esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Compressor powers between 15% and 75% and HTF flow rates of 50 L\(\cdot\)h\({}^{-1}\), 100 L\(\cdot\)h\({}^{-1}\), and 150 L\(\cdot\)h\({}^{-1}\) were tested, with a fixed HTF inlet temperature of 12 \({}^{\circ}\)C. Not all planned experiments were feasible in practice since the evaporation temperature set-point was achieved before any part of the PCM could be charged, being the only feasible tests in Mode 3b the ones performed at a compressor power of 15%. This is one of the drawbacks related to the direct implementation of the RPW-HEX in the refrigeration system, which needs to be further analysed in more detail in future research. The results showed that, after an initial transient phase during which the PCM was either charged by the refrigerant (Mode 3a) or discharged by the HTF (Mode 3b), steady state conditions, characterised by thermal equilibrium between the refrigerant, PCM, and HTF, were achieved in both Modes 3a and 3b. When steady state was attained, the PCM reached a state of partial charge, in which only some parts of the PCM were in solid state. The cold energy stored in the PCM in steady state strongly depended on the HTF flow rate. The higher the HTF flow rate, the lower the energy stored and the shorter the time to reach the steady state. For instance, the results of the experiments performed in Mode 3b, in Figure 24, show that at a compressor power of 15% the cold energy stored in the PCM decreased from 76 kJ to only 4 kJ by increasing the HTF from rate from 50 L\(\cdot\)h\({}^{-1}\) to 150 L\(\cdot\)h\({}^{-1}\). The cold energy stored in the PCM is also influenced by the compressor power according to the results of the experiments performed in Mode 3a, in which the energy increased from about 5 kJ to 145 kJ by increasing the compressor power from 15% to 45%. Moreover, a large temperature gradient throughout the PCM along the vertical direction was observed in both Mode 3a and 3b, being the temperature at the top of the RPW-HEX up to 20 \({}^{\circ}\)C higher than at the bottom. Since the RPW-HEX was designed to contain seven sections (blocks), defined by different geometry and material distribution, and energy storage capacities the study presented in this paper can only be considered as a preliminary analysis aimed at presenting the novel concept and assessing the behaviour only of the PCM located at the central section (block A) of the RPW-HEX, as shown in Figure 5. However, some of the drawbacks such as temperature stratification, low cold storage capacity, and impossibility of attaining stable operating conditions should also be analysed in detail in future research. Other aspects such as the size, the influence of the auxiliary parts of the RPW-HEX, and the effect of the PCM integration on the performance of the refrigeration system as compared to a more traditional heat exchanger without PCM will also be investigated in future research.
# Graph Planarity by Replacing Cliques with Paths +
## 4 _h_-Clique2 Pah Planarity and 1-Planarity  
In this section, we show that, when the given simple topological graph is 1-plane, _h_-C2 PP can be solved in linear time in the size of the input, for any \(h\). We consider all possible simple topological 1-plane cliques and show that the problem can be solved using only local tests, each requiring constant time. Note that we can restrict to the case _h_\(\leq 6\), since \(K_{6}\) is the largest 1-planar complete graph [11].  
Simple topological 1-plane graphs containing cliques with at most four vertices that cross each other can be constructed, but it is easy to enumerate all these graphs (up to symmetry) (see Figure 4). Note that such graphs involve at most two cliques and that, if \(K_{4}\) has a crossing, combining it with any other clique would violate 1-planarity (see Figure 4a,b). The next lemma accounts for cliques with five or six vertices.  
**Lemma 2**.: _There exists no \(1\)-plane simple topological graph that contains two cliques, one of which with at least five vertices, whose edges cross each other._  
Proof.: Consider a simple \(1\)-plane graph \(G\) that contains two disjoint cliques \(K\) and \(H\), with five and three vertices, respectively. Let \(K^{\prime}\) be the simple plane topological graph obtained from \(K\) by replacing each crossing with a dummy vertex. By \(1\)-planarity, every face of \(K^{\prime}\) is a triangle and contains at most one dummy vertex. Suppose, for a contradiction, that there exists a crossing between an edge of \(K\) and an edge of \(H\) in \(G\). Then, there would exist at least a vertex \(v\) of \(H\) inside a face \(f\) of \(K^{\prime}\) and at least one outside \(f\). Since \(H\) is a triangle, there must have been two edges that connect vertices inside \(f\) to vertices outside \(f\). If \(f\) contains one dummy vertex, then two of its edges are not crossed by edges of \(H\), as otherwise \(G\) would not be \(1\)-planar. Hence, both the edges that connect vertices inside \(f\) to vertices outside \(f\) cross the other edge of \(f\), a contradiction. If \(f\) contains no dummy vertices, then each edge of \(f\) admits one crossing. Let \(u\) be the vertex of \(f\) that is incident to the two edges crossed by edges of \(H\). Since \(u\) has degree \(4\) in \(K\), it is not possible to draw the third edge of \(H\) so that it crosses only one edge of \(K\), which completes the proof.  
