esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 3 UAV View Object-Detection Method Based on YOLOv7-Tiny
### The Improved YOLOv7-tiny Algorithm
#### 3.2.2 BiFPN Feature Fusion Network  
In the neck of the YOLOv7-tiny algorithm framework, the PANet [25] structure is traditionally used for feature extraction and fusion. PANet enhances connections between feature maps through top-down and bottom-up pathways, optimizing the flow of information and improving the algorithm's ability to transfer and merge key features across different resolution levels [25]. However, in UAV-view scenarios, shooting angles and altitude variations often lead to objects having different scales and semantic information across feature layers. PANet focuses primarily on information transfer between feature layers but lacks flexibility, making it difficult to adapt to dynamic aerial perspectives and multi-scale object variations. This limitation affects its ability to efficiently handle multi-scale and multi-semantic feature fusion, particularly in highly dynamic scenes and complex multi-scale object-detection tasks [24].  
The BiFPN structure [25] is adopted to address these challenges as an enhanced feature pyramid network incorporating bidirectional information flow. BiFPN introduces a bidirectional flow mechanism that allows simultaneous top-down and bottom-up feature fusion, improving information transfer across layers [25]. It also includes cross-level connections that retain top-down information transfer while adding bottom-up pathways, forming a more robust and interconnected network topology, as illustrated in Figure 3. This design enables each node in BiFPN to receive inputs from multiple levels, significantly enhancing multi-scale feature interaction. These characteristics make BiFPN particularly effective in detecting small or densely distributed objects, common in UAV-view scenarios.  
Additionally, BiFPN eliminates single-input nodes that do not contribute to feature fusion, reducing computational redundancy and lowering memory usage. This design optimizes computational resources, allowing the algorithm to perform real-time detection tasks in UAVs, where resources are typically constrained. Each node in BiFPN integrates information from multiple layers, improving the richness and robustness of features. This enhanced feature interaction enables a more accurate detection of objects of varying scales and improves the algorithm's ability to adapt to changes in aerial perspectives. Acting as a bridge between the backbone network and the prediction head, BiFPN ensures that coarse-to-fine feature representations are optimized. When applied to UAV aerial images with complex backgrounds, BiFPN prevents critical information from being lost during feature layer transitions, providing a solid foundation for precise object detection.  
Figure 3: PANet structure optimized for BiFPN structure.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 5 Simulation Results and Discussion
### Simulation of Decision-Making Result  
In this section, the anti-jamming decision-making is under the conditions of known jamming patterns and unknown jamming patterns. We utilize the py2 neo package to invoke the existing knowledge graph database which is relevant to the anti-jamming, and then perform the simulation experiment by Python. The simulation parameters details are shown in Table 4.  
The computational complexity of the proposed scheme is calculated by the communication timeslot, knowledge graph search time (about 60 ms per search), total number of timeslots and number of jamming patterns (i.e., \(100\times 0\times 3000\times\) number of jamming patterns).
# The Application of Satellite Image Analysis in Oil Spill Detection
## 2 Materials and Methods
### Methodology Description
#### 2.2.3 Classification
In Support Vector Machines (C) is a hyperparameter that determines the penalty for misclassification of observations. In addition, there is a parameter gamma (\(\gamma\)) for the Radial-Basis Function kernel. As a rule, the Grid Algorithm is used to optimize these values. The truth is that this method is ineffective and time-consuming. To simultaneously optimize the feature subset and the SVM kernel parameters, the Geomatica Banff software uses attempts to increase the classification accuracy rate by employing two computing optimization-based approaches: genetic algorithm (GA) and particle swarm optimization (PSO) [46]. The calculated parameter achieve values generally with the best accuracy for the training samples while reducing the possibility of model over-fitting. With these rules and parameters, the algorithm built a model that assigns new examples to classes [47; 48]. The Confusion Matrix and Accuracy statistics for SVM classification as results are presented in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CLASS** & **Training** & **Test** & **Total** \\ \hline Water & 88 & 46 & 134 \\ \hline Oil & 28 & 11 & 39 \\ \hline Ground & 81 & 54 & 135 \\ \hline Vegetation & 34 & 19 & 53 \\ \hline Snow & 38 & 19 & 57 \\ \hline Buildings & 18 & 9 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of Training and Test Samples. Figure 4: Segmentation Results.
