esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Parameter Estimation
#### 2.2.6 Weibull (W3)  
The equations needed to estimate the parameters with MOM have the following expressions [18; 20]:  
\[\mu=\gamma+\beta\cdot\Gamma\bigg{(}1+\frac{1}{\alpha}\bigg{)} \tag{72}\]  
\[\sigma^{2}=\beta^{2}\cdot\bigg{[}\Gamma\bigg{(}1+\frac{2}{\alpha}\bigg{)}- \Gamma\bigg{(}1+\frac{1}{\alpha}\bigg{)}^{2}\bigg{]} \tag{73}\]  
\[C_{s}=\frac{\Gamma\big{(}1+\frac{3}{\alpha}\big{)}-3\cdot\Gamma\big{(}1+\frac {2}{\alpha}\big{)}\cdot\Gamma\Big{(}1+\frac{1}{\alpha}\Big{)}+2\cdot\Gamma \Big{(}1+\frac{1}{\alpha}\Big{)}^{3}}{\bigg{(}\Gamma\big{(}1+\frac{2}{\alpha} \big{)}-\Gamma\Big{(}1+\frac{1}{\alpha}\Big{)}^{2}\bigg{)}^{1.5}} \tag{74}\]  
The shape parameter can be obtained approximately, depending on the skewness coefficient, using the following rational function [20]:  
\[\alpha=\frac{3.5947875-3.7374529\cdot C_{s}+2.7023151\cdot C_{s}^{2}-1.1835102 \cdot C_{s}^{3}+}{1+0.0931316\cdot C_{s}-0.12783\cdot C_{s}^{2}+0.0671082\cdot C _{s}^{3}+0.1113667\cdot C_{s}^{4}-} \tag{75}\]  
\[0.0425475\cdot C_{s}^{5}+0.0673629\cdot C_{s}^{6}-0.0157628\cdot C_{s}^{7}\]  
\[\beta=\frac{\sigma}{\sqrt{\Gamma\big{(}1+\frac{2}{\alpha}\big{)}-\Gamma\Big{(} 1+\frac{1}{\alpha}\Big{)}^{2}}} \tag{76}\]  
\[\gamma=\mu-\beta\cdot\Gamma\bigg{(}1+\frac{1}{\alpha}\bigg{)} \tag{77}\]  
The equations needed to estimate the parameters with L-moments have the following expressions [18; 20]:  
\[L_{1}=\gamma+\beta\cdot\Gamma\bigg{(}1+\frac{1}{\alpha}\bigg{)} \tag{78}\]  
\[L_{2}=\beta\cdot\Gamma\bigg{(}1+\frac{1}{\alpha}\bigg{)}\cdot\Big{(}1-2^{- \frac{1}{\alpha}}\bigg{)} \tag{79}\]  
\[L_{3}=\beta\cdot\Gamma\bigg{(}1+\frac{1}{\alpha}\bigg{)}\cdot\Big{(}1-2^{- \frac{1}{\alpha}}\bigg{)}\cdot\bigg{(}2\cdot\frac{2^{-\frac{1}{\alpha}}-3^{- \frac{1}{\alpha}}}{2^{-\frac{1}{\alpha}}-1}+1\bigg{)} \tag{80}\]  
An approximate form of the shape parameters can be adopted, depending on L-skewness (\(\tau_{3}\)), as follows [20]:  
\[\alpha=\frac{3.528107902-6.294082546\cdot|\tau_{3}|+2.767652838\cdot\tau_{3}^{ 2}}{1+4.599024923\cdot|\tau_{3}|-7.993601572\cdot\tau_{3}^{2}+2.423742593\cdot| \tau_{3}|^{3}} \tag{81}\]With parameter \(\alpha\) known, the parameters \(\beta\) and \(\gamma\) are determined with the following expressions:  
\[\beta=\frac{L_{2}}{\Gamma\Big{(}1+\frac{1}{\alpha}\Big{)}\cdot\Big{(}1-2^{-\frac {1}{\alpha}}\Big{)}} \tag{82}\]  
\[\gamma=L_{1}-\frac{\beta}{\alpha}\cdot\Gamma\Big{(}\frac{1}{\alpha}\Big{)} \tag{83}\]
## 1 Introduction
### The Proposed Method and Article Structure  
In this paper, we propose a feature extraction method based on the self-organizing mapping (SOM) of dictionary atoms. In this method, we first use K-SVD dictionary learning algorithm to construct a sparse dictionary. Then, self-organizing mapping is performed on the dictionary atoms, and the code vector of the neuron is compared with the target ERP waveform as a time-domain waveform. The code vectors with the largest cosine similarity value to the target ERP waveform are found. For the EEGsignal frame to be recognized, the cosine similarity between the to-be-recognized frame and the selected code vectors are calculated. These similarity values are the extracted classification features. Finally, the classifier is trained using these features to find the ERP waveform. In the testing phase, the SOM, sparse dictionary, and classifier of the training phase are reused, and the feature extraction operation is repeated for the EEG samples to be recognized. Compared to the three categories of methods mentioned previously, the proposed method has the following advantages: (1) It does not rely on multichannel data. (2) It can deal with non-stationary ERP waveforms. (3) It can make the most use of ERP fragments in sparse dictionary.  
