esa-sceva/satcom-chunk-collection huggingface dataset:
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
###### Abstract  
In network edge computing scenarios, close monitoring of network data and anomaly detection is critical for Internet services. Although a variety of anomaly detectors have been proposed by many scholars, few of these take into account the anomalies of the data in business logic. Expert labeling of business logic exceptions is also very important for detection. Most exception detection algorithms focus on problems, such as numerical exceptions, missed exceptions and false exceptions, but they ignore the existence of business logic exceptions, which brings a whole new challenge to exception detection. Moreover, anomaly detection in the context of big data is limited to the need to manually adjust detector parameters and thresholds, which is constrained by the physiological limits of operators. In this paper, a neural network algorithm based on the combination of Labeling Neural Network and Relevant Long Short-Term Memory Neural Network is proposed. This is a semi-supervised exception detection algorithm that can be readily extended with business logic exception types. The self-learning performance of this multi-network is better adapted to the big data anomaly detection scenario, which further improves the efficiency and accuracy of network data anomaly detection and considers business scenario-based anomaly data detection. The results show that the algorithm achieves 96% detection accuracy and 97% recall rate, which are consistent with the business logic anomaly fragments marked by experts. Both theoretical analysis and simulation experiments verify its effectiveness.  
anomaly detection; labeling neural network; relevant long short-term memory; compressing window; quartile method; KPI; deep learning +
Footnote †: journal: _Applied_  
software will send the network alert data to the operator for timely incident-related decisions [2]. However, anomaly detection for KPI is a typical big data environment where anomaly detection is generally inefficient due to the constraints of operators' physiological limits. Moreover, KPIs are presented in the form of time-series data, which makes them different from detection in non-time-series data and images [3]. It should be emphasized that the detection method proposed in this paper is mainly used to identify routine data anomalies, and network attack anomalies are not in the scope of this paper.  
In general, there are many challenges in designing services for time-series anomaly detection.
## 5 Validation of Different Waveguide Classes in an Optically Enabled Data Center System
### System Level Measurement Results  
Each waveguide under test was connected between the 850 nm optical interface of a Finisar BOA midboard optical transceiver (Finisar, Wuxi, China) connected directly to a Serial Attached SCSI (SAS) expander switch (Microsemi, Aliso Viejo, CA, USA) via the electro-optical midplane connection to one optically enabled 6 Gb/s SAS disk drive. The performance of the system with the connected drive was characterised using IOmeter (Open Source Development Labs, San Francisco, CA, USA), an open source I/O subsystem measurement and characterization tool for single and clustered systems [15]. The waveguide channels with the lowest insertion loss were chosen for the Mosquito (channel 5) and glass waveguide (set 7, channel 12). On the photo-addressed waveguide test board, a waveguide with a median insertion loss (channel 1) was chosen. The IOmeter system performance graphs in Figure 19 show the number of error-free data transfers in MB/s per data block size. Negligible difference in system performance is observed by inserting the waveguide section into the system. Although, this is expected given the short waveguide lengths and relatively low losses, it demonstrates that system level performance as a whole is not adversely affected, providing system level validation, in addition to channel level validation of at least short lengths of optical waveguide.  
Figure 18: System level measurement set-up showing the optically enabled system schematic with waveguides under test connected to the intra-system optical network.
# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
## 3 Data and Methods  
To the best of our knowledge, no prior datasets have been used to study earthquake magnitude categorization as a multi-label classification task. Therefore, to assess the proposed framework, we extract the dataset presented in [29] and further transform the data as utilized in [17]. In this section, we first introduce the properties and then the transformation procedure of the dataset.
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 2 Method
### ASPP Model  
To enhance small object detection performance in YOLOv8, we incorporate the Atrous Spatial Pyramid Pooling (ASPP) module at the backbone-neck junction, which significantly improves multi-scale feature representation capabilities. By replacing conventional pooling operations with atrous convolutions of varying dilation rates (6, 12, 18), ASPP constructs a hierarchical feature pyramid that captures contextual information across scales. This architecture addresses the inherent limitations of traditional pooling layers in small object detection [49].  
