esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
Once the shot and thermal noise contributions are known, the SNR is calculated as follows: [41]  
\[\text{SNR}=\frac{S}{N} \tag{15}\]  
The BER for OOK modulation can be calculated using [5]  
\[\text{BER}=Q\Big{(}\sqrt{\text{SNR}}\Big{)} \tag{16}\]  
where  
\[Q(x)=\frac{1}{2\pi}\int_{x}^{\infty}e^{-y^{2}/2}\,dy \tag{17}\]  
Equations (2) and (3) are used to plot the irradiance distribution from the two transmitters of the modeled VLC system. Then, Equations (6), (7), (9) and (11)-(15) are utilized to simulate the SNR of the VLC system against distance. Finally, the BER is plotted against distance using Equations (16) and (17).
## 3 Simulation Results
### IEEE 33-Bus Test System  
Figure 5 depicts the IEEE 33-bus distribution network used in this analysis. It is made up of 33 nodes and 37 branches. The distribution network data (line data, bus data) are available in [25]. Initially, the open branches (OB) were as follows [33-34-35-36-37]. The ENS and active power losses for the initial configuration were 7.0980 kWh/year and 202.6774 kW, respectively. The MATLAB environment was used to program the proposed load flow and implement the proposed optimization approaches.
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 1 Introduction
### Related Work
Recently, how to resist capture attacks from physical devices has become a hot topic in authentication protocol research. Thanks to the application of Physically Unclonable Functions (PUF), many security authentication protocols have emerged that resist sensor capture attacks. In 2024, [PERSON] et al. [22] proposed a multi-server authentication protocol based on PUF and chaotic maps to address the security issues of [PERSON] et al. 's scheme [23]. [PERSON] et al. [24] also proposed a PUF-based security authentication protocol to address the inability of [PERSON] et al. 's scheme [25] to resist capture attacks from roadside units. [PERSON] et al. [26] proposed a Blockchain- and PUF-based registration protocol for the Internet of Drones. The change from hash-based operations to complex cryptographic primitive-based schemes greatly improved the security of TWSNs. However, the difference between TWSNs and UANs makes it impossible to directly use TWSN's secure authentication mechanism for UANs. In 2019, [PERSON] et al. [27] proposed a cooperative authentication scheme for UANs, which relies on trusted nodes that independently assist in aggregating nodes during the authentication process. Later, [PERSON] et al. [28] presented a remote mutual authentication scheme based on chaotic maps for UANs. Based on the architecture of underwater wireless sensor networks, [PERSON] et al. [29] designed an authentication technique that establishes a session key for safe communication. In 2024, [PERSON] et al. [30] proposed a Blockchain-based Key Management Protocol for UANs, and [PERSON] et al. [31] proposed a deep learning and random forest algorithm-based dynamic trust model for UANs.
## 4 The Improved Self-Collision Detection Technology
### Equipment Parameters  
This simulation experiment mainly takes the mobile manipulator robot illustrated in Figure 1 as the research object. To establish the kinematic modeling of the robot, we need to determine the following related parameters:  
(1) Parameters of the mobile platform. The length, width, and height of the mobile platform are \(L_{c}*B_{c}*H_{c}=1104\) mm \(*\) 518 mm \(*\) 258 mm, respectively; the radius of the wheels is \(R_{c}=100\) mm; the midpoint of the driving axis in the body-fixed frame is \(D_{c}=[340,0,119]\) mm. At the initial moment, the position of the midpoint of the driving axis in the world frame is \(P_{d}=[0,0,100]\) mm; the rotation angle of the robot around the \(Z\)-axis of the midpoint-fixed frame is 0 rad.  
