esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Maneuver-Based Driving Envelope Generation Approach for Driving Safety Assessment
## 4 The Applications of the Driving Envelope  
### Intervention Time  
The proposed driving envelope can be utilized to determine the intervention time for semi-autonomous vehicles. The collision probability is used to determine the intervention time in [20]. The collision probability is computed by randomly generating N particles and computing the number of particles that collide with the traffic participants. Inspired by their work, we determine the intervention time for a human-in-the-loop shared driving system by calculating the probability of violating the safe driving envelope,  
We predict the future occupancy set of the host vehicle at each time step by randomly generating N particles based on the driver's input and vehicle state. The Constant Turn Rate and Acceleration (CTRA) kinematics motion model [38; 39] is used to predict the state of the host vehicle in a short prediction horizon. By adding uncertainty to the control input, the possible occupancy set of each time step can be obtained. For example, the generated particles at 1.5 s are shown in Figure 14.  
Figure 13: The driving envelope for lane-change and lane-change abortion. (**a**) The driving envelope for collision avoidance of static obstacle. (**b**) The driving envelope for lane-change before lateral movement. (**c**) The driving envelope for lane-change during lateral movement. (**d**) The driving envelope for lane-change abortion.  
The driving envelope is generated considering the prediction time horizon. If one particle is outside of the generated driving envelope, then it can be treated as an unsafe state. The greater the probability of locating outside the driving envelope at a future time step, the greater the probability of arriving at an unsafe state. The probability of locating outside the driving envelope at each time step can be obtained by counting how many particles are outside of the driving envelope.  
\[p_{outside}=\frac{N_{outside}}{N} \tag{20}\]  
where \(N\) is the total number of generated particles, \(N_{outside}\) is the number of particles distributed outside of the driving envelope. As shown in the Figure 14, the boundary paths of the proposed driving envelope are \(\tau_{1}\) and \(\tau_{2}\). If the lane is not straight, we convert the discrete points on boundary paths of the driving envelope from the reference frame into the cartesian coordinate, and connect the discrete points into line segments. Whether the point exceeds the safety zone is determined by judging which side of the line the point is on, as shown in Figure 15.  
When \(p_{outside}\) at a future prediction time step reaches a predefined threshold, the shared driving system can provide warning or intervention. An advantage of this method is that it can guarantee the exist of collision avoidance trajectories when the system intervenes, and the system will not intervene too early.
## 2 Literature Review and Research Hypotheses
### Research Hypothesis  
This study examines whether senior management support can improve team performance [57]. It is suggested that technological capabilities depend on learning processes and the organizational culture such as the collaborative environment and the environment itself, which are influential factors regarding team productivity. Therefore, this study postulates the following hypotheses arising from the theoretical review carried out:  
**Hypothesis 1 (H1).**: _There is a positive link between senior management support and BIM technology effectiveness when carrying out design projects._  
**Hypothesis 2 (H2).**: _The effect senior management support has on BIM effectiveness when carrying out design projects is positively mediated through technological learning._  
**Hypothesis 3 (H3).**: _The effect senior management support has on BIM effectiveness when carrying out design projects is positively mediated through a collaborative culture._  
From these hypotheses, a research model were formulated. The variables were obtained from the literature exposed in the previous sections, and the indicators used to measure the variables are presented in Section 3.3. The proposed model (Figure 1) had an independent variable \"senior management support\" (SMS), two mediating variables \"technological learning\" (TL) and \"collaborative culture\" (CCU), and one dependent variable \"BIM effectiveness\" (BIME).  
Control variables were those variables which monitored the behavior of the dependent variable. The direct effect was used to test the model without the mediating variables, while multiple effects were used to test the model with mediating variables.
