esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary Knowledge
### Energy Efficiency of the Proposed Schemes  
In this section, we briefly analyze the energy efficiency of the proposed scheme. The average transmit power per sub-block is denoted as \(k+\beta(n-k)R^{2}\). The power loss \(\eta\) of the conventional dither signals scheme compared with the proposed hybrid scheme [17] is denoted as:  
\[\eta =10\text{log}_{10}\frac{k+\beta(n-k)R^{2}}{k+(n-k)R^{2}} \tag{21}\] \[\approx\frac{10(1-\beta)(n-k)R^{2}}{[k+\beta(n-k)R^{2}]\text{ln}10 }\text{dB}\]  
For a practical system with \(\beta=0.5\), \(n=4\), \(k=2\), the power loss is around 4.34 (\(1-\frac{2}{(2+R^{2})}\)) dB, which is larger than 0. Therefore, it is obvious that the proposed scheme owns higher energy efficiency than that of the existing PAPR reduction schemes with dither signals.
## 3 Data Description  
The data used in this paper comes from the national freight management platform developed by the regulatory authority under the Chinese Ministry of Transport. The dataset records information on truck journeys daily, including 37 fields related to vehicle attributes, operational efficiency, activity areas, and safety behavior, among others, over one year from 1 January 2019 to 31 December 2019. From this sample, 600,000 trucks were randomly selected to have their operating information recorded in both January and December. These trucks were considered to be under normal operating conditions throughout the year. The pre-processed dataset contains a total of 74,993,891 records for 562,071 trucks.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Cellular Mobile Communications  
#### 7.2.1 The 5G and Beyond  
The 5G and its subsequent evolutionary stages constitute this transformative age of cellular mobile communications in the IoV, which rely on high-speed, low-latency connectivity to realize real-time communication of data and dynamic IoV applications [99]. The 5G communications lay a robust foundation for V2X communication goes beyond traditional cellular networks. It includes enabling the free exchange of data between vehicles and their various elements in the transportation environment. IoV offers numerous applications of 5G, including significant services like traffic management enabled with real-time data for devising adaptive solutions to congestion.
# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
## 2 Materials and Methods
### Fuzzy Control  
The WMR must have the ability to avoid obstacles and is not influenced by obstacles in the patrol process to successfully conduct outdoor patrols. Often, vehicles parked on the road are the obstacles that are most encountered in outdoor patrols. We used a laser rangefinder to detect the obstacle which is on the patrol path and the fuzzy controller to enable the WMR to avoid obstacles. The SICK LMS100 laser rangefinder is settled on the WMR. The angle 20\({}^{\circ}\) of the laser rangefinder on the WMR is marked as DL. The angle 90\({}^{\circ}\)of the laser rangefinder on the WMR is marked as DF. The angle 160\({}^{\circ}\) of the laser rangefinder on the WMR is marked as DR. Input variables _dl_, _df_, and _dr_ are presented by three linguistic labels: \"near\", \"medium\", and \"far\". The robot uses an ultrasonic sensor and laser sensor to detect the distance of surroundings. There are three inputs of the proposed fuzzy controllers for angle control, which are detected distances from different angles. The three inputs of the fuzzy controller in angles are 20\({}^{\circ}\), 90\({}^{\circ}\), and 160\({}^{\circ}\). For input variables 20\({}^{\circ}\) (_dl_), 90\({}^{\circ}\) (_df_), and 160\({}^{\circ}\) (_dr_), their fuzzy sets are \"near\", \"medium\", and \"far\" (the distance detection range is 0 to 200 cm). The output of the fuzzy controller is the turning angle. Fuzzy sets of the output variable for turning angle are five linguistic labels, including \"VLL (turn left very large)\", \"VL (turn left)\", \"S (go forward)\", \"VR (turn right)\", and \"VRR (turn right very large)\". We established a new kind of ideal fuzzy control model through the cross combination of Gaussian functions. This fuzzy control model is shown in Figure 17. The fuzzy truth table shows all the rules used to control the steering angle, as shown in Appendix C.  