ASPP (Atrous Spatial Pyramid Pooling), an advanced feature extraction module in deep convolutional neural networks, can be regarded as an enhanced version of traditional pooling layers. Its core design principle originates from an improvement upon Spatial Pyramid Pooling (SPP), where the introduction of atrous convolution (dilated convolution) technology enables multi-scale feature fusion while preserving the spatial resolution of feature maps.  
From a functional perspective, ASPP shares the same fundamental objective as conventional pooling layers--to extract comprehensive and efficient deep feature representations of input data through hierarchical feature abstraction. However, unlike traditional pooling layers, which rely on a single fixed-scale feature extraction approach, ASPP constructs a  
Figure 4: Structure diagram of C_OREPA module.  
multi-scale feature extraction pyramid structure by employing parallel atrous convolutional layers with different dilation rates. This innovative design allows the module to simultaneously capture contextual information at varying scales: smaller dilation rates focus on local fine-grained features, while larger dilation rates capture broader semantic contexts.  
In terms of implementation, the ASPP module typically consists of four parallel feature extraction branches: a standard \(1\times 1\) convolutional branch and three \(3\times 3\) atrous convolutional branches with different dilation rates (e.g., 6, 12, 18), supplemented by a global average pooling branch to incorporate image-level contextual information. This multi-scale feature fusion mechanism enhances the network's capability to process visual data with complex spatial structures. By integrating ASPP at the junction between the backbone and neck of YOLOv8, the receptive field is expanded, thereby improving multi-scale object detection performance--particularly in the recognition of small and densely clustered objects. The structure of the ASPP module is illustrated in the following Figure 5.
## 5 Conclusions  
In this paper, the flow and heat transfer characteristics of China RP-3 aviation kerosene in the curved rectangular regenerative cooling channel were numerically studied. A detailed pyrolytic chemical reaction mechanism was adopted to describe the hydrocarbon fuel pyrolysis. The secondary flow effect on fluid flows and heat transfer was studied by comparing the distribution of the fuel conversion and heat transfer coefficient in both a curved cooling channel and a straight cooling channel. In addition, the fluid flows and heat transfer in the curved cooling channel at different inlet mass flow rates and radii of the curvature were also investigated. The main results are summarized as follows:  
1. The secondary flow enhanced the fluid mixing and thermal exchange in the curved cooling channel. Additionally, the fuel conversion and heat-absorbing capacity was higher, and the heat transfer was effectively enhanced. At the outlet of the curved section, the heat transfer coefficient increased by around 90%.
2. With the increase of inlet mass flow rate, the centrifugal force became larger. The heat and components exchange between the near-wall region and the main flow was much more severe. The non-uniformities of the fuel temperature and conversion at the cross section decreased, which was beneficial for the promotion of fuel pyrolysis, and helpful for improving the utilization level of the fuel heat-absorbing capacity. The increasing inlet mass flow rate can effectively enhance the heat transfer in a curved cooling channel.
3. The radius of curvature was inversely proportional to the centrifugal force, so as the radius of curvature decreased, the secondary flow velocity increased. Therefore, the increasing radius of curvature raised non-uniformities of the fuel temperature and conversion at the cross section, which suppresses the fuel pyrolysis and reduces the utilized efficiency of the fuel heat-absorbing capacity. The increasing radius of curvature deteriorated the heat transfer in the curved cooling channel.  
