esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
Rank & & & & & & & & & & \\ (Fixed) & 41.1 & 8.11 & 4.22 & 6.89 & 7.44 & 3.11 & 9.67 & 2.78 & 7.67 & **1.00** \\ \hline Var\_Stream\_1 & 96.20 (9) & 94.77 (5) & 92.57 (6) & 86.77 (9) & 85.61 (10) & 95.27 (4) & 87.79 (8) & 96.84 (2) & 91.02 (7) & **97.02 (1)** \\ Var\_Stream\_2 & 96.73 (9) & 94.25 (9) & 91.71 (8) & 87.54 (9) & 86.81 (10) & 95.29 (4) & 87.23 (9) & 96.97 (7) & **97.37 (1)** \\ Var\_Sud\_1 & 94.14 (8) & 87.02 (6) & 83.72 (9) & 93.09 (5) & 78.78 (10) & 94.51 (5) & 86.37 (7) & 95.00 (2) & **95.33 (1)** \\ Var\_Sud\_1 & 95.28 (9) & 83.28 (9) & 88.84 (8) & 99.23 (8) & 84.72 (7) & 91.71 (4) & 83.16 (10) & 92.10 (3) & 83.99 (8) & **92.60 (1)** \\ Var\_Grad\_1 & 86.68 (8) & 87.14 (7) & 91.69 (4) & 93.32 (2) & 84.73 (10) & 90.51 (8) & 86.66 (9) & 91.89 (3) & 90.71 (5) & **95.19 (1)** \\ Var\_Grad\_2 & 87.69 (7) & 84.22 (8) & 90.79 (4) & 89.14 (5) & 84.10 (10) & 91.55 (3) & 84.21 (9) & 94.49 (2) & 88.99 (6) & **94.71 (1)** \\ Var\_Mix\_RS1 & 88.64 (5) & 76.66 (7) & 87.13 (9) & 89.06 (4) & 80.20 (7) & **91.20 (1)** & 75.61 (10) & 89.55 (3) & 78.94 (8) & 90.37 (2) \\ Var\_Mix\_SG1 & 83.37 (7) & 78.10 (10) & 86.79 (5) & 90.46 (10) & 79.52 (8) & 90.09 (2) & 78.30 (9) & 89.31 (4) & 83.69 (8) & 90.65 (5) \\ Var\_Mix\_SG1 & 91.99 (2) & 83.49 (8) & 88.75 (5) & 88.18 (6) & 84.56 (7) & 90.73 (4) & 79.66 (10) & 91.98 (3) & 82.81 (9) & **92.83 (1)** \\ \hline Avg. Rank & & & & & & & & & & \\ (Var) & 4.56 & 7.44 & 5.67 & 5.00 & 8.78 & 3.44 & 9.00 & 2.67 & 7.11 & **1.33** \\ \hline Covtype & 81.75 (6) & 72.71 (9) & 87.98 (2) & **89.52 (1)** & 76.31 (7) & 84.53 (4) & 67.90 (10) & 83.59 (5) & 75.06 (8) & 85.80 (3) \\ Shuttle & 94.46 (5) & 87.61 (9) & 97.65 (6) & **98.72 (1)** & 89.36 (6) & 95.81 (5) & 87.83 (10) & 89.08 (5) & 83.38 (10) & 95.26 (4) \\ hypothyroid & 88.66 (2) & **20.54 (1)** & 21.78 (6) & 0.62 (9) & 8.60 (7) & 13.35 (3) & 0.02 (10) & 12.46 (4) & 12.22 (5) & 15.57 (2) \\ competat\({}^{+}\) & 51.23 (4) & 43.29 (4) & 48.23 (5) & 51.87 (2) & 28.00 (10) & **52.89 (3)** & 23.13 (9) & 42.75 (7) & 36.89 (8) & 51.58 (3) \\ diabetes & 22.88 (8) & 21.99 (6) & 24.44 (4) & 23.29 (4) & 20.56 (9) & 24.36 (2) & 20.97 (8) & 21.19 (7) & 16.13 (10) & 23.41 (5) \\ CIC\_ISC & 99.39 (4) & 97.63 (8) & 99.40 (5) & **99.76 (1)** & 98.58 (7) & 99.41 (2) & 93.23 (10) & 98.84 (6) & 96.28 (9) & 99.32 (5) \\ \hline Avg. Rank & & & & & & & & & & \\ (Real)
## 2 Related Work
### Irregular Radio Communication and Anisotropic Network  
WSNs can be classified into isotropic and anisotropic networks based on their properties. The communication range of a node depends on the transmission power of the sensor node and the communication environment [17]. The incremental variation in radio signals with direction leads to irregularities in the radio direction pattern, and [PERSON] et al. proposed an RIM based on this characteristic [18].  
