esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Image Locating by Hologram Multiplexing in Off-Axis Digital Holography Display
## 4 Optical Experiments  
A series of optical experiments are carried out to investigate the proposed method further. The optical configuration has been represented in Figure 1. A resolution target (positive version) is used as the object and a laser source provide the illuminating wave of 532 nm wavelength. The recording chip of CCD has a 1536 \(\times\) 1040 array with each pixel size of 6.45 \(\upmu\)m \(\times\) 6.45 \(\upmu\)m. Only the central area of 1024 \(\times\) 1024 pixels is used for recording.  
One of the recorded holograms is shown in Figure 9a where there are many regular fine stripes. FT operation is carried out in Figure 9a, and the spectra distribution is given in Figure 9b. Five bright areas in Figure 9b are negative second order spectra, negative first order spectra, zero order spectra, positive first order spectra, and positive second order spectra from the top corner left to lower right corner consequently. The coordinates of the center of the positive first order spectra are found as \(\mu_{p}\) = 184 pixel and \(v_{p}\) = 89 pixel. Then, the reference tilt angles are calculated by using Equation (7), and the results are about \(\theta_{x}\) = 0.015 rad and \(\theta_{y}\) = 0.007 rad. Reference wave is reconstructed by Equation (8), and another FT operation is carried out by Equation (9) with the spectra distribution in Figure 9c. The whole spectra are shifted in Figure 9c after using the tilt reference wave so that the positive first order spectra are moved to the center of the spectrum plane. After choosing the positive first order spectra, the recovered image is achieved and shown in Figure 9d. The reconstructed image occupied the full field of view without the disturbances of the twin image and zero order term. The fact that the image recovered is displayed at the center of Figure 9d without any human operation shows the locating accuracy of the method here.  
Figure 8: Simulation results for a non-binary object. (**a**) Original image; (**b**) Recovered image by OADHM method.
###### Abstract  
The new generation of Android smartphones is equipped with GNSS chips capable of tracking multi-frequency and multi-constellation data. In this work, we evaluate the positioning performance and analyze the quality of observations collected by three recent smartphones, namely Xiaomi Mi 8, Xiaomi Mi 9, and Huawei P30 pro that take advantage of such chips. The analysis of the GNSS observation quality implies that the commonly employed elevation-dependent function is not optimal for smartphone GNSS observation weighting and suggests an application of the \(C/N_{0}\)-dependent one. Regarding smartphone code signals on L5 and E5a frequency bands, we found that they are characterized with noticeably lower noise as compared to E1 and L1 ones. The single point positioning results confirm an improvement in the performance when the weights are a function of the \(C/N_{0}\)-rather than those dependent on the satellite elevation and that a smartphone positioning with E5a code observations significantly outperforms that with E1 signals. The latter is expressed by a drop of the horizontal RMS from 8.44 m to 3.17 m for Galileo E1 and E5a solutions of Xiaomi Mi 9 P30, respectively. The best positioning accuracy of multi-GNSS single-frequency (L1/E1/B1/G1) solution was obtained by Huawei P30 with a horizontal RMS of 3.24 m. Xiaomi Mi 8 and Xiaomi Mi 9 show a horizontal RMS error of 4.14 m and 4.90 m, respectively.  
GNSS; smartphone; absolute positioning; GNSS noise assessment; Android; Galileo; GPS 2021 15 March 2021 18 March 2021 18 March 2021
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 4 Numerical Results
### Exclusion Zone
#### 4.1.3 The Effects of ES Elevation Angle on Exclusion Zone  
As discussed above, the ES elevation angle has a significant effect on the ES antenna gain, which greatly affects the exclusion zone. Depending on the location of the satellite, the ES antenna may have different tilt angles [3]. It is reported that the elevation angle generally varies from 5-48\({}^{\circ}\)[13]. Figures 18-21 demonstrate the effects of the elevation angle of the ES antenna in the exclusion zone for two typical angles, 15\({}^{\circ}\) and 48\({}^{\circ}\), respectively, as given in [13]. Note that, as the elevation angle increases, the off-boresight angle also increases. This results in lower ES antenna gain toward the north side, causing lower interference and hence a smaller exclusion zone.  
