esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Freight Transportation Carbon Emission Reduction Based on System Dynamics
## 3 System Flow Diagram and Structural Equation
### Structural Equation  
The structural equation in this paper is used to describe the quantitative relationship between state variables, rate variables, and auxiliary variables. The state variables, rate variables, and auxiliary variables in the system dynamics model correspond to the L equation, R equation, and A equation, respectively.  
The L equation includes:  
\[GDP.k=GDP.j+DT(EG.jk-ER.jk) \tag{1}\]  
\[CDE.k=CDE.j+DT(EMG.jk-ED.jk) \tag{2}\]  
The R equation includes:  
\[EG.jk=GDP.j\times EGC.j+IRR.j \tag{3}\]  
\[ER.jk=RAF1.j \tag{4}\]  
\[EMG.jk=ELEMC\times ELEC.k+DELMC\times DLC.jk \tag{5}\]  
\[ED.jk=DVEMC\times DVEC.k+EPRC\times RAF1.j \tag{6}\]  
The A equation includes:  
\[RAF1.k=GDP.k\times RAF1C \tag{7}\]  
\[IRR.k=RFR\times IVRA.k \tag{8}\]  
\[DVRO.k=IVRA.k \tag{9}\]  
\[DLV.k=PD.k\times IVRA.k \tag{10}\]  
\[ELV.k=IVRA.k-DLV.k \tag{11}\]  
\[DLEC.k=DLV.k\times DLUC.k \tag{12}\]  
\[ELEC.k=ELV.k\times ELUC.k \tag{13}\]  
\[DVEC.k=DVRO.k\times DVUC.k \tag{14}\]  
\[TCD.k=FV.k\times TR.k \tag{15}\]  
\[IVRA.k=TVD.k \tag{16}\]  
where \(j\) and \(k\) are time nodes. _Variable.k_ represents the current value of the variable. _Variable.j_ represents the past value of the variable, _Variable.jk_ represents the change in value between \(j\) to \(k\) of the variable. \(DT\) represents the timeline.  
In addition to the above equations, the diesel locomotive emission coefficient, electric locomotive emission coefficient, and diesel vehicle emission coefficient are provided in Table 3. The diesel locomotive unit energy consumption, electric locomotive unit energy consumption, and diesel vehicle unit energy consumption are provided in Table 4. The railway freight rate, rail freight environmental protection investment coefficient, and environmental protection investment reduction emission coefficient are determined as constants based on the data of previous years.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Combustion Value** & **Emission Factor** & **Emission Coefficient** \\  & **(kj/kg)** & **(kgCO\({}_{2}\)/T)** & **(kgCO\({}_{2}\)/kg)** \\ \hline Diesel fuel & 42,652 & 74,100 & 3.161 \\ Electricity & — & — & 0.604 \\ Petrol & 43,070 & 69,300 & 2.988 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Carbon dioxide emission coefficient of various energy sources.  
In addition, freight volume, economic growth coefficient, proportion of diesel locomotives, diesel locomotive unit energy consumption, electric locomotive unit energy consumption, and diesel vehicle unit energy consumption are set as table functions related to time. Data were obtained from the National Bureau of Statistics of China.
## 4 Experimental Results and Analysis
### Adversarial Data Injection and IDS Performance  
To simulate adversarial behaviour, we injected false data into 20% of the clients following the approach of [PERSON] et al. (2019). The data injection function, implemented in Python version 3.9.19, added Gaussian noise (\(\mu=0,\sigma^{2}=20\)) to 20% of these clients' data and randomised their labels to simulate label-flipping attacks. Figure 8 illustrates the impact of adversarial data injection on the MNIST dataset, highlighting the differences between normal data and adversarially altered data. The IDS was pre-trained on a mix of normal MNIST data and poisoned anomalies, achieving a validation accuracy of 99.86%. These results highlight the IDS's capability to detect subtle adversarial manipulations, even in scenarios with varying degrees of data injection. The IDS plays a critical role in maintaining the integrity of the federated learning process by identifying anomalous clients.
# Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox
## 4 Experimental Validation and Analysis  
To test the validity of the proposed AMC-RSN, two case studies are conducted in this section. The two different cases used for testing include the rolling bearing and gear datasets. To verify the anti-interference of the proposed AMC-RSN, it is worth noting that the collected vibration signals contain two faulted parts, one of which act as interference item. The experiment setup is shown as Figure 4a. The parallel gearbox contains two stages of gear reduction, with a primary ratio of 0.29 and a secondary ratio of 0.4. Two cases are conducted to study the effects of the proposed method. In both cases, the right-side rolling bearing of the input shaft is pre-set with ball fault, treated as an interference faulted part. In Case I, different fault types of rolling bearings (gears are healthy) in the intermediate shaft are combined with the above interference faulted part to form the multi-part multi-fault experiment setup. In Case II, different fault types of gears (rolling bearings are healthy) in the intermediate shaft are combined with the above interference faulted part to form the multi-part multi-fault experiment setup. In Case III, different mixed fault types of bearings and gears are set up in the intermediate shaft, and no interference fault is set in the input axis. Due to this setup, along with the noise generated by the machine operation, it will inevitably pose a challenge to the detection and classification of target faults.  
To enhance the dataset, all samples are overlapping samples with a sample length of 1024 and a stride of 512, the sampling form is shown in Figure 4b. Vibration data from the different datasets were collected using the SpectraQuest Drivertrain Dynamics Simulator (DDS). The data are collected by the accelerometer, and the arrangement of the sensor  
Figure 3: Diagnostic process of AMC-RSN.  
is shown in Figure 5, No. 6. The sampling time is 43.5 s, and the sampling frequency is 12,800 Hz. The length of each fault signal is 556,800, so the number of samples for each fault type after overlapping sampling is 1086. In all experiments, 80% of the training samples and 20% of the test samples are used. The details of the DDS are shown in Figure 5.  
The program uses PyTorch DL software with the Python 3.7 language. The CPU of the computer is configured as Intel Core i7-9750H and the GPU is configured as NVIDIA RTX2060. In the validation session, each test model is the optimal model obtained by 100 epochs of training. To avoid randomness, each method was trained ten times and ten test models were obtained. The Adam optimizer is used to optimize the learning rate during the training process. The loss function is a cross-entropy function.
## 2 Synthesis
### Hydrothermal/Solvothermal Method  
Hydrothermal/solvothermal method is a crystallization process at relatively low reaction temperature (< 200 \({}^{\circ}\)C) and higher pressure. It has gained plenty of successes for its outstanding advantages of low agglomeration, better product purity, higher dispersion and low cost in energy [65]. Different kinds of BCPs can be attained by the hydrothermal/solvothermal technique [36; 45; 46; 47; 48; 49; 50; 51; 52; 53].  
[PERSON] et al. [36] synthesized ZnO/BC composite by hydrothermal method (shown in Figure 2B). In a typical synthesis, waste biomass was washed and dried, followed by carbonizing under 500-600 \({}^{\circ}\)C. Then, the obtained material was activated via physical activation method under an inert atmosphere. Different weight ratios of BCPs were prepared. First, zinc nitrate and BC were mixed and stirred in distilled water, followed by dropwise adding NaOH (1 M) till the pH was 10. Then, the suspension was heated for 6 h at 120 \({}^{\circ}\)C. Later, the obtained material was centrifuged, washed, and dried overnight. The result showed ZnO exhibited reduced band gap energy, excellent activities, and reusability either under UV or visible-light irradiations by combined with BC.  
In another study, [PERSON] et al. [47] prepared TiO2/BC composite for photocatalytic degradation of acetaminophen under solar light via a multi-step solvothermal process. First, the biomass (lignin) was activated by using different agents. Then, the BC was suspended into the EtOH at room temperature to form solution A. Meanwhile, Ti(OBu)4 was diluted into EtOH (solution B). Later, solution B was dropwise added to solution A until complete homogenization. Afterwards, the hydrolysis of the Ti precursor was performed by adding dropwise water. The mixture was stirred and heated, followed by separation, washing, and drying overnight to obtain TiO2/BC. Through hydrothermal/solvothermal route, anatase phase with an average grain size of 10 nm was formed. TiO2-BC displayed much higher activities than bare TiO2 for the removal of acetaminophen under solar light.
## 3 The Proposed Optimization Hybrid Algorithm
### OPF Solutions IEEE 30-Bus Network [56]
#### 4.1.2 Case 2: Minimization of Piecewise Quadratic Fuel Cost  
Several thermal generating units can utilize fuel sources such as oil, coal or natural gas. The fuel cost coefficients of generators operating with a single fuel type are similar to those of Case 1. The fuel cost characteristics of the units located at buses 1 and 2 are expressed as:  
\[f_{i}(P_{Gi})=\sum\limits_{k=1}^{n_{f}}\alpha_{i,k}+b_{i,k}P_{Gi}+c_{i,k}P_{Gi}^ {2} \tag{47}\]  
where _nf_ denotes the number of fossil fuel alternatives for the \(i\)th generating unit, and \(a_{i,k}\), \(b_{i,k}\), and \(c_{i,k}\) are cost coefficients of generating unit \(i\) when the \(k\)th fuel is the alternative.  