## 1 Introduction
However, it is necessary to consider the influence of bit depth of the recorded holograms on the quality of the reconstructed image. This is because the proposed DH multiplexes holograms at multiple wavelengths on a monochrome image sensor, and available bit depth per wavelength is sacrificed. Furthermore, in the case of the proposed DH, it is worth evaluating whether the dynamic range of holograms is related to wavelength resolution because wavelength information is selectively extracted by using the wavelength dependency of the intensity changes induced by the phase shifts of holograms. In this paper, we investigate image quality in relation to the dynamic range of holograms formed by wavelength-selective phase-shifting DH. Image quality and wavelength resolution in relation to dynamic range are analyzed with numerically and experimentally obtained holograms.
## 8 Results and Discussion of Reviewed Papers
### DarkTrack2021  
[PERSON] and his team utilized the DarkTrack2021 benchmark as a foundation for developing the Segment Anything Model (SMA)-powered framework, SAM-DA. Their research [80] focused on effectively addressing illumination variation and low ambient intensity. They conducted a comparative analysis between their model and various methods, particularly the Baseline tracker UDAT [112] method. Their novel approach outperformed the Baseline UDAT method, achieving substantial improvements of 7.1% in illumination variation and 7.8% in low ambient intensity. The authors evaluated 15 state-of-the-art trackers and found that SAM-DA demonstrated the most promising results. Additionally, [PERSON] delved into Siamese Object Tracking in their another study [81], highlighting the significance of UAVs in visual object tracking. They also leveraged the DarkTrack2021 datasets as a benchmark to assess model performance in low-illumination conditions, with detailed results and the applied models presented in Table 6.
## 5 EKF-Based Approach for AE Source Localization during Quasi-Static Tensile Test
### EKF-Based Analysis of AE Data
In order to determine the space distribution of the localized AE sources, we have used an approach which consists in dividing the specimen into an array of rectangular elements within which a count of the number of AE hits is performed [66]. The size of the grid elements should be large enough to have a sufficient number of AE hits per element. At the same time, it should not be smaller than the accuracy of the proposed AE localization method. Under these conditions and according to [67], the width of the damage zone can be determined by drawing 80 % of the located AE hits. Indeed, it can be seen from Figure 14 that AE hits  
Figure 12: Identification of the time of arrival using the line profile of the scalogram at the average frequency \(\overline{f}=283.3\) kHz. are mainly concentrated around the damaged zone. The 80% criterion associated to the previously applied EKF method allow to have a very good estimation of the crack creation and propagation when the material is under test. Figure 14: Histogram of the AE events detected in the aluminum plate before (**a**) and after (**b**) the application the 80% criterion. Figure 13: Comparison of the AE source localizations performed with the AE system and the EKF method during the mechanical test. The path of the crack is obtained according to Figure 9c (solid). Approximate crack path is also plotted based on the AE system localization (dashed).
# Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders
## 4 Experimental Framework
### Loss Functions  
In both use cases, the optimization problem is similar, so the loss functions that we used in both cases are almost the same. As previously described, the training process consists of two phases:  
* **Autoencoding phase:** In this stage, we optimize a loss function with the aim of learning to reconstruct the inputs \(\mathbf{x}\) from the training dataset. The loss function employed for this purpose is the mean squared error (see Equation (11)): \[\mathcal{L}^{\text{AE}}(\mathbf{x},\mathbf{x}^{\prime})=\frac{1}{N}\sum_{i=1}^ {N}(\mathbf{x}^{(i)}-\mathbf{x}^{\prime(i)})^{2}\] (11) where \(N\) denotes the number of training samples.
* **Counterfactual generation phase:*
* During this phase, the weights of the autoencoder and the black-box model are maintained constant, and the generator is optimized to minimize a loss function that incorporates three distinct terms (as shown in Equation (5)).
