esa-sceva/satcom-chunk-collection huggingface dataset:
# MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes
## 4 Experimental Results and Analysis
### Benchmarking Datasets Test  
Then, datasets from the KITTI odometry benchmark [29] are used to evaluated MIM_SLAM. The datasets take advantage of the autonomous driving platform Annieway to develop novel challenging real-world computer vision benchmarks. The autonomous driving platform is equipped with a 360\({}^{\circ}\) Velodyne HDL-64E laser scanner and two high-resolution color and grayscale video cameras. Accurate ground truth is provided by a GPS localization system. We selected three typical types of environments: \"urban\" with building around (sequence 07), \"country\" on small roads with vegetation in this scene (sequence 03), and \"highway\" where roads are wide and lower dynamic (sequence 06).  
1. Sequence 03: This dataset is designed to verify that MIM_SLAM can achieve a low drift pose estimation in sparse vegetation environment. The mapping result is shown in Figure 9(a) and the trajectory and the ground truth are shown in Figure 9(a). Due to the scarce stable features in this scene, there is a bit of position deviation after 420 m of traveling compared with the ground truth in Figure 9(a). To evaluate the pose estimation accuracy, we use the evaluation method in the KITTI odometry benchmark which calculated the translational and rotational errors for all possible subsequences of length (100, 200,..., 800) meters. As is shown in Figure 11(a), both LOAM and MIM_SLAM achieve lower drift values compared with the standard ICP method, and MIM_SLAM is slightly worse than LOAM.
2. Sequence 06: We use this dataset to verify that MIM_SLAM can effectively process the data association of the closed-loop area shown in Figures 11 and 11. From Figure 11(b), it can be seen that the translational and rotational errors are lower compared to the standard ICP, and slightly better than LOAM.
3. Sequence 07: This urban road scene is highly dynamic and large-scale. The vehicle travels approximately 660 m at a speed of 6.2 m/s. The mapping result is shown in Figure 9(c) and the trajectory and the ground truth are shown in Figure 9(c). Intuitively, the details of the point cloud map are clear. The estimated pose and the ground truth are almost overlapping. The pose estimation error is shown in Figure 11(c) quantitatively.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Standard ICP** & **LOAM** & **MIM_SLAM** \\ \hline \multicolumn{4}{c}{Processing Time (unit: s)} \\ A & 0.0405 & 0.1036 & 0.0886 \\ B & 0.0416 & 0.1067 & 0.0932 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average time consumption per frame for different approaches/scenes.  
Figure 10: The mapping results in the three sequences.
## 4 Experimental Results
### Calculation of MMW Power Incident on GDD Cross Section  
The initial alignment of the quasi-optical setup was carried out using a laser source that was placed instead of the MMW source. Further alignments were performed by adjusting the position of quasi-optical dishes and the GDD in order to receive a stronger MMW signal. The MMW source horn antenna was located at the focal point of the OPM. The Gaussian beam approximation was used to calculate the beam waist (Equation (5)), and the total power collected by the GDD cross-section is calculated in Equation (6).  
\[\omega(z)=\omega_{0}\sqrt{1+\left(\frac{z}{z_{R}}\right)} \tag{5}\]  
where \(z_{R}=\frac{\pi\omega_{0}}{\lambda}\)  
\[P=P_{0}\left(1\ -\ e^{-\ \frac{D^{2}}{2\omega(z)^{2}}}\right) \tag{6}\]  
where \(\omega(z)\) is beam waist, \(z\) is the distance of propagation, \(\lambda\) is the MMW wavelength, \(Z_{R}\) is the Rayleigh range of a Gaussian beam, \(P_{0}\) is the total beam power emitted from the MMW source, and \(D\) is the diameter of the GDD.  
