esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
It can be observed that current ship classification studies based on AIS data mainly include two aspects: ship type identification and anomaly analysis. The former focuses on the classification of ship types by extracting ship static and motion features. The latter can detect the anomalous motion state mainly by analyzing the sailing state of ships. From the above research status, it can be seen that ship classification research based on AIS data still has the following problems:  
1. At present, the AIS data used for ship classification are mostly collected by shore-based AIS stations; therefore, the ship motion mode is relatively singular. This is because the coverage of shore-based AIS stations is relatively small. They can only monitor the maritime traffic situation in a specific sea area, which has certain limitations. Compared with shore-based AIS stations, spaceborne AIS receivers can realize AIS data collection worldwide and carry out large-scale offshore data mining. However, there are relatively few studies on ship behavior analysis and maritime traffic control in the open sea for spaceborne AIS. 2. Most current studies on ship classification are focused on cargo ships and oil tankers, which account for the vast majority of ships, resulting in a relatively singular type of ship classification for AIS data. The development of maritime surveillance technology requires research on various types of ships, but relatively few studies have been conducted on passenger ships, fishing ships, and other types of ships. 3. The existing studies mainly focus on the single geometric features of ships, and few studies consider the ship behavior characteristics, which is the necessary direction to further improve the performance of AIS ship classification. In general, current research on ship classification based on AIS data mainly focuses on a single ship feature mode in a small geographical range and timespan, and a comprehensive analysis of a large range and multiple features is still lacking. Shore-based AIS data can only reflect the spatiotemporal motion information of ships in a very small range, whereas space-based AIS can realize long-term and large-scale data reception, which is more suitable for ship classification and anomaly detection. Since the concept of spaceborne AIS has been proposed, especially with the construction of AIS constellations and the development of commercial operations, the quality of spaceborne AIS data has steadily improved. In the public literature, relatively few studies have been performed for spaceborne AIS data analysis. Making use of the advantages of spaceborne AIS data to perform more effective data mining is an important direction for ocean big data analysis and the intelligent develop ment of maritime surveillance in the future. Therefore, this paper considers spaceborne AIS data as the research object and extracts the geometric features and behavior characteristics of ships to realize ship classification and anomaly detection.
# Further Investigations into the Capacitive Imaging Technique Using a Multi-Electrode Sensor
## 2 Principle of the Multi-Electrode Sensor for Defect Detection  
The multi-electrode sensor uses multiple quasi-static fringing electric fields generated by an array of coplanar electrodes to acquire more information about the defects in the specimen and it has two  
Figure 1: Detection principle of CI: (**a**) CI sensor inspecting the non-conducting specimen; (**b**) CI sensor inspecting the conducting specimen covered by an insulation layer; CI: capacitive imaging.  
different detection mechanisms according to the electrical conductivity of the specimens. Taking a four electrode sensor as an example, the detecting principle of the multi-electrode sensor is shown in Figure 2. An AC voltage is applied to the driving electrode and an array of the sensing electrodes are connected to a measurement circuit via a Multiplexer (MUX).  
As shown in Figure 2a, when the specimen is non-conducting, the fringing electric field emanates from the driving electrode (No. 1) and terminates at the different sensing electrodes (No. 2-4). The three fringing electric fields between the driving electrode (No.1) and the different sensing electrodes (No. 2-4) are stratified, which can be used to acquire more information about the defects in the different electric fields. If a defect exists in a non-conducting specimen as shown in Figure 2a, the detecting results with the three sensing electrodes (No. 2-4) are different. The electric field between the driving electrode (No. 1) and the sensing electrode (No. 2) cannot detect the defect. The electric field between the driving electrode (No. 1) and the sensing electrode (No. 3) can detect the defect and the detecting result will show a trough in the process of a line scanning. The electric field between the driving electrode (No. 1) and the sensing electrode (No. 4) can also detect the defect but the detecting result will show two troughs in the process of a line scanning. Compared with these three line scans, it can be inferred that the defect is beyond the sensing limit of the driving electrode (No. 1) and the sensing electrode (No. 2) and within the sensing area of the driving electrode (No. 1) and the sensing electrode (No. 3). Thus its depth information will be approximated and that can be used to discriminate the different depths of the defects in non-conducting specimen.  