This article unfolds as follows: Section 2 provides a brief introduction to EEG sparse decomposition and the procedures of proposed method. In Section 3, the experiment material is explained, and we present the results produced by proposed method. In Sections 4 and 5, we discussed the advantages of the proposed method and potential further improvements.
## 5 Comparative Simulation Results and Discussion  
In this section, we present simulation results to compare the performance of the proposed method with existing linear and nonlinear control approaches. Commercial autopilots commonly rely on PID controllers due to their simplicity of implementation on small UAV platforms [35]. To establish a baseline, we compare our method with a cascaded anti-wind-up control scheme presented in [33], where the dynamics are linearized around the operating point and PID controllers with properly tuned gains are employed. Furthermore, we consider the hybrid P-PPC scheme introduced in [30] as a nonlinear control benchmark for our comparative analysis. The simulations conducted on the same Aerosonde model as described in Section 4. Through these comparative simulations, our goal is to showcase the superiority of the proposed approach over alternative approaches. By directly comparing the performance of our method with existing control methods, we aim to provide compelling evidence of its enhanced capabilities and advantages.
## 4 Case Study
### Unstructured Data Analysis  
In this step, Spyder was used as an auxiliary tool. Spyder provides an interactive Python language development environment, and it has advanced editing, interactive testing, debugging, and introspection features [56]. Due to the standard structure of a Chinese patent, Regular Expression \"re.findall(r\"background(+)contents',text)\" was used to extract each patent's background section. Then, a maximum of 200 verbs or nouns were extracted from each background section using TextRank. Here, the frequency of keywords in TF-IDF was set from 10 to 90%. Next, a coherence model in Gensim was used to estimate the appropriate number of clusters. The perplexity for different clusters is shown in Figure 12. It shows that a smaller perplexity value indicates a more appropriate number of clusters. The cluster K value was thus set to 12. Table 3 lists the final 12 classifications with 10 keywords (the closest to each center). Figure 13 shows the clustering results. The rightmost blue dot area is composed of four classifications. According to the keywords, they can be summed up as a \"shoot or process image\", corresponding to the execution system of a UAV under the law of system integrity. The orange dot area is composed of two categories, they can be summed up as \"control or flight\", according to the keywords, corresponding to the control system of a UAV under the law of system integrity. The leftmost green area is composed of three categories, and they all relate to \"telecontrol\", according to their keywords. The red dot area above contains two categories. According to their keywords, they can be summed up as \"engine or structures\", corresponding to the energy system and transmission system of an UAV under the law of system integrity. The last category of gray dots can be summed up as \"communication\" based on the keywords. Together, the patent set was regrouped into six patent classifications. Based on these proprietary technical features, six R&D themes were summarized, as in the last column of Table 3. They are the problem areas of existing products. Figure 10: UAV inventions that DJI applied for patents for from 1 January 2010 to 22 June 2022. Figure 11: Visualization of the IPC/date matrix based on patents DJI applied for from 2010 to 2020. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**IPC** & **VON** & **IPC** & **VON** & **IPC** & **VON** & **IPC** & **VON** & **IPC** & **VON** & **IPC** & **VON** \\ \hline G01C & 2 & G01P & 7 & G03B & 7 & H02G & 8 & H04W & 8 & H05K & 8 \\ G01R & 6 & H02P & 7 & B64D & 7 & G01B & 8 & G08C & 8 & G06F & 8 \\ G01S & 6 & F16F & 7 & H01R & 7 & H04M & 8 & H01Q & 8 & H01M & 8 \\ F16M & 6 & G05B & 7 & H04B & 8 & G05G & 8 & H04L & 8 & H04N & 8 \\ G05D & 6 & H02K & 7 & G02B & 8 & G06T & 8 & H02J & 8 & B64C & 8 \\ \hline \end{tabular}
\end{table} 
Table 2: IPCs and their value of technological novelty. Figure 12: The perplexity for each number of clusters. Figure 13: Result of K-means clustering.