(2) Parameters of the manipulator subsystem. The manipulator subsystem is redesigned based on the typical UR manipulator in Figure 2, and the initial rotation angle of each joint is set \((0,0,0,0,0)\). Therefore, the coordinate system of each joint is established based on the optimized method, and the specific parameters of the manipulator robot were shown in Table 1. In this table, \(d_{i}^{c}\), \(a_{i}^{c}\), \(a_{i}^{c}\) indicate the basic parameters obtained of the manipulator subsystem, which are determined by the classical kinematic modeling method, \(d_{i}^{t}\), \(a_{i}^{t}\), \(a_{i}^{t}\) are obtained by the traditional kinematic modeling method.  
(3) Parameters of the other objects. The height of the carrying load object is \(H_{Ld}=200\) mm, the radius is \(R_{Ld}=200\) mm, and the distance between the fixed position of the carrying load and the gravity of the mobile platform in the \(x\)-axis direction is \(L_{cx}=140\) mm. The height of the supporting platform is \(H_{sp}=137\) mm, the length and width of the lower surface are \(450\) mm \(*\) 358 mm, respectively; and the horizontal distance from the center of the lower support surface to the gravity of the mobile platform is \(297\) mm. Meanwhile, the length and width of the upper surface are \(360\) mm \(*\) 190 mm, respectively, and the horizontal distance from the center of the upper support surface to the gravity of the mobile platform is \(252\) mm. In addition, the maximum length, width, and height of the tool at the end of the manipulator subsystem are \(199\) mm \(*\) 89 mm \(*\) 89 mm, respectively.
## 4 Experiments and Analysis  
In order to analyze and compare the characteristic of the satellite repeat shift time for current global systems more comprehensively, all three methods were used to calculate the shift time. Multi-GNSS data collected from 10 to 30 June 2018 at two stations in the Asia-Pacific region were collected. The two stations are located in Australia, named CUT0 and CUT2, respectively. The sampling rate of the dataset was 1 Hz and the mask angle was 15\({}^{\circ}\). It should be noted that the use of the quantity of the CCM method is the single-difference observations between receivers, and the observations were denoised by a filter.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 3 Proposed Fingerprinting System
### Proposed Fingerprinting Strategies
#### 3.2.3 Coverage  
The _Coverage_ strategy only relies on the selected RF parameter to define the distance metric between RP \(i\) and TP \(j\) as follows:  
\[d_{Cov}(i,j)=\sqrt{\sum_{l=1}^{N}\Bigl{(}p_{l}^{i}-p_{l}^{j}\Bigr{)}^{2}}, \tag{6}\]  
where \(N\) is the size of the union of all NPCIs detected in any RP and TP and is thus independent from \(i\) and \(j\); whenever \(p_{l}^{i}\) and/or \(p_{l}^{j}\) are missing, a default baseline value \(p_{miss}\) is used for data padding. Note that the value of \(p_{miss}\) should be lower than the minimum value present in the data. Based on the range observed for the RF indicators, \(p_{miss}=-160\) dBm is used for RSSI and RSRP, and \(p_{miss}=-40\) dB is used for SINR and RSRQ. In this case as well, RPs are sorted based on increasing \(d_{Cov}(i,j)\), and the first \(k\) is selected for estimating the position of TP \(j\).
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 4 Experimental Results and Discussion
### Ablation Study Results
Furthermore, the ablation study underscores the importance of combining an adaptive similarity matrix with spatial self-attention. This method allows the model to concentrate more on the interactions between the similar node pairs during the spatial feature learning process. By accounting for these dynamic and long-range spatial correlations, our model can understand and predict more precisely. Especially when dealing with complex interactions between nodes in urban traffic networks, this mask spatial self-attention mechanism proves particularly crucial.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 3 Dangampo Site: Equipment Testing
### Results and Discussion  
#### 3.2.1 Side Scan Sonar  
The SRA images acquired by the SiSS displayed a dense distribution of shades distinct from the other images. Comparing the images acquired at 400 and 1250 kHz, at 400 kHz, only the SRA could be distinguished from the remaining seafloor in the images acquired, whereas the images obtained at 1250 kHz delivered an accurate image of the seafloor topography of the SRA. Despite a shortening of the propagation distance, higher frequencies obtain higher resolution images. However, the SRA shape in the images was distorted due to the unsteady speed, depth, and direction of the towfish. Despite repeated surveying around the SRA in an effort to acquire high-quality images, obtaining an accurate picture of the SRA remained difficult (Figure 2).
