esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Powder Classification
#### 3.1.3 Classification by Powder Specifications  
Figure 9 shows the relationship between the bulk density and grain size of the powders. Group A is indicated by filled circles, group B is indicated by cross marks, and group C is indicated by filled triangles. The filled squares indicated glass beads and expanded polystyrene beads with a diameter of 2 mm that were included as references. Figure 10 plots the measured sound absorption coefficients of these glass beads and expanded polystyrene beads. Although Figure 3 shows that the sound absorption curves of the sample powders had peaks at low frequencies, Figure 10 shows that the reference granular materials had sound absorption curves similar to those of general porous materials with peaks at high frequencies. These results were attributed to the gaps between the granular materials acting like pores. The red dashed line in Figure 9 indicates a bulk density of 0.1 g/cm\({}^{3}\) that can be used to separate group A from groups B and C. The blue dashed line indicates an areal density per grain layer of 0.0006 g/cm\({}^{2}\) that can be used to separate groups A and B from group C. However, Figure 9 clearly shows that individual groups cannot be classified by using the grain size. A threshold can be observed between the bulk densities of groups A and B for the first absorption peak frequency at 0.0868 \(<\rho<\) 0.124 g/cm\({}^{3}\). However, Figure 10 shows that sound absorption due to longitudinal vibration does not occur for powders with a low bulk density if the grain size is large, such as for polystyrene beads with a diameter of 2 mm. Thus, to distinguish between groups A and B, not only the bulk density but also the grain size must be considered.  
Figure 11 shows the relationship between the bulk density and areal density per grain layer of the powders. Groups A and B both had a low areal density per grain layer, and it is not possible to distinguish between the two groups using only this specification. The red and blue dashed lines can be used together as thresholds for classifying groups A and B. In other words, a lightweight powder can be predicted to be in group A (i.e., useful sound absorption characteristics) if it has a bulk density of 0.0868 g/cm\({}^{3}\) or less and an areal density per grain layer of 0.0006 g/cm\({}^{2}\).  
Figure 8: Relationship between the grain size and half-width of the powders.  
Figure 10: Sound absorption coefficients of granular materials with large particle sizes used as a reference (glass beads \(\varphi 2\), \(l=20\) mm; expanded polystyrene beads \(\varphi 2\), \(l=20\) mm).  
Figure 9: Relationship between the grain size and bulk density of the powders.
## 5 Test and Results Regarding Market Participation
### Input Data  
In this study, it is assumed that the SLB facility is located in Turkiye. It is hypothesized that the facility conducts arbitrage in the day-ahead electricity market through second-life EV batteries. The facility consists of 102 SLBs, with a total energy value of 5133 MWh. The batteries comprise 10 different types of aged EV batteries. These batteries are composed of those belonging to the best-selling EVs in Turkiye in 2023, namely Togg T10X, Tesla Model Y, Renault Megane E-Tech, MG4, Skywell ET5, Opel Corsa-e, Renault Zoe, Opel Mokka-e, Dacia Spring, and Mercedes-Benz EQB. All of these vehicles have lithium-ion batteries. Information on the batteries from each EV is presented in Table 3. Additionally, the table includes the number of batteries.  
It is assumed that the batteries in the facility have energy capacities in the range from 55% to 80%. Figure 3 presents the current capacity value of each battery. For example, while the fresh battery capacity of battery 10 is 88.5 kWh, the capacity value in the SLB facility is 55.76 kWh. Additionally, the charging and discharging efficiency of each battery is set at 95%. It is assumed that the batteries can be fully charged and discharged within one hour, while the minimum energy limit is set at 5%. There are 102 batteries in the SLB facility. The total energy capacity of these is 5133 kWh. However, due to the minimum battery capacity, only 4877 kWh of energy can be actively traded.  