The model used the degree of irregularity (DOI) to represent the percentage change in the maximum path loss per unit degree change in the direction of radio propagation. In practical scenarios, it is an irregular figure that approximates a circle. A high DOI can lead to interruptions in the communication between nodes and can alter the topological distribution of the network. Therefore, radio irregularity is one of the key factors contributing to the formation of anisotropic networks. The radiation pattern of the radio communication in different DOI states is shown in Figure 1.
## 2 Transmission Setup and Principles
### Modulation Error Ratio  
The \(MER\) is also an important indicator to evaluate the quality of a communication system, and it can well reflect the ability of digital receivers in restoring transmitted data [31]. The \(MER\) is measured using the ratio between the average signal power and the average error power [32], and is defined in \(dB\) as follows  
\[MER(dB)=10\log_{10}(\frac{P_{signal}}{P_{error}}) \tag{9}\]  
where \(P_{error}\) is the root mean square (RMS) power of the error vector and \(P_{signal}\) is the RMS power of the ideal transmitted signal. It can be found that the MER provides a similar evaluation on the quality of the received signal, compared to the concept of the signal-to-noise-ratio (SNR) [4].
# Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks
## 4 Route Formation Process
### Selection Criteria  
The forwarding node can be selected based on RSS, number of hops, and the number of neighbors of the forwarding node. If the protocol is used for delay sensitive data, then the smaller number of hops will be given more weight. If energy conservation is the prime goal, then RSS and least number of neighbors will be given preference over the number of hops. Below, a scenario is considered to understand the process of selection in more detail.  
1. Suppose node A (Figure 8) broadcasts the RR packets which are received by nodes B and C, because they are in the transmission range of node A;
2. The information contained in the RR packet is shown in Figure 9. The RSS between node A and the gateway (GW) is 2 (assumed). Node A is one hop away from GW and A has no knowledge about its neighbors so far. Node A learns about its neighbors when it broadcasts the RR packet and receives the acknowledgement(s);
3. When nodes B and C send the acknowledgement (RR_ACK), node A learns that it has become part of the routing path of nodes B and C;
4. Now, either B or C broadcasts the RR packet first (depends on the selected time slot);
5. Let us assume that node B will send the RR packet first. When B broadcasts RR packet it contains the information of signal strength between A and itself, number of hops it is away from the gateway through node A, and the number of neighbors (see Figure 10);
6. At this point, C compares the RSS, the least number of hops, and the least number of neighbors to form the routing path with A or B;
7. Node C can choose its path to the gateway by two routes: C \(\rightarrow\) A or C \(\rightarrow\) B \(\rightarrow\) A:  
Figure 8: Transmission range for the Route Request (RR) packet.
## 8 Energy Optimization Techniques in UAV-Based Cellular Networks
### Conventional Approaches
#### 8.1.4 Flight and Transmission Scheduling
Machine Learning Approaches  
One of the ways of achieving energy efficient UAV operation, particularly for multiple-UAV networks, is the proper scheduling of their mobility as well as battery recharging cycle. In this regard, the authors in [30] proposed an energy-efficient and fair 3D UAV-BS framework for scheduling the mobility of UAV-BSs as well as timely recharging of the UAV battery. A DRL algorithm based on DDPG was employed to solve the joint optimization of both the mobility and charging cycle of the UAV-BSs, leading to maximization of EE and achievement of fair user coverage. The work in [263; 264] considered the problem of user scheduling in UAV-based communication networks to minimize the energy consumption of the UAVs. Two offline optimal heuristic algorithms were proposed to solve the problem. However, since these solutions might not be suitable for online operations, an online actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm with less computational complexity was developed to solve the user scheduling problem. The proposed method resulted in a 25-30% reduction in energy consumption of the network compared to two DRL-based benchmarks. The CA and ML approaches that are applied for UAV-BSs flight and transmission scheduling in order to minimize their energy consumption are summarized in Table 4.