The objective function can be described by Equation (42).  
\[\begin{split}& J_{2}=\sum\limits_{k=1}^{NG}\alpha_{i,k}+b_{i,k}P_{Gi}+c_{i,k}P_{Gi}^ {2}+\,\lambda_{P}(\,P_{\text{G1}}-P_{\text{G1}}^{\text{lim}}\,)^{2}\,+\,\lambda _{V}\sum\limits_{i=1}^{NL}\,(\,VL_{i}-VL_{i}^{\text{lim}}\,)^{2}\\ &+\,\lambda_{Q}\sum\limits_{i=1}^{NG}\,(\,Q_{Gi}-Q_{Gi}^{\text{ lim}}\,)^{2}+\,\lambda_{S}\sum\limits_{i=1}^{NL}\,(\,S_{i}-S_{i}^{\text{lim}}\,)^{2} \end{split} \tag{48}\]  
According to Table 3, the fuel cost when the suggested algorithm is applied is 646.4715 (USD/h). The best result belongs to the hybrid TLTFWO algorithm when compared with the results of other techniques listed in Table 5, such as MSA [56], gbest guided ABC (GABC) [74], MFO [56], MPSO-SFLA [11], FPA [56], Levy TLBO (LTLBO) [4], social spider optimization (SSO) [14], a modified DE (MDE) [60], sparrow search algorithm (SSA) [75], an improved EP (IEP) [71], MICA-TLA [67], TLBO, and TFWO, where the TLTFWO provides best fuel cost than the reported results in the literature. Moreover, Figure 6 demonstrates the convergence behavior of the algorithms when applied to the OPF problem with minimum fuel cost (USD/h).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizer** & **Fuel cost (USD/h)** & **Emission (t/h)** & **Power Losses (MW)** & **V.D. (p.u.)** \\ \hline MSA [56] & 646.8364 & 0.28352 & 6.8001 & 0.84479 \\ GABC [74] & 647.03 & - & 6.8160 & 0.8010 \\ MFO [56] & 649.2727 & 0.28336 & 7.2293 & 0.47024 \\ MPSO-SFLA [11] & 647.55 & - & - & - \\ FPA [56] & 651.3768 & 0.28083 & 7.2355 & 0.31259 \\ LTLBO [4] & 647.4315 & 0.2835 & 6.9347 & 0.8896 \\ SSO [14] & 663.3518 & - & - & - \\ MDE [60] & 647.846 & - & 7.095 & - \\ SSA [75] & 646.7796 & 0.2836 & 6.5599 & 0.5320 \\ IEP [71] & 649.312 & - & - & - \\ MICA-TLA [67] & 647.1002 & - & 6.8945 & - \\ TFWO & 646.9425 & 0.2840 & 6.8026 & 0.9136 \\ TLBO & 647.5263 & 0.2838 & 6.8375 & 0.9102 \\ TLTFWO & 646.4715 & 0.2835 & 6.7380 & 0.9152 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The optimal results found by different algorithms in Case 2.
# Ten Years of TerraSAR-X Operations
## 3 Evolution of SAR Product Portfolio
### Extension of Basic Products
These developments were supported by the tremendous effort put into phase and signal delay calibration for the TanDEM-X mission, the intrinsic bistatic focusing approach of the TMSP (even applied for mono-static TerraSAR-X data), and the elimination of algorithmic approximations in its focusing kernel. Once atmospheric and ionospheric signal propagation effects are properly considered, pixel location accuracies in the 1-2 cm range are achievable with the precisely determined orbit products [20]. Figure 2: Example for the additional quicklook files with a color representation of the Synthetic Aperture Radar (SAR) image pixel statistics that supplement each TerraSAR-X level 1 product since April 2009: Single polarization Stripmap GEC product of Auckland, New Zealand acquired in June 2008. These quicklooks are adaptively “tuned” by the TerraSAR-X Multi-mode SAR Processor (TMSP) to support visual interpretation of the image content and not meant for tasks like scientific land cover classification.
