esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Training and Testing Data  
To learn the conditional six-dimensional probability distribution of parameters characterizing the particles of ionizing radiation, the values of the three conditional parameters for the training set were taken on a regular grid with mean energy between 5.6 MV and 6.4 MV in 0.1 MV steps, the focal spot size ranging between 0 mm and 4 mm in 0.5 mm steps, and beam divergence ranging between 0\({}^{\circ}\) and 3\({}^{\circ}\) in 0.5\({}^{\circ}\) steps. For each of these 567 grid nodes, 10\({}^{7}\) photons were generated by MC simulations and used for training. To obtain a smooth energy spectrum, photons originating from electron-positron annihilation (about 1% of all photons) were removed from the training data.  
Testing of the trained models was performed on 10 selected sets of electron beam parameters at points between nodes of the training grid. Each node \(P_{i}=(E_{i},r_{i},\alpha_{i}),i\in\{0,1,\ldots,9\}\) is described by a tuple containing initial beam energy, focal spot size, and beam divergence. Nodes chosen to test the ability of the model to interpolate between the training sets of parameters are given in Table 1.  
For each of the testing sets of the electron beam parameters, two phase spaces consisting of 7 \(\times 10^{8}\) particles were generated: the MC phase space, generated by PRIMO, in which photons, electrons, and positrons were present, and a GAN-based phase space in which only photons were present. These phase spaces were then used to assess the quality of our GAN models. Figure 1 presents a sample contour plot of the marginal distribution of phase space \(P_{0}\) projected onto position coordinates \(X\) and \(Y\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phase Space**} & **Beam** & **Focal Spot** & **Beam** \\  & **Energy** & **Size (mm)** & **Divergence** \\  & **(MV)** & **(Degrees)** \\ \hline \(P_{0}\) & 5.65 & 2.25 & 2.25 \\ \(P_{1}\) & 5.65 & 0.25 & 0.25 \\ \(P_{2}\) & 5.65 & 3.75 & 0.25 \\ \(P_{3}\) & 5.65 & 0.25 & 2.75 \\ \(P_{4}\) & 5.65 & 3.75 & 2.75 \\ \(P_{5}\) & 6.35 & 0.25 & 0.25 \\ \(P_{6}\) & 6.35 & 3.75 & 0.25 \\ \(P_{7}\) & 6.35 & 0.25 & 2.75 \\ \(P_{8}\) & 6.35 & 3.75 & 2.75 \\ \(P_{9}\) & 6.05 & 2.25 & 2.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test phase space parameters.  
Figure 1: Contour plot of the marginal distribution of particle \(X\) and \(Y\) coordinates for phase space \(P_{0}\). The red circle of radius 5.5 cm denotes the area outside of which particles in phase spaces were rejected for GAN training.  
Tests of the InfoVAE model were conducted for only one phase space with an average energy of 5.8 MV, focal spot size 1 mm, and beam divergence 0\({}^{\circ}\). The training space consisted of 6 million photons and the test space of 4 million photons.
# Enhancement of Mechanical Properties of PCL/PLA/DMSO2 Composites for Bone Tissue Engineering
## 1 Introduction
DMSO\({}_{2}\) has been reported to possess several advantageous properties that can mitigate the drawbacks of PCL/PLA composites, including a reduction in viscosity, an increase in degradation time, and improvements in geometric accuracy and surface tension [8; 34; 35]. Particularly, high surface tension enhances hydrophilicity, thereby increasing the composite material's cell-attracting properties within the biomaterial matrix. However, an insufficient reporting of studies on DMSO\({}_{2}\), which can potentially alleviate the drawbacks of PCL/PLA composites, has been observed in the existing literature. Recently, [PERSON] et al. [36] studied the material properties of PCL and DMSO\({}_{2}\) composites, with PCL acting as the matrix and DMSO\({}_{2}\) serving as a filler to customize the composite properties. The study identified a 15.5% decrease in the water contact angle of the PCL/PLA composites compared to the pure PCL and a decrease in yield strength along with an increase in modulus as DMSO\({}_{2}\) content was increased. The result was attributed to interface or adhesion challenges between PCL and DMSO\({}_{2}\). As a solution, they proposed the addition of a binder that functions as an adhesion promoter to increase the yield strength of PCL/DMSO\({}_{2}\) composites. While various materials based on polymers have been studied, there are still limitations to their applications and manufacturing processes, especially in the context of utilizing biomaterials in additive manufacturing. In addition, the material properties of the composite system comprising a PCL/PLA matrix and DMSO\({}_{2}\) filler system have been subject to limited investigation. In this study, the tailored composites of a PLA + 20 wt% PCL + 10, 20, and 30 wt% DMSO\({}_{2}\) system were characterized, investigated to enhance mechanical properties, such as yield strength, modulus, and hydrophilicity, and comparedto pure PLA, pure PCL, and PLA/PCL composites. The yield strengths, moduli, and hydrophilicities of PLA/PCL/DMSO\({}_{2}\) composites were increased with the concentration of DMSO\({}_{2}\) in the PLA matrix.