## 1 Introduction
That is why well-controlled experimental PVD methods of fabricating metal thin films of silver and gold with sophisticated surface morphology by self-assembling remains an extensive area of research. A lot of papers are devoted to island-like silver films with a thickness below the percolation threshold (non-continuous) obtained by vacuum sputtering methods. However, such films have limited applications due to low thermal conductivity, leading to SERS substrate degradation when exposed to exciting laser radiation power above several mW. By experimentally controlling evaporation process recipe parameters, it becomes possible to obtain continuous noble metal thin films with awell-controlled morphology down to atomically flat surfaces, crystalline structure, electrical and optical parameters [41; 42; 43; 44; 45; 46; 47]. In this paper, we investigate the possibility of creating thin silver films with complex self-assembled morphology using the electron-beam evaporation in vacuum, which makes it possible to obtain high-amplitude spectra of myoglobin deposited on the surface from low concentration solution. We experimentally determine the e-beam evaporation recipe parameters to get SERS-active substrates with complex surface morphology, that allow obtaining the maximum SERS signal amplification for a particular type of analyte. At the same time, we observe that proposed SERS-active substrates promote quite important processes of concentrating liquid analyte probes during drying process on the substrate surfaces with characteristic uniform sediment ring dimensions less than 200 microns in diameter, which is also quite a challenge. Finally, the mathematical data treatment of the film surface morphology is carried out, followed by machine learning analysis to provide automatic characterization and forecast technique of SERS-active substrate quality.
## 6 Calculation of Parameters for Mudslide Mitigation
### Sediment Yield from a Mudslide  
The total volume of sediment transported by a single mudslide event can be calculated using Equation (14):  
\[Q_{H}=Q(\gamma_{c}-\gamma_{w})/(\gamma_{H}-\gamma_{w}) \tag{14}\]  
where \(Q_{H}\) represents the total amount of sediment discharged by a single mudslide event (m\({}^{3}\)); \(Q\) denotes the total mudslide volume (m\({}^{3}\)); \(\gamma_{c}\) indicates the specific weight of solids within the mudslide (t/m\({}^{3}\)); \(\gamma_{w}\) is the specific weight of water (t/m\({}^{3}\)); and \(\gamma_{H}\) is the specific weight of solids in the mudslide (t/m\({}^{3}\)).  
Due to the lack of historical monitoring data for mudslides in this gully, the type and proportion of solids involved in mudslide movement were estimated based on current gully conditions. The specific weight of solids at the main gully catchment was assumed to be 2.5 t/m\({}^{3}\), while the specific weight of solids at the tributary gully catchment was assumed to be 2.4 t/m\({}^{3}\). The calculated sediment yields for each catchment are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Section**} & **Mudslide Specific** & **Water Specific** & **Solids Specific** & & \\  & **Weight** & **Weight** & **Weight** & & **Sediments Volume** \\  & \(\gamma_{c}\) & \(\gamma_{w}\) & \(\gamma_{H}\) & & \(Q_{H}\) (10\({}^{4}\) m\({}^{3}\)) \\ \cline{2-7}  & **(t/m\({}^{3}\))** & **(t/m\({}^{3}\))** & & & \\ \cline{2-7}  & & & & & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% \\ \hline Main gully catchment & 1.537 & 1 & 2.5 & 0.1457 & 0.1340 & 0.1165 \\ \hline Tributary gully catchment & 1.537 & 1 & 2.4 & 0.0952 & 0.0875 & 0.0761 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation of total sediment volume transported by a single mudslide event at the main gully and tributary gully catchments in Dashing Gully.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{Section} & \multicolumn{2}{c}{Mudslide duration} & \multirow{3}{*}{Peak mudslide discharge Qc (m\({}^{3}\)/s)} & \multirow{3}{*}{K} & \multirow{3}{*}{Total mudslide volume Q (10\({}^{4}\) m\({}^{3}\)/h)} \\ \cline{2-2} \cline{5-8}  & (s) & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% & & \(p\) = 1\% & \(p\) = 2\% & \(p\) = 3\% \\ \hline Main gully catchment & \multirow{3}{*}{3600} & \multirow{3}{*}{5.60} & \multirow{3}{*}{5.15} & \multirow{3}{*}{4.48} & \multirow{3}{*}{0.202} & \multirow{3}{*}{0.4069} & \multirow{3}{*}{0.3744} & \multirow{3}{*}{0.3255} \\ \cline{2-2} \cline{5-8} Secondary gully catchment & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation of total mudslide volume (Q) in the main gully and tributary gullies of the Dashing Gully for various rainfall frequencies.
# Overview of the Development of 3D-Printing Concrete: A Review
## 3 Performance Requirements of 3D-printing Concrete Materials
### Printability of 3D-printing Concrete Materials
#### 3.1.2 Extrudability  
Extrudability refers to the difficulty of 3D-printing concrete in the extrusion process and the continuity and surface quality after extrusion. The study of extrudability can ensure that the slurry can be continuously transported through the feed pipe and deposited smoothly through the nozzle of the print head. It is a guarantee for continuous printing construction and can ensure the integrity of the printing building.  
