esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Adaptive Rotational Convolution Module
#### 3.2.1 Motivation and Overview  
In the context of oriented object detection, objects in aerial imagery often appear at arbitrary angles, making it essential for the convolutional layers to be sensitive to these orientations. The ARC module introduces rotation-invariant convolutional kernels that adapt to the dominant orientation within each receptive field, enabling the model to extract more discriminative features regardless of the object orientation.  
The core idea behind the ARC module is to dynamically rotate the convolutional kernels based on the orientation of the local features detected in the input. This rotation is performed in real-time during the forward pass, ensuring that the feature extraction process is always aligned with the orientation of the objects.
## 4 Technique Validation and Discussion
### TEC Time Derivative  
Figure 11 shows the results of simulating the algorithm operation to recover the TEC time derivative. We obtained TEC time derivatives when solving the consistent set of equations, whose results are presented in Figures 6 and 9. The designations are the same as those in Figure 6. Similar to the results for the absolute vertical TEC (Figure 6), there is agreement of both general dynamics and the quantitative values of the TEC time derivative. This indicates that such data may be used to efficiently predict the vertical TEC in the station region.  
Figure 11: Simulating the TEC time derivative recovery for IRKJ (**a**), NTUS (**b**), and THU2 (**c**) stations. The black dotted line presents the results of the TuRBOTEC operation, the red line exhibits the IRI-2012 TEC time derivative data, and the blue line shows the difference between the results of the TuRBOTEC operation and those from the IRI-2012 model. The green line shows the similar differences, but for the IRI-plas simulation. The axis for the blue and green lines are on the right, for the red and black lines on the left.  
Figure 10: Errors in TEC estimations at different UT for various distances from IRKJ. \(\Delta\)I\({}_{\text{V}}\), is the difference between the model-specified values and the model-recovered data, panel (**a**) and panel (**b**) are for the distance from the station by latitude and longitude, respectively. The IRI-2012 simulation as of 10 April 2012 for the IRKJ geometry.  
Figure 12 presents the results of the TEC time derivative experimental recovery through the TuRBOTEC algorithm from the IRKJ data (red solid curve) and the GIM-obtained time derivative from CODE (black dotted line), from JPL (blue dot-and-dash line) for 5 March 2015, \(Kp_{max}=2.3\) (left panel), and for 17 March 2015, \(Kp_{max}=7.7\) (right panel). Like for the absolute vertical TEC data (Figure 7), there is qualitative and quantitative agreement with the GIM data, the divergence values from other laboratories are within the divergence interval of different laboratories among themselves.
## 4 Adaptive Target Tracking Algorithm Based on EKF  
For the application scenarios in this paper, the forward speed and heading of MRS are unknown. The noise statistics of forward speed and heading of AUV and MRS are unknown. It is assumed that the measurement noise statistics are Gaussian and known. The adaptive TT-EKF is applied to simultaneously estimate the position of AUV and MRS and the horizontal and vertical direction of the velocity components of MRS with unknown PNCM.  
The structure of proposed adaptive target tracking algorithm is shown in Figure 4. In the algorithm, Variational Bayesian algorithm is used to estimate the PNCM. A multilayer neural network which acts as a feedforward error compensator is embedded in target tracking system to deal with the prediction errors caused by the noisy input vector.
## 3 Parameter Determination Method for the JH-2 Model
### Determination of Parameters Concerned with HEL  
In the JH-2 constitutive model, \(HEL\) is an important concept throughout the whole computational process. Before determining the parameters of the JH-2 model, it should be noted that the normalizedparameters are all derived based on the constants concerned with \(HEL\), such as \(\sigma_{HEL}\) and \(P_{HEL}\) introduced above. Therefore, it is the primary task to obtain the values of \(HEL\), \(\sigma_{HEL}\), and \(P_{HEL}\). \(HEL\) is usually measured from the mechanical response of rock materials determined using flyer plate impact experiments using a light gas gun [38] or explosives [39]. With regard to granitic rock, [PERSON] et al. [40] proposed that the Hugoniot properties of Atlanta granite are similar to those of dolerite, the \(HEL\) value of which is around 4-5 GPa through a series of plate impact experiments. [PERSON] [41] determined a specific average value of \(HEL\) of 4.5 GPa for granite, which has been extensively adopted in recent dynamics research [26; 29; 30]. Therefore,\(HEL=4.5\) GPa is considered as a reliable experimental parameter used in the simulation in this study.  