## 4 Results and Evaluation
### Preparation
#### 4.1.2 Evaluating Indicators  
Given the unique nature of our generated models and the limitations of diffusion-model-based methods in spatial understanding, we found conventional metrics like R-precision or FID less applicable. This necessitated alternative evaluation approaches. This difficulty arose due to the lack of corresponding real-world models for our generated models and the limited spatial understanding of diffusion-model-based methods. To navigate this issue, we drew inspiration from subjective evaluation methods utilized in research [45] facing similar challenges. We devised a subjective assessment criterion tailored to our method. We engaged 30 participants to rate a range of 3D models on a 0 to 5 scale. To ensure scoring consistency, we provided detailed guidelines on how to match models with textual descriptions. Scores were allocated based on how well models matched textual descriptions: a score of 2 indicated that the model met some aspects of the text, 3 suggested that the model largely matched the text, and 4 implied that the model perfectly aligned with the text description.  
Figure 5: Unlike conventional multiagent systems, our agents do not have long-term memory. Instead, each time the terrain is modified, their memory only contains data from the last modification of the terrain feature, as longer-term memory is meaningless for the next modification the agent will make, and the only possible use of such memory, to undo the last change, can be completely bypassed without an LLM.
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 2 Related Work
### Some Improved Classic Backoff Algorithms  
In order to improve the throughput and energy efficiency of sensor nodes, different backoff algorithms were proposed in recent years. Dynamically changing contention windows and the size of contention windows can adapt to the dynamic changes of network load. However, they have a common shortcoming: energy efficiency is hardly improved.  
1 Multiplicative Increase Linear Decrease (MILD) algorithm [18]. When data collision occurs in the network, the contention window becomes two times larger than the original window. The contention window will decrease linearly after sending out data successfully. When the number of nodes in the network is not yet large, the contention window is large. This algorithm cannot reduce the contention window rapidly and causes larger transmission delays that reduce the utilization rate of channel.
2 Multiplicative Increase Multiplicative Decrease (MIMD) algorithm [19]. When collision happens, the contention window is doubled. When data is sent out successfully, the contention window becomes half the size of the original window. MIMD solves the problem of initializing contention window, but there is still no way to satisfy the dynamic changes of network topology and traffic.
3 Slow Decrease (SD) algorithm [19]. The current contention window of nodes becomes \(a\) times (\(a=0.85\)) as much as the original contention window while accomplishing data transmission. Moreover, this algorithm can improve throughput and fairness among nodes when the number of nodes is large. However, when there are fewer nodes, nodes need to successfully send multiple times to reduce contention window.
4 Gentle Distributed Coordination Function (GDCF) algorithm [20]. Parameter \(c\) is introduced in this algorithm and it refers to the number of messages. The current contention window decreases by half of the original window only if nodes successively succeed in sending \(c\) messages. The value of \(c\) is related to the number of nodes in the network. When the number of nodes is unknown, \(c\) is in the range of 4-16. The advantage of this algorithm is that it can provide priority services to the nodes working in the network. The contention window of nodes with smaller value of \(c\) decreases rapidly, so nodes have higher priority to access the channel. In this algorithm, throughput is greater than in the Binary Exponential Backoff (BEB) algorithm when the number of nodes is large.