## 4 Control System Design  
### Right Factorization  
Control systems are designed using operator theory, which allows for handling systems in the time domain without linearization and compensates for the BIBO stability of nonlinear control targets. First, right factorization is performed. The right factorization is illustrated in Figure 8. As shown in Figure 8, \(P:U\to Y\) is a plant, which is a DC-DC converter in this research. \(U\) represents the input space, and \(Y\) represents the output space. Additionally, \(W\) is referred to as a quasi-state space. When the plant \(P:U\to Y\) is factorized as shown in Equation (11) using a stable operator \(N:W\to Y\) and a stable and invertible operator \(D:W\to U\), this factorization is called right factorization.  
\[P=ND^{-1} \tag{11}\]  
Figure 8: Conceptual diagram of right factorization.  
Next, the concept of filtered right factorization is explained. Define the internal state signal space within the plant as \(\tilde{W}\), and assume the existence of a stable operator \(\tilde{N}:\tilde{W}\to Y\) and a stable and invertible operator \(\tilde{D}:\tilde{W}\to U\). Here, \(\tilde{N}\) and \(\tilde{D}\) constitute the right factorization of \(P\). Next, consider a stable operator \(Q:W\rightarrow\tilde{W}\). Using the operator \(Q\), we define the operators \(N\) and \(D\) as \(N=\tilde{N}Q:W\to Y\) and \(D=DQ:W\to U\)[29]. Considering the right factorization after passing through the operator \(Q\), we obtain the following equation:  
\[P =ND^{-1}\] \[=(\tilde{N}Q)(Q^{-1}\tilde{D}^{-1})\] \[=\tilde{N}\tilde{D}^{-1} \tag{12}\]  
From Equation (12), the results of the right factorization remain unchanged, even when using operators filtered through a filter. A block diagram illustrating this right factorization is shown in Figure 9.  
Thus, by designing an appropriate operator \(Q\), various characteristics can be imparted to the control system. In this research, the operator \(Q\) is designed to have low-pass filter characteristics, thereby enabling accurate tracking of the reference value.
## 3 Simulation Results
### Sensitivity Analysis  
In order to show the sensitivity, the algorithm has been implemented to solve the issue of optimal number of populations in the problem of optimal operation of MG with different initial populations in two single-objective and multi-objective modes. Populations of 25, 50, 75, and 100 are intended to solve the problem. Table 20 shows the impact of population change on cost and Table 21 displays the impact of initial population on pollution. Increasing the initial population has improved the answers. On the other hand, the running time of the program also increases. Figures 13 and 14 show the effects of the initial population on convergence respectively. It is clear that increasing population size improves the convergence of graphs. Figure 15 shows the Pareto Fronts with different populations; an increase in population makes the Fronts more dominant.  
Figure 11: Results of implementing different methods considering the limitations of upstream grid power exchange (cost and emission based).  
Figure 12: The results of implementing different methods without considering the limitations of upstream power exchange (cost and emission based).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Initial Population** & **Best Answer** & **Average Answers** & **Worst Answer** & **Standard Deviation** & **Time** \\ \hline
25 & 729.1 & 1072.94 & 1658.61 & 337.1 & 38 \\
50 & 669.55 & 739.35 & 1012.41 & 104.38 & 63 \\
75 & 656.33 & 686.9 & 726.5 & 22.07 & 95 \\
100 & 647.74 & 683.64 & 738.59 & 28.95 & 124 \\ \hline \end{tabular}
\end{table}
Table 20: The results of implementing the proposed algorithm with different initial populations with the aim of reducing costs.  
