esa-sceva/satcom-chunk-collection huggingface dataset:
# Relative Pose Estimation between Image Object and ShapeNet CAD Model for Automatic 4-DoF Annotation
## 4 Experiments  
### Object Detection and Segmentation  
To evaluate the performance of the proposed method, we use total ten indoor and outdoor objects, _Bed_, _Motobike_, _Table_, _Oven_, _Bag_, _Clock_, _Bottle_, _Chair_, _Car_, _and_ _Bench_ as shown in Figure 7. One common property of the test objects is that they are nonsymmetric. Symmetric objects such as bottle or cup is not used to reduce the pose ambiguity problem. In each object category, we collect 100 RGB images from the Internet. When collecting the images, we try to collect such images whose color and shape are similar with those of the corresponding CAD models as much as possible.  
In each object category, we have total ten CAD candidates. In fact, in each object category, there is a large number of CAD models in ShapeNetXCore. For example, in the _Clock_ category, there are three sub-categories and 651 CAD models in total. Thus, there are large overlaps in terms of similarity of color and shape. One of the purposes of our study is finding the matching pair between the real and the CAD model. Therefore, it needs to be the CAD models distinguishable in terms of color and shape. In each object category, we select ten CAD models which are commonly available in daily life and have different color and shape.
## 2 Design, Working Fluid, and Experimental Apparatus of the MF-AHPA  
### Design of the MF-AHPA  
As shown in Figure 1, extruded from 1100 aluminium, a 26-mm wide and 970-mm long MF-AHPA consists of nine parallel microchannels with a 3-mm thick shell. Note that these channels are fully separated by inner ribs. Once they are charged, these individual channels can work independently as heat pipes. The inner ribs also serve as fins, with an estimated fin efficiency of 67% when the heat transfer coefficient is 16,000 W/m\({}^{2}\).K. Also, 0.32-mm deep microgrooves are extruded on the upper and bottom walls, which can greatly prompt capillarity and substantially increase the surface area for evaporation or the condensation heat transfer rate. The specific dimensions of the cross-section are detailed in Table 1.  
The depth-width ratio is an important parameter for evaluating the groove quality of heat pipes. For example, a narrow and deep groove can generate larger capillary pressure and enhance the heat transfer limit, but results in a higher thermal resistance due to the formation of thicker liquid films. [PERSON] [33] found that in a rectangular grooved wick heat pipe, a groove height ratio of 1.4 and groove width ratio of 1.1 achieved a well-balanced heat pipe performance, i.e., a small thermal resistance and high heat transport limit. In this study, the depth-width ratio of grooves is designed and fabricated as 1.28 (0.32 mm/0.25 mm), which is close the optimal value of 1.27 (1.4/1.1) according to [PERSON]'s study [33].  
The configuration and working mechanism of a MF-AHPA are also schematically shown in Figure 1. Liquid return is improved by additional capillary pressure generated by the microgrooves. Since an individual channel works independently as a heat pipe, the MF-AHPA is more tolerable to leakage and non-condensable gas, and hence becomes more reliable than conventional heat pipes.  
The heat spreading is supposed to be significantly enhanced by the inner ribs in the present design. Figure 2 schematically presents the heat transfer path and liquid film distribution in grooves when heated from the upper surface as a solar collector in solar thermal systems. Without the inner ribs, evaporation only occurs on the heating side. With the integration of inner ribs acting as fins, heat can spread from the upper surface to the bottom surface, as schematically shown in Figure 2.  
Figure 1: Geometric configuration and working mechanism of the micro flat aluminium heat pipe arrays (MF-AHPA). (**a**) Real image of the MF-AHPAs; (**b**) multiple channels working mechanism; and (**c**) geometric configuration of the MF-AHPA shell.  
Thus, evaporation can be extended on all of the inner surface areas, which at least doubles the evaporating areas and can greatly enhance the overall heat transfer rate [34; 35].
# Convective-Microwave-IR Hybrid Drying of Kaolin Clay--Kinetics of Process
## 2 Materials and Methods
### Drying Procedures
Detailed description of the drying schemes is given in Table 2.  
