esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Test Setup
#### 3.1.1 PERFORM Case Study I: Single Obstacle  
As the first case study, an obstacle configuration given in Figure 8 is simulated to generate experimental test data and analyze the evaluation framework.  
Two different FIS are analyzed (a 3-MF system and 5-MF system) to observe what effects may result (if any) from varying the number of MF's.  
Figure 7: **(a)** The Chase Engineering Tank located at the University of New Hampshire (**b**) Small-Scale ASV Experimental Platform. Simulations are based upon the laboratory equipment shown here.  
The rule bases for this mission evaluation (arbitrarily chosen and without loss of generality) is summarized in Tables 1 and 2 for a 3-MF system and 5-MF system, respectively. Here, the linguistic input terms are italicized with CPA on the horizontal axis and path length given on the vertical axis. The linguistic pattern of Table 1 reads in the following manner:  
1. _If the total distance is short and the CPA is Close, then the performance score is Satisfactory._
2. _If the total distance is short and the CPA is Adequate, then the performance score is Very Satisfactory._
3....  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Linguistic Term** & **Close** & **Adequate** & **Far** \\ \hline
**Short** & Satisfactory & Very Satisfactory & Satisfactory \\ \hline
**Medium** & Satisfactory & Very Satisfactory & Satisfactory \\ \hline
**Long** & Poor & Satisfactory & Poor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of rules used for a 3-MF system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Linguistic Term** & **Very Close** & **Close** & **Adequate** & **Far** & **Very Far** \\ \hline
**Very Short** & Fair & Good & Very Good & Good & Fair \\ \hline
**Short** & Fair & Good & Very Good & Good & Fair \\ \hline
**Medium** & Poor & Fair & Good & Fair & Poor \\ \hline
**Long** & Very Poor & Poor & Fair & Poor & Very Poor \\ \hline
**Very Long** & Very Poor & Poor & Fair & Poor & Very Poor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of rules used in the example for a 5-MF system.  
Figure 8: Testbed for Case I (obstacle represented by the black box).  
To encourage the vehicle to remain a safe distance away from an obstacle, the linguistic term \"adequate\" is mapped to a better performance score. The ideal scenario is to have the vehicle remain a safe distance away from any obstacles while also maintaining the shortest possible path to a given destination within the given test environment.
## 2 LEV Charging Infrastructure Overview  
Existing LEV charging infrastructure is categorized into the following: conductive/plug-in charging, and wireless/inductive charging. Conductive charging uses a physical connection between the charger and the battery for charging. It is to be noted that conductive charging is the most developed and commonplace mode of charging vehicles. In terms of plug-in/conductive charging, on-board charging and off-board charging are the two prominent charging methods.  
Figure 2 shows the block diagram for on-board charging. As evidenced, most on-board LEV chargers involve the following two stages:  
* AC-DC and power factor correction (PFC): This comprises rectification followed by a PFC stage--normally a DC-DC converter--for maintaining supply current Total Harmonic Distortion (THD) as per IEEE-519 and IEC 61000-3-2 standards [14; 15].
* DC-DC stage: This DC-DC conversion stage is responsible for controlling the LEV battery charging process. The switch duty cycle is controlled by the battery voltage status/required charging current.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Model** & **Battery Type** & **Voltage Rating** & **Capacity** & **Charging Time** & **Fast Charging** \\ \hline \multirow{3}{*}{Two-wheeler} & Hero Electric Optima CX & Li-ion & 51.2 V & 30 Ah & 4–5 h & N/A \\  & Okinawa PraisePro & Li-ion & 72 V & 2 kWh & 2–3 h & N/A \\  & After 450 Plus & Li-ion & 51.1 V & 3.7 kWh & 5 h 40 min & 1–2 h \\ \hline \multirow{3}{*}{Three-wheeler} & Mahindra Treo & Li-ion & 48 V & 7.37 kWh & 3 h 50 min & N/A \\  & Piaggio Ape E City/Fixed & Li-ion & 48 V & 7.5 kWh & 3 h 45 min & N/A \\ \cline{1-1}  & Battery & & & & & \\ \cline{1-1}  & Terra Motors Y4A & Lead Acid & 48 V & 140 Ah & 6–8 h & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some commercially available LEVs and their battery/charging specifications.  
However, on-board charging tends to have an impact on the AC grid, due to the power electronic interface. Charging many LEVs from residential connections can have a negative impact on the voltage and current quality of grid.  