* _Data closeness loss:_ This loss function has to minimize the distance between the original samples \(\mathbf{x}\) and counterfactuals \(\mathbf{x}_{cf}\). Thus, we employ the mean squared error in both use cases: \[\mathcal{L}^{\text{G}}(\mathbf{x}_{cf},\mathbf{x}^{\prime}_{cf})=\frac{1}{N} \sum_{i=1}^{N}(\mathbf{x}^{(i)}_{cf}-\mathbf{x}^{\prime(i)}_{cf})^{2}.\] (12)
* _Validity loss_: This loss function has to ensure that the class given by the black-box model \(\mathbf{y}^{\prime}_{cf}\) matches the counterfactual class defined by the user, i.e., \(f(\mathbf{x}_{cf})=y_{cf}\). As the MNIST use case is a multiclass problem and the Gearbox use case is a binary classification problem, we use different loss functions here. On the one hand, for MNIST, we used the categorical cross-entropy loss: \[\mathcal{L}^{\prime}(\mathbf{y}_{cf},\mathbf{y}^{\prime}_{cf})=\frac{1}{N} \sum_{i=1}^{N}\sum_{j=1}^{C}\mathbf{y}^{(i)}_{cf,j}\cdot\log(\mathbf{y}^{ \prime(i)}_{cf,j})\] (13) where \(\mathbf{y}^{(i)}_{cf}\) is the one-hot encoded ground truth label and \(C\) is the number of classes.  
Figure 8: Proposed generator.  
On the other hand, for the Gearbox use case, we used the binary cross-entropy loss:  
\[\mathcal{L}^{\prime}(\mathbf{y}_{cf},\mathbf{y}^{\prime}_{cf})=\frac{1}{N}\sum_{ i=1}^{N}(\mathbf{y}^{(i)}_{cf}\cdot\log(\mathbf{y}^{\prime(i)}_{cf})+(1- \mathbf{y}^{(i)}_{cf})\cdot\log(1-\mathbf{y}^{\prime(i)}_{cf})) \tag{14}\]  
where \(\mathbf{y}_{cf}\) is 0 for normal data and 1 for anomalous data.
* _Data manifold closeness loss:_ This loss function ensures that the generated counterfactuals are close to the data manifold. Thus, it has to minimize the distance between the generated counterfactuals \(\mathbf{x}_{cf}\) and the reconstruction given by the AE for the counterfactuals \(\mathbf{x}^{\prime}_{cf}\). Therefore, the loss used for this was the mean squared error: \[\mathcal{L}^{\text{AE}}(\mathbf{x}_{cf},\mathbf{x}^{\prime}_{cf})=\frac{1}{N} \sum_{i=1}^{N}(\mathbf{x}^{(i)}_{cf}-\mathbf{x}^{\prime(i)}_{cf})^{2}.\] (15)
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework  
We considered UAVs, including drones in smart journalism from a technical perspective, are scarcely reported in the literature, let alone with an AI framework. Therefore, motivated by the related research review above, there are a few challenges which can be addressed to enhance the integration between UAV and AI technologies. Thus, our proposed novel technology aims to couple a drone with the artificial intelligent NN-RBFN framework to train the neural network to obtain an accurate channel propagation model to support aerial smart journalism. To obtain novelty and make a noticeable shift from existing work, there are key contributions to this work:  
* Testing and evaluating set propagation models, which are representatives of their types from aerial perspective using the NN-RBFN approach,
* The optimized propagation model would exhibit advantages of accuracy, strong connectivity, and wide media coverage,
* Validating obtained results against other well-known optimized propagation models as proof-of-concept using 3D Remcom Wireless Insite tools.  
This section starts with highlighting the proposed conceptual architecture that mainly includes propagation channel models and a link budget for drone communications with their mathematical background. Then, the rest of the section introduces the proposed NN-RBFN framework along with its mathematical background to optimize a channel model.
# Sketch-Guided Topology Optimization with Enhanced Diversity for Innovative Structural Design
## 4 Experiments
### Experiment of Human Controllability  
Figure 10 presents the experimental results of human controllability over the generated structural features by integrating the minimum allowed structural feature size \(l_{min}\) and the relative weight coefficient of the closed-shape-based constraint over the stroke-based constraint \(\lambda\) (\(\gamma_{Inc}=\gamma\cdot\beta_{Inc}\)). It is intuitive for designers to take control by simply fine-tuning these two hyper-parameters, as their control effects are precisely targeting at specific structural features and are independent from each other.  