An important factor affecting extrudability is the ratio of aggregate particle size to the diameter of the extrusion nozzle. If the aggregate particle size is too large, the extrusion nozzle will be blocked. Conversely, if the aggregate particle size is too small, its surface area will increase, and the amount of slurry that required to wrap the surface of the aggregate will increase, causing the concrete to easily crack. [PERSON] [23] found that the use of  
Figure 3: Jump table test employed for flowability measurement. Reprinted with permission from ref. [21]. Copyright 2017 Elsevier Ltd.  
Figure 2: Relationship between the flowability of cement paste and the optimum amount of natural sand. Reprinted with permission from ref. [18]. Copyright 2019 Elsevier Ltd.  
fine aggregate materials can ensure good extrudability and effectively prevent concrete materials from blocking the printing pipes and nozzles. [PERSON] et al. [24] found that when the maximum particle size of the sand in the mixture is 2 mm, and the weight ratio of the cement is 2, and when the ratio of the nozzle diameter to the maximum aggregate particle size is greater than 5, the printing material will not be extruded. Blockage occurred. [PERSON] et al. [25] found that while reducing sand, increasing the amount of cement will make it have better extrudability. When the ratio of fine aggregate to cement is 1.28, the ratio of fine aggregate to sand is 2. The cement ratio required for proper extrudability with minimum water is 0.48, the best nozzle diameter is 2 cm, and the extrudability is the best. [PERSON] et al. [26] found that when the content of fiber mixed in cement-based materials exceeds about 1.5%, it will cause the printing nozzle to block.  
The extrudability test is mainly evaluated by the apparent quality of the strip or the extrusion pressure. [PERSON] et al. [16] extruded a band with a total length of 4500 mm from a 9 mm wide nozzle without clogging or breaking, as a criterion for meeting extrudability, as shown in Figure 4a. [PERSON] et al. [27] used the process parameters with a print nozzle diameter of 15 mm and a printing speed of 100 mm/s, as shown in Figure 4b, and evaluated the extrudability by observing whether the mixture was blocking the pipe during the process of stacking 20 layers of strips with a length of 500 mm and a width of 350 mm. [PERSON] et al. [28] used a stamping extruder to quantify the extrusion pressure of different blends to characterize the extrudability.
## 3 Results and Discussions
By fitting with the embedded measurement result, series and parasitic parameters can be extracted rapidly. They are listed in Table 2. Analysis on these parasitic parameters originating from contact pads and interconnections provides an insight into RF measurement. Figure 2:0 (a) Schematic diagram of on-chip spiral inductor with GSG pads and its (b) topology and equivalent physical models. Figure 2:1 (a) Measured and modeled magnitude and phase of \(Y_{1I}\) and \(Y_{1Z}\) for the inductors with \(R_{\textit{int}}=600\) μm; (b) Inductances and \(Q\)-factors for the inductors with different \(R_{\textit{int}}\).
## 1 Introduction
In the frequency domain, frequency-based saliency detection methods, such as the Spectral Residual (SR) approach [23], Frequency-tuned (FT) method [24], and Maximum Symmetric Surround (MSS) method [25] have fewer parameters and less time consumption than those saliency detection methods in a spatial domain. However, in the case of large ripple, reflection, and other interference factors, redundancies of salient maps are difficult to eliminate, resulting in extreme high false alarm rates. In order to overcome the problems of small sample acquisition and several interference factors, i.e., ripple, reflection, and uneven illumination in floating objects detection, a simple but effective method combining spatial and frequency domains is proposed. In the spatial domain, after training with few positive samples, sliding image blocks are roughly classified by Support Vector Machines (SVM) [26; 27; 28] with a Fused Histogram of Oriented Gradient (FHOG) features [29] and GLCM features to detect floating objects. In the frequency domain, Gaussian high-pass filtering and phase spectrum reconstruction are implemented to preliminarily suppress the interferences. Then, global and local low-rank decompositions [30; 31] are carried out to remove the residual redundant regions. Finally, bounding boxes from different processing domains are combined by analyzing Intersection-over-Unions (IoUs) to frame real objects accurately. The main contributions of this paper are as follows:  
An effective combination of hand-crafted texture features, i.e., FHOG and GLCM. The new combined features describe floating objects more accurately than traditional HOG, GLCM, and LBP features in complex water scenes, while its time consumption of feature extraction is comparable to the traditional ones. A new anti-interference saliency detection method. The method processes a frequency spectrum and adopts low-rank matrix decomposition to accurately detect floating objects without any training samples. Note that the method effectively solves the problems caused by ripple, reflection, and uneven illumination, and performs better than other sample-free saliency detection methods. A novel floating object detection algorithm combining spatial-based texture detection and frequency-based saliency detection. The combined algorithm is characterized by small sample training, low time consumption, strong anti-interference ability, and high precision. The rest of this paper is organized as follows. Section 2 introduces the methodology, including the proposed algorithm framework, detailed implementations in different processing domains, and the strategy of combination. Section 3 presents and analyzes the comprehensive results. Parameter selections and comparative experiments are also conducted in Section 3. Section 4 draws the conclusion.
