esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Observation of Various Soliton Molecules in Different Fibre Lasers
### Nearly Zero Net-Cavity Dispersion Regime
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Wavelength** & **Dispersion** & **Saturable Absorber** & **Pulse Width** & **Pulse** & **Phase** & **Reference** \\ \hline
1 \(\upmu\)m & 0.047 ps\({}^{2}\) & NPE & 4.3 ps & 20.33 ps & - & [46] \\
1 \(\upmu\)m & 0.0054 ps\({}^{2}\) & NPE & 0.39 ps & 21 ps & - & [48] \\
1 \(\upmu\)m & 0.18 ps\({}^{2}\) & SESAM & 13.4 ps & 14.8 ps & \(\pi/2\) & [49] \\
1.5 \(\upmu\)m & \(-\)0.381 ps\({}^{2}\) & MoS\({}_{2}\) & 1.2 ps & 3.4 ps & \(\pi\) & [33] \\
1.5 \(\upmu\)m & \(-\)0.28 ps\({}^{2}\) & CNT & 0.9 ps & 3–20 ps & \(\pi,0,\pm\pi/2\) & [28] \\
1.5 \(\upmu\)m & \(-\)0.28 ps\({}^{2}\) & Graphene & 0.62 ps & 55 ps & - & [31] \\
1.5 \(\upmu\)m & anomalous & CNT & 370 fs & 1.5 ps & \(\pi\) & [29] \\
1.5 \(\upmu\)m & \(-\)0.1 ps\({}^{2}\) & NPE & 1.5 ps & 18.5 ps & - & [21] \\
1.55 \(\upmu\)m & 0.038 ps\({}^{2}\) & SESAM & 20.3 ps & 91 ps & - & [50] \\
1.55 \(\upmu\)m & 0.0017 ps\({}^{2}\) & CNT & 0.18 ps & 1.53 ps & \(\pm\pi/2\) & [51] \\
1.55 \(\upmu\)m & \(-\)0.12 ps\({}^{2}\) & NALM & 1.3 ps & 2.2 ps & \(\pi\) & [8] \\
1.55 \(\upmu\)m & anomalous & Soliton shaping & 2.9 ps & 5.2 ps & \(\pi\) & [52] \\
1.5 \(\upmu\)m & \(-\)0.047 ps\({}^{2}\) & NPE & 0.88 ps & 1.5 ps & \(\pi\) & [53] \\
1.5 \(\upmu\)m & \(-\)0.03 ps\({}^{2}\) & Graphene & 0.307 ps & 35 ps & - & [54] \\
1.5 \(\upmu\)m & \(-\)0.02 ps\({}^{2}\) & CNT & 0.71 ps & 2.5 ps/3.5 ps & \(\pm\pi/2\) & [55] \\
1.5 \(\upmu\)m & \(-\)0.27 ps\({}^{2}\) & Black phosphorus & 0.78 ps & 7.5 ps & - & [34] \\
1.5 \(\upmu\)m & anomalous & Graphene & 0.39 ps & 8.1 ps & - & [56] \\
2 \(\upmu\)m & \(-\)0.329 ps\({}^{2}\) & NPE & 0.78 ps & 2.5 ps & \(\pi\) & [23] \\
2 \(\upmu\)m & \(-\)0.098 ps\({}^{2}\) & NPE & 0.93 ps & 7–15 ps & \(\pi,0\) & [24] \\ \hline \hline \end{tabular}  
* NPE: nonlinear polarization evolution; SESAM: semiconductor saturable absorber mirror; MoS\({}_{2}\): molybdenum disulphide; CNT: carbon nanotubes; NALM: nonlinear amplifying loop mirror.  
\end{table} 
Table 1: Experimental observation of two-soliton molecules in mode-locked fibre lasers. Figure 13: (**a**) Autocorrelation function and (**b**) optical spectrum recorded at one output of the fibre laser in [22]. (Reproduced with permission from [22]. © The Optical Society of America (Washington, DC, USA), 2003). Based on a plethora of experimental evidences, it is clear that the state of soliton molecules is one of the intrinsic forms in mode-locked fibre lasers which is independent on cavity details such as dispersion regime, spectral window, saturable absorber and so on. However, the limited variation of experimental parameters implies discreteness and quantization of allowed soliton molecules, meaning that the properties of the attractors in such dissipative systems are sensitive to the detailed features of the systems.