To demonstrate the effectiveness of the presented model, four different case studies are conducted by predicting the next 24 h of electricity market prices for the 15 th day of
## 3 The Proposed Method and Scenarios
### System Initialization and Registration Phase  
Offline enrollment comprises the provision of credentials from the vehicles, encompassing their unique vehicle identification number, user identification number, password, origin, destination, type of service, and permissions (read, write, and modify). Road-side Units (RSUs) are also enrolled with their specific location, MAC address, IP address, and RSU ID. Prior to commencing the verification process, the Kerberos server will produce and transmit the confidential keys for each entity.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
###### Abstract
Environmental conditions during image acquisition and insufficient light levels may introduce noise in the image, which can significantly affect the quality of the captured image. Environmental conditions such as particles or smudges on the lens can also result in distorted or blurry images. Subsequently, noise due to variations in illumination conditions such as brightness, contrast, and color temperature can result in a loss of image quality and affect the accuracy of any analysis or interpretation performed on the image. Additionally, inadequate light levels can lead to underexposed images, making it challenging to capture important details and reducing overall visibility [19; 20]. Based on the above reasons, finding an accurate edge detection method for iced transmission line identification is crucial. According to previous researchers, conventional edge detection operators have categories, such as first-order derivative or gradient-based [21]. Traditional edge detection methods use low-level signs such as colors, brightness, textures, and gradients in the images [22]. However, there is a need for more accuracy to meet the application requirements. Traditional Canny and Hough transforms have often been applied to identify line icing [6], with Canny operators for edge detection of line icing and Hough transforms for straight lines [23; 24]. However, this method has interference noise, so other objects are sometimes detected. The lighting in the image also has a significant impact on this conventional edge detection [3]. The Canny edge detection algorithm has a few things to improve, such as fracture edges [25; 26]. Many edges of objects in the background are also detected, making it challenging to distinguish which object (the iced transmission line) is selected and which is in the background. [PERSON] et al. [18] proposed an edge detection algorithm for images of iced transmission lines based on wavelet transform and morphology fusion to keep image background and noise from disrupting the edge detection identification result. Based on the problems above, the main works and innovations proposed in this paper include:  
* An image optimization method that combines the image restoration method and image filter enhancement to alleviate the influence of noise and light on iced transmission line detection. * An enhanced multi-level threshold algorithm to segment background and target pixel areas. * A connected component labeling modification and mathematical morphological operations to refine the segmentation image, obtain the ROI, and determine the position of the top and bottom of the line. This paper is organized as follows: Section 2 summarizes PTLI identification and extraction. Section 3 illustrates the proposed method for transmission line icing identification. Section 4 introduces our proposed ice thickness calculation using 3D measurements. Section 5 presents the experimental results and discussion, and Section 6 states the conclusions and future work.
## 2 Related Work
### Contention-Free-Based MAC Protocol
In all these network design challenges, SDA protocol leads to the unreliability nature of the USN (underwater sensor node) or the channels. Based on this, the USN channel's packet drop and energy efficiency become more challenging. However, our proposed SDAA protocol depends on a clustered-based network design (CBND) that can reorganize the USN channel very well in a more energy-efficient manner. In addition, our proposed SDAA method utilizes multiple mobile sinks (MMS) for planning the appropriate path of underwater vehicle movement. Our proposed SDAA protocol reduces any communication overhead encountered in UVWSN. In addition, the challenge of the packet drop can be resolved through our proposed SDAA protocol using optimized secure data aggregation and authentication method deployed in CBND, which reorganizes the USN channels for greater energy efficiency and reduces packet drop in the network. EEST secure MAC protocol is an energy-efficient and secure transmission scheme based on chaotic compressive sensing. The development and implementation of EEST aimed to guarantee data security for the USN, or channels, to prolong the UWSN lifetime. EEST used compressive sensing and exploited sensor data spareness using the time domain. Therefore, the protocol used a long-time transmission period. Thus, delay in the network was a pronounced effect, leading to energy efficiency limitations. EEST protocol reduced the number of transmissions of the USN or channel data during data sampling in each frame period, eventually transmitting the data to reduce the energy consumption of each USN in the UWSN. EEST chaotic compression encryption is used only to encrypt data at the end of specific periods. This presupposes that delay and subsequent packet drop will be an issue. Another limitation is that the EEST protocol does not utilize MMS, and the UWSN architecture deployment was not in CBND. To solve these challenges in EEST, our proposed SDAA protocol employs authentication and secure data aggregation optimized methods. It deploys the CBND network which utilizes MMS to ensure there is no communication overhead in the USN or channel and that makes the reliability of data transmission in UWSN is possible. To solve all these design challenges encountered in the secure MAC protocols, such as SEFSC, SDA, and EEST, our proposed SDAA protocol develops a new secure system model and probability analytical models to reevaluate new performance metrics for the reliability, average packet drops, and energy efficiency of the UVWSN.