Combining the previous discussion with Lemma 2, we conclude that, for each subgraph of the input graph \(G\) that consists either of a combination of at most two cliques of size at most \(4\), as in Figure 4, or of a single clique not crossing any other clique, the crossings involving this subgraph (possibly with other edges not belonging to cliques) can only be resolved by removing its edges, which can be checked in constant time. In the next theorem, \(n\) denotes the number of vertices.  
**Theorem 2**.: \(h\)_-C2 PP _is \(O(n)\)-time solvable for simple topological \(1\)-plane graphs._
# Mobile Robot Path Planning Algorithm Based on NSGA-II
## 4 Experimental Simulation and Analysis  
### Environment Setup  
In the path planning of mobile robots, map establishment is an important step. Commonly used map representation methods include grid representation, geometric representation, and topological map representation. This paper adopts the grid map representation method. Grid map representation has the advantage of a simple structure, which can simplify complex terrain and facilitate model establishment. In the grid map, and white areas represent feasible areas, while black areas represent infeasible areas, i.e., obstacles, as shown in Figure 5.  
Figure 4: Path smoothing algorithm flow chart.  
Figure 5: Grid map.  
Let the map model be represented as  
\[Grid(x,y)=\begin{cases}1,&(x,y)=w\\ 0,&\text{else}\end{cases} \tag{14}\]  
In grid maps, commonly used encoding methods include real number encoding, binary encoding, and floating-point encoding. Among them, real number encoding is simple to operate and easy to calculate. Therefore, in this experiment, real number encoding was used to encode each grid in the map. According to the 20 \(\times\) 20 grid map used in the experiment, grids are encoded sequentially from left to right and from bottom to top as 0, 1, 2,..., 399. The relationship between the grid with the number \(O\) and its coordinates in the map is:  
\[\begin{cases}x=O\mod G_{size}+1,\\ y=\lfloor\frac{O}{G_{size}}\rfloor+1,\end{cases} \tag{15}\]  
where \(\lfloor\rfloor\) denotes the floor function, \(\mod\) denotes the modulo function, and \(G_{size}\) represents the number of grids per row.  
To verify the correctness and effectiveness of the algorithm, two grid maps of different complexity levels were selected, as shown in Figure 6. Simulation tests were conducted using MATLAB R2016a and compared with the standard NSGA-II algorithm and genetic algorithm.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 3 Proposed System Architecture
### Quantization for On-Edge Classification  
The TFLite framework stands out in its ability to handle latency, privacy, connectivity, size, and power consumption constraints [2]. This is especially useful when deploying deep learning models on devices that are usually incapable of handling such complex architectures. Quantization is one of the more popular approaches to optimize a model for deployment, along with pruning and clustering that mainly aim for easily compressible models [36]. Size and latency are optimized through quantization, with some loss of model accuracy. A summary of the two types of quantization chosen for this work can be seen in Table 5. Once a model is trained and converted to TFLite format, it is typically saved in floating-point 32 precision. Post-training F16 quantization then converts the model weights to floating-point 16 format, reducing the model size and allowing for faster, more efficient operations that reduce inference time. QAT [8], however, is a more advanced approach that adjusts model weights to lower precision only when needed during the training phase, not after. This reduces model size whilst still retaining its performance.
###### Abstract  
The auroral ovals around the Earth's magnetic poles are produced by the collisions between energetic particles precipitating from solar wind and atoms or molecules in the upper atmosphere. The morphology of auroral oval acts as an important mirror reflecting the solar wind-magnetosphere-ionosphere coupling process and its intrinsic mechanism. However, the classical level set based segmentation methods often fail to extract an accurate auroral oval from the ultraviolet imager (UVI) image with intensity inhomogeneity. The existing methods designed specifically for auroral oval extraction are extremely sensitive to the contour initializations. In this paper, a novel deep feature-based adaptive level set model (DFALS) is proposed to tackle these issues. First, we extract the deep feature from the UVI image with the newly designed convolutional neural network (CNN). Second, with the deep feature, the global energy term and the adaptive time-step are constructed and incorporated into the local information based dual level set auroral oval segmentation method (LIDLSM). Third, we extract the contour of the auroral oval through the minimization of the proposed energy functional. The experiments on the UVI image data set validate the strong robustness of DFALS to different contour initializations. In addition, with the help of deep feature-based global energy term, the proposed method also obtains higher segmentation accuracy in comparison with the state-of-the-art level set based methods.  