Based on mass measurement, the dry basis moisture content MC\({}_{\text{db}}\) (\(-\)) and drying rate DR (g/min) at a given time of the process were calculated in accordance with the following equations:  
\[\text{MC}_{\text{db i}}=(\text{m}_{\text{i}}-\text{m}_{\text{s}})/\text{m}_{ \text{s}}, \tag{1}\]  
\[\text{DR}_{\text{i}}=(\text{m}_{\text{i}-1}-\text{m}_{\text{i}})/(\text{t}_{ \text{i}}-\text{t}_{\text{i}-1}), \tag{2}\]  
where m\({}_{\text{i}}\) is the mass of sample (g) at t\({}_{\text{i}}\) (min) of the process; m\({}_{\text{s}}\) is the dry matter mass (g). The measured energy consumption was recalculated to specific energy consumption SEC, that is, energy (MJ) consumed per each gram of evaporated moisture:  
\[\text{SEC}=3.6\cdot(\text{EC}/\Delta\text{m}), \tag{3}\] \begin{table}
\begin{tabular}{c c c} \hline
**Schedule No.** & **Name** & **Description** \\ \hline
1 & CV & Convective drying. \\ \hline
2 & CV \(-\) MW & Convective drying enhanced with microwave during the whole process. \\ \hline
3 & CV + MW\({}_{60}\) & Convective drying enhanced with microwave in the first 60 min of the process. \\ \hline
4 & CV + (MW \(-\) IR)\({}_{40}\) & Convective drying enhanced with microwave and IR in the first 40 min of the process. \\ \hline
5 & CV + IR\({}_{80}\) & Convective drying enhanced with IR in the first 80 min of the process. \\ \hline
6 & CV + MW\({}_{\text{per}}\) & Convective drying enhanced with microwave periodically. The application of radiation was controlled by the time in the following scheme: \\  & & phase 1: CV + MW \(\rightarrow\) 60 min \\  & & phase 2: CV \(\rightarrow\) 30 min \\  & & phase 3: CV + MW \(\rightarrow\) 10 min \\  & & phase 4: CV \(\rightarrow\) 30 min \\  & & phase 5: CV + MW \(\rightarrow\) 10 min \\  & & phase 6: CV \(\rightarrow\) 60 min \\ \hline
7 & CV + (MW \(-\) IR)\({}_{\text{per}}\) & Convective drying enhanced with microwave and IR periodically. The application of radiation was controlled by the time in the following scheme: \\  & & phase 1: CV + MW + IR \(\rightarrow\) 40 min \\  & & phase 2: CV \(\rightarrow\) 30 min \\  & & phase 3: CV + MW + IR \(\rightarrow\) 10 min \\  & & phase 4: CV \(\rightarrow\) 30 min \\  & & phase 5: CV + MW + IR \(\rightarrow\) 10 min \\  & & phase 6: CV \(\rightarrow\) 60 min \\ \hline \end{tabular}
\end{table} 
Table 2: Drying schedules. where EC is the energy consumption (kWh), and \(\Delta\)m is the mass of evaporated moisture (g). The kinetics of each process was assessed on the basis of the plots of moisture content, temperature of the sample surface and drying rate versus time. The quality of the obtained products was visually assessed and presented in a digital photograph.
## 2 Materials and Methods
### Estimation of Phenolic Compounds by UPLC-Q-TOF-MS  
The qualitative and quantitative composition of phenolic compounds was estimated using an Acquity ultra-performance liquid chromatography (UPLC) system that was coupled with a quadruple-time of flight mass spectrometry (Q-TOF-MS) instrument (Waters Corp., Milford, MA, USA) equipped with an electrospray ionization (ESI) source. The conditions of UPLC-PDA and MS/MS were described in our previous study [27]. Chromatographic separation was performed using an Acquity HSS T3 C18 column (150 mm \(\times\) 2.1 mm, 1.8 \(\upmu\)m; Waters, Milford, MA, USA) at 30 \({}^{\circ}\)C with a flow rate of 0.45 mL min\({}^{-1}\), and an injection volume of 5 \(\upmu\)L. The mobile phase was composed of two eluent mixtures: 0.1% formic acid (A) and acetonitrile (B). The gradient employed was as follows: 0 min, 99% A; 12 min, 65% A; 12.5 min, 0% A; 13.5 min, 99% A. Phenolic compounds were identified using their UV-Vis characteristics and MS and MS\({}^{2}\) properties using data gathered in-house and from the literature.
## 3 Results  
### Long-Term Assessment Based on EK15 Echosounders  
The long-term data acquisition and fish detection tests were carried out from February 2015 to March 2016. Based on long-term monitoring, it was observed that the majority of fish were detected in the spring [23]. It should be noted that difficulties arose during the summer (false positive detections), caused by noise related to higher discharges as well as grass cutting. Noise was also problematic in autumn due to falling and entrained leaves, as well as in winter, caused by floating snow from urban snow clearance activities.  