Figure 17: RMa, UMa, and UMi scenarios with an ES antenna elevation angle of 5\({}^{\circ}\) for the conventional omnidirectional antenna.  
Figure 18: RMa, UMa, and UMi scenarios with an ES antenna elevation angle of 15\({}^{\circ}\) for the conventional omnidirectional antenna.
# Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD
###### Abstract  
In recent years, the multi-GNSS positioning application is becoming more and more popular, same to the low Earth orbit (LEO) satellite precise orbit determination (POD) based on the onboard multi-GNSS measurements. The third-generation Beidou navigation satellite system (BDS-3) provides a new option to obtain the LEO satellite orbit solutions. However, the receiver intersystem bias (ISB) of different GNSS is unavoidable in multi-GNSS data processing. This paper's main goal is absorption of the impact of the ISB between BDS-3 and BDS-2 on the LEO satellite POD. Taking GPS-based POD solutions for the reference orbit, this paper evaluates the orbit accuracy of BDS-2-based POD, BDS-3-based POD, BDS-2 and BDS-3 combined PODs with/without ISB. The BDS-3-based POD accuracy is 6.57 cm in the 3D direction, a 56% improvement over BDS-2-based POD. When the ISB between BDS-3 and BDS-2 is estimated, the BDS-2/3 combined POD accuracy of 5.37 cm in the 3D direction is better than that without ISB, which is a 64% improvement over BDS-2-based POD and 18% improvement over BDS-3-based POD. For GPS and BDS-2/3 combined POD, the GPS and BDS-3 joint POD solutions have the smallest RMS differences in overlapping consistency and smallest RMS differences compared to GPS-based POD. This study indicates that estimating the BDS-2/3 receiver ISB in BDS-2/3 joint POD could improve the orbit accuracy, and the GPS and BDS-3 joint POD solution is better than another combined POD. This paper will provide meaningful references for the LEO satellite multi-GNSS-based POD.  
low Earth orbit; precise orbit determination; intersystem bias; BDS-2/3-based POD; GPS and BDS joint POD 2022 20 February 2022  
Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD  
2022 May 2022
###### Abstract  
This study aims to assess the efficiency of caffeine mitigation in an aqueous solution through a combination of adsorption and the Fenton reaction, using granular activated carbon (GAC). The present study also investigates the reduction in the concentration of oxidation byproducts in the solution and the regeneration of the solid. The combined process was conducted in four consecutive cycles using optimal values determined in individual technique studies. For the individual adsorption study, a Box-Behnken design was employed, with varying pH (3 to 11), GAC concentration (1.0 to 10.0 g L\({}^{-1}\)), and contact time (10 to 120 min). In the individual Fenton study, based on a factorial design, concentrations of FeSO\({}_{4}\)-7H\({}_{2}\)O (4 to 20 mg L\({}^{-1}\)) and H\({}_{2}\)O\({}_{2}\) (25 to 150 mg L\({}^{-1}\)) were used at reaction times of 5 and 60 min. GAC was characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), specific area (BET), and pore size (BjH) throughout all stages of experimentation. The outcomes show that the adsorption achieved a 93.4% removal rate under the optimal experimental conditions (natural pH, 65 min, and 10 mg L\({}^{-1}\)) and the Fenton reaction achieved a 98.92% degradation rate at a 37.5 ratio of H\({}_{2}\)O\({}_{2}\)/FeSO\({}_{4}\)-7H\({}_{2}\)O. The combined process also achieved an efficiency of over 95.7% of caffeine removal in four cycles, reducing the Total Organic Carbon (TOC) by more than 47.65% and 20.6% at 5 and 60 min of the Fenton reaction, respectively. Regeneration efficiencies of 99.6%, 91.8%, and 93.8% for the other three evaluated cycles were obtained. These findings suggest that the combined process is a promising solution for the treatment of effluents contaminated with caffeine.  