As shown in Figure 2b, when the conducting specimen is covered by an insulation layer, the fringing electric field emanates from the driving electrode (No. 1) and terminates at the different sensing electrodes (No. 2-4) and on the surface of the conducting specimen. If a defect exists on the surface of the conducting specimen, it will influence the distribution of the different fringing electric fields and thus impacts the charge on the whole sensing electrodes (No. 2-4). The change on the charge of the different sensing electrodes contains more information about the defects, which may be used to discriminate the defects of the insulation layer and metal surface.
## 3 Feedback-Inversion Feedforward Design  
### Controller Design  
This section describes the feedback controller design method for the model matching problem, i.e., in particular the bound generation inequalities (of an equivalent SISO problem) are derived. The transfer matrix from \(R\) to \(E\) can be written from Figure 2 as follows.  
\[[I+PG]T_{e}=[M-PG_{R}] \tag{3}\]  
Figure 2: The 2-DOF feedforward-feedback control structure for the model matching problem in the MIMO control system.  
\(P\in P\) must be non-singular for the inverse to exist. Left multiply both the sides of Equation (3) by \(P_{0}P^{-1}\) to get  
\[\Big{[}P_{0}P^{-1}+P_{0}G\Big{]}T_{e}=\Big{[}P_{0}P^{-1}M-P_{0}G_{R}\Big{]}\]  
Define V: \(\equiv\) I - \(P_{0}P^{-1}\), so \(P_{0}P^{-1}=I-V\) and the above equation becomes  
\[[I-V+P_{0}G]T_{e}=\big{[}(I-V)M-P_{0}G_{R}\big{]}\]  
The nominal loop transmission matrix is defined as \(L_{0}\): \(\equiv\)\(P_{0}G\), so we can write the above equation as  
\[[I-V+L_{0}]T_{e}=[M-VM-P_{0}G_{R}] \tag{4}\]  
The inversion-based strategy suggests [8; 12] that the tracking feedforward controller as  
\[G_{R}=diag\bigg{(}\frac{m_{11}}{p_{110}},\cdots,\frac{m_{ii}}{p_{ii0}}\bigg{)} \tag{5}\]  
Further, we can write the Equation (4) as,  
\[[I+L_{0}]^{-1}=T_{e}[D_{e}]^{-1} \tag{6}\]  
Equation (6) is implemented in Figure 3 with the equivalent disturbance term \(D_{e}=[M-P_{0}G_{R}\) - \(V\) (\(M-T_{e}\))]. The plant uncertainty is transformed into an external output disturbance whose range is known, and hence, it is known as equivalent disturbance attenuation (EDA) method [12; 14]. The feedback controller \(G\) and the feedforward controller \(G_{R}\) is designed such that the closed-loop output (\(T_{e}\)) satisfies the system tolerances over the entire \(D_{e}\) set, then the Schauder fixed point theory can be used to prove that the original MIMO specifications are satisfied [12].
# Influence of Crowd Participation Features on Mobile Edge Computing
## 3 Opportunistic Transmission
### Control Plane: How to Collect the Heuristic Information?  
To optimize the performance of zero-information ORPs, many smart routing algorithms have emerged, including ProPHET [23], SimBet [24] and Bubble [25]. These protocols use various control information including the contact probability, social similarity and locations of nodes, so as to choose qualified relays. The method of collecting such heuristics obviously has a big influence on the routing performance. Note that while the existing works use elaborate methods to allocate packets, they collect the control information in an epidemic way. That is, any two nodes can swap the control information whenever they have a contact, resulting in a splurge on resource consumption and degrading the performance metric.  