For brittle materials, the relationship between the equivalent stress, \(\sigma_{HEL}\), and the hydrostatic pressure, \(P_{HEL}\), at the \(HEL\)[42] is given by  
\[P_{HEL}=\frac{V_{P}^{2}}{V_{S}^{2}}\frac{\sigma_{HEL}}{2},\]  
then  
\[P_{HEL}=1.246\sigma_{HEL} \tag{12}\]  
where \(V_{P}\) is the longitudinal wave velocity and \(V_{S}\) is the shear wave velocity.  
The expression of \(HEL\) is given as follows according to [3]  
\[HEL=K1\cdot\mu_{HEL}+K2\cdot\mu_{HEL}^{2}+K3\cdot\mu_{HEL}^{3}+\frac{4}{3}G \bigg{(}\frac{\mu_{HEL}}{1+\mu_{HEL}}\bigg{)}=P_{HEL}+\Delta P \tag{13}\]  
As can be obtained from Equation (13), \(\Delta P=\frac{4}{3}G\bigg{(}\frac{\mu_{HEL}}{1+\mu_{HEL}}\bigg{)}\) is the increased potential internal energy converted from the internal elastic energy, which is due to the decreased deviatoric stress. Then \(P_{HEL}\) can be expressed as  
\[P_{HEL}=HEL-\frac{4}{3}G\bigg{(}\frac{\mu_{HEL}}{1+\mu_{HEL}}\bigg{)} \tag{14}\]  
Therefore, the deviatoric strength component is \(S_{z}=-(HEL-P_{HEL})\). The sum of the three deviatoric stresses must be zero by the definition \(S_{x}+S_{y}+S_{z}=0\), and hence \(S_{x}=S_{y}=-S_{z}/2\). Substituting the normal stresses into Equation (2) gives  
\[\sigma_{HEL}=\frac{3}{2}(HEL-P_{HEL}) \tag{15}\]  
Combining Equation (12) with Equation (15) gives  
\[1.246\sigma_{HEL}+\frac{2}{3}\sigma_{HEL}=HEL \tag{16}\]  
Then \(\sigma_{HEL}=2.35\) GPa and \(P_{HEL}=2.93\) GPa. Substituting the stress, pressure, and shear modulus into Equation (14) gives \(\mu_{HEL}=0.0567\), where the shear modulus \(G\) has been computed based on the previous wave velocity measurement, which can also be computed from \(E\) and \(\
u\) through \(K1=E/3(1-2\
u)\) and \(G=E/2(1+\
u)\).
# Bearing Severity Fault Evaluation Using Contour Maps--Case Study
## 2 Method
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Nomenclature** & **Description** \\ \hline \(Fn\) & Fault levels \\ \(Rn\) & Shaft rotational speed \\ \(R_{Fn}\) & Record for each fault level in the time domain \\ \(E(R_{Fn})\) & Envelope spectrum of the record of each fault level \\ \(l\) & Sample time length in seconds \\ \(N_{Turns}\) & Number of shaft turns \\ \(w\) & Constant angular velocity \\ \(NR\) & New record to be diagnosed in the time domain \\ \(E(NR)\) & Envelope spectrum of the record to be diagnosed \\ \(F_{r}\) & Frequency of shaft rotation \\ \(N_{b}\) & Number of rolling elements of the bearing \\ \(S_{low}\) & Lower limit of the analysis section \\ \(S_{up}\) & Upper limit of the analysis section \\ \(X\) & Sample of data set under analysis \\ N & Length of the sample under analysis \\ \(R_{a}\) & Range of analysis \\ \(N_{int}\) & Number of intervals \\ \(X_{i}\) & Mean value of the amplitudes of the analysed sample \\ \(I\) & Width between intervals \\ \(f_{i}\) & Number of components in each interval \\ \(\overline{x}\) & Arithmetic mean of the distribution of amplitudes \\ \(X_{2}\) & Significant amplitude data set \\ \(N_{2}\) & Length of the significant amplitude data set \\ \(N_{iso}\) & Number of isolines \\ \(k\) & Multiple of the spacing value \\ \(BPFO\) & Ball Pass Frequency Outer \\ \(BPFI\) & Ball Pass Frequency Inner \\ \(BSF\) & Ball Spin Frequency \\ \(FTF\) & Fundamental Train Frequency \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The nomenclature used in the construction of contour maps and bearing diagnostics. Figure 1: Research Method. The literature has emphasised the importance of identifying frequency bands of a signal, with the presence of transient events associated with the bearing condition. However, in this study, we intended to analyse the behaviour of the footprint of the evolution of a fault, according to the frequency bands proposed in [16]. Therefore, various contour maps are made for the selected frequency bands. For each frequency band, several contour maps will be built around selected frequencies. In each contour map, the main fault frequencies and their most representative harmonics are displayed, with a range of \(\pm 1\) Hz. In this way, several contour maps are used to classify future measurements of the study machine and, thus, identify the state of fault that the bearing is in. Contour maps that present a unique relationship between frequency, amplitude and failure will allow for better future classification of the condition of this bearing throughout its useful life. The following sections provide more clarification about key parts of the described method.