caffee; adsorption; activated carbon; Fenton; regeneration +
Footnote †: journal: Applied Sciences  
16 July 2024  
Revised: 6 August 2024  
Accepted: 6 August 2024  
Published: 9 August 2024
## 5 Hovering Attitude Control of the Unmanned Helicopter
### Analysis of Manipulator's Operation  
The manipulator of the helicopter is shown in Figure 3, where the coordinate frame for each link is established. The base of the manipulator is fixed on the helicopter's belly, making the manipulator's basic frame \(\{B\}\) overlap with helicopter's center of gravity as much as possible. The frame of the terminal of the manipulator is named \(\{E\}\). The link's coordinate frame \(\{0\}\) equals the manipulator's frame \(\{B\}\), and the link's coordinate frame \(\{3\}\) equals the manipulator's frame \(\{E\}\). \(J_{ntj}(j=1,2,3)\) denotes each of the joints.  
For the \(i^{\text{th}}\) link of the manipulator \((i=0,1,2,3)\), its quality of \(m_{i}\) is assumed, and \(m_{0}=0.58\) kg is the helicopter's mass in the empty state. \(O_{i}C_{i}\in R^{3\times 1}\) denotes each link's center of gravity. The inertia tensor matrix of each link is defined as \(J_{i}=diag\{j_{ix},j_{iy},j_{iz}\}\).  
According to the analysis of the DH parameter method, the initial angle of the three joints of the manipulator are set as \(\theta_{i0}\), and the joint rotation angles are set as \(\theta_{i}\).  
Then, according to the positive kinematic analysis of the manipulator, the translation matrix of \(i^{-1}_{\text{ }}T\in R^{4\times 4}(i=1,2,3)\) can be expressed as  
\[i^{-1}_{\text{ }}T=\begin{bmatrix}i^{-1}R&i^{-1}P\\ 0_{1\times 3}&1\end{bmatrix} \tag{45}\]  
where \(0_{1\times 3}\in R^{1\times 3}\) denotes the zero matrix  
\[OM(\Theta)=\begin{bmatrix}OM_{x}\\ OM_{y}\\ OM_{z}\end{bmatrix}=\frac{1}{m_{t}}\sum_{i=0}^{3}m_{i}\cdot OC_{i} \tag{49}\]  
Thus, taking the initial parameters of Table 1 into Equation (49), the helicopter's mechanical parameters changed by the manipulator's joint angles \(\theta_{i}\) can be expressed as follows:  
\[\begin{bmatrix}D_{lr}\\ H_{tr}\\ H_{mr}\end{bmatrix}=\begin{bmatrix}0.32+OM_{x}\\ 0.012+OM_{z}\\ 0.077+OM_{z}\end{bmatrix} \tag{50}\]  
In addition, the entire inertia tensor matrix of the UAM can be calculated as  
\[I_{t}=I_{0}+\sum_{i=1}^{3}\left({}_{i}^{B}R^{T}\cdot J_{i}\cdot{}_{i}^{B}R+m_{ i}OC_{i}^{*T}\cdot OC_{i}^{*}\right) \tag{51}\]  
where \(m_{t}\) denotes the total mass of the UAM and \(I_{0}=J\) is the inertia tensor matrix of the helicopter itself. In addition,\({}_{i}^{B}R\in R^{3\times 3}\) is the third-order leading principal minor of \({}_{i}^{B}T\). \(OC_{i}^{*}\in R^{3\times 3}\) is the skew symmetric matrix constructed by \(OC_{i\,3\times 1}\), for which the construction principle is as follows.  
The column vector is assumed as \(\zeta=[x,y,z]^{T}\), yielding  
\[\zeta^{*}=\begin{bmatrix}0&-z&y\\ z&0&-x\\ -y&x&0\end{bmatrix} \tag{52}\]  
and for any column vectors \(v\in R^{3\times 1}\), there is the property of \(\zeta\times v=\zeta^{*}\cdot v\).  
