esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatial 4-DOF Laser Collimation Measurement System
###### Abstract  
A compact and miniaturized laser collimation system was proposed to measure the four-degrees-of-freedom of an optical payload in high-altitude space. Compared with other systems, this system has a simple structure and low cost, high measurement accuracy, and a large measurement range. The optical structure of the system was designed, the measurement principle of the four-degree-of-freedom was described in detail, the interference between the distance measurement and the angle measurement in the optical path was analyzed, and the installation error was analyzed. The error was minimized under different temperature conditions to improve the robustness of the system. An engineering prototype was built based on the system design scheme and an experiment was conducted to measure a target with a measured distance of 500 mm. The current indicators reached the requirements for the ground testing of optical payloads. The application of the system can be used to measure six degrees of freedom simultaneously by installing two systems in different coordinate systems. The system can also be used in industry; for example, by measuring the machine tool error in real time and compensating for it, the system can improve the positioning and motion accuracy. It can also be used for feedback control of the robot's motion by measuring and controlling it.  
4-DOF; laser collimation; space optical loading +
Footnote †: journal: Applied sciences  
**Clication:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Spatial 4-DOF Laser Collimation Measurement System. _Appl. Sci._**2024**, 14, 10891. [[https://doi.org/10.3390/appl42210491](https://doi.org/10.3390/appl42210491)]([https://doi.org/10.3390/appl42210491](https://doi.org/10.3390/appl42210491))
## 3 Conceptual Model and Numerical Implementation
### Numerical Model and System Abstraction
By default, at the top of the domain (grid point n in Figure 1), the von Neumann-type condition applies. In this case, \(s_{\text{d,n}}\), \(P_{\text{v,n}}\) and  
Figure 1: System abstraction discretization, boundary and initial conditions. \(RH\): relative humidity. \(P_{\
u,\text{an}}\) are used instead of \(s_{\text{d,0}}\), \(P_{\
u,0}\) and \(P_{\
u,1}\), respectively. FLoW1D has also been prepared to change this top boundary condition, and it is able to shift to a Dirichlet condition. To do this, cells H103, I103, J103 and so on must be equal to \"sn\" (cell C16), as a function of the \(RH\) value consigned in cell C15. Figure 2: FLoW1D (One-Dimensional Water Flow) interface. (**a**) Input data. (**b**) FLoW1D function. (**c**) Water content function. (**d**) Total water content. Therefore, in the default option (bottom, Dirichlet condition; top, von Neumann condition), FLoW1D will be activated for the cell range 3 to 103. According to the initial calculation proposal, 101 grid points have been adopted for the spatial discretization. The user can easily increase or reduce this number. The activation of FLoW1D for a column of cells, as an \"array formula\" (or \"CSE formula\"), requires the selection of the whole range of solution vectors and for the user to simultaneously press the keys Control, Shift and Enter [27]. Thus, the results of the FLoW1D function are the suction in each of the grid points in which the sample has been discretized for the desired computational times (see Figure 2b). Although the state variable used is suction, the magnitude measured directly in the WAC test is the water content. To obtain the value of \(w\) associated with the value of \(s\) in each grid point, the function \(ws\) was implemented. In this function, besides \(s\), the arguments are porosity \(\phi\) (cell C2), the van Genuchten parameters \(n\), \(m\) and \(\alpha\) (cells C5, C6 and C7, respectively), and the temperature \(\hat{T}\) (cell C8). The results of this function are shown in Figure 2c. Finally, it should be noted that the total mass of water contained in the sample is \(w_{\text{tot,cal}}\); this variable represents the variation of the accumulated water mass (g) in the tested material specimen, \(m_{w}\) (Figure 2d). This calculated variable is compared with the experimentally obtained value of \(w_{\text{tot,obs}}\). In summary, the main equation of FloW1D is Equation (1), which represents the water mass balance in the material. The state variable of this balance is \(w\) (water content per total volume unit). From the values of \(w\) at each grid point, it is possible to obtain the total mass of water accumulated in the tested sample (\(m_{w}\)) considering the total volume of the sample and the observation times.
