esa-sceva/satcom-chunk-collection huggingface dataset:
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 3 Geo-DMP Mobile System
### The Workflow of Geo-DMP
#### 3.2.1 On the Perspective of Transformations between HTTP Messages and DTN Bundles  
In this part, we concentrate on interactions on the gateway nodes that connect DTN-aware nodes with the client and server. As the process of contacting with the server side should be made clear, here we do not consider the situation of discovering replicas at intermediate DTN nodes. The entire procedure can be divided into four phases, as shown in Figure 4.  
_Phase 1: HTTP Request to DTN Query (Client Side)_  
The proxy at the Terminal Gateway (hereafter TG) keeps listening to HTTP requests and intercepts those asking for geospatial contents in light of predefined rules. Then it resolves the type and name of requested data from the URL, and checks whether this content is already available locally. If yes, it will return the content to the client instantly without resorting to DTN. Otherwise, a new query bundle is created with parameters of destination EID, destination regions, and content name. After that, the proxy invokes the DTN API to inject this bundle into the DTN network.  
_Phase 2: DTN Request to HTTP Request (Server Side)_  
Through multi-hop relaying of DTN nodes, when the query bundle arrives at the Server Gateway (hereafter SG), DTN extracts the content name from the BPQ block and hands it to the proxy module. The proxy then asks the back-end server for that content with a newly generated HTTP request.  
_Phase 3: HTTP Response to DTN Response (Server Side)_  
After processing inside the server (which is described in our previous paper [32]), the HTTP response is generated and returned to the SG. The proxy at SG unpacks the HTTP response and calls the DTN API to publish this content into the DTN network. Afterwards, this content is used to satisfy the awaiting query bundle and create the corresponding response bundle.  
_Phase 4: DTN Response to HTTP Response (Client Side)_  
Eventually, when the response bundle is routed back to the TG, DTN reads the content name from the BPQ block and informs the proxy that the content is ready. Thereupon, the proxy goes to fetch the content, stores the payload to the local cache, makes a new HTTP response, and finishes the procedure by returning it to the client.
## 4 Results and Discussion  
### Molecular Docking  
Discovery studio visualizer and AutoDock 1.6 applications were applied for molecular docking studies to predict ligand-protein interactions, bonding modes, bonding affinity based on energy, and ligands interaction with the receptor [17]. It can be seen in Figure 1, ChAlg in computer simulation describes that only 1 hydrogen bond presented at the 23 rd hydrogen atom of Alg with activation energy of 0.18 kcal/mol. In ChA/\(\alpha\)-M, there were 2 hydrogen bonds between Alg and \(\alpha\)-M placed at the 21 st and 22 nd hydrogen atom of Alg with activation energy of 0.514 and \(-\)0.87 kcal/mol, respectively. Alg forms the polyelectrolyte network by interacting between the dissociated functional groups: An anionic carboxyl group of Alg and a cationic amino group of Ch, intra and inter-chain hydrogen bonding, different parts of the polysaccharides structures and already created aggregates of Alg-Ch particles [27]. In addition, complexation of polyelectrolytes leads to coacervate and form hydrogel [28]. \(\alpha\)-M probably entrapped in the network through the 21 st and 22 nd hydrogen atom of Alg to fill in the voids between Ch-Alg, thus smoothing the surface of the HFs [29; 30]. These results confirm that the interaction between Ch, Alg, and \(\alpha\)-M occurred by hydrogen bonding, verifying \(\alpha\)-M as a receptor and Alg as a donor.