The link parameters of the UAM are shown in Table 3.
# Autonomous Operation Method of Multi-DOF Robotic Arm Based on Binocular Vision
## 2 Materials and Methods  
### Binocular Vision Positioning Principle  
The target-grabbing object is spatially positioned using a parallel binocular camera. The positioning principle is shown in Figure 1.  
In the picture, the coordinate system \(O_{1}X_{1}Y_{1}Z_{1}\) is the left camera coordinate system; \(P\) is a point in the left camera coordinate system with coordinates (\(X_{1}\), \(Y_{1}\), \(Z_{1}\)); \(O_{2}X_{2}Y_{2}Z_{2}\) is the right camera coordinate system; \(E_{1}\) and \(E_{2}\) are the left and right camera imaging planes, respectively; \(T\) is called the baseline distance, which represents the distance between the optical centers of the two cameras; the distance from the optical center to the imaging plane is the camera focal length, indicated by the symbol \(f\); and \(P_{1}\) and \(P_{2}\) are the image points of the \(P\) on the imaging planes \(E_{1}\) and \(E_{2}\), respectively. The coordinates of \(P_{1}\) in the left image coordinate system are (\(u_{1}\), \(v_{1}\)), and the coordinates of \(P_{2}\) in the right image coordinate system are (\(u_{2}\), \(v_{2}\)). In accordance with the corresponding geometric relationship, the coordinate of the \(P\) point in the left camera coordinate system are as in Equation (1); the parameters \(u_{0}\), \(v_{0}\),\(f_{x}\),\(f_{y}\), and \(T\) are determined via camera calibration methods.  
\[\left\{\begin{array}{l}X_{c}=\frac{T(u_{1}-u_{0})}{u_{1}-u_{2}}\\ Y_{c}=\frac{T_{f_{x}}(v_{1}-v_{0})}{f_{y}(u_{1}-u_{2})}\\ Z_{c}=\frac{T_{f_{x}}}{u_{1}-u_{2}}\end{array}\right. \tag{1}\]
# Well-Separated Pair Decompositions for High-Dimensional Datasets
## 4 Experiments
### Results
#### 4.3.1 Synthetic Datasets  
In Figure 3, the experiments were performed with the synthetic datasets. Only for \(d=2\) was the standard WSPD algorithm used, and for \(d>2\), NN-WSPD was used. On the boxplots in the left column, NN-WSPD was applied but without the refine step (Algorithm 1). From this one can see that many of the reconstructed dumbbells can violate the quality given by Equation (5), which the dumbbells are supposed to guarantee. However, most dumbbells are still well separated since a lot of valuable information for a WSPD was preserved by the nonlinear mapper \(f_{0}\). The results of the refine step (Algorithm 1) can be seen in boxplots in the right column in Figure 3. Note that after the refine step all dumbbells are indeed dumbbells (i.e., well separated), with the overall number of dumbbells that rarely gets larger than the starting WSPD size multiplied by a factor of at most three, independent of the dimension \(d\). We noticed that the higher the dimension \(d\) of the input set \(S\), the fewer refined dumbbells are needed. This is not that surprising bearing in mind the fact that when a Euclidean distance is defined using many coordinates there is less difference in the distances between different pairs of samples (see the curse of dimensionality phenomenon [21]).  
Figure 2: In both figures, the \(x\)-coordinate stands for the dimension, while the \(y\) coordinate stands for the number of dumbbells divided by the size of all pairwise distances, i.e., \(n(n-1)/2\). (**left**) Size of the dataset is \(n=5000\). Dumbbells are computed using the WSPD algorithm proposed in [1], and the size of a WSPD (i.e., the number of dumbbells) is reported. (**right**) The number of dumbbells stays constant with the NN-WSPD approach even when the dimension grows.  