Hence, researchers are exploring the possibility of DC charging of LEVs in a similar vein to DC fast charging as seen in conventional EVs [12]. As shown in Figure 3, these chargers are normally housed off-board and may not include a dedicated PFC stage. Hence, this may help improve the efficiency of the charging system. This mode of charging is ideal for integration into a DC micro-grid ecosystem [16], thus removing any dependence on the AC grid.  
Moreover, owing to safety concerns arising from the physical condition of the connector, recent years have seen researchers also explore LEV charging through wireless power transfer (WPT). In this process, LEV is charged with the help of energy transfer between transmitter coil (usually located at charging location) and receiver coil (usually fitted underneath the vehicle body). This mode of charging does not involve any physical connection between the vehicle and charger.
## 3 Results
### Simulation Results
#### 3.2.3 Simulation 3  
We consider an overlapping span of 0.5 to 5 m in both cantons with an increase of 0.1 m in each calculation. In all cases, a slope elevation of 0 m is considered, i.e., we use a horizontal beam without slope for overlapping span to obtain the optimum length of the beam to establish a suitable elevation. We continue with the same configuration of normal spans of 10 m.  
As in the previous cases, the analysis of the data obtained by the calculations is carried out in the common area, and, as a result, we obtain the graphs shown in Figure 12, which represent the average and maximum values of the pantograph stress on the contact wires in Newtons, the standard deviation, and the total distance of take-offs. In this case, and as expected, take-offs may occur in the common area of the catenary; thus, we show the graph indicating the total length of take-offs produced in this area.  
The abscissa axis of the graphs represents the length of the overlapping span in meters and the ordinate axis represents the effort (mean, maximum, minimum, and deviation) in Newtons obtained in the integration calculated with the given elevation.  
Analysis of the graphs (Figure 12) shows that the deviation of the stresses in the common zone is adequate in cases in which the span length is below 3.5-3.7 m, with slightly worse behavior at values below 1.5 m. Above 3.7 m, the behavior worsens significantly, and take-offs appear over longer distances. This behavior is explained by the absence of the section elevation effect compensated by the \"excessive\" length of the overlapping span, which causes it to lose the elevation and fall below the nominal height on the track. Thus, the pantograph \"collides\" with this beam with stresses of more than 2000 N.  
In the configuration analyzed, the optimum distance is around 3.5 m. From this length onwards, it is necessary to elevate the slope to achieve optimum values in the common area of the catenary. However, if we consider the results separated by the catton represented in Figure 13, we observe that, from 3.1 m, take-offs occur in the wire of catton 1 and, from 3.4 m, in the wire of catton 2. To ensure that the optimum distance is below these values, it is necessary to maintain contact of the plate with the two wires simultaneously in the common area.  
It is expected that the optimum distance of the overlapping span length is influenced by the length of the adjacent normal spans. We can check this situation by repeating this simulation with normal spans of 5 m instead of 10 m, as shown in the following simulation.
###### Abstract  
The RTD-A (robust, tracking, disturbance rejection and aggressiveness) controller is a novel control scheme that substitutes the classical proportional integral derivative (PID) controller. This novel controller's performance depends on the four controller tuning parameters (\(\theta_{R}\), \(\theta_{T}\), \(\theta_{D}\) and \(\theta_{A}\)). The tuning of RTD-A controller is more transparent than classic PID controllers. The RTD-A tuning parameters values lies between ZERO and ONE. Availability of a tool to design optimal parameters for this controller and evaluating the performance on a given system is necessary for the researchers. In this paper, the new simulation tool is presented to deal with the RTD-A control scheme. There are four graphical user interface tools included in the proposed tool and working of each tool is explained in detail. To demonstrate the proposed tool, two examples, which involve a liquid level control application and an air pressure control application, are presented in this work. The performance of the RTD-A controller is compared with PID controller. RTD-A controllers are tuned using optimization algorithms and their performances are observed and analyzed in both cases under deterministic and uncertain conditions.  
controller; optimization; regulatory; simulation tool; servo; tuning +
Footnote †: journal:
## 5 Performance Evaluation of the Charging Circuit
### Temperature Dependence of the Detection Circuit  
We placed the detection circuit into the GDJS series high-low-temperature test chamber providing a stable environmental temperature over the range of \(-50\)\({}^{\circ}\)C to \(30\)\({}^{\circ}\)C. The input voltage of the experiment was generated with programmable power-supply E3631A (Agilent, Santa Clara, CA, USA) to generate voltages from 2 V to 22 V. The output of the detection circuit was measured by an oscilloscope (MSO70404C, Tektronix, Beaverton, OR, USA). The interval of the experimental temperature was 20 \({}^{\circ}\)C. The interval of the measured voltage was set to 1 V. At each temperature, the high-low-temperature test chamber maintained the temperature for 20 min, during which  
Figure 12: Schematic of the auxiliary circuit.  
measurements were repeated 300 times. We took the average values to minimize statistical error and uncertainty.  