# Estimating Algorithmic Information Using Quantum Computing for Genomics Applications
## 1 Introduction  
The evaluation of metrics such as the algorithmic complexity and algorithmic probability of finite sequences are key in scientific inference. For example, in biological systems, a better understanding of the algorithmic structures in DNA sequences [1] and cellular dynamics [2] would greatly advance domains such as personalized medication. Also, data-driven approaches such as deep learning and lossless compression often fail in providing insights on the causal generative mechanism underlying the set of observations about the physical process under study. In this research, we follow the algorithmic information theoretic approach of enumerating the distribution of all generating automata (e.g., Turing machines). The uncomputable nature of these metrics is approximated in practice by restricting the resources available to the computational models, such as time/cycles and space/memory.  
Such approaches however remain intractable except for the simplest of cases. This is due to the exponential scaling of the number of possible automata that needs to be enumerated for experimenting on real-world dataset sizes. We explore the possibility of accelerating this technique on a (gate-based) circuit model of quantum computation. We use the design developed in [3] of the exact circuit required to simulate a superposition of resource-bounded stored-program automata. Thereafter, we present a full experimental framework for using this approach for inferring various metrics such as the algorithmic probability and algorithmic complexity. The output data strings or self-replicating programs evolve on the specified automata as quantum superposition for further downstream quantum algorithms. Although quantum search-based approaches require high qubit multiplicity and quality as compared to those available in the NISQ era, these search approaches cannot be dequantized for this use case of the unstructured and computationally irreducible database of program-output relations. That makes this particular algorithm an ideal candidate for demonstrating quantum supremacy with widespread application. We propose use cases in genomics of quantum accelerated viral genomics, meta-biology and synthetic biology.  
Our approach is rooted in various automata models, computability, algorithmic information theory and resource complexity estimation techniques. The necessary background for these is presented in Section 2. In Section 3, we introduce the quantum circuit implementation for evolving a superposition of classical automata and discuss cases where conditioning the resultant universal distribution as a quantum superposition state has a computational advantage. Experimental use case for self-replicating programs and algorithmic complexity is presented in Section 4. Applications in genomics which can benefit from the developed framework are explored in Section 5. Section 6 concludes the paper.
## 4 Time Differenced Filter Algorithm
### Filter Model  
With the state Equation (3) and observation Equation (6), the time differenced filter can be formed as follows:  
\[\left\{\begin{array}{l}\textbf{X}_{t_{i}}=\boldsymbol{\Phi}_{t_{i-1},t_{i}} \textbf{X}_{t_{i-1}}+\boldsymbol{\Gamma}_{t_{i-1}}\cdot\Delta\textbf{X}_{t_{i -1},t_{i}}\\ \textbf{Z}_{t_{i}}=\textbf{B}_{t_{i}}\delta\textbf{X}_{t_{i}}+\textbf{v}_{t_{i}} \end{array}\right. \tag{7}\]  
The architecture of the time differenced filter algorithm is shown in Figure 13. Firstly, preferable GNSS observations are selected based on C/N0 and ADRS. The observations with C/N0 less than 30 dBHz are rejected. Then, the IEPV is calculated with the screened carrier phase observations by constructing TDCP measurements, and a Chi-squared test is applied to the carrier phase residuals. If the Chi-squared test is not passed, a w-test is applied to locate possible cycle slip, and the corresponding carrier phase observation is excluded, and the IEPV is re-calculated. Then, with the derived high accuracy IEPV, the current position is predicted with Equation (3), and the a priori pseudorangeresiduals are generated. The Grubbs test is applied to eliminate the gross errors in the pseudorange observations. The remaining pseudorange observations are processed with the single point positioning (SPP) algorithm. An a posteriori residual check is carried out to detect possible pseudorange outliers. If the posteriori variance exceeds the threshold, the satellites with maximum posteriori residuals are excluded and the current epoch is reprocessed. Finally, the clean pseudorange observations and high precise IEPV are used in the filter model. With these methods, the gross errors in GNSS observations are detected and eliminated step-by-step, improving the reliability of the positioning results.  
Figure 13: Flow chart of the time differenced filter algorithm.