When the control parameter \(\lambda\) is set to be small, the drawn closed-shape region contains many branching structures. In this case, if the value of \(l_{min}\) is also small, the generated structure is highly complex, with low manufacturability, the drawn shape is not visually prominent, though the compliance is small; if \(l_{min}\) is larger, the structural complexity becomes lower, the hand-drawn shape is more prominent, but compliance increases significantly. When \(\lambda\) is set to be larger, there are fewer branching structures within the drawn shape region, leading to a result similar to a hard constraint on the design field boundary. The shape becomes more prominent, especially when the shape has a large area, which significantly affects the aesthetic styling. \(l_{min}\) has a bigger impact on compliance than \(\lambda\). These experimental results verify that the two control parameters have different effects on the structural compliance, and mechanical and aesthetic performance, enabling human designers to achieve a better balance between multiple factors in a flexible control manner.  
The flexibility of human controllability is valuable for practical application, as solving conflicts between different design factors is the main challenge in conceptual structural design. Mechanical and aesthetic performances are conflicting in most cases. Meanwhile, increased structural complexity lowers the manufacturability, as overly narrow structural  
Figure 9: Innovative design exploration results for automobile wheel spokes under sketch-based constraints. Randomly perturbed hyper-parameters include \(d\
u\), \(l_{min}\), and \(\mathbf{f}\). (**a**) Guided by sketch 1. (**b**) Guided by sketch 2. (**c**) Guided by sketch 3. (**d**) Guided by sketch 4.  
branches may be unfeasible to produce. Structural complexity also impacts aesthetic appeal, with simpler and regular forms generally considered more aligned with modern aesthetic standards.
# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
## 3 Experimental Architecture and Results
### Congestion Control  
The 5G-UAVs require a meticulous design in all the layers of their architecture. Figure 7 depicts how protocols and, for this experiment, the transport control layer (TCP) protocol, can significantly affect the behavior of the described autonomous 5G-UAV. Although many studies support user datagram protocol (UDP) connections for streaming data, autonomous robotics applications are more complex and require further use-case investigation to vali  
Figure 6: The 5G-UAV autonomous mission architecture. The remote controller is located in the edge cloud. An MPC controller is operating in the cloud and sends real-time control back to the UAV. The communication is established over 5G. S&SD: state and sensor data; C2: control and command data.  
Figure 7: The uplink latency of the 5G-UAV. Sensor offloading on the same 5G-UAV platform is turned on and off two times.  
date this claim. Figures 7 and 8 depict the latency behavior observed during data overflow (when the image sensor is enabled for transmission), where the behavior can be explained by the TCP congestion mechanism. More specifically, when data congestion occurs, the TCP congestion mechanism takes place, thus reducing the transmission and affecting the latency of the control data packets. Hence, here it is evident that without a congestion feedback mechanism that UDP does not offer by itself, the delay in the data packets would be even more significant. However, the observed behavior and the corresponding performance can be improved by including congestion feedback mechanisms on top of the transport layer protocols; some promising examples are SCream [22] and L4S [23]. Additionally, it is important to note that the experiments were conducted over stable and reliable physical channel conditions, and the data drop rate was very low and thus considered negligible.
