esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system's security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system's resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs--aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks.  
techno-economical control; UPFC; PSS; cyber-physical attacks
## 6 Extension with Side-Lobes/Back-Lobes as Well as the Path Loss Effect
### Antenna Model with Side-Lobes and Back-Lobes
#### 6.1.2 Directional Antenna  
As mentioned in Section 2, we consider an approximated radiation pattern of directional antennas [15]. In this model, the main lobe of a directional antenna is represented as a cone with angle \(\theta_{m}\) and side-lobes and back-lobes are approximated as a sphere with beamwidth \(2\pi-\theta_{m}\). Figure 10 shows the approximated radiation pattern of a directional antenna.  
We then to calculate the antenna gain of the main lobe of a directional antenna and the antenna gain of the side-lobes and back-lobes, which are denoted as \(g_{m}\) and \(g_{s}\), respectively. We first derive the maximum beamwidth \(\theta_{\mbox{max}}\) of the main beam. As shown in Figure 10, we consider a sphere with radius \(r_{d}\), where the radiated power of the main beam is concentrated within the small surface with area \(A\). The area \(A\) can be approximated as \(A=r_{d}\tan\theta_{\mbox{max}}/2\). We denote \(S\) as the surface area of the sphere. By Equation (3), we have  
\[g_{m}=\frac{\frac{P_{rad}}{A}}{\frac{P_{rad}}{S}}=\frac{\frac{P_{rad}}{P_{rad}} \frac{P_{rad}}{4\pi r_{d}^{2}}}{\frac{P_{rad}}{4\pi r_{d}^{2}}}=\frac{4}{\tan^ {2}\frac{\theta_{\mbox{max}}}{2}} \tag{4}\]  
We next derive the maximum approximated beamwidth \(\theta_{\mbox{max}}\) for \(g_{m}\).  
\[\theta_{\mbox{max}}=2\arctan\sqrt{\frac{4}{g_{m}}} \tag{5}\]  
Usually, the main beamwidth \(\theta_{m}<\theta_{\mbox{max}}\). Similar to [15], we simply choose \(\theta_{m}\) to be the largest multiple of 10 that is less than \(\theta_{\mbox{max}}\). Given \(\theta_{m}\) and \(g_{m}\), we now derive the antenna gain of side-lobes and back-lobes \(g_{s}\) as the following steps.  
Figure 10: Approximated radiation pattern of a directional antenna.  
By Equation (3), we have  
\[g_{m}\cdot\frac{P_{rad}}{S}\cdot A+g_{s}\cdot\frac{P_{rad}}{S}(S-A)=\eta\cdot P_{ rad} \tag{6}\]  
Solving the equation, we have  
\[g_{s}=\frac{\eta\cdot\frac{S}{A}-g_{m}}{\frac{S}{A}-1} \tag{7}\]  
where \(\frac{S}{A}=\frac{4}{\tan^{2}\frac{\pi\max}{2}}\) and \(\eta\) is usually set to be 1.  
We then calculate the main beam gain \(g_{m}\) by Equation (4). We next choose \(\theta_{m}\) to be the largest multiple of 10 that is less than \(\theta_{\textrm{max}}\). Finally, we calculate the side-lobe and back-lobe gains by Equation (7). Table 2 lists both the linear gains and logarithmic gains (dBi).  
As shown in Table 2, when the main beamwidth of a directional antenna is decreased, the ratio of \(g_{m}/g_{s}\) is significantly increased. In particular, when the main beamwidth is quite narrow (e.g., \(\theta_{m}\leq 10^{\circ}\)), we have \(g_{m}\gg g_{s}\).
# GPSMAP 66 sr: Assessment of Its GNSS Observations and Centimeter-Accurate Positioning
###### Abstract  
In 2020, Garmin released one of the first consumer devices with a dual-frequency GNSS chip and a quadrifilar helix antenna: GPSMAP 66 sr. The device is intended to serve as a positioning and navigation device for outdoor recreation purposes with positioning accuracies on the few meter level. However, due to its highly accurate GNSS dual-frequency carrier-phase observations, the equipment can also be used for centimeter-accurate positioning. We performed extensive test measurements and analyzed the quality of its code and carrier-phase observations. We calibrated the Garmin GPSMAP 66 sr antenna with respect to its phase-center offset and phase-center variations. We also performed dual-frequency GPS/Galileo precise point positioning (PPP) and precise relative positioning in baselines to virtual reference stations (VRS). We demonstrate and explain how centimeter-accurate positioning can be achieved with this novel kind of equipment.  