According to VDI's nominal horn specifications, the MMW beam waist radius refers to the input of the conical horn antenna and is fixed by that geometry to \(\omega_{0}=6.2\) mm. The MMW source was placed in the focal point of the OPM and the GDD was placed at the focal point of the convex lens. The approximate beam waist can be calculated for a given position in the quasi-optical setup, using the transformation of the complex beam parameter q\({}_{1}\) at the output of the MMW source (\(W\) = 6.2 mm and \(R=\infty\)) by the ABCD  
Figure 10: The effect of the 550 nm long pass filter on the GDD detector at full MMW source output power.  
transfer matrix for paraxial rays. The complex beam parameter \(q_{2}\) can then be calculated at that given position using Equation (4). Knowing the complex beam parameter \(q_{2}\) can allow the calculation of the beam waist (\(W\)) and the beam radius of curvature (\(R\)) at that given position using Equation (5).  
\[\left[\begin{array}{cc}A&B\\ C&D\end{array}\right]=\left[\begin{array}{cc}1&d_{3}\\ 0&1\end{array}\right]\left[\begin{array}{cc}1&0\\ -1/F_{\text{Lens}}&1\end{array}\right]\left[\begin{array}{cc}1&d_{2}\\ 0&1\end{array}\right]\left[\begin{array}{cc}1&0\\ -1/F_{\text{OPM}}&1\end{array}\right]\left[\begin{array}{cc}1&d_{1}\\ 0&1\end{array}\right] \tag{7}\]  
\[q_{2}=\frac{Aq_{1}+B}{Cq_{1}+D} \tag{8}\]  
\[\frac{1}{q_{2}}=\frac{1}{R}-\frac{i\lambda}{\pi W^{2}} \tag{9}\]  
The parameters in Equation (7) are as follows:  
\(d_{1}\) is the distance between the transmitter and the OPM (152 mm), \(F_{Lens}\) is the focal length of the lens (75 mm), \(FOPM\) is the focal length of both of the OPM components (152 mm), \(d_{2}\) is the distance between the OPM and lens (155 mm), and \(d^{3}\) is the distance between the lens and the GDD (75 mm).
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments  
### Datasets  
**UAV-VeID**[36] dataset contains 41,917 images covering 4601 vehicles. The UAV-VeID dataset comprises videos taken by drones at locations such as highway interchanges, intersections, parking lots, etc., under different backgrounds and lighting conditions. The flight height of the captured drone is about 15 to 60 m, and the camera's vertical angle is between 40 and 80 degrees. This method of shooting causes the size and angle of view of the target vehicle to change.  
**VeRi-UAV**[37] uses aerial photography to take pictures in multiple parking lots and on some roads. There is a training set and a test set for the VeRi-UAV. The test set has 4905 photographs of 111 automobiles, whereas the training set has 12,610 images of 343 vehicles. On this basis, about 15% of pictures are randomly selected from each vehicle ID to form a query set, and the remaining pictures form a gallery set.  
**Evaluation Metrics.** We used the frequently used Top1, Top5, Top10, and \(mAP\) to assess how well various ReID techniques performed on datasets. They demonstrate how accurately the query sample matches the ID in the gallery. A high Top-k value indicates that query sample-based ID identification accuracy is high. \(mAP\) utilized in vehicle reidentification, is used to gauge how well the ReID approach performs overall. It displays the searchability of all test photos with the same ID. The search accuracy of the \(k\) position can be represented by the product of \(P(k)G(K)\) and \(P(k)G(K)\). In the search sequence, \(G(k)\) denotes if the matched picture is present at position \(k\), \(P(k)\) denotes the likelihood that the first \(k\) search image contains the matched image, and \(sum_{k}=1 nP(k)G(K)\) can denote the retrieval accuracy for query \(q\) as a whole. The \(AP\) of query \(q\) is the absolute retrieval precision divided by \(N_{q}\). Formula (5) illustrates by defining \(mAP\) as the average of \(APs\) over all queries, and it may be used to evaluate the overall performance of a ReID model:  
\[mAP=\frac{\sum_{i=1}^{M}AP(q_{i})}{M}. \tag{5}\]  
The evaluation metric \(CMC@k\) is designed to describe the retrieval accuracy of matching locations as shown in Equation (6):  
\[CMC@k=\frac{\sum_{i=1}^{M}F(q_{i},k)}{M}. \tag{6}\]  
If query \(qi\)'s matched photos are in the top \(k\) images of the retrieved sequence, the evaluation index \(F(q_{i},k)\) indicates it. The evaluation metric \(CMC@k\) measures the average search precision of all queries at position \(k\) in the search sequence. The most frequent \(CMC@k\) values, Rank-1 and Rank-5, show the likelihood that an image will match in the top 1 and 5 positions of the retrieval sequence. This study uses \(mAP\) and \(Rank\)-\(N\) as evaluation markers, similar to most of the ReID work.