## 4 Analysis of Experimental Results
### Precise Identification Technology for Emergency Airdrop Stations
#### 4.2.3 Ablation Experiments  
To verify the validity of SimAM, GhoHGBlock, and LSCHead, four groups of ablation experiments were set up in this paper. The first group of experiments used YOLOv8n  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Configuration** & **Name** & **Type** \\ \hline \multirow{4}{*}{Hardware} & CPU & Intel(R) Xeon(R) Platinum 8255C \\ \cline{2-3}  & GPU & NVIDIA GeForce GTX3080 \\ \cline{2-3}  & Memory & 40 GB \\ \hline \multirow{4}{*}{Software} & CUDA & 11.8 \\ \cline{2-3}  & Python & 3.8 \\ \cline{2-3}  & Pytorch & 2.0.0 \\ \cline{2-3}  & Operating system & Ubuntu20.04 \\ \hline \multirow{4}{*}{Hyperparameters} & Learning Rate & 0.01 \\ \cline{2-3}  & Image Size & 640 \(\times\) 640 \\ \cline{2-3}  & Workers & 8 \\ \cline{2-3}  & Batch Size & 8 \\ \cline{2-3}  & Epoch & 80 \\ \cline{2-3}  & Optimizer & SGD \\ \cline{2-3}  & Momentum & 0.937 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental parameter configuration.  
Figure 12: Illustration of the physical situation of UAV load drop.  
to validate the dataset, and the second, third, and fourth groups of experiments add SimAM, GhoHGBlock, and LSCDHead, respectively, based on YOLOv8. The results of the experiments are shown in Table 8.  
The results from ID 0 to ID 1 indicate that the Simple Attention Module effectively improved the model's capability for feature extraction and attention allocation to target objects, thereby enhancing detection performance. The [EMAIL_ADDRESS]/% increased from 88.5% to 92.8%, while the [EMAIL_ADDRESS]-0.95/% also rose from 64.8% to 68.0%. The results from ID 1 to ID 2 show that the inclusion of the GhoHGBlock increased the [EMAIL_ADDRESS]/% to 93.1%, reduced Params by 23.0%, and decreased FLOPs by 16.1%, although the [EMAIL_ADDRESS]-0.95/% experienced a slight decline. This indicates that the GhoHGBlock managed to reduce the model complexity while maintaining a certain detection performance level. The results from ID 2 to ID 3 indicate that the introduction of the LSCDHead further improved the model's performance, with [EMAIL_ADDRESS]-0.95/% increasing from 67.3% to 68.2%, Params decreasing by 27.7%, and FLOPs decreasing by 22.1%. This demonstrates that the LSCDHead successfully implemented the idea of ShConv, achieving fewer Params and FLOPs while minimizing the loss in accuracy as much as possible.  
In summary, incorporating SimAM, GhoHGBlock, and LSCDHead positively affected the model's performance, resulting in an efficient and accurate object detection model.
# Users Empowered in Smart Grid Development? Assumptions and Up-To-Date Knowledge
## 3 Results
### Co-Design  
Many social scientists and some engineers critique the technological and economic rationality in the design of smart grid technologies and projects. [PERSON] et al. [8] (p. 260), for instance, stated: \"Our analysis shows that the projects employ a technology driven approach to household users with a focus on testing ready-made technologies rather than on improving technologies by including consumers\". For this reason, several scholars presuppose that if the smart grid technologies are co-designed, they will better address the users' needs [3; 7]. There were some examples where future users were addressed with images or prototypes of potential products. However, we could not find any study on future users purposively and structurally being involved in the preparation phase of pilot projects.  
That does not mean that users play no role at all in design processes. In the literature, we found several examples of projects in which the applied techniques or their applications were changed because of users' wishes and responses [15; 19]. [PERSON] and [PERSON] [19] showed how, in a project in which 20 households were to be equipped with the same air/water heat pump, it was installed in only seven households. After a negotiation process, the other households instead received a hybrid air/water heat pump with a gas boiler installed, sunwells, or a geothermal heat pump.  