Figure 3: The boxplots show on the vertical axes the bounds given by Equation (5), i.e., \(1+4/s\), for different datasets. Note that for dimension 2 a WSPD is computed directly on the input dataset.  
(leftmost boxplot in each image). For all other dimensions greater than two, autoencoder (AE) and mMDS neural network are used to map the input points to dimension two, and the bound \(1+4/s\) is shown for the reconstructed dumbbells in the original dimension. All boxplots on the right show the bound on the reconstructed dumbbells after the refining step. The blue line drawn on the plots in the right column shows the multiplicative increase in dumbbells before and after the refining stage.
## 3 Results
### Antibiotic Mim Activity
#### 3.2.2 Biofilm Inhibition and Destruction  
The ability of sildenafil to inhibit biofilm formation and destroy the formed biofilm was recorded (Figures 2 and 3). The percentage values above each bar represent the reduction in total biofilm biomass relative to the positive control for biofilm formation. Sildenal citrate was found to effectively inhibit biofilm formation at all tested concentrations (3.12-0.78 mg/mL). Notably, Figure 2G shows that at a sub-inhibitory concentration--half of the MIC (\({}^{{}^{\prime}}\)2 MIC, 1.56 mg/mL)--sildenafil citrate inhibited PA01 biofilm formation by 33.33%. Similar efficacy was observed in the clinical isolates (Figure 2A-F), with Figure 2C highlighting an inhibition rate of 87.17% at a reduced drug concentration.  
Figure 1: Biofilm formation profile of reference strain (PA01) and clinical isolates after 24 h (**A**) and 48 h (**B**). An analysis of variance followed by [PERSON]’s test was used, considering \(p\)-values \(<\) 0.05 to be statistically significant (***) when compared with the PA01 strain group. The study was performed in three replicates.  
Figure 2: Biofilm inhibition results. (**A**) 4 PAUR, (**B**) 6 PAST, (**C**) 7 PAES, (**D**) 8 PAUR, (**E**) 9 PAUR, (**F**) 10 PAUR, and (**G**) PA01, with C- representing the negative control and C+ the positive control. The percentages above each bar represent the reduction in total biofilm biomass compared to the positive biofilm formation control. Analysis of variance (ANOVA) followed by [PERSON]’s test was conducted, considering \(p\)\(<\) 0.05 as statistically significant (***), specifically when compared with the positive control (C+) group. The study was performed in three replicates.  
The results regarding biofilm destruction indicate that sildenafil can disrupt the protective matrix, which enables microorganisms to survive in adverse environments, thus hindering the treatment of biofilm-related infections [8,9]. Figure 3K illustrates that sildenafil citrate eliminated 46.87% of the biofilm at a sub-inhibitory concentration. For the isolates, the percentage of biofilm destruction at the MIC varied between 83.81 and 43.31%. Notably, the sample depicted in Figure 3G exhibited the highest rates of destruction--83.81% at the MIC, 73.38% at \({}^{1}/_{2}\) MIC, and 52.89% at \({}^{1}/_{4}\) MIC.