Figure 14: Impact of initial population on convergence of pollution (cost based).  
Figure 13: The influence of initial population on cost convergence.
# GPON PLOAMd Message Analysis Using Supervised Neural Networks
## 3 Data Characteristics
### Invalid Data Generation  
Supervised learning algorithms require a balanced number of samples for each classification set. The model described by this paper classifies message sequences into a set that satisfies the GPON standard and a set that does not. The standard communication samples are generated using procedures described in the previous section. The non-standard sequence samples are generated using three procedures that knowingly corrupt standard sequences (the input of these procedures is the entire captured communication). The first corruption procedure changes values in random PLOAMd messages using Equation (1).  
\[Value_{new}=(Value_{old}+128)\mod 255 \tag{1}\]  
The second procedure removes important message types from the activation process. The activation process consists of Messages 4, 8, 10, and 18, in this exact order (a more detailed explanation of these messages can be found in GPON recommendation [50]). The procedure runs for each message on a distinct instance of the captured dataset separately and deletes all occurrences of that message. The third procedure creates faulty messages by adding zero-mean Gaussian noise with standard deviation equal to 20 into the captured communication. The last procedure simulates modification of the GPON protocol by a device vendor. This procedure duplicates the important messages of activation process mentioned earlier. Each message is duplicated in a separate instance of the captured message list.  
All of the mentioned procedures are applied on separate instances of a filtered PLOAMd message sequence. The sliding window used for sequence sample generation runs through the faulty sequences with stride equal to 4. As a result, the number of generated faulty samples is almost the same as the number of correct sequence samples.
## 2 Materials and Methods
### Scanning Electron Microscopy (SEM) Analysis  
The microspheres were subjected to SEM analysis to find out more about their morphology. The morphology of the microspheres was influenced by the method by which they were prepared (Figure 2). Thus, the microspheres prepared by method 1 showed a smoother appearance and less rough or creased surface (m1_Ch01, m1_Ch02, and m1_Ch04) compared with the microspheres prepared by method 2, which showed a more wrinkled, creased, and rough appearance (m2_Ch01, m2_Ch02, and m2_Ch04).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Sq (\(\upmu\)m)** & **Sp (\(\upmu\)m)** & **Sv (\(\upmu\)m)** & **Sz (\(\upmu\)m)** & **Sa (\(\upmu\)m)** \\ \hline m1\_Ch01 & 11.644 \(\pm\) 0.89 & 194.762 \(\pm\) 0.78 & 153.671 \(\pm\) 0.18 & 311.558 \(\pm\) 0.67 & 6.761 \(\pm\) 0.28 \\ m1\_Ch02 & 11.614 \(\pm\) 0.55 & 139.356 \(\pm\) 0.43 & 116.797 \(\pm\) 0.58 & 276.878 \(\pm\) 0.47 & 6.514 \(\pm\) 0.41 \\ m1\_Ch04 & 10.913 \(\pm\) 0.13 & 123.208 \(\pm\) 0.51 & 110.873 \(\pm\) 0.25 & 250.229 \(\pm\) 0.76 & 5.838 \(\pm\) 0.27 \\ m2\_Ch01 & 32.648 \(\pm\) 0.57 & 405.192 \(\pm\) 0.73 & 416.041 \(\pm\) 0.08 & 821.233 \(\pm\) 0.38 & 16.271 \(\pm\) 0.14 \\ m2\_Ch02 & 27.865 \(\pm\) 0.46 & 315.118 \(\pm\) 0.35 & 309.497 \(\pm\) 0.33 & 581.519 \(\pm\) 0.83 & 14.840 \(\pm\) 0.99 \\ m2\_Ch04 & 20.650 \(\pm\) 0.03 & 272.022 \(\pm\) 0.17 & 179.762 \(\pm\) 0.67 & 494.881 \(\pm\) 0.28 & 9.145 \(\pm\) 0.82 \\ E1 & 39.059 \(\pm\) 0.02 & 586.594 \(\pm\) 0.06 & 460.286 \(\pm\) 0.71 & 1046.88 \(\pm\) 0.88 & 19.790 \(\pm\) 0.91 \\ E2 & 31.039 \(\pm\) 0.18 & 337.760 \(\pm\) 0.23 & 342.348 \(\pm\) 0.67 & 680.107 \(\pm\) 0.92 & 15.177 \(\pm\) 0.55 \\ E3 & 25.066 \(\pm\) 0.23 & 230.460 \(\pm\) 0.82 & 202.941 \(\pm\) 0.54 & 433.401 \(\pm\) 0.10 & 15.060 \(\pm\) 0.37 \\ \hline \hline \end{tabular} Legend: Stq—square root of the average roughness, Sp—maximum height, Sv—maximum depth, Sz—sum of Sp and Sv, Sa—average roughness.  