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 2 Materials and Methods
### Estimation of Operating Costs
#### 2.4.7 Comminution  
The comminution circuit included crushing and milling (or grinding). In this case study, the crushing circuit consisted of primary and secondary stages. The primary stage employed a gyratory crusher, with an average feed size (F\({}_{80}\)) of 750 mm and an average product size (P\({}_{80}\)) of 118 mm. In the secondary stage, a pebble crusher was used to process material with a F\({}_{80}\) in the \(-65+12\) mm fraction and an average P\({}_{80}\) of 12 mm. The total specific energy consumption required for crushing was approximately 1.94 kWh/t.  
Milling was conducted in wet mode. In the primary stage, a semi-autogenous grinding (SAG) mill was used to process material with an average F\({}_{80}\) of 118 mm and a P\({}_{80}\) of less than 65 mm. The secondary stage employed a ball mill with a F\({}_{80}\) greater than 150 \(\upmu\)m and a P\({}_{80}\) in the \(-150\)\(\upmu\)m +2.5 mm fraction. The total specific energy consumption for milling was 11.15 kWh/t, with an operating Bond work index of 10 kWh/t.  
The annual throughput of the comminution circuit reached an average of 10,350,000 tons, and considering that crushing and milling shared similar cost components, the operating costs for these processes were determined using the following equations:  
\[K_{Ct}=K_{Cmr}+K_{Clab}+K_{Cenc}+K_{Ceqd}+K_{Cc}+K_{CO} \tag{13}\]  
\[OPEX_{C}=\frac{K_{Ctc}}{Tph} \tag{14}\]  
where \(K_{Ct}\) is the total cost of crushing and milling (USD/year); \(K_{Cmr}\) is the cost of maintenance and repairs (USD/year); \(K_{Clab}\) is the labor cost inclusive of salaries and wages for operators and technicians (USD/year); \(K_{Cnc}\) is the cost of energy consumption (USD/year); \(K_{Ccq}\) is the cost of equipment depreciation (USD/year); \(K_{Cc}\) is the cost of consumables (USD/year); \(K_{CO}\) is the other costs (USD/year); \(OPEX_{C}\) is the operating cost for crushing or milling (USD/ton); \(K_{Ctc}\) is the total cost inclusive of the 10% contingency (USD/year); and \(Tph\) is the throughput (tons/year). Note that each cost component is related to crushing or milling, and throughput is considered for the entire comminution circuit.
## 3 Related Concepts
### Method Noise Thresholding  
It plays a very significant role in the process of dealing with the granular pattern in radar images. It can enhance the performance of any speckle-noise reduction techniques and lead to precise outcomes. This is quite an effective and general despeckling model to enhance the outcomes [41]. The speckle and despeckled images consist of a few arbitrary details of the image that represent the differences because of the inadequacy of the speckle-noise reduction techniques. We aimed to validate the performance and effectiveness of the despeckling methods by using some famous metrics such as SNR, PSNR, SSIM, and UIQI, which becomes classified into two categories: one is with the reference indexed, and another is without the reference indexed [42]. The application method noise is used in conventional and medical images and reaches the fields of the military, topology, agriculture, object detection, and classification [43]. The residual or unfiltered components can be filtered using any despeckling method. The method is implemented on unfiltered pixels of the noise threshold image [37]. It is a method based on the assumption that the wavelet coefficients are modeled as random variables with general Gaussian distribution (GGD) within each subband. Under this condition, a threshold is estimated to find the value that minimizes the Bayesian risk [38; 44].  
\[\mathrm{f_{i}(\sigma_{x})=\frac{\sigma_{n}^{2}}{\sigma_{x}}} \tag{5}\]  
where \(\sigma_{x}\) denotes image standard deviation evaluated in each wavelet sub-band and \(\sigma_{n}^{2}\) refers standard deviation of noise evaluated in each wavelet sub-band.  
The shrinkage threshold involves setting the elements whose absolute values are lower than the threshold to zero and then scaling the nonzero coefficients toward zero.  
\[\mathrm{S(y)=\left\{\begin{array}{ccc}0&,&\left|y\right|<T\\ \mathrm{sign}\left(y\right).(\left|y\right|-T)&,&\left|y\right|>T\end{array}\right. \tag{6}\]  
where T refers threshold value and \(\mathrm{S(y)}\) indicates nonlinear transform.