## 1 Introduction  
Carbon dioxide (CO\({}_{2}\)) is one of the most important gases in several technologically and sociopolitically relevant fields, like combustion and exhaust gas technology, breath gas analysis, agriculture, or pharmaceutical and food processing industry. CO\({}_{2}\) monitoring, storage, reduction, transport, and conversion technologies have become increasingly important, in particular as CO\({}_{2}\) gas is considered to be the primary anthropogenic greenhouse gas, accounting for the utmost part of the recent human contribution to the greenhouse effect.  
Recent progress in optoelectronics has triggered the use of quantum cascade lasers (QCLs) for spectroscopic laser absorption techniques in gas sensing applications [1, 2]. CO\({}_{2}\) gas is suited for such methods due to high extinction cross sections of its vibrational-rotational transitions in the mid-infrared spectral range. A key property of these techniques is their non-invasive character, that is, the sensor devices do not require direct contact to the medium of interest. Quantum cascade laser-based absorption spectroscopy (QCLAS) thus uniquely combines several major advantages, such as high sensitivity, exceptional time-resolution, and the possibility to perform real-time in situ measurements. In recent years, QCLAS has proved to be an essential probe of CO\({}_{2}\) in, e.g., biomedical breath analysis [3, 4, 5, 6, 7], the investigation of plasma and combustion processes [8, 9], or environmental and atmospheric gas sensing [10, 11, 12, 13].  
Vibrational-rotational cross sections of gases are generally sensitive to temperature, of which CO\({}_{2}\) by no means is an exception. As a result, the quantification of CO\({}_{2}\) gas concentration from specific spectral absorption lines is subject to a certain inaccuracy if the instant temperature of the medium is unknown. From an application point of view, this issue is particularly relevant if the system under investigation experience strong dynamic variations in gas temperature too fast to be tracked by conventional temperature sensing devices like platinum resistance thermometers and thermocouple elements. An inherent disentanglement in the determination of CO\({}_{2}\) concentration and temperature by laser spectroscopy would thus support efforts in resolving fast changes of these parameters in real-time with high precision, as required for instance in online measurements of CO\({}_{2}\) as biomarker in exhaled air for clinical testing, etc.  
In the present paper, we report on a proof-of-concept study of a QCL-based absorption method that is optimized to measure temperature _and_ CO\({}_{2}\) concentration of gas mixtures in a range close to room temperature on a nanosecond time scale. The method employs strong CO\({}_{2}\) absorption lines with opposite temperature dependence which are probed by a single nanosecond frequency down chirped IR pulse of a distributed feedback QCL operated in intrapulse mode [14] with scanning rates up to \(\sim\)40 kHz. We demonstrate measurements on simulated breath gas flow and discuss the method with respect to potential improvements.
# Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades
## 1 Introduction
We used published experimental data regarding changes in resonant frequencies and attenuation to estimate the impact of cracks on non-linear blade vibration parameters. The suggested methods used to measure wideband signal modulation are based on the DEMON algorithm, which is widely used in submarine detection. Laboratory tests were conducted using small steel samples (10 inches in length) under cyclic loading. These tests demonstrated the feasibility of measuring the modulation of the wideband signal in laboratory conditions using the DEMON algorithm. The structure of the article and steps performed are illustrated in Figure 2.
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 5 Experiment and Evaluation
### Latency  
This experiment examines the relationship between link delay and the number of competing streams under the Ad-BBR algorithm in a multi-stream contention scenario. The setup involves 1, 2, 4, 8, and 16 streams competing for a 100 Mbps bottleneck bandwidth with a 1 BDP bottleneck buffer. The link delay is observed and plotted as a function of the number of streams, with the results presented in Figure 16.  
Figure 16: Link delay vs. flow count under (100 Mbps, 1 BDP) conditions.  
Figure 15: Retransmission rate vs. flow counts with 0.1/1 BDP buffer.  
When there is only one flow in the link, all six algorithms exhibit relatively low latency. As the number of flows increases, the latency rises for all algorithms. However, packet loss-based algorithms like Cubic and Reno experience a sharper and more significant latency increase compared to BBR-related algorithms. This is because Cubic and Reno attempt to achieve higher throughput with limited buffer space, leading to faster queue buildup and increased queuing delays. BBRv1 exhibits more drastic delay variations than improved BBR algorithms due to its data over-injection problem and the dominance of long RTT flows. Among them, the BBQ algorithm achieves better queue delay reduction through its joint bandwidth- and queue-awareness mechanism, making its queue delay more stable and lower than BBRv3 in this experiment. Compared to other algorithms, Ad-BBR demonstrates more stable and minimal delay variations. Its delay increase with the growing number of streams is the smallest, and it maintains the lowest latency for a given number of streams. This performance represents a \(\sim\)20% improvement over BBRv1 and represents a \(\sim\)17% improvement over BBRv3, highlighting the importance of reducing data over-injection and enhancing RTT fairness in minimizing queue delays.