Considering this fact, we design ELECTION, a hierarchical information collection scheme, to improve the routing performance [26]. It has two important features. One is the effectiveness, which employs the packet delivery ratio to measure the degree to which the delivery is successful. The other is the efficiency, which refers to the degree of energy saving, measured by the exchange times of the control information. ELECTION first classifies nodes into core and edge nodes. The former is responsible for collecting and diffusing control information, and the latter receives control information from the former and sends their control information to the core nodes. Note that the information exchange happens only between two core nodes or between one core node and an edge node. That is, two edge nodes cannot be permitted to swap control information even if they encounter each other, so as to reduce the number of information exchanges and save network resources. ELECTION then transforms the information collection problem into the problem of minimum coverage, which is submodular. A greedy algorithm to construct the coverage set is designed and has been proven to converge the optimum solution with a probability of at least \(1-1/e\).
# An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
## 2 System Model and Existing Schemes
### Existing Schemes
However, these schemes have excluded the nodes that are notin the current non-cooperative path from participating the cooperative transmission. By no means we can say that the last \(l\) nodes in the non-cooperative route is the optimal transmission set for the \(k^{\text{th}}\) hop data transmission. In addition, PC-\(l\) and FACR need to re-solve Equation (1) at each hop, using Dijkstra and Bellman-Ford algorithm for example. This may introduce the excessive delay and signalling overhead. In the next section, we will propose a two-stage cooperative routing scheme to solve these problems.
# Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory
## 5 CDS Implementation Case Study: OFDM Long-Haul Fiber Optic Communications  
Figure 8 shows the CDS architecture block diagram for an OFDM long-haul fiber optic link. As mentioned before, we select the fiber optic link as an example of a NGNLE. In general, \(\hat{\mathbf{X}}_{n}^{k}\) can be  
supplied by a database during a training period. However, the estimated \(\hat{X}_{n}^{k}\) after FEC can be used instead of the database shown in Figure 7 in fiber optic link for model extraction. The bit error rate after forward error correction (FEC) is between \(10^{-15}\) and \(10^{-12}\)[34] when the BER before FEC is less than the FEC-threshold. In addition, the decision-making error can be calculated as the BER. Therefore, the BER can be estimated in two ways. First, if the BER is less than the FEC threshold, then the BER after the FEC can be measured and the BER before FEC can be found using extrinsic information transfer (EXIT) chart [25]. If the BER is more than the FEC threshold, we can estimate the BER using 16 or 32 training frames that are used for linear distortion mitigation. The channel estimation and compensation of linear impairments is done using the linear adaptive channel equalizer that uses 16 training frames to mitigate linear distortion [40]. Therefore, the prospective internal reward can be calculated as follows (see Equation (22) and [20; 21; 22; 23]):  
\[rw_{n}^{k,t}=\frac{BER_{k+t}^{n}}{\left(R_{t}^{k}-R_{ref}\right)^{4}}, \tag{25}\]  
\[R_{t}^{k}=R_{ref}+t\times d, \tag{26}\]  
where \(d\) is 4 Gb/s discretization step for data rate and \(R_{ref}\) is an available reference data rate that is set equal to \((R_{1}^{1}-d)\). Also, the implementation of Equations (17)-(19) for a fiber optic communication system is presented in [21; 23].  
Figure 8: CDS architecture for long-haul NGNLLE OFDM-based fiber optic link.  