A sample of the long-term dataset is presented for April 2015 (Figure 7), taking into account the abiotic parameters from the river gauging station at Rattenberg. The number of detected fish-echoes (contacts) resulted in between 2 and 23 tracks per day, with multiple detections being likely.  
Figure 7: EK15 fish contacts summary compared with the abiotic parameters of water temperature, runoff and turbidity during April 2015. Discharge and turbidity are from gauging station Rattenberg (about 20 km upstream), while water temperature was measured in the headrace channel of the HPP Kirchbichl. Additional measurements of turbidity at Kirchbichl show, that the data from the upstream gauging station is representative.
# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 3 Proposed Methodology
### Model Equation of CNN and VGG16  
#### 3.6.1 Convolution Layers  
The following Equation (5) illustrates the steps involved in applying the ReLU activation function to the outcome after sliding the convolutional kernel over the input image, multiplying the local input values by the weights of the kernel, summing these products, and adding a bias term. This procedure creates the feature map that represents the learned features from the input image for each point (i, j) in the output feature map [21,22].  
\[\text{Conv\_Relu}[i,j]\quad=\quad\text{ReLU}\left(\sum_{m=0}^{M-1}\sum_{n=0}^ {N-1}(Input[i+m,j+n]\times Kernel[m,n])+\text{Bias}\right) \tag{5}\]  
An activation function called the Rectified Linear Unit (ReLU) is applied element-by-element to its input. According to its definition, \(\text{ReLU(x)}=\text{max(0,x)}\), it is set to zero if the input value is less than or equal to zero; otherwise, it remains unchanged. ReLU adds nonlinearity to the network and facilitates learning intricate patterns. During the convolution operation, the \"input\" refers to the pixel value in the input image at the location (\(i+m,j+n\)). It represents the local area of the input image that lines up with the convolutional kernel's current position. The convolutional \"kernel\" is a tiny matrix of trainable weights, commonly referred to as a filter. It is convolved (multiplied element-by-element) with the matching values found in the nearby area of the input image. These weights specify the characteristics that the kernel seeks out in the input. A constant value is added as the \"bias\" term following the convolution procedure. It gives the neural network an extra parameter that can be learned and that can cause an offset or shift in the output. The bias enables the network to correct for any systematic inaccuracies or data fluctuations.
# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 5 Evaluation
### Results and Simulation Analysis
\(\bullet\) _Average End-to-End Delay:_  
This can be achieved by computing the mean of end-to-end delay of all messages that are delivered successfully. Hence, this depends partially on the ratio of packet delivery. As there is an increase in the distance among destination and source, the packet drop probability increases. The average end-to-end delay is comprised of possible network delays, such as queuing, buffering, propagation, latency, transfer time, and packet resending at the MAC layer. The CACIOV algorithm demonstrates significant performance improvement in contrast to other algorithms in terms of average end-to-end delay. it is obvious that by expansion the vehicles number from 40 to 80 in network; this metric increases for CACIOV and has a negligible increase in cases where there are more than 80 vehicles, which is clarified in Figure 8. Also, by expanding the vehicle number from 40 to 120, delay increases significantly for AODV and we see a sudden delay reduction by increasing vehicles from 120 to 200. ACO has a small delay increase in cases where there are 40 to 160 vehicles in the network. Our interpretation from CACIOV protocol behavior is expecting negligible increment of delay while the number of vehicles is increasing. We conclude that the impact of our proposed mechanism on the satisfaction of the requirements of real-time applications in IoV increases by minimizing the end-to-end delay. \(\bullet\) _Average Number of Cluster:_  
A lower number of clusters portrays better communication that the CACIOV technique achieves on the 4 km\({}^{2}\) grid size. There are two main factors influencing the number of clusters, which are the grid size and the range of transmission. Due to high transmission range, node communication in a larger area of coverage becomes efficient, which reflects minimum clusters in a network. Per node, the range of transmission employed is dynamic for observing their impact on various conditions of networks. The minimum number of clusters mean minimum CHs and more stable and reliable network and clusters. Results of the comparison reveal that when traffic density in the network is high, CACIOV performs better by forming a minimum number of clusters that shows reliability of our protocol over others. The worst performance belongs to AODV, which has a maximum number of clusters in any traffic density. it is obvious that by increasing the number of vehicles from 40 to 200 in the network, this metric increases, and can be seen in Figure 9. Consequently, increasing the number of vehicles leads to having a greater number of clusters. Figure 8: Average end-to-end delay.