It can be concluded that co-design is rare and has so far yielded little insights in the preferences of users. The documentation on interactions between users and project managers in the course of actual projects provides evidence that, at times, feedback from users does influence choices made in these projects. Allowing room for such forms of co-design during the use phase of projects may instigate a countervailing power against the misconceptions of leading engineers.  
In general, it seems useful to think of co-design not at the level of products and services only but at the level of projects as well. In this way, socio-technological aspects regarding the local energy system and stakeholder relationships will be part of the negotiations rather than merely the interaction between residents and technology.
## 2 FMCW LiDAR
### FMCW LiDAR Key Parameters
#### 2.3.2 Distance Resolution and Ranging Accuracy  
The distance resolution of the FMCW LiDAR system, indicative of the minimum distinguishable separation between targets within a single measurement. The distance resolution is primarily influenced by the modulation bandwidth \(B\) of the light source, and the distance resolution must satisfy the following criterion [14]:  
\[S_{r}=\frac{c}{2\cdot n\cdot B}. \tag{10}\]  
The equation shows a negative correlation between distance resolution \(S_{r}\) and the modulation range \(B\) of the light source, with \(c\) denoting wavelength and \(n\) the refractive index. Expanding the modulation bandwidth \(B\) of the light source leads to a reduction of the distance resolution \(S_{r}\); i.e., the distance resolution becomes smaller, and the accuracy of the system increases. Therefore, expanding the modulation range of the light source emerges as a viable strategy for enhancing the ranging resolution of the LiDAR. In FMCW LiDAR, the distance resolution is mainly influenced by the light source's performance parameters, making the generation of high-quality frequency-modulated optical signals a key area of research focus within the scientific community [15].  
The distance resolution and SNR will jointly affect the ranging accuracy of FMCW LiDAR; the accuracy of the LiDAR's range measurements is intricately related to the SNR.  
When the SNR exceeds a certain threshold, the ranging data distribution aligns with a normal distribution, ensuring high accuracy. However, a decline in SNR broadens the data distribution range, consequently diminishing the ranging accuracy.  
In addition, the period of the FMCW LiDAR also indirectly affects the ranging accuracy. The period mainly affects the speed and density of point cloud generation, but it also affects the ranging accuracy of the system. A shorter period usually increases the sampling frequency, which, in turn, increases the point cloud density, providing more-accurate distance measurements in real-time processing. However, a period that is too short may introduce nonlinear errors, which may have a negative impact on ranging accuracy. These errors usually come from inaccurate frequency modulation or problems with the dynamic response of the system. Therefore, finding a suitable period to balance the sampling rate and error is the key.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation
### Transceiver Design
#### 3.5.4 Noise Simulation  
White noise is simulated by adding the signal generated by a \"Noise Source\" block to the original signal before sending it to the receiver. The value of the noise voltage is calculated using Equation (6) [13].  
\[VNoise=\ \left(\surd(2\times Bitsym\times 10(SNRdB/10))\right)\ -\ 1 \tag{6}\]  
Figure 9: GMSK receiver design in GNU Radio.  
where  
* _VNoise_: noise voltage
* _Bitsym_: number of bits per symbol
* _SNRdB_: signal-to-noise ratio in dB  
The signal-to-noise ratio (SNR) is changed to control the value of the noise voltage while observing the reception of the signal in the other terminal. Figure 10 shows the GNU Radio blocks used to simulate the channel noise.
# Morphological and Molecular Alterations Induced by Lead in Embryos and Larvae of _[PERSON]_
## 4 Discussion  
Over time, several species have been used as surrogate indicators of exposure to pollutants in order to estimate the risk to human health. Still, the accuracy with which vertebrate studies predict the effects of putative toxic substances in the environment has long been a topic of interest and concern [40]. Given its well-demonstrated translational value for human toxicity research, in recent years, the use of zebrafish has undergone rapid development, becoming a prominent model in a variety of scientific disciplines, including toxicology [41]. In particular, in all vertebrates, both gene programming and development are highly conserved during the early stages of life, also allowing the maintenance of numerous morphological similarities through the evolutionary scale [40,42]. Several endpoints have been evaluated to disclose signals of lead toxicity in fish sentinel species, including zebrafish. Neurotoxicity, oxidative stress induction, and hematological alterations are the mainly reported outcomes of lead exposure in both adults and embryos [8,22,23,24,29,30,34,44,45,46,47]. However, all these literature data relate to high lead concentrations, thus leaving a gap of knowledge regarding the adverse effects of lead at environmentally relevant concentrations [9,53].  