In addition, we also tried five inputs, three membership functions, and 243 fuzzy rules. Compared with 243 fuzzy rules, 27 fuzzy rules were found to be sufficient to complete a patrol and avoid obstacles.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Point Cloud Segmentation for Corn Populations
#### 2.5.2 Algorithm for Approximate Segmentation of Column Space  
In order to extract the corn single-plant point cloud and determine its height, we provide in this work a column space approximate segmentation approach that combines the column space algorithm with AABB enclosing box segmentation. To remove the leaf point cloud and keep the stalk point cloud, the angle between the point cloud's normal vector and the horizontal direction is first determined. The height of a single plant is then estimated by segmenting the stalk point cloud using an AABB bracket box. The specific process is shown in Figure 3.  
The steps are as follows. Determine the angle between the point cloud's normal vector and the horizontal direction. Set the angle threshold to extract the stalk point cloud. Set the AABB bracket box's width and length to obtain the height data, and use the bracket box's length to calculate the height of a single plant. Both incorrect and excessive segmentation are successfully decreased by this technique.  
Figure 2: UAV planning diagram.  
In three-dimensional space, the normal vectors of crop leaves and stalks differ, and segmentation can be achieved by adjusting the normal vectors' angle. Normal vectors must be computed from 3D coordinates, while the corn population point cloud data are reconstructed from image sequences that contain color information and 3D coordinates. In order to approximate the surface normal vectors and achieve the segmentation base data support, normal estimation fits the point cloud's local surface to a plane and derives the normals using the least squares approach. The plane equation is presumed to be  
\[cos\alpha\cdot x+cos\beta\cdot y+cos\gamma\cdot z+p=1 \tag{1}\]  
\[ax+by+cz=d(d\geq 0),\ a^{2}+b^{2}+c^{2}=1 \tag{2}\]  
where \(cos\alpha\), \(cos\beta\), and \(cos\gamma\) are the cosine of the direction of the normal vector at the point (\(x\), \(y\), \(z\)) in the plane, and \(\left\lvert p\right\rvert\) is the distance from the origin to the plane. Finding the equation of the plane is transformed into finding the four parameters \(a\), \(b\), \(c\), and \(d\).
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 3 Design of the HECRA
### Cluster Formation Phase  
Once the process of cluster head selection is finished, the CH broadcasts the cluster formation message to its communication range. Each CM node listens to its surroundings during this phase to receive cluster formation messages from different nodes. These messages contain the ID and residual energy information of the corresponding CH.  
Depending on the received cluster formation messages, the sensor node will select clusters based on the following. If the sensor node receives only one cluster formation message, it will join the corresponding CH. If the sensor node receives more than one cluster formation message, it evaluates these messages and joins the cluster where the CH with more residual energy is located. In particular, because the CHs \(CH=\{CH_{1},CH_{2},\ldots,CH_{m}\}\) corresponding to the \(m\) received cluster formation messages, the sensor node \(j\) will select its own CH \(H_{j}\) based on the following formula:  
\[H_{j}=CH_{\underset{i}{argmax\left(E_{i}\right)}},1\leq i\leq m \tag{9}\]  
where \(i\) is the number of sending message CHs, \(E_{i}\) denotes the residual energy of the sending message CH, and node \(j\) selects the CH with the maximum residual energy and joins the cluster.  
If the sensor node does not receive the cluster formation message, it proves that the node cannot communicate with any other node; then, the node goes into a sleeping state and waits for the broadcast message.
# Optimization of Airflow Organization in Bidirectional Air Supply Data Centers in China
## 2 Materials and Methods
### Numerical Simulation
#### 2.2.2 Simplification Assumptions  
Full-scale three-dimensional modeling of the data center room was avoided to prevent excessive computational nodes and grid elements, which would significantly increase temporal costs. Figure 3a shows the original computer room model. The physical model was constructed using SpaceClaim, where all room geometries were simplified as cuboids to reduce simulation complexity. Structural columns and cable trays that may affect airflow distribution were modeled at their actual locations. To decrease computation time, the following assumptions were applied to the physical model:(1) The data center enclosure was assumed to be well-sealed, with pressure variations small enough to satisfy the Boussinesq approximation.  
(2) The airflow was treated as an incompressible fluid.  
(3) The interior walls were considered thermally insulated, rendering heat transfer through building envelopes negligible.  
(4) IT equipment power was modeled as time-invariant, with constant heat dissipation rates maintained throughout simulations.  
Figure 3b presents the computational mesh. Polyhedral meshing was performed using the meshing module of ANSYS Fluent 2021R1, resulting in 6,972,368 total cells.