G 202  
It proved to be a major advantage of the equipment that GNSS observations can be stored internally in RINEX 3.04 format [7] and that these files can be easily transferred to a computer for postprocessing. The GPSMAP 66 sr is not designed to allow centimeter-accurate GNSS real-time positioning on the device itself. Therefore, it should be considered as a GNSS receiver with the potential to obtain centimeter-accurate positions in postprocessing only.  
In Section 2, we provide an overview of the observation data we gathered, as well as the data processing. Section 3 deals with the assessment of the observation quality and antenna phase-center calibration. In Section 4, we demonstrate how centimeter-accurate positions can be obtained with GPSMAP 66 sr either with precise point positioning (PPP) or in baseline mode with respect to a virtual reference station (VRS).  
Throughout this paper, we identify the GNSS satellite signals by two-character codes: a letter that describes the satellite system (G--GPS, R--GLONASS, E--Galileo) and a digit for the frequency number (1, 2, or 5), following the RINEX format.
## 4 Conclusions and Future Work  
TLS point clouds cannot cover the entire surface of the target object, due to block of view and limitation of measurement condition. In order to improve measurement accuracy, we improved point clouds for displacement assessment of slope surface by registering TLS point clouds and UAV photogrammetry point clouds. By comparing only using UAV, only using TLS and the method proposed in this paper to measure surface displacement, it can be known that surface displacement measured by using the improved registration point clouds is more accurate. Compared with the existing research, its accuracy has been improved to a certain extent, and it can further provide accurate data for analysis of slope safety monitoring. The identification of monitoring points is the basis of displacement calculation. In this study, the corresponding RGB color space values and HSL color space values of different period of multiple monitoring were analyzed found that the light intensity of different has a great impact on three values \(R\), \(G\) and \(B\) in the RGB color space, but has little effect on the values of \(H\) and \(S\) in HSL, so it is proved that HSL color space is more suitable as the basis for monitoring point identification. Through the verification of the relationship among flight height, monitoring point area and measurement error, it is found that the measurement error decreases to a certain extent with the increase of monitoring point size. In order to ensure the effectiveness of the method proposed in this paper, the monitoring point area should also be increased with the increase of relative flight height, and the relative flight height of UAV should be controlled below 60 m. If the altitude exceeds 60 m, the measurement error will increase sharply.  
There are some deficiencies in this paper. As the color of the monitoring point gradually disappears under the influence of external factors, it is necessary to recolor the monitoring points in the later stage to ensure the successful identification of the monitoring points, which cause in additional work. During monitoring, the process of generating point clouds by TLS takes a long time. The author will continue to carry out field tests on the shortcomings of this paper to reduce unnecessary workload and improve monitoring efficiency.  
Conceptualization, [PERSON], [PERSON], [PERSON] and J.H.; methodology, [PERSON] and G.Z.; software, [PERSON] and J.H.; validation, [PERSON], [PERSON] and S.H.; formal analysis, [PERSON], B.L. and L.G.; investigation, [PERSON], [PERSON] and S.H.; resources, [PERSON] and J.H.; data curation, [PERSON], [PERSON], S.H. and J.H.; writing--original draft preparation, [PERSON], [PERSON] and B.L.; writing--review and editing, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Yunnan province, grant number 2018 BA066.  
Not applicable.  
The authors declare no conflict of interest.
## 4 DLR+ Router Overview
### Router Architecture  
The core of the router has two fundamental modules that allow the router, upon a connection-up event, to choose the best next hop from its current connections and the best next message to send from its queue, but also to share information to other nodes (upon request), so they can decide whether or not to pass a packet to it. Such modules are called, respectively, the connections manager and the fitness center, which in turn has two independent modules for the messages and for the host itself (Figure 2).