To fully utilize resources and ensure stable data transmission, further research on and optimization of urban rail Ad Hoc network routing protocols are needed to improve the performance of urban rail Ad Hoc network routing protocols and better adapt to the communication environment of urban rail. This paper proposes a dynamic addressing hybrid routing mechanism based on static configuration (DASC) in urban rail Ad Hoc networks. The contributions are as follows:  
1. In the static configuration stage, first, the improved AODV routing discovery algorithm combines the characteristics of fixed node location in urban rail scenarios and considers the remaining link lifetime between the trains and the fixed wayside nodes after clustering. It not only reduces network overhead but also prolongs the network's lifetime. 2. Then, fixed nodes are selected and the path to each router is written in advance for static configuration to eliminate the delay in the routing discovery process. 3. After the static configuration is completed, the cluster head dynamically monitors the packet loss rate of each link to determine whether there are links that need to be replaced. When a link that needs to be replaced is found, the dynamic addressing phase is enabled. The source node serving initiates a route replacement request to enable the route discovery algorithm, which updates the path for different types of communication services, effectively improving the packet delivery rate. The rest of this paper is structured as follows: Section 3 introduces the network model. Section 4 presents the routing algorithm and proposes the specific network operation steps. In Section 5, the simulation results of routing discovery overhead, remainder link lifetime, packet delivery rate, throughput, and end-to-end delay are shown and compared with that of traditional AODV. Finally, Section 6 concludes the paper.
# Solid-State Transformer-Based DC Power Distribution Network for Shipboard Applications
###### Abstract
also utilized in other power distribution aspects, such as Var compensation, active filtering, FACTS, fault isolation, and current limiting [23]. SSTs provide voltage transformation between various levels through the employment of power electronic converters and a high-frequency isolation transformer. The advantages of this technology over a traditional distribution transformer include no mineral oil, less weight, smaller size, and the mitigation of all power quality issues at the load side. In addition, SSTs can also feature DC buses capable of supporting variable speed drives and interfacing energy storage devices, e.g., batteries, as well as different renewable sources. This function permits the optimal selection of energy flow with respect to the vessel demands [24]. Smart SST also mitigates secondary faults and features very low no-load losses. Besides, it can also deliver power at a diverse frequency range from DC up to several hundreds of Hertz, i.e., 400-800 Hz, which suits a wide range of aboard electric loads and represents notable merits suitable for hybrid AC/DC electrical systems [16]. Concerning the propulsion system of the electric ship, there are several electric motor types that are utilized, including induction motors (IMs), permanent magnet (PM) machines, and electrically excited synchronous motors [25]. According to the analysis of DC shipboard microgrids introduced in Ref. [9], IMs are preferably used in present electric ships. The advantages of IMs are countless and include low cost, robustness, reliability, and a low maintenance requirement. On the other hand, PMSMs have the highest efficiency amongst all other powertrains. Multi-phase machines constitute powerful candidates in high-power machine applications. Unlike three-phase machines, multiphase ones offer an enhanced fault-tolerance capability and reduced per-phase current rating by splitting the power among more phases. Of the different available phase orders, six-phase-based propulsion motors are commonly employed in recent electric ship propulsion systems [26]. This paper presents a complete DC grid-based network for the shipboard electric power distribution system based on a hybrid AC/DC multiport SST and a multiphase-based propulsion system. The proposed system is designed based on the typical voltage levels employed in most ships with different sizes. The required system controller is implemented to ensure unity power factor operation for the generating units and guarantee power balance during different load conditions. Besides, the integration of high-power six-phase winding electric motors employed in the propulsion system to the local DC grid and their effect on the SST-based microgrid are also investigated. Simulations on the whole system have been carried out to investigate the effectiveness of this architecture in electric vessels under different operational modes. A small-scale 2 kW prototype system is constructed to corroborate the fidelity of the claimed system advantages.
## 5 Experimentation  
As can be seen in Figure 11, the prototype of the diplexer was constructed by employing a FR4 substrate with a thickness of 1.5 mm and dimensions of 32 mm by 26 mm. A Vector Network Analyzer (VNA) using the ARINST VNA-DL version, as in Figure 12, was utilized to validate that the diplexer was functioning properly as compared with simulated frequency responses. The substrate material undergoes a series of treatments prior to the Gerber file being sent to the machine by the AWR simulator. Before using the equipment, several procedures are completed using a CNC Machine 6090 to fabricate the diplexer prototype. The last step is to connect SMA cables to all three ports to check the built-in filters' functionality in the diplexer based on their S-parameters by VNA.  