auroral oval; convolutional neural network; image segmentation; level set; adaptive time-step
## 3 Results
### Classification Accuracy and Mangrove Cover Changes
Figure 3 shows the mangrove areas, their gain, and their loss, and unchanged mangrove areas between 2000 and 2019. The total unchanged mangrove areas over the 19-year period was 8483.44 ha, and the total loss was 5128.80 ha. Despite the loss, some new (2171.38 ha) mangrove areas were detected. This means that a net loss of 2907.29 ha (21.35%) has occurred in IM over the last 19 years. This is equal to a 1.12% annual mangrove loss in the IM region, which is higher than Malaysia's mean depletion rate of 0.41%, based on the estimation of [PERSON] and [PERSON] [53]. The southwestern part of the IM, where the Pulau Kukup, Tanjung Piai, and Sungai Pulai mangrove forests (Ramsar sites) are located (Figure 3c), faced significant loss over the study period. On the other hand, at the eastern side of IM, new patches of mangroves were noticed. Figure 4a shows the pinned locations of gain and loss of mangroves over the IM region. Table 4 lists the locations and types of transition from (loss) and to (gain) mangrove area, with Table 5 showing the conditions of some selected loss and gain areas, including when these transitions occurred. During the identification of the gain and loss locations, we also identified around 40 sites in IM that were used for aquaculture activities (Figure 4b). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Gain ID.** & **Places/Location** & **Transition Type** \\ \hline
1 & Pulau Kukup & Eroded mangrove \\
2 & Serkat Berkat Village & Oil palm \\
3 & Tanjung Piai & Eroded mangrove \\
4 & Perpat Pasir Village & Oil palm \\
5 & Sungai Bohr Village & Urban \\
6 & Pulai River—Custom & Urban \\ Administration Building & & \\
7 & Forest City Golf Hotel/ Resort & Urban—resort \\
8 & Sri Tulang Residential Project & Urban—residential \\
9 & Bayu Senibong Residential zone & Bare land \\
10 & Changkat Village & Aquaculture \\
11 & Sri Aman Village & Aquaculture \\
12 & Sri Aman Village & Aquaculture \\
13 & Sri Aman Village & Aquaculture \\
14 & Tanjung Langsat & Urban—port expansion \\
15 & Tanjung Langsat & Urban—port expansion \\
16 & Kampung Kong Kong & Aquaculture \\
17 & Kampung Kong Kong & Aquaculture \\
18 & Layang River & Eroded mangrove \\
19 & Tiram River Fishing Village & Aquaculture \\
20 & Tiram River Fishing Village & Aquaculture \\
21 & Tiram River Fishing Village & Aquaculture \\ \hline
**Gain ID.** & **Places/Location** & **Transition Type** \\ \hline
1 & Kong Kong River & Eroded mangrove \\
2 & Kong Kong River & Eroded mangrove \\
3 & Tanjung Langsat Village & Aquaculture \\
4 & Melayu Village & Aquaculture \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The loss and gain of mangrove areas and their transition to other land use/cover types in IM between 2000 and 2019.
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
## 2 V2A Charging Mode
### V2G Charging Scenario  
V2G signifies the discharging power released from EVs to the power grid, as shown in Figure 5. The given framework consists of residential households, a battery swapping station, distributed renewable energy resources, an EV charging station, smart buildings, smart home, and other essential power grid elements. Based on practical applications and voltage levels, the energy distribution can be categorized into three clusters [10]: the first cluster performs charging/discharging for EVs at low-voltage networks such as at home or building parking lots; the second cluster implements EV charging/discharging at charging stations, particularly for fast power exchange of EVs at medium-voltage networks; the third cluster is EV batteries that are swapped at battery swapping stations while the discharged batteries are collectively recharged. It should be noted that the third role is not advised for V2G operation.  