## 2 Related Works
### Geographic Routing  
Geographical routing utilizes location information for path establishment between source and destination. This scheme uses geographic position information to send packet towards the closer  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbols** & **Description** \\ \hline \(C_{n}\) & Number of logical cubes \\ \(N_{n}\) & Total nodes deployed \\ \(S_{s}\) & A set of sonobuoys \\ \(\Lambda\) & Flag to indicate that latest neighbor information \\ \(F_{sel}\) & Potential forwarder nodes set \\ CC & Current cube \\ NC & Neighbor cube \\ \(TC\) & Target cube \\ \(P_{BER}\) & Bit error rate probability \\ \(P_{CR}\) & Probability of collision rate \\ \(ADV\) & Advancement towards destination \\ \(T_{proc}\) & Processing time at each node \\ \(T_{p}\) & Propagation delay \\ \(NADV\) & Normalized advancement towards destination \\ \(MS_{n}\) & Number of mobile sonobuoys deployed in the network \\ \(DFM\) & Data forwarding \\ \(ET_{X}\) & Transmission energy \\ \(R_{RX}\) & Receiving energy \\ \(D_{lj}\) & Straight line distance from node \(i\) to node \(j\) \\ \(E_{consumed}\) & Energy consumed which includes transmission and reception energies \\ \(DR\) & Data rate \\ \(E_{DA}\) & Energy dissipated in data aggregation \\ \(R_{c}\) & Communication range of a node \\ \(P_{TX}\) & Transmission power for transmission data packet \\ \(P_{RX}\) & A power required to receive a data packet \\ \(PDR\) & Packet delivery ratio \\ \(E_{th}\) & The residual energy threshold which must be greater or equal than residual energies \\ \(D_{DT}\) & Delay occur in transmitting data packet directly \\ \(D_{MHT}\) & Delay occur in delivering data packet through multiple hops \\ \(T_{hold}\) & Time required to hold a packet \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclature.  
destination from each hop till packet reaches the sink. Unlike the proactive routing that bears large overhead to maintain full path, geographic paradigm relies on one or two hop information for routing data packets. This feature enhances scalability of large sensor networks. In geographic routing, services like geocasting can be used to get geographic information for data forwarding within a geographic region [12]. Considering geographic information, we categorize the existing protocols and schemes into two hierarchies: sender-based and receiver-based underwater routing protocols are tabulated in Table 2. These hierarchies are further divided into two categories based on information type: either location information or depth information as shown in Figure 1.
## 4 Experiment
### Impact of the Negative Sampling Ratio (RQ2)  
We explore the effect of negative sampling ratio \(\rho\) on the accuracy of DACR recommendation results with extensive experiments, that is, the influence of the number of sampled un-interacted instances corresponding to each real interaction instance on the experimental results. The experimental results of different negative sampling ratios are tested on the Lastfm dataset. Figure 3 shows the experimental results.  
From Figure 3, we first observe that when the value of \(\rho\) is set to 3, HR@10 achieves maximum performance, and when \(\rho\) is equal to 6, NDCG@10 achieves maximum performance. Increasing the negative sampling ratio of the instance will enhance the predictive performance of the DACR. However, the quantity of model parameters is also increased, resulting in a substantial increase in training time. This will reduce the prediction performance [13]. In summary, we believe that the best negative sampling ratio should be between 3-6, which can not only guarantee the prediction and recommendation results of the model but also reduce the parameters that the model needs to train, thereby saving training time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Statistics**} & \multicolumn{2}{c}{**ml-1m**} & \multicolumn{2}{c}{**Lastfm**} & \multicolumn{2}{c}{**AMusic**} \\ \cline{2-7}  & **HR@10** & **NDCG@10** & **HR@10** & **NDCG@10** & **HR@10** & **NDCG@10** \\ \hline ItemPop & 0.4535 & 0.2542 & 0.6628 & 0.3862 & 0.2483 & 0.1304 \\ ItemKNN & 0.6624 & 0.3905 & 0.8673 & 0.5617 & 0.3510 & 0.1989 \\ BPR & 0.6725 & 0.3908 & 0.6249 & 0.3466 & - & - \\ DMF & 0.6565 & 0.3761 & 0.8702 & 0.5804 & 0.3744 & 0.2149 \\ DeepCF & 0.7220 & 0.4378 & 0.8995 & 0.6186 & 0.4116 & **0.2601** \\ DeepUCF+a & 0.7264 & 0.4420 & 0.9014 & 0.6193 & - & - \\ Ours(DACR) & **0.7352** & **0.4485** & **0.9133** & **0.6308** & **0.4189** & 0.2564 \\ Improvement & 0.88\% & 0.65\% & 1.19\% & 1.15\% & 0.73\% & \(-\)0.37\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of evaluation for different models.  
Figure 3: The effect of \(\rho\) on Lastfm.