esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 OTFS-Based Radar Sensing
### Two-Dimensional Matched-Filter Operation  
In this subsection, our objective is to provide a detailed explanation of the 2D MF operation for integer parameter estimation. When we initially examine the representation of the received OTFS symbol matrix in the DD domain, the direct identification of target elements within \(\mathbf{Y}_{\text{DD}}\) (received data in the DD domain) becomes challenging due to the presence of IS. These symbols are influenced by the time-varying channel, which leads to the emergence of overlapping responses in the received DD domain signals. These responses arise from both the channel characteristics and the superimposed IS. In this context, we draw inspiration from pulse compression radar sensing and implement a 2D MF-basedestimator. This estimator is likened to performing simultaneous pulse compression in both the DD dimensions, which, in turn, allows us to effectively capture the DD parameters.  
To represent the matrix associated with the 2D MF as \(\mathbf{V}\), the diverse DD indices of the MF coefficient are denoted as:  
\[V[k,l]=\sum_{n=0}^{N-1}\sum_{m=0}^{M_{S}-1}Y_{\text{DD}}^{*}[n,m]X_{\text{DD}} [[n-k]_{N_{T}},[m-l]_{M_{S}}]. \tag{15}\]  
To provide a more lucid explanation of the proposed approach, we have devised a simplified radar sensing example using OTFS principles. In this illustrative scenario, we assume the existence of five targets, denoted as \(P=5\). The symbols were randomly generated from a four-QAM scheme. We set the parameters \(M_{S}\) to 32 and \(N_{T}\) to 16, with specific DD indices assigned to each target, represented as \(\{4,9,3\}\) and \(\{7,1,4\}\), respectively. The resulting symbol matrix in the DD domain, under this particular configuration, is visually represented in Figure 1. As illustrated, the matrix \(Y_{\text{DD}}\) exhibits a dense population, primarily attributable to the overlapping responses originating from the DD domain symbols. The task of target identification within this matrix is inherently intricate. However, through the application of a 2D MF computation, the targets become more localized. This process can be likened to a specialized form of pulse compression in the DD domain, analogous to its role in radar sensing. Ultimately, this improves the retrieval of responses in the DD domain.
## 4 Design Implementation
### System Execution Flow  
In Figure 7, The processing flow of input stream signals is depicted by the green dashed arrow, as shown above in Figure 6. When the device begins to receive stream signals, the \"Key Stream Generator\" continuously generates OTP keys for real-time data encryption. The encrypted datagrams (i.e., ciphertext) are temporarily stored in the \"Input FIFO Buffer.\" This buffer can be configured to issue an interrupt signal to the micro-processor when it reaches a fill of half-full, near-full, or full capacity. The micro-processor then controls the SG-DMA controller to transfer the encrypted datagrams from the \"Input FIFO Buffer\" to the external memory on the system board. Conversely, datagrams from the Internet are first stored in the external memory, then the micro-processor can control the SG-DMA controller to move the encrypted datagrams (i.e., ciphertext) from the external memory to the \"Output FIFO Buffer.\" Subsequently, the datagrams are decrypted using the generated OTP keys and then emitted as stream signals.  
Compared to merely utilizing software [51] or simulation [52] for computation and processing, the distinctive feature of this study lies in the integrated design of hardware and software. This effectively alleviates the computational burden on the micro-processor, enabling real-time streaming data encryption and decryption on IoT devices. Next, components of the FPGA system bus (depicted on the left side of Figure 6), encompassing the micro-processor, fundamental controllers, and application-specific key accelerators, will be expounded upon in the forthcoming sub-section.
# CFD and Experimental Comparison for Micro-Pump Performance in Space Applications: A Case Study
## 2 Materials and Methods
### Design Considerations
#### 2.1.1 Impeller  
For the impeller design, the most important parameter to choose is the rotational speed or specific speed, calculated using Equation (1).  
\[n_{q}=\frac{n\sqrt{Q}}{H^{0.75}} \tag{1}\]  
where \(n_{q}\)--Specific speed; \(n\)--speed [rpm]; \(Q\)--volumetric flow or capacity [m\({}^{3}\)/s]; \(H\)--head [m]  
Figure 2: Predimensioning algorithm for impeller design.  