## 6 Experimental Results
### Environment and Scenario
Each recorded sequence takes between 30 and 70 s and contains the following messages types: raw data from two GNSS receivers (10 Hz), poses of a bus calculated based on GNSS data (10 Hz), processed CAN messages (100 Hz), the output of a bus state estimator (40 Hz), the output of a feedback controller (40 Hz), and a single reference path generated individually for each manoeuvre.
## 3 Results
### TCE Suppresses Platelet Activation by Inhibiting MAPK and PI3K/Akt Signaling Pathways  
The MAPK and PI3K/Akt pathways are critical intracellular signaling cascades that mediate platelet activation and aggregation [31]. MAPK, phosphorylated by agonists, plays a pivotal role in platelet granule release and TXA\({}_{2}\) production. In this study, collagen (2.5 \(\upmu\)g/mL) induced robust phosphorylation of ERK and p38 compared to unstimulated platelets. However, TCE pretreatment significantly suppressed these collagen-induced phosphorylations (Figure 6A).  
Similarly, the PI3K/Akt/PDK/GSK pathway has been identified as a key intracellular signaling cascade regulating platelet aggregation, granule release, and integrin \(\alpha\)IIb\(\updelta\)3 activation [32; 33; 34]. To elucidate the mechanism by which TCE affects platelet activation, we examined the impact of TCE on the PI3K/Akt/PDK/GSK pathway. Collagen (2.5 \(\upmu\)g/mL) markedly increased the phosphorylation of PI3K, PDK1, Akt, and GSK3\(\updelta\) in platelets. In contrast, TCE pretreatment significantly inhibited the phosphorylation of these signaling proteins (Figure 6B). These results suggest that TCE attenuates platelet activation by targeting both MAPK and PI3K/Akt pathways.  
Figure 5: Effects of TCE on TXB\({}_{2}\) synthesis and COX-1 activity. (**A**) Human platelets were pretreated with TCE for 2 min and subsequently stimulated with collagen (2.5 \(\upmu\)g/mL) for 5 min. TXB\({}_{2}\) levels in the supernatants were measured. (**B**) Effects of TCE on COX-1 activity. Data are expressed as mean \(\pm\) SD (\(n=3\)). Statistical significance is indicated as * \(p<0.05\) and ** \(p<0.001\) compared to collagen-treated platelets, and *\(p<0.001\) compared to untreated platelets.
# Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance
## 3 Case Study
### Comparative Study  
The diagnostic results based on the steady-state data and transient data are compared in this section. Up to now, extensive research has been conducted to investigate the effects of physical faults on the performance of gas turbines. From the experimental results, the theoretical value of the ratio of the compressor flow rate degradation over the efficiency degradation is three [28; 29; 30]. This theoretical value was chosen as the criterion for the comparison of different diagnostic methods.  
For comparison, two pieces of relatively stable steady-state data are selected for the same kind of fault diagnosis, as in Figure 13. They are the input for the steady-state diagnosis. The input for the transient process is the two complete pieces of data before and after washing.  
Component degradation is the relative value of the clean component. The improvement value of component degradation is selected for this comparative study. The diagnostic result of transient process analysis method is better than using steady state measurements, according to the result comparison, see Table 3. For convenience, diagnosis based on the steady-state is called Method A, and diagnosis based on the transient process is called Method B below.  
Figure 12: Integrating result of the compressor discharge temperature.  
Figure 13: Selected working condition for diagnosis.  
* The improvement of the compressor flow rate degradation is 13 times the value of its efficiency degradation based on Method A. It does not meet the theoretical value of three. The result of Method B is 1.96, closer to the theoretical value.
* These ratios for high pressure turbines are 3.86 and 5.33. It seems that Method A is more accurate than Method B for high-pressure turbines. Thus, the health condition of high-pressure turbines can be monitored by both Method A and Method B. However, washing decisions are usually made by compressor degradation. This means that Method B is more suitable for supporting maintenance scheduling.