## 5 Developments in Improving Biodiesel Production from Animal Fats  
China has been the most active country in publishing patents in the period 1999-2018 with 647 patents on biodiesel. The US had 266 patents, with more than 50% of them focused on reactors technology and processing methods [11]. There has also been patenting activity on pretreatment methods as well as on catalysts for improving the transesterification process. Specific examples of patents for biodiesel production from animal fat waste are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & **EN14214** & \begin{tabular}{c} **Poultry** \\ **Fat** \\ \end{tabular} & \begin{tabular}{c} **Poultry** \\ **Fat** \\ \end{tabular} & \begin{tabular}{c} **Pickken** \\ **Fat** \\ \end{tabular} & \begin{tabular}{c} **Chicken** \\ **Fat** \\ \end{tabular} & \begin{tabular}{c} **Chicken** \\ **Fat** \\ \end{tabular} & \begin{tabular}{c} **Chicken** \\ **Fat** \\ \end{tabular} & \begin{tabular}{c} **Park** \\ **Lard** \\ \end{tabular} & \begin{tabular}{c} **Mutton** \\ **Tallow** \\ \end{tabular} & \
\begin{tabular}{c} **Beef** \\ **Tallow** \\ \end{tabular} \\ \hline Density at 15 \({}^{\circ}\)C (kg/m\({}^{3}\)) & 860–900 & - & 877 & 830 & 870 & 883 & 873 & 870 & 856 & - & 870 \\ Viscosity at 40 \({}^{\circ}\)C (mm\({}^{2}\)/s) & 3.50–5.00 & 4.50 & 6.86 & 3.5 & 5.4 & 4.94 & 5.08 & 4.74 & - & 4.82 & 5.35 \\ Cetane number & \textgreater{}51.0 & - & - & 50 & 58.4 & - & - & 56.9 & 59.0 & - & - \\ Acid value (mg KOH/g) & \textless{}0.50 & 0.044 & 0.55 & - & 0.8 & 0.22 & 0.22 & 0.23 & 0.65 & 0.147 & 0.20 \\ Iodine value (g/100 g) & \textless{}120 & - & 78.8 & - & - & - & 75.6 & 66.7 & 126 & - & 44.4 \\ Water content (mg/kg) & & & 1201 & & & 200 & 184 & 500 & 200 & & 374 \\ Flash point (\({}^{\circ}\)C) & \textgreater{}120 & \textgreater{}160 & 172 & 50 & 174 & 171.8 & 147 & 175 & - & \textgreater{}160 & 171 \\ Pour point (\({}^{\circ}\)C) & - & - & - & -6 & - & -6 & - & - & -5 & - & 10 \\ Cloud point (\({}^{\circ}\)C) & - & 6.1 & - & - & - & -5 & - & - & -4 & 16 & - \\ Free glycerin (\%) & - & 0.002 & - & - & - & 0.19 & - & - & - & 0.008 & - \\ Cold filter plugging point (\({}^{\circ}\)C) & 8 & 2 & 3 & - & - & - & 5 &
\begin{tabular}{c} -20 \\ to 5 \\ \end{tabular} & - & 14 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Properties of biodiesel produced from animal fat waste.
# Multiplexing Perfect Optical Vortex for Holographic Data Storage
## 4 Discussion
### Effect of Multiplexing Number on BER  
Figure 7a demonstrates the influence of varying the multiplexing number (\(K\)) on the quality of the reconstructed data. As the multiplexing number increases, there is a slight enhancement in the quality of the reconstructed data when utilizing perfect optical vortex (POV) compared to conventional optical vortex (OV). However, it should be noted that both POV and OV are subject to the effects of the multiplexing number.  
