esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 3 Method
### HMM-Based KPI Anomaly Detection
#### 3.5.1 Construction of Hidden and Observed States and State Transition Matrix  
In the distributed system, when an abnormal situation occurs, each KPI may be a direct impact indicator of another KPI; that is, each KPI may be an implicit state or an observation state. Therefore, this paper takes KPIs as being either in the hidden state or the explicit state, while the hidden state and the observation state are the same. However, in a real system, there are exceptions related to user timeout and data reading, and other abnormal states. The hidden state set \(S\) and observation state \(V\) in HMM that are applicable to a distributed system are represented by \(S=V=(s_{1},s_{2},\ldots,s_{N})\), where \(N\) is the number of hidden states and observer states in the HMM. The structure of this is shown in Figure 4, where element \(a_{ij}\) denotes the probability at time \(t\) that the hidden state of the system transfers to state \(s_{j}\) at time \(t+1\), and \(b_{i}(v_{k})\) denotes the probability of the observed state \(v_{k}\) being emitted by the system under state \(i\) at time \(t\).  
After determining the observed state set and hidden state set of the model, it is necessary to construct the state space. In this paper, the KPI data in the system are analyzed, and the observation state is the same as the hidden state, the number of which is \(N\). The state transition matrix is constructed as follows:  
\[A=\left(\begin{array}{ccc}a_{11}&\cdots&a_{1N}\\ \vdots&\ddots&\vdots\\ a_{N1}&\cdots&a_{NN}\end{array}\right) \tag{11}\]  
Here, the elements in the final correlation score matrix obtained in the previous section are transformed according to the requirements of \(\sum_{j=1}^{N}a_{ij}=1\) in the state transition matrix of HMM. The transformation formula for each transition probability in the matrix is as follows:  
\[a_{ij}=\frac{m_{ij}}{m_{i1}+\cdots+m_{iN}},i\in(1,N),j\in(1,N) \tag{12}\]  
In this paper, according to the data characteristics of distributed systems, the observation emission matrix is the same as the state transition matrix of hidden states. The internal  
Figure 4: Hidden Markov State Model.  
transition probability of the matrix is the same as that of the state transition matrix. The observation emission matrix is obtained as formula (13) shows:  
\[B=A=\left(\begin{array}{ccc}b_{11}&\cdots&b_{1N}\\ \vdots&\ddots&\vdots\\ b_{N1}&\cdots&b_{NN}\end{array}\right) \tag{13}\]  
In the state transition matrix and observation emission matrix, horizontal represents the observable state, and vertical represents the hidden state. \(b_{ij}\) represents the probability that the jth hidden state generates the ith observable state.
# Nickel (Ni\({}^{2+}\)) Removal from Water Using Gellan Gum-Sand Mixture as a Filter Material
## Materials and Experimental Preparation
### Experimental Process
#### 2.2.3 Adsorption Isotherm and Kinetic Study  
To study the nickel adsorption mechanism of the GG-sand mixture, an equilibrium isotherm study was conducted. Different concentrations of nickel (10-1000 mg/L) were used to determine the equilibrium isotherm. Samples (40 mL) with designated nickel concentrations were mixed with 5 g of GG-treated sand (0 HBL, 0.5 HBL, 1.0 HBL, and 2.0 HBL) in 50 mL Falcon tubes. The samples were mixed at 120 rpm at 25 \({}^{\circ}\)C for 24 h and filtered using a 0.45 \(\upmu\)m nylon filter for further analysis.  
The empirical equilibrium adsorption isotherm was modeled using the Langmuir and Freundlich isotherm equations. The adsorption isotherm provides insight about the interaction between the adsorbent and adsorbate. The Langmuir model is written as follows:  
\[q_{e}=\frac{q_{\text{max}}k_{L}C_{e}}{1+k_{L}C_{e}}, \tag{1}\]  
where \(q_{e}\) is the metal uptake per adsorbent (mg/g), \(q_{max}\) is the maximum adsorption, \(k_{L}\) is the Langmuir constant, and \(Ce\) is the equilibrium metal concentration (mg/L). Langmuir isotherm fitting indicates a monolayer process where the \(k_{L}\) can be a criterion of adsorbate affinity to the adsorbent. A high value of \(k_{L}\) indicates a strong interaction. Conversely, the Langmuir isotherm can be directly related to the homogenous surface with uniform adsorption energy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Flow Rate [mL/min]** & **Thickness of HBL [cm]** & & **GG Contents [\%]** & \\ \hline
0.04 & & & & & \\
0.20 & 5 & 0 & 0.5 & 1.0 & 2.0 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental test conditions.  