The first design of the impeller is evaluated by a fast (reduced complexity) CFD analysis where the impeller efficiencies are calculated and a second design iteration of impeller is generated. The process is repeated until the CFD efficiencies are lower than 5%. Figure 3 shows the extracts from the algorithm program developed in-house, where the discretization of the blade points can be seen. Subsequently, Figure 4 shows the final version of the closed impeller that was further analyzed using CFD and the printed model. Due to the proprietary nature of the design and to safeguard intellectual property rights, specific dimensions and detailed specifications of the closed impeller cannot be disclosed in this publication. The overall exterior dimension of the closed impeller is 39.7 mm with an axial height of 8 mm (without labyrinths at this stage of design).  
Figure 4: 3D model of the (**a**) impeller blade geometry representation; (**b**) closed impeller configuration; (**c**) In625 printed impeller; (**d**) Labyrinth groove profile.  
Figure 3: Impeller blade discretization: (**a**) meridional section; (**b**) front view.  
An important issue to be taken into account is the clearance between the rotor and the casing. Through this clearance, the liquid can flow backwards from the high pressure at the rotor outlet and the low pressure of the inlet. For the labyrinth design, a tapered asymmetrical groove profile is used, as seen in Figure 4d. Those grooves create vortices and extract flow rate from the leakage flow.
## 3 Array Verification Lens Element Function  
In order to verify the proposed functional unit-cell performance, a fixed-beam transmit-array with a \(\lambda\lambda_{0}\times\lambda\lambda_{0}\) (\(\lambda_{0}\) is the wavelength at the center operating frequency 25.8 GHz) size was designed and simulated. The schematic diagram of the transmit-array is shown in Figure 4a, and the phase distribution is demonstrated in Figure 4b. The transmit-array was fed with a horn antenna with a gain of 14.3 dBi and a focal length of 108 mm. The proposed transmit-array was studied using Ansys HFSS 2020. The gain pattern and axial ratio of several frequency points of the transmit-array in the frequency range of 24.5-27 GHz are presented in Figure 5. At 25.8 GHz, the gain of the main lobe with a 3 dB beamwidth of 6.7\({}^{\circ}\) reached 26.3 dBi, and the maximum SLL was \(-\)18 dB. Figure 6 presents the simulated co-polarization and cross-polarization gains versus frequency. The transmit array exhibited 11.5% 1 dB gain bandwidth. The maximum gain is 26.5 dBi, leading to the corresponding aperture efficiency of 45.2% at 25.8 GHz. Due to the existence of metallic vias and six metal-pattern layers in the transmit-array element, the fabrication processing became complicated and difficult, so we mainly focused on the design and novelty concept.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Center Frequency** & **Period of the** & **Thickness of the** & **Phase** & **Polarization** & **Measurement** \\  & **(GHz)** & **Element (\(\lambda_{0}\))** & **Element (\(\lambda_{0}\))** & **Modulation** & **Transformation** & **Measurement** \\ \hline
[1] & 140 & 0.58 & 0.28 & 2 bit & \(\times\) & \(\surd\) \\
[3] & 12.25 & 0.6 & 0.5 & 3 bit & \(\times\) & \(\surd\) \\
[9] & 29.75 & 0.25 & 0.33 & 6 bit & \(\times\) & \(\surd\) \\
[10] & 29.5 & 0.5 & 0 & 3 bit & \(\surd\) & \(\surd\) \\
[11] & 25 & 0.29 & 0.81 & 3 bit & \(\times\) & \(\surd\) \\
[16] & 9 & 0.48 & 0.06 & \(\times\) & \(\surd\) & \(\surd\) \\
[17] & 25 & 0.44 & 0.09 & \(\times\) & \(\surd\) & \(\surd\) \\
[18] & 10 & 0.72 & 0.11 & \(\times\) & \(\surd\) & \(\surd\) \\
[19] & 9.4 & 0.31 & 0.19 & \(\times\) & \(\surd\) & \(\surd\) \\
[24] & 27 & 0.54 & 0.18 & 2 bit & \(\surd\) & \(\surd\) \\
[25] & 300 & 0.5 & 0.26 & 3 bit & \(\surd\) & \(\surd\) \\ \hline
**This work** & **25.8** & **0.31** & **0.32** & **3 bit** & \(\surd\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of the elements.  