Here, we investigated the effects of lead exposure during development in zebrafish, providing evidence of the high toxicity of this heavy metal even at very low doses. To the best of our knowledge, the present study demonstrated for the first time that short-term exposure to environmentally relevant concentrations of Pb induces severe morphological modifications in the early stages of zebrafish and modulates the expression of several genes involved in the toxicological responses of fish to heavy metals. The results from the current study also confirm that embryos and larvae can act as an early warning indicator of pollution and may play a major role in environmental monitoring to protect ecosystems and human health.
# Fault Diagnosis of Vehicle Gerboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
## 2 Principle of the Method
### Dimensionality Reduction of Fault Feature Construction
#### 2.2.1 Fault Feature Vector Extraction  
(1) Mixed domain feature  
Time-domain statistical metrics are classified into dimensional and dimensionless types. Dimensional characteristic metrics usually include specific physical quantities, such as the amplitude and units of the signal. Dimensionless characteristic metrics provide a more abstract and generic way to describe the nature of the signal. Frequency domain analysis reveals the frequency characteristics of a signal by decomposing it into components of different frequencies. These metrics are used not only to describe the frequency distribution of the signal but also to assist in identifying significant frequency components and trends in the frequency domain. Therefore, in the time domain, dimensional characteristic indices such as mean value, absolute mean value, peak-to-peak value, and kurtosis are applied, along with dimensionless characteristic indices such as pulse factor and waveform factor. In the frequency domain, statistical indices such as the mean value of the spectrum amplitude sample, spectrum amplitude sample variance, and spectrum amplitude sample kurtosis are utilized.  
(2) Wavelet domain features  
To extract wavelet domain features, a multi-layer wavelet packet decomposition is performed on the signal. The resulting decomposition produces \(L=2^{l}\) frequency bands, where \(l\) represents the number of decomposition layers. The calculations of energy for each frequency band and the subsequent calculation of total energy (\(E\)) are conducted as follows:  
\[E=\sum_{i=1}^{L}E_{i} \tag{19}\]  
The energy entropy of the wavelet packet is as follows.  
\[Energy=-\sum_{i=1}^{L}\left(\frac{E_{i}}{E}*\text{log}\left|\frac{E_{i}}{E} \right|\right) \tag{20}\]  
Wavelet packet singular spectrum entropy (Qiyi) is employed as an index to gauge the singularity or irregularity of the signal. It is calculated as follows:  
\[Qiyi=-\sum_{i=1}^{L}\left(\frac{\lambda_{i}}{\sum_{i=1}^{L}\lambda_{i}}*\text {log}\left|\frac{\lambda_{i}}{\sum_{i=1}^{L}\lambda_{i}}\right|\right) \tag{21}\]  
where, \(\lambda_{i}\) represents the singular values obtained through singular value decomposition after processing the original signal into a matrix.  
The singularity of a signal is assessed through wavelet packet singular entropy, which analyzes the distribution of coefficients in the wavelet packet transform. A lower value of singular entropy signifies a more uniform distribution of coefficients, indicating that the signal is regular and smooth. Conversely, a higher singular entropy value suggests an uneven distribution of coefficients, implying the presence of more irregular or singular components in the signal. Consequently, wavelet packet singular entropy is commonly employed to identify bursts, noise, or abnormal behavior in a signal.
## 3 Experiments and Results
### Model Training  
Model training is a process where the model learns patterns and rules from data. First, the preprocessed training data are provided as input into the model to initiate the forward propagation process. Based on the input data and internal parameters, corresponding prediction results are then calculated. Following that, a loss function is introduced to calculate the loss value according to the difference between the prediction results and the true labels. Next, using the back-propagation mechanism, the gradients are calculated in reverse along each layer of the model's structure. Then, an optimization algorithm is finally used to update the model parameters in a timely manner according to the calculated gradients, enabling the model to gradually approach the optimal state. After multiple rounds of iteration, the optimal model was obtained to achieve accurate prediction or classification of the data.  