## 5 Experiments
### Ablation Studies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**MOSI**} \\ \cline{2-5}  & **Acc\({}_{2}\)\(\uparrow\)** & **Precision** & **Recall** & **F1 \(\uparrow\)** \\ \hline Ours & \(85.12\pm 0.28\) & \(83.73\pm 0.47\) & \(82.61\pm 0.54\) & \(84.32\pm 0.29\) \\ TFN & \(80.04\pm 1.27\) & \(79.22\pm 1.46\) & \(78.63\pm 0.51\) & \(80.78\pm 0.99\) \\ LMF & \(82.53\pm 1.35\) & \(73.74\pm 1.39\) & \(75.59\pm 1.44\) & \(79.97\pm 0.67\) \\ MFM & \(81.73\pm 0.26\) & \(80.89\pm 0.75\) & \(79.42\pm 0.50\) & \(78.65\pm 0.08\) \\ ICCN & \(83.07\pm 1.29\) & \(82.25\pm 0.90\) & \(81.86\pm 0.71\) & \(83.04\pm 0.11\) \\ MISA & \(82.49\pm 0.34\) & \(83.57\pm 0.50\) & \(80.75\pm 0.78\) & \(81.69\pm 0.63\) \\ FDMER & \(83.68\pm 0.33\) & \(80.91\pm 0.52\) & \(73.19\pm 0.86\) & \(78.72\pm 0.27\) \\ \hline \hline \multirow{2}{*}{Model} & \multicolumn{4}{c}{MOSEI} \\ \cline{2-5}  & Acc\({}_{2}\)\(\uparrow\) & Precision & Recall & F1 \(\uparrow\) \\ \hline Ours & \(86.14\pm 0.33\) & \(87.41\pm 0.45\) & \(88.29\pm 0.53\) & \(86.34\pm 0.28\) \\ TFN & \(82.52\pm 0.79\) & \(76.27\pm 1.32\) & \(80.48\pm 1.46\) & \(78.15\pm 0.33\) \\ LMF & \(82.06\pm 1.34\) & \(80.92\pm 0.40\) & \(80.02\pm 0.39\) & \(79.12\pm 0.30\) \\ MFM & \(84.45\pm 0.34\) & \(83.11\pm 0.78\) & \(82.28\pm 0.92\) & \(80.36\pm 0.13\) \\ ICCN & \(80.25\pm 0.08\) & \(83.01\pm 1.36\) & \(85.94\pm 0.63\) & \(82.22\pm 0.78\) \\ MISA & \(84.57\pm 0.32\) & \(80.95\pm 0.11\) & \(85.17\pm 0.76\) & \(82.31\pm 0.36\) \\ FDMER & \(86.17\pm 0.75\) & \(80.83\pm 0.99\) & \(83.01\pm 0.37\) & \(81.85\pm 0.09\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison on multimodal sentiment analysis benchmark. Practical applicability: Contrastive learning can be more easily applied in practical scenarios as it does not require generating additional data, which is an advantage for certain applications. The results are shown in Table 5. Improved generalization: Contrastive learning helps the model learn relationships between multiple modalities, aiding better generalization to new data. Generating modality data methods often performs poorly when dealing with new data as they struggle to capture complex relationships between multimodal data. **The importance of modal completion. ** We randomly discard the modes to evaluate the effect of modal completion on improving data integrity and utilization. We find that modal completion can bring a better emotional analysis effect than no modal completion. This shows that modal completion can improve the integrity and utilization of data so that the model can better deal with the situation of missing modes. On both datasets, the effect of sentiment analysis with modal completion is significantly better than that without modal completion. The results are shown in Table 6.
## 5 Proposed Solution  
The problem \(P1\) is divided into four sub-problems pertaining to communication scheduling as well as trajectory and computing resource optimization using Lagrangian dual decomposition. The communication delay is optimized in the first sub-problem \(SP_{1}\). Branch and bound is used to solve optimization sub-problems \(SP_{1}\) and \(SP_{2}\) to find an optimal solution from a set of candidate solutions. Branch and bound is used to allocate limited UAV resources to vehicles to minimize the objective function. For a given delay, the trajectory and computing resource are jointly optimized in \(SP_{2}\). Next, we apply binary relaxation to transform \(P1\) into a linear programming problem. Binary relaxation transforms the sub-problems into convex problems, where the complex variables are relaxed into real variables. In sub-problems \(SP_{3}\) and \(SP_{4}\), the objective function to maximize the data rate, energy efficiency, and the constraints are non-convex and are solved using successive convex approximation (SCA). To integrate the solutions from the four sub-problems with equality and inequality constraints, we use successive quadratic programming (SQP), which is an iterative approximation of the nonlinear objective function and constraints using quadratic models, updating them in each iteration. Figure 3 illustrates the proposed FRL-based solution approach for UAV trajectory control and power optimization for low-latency C-V2X communications.