## 5 Trust Model
\begin{split}& T_{Y_{continuity}}^{X,t_{1}\cup\ldots\cup t_{n}}=[B_{Y}^{X,t_{1} \cup\ldots\cup t_{n}},D_{Y}^{X,t_{1}\cup\ldots\cup t_{n}}],\quad T_{Y_{ Uncertainty}}^{X,t_{1}\cup\ldots\cup t_{n}}=[N_{Y}^{X,t_{1}\cup\ldots\cup t_{n}},E_{Y}^{X,t_{1} \cup\ldots\cup t_{n}}]\\ & B^{X,t_{1}\cup\ldots\cup t_{n}=1/n(B_{Y}^{X,t_{1}},\ldots,+B_{ Y}^{X,t_{n}})},\quad D^{X,t_{1}\cup\ldots\cup t_{n}=1/n(D_{Y}^{X,t_{1}},\ldots +D_{Y}^{X,t_{n}})}\\ & N^{X,t_{1}\cup\ldots\cup t_{n}=1/n(N_{Y}^{X,t_{1}},\ldots,+N_{ Y}^{X,t_{n}})},\quad E^{X,t_{1}\cup\ldots\cup t_{n}=1/n(E_{Y}^{X,t_{1}},\ldots +E_{Y}^{X,t_{n}})}\end{split}  \tag{11}\]  
The certainty of an event mainly comes from belief and disbelief, while uncertainty includes prior and posterior state of an event. Using Definition 3 and 4, we define trustworthiness \(\gamma^{j}\) using entities consensus operation to combine trust opinions generated by vehicles \(\left\{S_{j,i}\right\}_{i=1}^{n_{j}}\) in time interval \(\left\{t\right\}_{t=t_{1}}^{t_{n}}\) as  
\[\gamma^{j}=T_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots,\cup t_{n}} \tag{12}\]  
The \(\gamma^{j}\) can be calculated with respect to vehicle consensus or time interval method. The vehicle consensus is given as:  
\[\gamma^{j}=T_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}}=T_{1}^{j,t _{1},\ldots\cup t_{n}}\oplus\ldots\oplus T_{i}^{j,t_{1},\ldots\cup t_{n}} \oplus\ldots\oplus T_{n_{j}}^{j,t_{1},\ldots\cup t_{n}}\]  
and second with respect to time as follow  
\[\gamma^{j}=T_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}}=B_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}},D_{1,\ldots,j,\ldots,t_{n}}^{j,t_{ 1},\ldots\cup t_{n}},N_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}},E_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}}\]  
where  
\[ \begin{split}& B_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t _{n}}=1/n(B_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}}+\ldots+B_{1, \ldots,j,\ldots,t_{n}}^{j,t_{n}}),\quad D_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1}, \ldots\cup t_{n}}=1/n(D_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}}+ \ldots+D_{1,\ldots,j,\ldots,t_{n}}^{j,t_{n}})\\ & N_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}}=1/n(N_{1, \ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}}+\ldots+N_{1,\ldots,j,\ldots,t _{n}}^{j,t_{n}}),\quad E_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1},\ldots\cup t_{n}}=1 /n(E_{1,\ldots,j,\ldots,t_{n}}^{j,t_{1}}+\ldots+E_{1,\ldots,j,\ldots,t_{n}}^{j,t _{n}})\end{split}  \tag{13}\]  
According to Definition 3, each trust opinion has the same impact over time. Further, it is important that newer trust opinions have higher impact on trustworthiness, while previous trust opinions also taken into account. One solution is to use a time factor e.g., \(T\in[0,1]\) adding time impact into prior trust opinion, where greater \(T\) indicates newer opinion.