The actions space for this specific case study is (see Equation (16)):  
\[C=\left\{c_{k}\left|\begin{array}{c}\text{data rate }\ R_{k}>R_{ref},\ \text{or}\\ \text{Internal commands}=\left\{\begin{array}{c}m=1,\\ m=0\end{array}\right.\end{array}\right.\right\} \tag{27}\]
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 3 Controller Design of USV
### Surge Control  
Longitudinal velocity tracking error variables can be defined as follows:  
\[\underset{t\rightarrow\infty}{\lim}u\to u_{d} \tag{26}\]  
In the pursuit problem, \(u_{d}\) is the pursuer's maximum speed. The surge following error \(u_{e}\) is described as follows:  
\[u_{e}=u-u_{d} \tag{27}\]  
The Lyapunov function considering longitudinal thrust and external disturbance is designed as follows:  
\[V_{u\epsilon}=\frac{1}{2}{u_{e}}^{2}+\frac{1}{2}\tilde{\tau}_{\epsilon u}^{2} \tag{28}\]  
Taking the derivative of the above formula, we obtain the following:  
\[\dot{V}_{u\epsilon}=u_{e}(\frac{m_{22}}{m_{11}}vr-\frac{d_{11}}{m_{11}}u+\frac {1}{m_{11}}(\tau_{u}+\tau_{\epsilon u})-\dot{u}_{d})+\tilde{\tau}_{\epsilon u} \dot{\tilde{\tau}}_{\epsilon u} \tag{29}\]  
In order to ensure the convergence of the velocity term, the design surge control law for \(\tau_{u}\) is chosen as follows:  
\[\tau_{u}=m_{11}(-k_{u}u_{e}-\frac{m_{22}}{m_{11}}vr+\frac{d_{11}}{m_{11}}u+ \dot{u}_{d})-\dot{\tau}_{\epsilon u} \tag{30}\]  
where \(k_{u}\) is a positive constant. If the surge force designed in Formula (31) can ensure the function value is always negative, then the actual velocity of the USV will be consistent with the desired value. Substituting the above expression into Formula (29), we have the following:  
\[\dot{V}_{u\epsilon}=-k_{u}u_{e}{}^{2}-k_{uu}\tilde{\tau}_{\epsilon u}^{2}<0 \tag{31}\]  
According to Formula (31), the velocity error will gradually converge to zero, and the velocity of the pursuer will reach the desired rate. When the direction and velocity of the pursuit USV reach the desired value and converge continuously, the escaping target can be intercepted by the pursuer.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 5 Joint Optimization Scheme for Communication Rate  
After intelligent channel selection, the relay drone can avoid the impact of jammers and achieve information transmission on interference-free channels. To further efficiently decouple the multivariable mixed-integer nonlinear optimization problem, this section divides the joint optimization problem of communication rate into the following four subproblems: relay drone modulation method optimization, relay drone transmission power optimization, three-dimensional trajectory optimization, and cluster drone power allocation factor optimization. These subproblems are iteratively solved alternately until convergence, thereby maximizing the communication rate of the cluster drones.
# Effect of Tuned Spring on Vibration Control Performance of Modified Liquid Column Ball Damper
## 3 Parameter Optimization
### Design Guidelines for Tuned Liquid Column Ball Spring Damper  
Based on the current numerical investigations and the previous literature, the following guidelines are proposed to select the appropriate optimal parameters for the design of TLCBSDs and TLCBSRDs.  
1. Choose the mass ratio for the liquid portion of the damper (0.5% to 5%) [43]. The higher the mass ratio, the better the efficiency of the damper will be.
2. For economical design, select a length ratio \(\dot{\alpha}\) in the range of 0.7 to 0.9 [40].
3. Select an optimum tuning frequency of the liquid column of the damper in the range of 0.96 to 1.04 (Figure 4)
4. Find the length of the damper using the chosen optimal tuning frequency.
5. Find the cross-sectional area of the tube using the length and mass ratio.
6. For optimum results, take the ball-tube diameter ratio = 0.8 [43].
7. In the case of a TLCSSD, for optimum results choose a tuning frequency for the spring-ball attachment equal to or greater than 1.4.
8. For a TLCSRD arrangement, select an optimal tuning frequency for the spring-ball attachment equal to or greater than 1.6.  