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 4 Numerical Experiments  
In this section, we report the results of simulation tests to assess the PLR and average delay of both frame-synchronous and frame-asynchronous slotted ALOHA with \(k\) multi-user detection (MUD). This means that we assume that the receiver is able to decode perfectly up to \(k\) simultaneous transmissions in a slot, but fails if the slot contains more than \(k\) packets. Using MUD jointly with coded ALOHA improves both the throughput and energy consumption of the system, which is especially important in applications with massive number of devices (e.g., in massive machine type communications (mMTC), as envisioned for 5G/6G networks). However, even though analytical results for \(k\)-MUD coded ALOHA exist in the literature for Poisson arrival point processes [22; 37], the analysis turns out to be intractable when more general, non-memoryless or correlated arrival processes are considered. In other words, the memoryless property of the arrival process is a key property for analytical tractability of the model. Motivated by the strong impact that heavy tails and LRD have on general queuing systems [36], our purpose in this work is to gain some understanding on this expected impact through simulation tests.  
In all the simulation test cases that follow, we used replica factors \(\ell=3\) and \(\ell=8\), and the \(k\)-MUD capability is \(k=1,2,3\). A small MUD capability is generally enough to sustain a high throughput, while keeping the complexity of the receivers to a practical level. SIC quickly becomes very complex as the number of receivers increases [42].  
In the next sections, we empirically test the performance of the system when the arrivals are heavy-tailed and/or correlated with SRD or LRD. It is important to note that in all the figures the x-axis plots \(\lambda_{a}/k\), so the load offered to the system increases with \(k\).
###### Abstract
Generally, the use of a GNSS CORS, in close proximity to artificial Persistent Scatterers (PS), such as CRs, serves as a valuable confirmation tool to the PS-estimated displacements [6]. However, the CRs should be installed in such a way, that the GNSS signal is not affected mainly by multipath. Figure 1: Site Locations of the Permanent Segment. Green triangles indicate the reference stations. Moreover, the GNSS reference station, should be installed at sites that strictly adhere to strict guidelines set by world renowned services (IGS, UNAVCO, EPN), which require increased monument stability to isolate the station from surface soil movement and, thus, avoid the introduction of biases in the estimation of displacements. In the current scientific literature on the collocation of GNSS/InSAR infrastructures, there are no specific guidelines or methodology for the determination of the most suitable locations, using a holistic approach in terms of criteria and required data. Therefore, the objective of this paper is to propose and present a semi-automatic multicriteria analysis and evaluation of candidate sites for the installation and collocation of a permanent GNSS CORS and a pair of InSAR CRs, one for each pass, taking into account the crucial parameters that affect not only their individual localization but also their interspatial relationship.
# Theory and Design of Blink Jamming
## 3 Modelling Formation Jamming
### Antenna Tracking System  
While the monopulse antenna system measures the error signal for the azimuth and elevation it performs no corrective action to reduce the error signals, that is the purpose of the antenna tracking system. First the antenna tracking loop, shown in Figure 15, takes the error signal for the azimuth and elevation positions and uses a second order tracking loop to estimate the target position based on the prior measurements and current indicated angle. The tracking loop was implemented as a second order transfer function of the form:  
\[G(s)=\frac{\omega_{n}^{2}}{s^{2}+2\zeta\omega_{n}+\omega_{n}^{2}} \tag{19}\]  
where \(\zeta\) is the damping coefficient, and \(\omega_{n}\) is the natural frequency. The antenna tracking system is based on threat models known to be used in missile tracking radars [15; 16]. The precise values were varied using a critically damped transfer function with a natural frequency in the 5 to 10 Hz range. It was implemented as an analogue model in (19), to reflect the analogue nature of the antenna movement and its servo motors.  
The updated azimuth and elevation estimates were then used to move the antenna for the next Simulink model simulation step, shown in Figure 16. This follows the standard architecture of a radar control system where the parameter--azimuth or elevation--is measured, the measurement is compared to the estimate from the control system, the control system updates the estimate, and the antenna is pointed to the new estimate.  