Figure 3: Schematic of the pressurized upward flow system.  
The Freundlich equation is written as follows:  
\[q_{e}=k_{F}C_{e}^{1/n}, \tag{2}\]  
where \(k_{F}\) is the Freundlich constant and n is the empirical constant. Freundlich isotherm empirically considers a multi-layer coverage on rough surfaces. The model fitting of the experimental data was checked with the correlation coefficient \(R^{2}\).  
To understand the kinetic uptake of nickel adsorption with time, GG-treated sands with 25 mg/L, 50 mg/L, and 100 mg/L nickel solutions were tested and taken according to the desired time. The experimental conditions were the same as those mentioned above.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 3 Results
### System Stability Evaluations
#### 3.1.2 Water Quality Data Stability Test  
The water quality sensors used for this study showed stable readings until the rechargeable NiMH batteries were depleted (Figure 11). The available sensing time was approximately 10 h at 10 s intervals. Temperature correction was necessary to obtain stable readings with both the EC and the DO sensors. The temperature correction coefficients to minimize  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Float Type**} & \multicolumn{2}{c}{**Stable Hovering Time (s)**} \\ \cline{2-3}  & **Wind Scale 1–2** & **Wind Scale 2–3** \\ \hline Polystyrene-foam bar & & \\ (Figure 6a) & 41 \(\pm\) 26 & 14 \(\pm\) 1 \\ \hline PET bottle & & \\ (Figure 6b) & 43 \(\pm\) 25 & 34 \(\pm\) 6 \\ \hline Coiled polyethylene-foam disc & & \\ (Figure 6c) & Not Determined & Not Determined \\ \hline Drone-only & \(>\)180 & 90 \(\pm\) 47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flight stability test results. Wind scale corresponds to the Beaufort wind force scale.  
the standard deviation of the readings differed for the two DO sensors (4.8% K\({}^{-1}\) for Atlas Scientific's DO sensor and 2.9% K\({}^{-1}\) for the DFRobotTM DO sensor). The water temperature values fluctuated, but the ratio of the water temperature to the reference water temperature (measured using a continuous power source) was within the range of 0.99-1.01 (Figure 11). The air pressure values measured using this test increased gradually from 1004 hPa to 1007 hPa. The fluctuation of corrected DO values by the reference water temperature (for membrane permeability correction of the oxygen electrode) (Figure 11) fell within the range of \(\pm\)5% of deviation of the reference value of oxygen-saturated water with saturated air humidity [29], except for the last three data points before termination by battery depletion. Figure 12 (for the SplashDroneTM 4 system) shows that the DO data fitted well to the standard value predicted by the equation reported by [PERSON] (1980) [29]. The EC values in Figure 12 declined sharply before battery depletion. The pH values were stable in this room-temperature test (Figure 12).  
Figure 11: Results of data stability testing of the system for the SplashDroneTM 3+ with rechargeable batteries: (**a**) pH values of a standard solution (potassium phthalate buffer, pH 4.01); (**b**) electrical  
conductivity values of a 1.41 dS m\({}^{-1}\) standard solution (141 mS m\({}^{-1}\)); (e) dissolved oxygen concentration values of the drawn tap water circulated continuously with air bubbling; (d) water temperature values of the tap water; (e) the ratio of water temperature and reference temperature values (the reference temperature values were obtained with stable power source). The EC values were corrected by the water temperature data to represent the values at 298 K. Temperature compensation for the DO measurement was conducted only for the membrane permeability fluctuation (not for standardization).
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 5 Numerical Results
### Impact of the Number of Multipaths on the Authentication Performance  
We show in Figure 4 the ROC curves under different numbers of multipaths with the settings of (SNR = 10 dB, \(N_{r}\) = 64, \(k_{\phi}\) = 2 dB, \(k_{\theta}\) = 2 dB, \(k_{\alpha}\) = 2 dB). From Figure 4, we can see that the authentication performance (\(P_{f}\), \(P_{d}\)) under \(L\) = 15 multipaths outperforms that of the others, and the performance under \(L\) = 5 multipaths is poorest. This means that more multipaths can contribute to the authentication performance. We can also see that when \(P_{f}\) is fixed, \(P_{d}\) increases when the number of multipaths increases.  
Figure 4: ROC curves under different numbers of multipaths with the settings of (SNR = 10 dB, \(N_{r}\) = 64, \(k_{\phi}\) = 2 dB, \(k_{\theta}\) = 2 dB, \(k_{\alpha}\) = 2 dB).  