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 5 Conclusions  
In this article, we generate echo signals of different underwater acoustic targets based on the bright spot echo model, and generate a data set of simulated target echo signals through weighting processing. The samples in the data set contain echo signal characteristics, target azimuth and distance information. Then we built a DCNN model to learn the echo signal of underwater acoustic targets. We trained and tested the model with a data set of analog signals, and the results showed that the accuracy of the model was high enough to be used for active tracking. Finally, we validate the proposed DCNN-KFAT method with simulations and pre-recorded sea trial data. By analyzing the simulation results, the method has a significant improvement in active tracking and can more accurately distinguish similar different targets. It is simpler and more accurate than the data association and target judgment of the KFAT method. The data recognized by DCNN-KFAT method include target category, target azimuth and target distance. In the process of target data association and target determination, the target data can be correlated very accurately. It solves the problem that KFAT loses a target after encountering the overlap of two target azimuths, and has a significant improvement in tracking accuracy and range.  
The research results of this paper can be used for active tracking of underwater acoustic targets and building target datasets for deep learning training and recognition. The DCNN-KFAT method can improve the range and accuracy of tracking, and can solve the data correlation problem in the process of hydroacoustic target tracking, which can be used to improve the engineering application problem of lost targets. The next step will be to test in a real marine environment to verify the performance of the DCNN-KFAT method proposed in this paper in practical applications.  
Conceptualization, M.W. and Z.Z.; methodology, B.Q. and C.Z.; software, B.Q. and H.X.; validation, B.Q., H.X. and C.Z.; formal analysis, C.Z.; investigation, H.X.; resources, M.W. and B.Q.; data curation, H.X.; writing--original draft preparation, B.Q.; writing--review and editing, B.Q., M.W. and C.Z.; visualization, B.Q.; supervision, M.W. and Z.Z.; project administration, M.W.; funding acquisition, M.W. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the Key Laboratory of Underwater Acoustic Environment, Institute of Acoustic, Chinese Academy of Science and Key R&D Program of Zhejiang Province (No. 2021C03013).
# A Novel Method Using 3D Interest Points to Place Markers on a Large Object in Augmented Reality
## 2 Proposed Method for Placing Multiple Markers
### Scoring Step  
In this step, the score for each candidate is calculated to determine which of all candidates of the preprocessed mesh is suitable as a position for the marker placement (hereafter MPS score, Marker Placement Suitability score). The proposed method automatically places the marker in the candidate with the highest calculated score. Based on the conditions for reducing the registration error due to the inevitable problems mentioned in the introduction, two hypotheses were considered for the calculation of the score.  
**Hypothesis 1**.: _From each area of an evenly divided virtual object, the face closest to the center of the area (CoA) would be the most suitable candidate. This is because the closer the markers are to the CoA, the more evenly they are placed._  
**Hypothesis 2**.: _Face with more interest points around it would be the most suitable candidate. This is because the more interest points around it, the easier it is to identify._  
This step is performed through the following processes: (1) centrality computation of segmented area representing scores for candidates based on hypothesis 1, (2) density computation of interest points representing scores for candidates based on hypothesis 2, and (3) calculation of the MPS scores by considering these comprehensively.
## 4 Image-Based Visual Servoing with Limited Field-of-View
### A Switched Approach  
The controller designed in the previous subsection should work under an ideal situation that the feature points are always in the field-of-view. The system is built based on partial 3D information. It cannot be guaranteed that the visual features remain in the field-of-view during the stabilization task of the mobile robot. Thus an image-based switched approach is proposed to ensure the features remain in view.  