## 3 Problem Statement and Formulation
### Problem Formulation
Thus, the utility rate for user \(i\) is the downlink spectral efficiency of link \(i\), which is given by:  
\[R^{i}(\mathbf{p}_{1},\ldots,\mathbf{p}_{N})=\sum_{k=1}^{K}\log_{2}(1+SINR_{i} (k))\Delta f_{k} \tag{7}\]where  
\[SINR_{i}(k)=\frac{P_{i}(k)\abs{h_{ii}(k)}^{2}}{\sum_{j\
eq k}^{K}P_{j}(k)\abs{h_{ ij}(k)}^{2}+\sigma_{i}^{2}(k)} \tag{8}\]  
\(R^{i}\) is the available utility function to player \(i\) with given power allocations \(\mathbf{p}_{1},\ldots,\mathbf{p}_{N}\) while \(\Delta f_{k}\) is the bandwidth of the \(k^{th}\) interval. As defined above, \(h_{ii}(k)\) and \(h_{ij}(k)\) are the direct channel gains and cross coupling functions, respectively. Here, we consider two special cases for the noise term in (8). In the first case, when the noise term \(\sigma_{i}^{2}(k)>0\), there is an external noise in the \(i^{th}\) user at frequency \(k\) and optimization problem becomes more complex. In the second case, when \(\sigma_{i}^{2}(k)=0\), the noise term can be ignored, and the spectral efficiencies might become large using FDM strategies. Hence, the available capacity \(R^{i}\) for each player \(i\) is continuous over multiple channels. Our goal is to model the payoff vector \(\mathbf{R}\) for frequency response, so we can simplify the problems for N-player games. Let the GIG is given by \(GI_{\{I_{1,\ldots,k}\}}=\{\mathbf{R},\ \mathbf{B}\}\) for N-players, the continuous payoff vector \(\mathbf{R}\) be denoted as  
\[\mathbf{R}=\left\{R^{1},\ldots,R^{N}\right\} \tag{9}\]  
where \(\mathbf{B}\) and \(R^{i}\) are the strategy set and the available capacity defined in (4) and (7), respectively. Moreover, we also consider a Nash equilibrium (NUM) problem intending to optimize the entire network performance under the constraints power budget and QoS conditions. Note that the QoS requirements are applicable if and only if the game players are satisfied without cross-coupling interference from the interfering transmitters. Given all other players \(i\) and player \(n\) in a \(\mathbf{p}\) strategy, the Nash equilibrium for QoS constraints is formulated as [51]  
\[R^{i}\big{(}\mathbf{p}_{1},\ldots,\mathbf{p}_{n-1},\ \mathbf{p},\ \mathbf{p}_{n+1}, \ldots,\mathbf{p}_{N}\big{)}\leq R^{n}(\mathbf{p}_{1},\ldots,\mathbf{p}_{N}) \tag{10}\]  
It can be seen that the problems mentioned above are homogeneous with respect to power allocation and QoS requirements, thereby depending on the power and QoS constraints. Specifically, the problem in (9) is a non-cooperative game with respect to the payoff vector \(\mathbf{R}\). Therefore, it can be solved efficiently using interference game methods for convex non-cooperative N-player games. We solve the problem using the Gaussian Interference game model and consider it as a baseline to design the reward function of DRL scheme proposed in the next section.
## 6 Experimental Results and Discussion
### Performance on Entities with Different Frequencies  
Considering the long tail distribution of entities in the knowledge graphs [7], in this experiment, we divide the data based on the frequency of entities and observe the performance of the proposed method in reasoning. Specifically, the validation set of FB15k-237 is separated into four subsets according to the frequencies of the entities: 1-10, 10-20, 20-30, and \(>\)30. As the quantities of the four subsets are different, we randomly sample 500 query triples from each subset. The reasoning results are depicted in Figure 3.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**WN18 RR**} & \multicolumn{3}{c}{**FB15k-237**} \\  & & **MRR** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** \\ \hline
1 & w/o in-context triples & 0.326 & 0.177 & 0.528 & 0.233 & 0.151 & 0.431 \\
2 & directly connected in-context triples & 0.315 & 0.171 & 0.503 & 0.226 & 0.151 & 0.422 \\
3 & in-context triples guided by logical rules & 0.457 & 0.403 & 0.552 & 0.326 & 0.254 & 0.547 \\
4 & in-context triples by logical rules and LLM’s feedback & 0.517 & 0.494 & 0.571 & 0.343 & 0.264 & 0.558 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study of proposed method.  