## 1 Introduction  
Silicon photonics has emerged as a powerful technology for optical networks in datacom and computercom with the commercial success of silicon photonic transceivers [1; 2; 3]. In the past decade, silicon photonics has been rapidly matured with the process standardization and the process design kit (PDK) development of silicon photonic foundries [4; 5]. Large-scale silicon photonic integrated circuits have been demonstrated for a variety of applications, such as photonic switches [6; 7; 8], optical phased arrays (OPAs) [9; 10; 11; 12], and programmable photonic processors [13; 14; 15; 16; 17]. A 3-dB optical power splitter is one of the key components to build high-density integrated photonic devices with multi-channel and parallel processing. A 3-dB optical power splitter can be employed in integrated Mach-Zehnder interferometers (MZI) to demonstrate intensity modulators or 1 x 2 switches [18; 19; 20], where the light traveling in a waveguide is split into two arms, the sensing and the reference arms. The optical power uniformity between two arms is extremely significant, which has motivated us to design a compact, low-loss, and excellent power uniformity optical power splitter. The 3-dB power splitters can be also utilized to demonstrate 1 x N splitters [9; 12; 21; 22]. Notably, low-loss and uniform 1 x N power splitters are essential devices for large-scale silicon photonic OPAs. Various silicon photonic 3-dB power splitters have been demonstrated with multimode interferometers [9; 12; 22; 23; 24], directional couplers (DCs) [25], asymmetric waveguide-based phase control [26], adiabatic couplers with S-bend-based Y-branches [27], and parameterized Y-junctions [28], which typically exhibit non-negligible excess losses or limited bandwidths.  
Recently, low-loss and broadband power splitters based on adiabatically tapered waveguides have been reported [29; 30]. However, these power splitters were designed with silicon strip waveguides, which cause tight mode confinement and weak coupling strength between waveguides. As a result, the designed critical dimensions (CDs) such as tapered tips and waveguide-gaps (30-50 nm), are beyond the deep ultraviolet (DUV) lithography limit of typical silicon photonics foundries. In this paper, we reported the experimental demonstration of power splitters based on adiabatically tapered silicon rib waveguides. The shallow-etched rib waveguides offered relatively strong coupling and relaxed critical dimensions (CDs \(\geq\) 200 nm), which allowed the device dimensions to be compatible with standard processes of existing silicon photonic foundries. The demonstrated device had a low excess loss of 0.06 dB and a wide operating wavelength range over 100 nm (1470-1570 nm). The fabrication tolerance analysis confirmed that the proposed power splitter is fabrication insensitive within a large deviation range of \(\pm\)50 nm for both the width and gap.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 3 Experimental Evaluation
### Analysis of Experimental Results  
#### 3.5.1 Track Deviation Detection  
The MOD algorithm can be used to preprocess the track data and detect the track deviation from the route in the data. Take longitude, latitude and height for example, Figure 4 shows the track deviation detection result based on MOD, in which the red dotted line indicates the track deviated from the route and the green solid line indicates the normal track. Figure 3(a)-d respectively show the flight track of flights CCA1315, CCA1369, CCA1883 and CCA1803 within a month. According to the detection results, the tracks that obviously deviate from the fixed flight route are removed from the data set.  
Figure 4: Track Deviation Detection Result.  
In order to verify the effectiveness and innovation of the proposed method, we can compare the model in this paper with the LSTM model, Bi-LSTM model, BP model, LR model and CNN model. The results are shown in Table 3.  
To solve the adaptive problem of the threshold of aircraft anomaly detection, the SVDD classifier is obtained based on the difference sequence between the predicted sequence and the real sequence, and the automatic detection of track outliers is realized. Next, 100 tracks' data is randomly extracted from the data set. The flight speed in a certain period of time is changed to 0.5 times of the original speed, the flight height is increased by 50 m, and the heading is reduced by 10 degrees. The modified track data and normal track data are added into the SVDD classifier for testing. Out of the 100 abnormal test samples, a total of 96 test samples showed that the distance between the predicted value and the true value to the center of the hypersphere is greater than the threshold value r, so the detection accuracy is 96%. Out of 265 normal test samples, a total of 258 test samples showed that the difference between the predicted value and the true value is less than the threshold r from the center of the hypersphere, thus the recall rate of detection is 97.36%. Table 4 presents a comparison of the detection effects of the models in this paper (the LSTM model, Bi-LSTM model, BP model, LR model and CNN model) on track outliers of aircraft.  