Conceptualization, Y.C. and K.G.; methodology, K.G.; software, S.L.; validation, Y.Z., L.W. and Z.C.; formal analysis, S.L.; investigation, K.G.; resources, Y.C.; writing--original draft preparation, Y.Z.; writing--review and editing, K.G.; visualization, L.W.; supervision, Y.C.; project administration, S.L.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51976046, 51806048 and 51776175), the National Key Laboratory of Science and Technology (Grant No. 6142701190106), Key Basic Research Projects of the Foundation Strengthening Program (Grant No. 2019-JCQ-ZD-083-05), the Shenzhen Technology Project (Grant Nos. ZDSYS201707280904031, GXWD20201230155427003-20200823111310002, and JCYJ20180306171941256), and the Open Cooperative Innovation Fund of Xi'an Institute of Modern Chemistry (Grant No. SYJJ200313).  
Not applicable.  
All data can be accessed from the corresponding author upon request.  
The authors declare no conflict of interest.
## 5 Experiments and Results
### Ablation Study
#### 5.1.5 The Effect of the Attention Module  
In deep learning models, not all channels in the input image contribute equally to classification, as some may contain irrelevant or noisy data. To address this, we used a channel attention mechanism in our backbone model and fusion process. This experiment demonstrated that channel attention positively impacts performance. Table 6 shows that integrating this mechanism into the ConvNeXt fusion model improves most evaluation metrics. The F1-score increased from 94.54% to 94.72%, and the HL decreased from 0.0364 to 0.0355, while mAP and RL showed slight improvements.
## 3 The Derivation of CH\({}_{4}\) Diffusivity and Permeability by MIP
### Permeability  
The relationships between absolute permeability and porosity, pore size were acquired based on MIP data with percolation theory [23; 24]:  
\[\text{k}=\frac{1}{89}(L_{max})^{2}\bigg{(}\frac{L_{max}}{L_{c}}\bigg{)}\varphi S (L_{max}) \tag{14}\]Here k is absolute permeability (Darcy); \(L_{c}\) is pore size; \(L_{max}\) is the value of the pore size with the maximum hydraulic conductance; \(\varphi\) is porosity and \(S_{(Lmax)}\) is the fraction of total porosity \(\varphi\) filled at \(L_{max}\). To acquire this characteristic length (pore size) \(L_{c}\) from the mercury porosimetry, pressure is determined at the inflection point in the rapidly rising range of the cumulative mercury intrusion as previous research [2; 23; 24].  
Before the MIP measurements, permeability with He and effective diffusion coefficients have been measured. The experimental CH\({}_{4}\) diffusion by using unipore diffusion model and the theoretical calculated effective diffusion coefficients (\(De\)) of the same coal samples will be compared. The important parameters including effective diffusion coefficient (\(D_{e}\)), diffusivity (\(D^{\prime}\)) and cementation factor (m values) by Equation (3) are presented in Table 2.
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 2 Materials and Methods
### Model Architecture Design
#### 2.2.5 Parameter Initialization and Training Strategy  
After completing the previous model construction, we proceed with the training of the disaster classification model. For the ResNet-50 model, a proper parameter initialization strategy is essential for accelerating convergence. Therefore, the following initialization strategies were applied to optimize the training process.  
For the convolutional layers, this study employs the Kaiming normal initialization method, where the convolution kernel weights are denoted as \(W\), and each element follows Equation (14). This approach ensures that activations maintain a reasonable variance at initialization, enhancing training stability. For the fully connected layers, we initialize parameters using a zero-mean Gaussian distribution, with the distribution satisfying Equation (15) to maintain a well-balanced initialization. Furthermore, in the multi-class disaster classification task, we define a loss function (Equation (16)) to optimize model performance. For parameter updates, this study adopts the Adam algorithm (Equation (17)), which not only adapts the learning rate dynamically but also incorporates momentum, making it a widely used optimization method in deep learning network training.  
\[W\sim\mathcal{N}\bigg{(}0,\frac{2}{(1+a^{2})\cdot fan_{in}}\bigg{)} \tag{14}\]  
where \(fan_{in}\) represents the size of the input channels, referring to the number of elements involved in summation within the convolutional kernel; \(a\) denotes the negative slope coefficient of the ReLU activation function.  