## 4 Experimental Results
### Integrated Detection Algorithm
#### 4.2.2 Effectiveness of IDA Detection Algorithm against Target Attacks  
The effectiveness of the IDA detection algorithm against target attacks was evaluated using the FC attack algorithm with 100 attack items and 984 normal data items in the ImageNet dataset and 200 attack items and 1800 normal data items in the Cifar10 dataset. As described in Section 3.2.2, the detection of target attacks in the IDA algorithm is performed based on the distance between the output values of the test items and those of the training items, as recorded in a collision table. Table 7 shows the experimental results for the mean and standard deviation values of the L2-norm distance in the FC attacks against the ImageNet and Cifar datasets. It is observed that the mean L2-norm distance for the normal data is very large, whereas that for the abnormal data is very small. Table 8 shows the corresponding detection statistics for the two datasets. For both datasets, the accuracy rate is greater than 99%. In other words, the ability of the IDA algorithm to detect the FC attack is confirmed for both datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistic**Algorithm & **TensorClog** & **Category Diverse** & **TensorClog** & **Category Diverse** \\  & **Clip 0.05** & **Clip 0.05** & **Clip 0.1** & **Clip 0.1** \\ \hline Accuracy Increment & 0.0266 & 0.1990 & 0.0748 & 0.3690 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy improvements obtained by Data Washing algorithm for TensorClog and Category Diverse paralysis attacks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classific**Algorithm & **Original** & **FC Attack** & **Original** & **FC Attack** \\  & **ImageNet** & **ImageNet** & **Cifar10** & **Cifar10** \\ \hline Mean & 18.2909 & 2.9688 & 19.3545 & 3.6706 \\ \hline Standard deviation & 2.6572 & 0.2671 & 2.7426 & 0.5807 \\ \hline \hline \end{tabular}
\end{table}
Table 7: L2-norm statistics between target test data and collision table data for FC attacks against ImageNet [37] and Cifar [10] datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistic**Algorithm & **FC Attack**[6] & **FC Attack**[6] & **CP Attack**[7] \\  & **ImageNet**[37] & **Cifar10[36] & **Cifar10[36]** \\ \hline Accuracy & 99.00\% & 100\% & 93.33\% \\ \hline Target Number & 100 & 200 & 10 \\ \hline Misclassified & 1 & 0 & 0 \\ \hline Target’s False Positive Rate & 1.00\% & 0.00\% & 0.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of FC and CP target attacks with Data Washing algorithm.
## 5 Discussion
### Classifying Challenges
#### 5.3.4 Low-Connectivity Challenges  
These have limited influence and connectivity within the organizational network. Their resolution may not have a cascading impact on other innovation challenges. They include \"rigid budgeting processes\" (C1), \"rigid and formal organization structure\" (C6), \"lack of commitment and support from top management\" (C9), \"lack of incentives and rewards\" (C12), \"lack of integration between government entities for shared services\" (C20), and \"regulations and legislations that limit service innovation\" (C22).
## 2 System Overview and Circuit Analysis  
### System Overview  
Figure 2 depicts the schematic diagram of the proposed SWPIT system based on the DPSK method. The system can be divided into three units: power supply unit, forward communication unit and backward communication unit. For the power supply unit, a full-bridge inverter is used to convert the DC source into the high-frequency AC source. To eliminate reactive power caused by the low-coupling coils, two compensation capacitors (\(C_{\mathrm{P}}\) and \(C_{\mathrm{s}}\)) are inserted between the power converters and the coils. On the secondary side, conventional passive rectifier is replaced by a semi-bridgeless active rectifier (S-BAR) to regulate the equivalent resistance and output power. For the communication unit, the transmitted data (Data in) are respectively sent to modulators for regulating phase-shifting angles (\(\varphi_{\mathrm{P}}\) and \(\varphi_{\mathrm{s}}\)) and driving the full-bridge inverter and the S-BAR. Then, by detecting the instantaneous current (\(i_{\mathrm{P}}\) and \(i_{\mathrm{s}}\)) in the compensation circuits, the received data (Data out) is recovered from demodulator. Notably, the modulator not only modulates transmitted data, but also regulates the voltage on the primary side or the impedance on the secondary side. Owing to the modulator and inductive link are shared, the cost and volume of the designed SWPIT system are significantly reduced.