Figure 3: ROC curves under different numbers of receiver antennas with the settings of (SNR = 10 dB, \(L\) = 20, \(k_{\phi}\) = 2 dB, \(k_{\theta}\) = 2 dB, \(k_{\alpha}\) = 2 dB).
## 3 Methodology
### Simulation of Half-Effect Absorption Cooling System  
For the analysis of the performance of the half-effect cycle, mathematical models were developed based on the first law of thermodynamics and the thermodynamic properties of the working mixture. The physical and thermodynamic properties of NH\({}_{3}\)-LiNO\({}_{3}\) were obtained from [PERSON] et al. [25]. Meanwhile, the properties of pure NH\({}_{3}\) were provided by [PERSON] [26] and [PERSON] [27]. The assumptions for the modeling of the thermodynamic cycle were the following:  
1. The system operates in thermodynamic equilibrium;
2. The analysis is made under steady-state conditions;
3. The solution is in a saturated state when leaving generators and absorbers;
4. The refrigerant is in a saturated state when leaving the condenser and evaporator;
5. Heat losses and pressure drops in the tubing and the components are considered negligible;
6. Flow through the valves is isenthalpic.  
In addition, isentropic efficiencies of 0.8 and 0.7 were considered for the pumps and the solution heat exchangers, respectively, [28] and the heat supplied to both generators was at the same temperature, \(T_{\text{G\_HP}}=T_{\text{G\_LP}}\). Steady-state mass and energy balances for the components of the half-effect cycle were established as follows:  
Low-Pressure Generator (G\({}_{LP}\))  
\[\dot{m}_{3}=\ \dot{m}_{4}+\dot{m}_{17}, \tag{12}\]  
\[\dot{m}_{3}X_{3}=\ \dot{m}_{4}X_{4}+\dot{m}_{17}X_{17}, \tag{13}\]  
\[\dot{Q}_{\text{G\_LP}}=\dot{m}_{4}H_{4}+\dot{m}_{17}H_{17}-\dot{m}_{3}H_{3}, \tag{14}\]  
Low-Pressure Absorber (A\({}_{LP}\))  
\[\dot{m}_{1}=\ \dot{m}_{6}+\dot{m}_{16}, \tag{15}\]  
\[\dot{m}_{1}X_{1}=\ \dot{m}_{6}X_{6}+\dot{m}_{16}X_{16}, \tag{16}\]  
\[\dot{Q}_{\text{A\_LP}}=\dot{m}_{6}H_{6}+\dot{m}_{16}H_{16}-\dot{m}_{1}H_{1}, \tag{17}\]  
High-Pressure Generator (G\({}_{HP}\))  
\[\dot{m}_{9}=\ \dot{m}_{10}+\dot{m}_{13}, \tag{18}\]  
\[\dot{m}_{9}X_{9}=\ \dot{m}_{10}X_{10}+\dot{m}_{13}X_{13}, \tag{19}\]  
\[\dot{Q}_{\text{G\_HP}}=\dot{m}_{10}H_{10}+\dot{m}_{13}H_{13}-\dot{m}_{9}H_{9}, \tag{20}\]  
High-Pressure Absorber (A\({}_{HP}\))  
\[\dot{m}_{7}=\ \dot{m}_{12}+\dot{m}_{17}, \tag{21}\]  
\[\dot{m}_{7}X_{7}=\ \dot{m}_{12}X_{12}+\dot{m}_{17}X_{17}, \tag{22}\]  
\[\dot{Q}_{\text{A\_HP}}=\dot{m}_{12}H_{12}+\dot{m}_{17}H_{17}-\dot{m}_{7}H_{7}, \tag{23}\]
# GPU-Enabled Serverless Workflows for Efficient Multimedia Processing
## 5 Results and Discussion
### AWS Batch Auto Scaling  
As mentioned above, AWS Batch compute environments are based on ECS cluster. These clusters execute the jobs in autoscaled groups of EC2 instances whose size grows and shrinks according to the workload. The main feature of this service is the scale to zero, which allows a real pay-per-use model. However, the time taken by the scheduler to launch and terminate instances is considerably longer than that of Functions as a Service platforms. This is due to the usage of traditional virtual machines instead of the container-based  
Figure 6: Comparison between CPU and GPU instances for video object detection.  
microVMs used by AWS Lambda [36]. Furthermore, the boot and initialisation time of the instances must also be considered.  
Figure 7 shows the measured times after launching 20 jobs into empty job queues of compute environment in AWS Batch. The first box displays the time taken by the scheduler to launch an instance since a job is received. This time, which averages 166.8 s, indicates that executions delegated to AWS Batch are likely to have a substantially longer start-up time than on FaaS platforms. Depending on the requirements of the application to be deployed, it could be advisable to adjust the compute environment to keep one instance up and running, although this would have a negative impact on the deployment cost and the main advantage of the serverless paradigm would be lost.  