Figure 4: The designed transmit-array lens antenna with proposed elements: **(a)** schematic diagram of the transmit-array lens antenna; **(b)** phase distribution of the transmit-array antenna in degrees.  
Figure 5: Radiation properties of the transmit-array with the proposed unit-cells over the frequency range 24.5–27 GHz: (**a**) gain pattern at 25 GHz; (**b**) gain pattern at 25.6 GHz; (**c**) gain pattern at 25.8 GHz; (**d**) gain pattern at 26.2 GHz; (**e**) gain pattern at 26.8 GHz; (**f**) axial ratio of transmit array in dB.  
Figure 6: Co-polarization and cross-polarization gains of the transmit array versus frequency.
## 2 Theoretical Aspects
### Model Identification by Swept Sine Excitation
The identification procedure of the Hammerstein model, and therefore of the functions \([h(t)]\), is based on this correspondence between the \([h(t)]\) and the \([g(t)]\): assume that the harmonic function: \(x(t)=R\ Cos(\phi(t))\) at the input is such to modify its instantaneous frequency by following an exponential law, i.e., assume that, if \(T_{0}\) is the duration of the swept sine signal ranging between the frequencies \(f_{MIN}\) and \(f_{MAX}\), the angular frequency is defined by \(\omega(t)=d\phi(t)/dt=2\ \pi\ f_{MIN}\ Exp(t/L)\), where the constant L is defined as \(L=T_{0}/ln(f_{MAX}/f_{MIN})\) and describes how quickly the frequency changes over time. It is easy to verify that the k \(-\) th harmonic of the input signal corresponds to a simple shift of the signal \(x(t)\) by the quantity \(\Delta t_{k}=L\ ln(k)\). In fact, we can write for the instantaneous frequency \(f(t)\):  
\[f(t+\Delta t_{k})=f_{MIN}\ Exp[(t+\Delta t_{k})/L]=f_{MIN}\ Exp[(t+L\ ln(k))/L ]=k\ f_{MIN}\ Exp[t/L]=k\ f(t) \tag{5}\]  
If, moreover, the input signal complies with specific constraints on its instantaneous phase, as detailed in [15], we can process the output \(y_{H}(t)\) with the matched filter, i.e., a filter whose impulse response \(\psi(t)\) is such that its convolution with the exponential swept sine signal \(x(t)=R\ Cos(\phi(t))\) produces \(\delta(t)\), a band-limited approximation of Dirac's delta function \(\delta(t)\). The output of the matched filter \(\psi(t)\) will then be given by:  
\[u(t)=y_{H}(t)\otimes\ \psi(t)=\left[\ Cos[k\phi(t)]_{c}\right]^{T}\otimes\left[g(t) \right]\otimes\ \psi(t)=\left\{\left[\delta(t+\Delta t_{k})\right]^{T}\right\}\otimes\left[g(t)\right] \tag{6}\]where the \(k-th\) element of the vector \(\left[\hat{\delta}(t+\Delta t_{k})\right]\) is the band-limited approximation of the Dirac delta function shifted in time by the quantity \(\Delta t_{k}=L\ ln(k)\) defined above. The output \(u(t)\) of the matched filter, when the signal \(y_{H}(t)\) is input, consists therefore of a sequence of impulsive functions \(g_{i}(t)\) starting at the times identified by the shifts \(\Delta t_{k}=L\ ln(k)\) related only once the input swept sine signal is defined, to the order \(k\) of the harmonic. If the parameter \(L\) is large enough to keep the different \(g_{i}(t)\) apart, each of the \(g_{i}(t)\) functions can be obtained by simply extracting an appropriate section in time of the output signal \(u(t)\), starting at a time instant identified by \(\Delta t_{i}\). The duration of the time window beginning at \(\Delta t_{k}\) depends on the length of the impulse responses \(h_{k}(t)\) and can only be established by trial and error. Once the \([g(t)]\) functions have been obtained by sectioning the output \(u(t)\) of the matched filter, from the \([g(t)]\) it is possible to derive the \([h(t)]\) functions that identify the Hammerstein model through a simple linear transformation:  
\[[h(t)]=[R_{c}]^{-1}[A_{c}]^{T}[g(t)] \tag{7}\]  
Figure 2 shows graphically the processing procedure to obtain the functions \([g(t)]\).