Suppose that the motion of the robot is on a plane, the image points only escape the field-of-view from the left and right sides. A heuristic approach is to set two bounds at the left and right boundaries. If any feature point falls within the boundary, the control process switches to the image-based controllers which are designed as following  
\[\left\{\begin{array}{l}w=k_{t}\big{(}\min(u_{i})-\min(u_{i}^{*})\big{)}\\ v=-k_{v}v_{i}\big{(}v_{i}-v_{i}^{*}\big{)}\end{array}\right. \tag{34}\]  
and  
\[\left\{\begin{array}{l}w=k_{t}\big{(}\max(u_{i})-\max(u_{i}^{*})\big{)}\\ v=-k_{v}v_{i}\big{(}v_{i}-v_{i}^{*}\big{)},\end{array}\right. \tag{35}\]  
where \(v_{i}\) is the coordinate in \(v\) direction of the corresponding image point with the \(\min(u_{i})\) or \(\max(u_{i})\). \(k_{t},k_{v}\in\mathcal{R}^{+}\) are positive control parameters. The control laws (34) are applied when an image point falls into the boundary area on the left. And the control laws (35) are applied when an image point falls into the boundary area on the right. The purpose of both sets of controllers is to bring the image points near the boundary back to the center of the field-of-view. In addition, a zone needs to be set in the center of the image to ensure that the controller can switch back to (23) and (24) when the image points fall into that zone. Together with this simple switching mechanism, it is guaranteed that the feature points could be kept in the field-of-view during the stabilization task of the mobile robot under the control of (23) and (24).
# A Study on Visually Induced VR Reduction Method for Virtual Reality Sickness
###### Abstract  
In this paper, we propose a new type of visually induced reduction method for virtual reality (VR) sickness. This method induces a gaze based on a visual guide (VG). Although VGs are used in commercial VR game contents as crosshairs, no studies have been conducted related to VR sickness. VGs can have various properties determined by position, size, shape, color, etc., and it was necessary to investigate which properties affect VR sickness. In particular, size and position of VG are properties that directly affect the user's gaze movement. Therefore, in this paper, five VR sickness reduction methods with different position and size of VG are proposed. Then, an experiment was conducted to investigate the effectiveness of the proposed VR sickness reduction method. To this end, a SSQ (including nausea, oculomotor discomfort, disorientation, and total score) and a questionnaire on fatigue and immersion were used. From the experimental results, a VG with a size 30% that of the aspect ratio, and a position synchronized to the user's head movement direction, was most effective in terms of VR sickness reduction and immersion.  
VR sickness; VR sickness reduction method; SSQ; visual guide; crosshair; gender +
Footnote †: 2021: 11, 6339. [[https://doi.org/10.3390/app11146339](https://doi.org/10.3390/app11146339)]([https://doi.org/10.3390/app11146339](https://doi.org/10.3390/app11146339))
a correlation between VR interaction and VR sickness symptoms according to cognitive level [8]. Also, Virtual Guiding Avatar', that combines various motion properties with an independent visual background (IVB), was used to reduce VR sickness [9].  
Existing studies have mainly dealt with VR sickness caused by camera speed, complexity of background, and unpredictable object movement in VR HMD environments. Studies have also tried to reduce VR sickness by artificially inducing users' gaze through FOV and IVB. However, these methods have a problem in that they reduce VR fidelity by causing a sensory conflict between content and users. To solve this, it is necessary to provide a new visually induced reduction method that can reduce VR sickness while maintaining VR fidelity. Therefore, in this paper, we propose a new type of visually induced VR sickness reduction method using a visual guide (VG) that can induce a user's gaze. Although VGs are used in commercial VR game contents as crosshairs, there have been no studies conducted related to VR sickness. This VG can have various properties determined by position, size, shape, color, etc., and it is necessary to investigate which properties affect VR sickness.  
Thus, in this paper, VR sickness reduction methods based on VGs with different properties are designed, and an experiment is conducted to verify the effectiveness of VR sickness reduction methods. The experimental results are analyzed using a simulator sickness questionnaire (SSQ: nausea, oculomotor discomfort, disorientation, and total score) [10,11] and a questionnaire on fatigue and immersion.
## 4 Results
### Different Droop Functions in Active Power Curtailment Method  
#### 4.1.1 Linear Droop  
This case represents the method in which all the PV inverters have linear droop-based APC control. All the PV inverters have same droop coefficient which linearly curtails power. The droop coefficient is calculated so that the curtailment occurs between critical voltages, i.e., 1.05 p.u. and 1.058 p.u. The droop coefficient was calculated as  
\[m=\frac{P_{max}}{(V_{cr1}-V_{cr2})}=2.5\frac{\text{kW}}{\text{V}}. \tag{7}\]  
where \(P_{max}\) is the required maximum power that needs to be curtailed by PV inverter to limit the voltage from \(V_{cr2}\) to \(V_{cr1}\).  