* There is not any maintenance work being carried out for power turbines in this process. Therefore, the improvement of both its flow rate and efficiency should be zero, theoretically. This proves that Method B is more accurate for power turbine diagnosis.
## 8 Anonymity and Security Analysis
### Security against Active Attacks
#### 8.2.2 Hard-Active Attacks  
If ADV physically compromises SN then it can launch denial of service attacks (DoS), which is an effort to temporarily or indefinitely suspend transmission in the network. It consumes the resources such as bandwidth, memory, storage, and processor time. When ADV compromises SNs, it would be able to send massive valid messages to consume system resources. The ADV will be able also to launch replay attacks where ADV gets credentials of the some sensors and attempts to mimic the sensors to send messages to other neighbors. The other attacks such as, forging attack, packet alternation, packet dropping and packet injection are all only possible to physically captured nodes. However, it cannot propagate easily behind neighbors. Nothing could be worse than having physically captured nodes where ADV has full control over the sensors. Good IDS can detect such attacks and respond by removing the compromised nodes immediately. The most danger tactic of hard-active attacks is to prevent the real messages from following normal paths to the BS and force the messages to traverse through certain routes. Our main contribution to handle this attack is to put in place a seamless and efficient protocol to add and remove SNs while WSN in action.
## 2 Materials and Methods
### Characterization  
FTIR spectra (Bruker Corporation, VX70) were measured from 400 to 3800 cm\({}^{-1}\) by the KBr pressing plate method. The morphological characterization was examined by a scanning electron microscope (SEM, JSM-6700F, JEOL, Tokyo, Japan) and transmission electron microscope (TEM) (JOEL JEM-2100) with an accelerating voltage of 120 kV. The crystalline phases present in the samples were measured by X-ray diffraction (XRD) by using PANalytical-X'Pert PRO MPD with a Cu K\(\alpha\) X-ray source (\(\lambda\) = 1.5405 A). The XPS spectra (wide scans and C 1s, O 1s) were performed by using VG Scientific ESCALAB Spectrometer with Mg K\(\alpha\) radiation (\(\lambda\) = 1253.6 eV). The sedimentation rate of CHC/GO/CNC suspensions were evaluated by the LUMiSizer(r), which is an analytical centrifuge that instantaneously measures the extinction of the transmitted light across the entire length of your sample using STEP-Technology (space- and time-resolved extinction profiles, ISO 13318-2). The water-absorption capability (W\({}_{\text{c}}\)) was calculated by the percentage of weight change of the dry sample (pressing disk, 1 cm of diameter \(\times\) 1 cm of height) [17]:  
\[\text{W}_{\text{c}}\ (\%)=(\text{W}_{\text{w}}-\text{W}_{\text{d}})/\text{W}_{ \text{d}}\times 100\% \tag{1}\]  
where W\({}_{\text{d}}\) and W\({}_{\text{w}}\) are the weights of the sample before and after the water-absorption test (pH = 7). The moisture absorption sample (W\({}_{\text{w}}\)) was exposed to the same environment (T = 24 \({}^{\circ}\)C, RH = 50%) and immersed in DI-water for 1 h. In the calculation method of W\({}_{\text{w}}\), the excess water was removed by using a filter.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments  
In this section, we firstly introduce the information about the datasets, including the information about the receivers and antennas, the position of the stations, the visible satellites of different GNSSs, etc. We then describe some analyses of the quality of different signals in terms of the pseudo range multipath and the noise of phase observation. Finally, the stability of ISB between overlapping frequencies and the performance of single-system, multi-system LCM RTK, and TCM RTK is analyzed.