## 1 Introduction
There are also a handful of examples where deterministic models have also been used to improve chemotherapy delivery from sustained-release systems [33; 34],  
Mathematical modelling has also been used to determine optimal dosage protocols for oncolytic virotherapy and immunotherapy [35; 36; 37; 38]. Using ODEs calibrated with experimental data, [PERSON] et al. [38] demonstrated that lower oncolytic virus doses combined with higher dendritic cell doses resulted in an optimal robust therapy. Optimising the scheduling of systemic doses of DCs and oncolytic virus, [PERSON] et al. [37] found that tumour eradication could be achieved only when oncolytic viruses were administered before DCs. In work by [PERSON] and [PERSON] [29], an optimised schedule for combined virotherapy and chemotherapy treatment was obtained using a genetic algorithm. So far, however, no one has optimised the release profile of viro-immunotherapy from a sustained-delivery system. The challenges faced in developing such a system lie in de-complexifying the immune response to viro-immunotherapy, while still accurately representing a relevant experimental treatment. In this article, we develop a novel system of ODEs to optimise the effectiveness of an oncolytic adenovirus genetically modified to express IL-12 and GM-CSF loaded onto a hydrogel with a population of immature DCs [10]. The parameter values in the model are optimised to in vitro and in vivo measurements relating to the release of this therapy from a gelatin-based hydrogel. With this calibrated model, we conduct a numerical investigation of the hydrogel release profile and investigate optimised sustained-delivery protocols. Implementing a genetic algorithm, we then determine the true optimal release curves and discuss how complex release curves may impose a less effective treatment.
###### Abstract  
In this paper we propose a novel method for tracking the respiratory phase and 3D tumor position in real time during treatment. The method uses planning four-dimensional (4D) computed tomography (CT) obtained through the respiratory phase, and a kV projection taken during treatment. First, digitally rendered radiographs (DRRs) are generated from the 4 DCT, and the structural similarity (SSIM) between the DRRs and the kV projection is computed to determine the current respiratory phase and magnitude. The 3D position of the tumor corresponding to the phase and magnitude is estimated using non-rigid registration by utilizing the tumor path segmented in the 4 DCT. This method is evaluated using data from six patients with lung cancer and dynamic diaphragm phantom data. The method performs well irrespective of the gantry angle used, i.e., a respiration phase tracking accuracy of 97.2 \(\pm\) 2.5%, and tumor tracking error in 3D of 0.9 \(\pm\) 0.4 mm. The phantom study reveals that the DRRs match the actual projections well. The time taken to track the tumor is 400 \(\pm\) 53 ms. This study demonstrated the feasibility of a technique used to track the respiratory phase and 3D tumor position in real time using kV fluoroscopy acquired from arbitrary angles around the freely breathing patient.  
Keywords:lung tumor tracking; 4D computed tomography; diaphragm tracking; radiotherapy; image-guided radiation therapy; image registration +
Footnote †: journal:
## 3 Materials and Methods
### AOp Prediction Model Development
#### 3.1.3 Multilinear Regression (MLR) Model  
MLR is a technique commonly used to illustrate the linear relationship between one or more independent variables and a single dependent variable. This relationship is based on the premise of reducing the sum of squares of the distinctions between the predictor and predicted variables [47]. The general equation of MLR can be written as shown in Equation (14):  
\[Z=c_{o}+c_{1}y_{1}+c_{2}y_{2}c_{3}y_{3}+\ldots\ldots\ldots c_{n}y_{n}+\mu \tag{14}\]  
where \(Z\) and \(y_{i}\) to \(y_{n}\) are the predicted and predictor variables, respectively; \(c_{o}\) is the intercept; \(c_{1}\) to \(c_{n}\) are the coefficients of the predictor variables; and \(\mu\) is the predictor-associated error.  
The purpose of the developed MLR model is to compare the developed soft computing and empirical models with those of the MLR-generated model for AOP prediction. The same sets of data used to generate the soft computing models were used to develop the MLR equations. However, the datasets were not normalized before the MLR simulation was performed. MLR modeling was performed using OriginPro 2021 software. Equation (15) shows the generated MLR equation for AOP prediction.  
\[AOp=75.3177-10.5074B+4.98115-0.0308D+2.1664L-6.7785 PF+0.6264Q-0.0108 DIS-0.0587 RMR \tag{15}\]