Furthermore, the relationship between the multiplexing number and storage capacity is investigated. An experiment was conducted under the same experimental conditions, and the receiving area of the CCD camera remained constant. Figure 7b presents the results of this experiment, illustrating the relationship between the multiplexing number and the storage capacity of both conventional OV and POV methods. At the initial stage of the experiment, with a small multiplexing number, OV has an advantage in terms of storage capacity. This is because OV can utilize smaller topological charges to encode data, allowing for a higher density of data storage. However, as the multiplexing number increases, the storage capacity of OV reaches its limit, requiring the use of larger topological charges to maintain the data density. On the other hand, POV demonstrates its advantage in increasing storage capacity as the multiplexing number increases. With the ability to efficiently use topological charges, POV can store more data within the same storage area compared to OV. For instance, when the multiplexing number is set to 14 (\(K=14\)), the storage capacity of OV is 480 bits, while the storage capacity of POV is 780 bits. This demonstrates that POV achieves a significant increase of up to 50% compared to OV in terms of storage capacity. This significant improvement implies that employing POV in multiplexing holographic data storage enables a larger number of data pages to be stored within a given storage area. As a result, the utilization of POV offers the potential to enhance storage capacity and efficiency, offering a more compact and efficient solution for data storage.
## 2 Problem Formulation
### Platoon Control Problem Formulation  
In this paper, the information flow topology of predecessor following is considered as shown in Figure 1. Each vehicle adjusts its driving status according to its predecessor to accomplish the goals of the platoon. This paper utilizes the CTH (constant time headway) spacing policy, and the configuration of the vehicle platoon system under this policy is depicted in Figure 2.  
The spacing error \(e_{i}(t)\) is defined as  
\[e_{i}(t)=d_{i}(t)-{d_{i}}^{*}(t),i\in\{1,\ldots,N\} \tag{6}\]  
where \(d_{i}(t)\) represents the relative spacing between the \(i\)th vehicle and its predecessor. The desired spacing is specified by \(d_{i}^{*}(t)=h_{i}v_{i}(t)+\delta_{i}\), where \(\delta_{i}>0\) and \(h_{i}>0\) denote the standstill spacing and time headway, respectively. The velocity error of the \(i\)th vehicle is  
\[e_{v_{i}}(t)=v_{i-1}(t)-v_{i}(t),i\in\{1,\ldots,N\} \tag{7}\]  
To ensure that the vehicle keeps a desired spacing from the preceding vehicle and achieves velocity synchronization with the leader, the control goals can be formally described as follows:  
1. Each vehicle keeps the predetermined inter-vehicle spacing and achieves velocity synchronization with the leader vehicle;
2. The matched and mismatched disturbances of the vehicle platoon are estimated by utilizing the developed FTDO without requiring to know the exact upper bounds of the disturbances;
3. An FTDO-based sliding mode control scheme is proposed to compensate for the adverse influences caused by matched and mismatched disturbances and to ensure the vehicle platoon's stability.
# Creep Behavior of Rocks and Its Application to the Long-Term Stability of Deep Rock Tunnels
## 7 On the Applicability of Constitutive Models of Rock Creep to Deep Tunnels
### Isothermal Conditions  
Most laboratory experiments on rocks are carried out under isothermal conditions and the results are used to establish constitutive creep equations. However, throughout the life of a deep tunnel, the host rocks are confronted with often considerable temperature fluctuations. Temperature rise is one of the most adverse conditions typically faced by deep rocky environments [156; 173]. Indeed, geothermal gradient cannot be ignored in deep rocky media. Generally, the temperature increases by 3 \({}^{\circ}\)C/100 m depth [156]. However, at the same depths, it can be varied in different rock formations [156]. For example, located at a depth of 1347 m, the host rocks of the Sangzhuling tunnel (Lhasa-Linzhi Railway) face temperature variations, the highest of which is 89.9 \({}^{\circ}\)C, which seriously affects the longevity of its structures [173]. As already expressed (Table 2), temperature changes have considerable effects on rock creep. Moreover, the coupling effects of temperature gradients and the evolution of damage are very unfavorable for maintaining the long-term stability of underground structures. Therefore, it is crucial to expose rock specimens to strong temperature gradients for a long period of time in order to adequately interpret their behavior. Thus, isothermal conditions cannot accurately reflect the actual behavior of deep rocks. This is another reason why constitutive creep models established under such conditions cannot be applied as they stand to ensure the long-term stability of deep rock tunnels.