\[W_{fc}\sim\mathcal{N}(0,0.01),b_{fc}=0 \tag{15}\]  
where bias initialization is set to 0, and standard deviation is set to 0.01.  
\[\mathcal{L}(y,\hat{y})=-\sum_{i=1}^{N_{classes}}y_{i}\text{log}\hat{y}_{i} \tag{16}\]where \(N_{classes}\) represents the total number of classes; \(y_{i}\) denotes the indicator of the true label for class \(i\) (0 or 1); \(\hat{y}_{i}\) represents the predicted probability of the sample belonging to class \(i\).  
\[\theta_{t+1}=\theta_{t}-\frac{\hat{m}_{t}}{\sqrt{\hat{v}_{t}+\epsilon}} \tag{17}\]  
where \(\theta_{t}\) represents the parameter vector at the \(t\)-th iteration; \(\hat{m}_{t},\hat{v}_{t}\) are the bias-corrected estimates of the first and second moments, respectively; \(\epsilon\) is a numerical stability constant, set to \(10^{-8}\) in this experiment.  
With the model architecture design, parameter initialization, and training strategy established in the previous sections, the model can now be fully trained and deployed to achieve classification and recognition of various disaster images. The following section will further discuss the model's prediction performance and compare it with the traditional neural network model (VGG16).
# Patterns of Arctic Tundra Greenness Based on Spatially Downscaled Solar-Induced Fluorescence
## 2 Data and Methods
### Trend Analysis  
For the spatial pattern of Arctic tundra greenness, the annual trends (2007-2013) of the NDVIs and SIF were calculated by least square regression using the Google Earth Engine platform [46]. The trends were checked using the annual maximum values of the NDVIs and SIF, not just within the growing season. This not only avoided a lack of temporal information, but also made more biological sense for the relationship between greenness and vegetation growth [47]. The slope coefficient obtained from the regression line was used to describe the greenness trend.  
For the temporal pattern of Arctic tundra greenness, the study area (CAVM) was divided into two sub-regions including North America and Eurasia. The annual greenness of each region (Arctic, North America, and Eurasia) was calculated as the averaged values of the NDVIs and SIF using whole year data.  
We supposed that the tundra was greening if the trend of the annual maximum values of the NDVIs or SIF was positive, and tundra was browning with a negative trend from 2007 to 2013. The trend was validated using EC flux measurements (maximum monthly GPP for each year) within the study area. A two-sided \(t\)-test was used to determine the statistical significance of the GPP, NDVIs, and SIF. A \(p\)-value \(<\) 0.05 was assumed to be significant. Compared to NDVI data, no data existed higher than 75\({}^{\circ}\) N for SIF.
# Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs
## 3 Preliminary Investigations
### Ocean Current Motion Model  
It is necessary to estimate the tide and ocean current as these are the main factors affecting the movement of underwater sensor nodes deployed along the seashore.  