\end{table}
Table 4: Calculation of microsphere roughness.  
In previous research, it was observed that the surface of chitosan microspheres containing _Cassia fistula_ L. ethanolic extract appeared to be rough, rubbery, fibrous, and folded. This was in contrast to the microspheres without extract [82]. Additionally, SEM images of certain chitosan and casein microparticles loaded with _Acalypha indica_ extract, which were produced through complex coacervation, displayed a porous and spherical appearance [79].
## 4 Prototype Fabrication and Measurement  
From the validation of the planar antenna array performance in the simulation, the optimized antenna array was fabricated as shown in Figure 6a. It was identically implemented on both sides of the entire circuit board with a footprint of 90 mm \(\times\) 36 mm. Practically, the antenna will be fed by a chip which is on top of the integrated circuit. Nevertheless, for the measurement, the port of the vector network analyzer (VNA) and the antenna has to be connected by using a semi-rigid 034 pigtail cable assembly. The reflection coefficient of the proposed antenna array is measured using a VNA as shown in Figure 6b. For the radiation pattern observation, the antenna is placed on the rotation in a microwave anechoic chamber to experiment with the far field as shown in Figure 6c.  
The measurement result displayed in Figure 7a reveals the achieved bandwidth to be 10.7%. Although, in simulation, the external cable used in the measurement was considered, avoiding all the effects from the cable and soldering is not possible. Thus, although the measurement suffers from S11 distortion, overall, it still is a good match. The expected gain enhancement of the array antenna is obtained, and its result is plotted in Figure 7b with the highest gain of 8.4 dBi. At lower frequencies, the increment is not as significant as the gain at higher frequencies. This phenomenon is caused by the change of the reflection coefficient of the single and array antenna. For the radiation, the 1D pattern shape comparison at 7.1 GHz, 7.5 GHz, and 7.9 GHz of simulation and measurement results are presented in Figure 7c in the H-plane and E-plane cut views. In detail, the HPBW of 40-50\({}^{\circ}\) of almost the entire operating frequency range from 7.1 GHz to 7.9 GHz is indicated in Table 2. This HPBW is significantly narrower than the first design of a single antenna.  
Figure 5: Antenna array reflection coefficient: (**a**) impact of L-feed on reflection coefficient; (**b**) the S11 of single antenna and 2 \(\times\) 2 array antenna.  
Figure 6: The antenna array measurement: **(a)** fabricated array antenna; **(b)** S-parameter measurement setup; **(c)** radiation pattern measurement setup.  
Figure 7: Comparison of simulated antenna with fabricated antenna: (**a**) the S11 comparison of simulation and measurement; **(b)** the gain of single and array antenna; **(c)** radiation pattern comparison of simulation and measurement.