## 5 Simulation Results
### Probability Results  
As depicted in Figure 4, it is apparent that approximately 28 percent of the NEO satellites are located within the uplink exclusion region, whilst approximately another 16.5 percent fall under the joint uplink and downlink exclusion zone. It is notable that the likelihood of occupying this exclusion area remains relatively consistent. Moreover, it can be observed that approximately 16.5 percent of the NGEO satellites have the capability of utilizing accessible uplinks within the inline interference avoidance area. This proportion remains stable despite an increase in the number of satellites.  
As illustrated in Figure 5, the quantity of decoupled access UTs attains its peak value at \(\rho_{L}/\rho_{H}=1.5\), subsequently diminishing gradually as \(\rho_{L}/\rho_{H}\) escalates. This is due to the downlink SINR of the \(L\)-th layer satellite progressively surpasses that of the \(H\)-th layer satellites; hence, the ratio of case 1 augments.  
Figure 6 accentuates that the quantity of coupled access UTs enhances with augmenting \(P_{H}\). Consequently, if the \(H\)-th layer satellites possess elevated transmission power, the likelihood of downlink and uplink decoupling tends to escalate. We discern from this figure that the probability of case 3 will not exhibit substantial growth when \(P_{H}>26\) dBW.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Values** \\ \hline Carrier frequency for uplink & 30 GHz \\ Carrier frequency for downlink & 20 GHz \\ Signal bandwidth & 10 MHz \\ Maximum antenna gain of the GEO satellite & 47 dBi \\ Maximum antenna gain of the GEO UT & 41 dBi \\ Maximum antenna gain of NGEO satellites & 41 dBi \\ The mean number of the NGEO satellites (\(\rho_{H}\cdot\theta_{\text{max}}\)) & 40 (H-th tier) \\ EIRP of the NGEO UTs & 54 dBW \\ Maximum antenna gain of the NGEO UTs & 37 dBi \\ The mean number of NGEO UTs (\(\rho_{E}\cdot\theta_{\text{max}}\)) & 100 \\ Exclusion angle for uplink (\(\alpha_{U}\)) & 3\({}^{\circ}\) \\ Exclusion angle for downlink (\(\alpha_{D}\)) & 8\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 4: Probability of satellite locations.  
Figure 5: Association probability with \(P_{H}=23\) dBW and \(P_{L}=20\) dBW.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 3 Methodology
### Storage Methods of the BuildingTree
#### 3.3.2 Generation of the CLOD BuildingTree Model  
The BuildingTree structure combines the generated LOD3 ES and CLOD models. In the BuildingTree, the metadata is used to determine which of the LOD3 ES or CLOD models should be visualized based on the stored height, area (defined as projected area on the ground) and location (building centroid) and the metadata are used in dynamic visualization to determine how many parts from one building (in CLOD model) should be loaded into the client browser from the server. This will reduce the loading time and reduce network traffic which is important in a mobile situation. Furthermore, the metadata store pointers to the LOD3 ES and CLOD models.  
The leaves of the BuildingTree are defined below. The attribute values are stored in normal text and the pointers to other leaves in Roman style, e.g., _lod3 es_ and _clod_ are references to the LOD3 ES and CLOD.  
BuildingTree root = {id, _lod3 es_, _clod_, _Metadata_}  
_lod3 es = {building_id, polygon set of building exterior shell}  
_clod = {building_id, CSG1, CSG2... CSGn}  
_Metadata =_ {location, area, height, type, _lod3 es_info_, _clod_info_}  
_lod3 es_info_ = {size, number_of_polygons}  
_clod_info_ = {number_of_CSGs}  
_CSGi =_ {area, height, type, scale, centroid, rotation}  
The metadata containing the root of the BuildingTree is actually an index that can be used to determine how many details should be loaded in the visualization. The CSG are stored in the CLOD as a tree structure. In our implementation, a building usually has less than 5 CSG parts which are ordered by their volume (area*height). The size of the BuildingTree root is small compared to the models, therefore, we could load all the roots in a nearby area, based on which of the 3D data of LOD3 ES or CLOD can be dynamically loaded.