The second box shows the boot time of the EC2 instance's operating system, which is on average 129 s with low standard deviation. This happens because the vendor manages compute environments that always run the same Amazon ECS-optimised AMI and only handles the ECS container agent startup as well as the download of the FaaS Supervisor component from the _cloud-init_ user data.  
Lastly, the time taken by the AWS Batch scheduler to scale down to zero the number of instances when the job queue becomes empty is displayed in the third box. This time, just like the launch one, depends on the internal operation of the scheduler and represents an additional cost to the actual usage of the machines. However, managed services such as AWS Batch represent a viable platform for sporadic executions of long-running, resource-intensive accelerated jobs.
# The Analysis of the Applications of Crop Seed Tape Sowing Technology and Equipment: A Review
## 5 Discussions and Prospection of Seed Tape Sowing
### Fundamental Issues in Seed Tape Sowing That Still Need Further Discussion
#### 5.1.1 Economic Factors  
Seeds should be made into seed tape first in the application of seed tape technology to crop production compared with traditional sowing. The additional process means more input in crop cultivation which include more equipment, more manpower and resources. Therefore, the upfront costs will increase. In the Xinjiang province of China, seed tape was originally introduced for cotton cultivation and the cost in the experiment was higher than conventional planting [102]. In addition, the production of seed tapes requires special equipment. People think that the production of wrapping seeds into seed tape is more cumbersome and not as convenient as direct seeding. With the continuous maturity of precision seeding technology, people are more and more inclined to precision direct seeding than seed tape sowing which is the main reason that seed tape is not widely used.  
To break this dilemma, we should give full play to the advantages of seed tape technology. Ensure that the seed tape is of high quality and high seedling emergence rate.  
Figure 12: Stress analysis of soil particles [30].  
Factors such as increased inputs and seed savings, labour costs and crop economic benefits due to the production of seed tape should be considered. Determine the crops and suitable seed tape material that could bring higher economic benefits to use seed tape sowing.
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 3 Efficient Soft Iterative Decoding Algorithms for HARQ with Rateless Codes in Satellite Systems
### HARQ with Rateless LDPC Codes
#### 3.2.2 Soft Iterative Decoding Algorithms
:  
\[r_{i}=\log\left(\frac{\Pr(c_{i}=1|\gamma_{i})}{\Pr(c_{i}=0|\gamma_{i})}\right). \tag{4}\]6  
With the proposed parallel decoding algorithm, both component decoders utilize the combined information from both decoders as in Equation (17), resulting in increased reliability and eventually early convergence.
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 2 The HES Architecture and Modeling in Stateflow  
In general, the HES operations are realized through different levels of control hierarchy [5; 14; 15]. The hierarchical structures can be classified into three levels: primary, secondary, and tertiary, as depicted in Figure 1. The primary control, which is the lowest level, is responsible for updating the values of the current, voltage, and frequency of the system. The most common example for HES is the V-I droop control [16]. On the other hand, the tertiary control (the highest level) is utilized to ensure optimal operation of the HES over a much longer term. Its fundamental role is to manage the energy flow among the sources of the HES and utility grid in order to achieve certain designed objectives, such as load scheduling, energy management, and optimization. The secondary level control is intermediate between the two. It mainly focuses on power flow coordination and restoration of the voltages of the dc bus to the nominal value. In addition to the control tasks, it must be emphasized that in the literature, the expressions \"power management\" and the \"energy management\" are distinct from each other in terms of time scales [5]. The key parameters in primary control are the instantaneous values of the electrical parameters. To achieve a meaningful response, its control strategies require very small control sampling time, i.e., from tens of micro to milliseconds [17]. For the tertiary level, since the evaluation of the objectives is long-term, the typical sampling time can be in minutes or hours. The scope of this work is oriented towards tertiary control. Various research works dedicated to tertiary control are reported in [7; 18; 19].
# Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation
## 5 Discussion  
In addition to the advantages of the proposed method, there is an additional issue that needs to be illustrated (Figure 13). In an HS-2D-DIC, the out-of-plane rotation-induced virtual strain is neglected in Equation (2). The resulting measurement error was not analyzed due to the complex situations in practical vibration measurements. Here, we take the cantilever panel vibration experiment in Section 3.4 as an example to discuss the error. From the experiment, it is noted that the amplitude of the out-of-plane displacement on the free edge of a 400 mm length panel is \(\sim\)1 mm, and thus the corresponding vibration angle \(\theta\) is 0.14\({}^{\circ}\). Therefore, the out-of-plane rotation-induced virtual strain \(\varepsilon_{r}=(1-\cos\theta)/1\) can be estimated, which is 3 \(\mu\epsilon\). By selecting three points, A, B, and C, at distances of 400 mm, 200 mm, and 40 mm, respectively, from the fixed edge of the panel, the out-of-plane displacement-induced virtual strains of these three points can be calculated. They are \(\varepsilon_{A}=10^{3}\)\(\mu\epsilon\), \(\varepsilon_{B}=5\times 10^{2}\)\(\mu\epsilon\), and \(\varepsilon_{C}=10^{2}\)\(\mu\epsilon\), respectively. By comparing the out-of-plane rotation and out-of-plane displacement-induced virtual strains, it can be seen that the former is negligibly small. Since the rotation- or deformation-induced measurement errors are different in various vibration experiments, we plan to develop a single-camera high-speed 3D-DIC system and conduct a series of comparative experiments in our following research paper to discuss the proposed method's measurement errors in detail.  
**Author Contributions:** Conceptualization, Y.L. and B.D.; methodology, Y.L.; software, Z.N.; validation, Y.L. and P.H.; formal analysis, Y.L.; investigation, Z.N.; resources, P.H. and Y.B.; data curation, Y.L. and Z.N.; writing--original draft preparation, Y.L.; writing--review and editing, Y.B. and B.D.; visualization, P.H.; supervision, S.X., B.D. and Y.B.; project administration, S.X. and B.D.; funding acquisition, S.X. and B.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (62171140, 61705047, and 61727810), Natural Science Foundation of Guangdong Province (2021A1515012598 and 2021A1515011945), and Young S&T Talent Training Program of Guangdong Provincial Association for S&T (SKXRC202225).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 13: The schematic of assessing out-of-plane-rotation- and out-of-plane-displacement-induced virtual strains in the cantilever panel vibration experiment.  
Data Availability Statement: Data that support the findings of this study are available from the corresponding author upon reasonable request.  
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 4 Optical OFDM Signaling
### Improving Spectral Efficiency with Respect to Dco-OFDM  
To improve the spectral efficiency of ACO-OFDM, several techniques have been proposed. In [32], enhanced U-OFDM (eV-OFDM) was proposed, which combines multiple U-OFDM signals at the Tx (by sending them on different \"depths\") and performs signal detection at the Rx based on successive interference cancellation to remove the interference between the signals of the different depths. The proposed asymmetrically clipped DCO (ADO) -OFDM [33] uses ACO on odd sub-carriers and DCO signaling on even sub-carriers. The hybrid ACO (HACO) -OFDM [34] is similar to ADO but uses PAM-DMT on even sub-carriers (instead of DCO), where the negative part of the corresponding signals is clipped before transmission. The recently proposed LACO-OFDM consists in a layering of multiple ACO signals, which depends on the signal's symmetry propeties (see Section 5.3 for more details). It has the advantage of improved spectral efficiency that can approach that of DCO [25]. It also benefits from more flexibility, in the sense that the overall transmission data rate and performance can be adjusted by setting independently the signal constellation size and the allocated power within each layer. Another proposed scheme, called hybrid PAM-DMT (HPAM-DMT) [35], uses a similar concept as LACO, where the transmitted signal is composed of different \"groups\" of signals: The first group consists of a real PAM-DMT (RPAM-DMT) signal, and the successive groups are obtained from RPAM-DMT modulated signals sent on specific sub-carriers. The advantage of HPAM-DMT over LACO is its lower computational complexity at the Rx, but this comes at the drawback of a lower power efficiency.  
A detailed comparison of the above-mentioned techniques can be found in [36; 37; 38]. In particular, it was shown in [38] that the two-layer LACO and HACO are more power-efficient compared to ADO for a given spectral efficiency. For a spectral efficiency of larger than \(\sim\)4 bps/Hz, the four-layer LACO-OFDM outperforms the other proposed hybrid schemes in terms of power efficiency [38].
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Frequency range & 2.620–2.630 (GHz) \\ Duplexing & TDD and FDD \\ Channel coding & Turbo code \\ Channel bandwidth & 10 (MHz) \\ FFT size & 1024 sub-carriers \\ CP length & 80, 72 normal \\ Total symbols & 140 s \\ TX block configuration & 50 Resource blocks \\ Modulation schemes & QPSK and QAM \\ Multiple access schemes & OFDM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for measurements.