## 4 Methodology  
The methodology used in this study involves seismic inversion of actual seismic data, followed by the creation of pseudo-wells with varying reservoir properties. Synthetic seismic gathers were generated from these pseudo-wells using AVO modeling techniques, and the resulting data were utilized to construct a neural network. Seismic inversion is a technique used to estimate subsurface physical properties by analyzing seismic data, which provide information about the reflectivity of geological layers. Pseudo-wells are synthetic boreholes created to simulate the subsurface properties of a reservoir. By generating synthetic seismic data from pseudo-wells with different reservoir properties, we can train a neural network to recognize patterns and relationships between the synthetic data and the subsurface properties. This methodology provides a powerful tool for analyzing subsurface structures and predicting reservoir properties.  
Figure 3: Velocity, density, and porosity logs from Well A. The reservoir interval is indicated as a grey zone.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 5 Numerical Experimental Analysis
### Impact of Underwater Acoustic Channels on Communication  
In underwater collaborative search missions, numerous state variables are considered, including depth, position, velocity, acceleration, attitude, and detection error. The proposed method simplifies this complexity by constructing a behavior model that requires only key information rather than all available state data. This approach effectively reduces the data volume in communication packets, thereby lowering the packet loss rate. This is particularly meaningful, as the packet error rate \(P_{PER}\) is directly influenced by data size, as shown in the following formula:  
\[P_{PER}=1-(1-P_{BER})^{n} \tag{4}\]  
where \(P_{BER}\) is bit error rate, and can be calculated based on Signal to Noise Ratio (SNR) and the modulation method, and \(n\) represents the number of bits in a packet. The probability of error-free reception is equivalent to the probability that all bits are received correctly. Figure 8 illustrates the impact of different data lengths on the packet loss rate under consistent underwater acoustic channel parameters. Table 5 lists the packet error rates at distances of 600 m, 700 m, and 800 m.  
Figure 8 illustrates that as the communication distance increases, the PER rises sharply, particularly beyond 600 m. The data size significantly affects this trend, with larger data packets (288 bits) exhibiting a much steeper increase in PER compared to smaller packets (72 bits). This is because larger data packets are more susceptible to errors over longer  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Source Level (SL) & 113 dB \\ Frequency (\(f\)) & 34 kHz \\ Bits & 144 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters for underwater acoustic channel.  
Figure 8: Variation in packet loss rate with distance under different data size.  
distances, highlighting the importance of minimizing data size to maintain reliable communication in underwater environments. The data in Table 5 further quantifies this effect.  
Since there is currently no existing research on intent recognition in the AUV domain, it is not possible to directly compare the data volume of the proposed method with other approaches. But, since intent recognition can combine multi-dimensional information to predict the development trend of the swarm from a decision-making perspective, it not only reduces the need for real-time data compared to trajectory prediction but also better handles unexpected situations. This capability enables swarm collaboration under more challenging conditions, further reducing dependence on communication. Although the data packet length set in this study may not fully match real-world scenarios, the strategy of reducing data volume by constructing a behavior model to lower the packet loss rate is feasible. This approach provides valuable insights and a practical foundation for future research in this area.
## 5 Signal and Noise Evaluation
### System Characteristics in Terms of CDMA Performance  
Now we can characterize our system in terms of _processing gain_ (Equation (5)) and _SNR_ (Equation (6)):  
\[PG=\frac{I_{s}}{l}=\frac{lN+2\delta}{2l} \tag{5}\]  
\[SNR=\frac{I_{s}}{I_{B}}=\frac{lN}{2\delta}+1 \tag{6}\]  
From the above analysis we can expect how the demodulated image will look like: bright image of the intersections of the projected pattern with the object's surface against the completely black background--exactly what we expect from the spatial distortion map. A projected pattern will be perceived much brighter than it would appear without modulation. High contrast with the black background makes the detection of projected pattern quite simple.  
For static scenes, high _SNR_ is expected since \(\delta\) tends to zero. For dynamic scenes, some \"gray trails\" left by the edges of moving objects could be noticed. We can suppress these artifacts by setting the threshold while detecting the projected lines. Increasing the sensor's frame rate will also lead to noise reduction since the integrated frame will accumulate fewer deviations.
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 2 Congestion and Awareness Control  
To date, congestion and awareness control protocols have been normally designed and evaluated separately, although both will be required for the reliable and efficient operation of vehicular networks. This section demonstrates potential negative interactions among these two protocols, and the need to carefully coordinate their operation.