# Towards Explainable Pedestrian Behavior Prediction:
## 2 Proposed Method
### Phase 1: Meta-Dataset Generator  
The first phase involves the potential of feature extraction which begins with the pedestrian action dataset (JAAD and PIE) consisting of videos, images and annotations, then a feature extractor component is employed to extract features using deep learning and neural networks, creating a meta-dataset that contains the extracted features for each pedestrian. It is important to highlight that the meta-dataset maintains a balance between the number of pedestrians who cross the street and those who do not. This balanced representation ensures that the extracted fuzzy rules are equally informed by both scenarios.  
As mentioned previously, our approach focuses on providing insights into the explainability of the pedestrian behavior predictor, with an emphasis on considering the overall processing pipeline. In particular, we aim to ensure that the pedestrian-related inputs are inherently explainable. To support this goal, we identified a set of features that offer meaningful contextual information and contribute to a comprehensive representation of road user behavior.  
The selection of these features was guided by a contextual analysis of various road scenarios, interpreted from both the driver's and the pedestrian's perspectives. This analysis allowed us to capture the key elements that influence pedestrian behavior and driver decision-making, ensuring that the model is grounded in real-world dynamics. By incorporating features that reflect how road users perceive and respond to their environment, we enhance the interpretability and relevance of the predictive model in practical applications.  
Therefore in this study, we carefully choose six features to serve as inputs for the pedestrian behavior predictor. Among these features, five are derived through the implementation of neural networks, while one feature is obtained directly from the pedestrian crossing datasets. The following list outlines the selected features:  
* **Body Orientation.** This describes the pedestrian posture through an angle from 0\({}^{\circ}\) to 360\({}^{\circ}\).
* **Attention.** This describes the attention of the pedestrian, indicating whether the pedestrian is looking at the ego vehicle.
* **Action.** This describes the motion state of the pedestrian, classifying between the following actions: stand, walk, wave, run, or undefined (used when the pedestrian action is not clear).
* **Proximity to the road.** This describes if the pedestrian is near the road. This feature is classified into three levels according to the pedestrian closeness to the road: near, medium distance, or far.
* **Zebra Crossing.** This represents the presence of a zebra crossing in the scene.
* **Distance to ego vehicle.** This represents the estimated distance between the pedestrian and the ego vehicle.
## 1 Introduction  
### Background and Purpose  
As of 2023, South Korea had approximately 26 million registered vehicles, with around 21 million being passenger cars and 4 million trucks. The total annual driving distance is approximately 335.6 billion kilometers, with highways alone accommodating about 8.5 million vehicles daily. In contrast, the total road length is 111,314 km, resulting in a ratio of 4.7 km per 1000 vehicles, which is the lowest among the Organization for Economic Cooperation and Development (OECD) countries. The number of traffic accidents in 2023 was approximately 200,000, with 152,935 being vehicle-to-vehicle collisions and 5017 of these occurring on expressways. Over a five-year period starting in 2017, as shown in Figure 1, South Korea ranked second to the United States among five OECD countries in terms of the number of traffic fatalities per 100,000 people, with a death rate per 10,000 vehicles similar to that of the United States, indicating a high level.  