Figure 3 depicts the reasoning results on query triples with different entity frequencies. The \(x\)-axis of each sub-figure represents different frequencies of entity, and the \(y\)-axes denote the MRR (Figure 2(a)), Hits@1 (Figure 2(b)), and Hits@10 (Figure 2(c)), respectively. We can conclude that the performance of the traditional reasoning methods (RotatE and AnyBURL) is greatly affected by the frequency of entities. Only when the frequency of entities is high can better results be achieved (only when the frequency of entities is larger than 20 are the results of RotatE slightly better than those of GPT\({}_{one-shot}\) on MRR in Figure 2(a)). One reason could be that the traditional data-driven models perform better on entities with higher frequencies. In contrast, our model performs well across varying frequencies with the best performance occurring at the lowest ones.
## 6 Outlook for Future Study  
Benefiting from the development of computer technology, modern measurement techniques and related sound theory, great achievements have been realized regarding the analysis of the flow-induced noise of centrifugal pumps. By summarizing the study status, there are still some important areas for flow-induced noise of centrifugal pumps needing to be focused in the future.  
From in-depth study on the generation mechanism of flow-induced noise, the contributions of different flow phenomena to overall flow-induced noise are not clear. Besides, there is no sufficient study on the generation mechanism and control strategy of noise induced by complex turbulence, back flow, rotating stall and water hammer. With the development of centrifugal pumps in the direction of large flow, high power and high load, as well as the demand for the change of operating conditions, the impact of these kinds of noise cannot be ignored. Therefore, an in-depth study on the generation mechanism of flow-induced noise is essential.  
The improvement of the flow-vibration-acoustic-coupling method. The pressure fluctuations could induce noise and also induce the vibration of pump structures. The vibration could not only affect the flow field but also induce noise. Meanwhile, the generated-noise could affect structure vibration. Therefore, it is essential to further improve the noise simulation method by combining the flow field governing equation, the elastic structure dynamic equation and the sound field governing equation and establishing the coupling equation.  
1. Analysis model establishment of the generation, distribution and propagation mechanism for flow-induced noise. The analysis of the propagation mechanism of flow-induced noise, which has been proved that it is affected by the synergy of flow and sound fields in the process of fluid flow [131], is not involved in current study of the flow-induced noise of centrifugal pumps. It is beneficial for the prevention and control of flow-induced noise to establish the analysis model of the generation, distribution and propagation mechanism for flow-induced noise. The analysis model could be used for systematically analyzing the generation mechanism, the proportions of noise sources with different properties, the spatial-temporal distribution characteristics and the propagation characteristics of flow-induced noise.
2. Development of high precision and fast numerical simulation method. There are three-dimensional steady and transient iterative calculation processes in the numerical simulation of flow-induced noise, which are tedious and time-consuming. To obtain high precision results, a large amount of grid system is required. Predictably, the development of high precision and fast simulation method could promote the study of flow-induced noise.
3. Noise visualization experiment. To acquire the spatial distribution and dynamic changing characteristics of the flow-induced noise of centrifugal pumps more accurately, the real-time measurement of the whole sound field of centrifugal pumps should be conducted by developing the noise visualization technology.
# Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images
## 3 Results
### Tube Current of 231 mAs  
Figure 12 shows the impact of iterative filtration on an image using a tube current of 231 mAs. In the original image, the noise peaks at a frequency of about 0.2 mm\({}^{-1}\). By filtering it for many iterations, the noise at high frequencies gradually decreased, leaving more at a low frequency.  
Figure 13 shows the NPS characteristics of the original image and the filtered image at up to five iterations for at tube current of 231 mAs. For five iterations of filtration, the noise magnitude decreased by more than 60%. The peak frequency dramatically shifted from 0.22 to 0.06 mm\({}^{-1}\) and the average frequency gradually decreased from 0.26 to 0.17 mm\({}^{-1}\).  
Figure 12: NPS on repeated filtered images at tube current of 231 mAs.  
Figure 13: Noise measurement on homogeneous phantom with tube current of 231 mAs. (**a**) Noise, (**b**) Percentage of reduced noise, (**c**) Peak frequency, and (**d**) Mean frequency.  