As shown in Table 4, the accuracy and recall of the algorithm in this paper are both higher than those of other methods, in particular the recall rate is much higher than that of other methods. A high recall rate means that the model can detect more real outliers, which is very important in anomaly detection.
## 3 Combined Inertial Control Based on CAE and DNN  
In the event of a load disturbance, an online calculation of the optimal combined inertial control parameters is required during the disturbance burst, owing to the uncertainty surrounding factors such as wind speed, the proportion of wind power, and the magnitude of the load disturbance of these three variables. If the traditional time-domain simulation method is used to manually adjust the combined inertial control parameters based on experience, it will take a long time and may not be able to find the optimal control parameters. Additionally, with these three variables constantly changing, the corresponding combined inertial optimal control parameters are often different, which greatly increases the difficulty of frequency regulation. In order to reduce the time required in the online calculation process, taking into account the accuracy of the control parameters, a combined inertial intelligent control for wind power frequency regulation based on CAE and DNN is presented. Through the acquisition of optimal control parameters for varying wind speed, wind power proportion, and load disturbance scenarios, the power system can swiftly and precisely determine the corresponding combined inertial control parameters when a load disturbance occurs.
## 6 Conclusions  
In our paper, a multicast model of sensor network can be formulated as a \"tree forest\" type architecture that jointly considers the routing problem and link capacity assignment through various mathematical programming techniques. Fairness can also be achieved in the inter-group and intra-group end-to-end delay in multi-rate multicast wireless sensor network. Our contributions in this research are the solution of an NP-complete problem through mathematical programming techniques to determine the optimal solution LR and LB. Finally, the Lagrangean Relaxation Method and Optimization-based algorithm are provided to solve this problem and have been proven to have good quality after verification with other simple algorithms and LB value. The LR based algorithm and our heuristic are implemented to prove that solution quality is better than that of SA. The solution utilizes  
Figure 11: Inter-group end-to-end delay.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Number of** & **Number of** & **LB** & **LR** & **SA** & **Gap (\%)** & **I. R. (\%)** \\
**Group** & **Destination** & & & & & \\ \hline
2 & 2 & 0.025702 & 0.450961 & 0.569503 & 94.3007\% & 20.8150\% \\  & 3 & 0.026917 & 0.538237 & 0.663245 & 94.9990\% & 18.8479\% \\  & 4 & 0.075271 & 0.64264 & 0.883838 & 88.2873\% & 27.2898\% \\  & 5 & 0.08825 & 0.858677 & 1.43812 & 89.7226\% & 40.2917\% \\
3 & 2 & 0.025748 & 0.65759 & 2.67525 & 96.0845\% & 75.4195\% \\  & 3 & 0.025641 & 2.45674 & 3.44923 & 98.9563\% & 28.7742\% \\  & 4 & 0.025641 & 4.6933 & 7.26076 & 99.4537\% & 35.3608\% \\  & 5 & 0.025641 & 5.62759 & 8.62115 & 99.5444\% & 34.7234\% \\
4 & 2 & 0.025641 & 1.25577 & 2.21255 & 97.9581\% & 43.2433\% \\  & 3 & 0.037905 & 1.78956 & 2.60234 & 97.8819\% & 31.2327\% \\  & 4 & 0.025641 & 2.16318 & 4.63587 & 98.8147\% & 53.3382\% \\  & 5 & 0.025641 & 3.04591 & 3.29655 & 99.1582\% & 7.6031\% \\
5 & 2 & 0.025641 & 1.23 & 1.79299 & 97.9154\% & 31.3995\% \\  & 3 & 0.025641 & 1.98478 & 2.40776 & 98.7081\% & 17.5674\% \\  & 4 & 0.025641 & 5.05017 & 6.04086 & 99.4923\% & 16.3998\% \\  & 5 & 0.025641 & 5.69069 & 25.1829 & 99.5494\% & 77.4026\% \\ \hline \end{tabular}
\end{table}
Table 5: Experiment result explanation.  
the Lagrangean Relaxation Method in conjunction with novel optimization-based heuristics. Computational experiments have been conducted to evaluate the performance of the proposed algorithms. In conclusion, our contribution has been perfectly solving the complicated optimization problem through the Lagrangean Relaxation Method with more efficiency and effectiveness.