Figure 14: Time-history responses of the main structure displacement subjected to impulsive loading: (**a**) structure with and without a TLCBD, (**b**) structure with and without a TLCBSSD, (**c**) structure with and without a TLCBSRD, and (**d**) structure with normalized peak response without a damper, and with a TLCBD, TLCBSSD, and TLCBSRD.
## 2 System Model and the Correction of Offset, Gain, Time Skew, and Bandwidth Mismatch
### Correction of Offset, Gain, Time Skew, and Bandwidth Mismatch  
ADC\({}_{0}\) is used as the reference ADC. Additionally, offset, gain, time skew, and bandwidth mismatch of the reference ADC are assumed to be zero. Hence, Equation (3) can be rewritten as follows:  
\[Y_{i}(j\omega)=\Delta g_{i}e^{-j\omega\Delta\tau_{i}}H_{\Delta\omega_{\rm ij}} (j\omega)X_{i}(j\omega)+\Delta o_{i}, \tag{5}\]  
here \(\Delta o_{i}\), \(\Delta g_{i}\) and \(\Delta\tau_{i}\) are, respectively, the offset, gain, and time skew relative to the reference channel. Additionally, \(H_{\Delta\omega_{i}}(j\omega)\) can be expressed as follows:  
\[H_{\Delta\omega_{i}}(j\omega)=\frac{1+j\frac{\omega}{\omega_{\rm bi}}}{1+j\frac {\omega}{\omega_{\rm bi}}}, \tag{6}\]  
where \(\omega_{\rm br}\) is the cutoff frequency of the sample and the hold circuit of the reference channel.  
In the TI-ADCs system, offset and gain can be corrected by the adder and multiplier, respectively. Additionally, Figure 2 is the proposed structure of correction offset and gain. In Figure 2, \(\widetilde{o_{i}}\) is the value of estimating the offset, \(\widetilde{g_{i}}\) is the value of estimation of the gain and \(\mathbf{y}_{\rm icalo,g}[n]\) is the output after calibrating the offset and the gain.  
When the filter is used to correct the time skew and the bandwidth mismatch at the same time, the frequency response of the filter can be represented as follows:  
\[H_{\Delta\tau_{i}\Delta\omega_{i}}(j\omega)=e^{i\omega\tau}\times\frac{1+j\frac {\omega}{\omega_{\rm bi}}}{1+j\frac{\omega}{\omega_{\rm br}}}. \tag{7}\]  
The IDTFT of \(H_{\Delta\tau_{i}\Delta\omega_{i}}(j\omega)\) can be expressed as \(h_{\Delta\tau_{i}\Delta\omega_{i}}[k]\). When there are time skews and bandwidth mismatches in the TI-ADCs system, the correction of these mismatches can be expressed as follows:  
\[y_{\rm ical\ o,\ g\ \tau,\ \omega}[n]=y_{\rm ical\ o,\ g}[n]*h_{\Delta\tau_{i} \Delta\omega_{i}}[k]. \tag{8}\]  
Here \(*\) is the convolution and \(\mathbf{y}_{\rm icalo,g,\tau,\omega}[n]\) is the output after calibrating the offset, gain, time skew, and bandwidth mismatch. When \(h_{\Delta\tau_{i}\Delta\omega_{i}}[k]\) is an \(N\)-tap filter, Equation (8) can be rewritten as follows:  
\[y_{\rm ical\ o,\ g\ \tau,\ \omega}[n]=\left\{\begin{array}{ll}\sum\limits_{k=1}^ {N}y_{\rm ical\ o,\ g}\Big{[}n-k+1+\frac{N}{2}\Big{]}\times h_{\Delta\tau_{i} \Delta\omega_{i}}[k],\ \text{when}\ N\ \text{is even}.\\ \sum\limits_{k=1}^{N}y_{\rm ical\ o,\ g}\Big{[}n-k+1+\frac{N-1}{2}\Big{]} \times h_{\Delta\tau_{i}\Delta\omega_{i}}[k],\ \text{when}\ N\ \text{is odd}.\end{array}\right. \tag{9}\]  
Figure 2: Proposed structure of correction offset and gain.  