The movement of nodes in seawater was simulated by a kinematic motion model in [40], which is used on the seashore to handle the specific distribution of nodes. Basedon the assumption of the superposition of tidal and remnant flow fields, the Gaussian radial basis function curve is multiplied by different influence factors and tidal components of ocean currents to approximate the movement of sea water. The accurate and real-time seawater motion model is obtained by the selection of the spatial basis function and optimization of model parameters. The model is approximated as  
\[\mathrm{y}(v,t)=\left\{\begin{array}{l}\varphi\cdot s\cdot d\cdot\sum_{i}^{N} \sin(\lambda_{i}\cdot\mathfrak{t})\cdot\psi\cdot\cos(\eta_{i}\cdot\mathfrak{t })\cdot\psi+\sum_{i}^{N}\varphi\cdot s\cdot\cos(2\lambda_{i}\cdot\mathfrak{t}) \cdot\psi+\mu\cdot\psi\\ -s\cdot d\cdot\sum_{i}^{N}\cos(\lambda_{i}\cdot\mathfrak{t})\cdot\psi\cdot \sin(\eta_{i}\cdot\mathfrak{t})\cdot\psi+p\cdot\psi\end{array}\right. \tag{9}\]  
\[\psi(x,x^{\prime})=e^{-\frac{\left|x-\
u\right|^{2}}{2\sigma^{2}}} \tag{10}\]  
where \(N\) is the tidal number of ocean currents; the time basis functions are denoted by \(\sin(\lambda_{i}\cdot\mathfrak{t})\), \(\sin(\eta_{i}\cdot\mathfrak{t})\), \(\cos(\eta_{i}\cdot\mathfrak{t})\), and \(\cos(2\lambda_{i}\cdot\mathfrak{t})\); and the frequencies of these functions are \(\varphi\), \(\lambda\), and \(\eta\), respectively. \(\psi\) is the Gaussian radial basis function; \(\mathrm{v}\) is the resultant velocity vector of \(\mathrm{V_{y}}\) and \(\mathrm{V_{x}}\); and \(S\), \(d\), \(\mu\), and \(p\) are the impact factors that represent the salinity of the sea water, density, temperature, and pressure, respectively.  
As shown in Figures 2 and 3, the motion of sea water shows some semi-periodic and space-time correlation properties. The movement of nodes underwater will change weakly and regularly, so the trajectory of sensor nodes will be predicted by the velocity vector in each direction with limited coverage of the nodes. In the following section of this paper, we will analyze the routing void caused by node drift based on this movement model.  
Figure 3: Velocity along the coast.  
Figure 2: Velocity perpendicular to the coast.
## 4 SDR Receiver Performance Evaluation
So, the loss of the GPU2 version is still considered negligible with respect to the ideal BER values. For example, for ACM 6, the BER drops to \(10^{-6}\) for an \(Eb/N0\) value around 3.7 dB, whereas in the ideal curves, the \(Eb/N0\) value is about 3.3. In this case, we have an implementation loss of around 0.4 dB. For ACM 22 and ACM 23 (32 APSK and 64 APSK), instead, the BER value of \(10^{-6}\) is reached with a \(Eb/N0\) of around 9 dB; on the other side, the ideal value is around 8.4/8.5 dB, with an implementation loss that increases with respect to lower ACMs. Finally, the GPU3 version of the code, that is the further-optimized implementation, shows, as expected, the best performances. Concerning the other GPU versions (GPU1 and GPU2), it shows a performance regain of more than 10\(\times\), while for ACM 27, it has a gain factor larger than 5500\(\times\) with respect to the SW EGSE. In comparison with the ideal BER curves, the GPU3 system shows an implementation loss of between 0.7 dB and 0.9 dB, as shown in Figure 9. Overall, we consider the error decoding performance of the system, comprising windowing and simplified max\({}^{\star}\), to be more than acceptable: the acceptable error recovery performance of a system may vary from case to case and is strongly related to the requirements of the single mission. However, we think that a 1 dB implementation loss can be tolerable for most of the ESA EO missions. In the end, the proposed solutions have demonstrated to have higher data rate performance than the state-of-the-art SW system, with an implementation loss that is negligible. With respect to the HW solution, the GPU3 version allows us to decode more than 200 CWs per second (in the worst case), while a HW receiver can decode up to 60 k CWs per second. Obviously, the SDR is still much slower than a HW receiver, but, given the flexibility and the low-cost of the system, it can be an acceptable alternative of HW receivers, especially for testing transmitters or for receiving data in science missions. Also, it is important to note that using a higher-performance GPU would provide a proportional performance gain for the implemented receiver, allowing us to speed up the overall execution. Furthermore, given the flexibility of the developed code, and thanks to CUDA framework, the code can be easily re-used on different NVIDIA GPUs or extended to multiple GPUs of the same family, potentially doubling the speed of the system. Figure 7: BER curves on linear AWGN channel, drawn for _GPU1_ implementation.