In this specific model of the antenna control system, see Figure 16, the initial pointing azimuth and elevation are provided at the simulation initialization. The sub-system then receives the change in pointing angle from the control system, and updates the memory block storing that value. The resulting  
Figure 15: The Simulink model that takes the angle error signals in azimuth and elevation and, using a tracking loop, updates the estimate of the target position for the antenna.  
pointing angle is then fed back into the control system via a Simulink \"Goto\" block, while also stored to memory at every time step for later analysis.
## 3 Results and Discussion
### Circular Dichroism  
Circular dichroism (CD) is a spectroscopic technique used to survey the stability and folding properties of a protein under certain conditions, namely in situations of protein denaturation (such as temperature, chemical compounds, and changes in pH). CD spectra evaluated the conformation of the secondary structure through the molecular arrangement of the three polypeptide chains in three extracted marine collagens at 4 \({}^{\circ}\)C, as visible in Figure 2C. The spectra of _COLRp_I_, _COLRp_II_, and _COLRp_III_ showed a similar minimum negative peak at 197 nm, indicating that the protein molecular exhibits a random coils conformation or the disarrangement of collagen polypeptide chains. These results agree with other studies [(54; 55)]. Besides that, collagens have a positive peak characteristic of  
Figure 2: Results from the characterization of _R. hippoglossoides_ fish collagens isolated from skin byproducts using three methods. (**A**) Extraction yield. (**B**) SDS-PAGE gel patterns, whereas number 1 represents protein molecular weight marker; 2 denotes standard collagen from bovine skins (_COL_Bovine_); 3, 4, and 5 represent _COLRp_I_, _COLRp_II_, and _COLRp_III_ collagen profiles, respectively. (**C**) Circular dichroism. (**D**) FTIR spectra.  
triple helix conformation in the range of 216 to 232 nm (_COLRp_1_ and _COLRp_II_) and 218 to 225 nm (_COLRp_III_) with a maximum peak at about 220 nm. This CD profile demonstrated that the native structure and biochemical properties of the extracted collagen protein are well-preserved independently of the extraction methods tested.
# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 3 Experiment Setup and Results
### Results and Discussions
#### 3.3.4 Snapshot Size  
As represented by Equation (18), the snapshot data size depends linearly on the sampling rate and signal duration. The data sizes corresponding to the chosen settings are computed and represented in Table 3 together with the resulting SRTK fix rates when these settings are used.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & **5 km** & **10 km** & **15 km** & **20 km** & **25 km** & **30 km** & **35 km** & **40 km** & **45 km** & **50 km** \\ \hline
**40 ms** & 92.53 & 90.87 & 87.14 & 82.16 & 74.69 & 66.39 & 53.11 & 51.45 & 40.66 & 31.54 \\
**60 ms** & 95.44 & 95.02 & 90.46 & 84.65 & 78.01 & 70.54 & 55.6 & 46.89 & 37.34 & 28.63 \\
**80 ms** & 98.34 & 98.76 & 94.19 & 88.38 & 80.91 & 74.69 & 59.34 & 56.02 & 43.98 & 27.8 \\
**100 ms** & 100 & 99.17 & 96.27 & 90.46 & 84.65 & 76.35 & 63.07 & 62.24 & 48.55 & 32.78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RTK fix rate (in percentage) for different integration times at different baselines.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **5 km** & **10 km** & **15 km** & **20 km** & **25 km** & **30 km** & **35 km** & **40 km** & **45 km** & **50 km** \\ \hline
**40 ms** & 5.92 & 5.56 & 4.99 & 4.35 & 3.69 & 3.06 & 2.51 & 2.38 & 2.03 & 1.73 \\
**60 ms** & 6.58 & 6.18 & 5.49 To show the relationship between the SRTK fix rate and the data size, all their values at 15 km baseline distance have been presented in Figure 10. Note that results from different integration times are represented by different colored markers to show that snapshot data size increases along with the integration time. In general, larger snapshot sizes result in a higher possibility of fixing SRTK ambiguities. However, also notice that the smallest data size used in this research is 127.2 kB and results in a 78.84% fix rate, while the largest data size used is 1590 kB, which is 12.5 times the smallest data size, and brings a fix rate of 96.27%. There is an obvious trade-off to consider between the data size and the expected accuracy. Assuming a minimum acceptable fix rate of 90%, the smallest data size able to achieve this goal in the test scenario is 254.4 kB, by using 12.72 MHz of bandwidth and 40 ms of integration time, for an observed fix rate of 93.36%.