Figure 14 shows the spatial resolution of each filter iteration at a tube current of 154 mAs. Table 5 shows the MTF50 and MTF10 values for a tube current of 231 mAs. In Figure 14, the MTF curves coincide, indicating that iterative bilateral filtering leads to no change in spatial resolution. The MTF values shown in Table 5 also do not show any change for a tube current of 231 mAs, identical to a tube current of 154 mAs (Table 4).
## 2 Pointing Strategy and Model Establishment  
### Pointing Scheme and Maneuvering Requirements  
In the scientific mode, to ensure the precision of space gravitational wave detection, fewer movable components inside the spacecraft are preferred. Each spacecraft in the TianQin mission consists of two MOSAs. To enhance the detection accuracy, we propose a single MOSA pointing scheme, as illustrated in Figure 1a. One of the MOSAs is adjusted through the telescope pointing mechanism to compensate for the breathing angle variation \(\theta\) within the TianQin constellation plane. Simultaneously, a pointing adjustment device is also installed on the other MOSA as a backup mechanism.  
Figure 1: Pointing scheme for breathing angle compensation. (**a**) Pointing scheme. (**b**) Coordinate system relationship.  
Additionally, the out-of-plane angle variations in the TianQin constellation are compensated for by adjusting the spacecraft's attitude using micro-propulsion devices; however, this aspect is not considered in this paper. Given the complex coupling relationship between the spacecraft and the telescope, a decoupling analysis was conducted, treating them separately. Through the design of a single-input-single-output (SISO) controller, control of the telescope pointing mechanism's single degree of freedom is achieved [18].  
The spacecraft reference frame (\(\text{O}_{\text{i}}\)-\(\text{x}_{\text{i}}\)y\(\text{i}\)z\(\text{i}\)) and the inertial reference frame (O-XYZ) was established, as shown in Figure 1b. The maneuvering requirements for telescope pointing were computed using satellite orbit data [19].  
The arm length \(r_{ij}\) between spacecraft \(i\) and \(j\), where \(i,j\in\) (1,2,3), can be expressed in an inertial reference frame.  
\[r_{ij}=\left\|\mathbf{r_{ij}}\right\|=\left\|\mathbf{R}_{i}-\mathbf{R}_{j}\right\|, \tag{1}\]  
where \(\mathbf{R}_{i}\) represents the position of the spacecraft SCi in the inertial reference frame. Define the unit vector as \(\mathbf{n}_{ij}=\mathbf{r}_{ij}/\left|\mathbf{r}_{ij}\right|\). The angle \(\alpha_{i}\) between the sight lines of the telescopes in the spacecraft SCi can be expressed as  
\[\alpha_{i}=\arccos(\mathbf{n}_{ij}\cdot\mathbf{n}_{ik})=\arccos(\frac{\mathbf{r}_{ij} \cdot\mathbf{r}_{ik}}{\left\|\mathbf{r}_{ij}\right\|\times\left\|\mathbf{r}_{ik}\right\|}). \tag{2}\]  
Substituting the changes in the satellite orbits in the inertial coordinate system equation into Equation (2), the trajectory variations in the breathing angle among the three spacecraft over three months can be obtained, as illustrated in Figure 2. It can be observed in the figure that the breathing angle variation in the TianQin constellation did not exceed \(\pm 0.1^{\circ}\) over the three months.