## 3 Materials and Methods
### Experimental Datasets  
In order to validate the processing schemes of the GLONASS IFCBs on PPP-based time transfer, observations from 36 IGS stations and three stations of the time-keeping laboratory were chosen, which covered a 71-day period of Day of Year (DOY) 227-297 in 2017. The selected stations are equipped with receivers from seven manufacturers (see Table 2). The detail information of these stations is listed in Table 3. Figure 1 display the distribution of the selected stations, which can track GPS and GLONASS. The PT11 provided by PTB (Physikalisch-Technische Bundesanstalt) is employed as the center node, and 38 time-links were conducted in our work. Note that the selected GNSS stations are equipped with high-precision atomic clocks (H-MASER or CESIUM). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Manufacturer** & **Receiver Type** & **Number of Stations** \\ \hline JAVAD & TRE\_G3 TH DELTA & 10 \\ LEICA & GR10 & 1 \\  & GR25 & 1 \\  & GRX1200 + GNSS & 1 \\  & GRX1200 GGPRO & 1 \\ NOV & OFME & 1 \\ SEPT & POLARX3 ETR & 1 \\  & POLARX4 & 7 \\  & POLARX4 TR & 8 \\ TPS & NETG3 & 1 \\ TRIMBLE & NETR8 & 1 \\  & NETR9 & 6 \\ SUM & & 39 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of Global Navigation Satellite Systems (GNSS) receivers of the selected stations. Figure 1: Geographical distribution of the selected 39 global tracking stations.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 2 Predicting Rain Attenuation Impairing FSO Links
### Specific Attenuation  
Specific attenuation due to rain is independent of the wavelength in the optical transmission windows usually adopted by commercial FSO systems, which are the classical 0.780-0.850 and 1.520-1.600 \(\upmu\)m bands [9]. For practical purposes, the specific attenuation \(\gamma\) (in dB/km) is often calculated from the rain rate \(R\) (in mm/h) through simple power-law relationships, i.e.,  
\[\gamma=kR^{\alpha}. \tag{1}\]  
For a given rain rate, the coefficients \(k\) and \(\alpha\) are rather sensitive to the distribution of raindrop size, namely to the DSD of precipitation, as clearly shown, for example, in [16]. Table 1 reports the best-fit \(k\) and \(\alpha\) coefficient derived from the values of \(\gamma\) calculated through classical scattering theory [16], assuming that the DSD is modeled with a Gamma function [26], with the latter only being dependent on the so-called shape parameter \(\mu\) and on the rain rate. Measurements show that \(\mu\) can range between \(-3\) and \(8\)[27].  
Figure 1 shows the curves in (1), with the coefficients in Table 1 for six different values of \(\mu\). The DSD impact depends on \(R\) and it is higher at very low or very high rain rates. For instance, if \(R\) = 50 mm/h, \(\gamma\) ranges from less than 10 dB/km (\(\mu\) = \(-3\)) to more than 25 dB/km (\(\mu\) = \(7\)). On the other side, microwave attenuation is not as sensitive to the DSD as optical attenuation. Finally, we point out that specific rain attenuation in the optical bands is of the same order of magnitude as in the V-band  
(40-75 GHz): for instance, if \(R=50\) mm/h and \(\mu=1\), \(\gamma=19\) dB/km at 50 GHz, whereas \(\gamma=18\) dB/km in the above optical windows.
# Metal-Organic Frameworks Toward Electrocatalytic Applications
## 1 Introduction
One can thus expect that the MOF-based materials installed with three-dimensional (3D) arrays of well-separated active sites should be excellent candidates as the thin-film catalysts for electrocatalysis due to their high \"accessible\" active-site density and regular porosity for ion diffusion; the choice of water-stable MOFs can further extend the use of these materials in the electrocatalytic applications that need to be operated in aqueous solutions, such as water splitting and electrochemical sensors. However, most MOFs are electrically insulating at low bias [29; 30], which hinders the charge transport between the active sites in MOFs and strongly limits the practical use of MOFs in electrochemical applications. Thus, rendering efficient charge transport within the catalytic MOF thin film and maintaining the framework stability in the electrolyte solution become the main challenges for utilizing MOFs in electrocatalytic applications. Although it still constitutes an emerging subfield, the use of MOFs and relevant materials for electrocatalytic applications has attracted much attention in recent years, as revealed in Figure 1. In this review, we aim to focus on the strategies to overcome the aforementioned challenges for utilizing MOFs in electrocatalytic applications, and some examples are highlighted to show the recent progress in utilizing MOFs for electrocatalysis and relevant applications. Figure 1: The number of papers with the topics of metal–organic frameworks (MOFs) and electrocatalysis, electrocatalytic, or electrochemical senor, published in each year, obtained from Web of Science database. It should be noted that all publications using MOF-derived and MOF-templated materials are counted.