## 4 Validation of the Numerical Model  
The static pressure distribution along the steam ejector upper wall was validated against the experimental measurements reported by [PERSON] et al. [47]. As shown in Figure 6, the overall static pressure profile showed good agreement with the experimental data, and the position of the shockwave was also well aligned. Within the mixing chamber section (i.e., axial position from 0.06 m to 0.15 m), the predicted static pressure distribution was slightly lower than the experimental data. One possible reason for this error might be the over-predicted condensation effect in the simulation. Furthermore, the remaining static pressure distribution profile of the numerical result was over- predicted against experimental data. Considering deviations shown in Figure 6, there are limitations in the experimental data collection, and it is extremely difficult to obtain measurements in the steam ejector. In this study, the experimental data was obtained from the wall of the steam ejector, where the solution variables have large gradients and the momentum and other scalar transports occur most vigorously. Also, the steam of the primary flow was generated by the boiler, where the state is not the pure dry saturated water steam, and this has a negative effect on the experimental testing data. Additionally, before the primary flow passed through the nozzle, part of steam condensed inside the boiler, which makes the experimental pressure lower than the numerical simulation results. Apart from the deviations in static pressure at axial positions from 0.15-0.25 (which are clarified in the response above), the numerical predictions were in good agreement with experimental results. The trends between the experimental and numerical results are the same, which is that the pressure increased due to the shock  
\begin{table}
\begin{tabular}{c c c} \hline
**Inlet Fluid** & **Temperature (K)** & **Pressure (Pa)** \\ \hline Primary fluid & 393.15 & 200,000 \\ Secondary fluid & 283.15 & 1200 \\ \hline \end{tabular}
\end{table}
Table 2: Temperature and pressure configurations for the primary and secondary inlets.  
Figure 5: Comparison of velocity distribution along the axial position for different meshes.  
wave happened at the throat. Also, the value of pressure was the same around 1200 Pa at the mixing chamber and eventually increased to 3000 Pa at the exit of diffuser. Therefore, the numerical results are validated against experimental results. Figure 7 illustrates the axial pressure distribution results from the wet steam model and ideal gas model against the experimental data. Overall, the numerical results from the wet steam model are in better agreement with the experimental data in comparison to the ideal gas case. With the consideration of the condensation effect in the wet steam model, there was a significant improvement in the pressure predictions within the mixing chamber (the axial position from 0.06 m to 0.15 m). Otherwise, the difference is small between the ideal gas and wet steam models for the rest of the profile.
## 3 Implementation and Experiments
### Implementation Setup  
All our experiments are performed on an Intel Xeon E5-1620 3.6 GHz machine with 64 GB memory under the Debian GNU/Linux 6.0 operating system. The heuristic is implemented in Haskell, as Haskell is reasonably fast [36] and makes parallelization easy. Pattern vectors are stored in the standard list data structures provided by Haskell; the input matrix is stored as a list of lists. The ILP implementation uses ILOG CPLEXby its C++ API. Both implementations are licensed under GPLVersion 3. The source code is available. ([37]).
## 2 Materials and Methods
### CFD Simulations  
#### 2.3.1 Geometry and Design of Simulation Cases  
The size of the geometry of the NVD building (1:100 scale) was based on the wind tunnel experiment as described in Section 2.2.1. A simplified building geometry was used for the CFD simulation without considering interior partitions except elevated cow resting areas and feeding alley. This is because previous research has shown that a high level of detail in the CFD model would not influence the overall airflow inside the room but significantly increase grid and computational cost [29].  