Regarding fatal accidents on expressways, the fatality rate is approximately 0.03 deaths per accident, while on other roads, it is 0.006 deaths per accident, making the fatality rate on expressways approximately five times higher than that on other roads. A major cause is high speeds on expressways; however, secondary accidents also play a significant role. According to traffic accident analysis data published by the Road Traffic Authority, the mortality rate for general accidents on highways averages 12.1% over five years, whereas the fatality rate of secondary accidents reaches 60.3%, which is approximately five times higher than that of general accidents [1]. Secondary highway traffic accidents are serious problems in terms of traffic safety. Such accidents refer to those that occur at the same location or nearby after an initial accident, often causing a greater loss of life or property damage. Instances of secondary accidents include collisions with individuals standing on the road for cleanup after a minor initial accident, hitting injured persons lying or thrown outside the vehicle in a primary accident, and strong collisions with slow-moving or stationary vehicles that are not noticed owing to an accident. Accidents on expressways often occur not only due to secondary collisions but also in areas where the speed of vehicles on certain lanes decreases, such as at merging and diverging sections, like interchanges and junctions. Typically, the traffic information provided is based on fixed nodes and links in a road network map, which makes it difficult to perform detailed analyses of lanes where sudden speed differences occur due to traffic accidents or merging and diverging sections.  
This study aims to map vehicle data and analyze traffic patterns on expressways to assess the impact of accidents and the effects of merging and diverging sections, with the goal of identifying risk factors, such as secondary accidents.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 3 Isolation Mechanisms
### Analysis of Parasitic Decoupling Structure  
The resonant frequency analysis of the parasitic decoupling structure is shown in Figure 4. It is performed by using wave ports in a full wave electromagnetic solver (Ansys HFSS(r)). The analysis setup and overall transmission loss over the UWB band is shown in Figure 4. Top and bottom side of an air box containing the resonant structure is excited with wave ports whereas the front and backside of the box is assigned perfect magnetic conductors. The other two sides (left and right) are assigned with the perfect electric conductor boundary conditions. It is apparent that different resonant components of the decoupling structure contribute in achieving the isolation between antennas placed across or diagonally. An overall isolation of 20 dB is provided by this parasitic structure.
# Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems
## 3 Mathematical Model  
To address the \"last mile\" travel demand for connecting urban rail transit stations, this study develops an operational model for feeder flex-route transit to coordinate with urban rail transit schedules. The model applies to a multi-vehicle flex-route transit service system. Considering factors such as bus operation time, fleet configuration, service standards, and policies, this study determines the optimal departure interval, departure frequency, departure time, and fleet size for flex-route transit. This enables the formulation of a collaborative optimization scheme for scheduling variable-route buses, enhancing connectivity and improving public transit service levels.
## 3 Security Issues Facing CVs and Solutions  
CVs adopt wireless communication to allow vehicles, roadside units, and mobile devices to communicate with each other and exchange critical information. In essence, CV is a kind of distributed system and imposes severe security and privacy challenges. It is anticipated that vehicle connectivity coupled with AV will revolutionize transportation systems, improve safety, and reduce costs, emissions, and energy consumption. In fact, the adoption of diverse types of vehicle connectivity on AV enables the exposure of potential vulnerabilities of the vehicle and results in a heightened risk of cyber-attacks on the CAV.  
CVs incorporate multiple specific types of wireless communication to provide the information needed to implement vehicle-to-everything (V2X) applications, such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), vehicle-to-sensor (V2S), etc. Figure 3 shows the various communications of CV in the future. The Vehicular Ad Hoc Network (VANET) adopts Dedicated Short Range Communication (DSRC) to enable V2V and V2I. The cellular network is gradually standardized using 3 GPP to support Cellular-V2X (C-V2X) services [35]. Bluetooth can be considered in V2P due to its energy-saving properties. Wi-Fi also satisfies some types of V2X communications with the advantages of low cost and the ease of deployment. However, all these communications have their own vulnerabilities and face some security issues. Therefore, in this subsection, we investigate the security issues facing these communications of CVs and identify some open issues for these communications if any of them are applied in CAVs.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Methods Deployed Across Studies
#### 3.3.2 Regression  
Regression models were used to predict various changes in the system under study. Specifically, [16; 30] designed regression models to estimate the temperature response of buildings during a DR event. For example, [30] estimated the temperature drop when heating was turned off. Moreover, [19; 46; 53; 58] designed regression models to estimate the amount of expected load reduction during a DR event. Moreover, [25; 27; 52] used regression to identify indicators of flexibility potential, which can be applied in the flexibility quantification process or used to inform other techniques within the methodology.