Based on Equation (9), Figure 3 shows the proposed structure of correction time skew and bandwidth mismatch. Additionally, \(N\) is even in Figure 3. Based on Figures 2 and 3, the offset, gain, time skew, and bandwidth mismatch can be corrected.
# Urban Biorefinery Demonstration: Production of Polyhydroxyalkanoates from a Municipal Solid Waste
## 2 Materials and Methods
### Scl-PHA Accumulation  
Achieving steady conditions in the enrichment fermenter is crucial for the accumulation of the scl-PHA. To accumulate scl-PHA, MMC requires the limitation of an essential nutrient, typically nitrogen (N) or phosphorus (P). In this case, phosphorus was set as the limiting nutrient and it was removed via NaOH precipitation from the clarified VFA-rich digestate stream. The resulting C:N:P ratio was 1:0.08:0 in the accumulation stage diet. Under these conditions, the culture is metabolically driven to store carbon sources as intracellular scl-PHA [59].  
Scl-PHA accumulation was run in a 3 m\({}^{3}\) fermenter (Figure 1b, F-3000 series, Bionet, Murcia, Spain), geometrically similar to the 20 m\({}^{3}\) fermenter but with stirring capabilities  
\begin{table}
\begin{tabular}{c c} \hline
**Total COD (mg/L)** & **62,165.00 \(\pm\) 8922.16** \\ \hline Soluble COD (mg/L) & 42,483.13 \(\pm\) 8374.85 \\ N-NH\({}_{4}\) (mg/L) & 1397.25 \(\pm\) 161.12 \\ NT (mg/L) & 6450.00 \(\pm\) 1990.81 \\ NO\({}_{3}\)\({}^{-}\) (mg/L) & 26.13 \(\pm\) 14.99 \\ NO\({}_{2}\)\({}^{-}\) (mg/L) & 2.93 \(\pm\) 1.48 \\ Ca (mg/L) & 2888.75 \(\pm\) 620.89 \\ PT (mg/L) & 84.38 \(\pm\) 30.53 \\ P-PO\({}_{4}\) (mg/L) & 40.70 \(\pm\) 19.25 \\ SST\({}_{\rm T}\) (g/L) & 2.06 \(\pm\) 0.93 \\ SSV\({}_{\rm V}\) (g/L) & 1.55 \(\pm\) 0.83 \\ Fe (mg/L) & 110.33 \(\pm\) 60.37 \\ Mn (mg/L) & 15.31 \(\pm\) 2.45 \\ Zn (mg/L) & 4.96 \(\pm\) 2.87 \\ Cu (mg/L) & 5.00 \(\pm\) 1.30 \\ Ca (mg/L) & 2888.75 \(\pm\) 620.89 \\ Na (mg/L) & 1555.00 \(\pm\) 77.78 \\ K (mg/L) & 3930.00 \(\pm\) 49.49 \\ SO\({}_{4}\) (mg/L) & 1778.00 \(\pm\) 142.02 \\ \hline \end{tabular}
\end{table}
Table 2: Average analytical results of the VFA-rich solution generated.  
up to 190 rpm. The fermenter operated in batch mode, starting with an initial working volume of 1 m\({}^{3}\) and filled with continuous feeding of pretreated VFA-rich digestate. The feeding flow rate was adjusted to each trial, based on the maximum VFA consumption rate at steady conditions and the soluble COD of the fermentation broth in the enrichment fermenter. The accumulation process was performed in 39 batches, with continuous feeding of phosphorus-free VFA-rich digestate from the daily discharge of the enrichment fermenter at steady conditions. Each accumulation lasted 8 h, with agitation and aeration control set to maintain dissolved oxygen (DO) above 20%, i.e., ensuring no oxygen limitations. Temperature and pH were monitored but not controlled.