The output voltage and average error results are shown in Figure 13. The results demonstrate that the output of the detection circuit had a strong upward trend, proportional to the increase of input, and the temperature dependence of the detection circuit was within a narrow range. The R\({}^{2}\) of the output of the detection circuit was greater than 0.99. The maximum average error of the output voltage of the detection circuit was 0.017 V at 10 V, and the minimum average error of the output voltage was \(-\)0.027 V at 6 V. Therefore, the experimental results show that the detection circuit has excellent stability at low temperatures, and can be used in the charging circuit for the polar regions.
## 3 Solving Method
### Attention Decision Network  
The attention mechanism can reduce the computational load of a neural network by allowing it to focus more on inputs related to the target output. The attention function can be described by mapping key-value pairs and a query to an output [48]. The attention function used in this study is expressed as follows:  
\[Attention=\text{Softmax}(\text{tanh}(W[Q,K]))V, \tag{13}\]  
where \(W\) is the network parameter and \(Q\), \(K\), and \(V\) are vectors representing the queries, keys, and values, respectively. This section describes the attention decision network in detail. Figure 4 illustrates the architecture of the proposed ADN.  
The original attributes \(O=transpose([o_{1}\), \(o_{2},\cdots,o_{M}])\) and the direct attributes \(D_{t}=transpose(\left[d_{1}^{t},\ d_{2}^{t},\cdots,d_{M}^{t}])\) are input into the ADN. According to Equations (8) and (9), the dimensions of \(O\) are \((2,M)\) and the dimensions of \(D_{t}\) are \((3,M)\). \(O\) and \(D_{t}\) are encoded by two one-dimensional convolutional networks, and the encoded \(O\) and \(D_{t}\) are defined as \(E\)-\(O\) and \(E\)-\(D_{t}\), respectively. \(E\)-\(OD_{t}\) represents the concatenation of \(E\)-\(O\) and \(E\)-\(D_{t}\). \(E\)-\(O\) in our attention layers are defined as value vectors. \(E\)-\(OD_{t}\) represents the key vectors.  
\(a_{t-1}\) is the output of the last time step, and \(O(a_{t-1})\) is the \(a_{t-1}\)-th element in \(O\). Therefore, the dimension of \(O(a_{t-1})\) is \((2,1)\). \(O(a_{t-1})\) was treated as the sequence attribute input. After the sequence attribute input was encoded, a GRU layer was employed to capture long-term information, where GRU-\(h_{n}\) is a query vector.  
With the key vectors \(E\)-\(OD_{t}\) and query vectors GRU-\(h_{n}\), a concatenation operator, two linear layers, an activation layer, and a softmax layer were used to calculate the attention weight (\(AW\)) vector.  
\[AW=\text{Softmax}(\text{tanh}(W_{AW}[OD_{t}h_{n}])), \tag{14}\]  
where \(OD_{t}h_{n}\) represents the concatenation of \(E\)-\(OD_{t}\) and GRU-\(h_{n}\). \(W_{AW}\) denotes the linear layer parameter. The context vector was then calculated as follows:  
\[Cont=AW\cdot E\text{-}O \tag{15}\]  
_Cont_ indicates the important information extracted from the inputs. Layers from _Concatenate1_ to _Torch.bmm_ are the attention layers. Then, we concatenated _Cont_ and \(E\)-\(O\) and, as shown in Figure 4, after two linear layers, one activation layer, and a dropout layer, we obtained the score for each action.  
The dimension of \(Mask_{t}\) is \((1,M)\). The \(i\)-th element in \(Mask_{t}\) equals \(1\) if task \(i\) is feasible and has not been scheduled, and it equals \(0\) otherwise. \(P_{t}\) is the probability distribution of the task selection. During the training process, the action selection is determined by sampling based on a probability distribution.