After applying droop-based APC control, the maximum voltage at houses 11 and 12 is reduced to 1.054 p.u. from 1.065 p.u of base case as shown in Figure 8, which is less than \(V_{cr2}\). Similarly, none of the houses exceed the voltage limit of \(V_{cr2}\). Figure 8 also shows the power curtailed during APC implementation with linear droop. The maximum power curtailed at 1 pm was 2.17 kW. However, due to the power curtailment, the total energy output from PV reduced from 489.60 kWh in the base case to 466.87 kWh with the linear droop-based APC. The total energy loss due to curtailment was 22.72 kWh.
## 2 Application of Thermography in Pig Breeding
### Growing Pig Efficiency  
In the context of evaluating temperature changes related to food intake and its energy content, [PERSON] examined the effects of four diets that were characterized by increasing levels of metabolizable energy and equal to 0.75, 1.5, 2.5, and 4.7 (ad libitum) times compared to the maintenance energy diet (MEm) [17]. The thermographic images have been taken three times a day for three consecutive days, starting from the fifth day after the onset of the trial, because the first four days were used for the adaptation to such diets. The pigs have been photographed at a 2 m distance, perpendicularly to the flank, while standing free in their stalls. This study has confirmed that weight gain and feed ingestion increase the body surface temperature, and, for the first time, it has shown the applicability of thermographic tracking to the study of temperature changes associated with feed ingestion. Moreover, it has highlighted the need to consider the timing of the thermographic shooting of the pig and the distance from the last feeding since the associated temperature variation can affect the detected value.  
[PERSON] evaluated the application of thermography to the measurement of radiant heat loss in relation to the metabolic activity, in order to identify the most growth-efficient pigs [18]. The thermographic camera was placed above an automatic feed station capable of weighing the amount of supplied and remaining food, as well as recording the individual's actual weight. The part of the body detected by the camera was, therefore, the back. The data surveys involved 141 pigs that had been checked as actually weighing around 35, 65, and 105 kilos. The performance of a 24 h fasting period revealed a decrease in the maximum dorsal surface temperature by 0.28 \({}^{\circ}\)C. Moreover, this temperature presented a negative relationship with ingestion and growth. This suggested the possibility of identifying, through thermography, individuals with more efficient metabolic processes associated with lower energy losses in the form of radiant energy.  
[PERSON] has recently evaluated whether, during the growth of animals, the use of thermography could identify fatter and leaner individuals [19]. Their initial hypothesis was that the increase in the back fat layer, due to its insulating power, could reduce the surface temperature detected on the back. However, the obtained results showed that we cannot discriminate this way since the changes in the fat layer associated with the growth, measured by ultrasound, are not as remarkable as to cause a decrease in the surface temperature of pig backs. Furthermore, the methods employed to evaluate this possible application of thermography have shown how the effects of the environmental conditions in which measurements are recorded can significantly interfere when working on the detection of limited thermal variations.
## 4 Experimental Results
### Visual Result Comparison  
Visual quality analysis is one of the vital evaluation parameters for the reconstruction of compressed images. In this subsection, the visual quality of the reconstructed frames using proposed JD is analyzed. The proposed framework performs better in terms of PSNR and SSIM at lower sub-rate. However, it is also important to verify that the frame produced at lower subrate are visually perceptible. Thus, we perform the analysis on three different video sequences having low, moderate and high motion contents.  
Figure 6 presents the visual results of the random center frame of each GoP selected from the reconstructed video sequence (the similar visual quality was observed for all center frames of each GoP) by using JD-TV and BCS-TV-AL3. The result shows that the visual quality of reconstructed frames by using proposed method at lower subrates is better visually perceptible than the conventional method. In addition, for low motion content video sequence (Mother-Daughter) the proposed JD-TV shows higher visual quality (due to precise frame prediction and residual reconstruction) as compared to moderate (News) and higher Motion content video sequences.  
Figure 6: \(Cont\).  
It should also be noted that at a lower subrate (0.05) due to the insufficient estimation information of motion few distortions are detected (red dotted circle). Further, for video sequences with fast-moving objects (Mobile Calendar), the JD-TV is vulnerable to a few distortions as emphasized by the red spotted circle.