## 3 Result
### Performance of the Proposed Model
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Literature** & **Contribution** & **Hardware Complexity** \\ \hline
[4] & \(\bullet\) Conditional empirical mode decomposition (CEMD) & \(\bullet\) CEMD involves iterative processes that do not fully leverage the hardware feature \\  & \(\bullet\) 1D multi-scale CNN: consists of three 1D CNN layers of different sizes. & \(\bullet\) Requires moderate computational resources \(\bullet\) Integrating multiple classifiers increases the memory requirement \\ \hline
[6] & \(\bullet\) Multi-domain feature extraction and sparse feature fusion (MFESF), such as the power spectrum estimation, time-frequency energy & \(\bullet\) Mentioned feature extraction techniques require a relatively low hardware implementation complexity \\ \hline
[7] & \(\bullet\) Temporal Two 1D Convolutional layers CNN: three different kernel sizes (1 \(\times\) 45, 1 \(\times\) 65 and 1 \(\times\) 85) \(\bullet\) Data Augmentation(DA) & \(\bullet\) 1D structure reduces computational parameters \(\bullet\) 9 branches increases implementation complexity \\ \hline
[8] & \(\bullet\) CWT \(\bullet\) 30 \(\times\) 30 kernel CNN & \(\bullet\) 30 \(\times\) 30 significantly increases computational complexity \\ \hline
[9] & \(\bullet\) Two-Branch CNN fusing temporal and frequency features & \(\bullet\) Single Convolutional Layer: Facilitates hardware implementation \(\bullet\) 64 \(\times\) 1 Kernel: Too large for efficient hardware implementation \\ \hline Our & \(\bullet\) Two-branch lightweight CNN \(\bullet\) CWT & \(\bullet\) 5 \(\times\) 5 kernel CNN small kernel good for reduce hardware memory and computing resource usage \(\bullet\) Many hardware acceleration techniques for CNNs and CWTs \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Compare the model structures to the comparative literature. Table 5 compares the models including TBTF-CNN [9], CWT-CNN [8], and our proposed model. On average, our proposed model achieves an average accuracy of 84.1%, achieving the highest accuracy compared with TBTF-CNN (81.3%) and CWT-CNN (83.0%). It is worth noting that our model achieved the highest accuracy for subjects 5, 6, 8, and 9, which were 91.7%, 87.2%, 93.3%, and 95.2%, respectively.
## 4 Experiments and Results
### Experiment Results  
#### 4.2.1 Determination of Time Scales of Features  
Considering that we predict the price trend in combination with multiple time scale features (\(F_{1}\), \(F_{2}\) and \(F_{3}\)), we need to determine appropriate time scales for these features in order to predict more accurately. Specifically, since we directly treat the daily data as the feature sequence (\(F_{1}\)), we only need to determine time scales for features learned by CNN (\(F_{2}\) and \(F_{3}\)). Time scales of \(F_{2}\) and \(F_{3}\) correspond to the receptive fields, which are determined by kernel size and stride of max-pooling operation and convolution operation. Since we fix other parameters as common values, we only need to adjust kernel sizes of two convolution layers to find more appropriate time scales. The results are shown in Figure 5. Figure 5a,b represent experimental results when predicting price trends one week and one month later, respectively. The abscissa represents the convolution kernel size of the first layer of CNN, while the ordinate represents the convolution kernel size of the second layer of CNN. The larger the convolution kernel size, the larger the time scale of features. The numerical values in the figure represent Accuracy of the model corresponding to the point.  
In Figure 5, we observe that when predicting the price trend one week later, the convolution kernel sizes of two convolutional layers are preferably 7 and 5, respectively. Therefore, the time scales of \(F_{2}\) and \(F_{3}\) are 8 trading days and 18 trading days, that is, each point in \(F_{2}\) is obtained by the closing price sequence of 8 trading days, and each point in \(F_{3}\) is obtained by the closing price sequence of 18 trading days. Similarly, when predicting the price trend one month later, the sizes of two convolution kernels are preferably 9 and 7. In this case, the time scales of \(F_{2}\) and \(F_{3}\) are 10 trading days and 24 trading days.  
Figure 5: The determination of time scales of features. (**a**) Results for predicting the price trend in one week; (**b**) Results for predicting the price trend in one month.  
In addition, we can find that time scales of the features used to predict the price trend in a month is larger than that used to predict the price trend in a week. This is consistent with our experience, that is, larger-grained data are used for long-term forecasting, and smaller-grained data are used for short-term forecasting.