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 3 Transistor Level Design of the PLL
### Charge Pump and Phase/Frequency Detector
#### 3.1.3 Comparison between the Two PFD/CP Architectures  
In order to compare the two different solutions (CMOS and CML versions of the PFD/CP), two PLL testbenches have been built in Cadence. In these testbenches, the VCO model with parasitic effects is considered, while the CP's and PFD's schematic view has been used to reduce the simulation time. Finally, a Verilog-A frequency divider, which can be found in the Cadence's _rf-library_, has been used. For the Phase Noise comparison, because of the non-linear nature of the PLL, a direct time domain noise analysis was necessary. This has been done exploiting the Transient Noise Analysis option embedded in the Spectre RF's Transient Analysis. Since this type of simulation is time consuming, a resolution bandwidth of 500 KHz to reduce the simulation time has been set. In Figure 14, the Phase Noise results are shown: the two solutions have almost the same behavior in terms of noise, but the CMOS solution shows higher peaks at multiples of the reference frequency because of the worse current matching during the switch of the CP currents from ON to OFF and vice versa. Instead, in Table 1, the comparison in terms of DC current matching of the CP and power consumption between the CML and CMOS solutions is summarized. The DC current mismatch has been measured performing a DC  
Figure 13: Triple modular redundant PFD architecture in (**a**) and simple PFD architecture in (**b**).  
simulation on the CP circuits and measuring the output current when the output node is forced at 520 mV with an ideal voltage source, and the inputs are such that both the source and sink current are ON. Considering that (1) the noise performance difference is negligible, (2) the CMOS solution has a better current matching in the worst case, and (3) the CML solution shows 25 times higher power consumption, then the CMOS solution has been chosen and has been developed at layout level for the whole PLL design.
# ATLAS: Latest Advancements and First Observations
## 2 Materials and Methods
### ATLAS: Technical Summary  
ATLAS is divided into a transmitting part and a receiving part. The transmitter, illustrated in a simplified depiction in Figure 2, is the subsystem responsible for generating and outputting the RF signal. Signal generation commes with a frequency synthesizer that generates the carrier frequency set at 5.56 GHz. This carrier signal is subsequently directed into a PIN modulator, which modulates the carrier in amplitude. The PIN modulator is controlled by a user-defined pulse shape. Following modulation, the signal proceeds to the power amplifier, where its peak power is adjustable within a range spanning from 155 W to 2 kW, facilitating versatile power configurations. For the observations shown in this article, the peak power was set to 1.6 kW. Table 1 summarizes all relevant parameters of the transmitter chain.  
The receiver, depicted in Figure 3, includes an analog chain that is responsible for the demodulation and amplification of the received echoes. It starts with a low-noise amplifier (LNA), followed by a downconversion to a 400 MHz intermediate frequency (IF) and filtering (80 MHz of bandwidth). The local oscillator is provided by the same reference signal from the transmitter, guaranteeing a coherent conversion. The IQ detector converts the signal to a baseband and separates it into the I/Q components, which are then digitized at a maximum sample rate of 100 MS/s and stored in a computer for further processing. Table 2 summarizes all relevant parameters of the receiver chain.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{Operating Frequency} & 5. 56 GHz \\ \hline Peak power & 1.6 kW \\ \hline Waveform & AM Chirp [5] (Section 4) \\ \hline Pulse repetition frequency & 33 Hz \\ \hline Number of pulses & 100 \\ \hline Emitter bandwidth & 2 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: ATLAS transmitter characteristics.  
\begin{table}
\begin{tabular}{c c} \hline \hline LNA noise figure (\(<\)15 \({}^{\circ}\)C) (\(N_{f}\)) & 0.7 dB \\ \hline IF filter BW (\(B\)) & 80 MHz @ \(-\)3 dB \\ \hline Maximum sample rate & 100 MS/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: ATLAS receiver characteristics.  
Figure 2: Functional diagram of the transmitter subsystem.