The air flow in the wind tunnel can be modeled using the laws of conservation of mass and momentum (i.e., the Navier Stokes equation). The construction of the building geometry and the mesh were done by ANSYS ICEM CFD in ANSYS Workbench 2019 R2 platform (ANSYS Inc). Eight opening scenarios (Table 1) were generated for the CFD study where SM (only middle part is open) and SMBD (middle and bottom parts of side walls and doors open) scenarios were similar to the field practices (Figure 1a,b). The SWC scenario (Figure 5) i.e., Case 4 was similar to the experiment in the BLWT based scale model (Figure 2) studies and also chosen for CFD model validation. Other seven opening scenarios were studied using the CFD simulations to investigate airflow patterns and find better opening control strategies considering local airflow conditions in steady state condition.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Cases**} & **Opening** & **U at WT** & **A\({}_{\text{inlet}}\)** & **A\({}_{\text{outlet}}\)** & **A\({}_{\text{roof}}\)** & **A\({}_{\text{doors}}\)** & **A\({}_{\text{fa}}\)** & \(\sum\)**A\({}_{\text{outlet}}\)** \\  & **Combination** & **Inlet (m\(\cdot\)s\({}^{-1}\))** & **(m\({}^{2}\))** & **(m\({}^{2}\))** & **(m\({}^{2}\))** & **(m\({}^{2}\))** & **(m\({}^{2}\))** & **(m\({}^{2}\))** \\ \hline Case 1 & SM & 8 & 0.019 & 0.019 & 0.002 & & & 0.021 \\ Case 2 & SMT & 8 & 0.031 & 0.031 & 0.002 & & & 0.033 \\ Case 3 & SMB & 8 & 0.032 & 0.032 & 0.002 & & & 0.034 \\ Case 4 & SWC & 8 & 0.043 & 0.043 & 0.002 & & & 0.045 \\ Case 5 & SWFD & 8 & 0.043 & 0.043 & 0.002 & & 0.004 & 0.049 \\ Case 6 & SMTD & 8 & 0.031 & 0.031 & 0.002 & 0.012 & & 0.045 \\ Case 7 & SMBD & 8 & 0.032 & 0.032 & 0.002 & 0.012 & & 0.046 \\ Case 8 & SWCD & 8 & 0.043 & 0.043 & 0.002 & 0.012 & & 0.057 \\ Case 9 & SM & 1 & 0.019 & 0.019 & 0.002 & & & 0.021 \\ \hline \end{tabular}
\end{table}
Table 1: Description of computational fluid dynamics (CFD) simulation cases. Cases 1 to 8 are the cases with eight opening combinations (as described in Figure 5) of NVD building using the wind tunnel inlet velocity of 8 m\(\cdot\)s\({}^{-1}\). Cases 9 to 18 are the cases with ten wind speeds with a constant opening configuration (i.e., SM-Middle part of side walls open).  
Figure 4: (**a**) Non-dimensional air velocity and (**b**) turbulence intensity profiles of the incident airflow inside wind tunnel.
## 4 Results Analysis
### Sensor Signals of Pipeline Structures with Dual Defects at the Same Circumferential Position
To further analyze the effect of the relative circumferential position of the damage, a comparison was made between case 14 (where the second defect was located 1350 mm away from the piezoelectric element sensor, with a relative circumferential position of 0\({}^{\circ}\) between the two defects) and case 22 (where the second defect was located 1350 mm away from the piezoelectric element sensor, with a relative circumferential position of 90\({}^{\circ}\) between the two defects), as shown in Figure 11.  
Figure 11: _Cont._  
Figure 10: The two-dimensional damage index matrix for dual defects in pipeline structures with different circumferential positions \({}^{6}\). \({}^{6}\) The axial distance of the damage shown in the figure refers to the distance between the second defect and the piezoelectric element sensor. The first damage is always located at 750 mm from the piezoelectric element sensor. Within the time range of 0.00035 s to 0.0009 s, in the amplified time-domain signal, some random wave packets could be clearly observed. This was because the signal in this area not only contained the reflection echoes but also some random signals, but the amplitude of the random signals could be neglected compared to that of the reflection waves. Figure 11 shows that the time and amplitude of the damage reflection echoes (reflection echo 1 and 2 of the first defect, and reflection echo 1 and 2 of the second defect) received by the piezoelectric element sensor changed, and the waveform became more apparent, as the relative circumferential position of the two defects increased (from 0\({}^{\circ}\) to 90\({}^{\circ}\)). When the relative axial distance of the damages remained unchanged, changing their relative circumferential position would cause changes in the reception time and the amplitude of the damage reflection echoes.
## 3 Methods
### Network Architecture  
#### 3.3.1 FcaNet  
In this study, the aim is to obtain high-quality EEG feature information. To achieve this goal, we incorporated an attention module called FcaNet [37] into the backbone network, which is used to lower the weight of low-quality EEG information. FcaNet is a novel attention module based on SENet [38], which was initially used in target detection. Unlike the global average pooling (GAP) used in SENet to squeeze the feature map, FcaNet uses two-dimensional discrete cosine transformation (2D-DCT) for the same purpose. GAP corresponds only to the lowest frequency portion of the 2D-DCT, resulting in the loss of the  
Figure 4: (**a**) EEG; (**b**) sparse transform; (**c**) compact transform; and (**d**) sensitive transform.  
remaining frequency portions in the channel. A comparison of SENet and FeaNet is shown in Figure 5.  