## 4 Circuit Description
### Circuit Module Design
#### 4.2.3 Periodic Sequence Generator  
In order to solve the problem of generating high rate periodic sequences, some approaches have been discussed in [20]. Finally, a special way is adopted. Several shift-registers are connected in series becoming a shift-register circle. Before the register works, the initial value of each register has to be set by switches. Then four channels of sequences come from four different taps. However, in this way the sequences have high correlation, which may leads to reconstruction failure. Fortunately, we learn that the Xilinx FPGA chip is equipped with Rocket I/O transceiver. And the GTZ (Gigabit Transceiver Z) transceivers are capable of running up to 28.05 Gb/s [22]. Each transceiver includes an independent transmitter and receiver. Based on this, we come up with an idea to use a transceiver to transmit the sequences without receiving them. Finally, we choose the chip Virtex-5 series XC5 VFX30T with four GTX transceivers as the main controller. We then use one GTX transmitter to generate periodic sequences. The sequences are written in a ROM firstly. Then the transceiver is controlled to output the  
Figure 7: The circuit diagram of attenuator (**a**) and modulator (**b**).  
sequences circularly. As for the interface between the mixer and transmitter, since the output of the transmitter is Current Mode Logic (CML), it is just compatible with the LO of the mixer [23].  
From Equation (6), we can see that the output of mixer contains energy spread all across the spectrum. A LPF is needed before sampling process by low rate ADC. The main purpose of the LPF is anti-aliasing filtering in case output signal exceeds the bandwidth of ADC. Besides, the filter needs to be differential corresponding with the output of mixer. We use the common simulation software Advanced Design System (ADS) to simulate a Chebyshev low pass filter of order seven. The cut-off frequency of passive filter is around 33 MHz, its circuit diagram **a** and frequency characteristic **b** are shown in Figure 8.
## 7 Simulation and Evaluation  
In this section, we adopt the Iridium LEO satellite network scenario and conduct a performance simulation of our BAPC protocol. The experimental environment is the NS2 platform, which uses the Ethereum key generation algorithm and signature verification algorithm.
## 2 History of Closed Loop Systems
A very early concept, [PERSON]'s two-compartment model [10], gave way to [PERSON]'s three-compartment model [11; 12]. Later, the [PERSON]/[PERSON] well-known minimal model of carbohydrate metabolism was published [13], and continues to provide an important contribution to some closed loop algorithms. This model generates indices of insulin action and insulin secretion and utilizes frequent glucose and insulin measurements during glucose clamp testing or intravenous glucose tolerance testing. It assumes a closed-loop relationship between glucose, insulin secretion and insulin action between a single glucose compartment and two insulin compartments [13]. It has been instrumental in defining the disposition index, a constant product of insulin sensitivity and insulin secretion [14]. [PERSON] et al have modified the original model in order to improve the accuracy of estimating insulin action and glucose secretory characteristics [15]. The ultimate role of any insulin delivery system is provision of insulin in a manner mirroring, as closely as possible, the human beta-cell. Beta-cell physiology and pathophysiology is an important topic of research and recent work emphasizes the acute effects of incretins and the chronic effects of amyloid [16]. Earlier beta cell reviews were silent on these issues and instead emphasized the many hormonal and non-hormonal input signals which regulate pancreatic insulin secretion [17]. These reviews of pancreatic beta cell secretion reveal something very important to this discussion. The strongest single regulatory factor affecting beta-cell insulin secretion is the concentration of glucose. Nonetheless, there are many other important input factors, including other pancreatic hormones, pituitary hormones, autonomic neural signals, paracrine compounds, catecholamines, other stress hormones, intestinal hormones such as glucagon-like polypeptide 1 (GLP-1), and even autocrine effects. For this reason, it is important that the reader understand how unlikely it is to achieve perfect glycemic control by using glucose concentration as the sole input for insulin delivery. Indeed, the term artificial pancreas is a presumptuous misnomer. It is impossible to perfectly replicate natural beta-cell control of insulin secretion in any one individual. Although perfection cannot be the goal, creation of an artificial pancreas (or \"automated pancreas\") is still an important and laudable goal, given the potential to decrease acute and chronic diabetes complications.