The discharging capability of a single EV, however, is normally limited due to its small capacity. To explore the full storage potential of V2G, a group of EVs is usually coordinated and governed through an internal agent, e.g., an EV charging station or an EV aggregator [16]. The EV user can choose an aggregator for V2G participation via signing a contract, while the aggregator can obtain regulation revenues through the incentives offered by the power operators. A thorough review of V2G can be found in [6] and the opportunities offered by V2G are briefly summarized below:  
(1) V2G can be operated in various places, e.g., residential areas, commercial parking lots, and work places, as displayed in Figure 5; (2) with V2G participation of massive EVs, adequate power supply can be provided for a power regulation service; (3) V2G activity has the potential to improve the power stability, energy efficiency, and reliability of power grids; (4) V2G can be arranged to coordinate with distributed renewable energy resources; (5) V2G is considered an important element for the realization of smart grid.  
The above activities constitute the paradigm of the V2A charging mode. Based on the above introduction, a single EV can perform different activities depending on the parking places, normally: (1) G2V, V2H, and V2G at home; (2) G2V, V2B, and V2G in building garages; (3) G2V, V2V, and V2G at a commercial charging station.  
Figure 4: Paradigm of V2V.
## 3 Analysis of Compound Strata and Surface Settlement
### Numerical Model Establishment

Table 5: Physical and mechanical properties of various components. Figure 9: Schematic of tunneling parameter settings. Figures 11 and 12 present the numerical simulation results and actual monitoring results of \(S_{\rm max}\) and \(i\) values for each monitoring section. The fitting degree between the simulation values and the actual monitoring results is high, and the curve trends are essentially the same, indicating the reasonability of the numerical calculation results. It is inferred from the graphs that (1) the surface settlement caused by shield tunneling varies due to the distinct physical and mechanical properties of strata. Therefore, the \(S_{\rm max}\) and \(i\) values change in a stepwise manner as \(L\) varies from soft-rock strata to hard-rock strata within a certain range on both sides of the composite surface, where the maximum surface settlement \(S_{\rm max}\) gradually decreases from 15.32 mm to 3.92 mm, and the settlement trough width \(i\) gradually increases from 10.25 m to 18.7 m in the direction from soft-rock strata to hard-rock strata. (2) The \(S_{\rm max}\) values are significantly impacted by the \(L\) distance from the monitoring section to the composite surface in the range of \(L=-\)35\(\sim\)25 m (\(-\)2.1D\(-\)1.7D, D is the tunnel excavation diameter), and, similarly, \(i\) values are highly influenced by the \(L\) distance in the range of \(L=-\)50-30 m (\(-\)3.1D\(-\)1.9D). Therefore, we believe that the range of \(L=-\)50-30 m (\(-\)3.1D\(-\)1.9D) is the composite strata settlement change range, where the surface settlement amplitudes vary greatly at each section along the direction of shield tunneling, requiring close monitoring during construction. (3) At \(L=0\), the monitoring section is located at the junction of the composite surface of the two strata. The \(S_{\rm max}\) value accounts for about 46% of the maximum \(S_{\rm max}\) value in interval 6 of the strata and is approximately 1.4 times the minimum \(S_{\rm max}\) value in stratum 7 of the strata. Meanwhile, the \(i\) value is about 1.5 times the minimum \(i\) value in stratum 6 of the strata and accounts for around 88% of the maximum \(i\) value in stratum 7 of the strata. In the composite strata settlement change range, the influence of the composite stratum on \(S_{\rm max}\) is greater than on \(i\), and it has a greater effect on the surface settlement of the soil strata rather than the rock strata. Figure 11: Comparison of simulated and measured values of \(S_{\rm max}\) for various monitoring cross-sections. Figure 10: Vertical displacement profile of strata after shield tunneling.
## 5 Experimental Evaluation  
### Experimental Procedure  
To evaluate the performance of the proposed control strategy with the developed lower-limb robotic orthosis system, various experiments are conducted with the participation of eight healthy male subjects who do not have neurological disorders. The detail information of these subjects are given in Table 4. All subjects gave their written informed consents for inclusion before they participated in the experiments. The experiment protocol was approved by the Ethics Committee of Shibaura Institute of Technology.  
The system is evaluated in two gait training modes including trajectory tracking mode and compliance control mode. The experiment time for each subject is about 10 min. In the first 5 min when  
\begin{table}
\begin{tabular}{c c} \hline
**Information** & **Value (Mean \(\pm\) SD)** \\ \hline Age (Years) & 29.7 \(\pm\) 3.9 \\ Body weight (kg) & 62.4 \(\pm\) 8.8 \\ Height (cm) & 166.2 \(\pm\) 4.0 \\ Shank length (cm) & 47.1 \(\pm\) 1.4 \\ Thigh length (cm) & 45.3 \(\pm\) 3.6 \\ \hline \end{tabular}
\end{table}
Table 4: The information of eight subjects.  