Figure 11: The fabricated diplexer prototype.  
Figure 12: The setup for measurement based on the selected S-parameter (S11) with 50-ohm termination.  
Following the completion of all of the necessary preparations for the actual testing, the constructed diplexer was connected to the Vector Network Analyzer to perform measurements as in Figure 12. Quantification of S11, S21, S31, and S32 was accomplished by the diplexer under test. Following the completion of the measurements, comparisons were made between the results obtained from the experiment and those obtained from the simulation, as depicted in Figures 13-15 in terms of S-parameters and phase and group delay. All measurements are in acceptable agreement with the simulations.  
Figure 14: Comparison between simulations and measurements in terms of GD.  
Figure 13: Comparison between simulations and measurements in terms of S-parameters.
## 4 Simulation Results
### Case Study 2: PE-Based Power Systems  
In this case study, instead of G4, which is a synchronous generator, a wind farm with the same power rating (800 MVA) is used. In this way, wind power penetration is 11.2% (excluding the external generation, G1). The control model of the wind turbine is shown in Figure 6. The wind farm is considered as a parallel combination of the wind turbines.  
Considering that G5 trips at \(t=50\) s, the system stands stable; however, its frequency response becomes worse compared with the conventional method, due to the less inertia of the system. The frequency response of the system is shown in Figure 7. Although Figure 7 is very similar to Figure 4, the frequency nadir is higher in comparison with case study 1. This shows less inertial response in case study 2, due to the higher penetration level of wind turbines.  
Bus voltages for some buses are shown in Figure 8. It can be seen that the bus voltages oscillate more than 5%, which may not be desired for the transmission system, and may converge to some protection system actions. It should be noticed that voltage range can be varied from system to system, and acceptable voltage variation range can be defined by the grid codes [74]. Although in this simulation, the protection system actions are not considered, an under-voltage protection system may disconnect a transmission line, and may lead to a cascading failure of the system components.  
Like case study 1, it should be noticed that the process of the security analysis should be repeated for all contingencies. If the system is stable for all contingencies, then it can be said that the system is secure.  
Figure 6: Hardware and control model of the wind turbine used in the simulation used for security analysis in case studies 2 and 3.  
Figure 7: The PE-based power system frequency response to the generator 5 trip at \(t=50\) s for the PE-based power system case study (case study 2).
# Omni Wheel Arrangement Evaluation Method Using Velocity Moments
## 6 Discussion  
In the previous section, we showed that it is practical to consider the balance of velocity moments in the design of asymmetric Omni wheel robots. On the other hand, there are cases in which it is difficult to completely satisfy the balance of velocity moments, as in the present experiment. In such cases, the sensitivity of the sum of the velocity moments is vital because it is possible to obtain the permissible sum of the velocity moments from the permissible angular velocities. This section discusses this sensitivity.  
The experimental results confirm that the sensitivity varies with the wheel arrangement and the number of wheels. As values to evaluate wheel placement, the Table 6 shows the average and root mean square of the distance \(l\) of the wheel from the origin and the distance \(l^{\prime}\) between the wheel's resulting velocity vector and the origin in each arrangement. The root-mean-square was introduced to accommodate changes in value due to positive and negative distances. Based on the experimental results with the 4-wheeled robots, the value with the largest Not E-to-O 2 and the smallest Not E-to-O 3 or the value with the largest Not E-to-O 2 and the smallest Not E-to-O 3 is suitable to represent the relationship with sensitivity. Therefore, the root-mean-square of \(l^{\prime}\) may be the most appropriate value. However, further verification is necessary because \(l^{\prime}\) is not simply proportional to sensitivity. In addition, the root-mean-square of \(l^{\prime}\) between 4- and 6-wheeled robots is more significant for 6-wheeled, while the sensitivity is lesser for 6-wheeled, suggesting that the sensitivity becomes lesser as the number of wheels increases.  
The experimental results confirm that the wheel arrangement may rotate without following inverse kinematics. The generalized kinematics used in this study was obtained geometrically on the condition that the Omni wheel does not slip. It is considered that the robot rotates due to the slippage of the Omni wheel if the propulsive forces are not balanced. If this hypothesis is correct, the sensitivity to the sum of the velocity moments must be obtained by considering the slippage due to friction on the Omni wheel. Therefore, a mechanical approach that takes friction into account rather than a geometric approach is considered necessary to verify the details of the sensitivity.  
Figure 11: The relationship between the sum of the velocity moments generated by the command velocity and the angular velocity.