# An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology
## 5 Conclusions  
In this paper, based on the development of communication technologies, we temporarily analyze the utility value of future reconfigurable antennas and propose an L-slot MEMS frequency reconfigurable antenna for the U-band. The reconfigurable function of the antenna is performed by RF MEMS switches. The L-slot widens the frequency range and creates reconfigurable conditions for it. Two switches with four matching states enabled the antenna to achieve better impedance matching at 42.36, 47.65, 53.13, and 56.72 GHz. The results show that the proposed L-slot MEMS frequency reconfigurable antenna is one of the ideal choices for future millimeter wave communication systems. In addition, a process preparation scheme is proposed in this paper. However, due to the slow progress of the work, only the state without the switch is tested. The test results are within acceptable limits. Preparation and testing will continue in the future.  
Conceptualization, Y.C.; methodology, Y.C. and H.G.; software, Y.C.; validation, Y.L., J.L. and Y.Z.; formal analysis, Y.C. and Y.L.; investigation, Y.C. and H.G.; resources, Y.C.; data curation, Y.C.; writing--original draft preparation, Y.C. and H.G.; writing--review and editing, J.L. and Y.Z.; visualization, H.G. and Y.L.; supervision, Q.W.; project administration, Q.W. and M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Measurement **(a)** A prototype of the antenna as a state without switches; **(b)** Microwave performance test of the antenna.  
Figure 13: Experimental verification of the state without switches **(a)** The comparison of the S11 parameter between the simulation and measurement; **(b)** The comparison of the VSWR parameter between the simulation and measurement.  
**Funding:** This research was funded by Double First-Class Talent Plan Construction (11012315), Double First-Class Disciplines National First-Class Curriculum Construction (11013168), Double First Class Disciplines Construction (11013351), and National Future Technical College Construction Project (11013169).  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 3 Improved Trimming Ant Colony Optimization (ITACO) Algorithm
### Triangular Trimming Path Optimization  
Triangular pruning utilizes the principle that the sum of the lengths of any two sides of a triangle is greater than the length of the remaining side. In this paper, triangular pruning is introduced to further optimize the paths generated by the ant colony optimization algorithm. By traversing all nodes on the path, redundant nodes are removed to shorten the path length. In path planning, the application of triangular pruning primarily involves eliminating triangular regions that cannot possibly be part of the optimal path, thereby reducing the search space and computational load, and improving the efficiency of path planning.  
The main idea is to connect the first node with the second node. If there are no obstacles between them, the second node is discarded. Next, the first node is connected to the third node. If there are no obstacles between them, the third node is discarded. This process continues sequentially, connecting the first node with the fourth node, and so on, until an obstacle is encountered between the first node and the node to be connected. The node immediately before the obstacle is then connected to the first node to form a new path. This node becomes the new starting point, and the process is repeated until the final node is reached. The pruning process is illustrated in Figure 1.  
Figure 1: Triangular pruning path optimization.  
In Figure 1, graphical elements are defined as:  
* Black squares: Obstacle locations
* Circled numbers: Path nodes (1, 2, 3,...)
* Black solid line: Initial candidate path
* Blue bold line: Optimized path after pruning
* Red dashed line: Invalid obstacle-crossing paths  
The path optimization process through the triangular pruning technique:  
1. The algorithm begins by connecting nodes sequentially (1\(\rightarrow\)2\(\rightarrow\)3\(\rightarrow\)4\(\rightarrow\)5...)
2. When the direct connection between nodes 1\(\rightarrow\)5 fails due to obstacle interference: 1. The system backtracks to the last viable node (node 4) 2. Establishes new valid segment (1\(\rightarrow\)4) 3. Continues connection from node 4 onward
3. This pruning process iterates until reaching the final node  
The introduction of triangular pruning effectively reduces redundant path length in path planning.