## 4 Experiment
### Experiments on Real-World Dataset  
The results of the methods on the real-world dataset are shown in Figure 8. To compare the results of processing more clearly, we also zoomed in on some areas of the images. The red frame represents the original image and the green frame the enlarged image. Our model could be well adapted to the rainy images under different conditions. When there were few rain streaks, our method and the DCNN method were better able to remove them. However, in the enlarged area, the image generated by the DCNN lost important details from the original. When the rain streaks were large in number, our method removed most of them, while each of the other methods left a certain residue in the resulting image. Our method thus removed more rain streaks and retained more image detail, and the resulting image was thus clearer.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Rainy Image** & **GMM [9]** & **UGSM [8]** & **DCNN [16]** & **FS-GAN [28]** & **Ours** \\ \hline MSE & 0.0044 & 0.0013 & 0.0008 & 0.0014 & 0.0009 & 0.0006 \\ SSIM & 0.8292 & 0.8803 & 0.9236 & 0.9352 & 0.9143 & 0.9614 \\ PSNR & 24.77 & 29.55 & 31.63 & 28.97 & 31.61 & 33.64 \\ VIF & 0.4832 & 0.4935 & 0.5418 & 0.5542 & 0.5111 & 0.5916 \\ LPIPS & 0.2386 & 0.1672 & 0.1058 & 0.0516 & 0.0721 & 0.0350 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average value of MSE, SSIM, PSNR, VIF, and LPIPS on Rain100 L dataset.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 5 Conclusions  
In this paper, a robotic manipulator has its state estimated using two different filters, each of which applies to a different model. After a description of the different components of the manipulator and of some of the possible solutions to the state estimation problem, both models are depicted in a modular and reusable way.  
The filters are then implemented and tested in a real platform. During the implementation of the filters, the correctness of the two used models is verified with extensive tests on the individual sensors. These reveals flaws in each individual sensor. However, when combined, these flaws are corrected. The only one that is uncorrectable by sensor fusion is the one from the joint model, which originated from the accelerometers measuring a small horizontal acceleration. This acceleration is caused by a slight tilt of the manipulator; however, the model justifies it as centrifugal acceleration, which causes the model to portray the movement of the manipulator as rapidly spinning along one axis. This problem is corrected by simplifying the accelerometers' model, by removing its ability to consider centrifugal acceleration.  
Despite this flaw of the joint model, it still outperforms the link model in an accuracy test. This test is performed resorting to an external structure with specified points marked in multiple strategic predefined locations. The estimate of the position of the manipulator's tip is compared to the coordinates of the points of interest when the tip is driven to those points. Both models suffer from drift around an axis. Despite that fact, during the execution of the accuracy test, the joint model is able to keep its maximum vertical error under 5 mm. This is an error that is not affected by drift and therefore represents the accuracy the filter would have achieved if the drift problem were not present.  
A possible solution to this issue would be to find a way to correctly calibrate the magnetometers. These would provide measurements that would compensate for drift in the direction in which it existed. Besides an improved calibration technique, the proposed models can be expanded to work with other sensors.  
Conceptualization, J.M., V.H.P., and P.C.; methodology, J.M. and P.C.; software, P.C., V.H.P., and J.M.; validation, J.M., V.H.P., and P.C.; investigation, J.M.; writing, original draft preparation, J.M.; writing, review and editing, J.M., V.H.P., J.G., and P.C. All authors read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Materials
#### 2.1.2 Al-Qatif Clay  
Herein, unprocessed clay was used, and natural swelling clay was obtained from Al-Qatif city (coordinates; 26.5764917, 49.9982360), which is known to be composed of highly plastic green to dark brown clay. The city is located along the shoreline of the Arabian Gulf. It is about 400 km to the east of Riyadh, Saudi Arabia. The properties and swelling characteristics of Al-Qatif were extensively investigated [25; 26; 27; 28; 29; 30; 31]. All studies confirmed that the Al-Qatif clay is highly expansive due to its mineralogy and the amount of smectite mineral content. Tables 1 and 2 present the physical and chemical compositions of Al-Qatif clay, respectively. The liquid limit was measured in the range of 130 to 150, while the plasticity index varied from 70 to 80. This is classified as CH (highly plastic clays in accordance with the unified classification system, USCS).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Range** \\ \hline Material passing sieve number 200 & \textgreater{}90\% \\ Liquid Limit \% & 130–150 \\ Plastic Limit \% & 60–70 \\ Plasticity Index & 70–80 \\ Maximum Dry Density (\(\text{kN}/\text{m}^{3}\)) & 11.5–12 \\ Optimum Moisture Content & 32–40\% \\ Swell percent (ASTM D4546) & 16–18\% \\ Swelling pressure (ASTM D4546) & 500–800 \(\text{kN}/\text{m}^{2}\) (\(\text{y}=12\text{ kN}/\text{m}^{3}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The physical properties of the Al-Qatif clay. After [27].