# Fabrication of Random Microlens Array for Laser Beam Homogenization with High Efficiency
## 3 Fabrication of Random Microlens Array (rMLA)
### Fabrication of rMLA  
Two types of rMLAs are fabricated in this paper, which are the honeycomb rMLA and the rectangular rMLA, respectively. The fabrication requires the pretreatment of the surface of the glass substrate--that is, the random micropores array with different structures needs to be fabricated on the surface of the substrate.
# Rentable CDN Using Blockchain and Proof of Provenance
## 3 Proposed Method
### Proof of Provenance  
The main concern for decentralized CDN is the trust between Downloader and Uploader. It is very easy for Uploader to claim that they uploaded more than they did. Conversely, Downloader can claim that they did not download from a specific Uploader. So, how can we use provenance to solve this?  
Provenance is widely used to assist with authenticating objects, such as artworks and wines, by comparing it with a historic record. It also is used in crime investigation to track evidence through a chain of commands. Like the examples, the proposed proof of provenance tracks the origin of the content and can prove that the content has been legally obtained from rentable CDN.  
The structure of the proof of provenance is described in Table 3. It contains content delivery contract ID, provenance flag, Uploader's address, and Verifier's signature. As mentioned before, Uploader's proof of provenance must be sent to Downloader during the handshake for verification.  
Every time the content is finished downloading, Verifier issues a challenge and response query to Downloader to check if they correctly downloaded the whole content. If they respond correctly, then a proof of provenance is generated and is added as a part of payment settling transaction. This proof is then used as a reference for any future content delivery process to prove the provenance of the content.  
Figure 7 shows how the proof of provenance is distributed. In the beginning, only Manager can deliver the content, because no one else has the content. The content registration contract ID is substituted as content delivery contract ID for the proof of provenance because that is how [PERSON] acquired the content. Naturally, when Downloader finishes downloading and successfully has a record of proof of provenance on Blockchain, they can become Uploader and deliver contents to other Downloader(s).  
During the payment settlement, if there is an incorrect proof of provenance (or cannot be verified) in the proof of piece, that Uploader is not eligible for the payment and Downloader is not eligible for proof of provenance for this content delivery contract. Furthermore, any unclaimed payment including the case of incorrect proof of provenance is paid to Verifier to prevent Downloader from cheating.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 6 HWLNets in IoT Applications
### Positioning in HWLNets  
WiFi and LiFi have different positioning errors. Due to the tight deployment of LiFi and short range, it can calculate the position of users within 10 to 30 cm, whereas WiFi accuracy ranges from one to seven metres [87]. LiFi is also more cost effective due to the fact that existing lighting LED infrastructure can be used and because the user equipment in LiFi systems use angle diversity receiver to detect the device's orientation, meaning it is easier to share data with an indoor positioning system of LiFi APs [88]. The problems in hybrid systems indoor positioning differ as a spectrum is used. Radio waves can have multipath fading when signals reach a receiver via many paths, and their relative strengths and phases change due to building materials and structures. Visual light obviously suffers from blockages by objects or even users themselves, which can influence connectivity.  
In the hybrid system, one spectrum can supplement the other using its strengths where they can apply. Research [89] was done using LiFi and ZigBee with a proximity position algorithm but had low accuracy. The next research proposed a two-stage model [90] that uses LiFi to establish a possible area where a user device can be found, then with radio frequency RSS pinpoints the exact location. The error margin of this system is 20 cm. Finally, a two-stage system can be used inversely for RF to locate the approximate area, and then the nearest LiFi AP can calculate the position of the user device [91] with an error of 5.8 cm.