## 2 Methods
### Effect of Incorrect Estimation  
The effect of an incorrect estimation of the effective average dielectric properties is considered by analysing the PSF. Firstly, a simplified skinless two-dimensional environment is considered, which is then compared to the imaging system described in this paper. The simplified environment is described in [59], where the backscattered signals are described as:  
\[S_{\mathbf{s},\mathbf{s}}^{\prime}(\omega)=\frac{j\omega\sqrt{\varepsilon_{r}} }{2\pi c_{0}}P(\omega)\int\limits_{\mathcal{D}}\frac{1}{\|\mathbf{r}-\mathbf{ s}\|}\exp\frac{-2j\omega\sqrt{\varepsilon_{r}}}{\|\mathbf{r}-\mathbf{s}\|} \chi(\mathbf{r})d\mathbf{r} \tag{3}\]  
Figure 1: Block diagram of the proposed imaging system. Focal quality metrics are used in a parameter-search algorithm to identify the best-case average dielectric properties, \(\varepsilon_{r}^{B^{\prime}}\). The proposed algorithm is described: from a set of images reconstructed with different average dielectric properties estimates, select the image that the measure of image quality, \(\Phi\), weights most highly. All FQMs used in this study are summarised in Table 1 divided into five FQM families based on the following:  
* the Discrete Cosine Transform (\(\phi^{\text{F}}\));
* image gradient (\(\phi^{\text{G}}\));
* Laplacian approximation (\(\phi^{\text{L}}\));
* image statistics (\(\phi^{\text{S}}\));
* and the Discrete Wavelet Transform (\(\phi^{W}\)). The discrete cosine transform (DCT) is a Fourier transform that uses cosines as basis functions. The DCT directly measures the frequency content of the image, as an estimation of the image quality. The energy of the AC components of the DCT (which is an estimate of the variance of the luminance of an image) has been used as a focal quality measure [68]. However, it was found that the energy of the AC components is sensitive to image contrast and that the ratio of the AC energy to the DC
# Translating Workflow Nets to Process Trees: An Algorithmic Approach
## Preliminaries
### Overview  
The core idea of the approach concerns searching for fragments in the given WF-net that represent behavior that is expressible as a process tree. The patterns we look for bear significant similarity with the translation patterns defined in Figure 5, i.e., they can be considered as a _strongly generalized reverse_ of those patterns. When we find a pattern, we replace it with a smaller net fragment representing the process tree that was identified. We continue to search for patterns in the reduced net until we are not able to find any more patterns. As we prove in Section 3.4, in the case the final WF-net contains just one transition, its label carries a process tree with the same labeled-language as the original WF-net.  
Consider Figure 6, in which we sketch the basic idea of the algorithm, applied on the example WF-net \(W_{1}\) (Figure 3). First, the algorithm detects two _choice constructs_, i.e., one between the transitions labeled \(b\) and \(c\) and one between the transitions labeled \(g\) and \(h\). The algorithm replaces the fragments by means of two new transitions, carrying labels \(\times(b,c)\) and \(\times(g,h)\), respectively (Figure 6a). Subsequently, a concurrent construct is detected, i.e., between the transitions labeled \(\times(b,c)\) and \(d\). Again, the pattern is replaced (Figure 6b). A sequential pattern is detected and replaced (Figure 6c), after which a loop construct is detected (Figure 6d). The resulting process tree, i.e., carried by the remaining transition in Figure 6e, \(\rightarrow(a,\circlearrowright(\rightarrow(\land(\times(b,c),d),e),f), \times(g,h))\), is equal to Figure 4.  
Figure 6: Application of the algorithm on the running example, i.e., \(W_{1}\). The label of \(t^{\prime}_{6}\), i.e., \(\kappa(t^{\prime}_{6})\), depicted in Figure 6e, is the resulting process tree. The resulting process tree, i.e., \(\rightarrow(a,\circlearrowright(\rightarrow(\land(\times(b,c),d),e),f), \times(g,h))\), is equal to Figure 4. (**a**) Result of the first two rounds of the algorithm. The first two patterns that can be found are choice constructs, between \(b\) and \(c\) and between \(g\) and \(h\), respectively. (**b**) Result of the third round of the algorithm on the running example. We find a concurrent construct between transition \(t^{\prime}_{2}\) and \(t_{4}\). (**c**) Result of the fourth round of the algorithm. We find a sequential construct. (**d**) Result of the fifth round of the algorithm. We find a loop construct. (**e**) Result of the final round of the algorithm. We find a sequence construct.