## 3 Results of Complete Soft Galloping (SG) Responses and Complete Hard Galloping (HG) Responses
### Hard Galloping (HG)
#### 3.2.3 Response Path 3: Decreasing Velocity and Applying a Threshold Initial Displacement  
When the flow velocity reduces from a high value (\(U_{r}=14.125\)), and the prism is manually pushed by a threshold initial displacement (exceeding 1 \(\times\)\(D\)), the prism first enters the galloping branch accompanied with a high \(A^{*}\) (2.4) and a low \(f^{*}\) (0.78). As \(U_{r}\) decreases from 14.125 to 11.625, \(A^{*}\) monotonically decreases to 1.799, and \(f^{*}\) basically maintains at 0.78 with a slight downward trend, indicating that the prism experiences the galloping branch. This characteristic is the same with SG response path 3 (shown in Figure 9). Nevertheless, when \(U_{r}\) is reduced lower than 11.625, the response of the prism is totally different from SG response path 3. When \(U_{r}\) reaches 11.625, oscillation is suddenly suppressed, the oscillation mode is dramatically transitioned from galloping to VIV, accompanied by a sudden collapse in \(A^{*}\) from 1.799 to 0.184, and there is a sudden jump in \(f^{*}\) from 0.78 to 1.029. The transition process is clearly observed in Figure 10c,d. As \(U_{r}\) continues to decrease, the prism experiences VIV upper branch and VIV initial branch and only presents the characteristics of VIV. It is noted that, even with a threshold initial displacement to push the prism for \(U_{r}<11.625\), the prism cannot be initiated to galloping.
## 5 Analysis of Behavior and Results
### Simulation Results at Spring/Summer ToU Tariff
#### 5.1.1 Spring Day Scenario  
The spring day simulated scenario is implemented by taking under consideration the power consumption profile (Figure 8) for an average day of spring and the ToU tariff of spring/summer (Figure 1a). Additionally, the weights for the 16 time slots of the discharging zone are all set 1. In that context, the optimization algorithm will not prioritize the load cover at any slot of the zone.  
Considering that the day-ahead load demand matched the actual load demand Figure 10a is obtained. As it is observed, the EMS distributed the stored energy at the BESS almost equally to the 16 hourly time slots of the discharging zone (around 245 Wh per time slot). This was expected since all 16 weights were set to 1 and there was no mismatch between the forecasted load demand and the actual one. In addition, the overall power supplied to the household was 3979 W and the load at the BESS at the end of the discharging zone was 18 Ah. In order to fully charge the battery stack during the 8 h charging zone the needed current was calculated and found 10.25 A. Figure 10b depicts the discharging (positive currents) and charging currents (negative currents) by starting from the first time slot (7 a.m.) of the discharging zone and ending to the end of the charging zone (7 a.m. of next day).  
Additionally, Figure 11 depicts the BESS parameters which were defined by the KiBaM model by starting from the first time slot of the discharging zone and ending to the end of the charging zone. More specifically is displayed the SOC in %, voltage in V, power in W and load in Ah. Calculating the daily energy cost for the household with and without the BESS it was found that the cost is 1.935  
Figure 10: BESS operation schedule: (**a**) Energy distribution; (**b**) Charging and discharging currents.  
Figure 9: Desirable ageing trajectory for the BESS.  
with the BESS while it is 2.053 \(\upepsilon\) without the BESS. This implies that the daily profit is 0.118 \(\upepsilon\) and for the whole season would be 10.738 \(\upepsilon\). The low profit during spring is an outcome of the corresponding ToU tariff which permits only one full charge/discharge cycle per day. Finally, the estimated capacity loss for the whole day is 0.0987 Ah while the capacity loss set point is 0.0984 Ah.
## 3 Results and Discussion
### Synthetic Data
#### 3.2.7 Cumulative Sum  
Cumulative sum displays the distribution per column for original and generated synthetic data in Figure 11. In the figure, we show the count distribution of _Pediococcus acidilactici_ for original and generated synthetic data for four different models of GAN. Cumulative sum can be used to evaluate both continuous and categorical values. We can see from Figure 11 that the distribution of _Pediococcus acidilactici_ using TGAN-skip-WGAN-GP is the most closely related.