Given an input \(X\), it is divided into n parts along the channel dimension: \(\left[X^{0},X^{1},X^{2},\ldots,X^{n-1}\right]\), where \(i\in\{0,1,2,\ldots,n-1\}\), \(X^{i}\in\mathrm{R}^{H\times W\times C^{\prime}}\), \(C^{\prime}=\frac{C}{n}\). For each part, the corresponding 2D-DCT frequency portion is distributed, and the result of 2D-DCT is used as the compression result noted by the channel. The following equation shows 2D-DCT:  
\[\mathrm{Freq}^{i}=2\mathrm{DDCT}^{u,v_{i}}(X^{i})=\sum_{h=0}^{H-1}\sum_{w=0}^ {W-1}X^{i}_{h,w}B^{u,v_{i}}_{h,w} \tag{11}\]  
\[B^{u,v_{i}}_{h,w}=\cos(\frac{\pi h}{H}(u_{i}+\frac{1}{2}))\cos(\frac{\pi w}{W} (v_{i}+\frac{1}{2})) \tag{12}\]  
\[s,t,i\in\{0,1,\ldots,n-1\}\]  
where H, W, and \((u_{i},v_{i})\) are the height, width, and 2D index of \(X^{i}\), respectively. The whole feature information compression vector can be represented by cascading as:  
\[\mathrm{Freq}=\mathrm{compress}(X)=\mathrm{cat}([\mathrm{Freq}^{0},\mathrm{Freq }^{1},\ldots,\mathrm{Freq}^{n-1}]) \tag{13}\]  
The complete FeaNet framework can be described as follows:  
\[\mathrm{ms\_att}=\mathrm{sigmoid}(\mathrm{fc}(\mathrm{Freq})) \tag{14}\]
## 2 Preliminary  
### Petri Nets (PN)  
A Petri Net model is a five tuple \(N=(P,T,F,W,M_{0})\), which is also a kind of directed model. \(P\) and \(T\) are two finite, nonempty and disjoint sets. \(P\) is a set of places and \(T\) is a set of transitions. \(F\subseteq(P\times T)\cup(T\times P)\) implies the set of arcs of this net, which is symbolized by arrows going from places to transitions or from transitions to places denotes a flow relation in the Petri Net model. \(W:(P\times T)\cup(T\times P)\rightarrow\mathbb{N}\) is a mapping that assigns a weight to an arc: \(W(x,y)>0\) for each \((x,y)\in F\), where \(x\), \(y\in(P\cup T)\) and \(\mathbb{N}\) is the set of non-negative integers. \({}^{\star}\chi=\{y\in P\cup T|(y,x)\in F\}\) is called the preset of x, and \(x^{\star}=\{y\in P\cup T|(x,y)\in F\}\) is called the postset of \(x\). A marking \(M\) is a multiset of Petri nets places, which allocates tokens to each place of a Petri net model. \(M(p)\) denotes the number of tokens in place \(p\). \(M_{0}\) is the initial marking of \(N\). A net is pure if \(\forall(x,y)\in(P\times T)\cup(T\times P),W(x,y)>0\), implying that \(W(y,x)=0\). The incidence matrix \([N]\) of the pure net \(N\) is a \(|P|\times|T|\) integer matrix with \([N](p,t)=W(t,p)-W(p,t)\).  
A transition \(t\in T\) is enabled at marking \(M\) if \(\forall p\in{}^{\star}t,M(p)\geq W(p,t)\), which is denoted as \(M[t]\). Once an enabled transition \(t\) fires, it generates a new marking \(M\), denoted as \(M[t]M\), where \(M^{\prime}(p)=M(p)-W(p,t)+W(t,p)\). \(M[)\) represents the set of all markings reachable from \(M\) by firing any possible sequence of transitions. \(M_{0}[)\) is the set of reachable markings of net \(N\) with initial marking \(M_{0}\), often denoted as \(R(N,M_{0})\). It can be graphically represented by a reachability graph, which can,in turn, be denoted as \(G(N,M_{0})\). It is a directed graph in which each node represents a marking in \(R(N,M_{0})\), and arcs are labeled by the fired transitions.  
Let \((N,M_{0})\) be a net system with \(N=(P,T,F,W)\). A transition \(t\in T\) is live at \(M_{0}\) if \(\forall M\in R(N,M_{0}),\exists M^{\prime}\in R(N,M_{0}),M^{\prime}[t)\). \((N,M_{0})\) is live if \(\forall t\in T\), \(t\) is live at \(M_{0}\). It is dead at \(M_{0}\) if \(\
exists t\in T,M_{0}[t)\).  
A P-vector is a column vector \(I:P\rightarrow\mathbb{Z}\) indexed by \(P\). P-vector \(I\) is called a P-invariant (place invariant, PI for short) if \(I\
eq 0\) and \(I^{T}[N]=0^{T}\). P-invariant \(I\) is said to be a \(P\) semi flow if \(I\succeq 0\). Let \(I\) be a PI of \((N,M_{0})\) and \(M\) be a reachable marking from \(M_{0}\). Then, \(I^{T}M=I^{T}M_{0}\).