Figure 6: Compliance control architecture of the AIRGAIT robotic orthosis.  
the trajectory tracking mode is tested the robot compliance is set to the minimum value so that the movement of the subject lower limb is dominated by the robotic orthosis. The subject is also asked to completely relax. Therefore, the data of \(T_{int}^{passive}\) in 30 GCs are collected and saved together with the desired and measured trajectories. In the next 5 min of the experiment, the robotic orthosis is switched to the compliance control mode. In this case, the subject is asked to be more active in moving. The data are also recorded in last 30 GCs for further analysis.  
During the experiments, the body weight support system is used due to the safety requirements for the subjects. The reference trajectories of the hip (\(\theta_{1}^{\circ}\)) and knee (\(\theta_{2}^{\circ}\)) mono-articular actuator are modified from the gait data profile in textbook [35] according to each subject with the maximum of hip and knee flexion/extension angles are \(+20^{\circ}/-20^{\circ}\) and \(45^{\circ}/0^{\circ}\), respectively. The speed of the treadmill is set at 2.2 km/h. The sampling frequency of the control system is 100 Hz. Low pass filters with unity gain and 6 Hz cut-off frequency are employed to reduce the noise from signals getting from the load cells, pressure sensors as well the angle sensors. All analyses are carried out by MATLAB (MathWorks, Natick, MA, USA) software version R2016a. The parameters of the controllers after being well tuned are provided in Table 5.
## 2 Evaluation Method of Inertia Distribution Characteristics
### Inertia Response Model of GFM-ESS  
A GFM-ESS primarily consists of energy storage elements, power conversion system, and GFM converter. The control strategy of converter utilizes virtual synchronous machine (VSM) control [25], which encompasses the virtual governor, the rotational rotor equation, the virtual excitation regulator, and the stator electrical equation. The topology and control diagram of the GFM-ESS is shown in Figure 1.  
The VSM control emulates the governor's external characteristics by establishing droop characteristics between active power and system frequency, which can be expressed as Equations (1) and (2).  
\[P_{\text{m}}=P_{\text{ref}}+\Delta P=P_{\text{ref}}+k_{\text{p}}(\omega_{\text {ref}}-\omega_{\text{grid}}) \tag{1}\]  
\[\left\{\begin{array}{l}J\frac{\text{d}(\omega-\omega_{\text{ref}})}{\text{d }t}=\frac{P_{\text{m}}}{\omega_{\text{ref}}}-\frac{P_{\text{e}}}{\omega_{ \text{ref}}}-D(\omega-\omega_{\text{ref}})\\ \frac{\text{d}\omega}{\text{d}t}=\omega-\omega_{\text{ref}}\end{array}\right. \tag{2}\]where \(\omega_{\rm ref}\) and \(\omega_{\rm grid}\) represent the rated and actual angular velocities of the power system, \(k_{\rm p}\) denotes the droop coefficient, and \(P_{\rm m}\) and \(P_{\rm ref}\) correspond to the mechanical power and reference active power.  
Equation (3) represents the reactive power control loop, which generates the reference internal voltage by simulating the excitation mechanism of SG.  
\[\left\{\begin{array}{l}E=E_{0}+k_{\rm qi}\Delta Q/s+k_{\rm v}\Delta U\\ \Delta Q=Q_{\rm ref}-Q\\ \Delta U=U_{\rm ref}-U\end{array}\right. \tag{3}\]  
where \(k_{\rm v}\) represents the reactive voltage proportional coefficient, \(k_{\rm qi}\) denotes the reactive power integral coefficient, \(E\) corresponds to the internal voltage, \(\Delta Q\) represents the reactive power change, \(U_{\rm ref}\) represents the voltage of point of common coupling (PCC), \(\Delta U\) represents the deviation of voltage, and \(E_{0}\) signifies the no-load electromotive force.  
The GFM-ESS implements frequency regulation in power systems through the active power-frequency control loop depicted in Figure 2, where \(k_{\rm f}\) denotes the active droop coefficient, \(J\) represents the virtual inertia coefficient determining inertial response characteristics, and \(D\) represents the damping coefficient.  
Figure 1: The topology and control diagram of the GFM-ESS unit.  
Figure 2: The power control loop of the grid-forming energy storage system.