# A Comprehensive Survey for Deep-Learning-Based
## 5 Methodologies
### Abnormality Detection with Model-Driven Methods  
Abnormality detection with model-driven methods is a well-established research areas, which adopts many advanced signal-processing techniques [5; 100] to get the indicators. To deliver an effective survey, we will start by introducing the well-known linearized power flow equations [5], which can be expressed as follows:  
\[\left[\begin{array}{c}\Delta\mathbf{F}\\ \Delta\mathbf{G}\end{array}\right]=\left[\begin{array}{cc}\mathbf{F}_{\theta }&\mathbf{F}_{\mathbf{v}}\\ \mathbf{G}_{\theta}&\mathbf{G}_{\mathbf{v}}\end{array}\right]\left[\begin{array} []{c}\Delta\theta\\ \Delta\mathbf{v}\end{array}\right]=\mathbf{J}\left[\begin{array}{c}\Delta \theta\\ \Delta\mathbf{v}\end{array}\right], \tag{1}\]  
where \(\mathbf{v}\), \(\mathbf{\theta}\) represent the voltage magnitude and node angles, respectively. It is well accepted that the minimum singular value of the power-flow Jacobian matrix \(\mathbf{J}\) can be trusted as a static abnormality indicator [5]. Equation (1) can be rewritten as follows:  
\[\mathbf{A}=\mathbf{B}^{T}\mathbf{C}, \tag{2}\]  
where \(\mathbf{A}=\left[\begin{array}{cc}\Delta\mathbf{\theta}&\Delta\mathbf{v} \end{array}\right]^{T}\in\boldsymbol{C}^{p\times N}\); \(\mathbf{\boldsymbol{p}}\) and \(\mathbf{N}\) denote the number of buses and variables, respectively; \(\mathbf{B}^{T}=\mathbf{J}^{-1}\); and \(\mathbf{C}=\left[\begin{array}{cc}\Delta\mathbf{F}&\Delta\mathbf{G}\end{array} \right]^{T}\).  
From the above, we can see that the maximum singular value of \(\mathbf{B}\) also equivalently indicates the abnormality. Interested readers are referred to the works of [4; 48] for technical details. Based on Equations (1) and (2), abnormality detection with model-driven methods is widely adopted in classical power systems. However, with an increase in the size of the power system, such a method will pose more of a challenge in solving the large-scale power flow equations. More essentially, the new techniques are hardly modeled in the classical methods, which calls for new solutions for abnormality detection.
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 2 Background
### LoRa and LoRaWAN Basics
#### 2.1.2 Activation Procedure  
In LoRaWAN, two options to handle the initial connection between EDs and the NS are defined. The former one is activation by personalization (ABP), which implies that all credentials are provisioned offline and is not recommended for commercial deployments due to insufficient security. The latter (and the recommended) one is over-the-air activation (OTAA). In the current paper, unless stated otherwise, we use OTAA (as defined in the standard version 1.0.x). Figure 3 visualizes the key phases and operations composing OTAA.  
In phase (1), the initial credentials, i.e., the DevEUI, AppEUI and AppKey, must be stored on an ED. The former represents the unique identifier of a device, while the AppEUI determines the application to handle the data and a special key associated with it. The join-request message (JoinReq) frame is composed of 8 bytes of AppEUI, 8 bytes of DevEUI and the 2 bytes of DevNonce. The message would appear as: \(|AppEUI|DevEUI|DevNonce|\). The message is protected by MIC, but not encrypted. For our needs, it is essential to explain the value of DevNonce. The DevNonce value is the last 2 bytes of the JoinReq message. Because the message is not encrypted, this value can be eavesdropped. DevNonce is unique, and is usually a randomly generated value. It changes every time a device attempts to connect. Note that the NS stores DevNonce values to prevent the reuse of an old value.  
In phase (2), the GW verifies the MIC, and NS checks the DevNonce value. If the checks pass, in phase (3), NS generates DevAddr, AppNonce and NetID values and shapes a join-accept (JoinAcc) message. The JoinAcc message consists of 3 bytes of AppNonce value, 3 bytes of NetID value, 4 bytes of DevAddr value, 1 byte of DL settings value, 1 byte RxDelay value and 16 bytes of an optional list of channel frequencies (CFList). The JoinAcc thus is as follows: \(|AppNonce|NetID|DevAddr|DLSettings|RxDelay|CFList|\).  
The value of AppNonce, which is the first 3 bytes of the encrypted JoinAcc message, is generated randomly, in the same manner as the DevNonce. For the JoinAcc message,  
Figure 3: OTAA procedure in LoRaWAN protocol version 1.0.x.  
the MIC is generated again, and the AppKey is further used to encrypt the frame before transmission. In phase (4), the AppNonce and DevNonce are shared by the NS and the ED. NwkSKey and AppSKey values are generated from the AppNonce and DevNonce values. In phase (5), the key credentials, such as AppSKey and DevAddr, is sent to the application server. Then, the transmission of the application layer messages can begin.