The model was trained using the above parameters, as seen in its curves for accuracy and loss values (Figure 8).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Optimizer & AdamW \\ Learning rate & \(1\times 10^{-4}\) \\ Convolution kernel size & \(3\times 3\) \\ Number of convolution kernels & 64 \\ Encoder layers & 2 \\ Number of multiple heads of attention & 4 \\ Attention dimension & 128 \\ Dropout & 0.5 \\ Batch size & 32 \\ Epochs & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental model parameters used in this paper.  
After 30 rounds of training, it started to stabilize. By comparing Figure 5, it can be concluded that the optimization strategy of AdamW was selected for model training, which accelerates model convergence and reduces significant fluctuations in the loss curve. By comparing Figure 6, it can be seen that setting the batch processing parameter to 32 and the learning rate to \(1\times 10^{-4}\) during model training reduces the risk of underfitting and overfitting, while also improving model training efficiency.  
The confusion matrix of the classification results can be seen in Figure 9 below.  
The confusion matrix intuitively and clearly demonstrates the differences between true and predicted labels, as well as the classification results of each category, indicating  
Figure 8: Comparison of model accuracy and loss curves.  
Figure 9: Classification confusion matrix.  
that the model has a high classification capability. The number of correct identifications for severe fault types is 174, suggesting that the model performs best in identifying severe fault categories. However, the number of minor fault types misidentified as normal water pumps is 20, indicating that the model's diagnostic capability between minor faults and normal conditions requires improvement.
# Dual Enhancement Network for Infrared Small Target Detection
## 4 Experiment
### Datasets  
Existing open-source datasets for infrared small target detection are scarce, and in this paper three currently used mainstream infrared small target detection datasets were selected for study, including NUAA-SIRST [20], NUDT-SIRST [24], and IRSTD-1K [22].  
The NUAA-SIRST dataset is a publicly available single-frame dataset containing 427 infrared images covering a total of 480 targets. The dataset also includes infrared images at 950 nm wavelength, and the target labelling approach consists of five forms applicable to different detection models: image classification, instance segmentation, bounding box regression, semantic segmentation, and instance point recognition tasks. The training set is 50%, the validation set is 20%, and the test set is 30%.  
NUDT-SIRST is a synthetic dataset containing 1327 images with a resolution of 256 \(\times\) 256. Compared with the real dataset, NUDT-SIRST has the advantages of accurate labelling, a large number of target categories, rich target sizes, and diverse cluttered backgrounds.  
The IRSTD-1K dataset provides 1000 real images with a variety of target shapes, different target sizes, and a rich clutter background with precise pixel-level annotations in the background. The dataset is divided into two folders, where IRSTD1k_Img contains the real images and IRSTD1k_Label contains the label mask.
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Packet Formulation  
This section explains how the packets from the ChipSat RF transmitter are formed. The data use forward error correction (FEC) and matched filtering (MF) to increase reliability. These methods were suggested by Dr. [PERSON] because they are often used in satellite communication. They are not common for small sat communications. But both FEC and MF improve the signal-to-noise ratio, which is needed for long-range transmissions. Matched filtering also helps separate the signal from the noise floor. Since the power budget is low, another way to make the signal more reliable is to add extra bits to the signal. Padding also works like a gain-of-sorts. By repeating the original binary sequence when coding it, if the signal faces interference, the original data can be recovered from the repetition. Also, these techniques make the transmitted data more secure since the  
Figure 17: 4 GFSKs. Four different frequencies corresponding to the four different possible 2-bit sequences. Image Credit: “802.11 Wireless Networks: The Definitive Guide” [36].  
Figure 18: Four-channel Gaussian frequency shift keying (GFSK). Image Credit: “802.11 Wireless Networks: The Definitive Guide” [36].  
sequences used for MF and FEC encoding would have to be known to decode the signal (more detail in the relevant sections).