## 4 Experiment Validation
### Case 1: Bearing Fault Diagnosis Based on CWRU Bearing Datasets
#### 4.1.4 Comparison between Different Methods  
To test the differences between DWSC and other methods, such as Resnet, DCNN, ShuffleNet, MCCNN, and boosting type of integrated algorithm Light Gradient Bosting Machine (LightGBM) that has eminent performance in the field of machine learning, a total of ten comparative experiments were conducted, and the comparative results of accuracy are shown in Figure 11. The average value and SD of fault classification accuracy of each method in ten experiments are listed in Table 3. The average accuracy of DWSC is 100%, followed by MCCNN, with an average accuracy of 98.17% and SD of 0.015. ShuffleNet and ResNet have similar accuracy, 87.89% and 84.33%, with SD of 0.055 and 0.062, respectively. DCNN and LightGBM have the lowest accuracy, 71.11% and 68.78%, with SD of 0.033 and 0.046, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Layer** & **Structure** & **Parameters** & **Title 4** \\ \hline
1 & Depth-wise separable convolutions & Channel = (3, 3) & \\  & & Kernel size = (1, 3) & \\ \hline
2 & Point-wise separable convolutions & Channel = (3, 16) & \\  & & Kernel size = (1, 1) & Batch size = 32 \\ \hline
3 & Conv1 + BN1 & Channel = (16, 32) & Initial learning rate = 0.01 \\  & & Kernel size = (1, 3) & MaxPool = (1, 2) \\ \hline
4 & Conv2\_1 + Conv2\_2 + BN2 & Channel = (32, 64), (64, 128) & Dropout = 0.5 \\  & & Kernel size = (1, 3), (1, 5) & Ceil mode = True \\ \hline
5 & Conv3 + BN3 & Channel = (128, 128) & Optimizer = [PERSON] \\  & & Kernel size = (1, 3) & Test sample rate = 20\% \\ \hline
6 & Conv4 + BN4 & Channel = (128, 32) & \\  & & Kernel size = (1, 5) & \\ \hline
7 & Conv5 + BN5 & Channel = (32, 1) & \\  & & Kernel size = (1, 1) & \\ \hline
8 & Linear & Input = 128 & \\  & & Output = 9 & \\ \hline \end{tabular}
\end{table}
Table 2: Structure and parameters of DWSC and other layers.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Average (\%)** & **Standard Deviation** \\ \hline LightGBM & 68.78 & 0.046 \\ \hline ShuffleNet & 87.89 & 0.055 \\ \hline ResNet & 84.33 & 0.062 \\ \hline MCCNN & 98.17 & 0.015 \\ \hline DCNN & 71.11 & 0.033 \\ \hline Proposed Method & 100 & — \\ \hline \end{tabular}
\end{table}
Table 3: Average accuracy and SD of the different methods.  
Figure 8: Training and test results. (**a,b**) are the changes of the accuracy and loss of the training and testing datasets with epoch, (**c,d**) are the visualization results of the training and testing results by t-SNE, (**e**,f**) are the confusion matrix of the training and test results.
## 1 Introduction
A set of potential forwarders can be created using opportunistic connection random graph (OCRG) and formation of a spanning tree can be used to demonstrate that the candidate nodes in OR will forward the packet according to some criteria (i.e., optimal link and path connectivity calculations in our case). However, the sensed data being generated as a result of deploying a WSN in a NAN of SGs might have different attributes like delay tolerance and delay sensitivity [13]. For example, the monitoring data (e.g., power load) generated by a sensor network which is part of a SG can always be delay sensitive and should be transmitted to the data processing (or sink) node within certain time limits thereby increasing the bandwidth requirements, whereas the control data (e.g., changing power load) generated by the sensor network in SGs can often be considered as delay tolerant data and is not required to be received by the data processing (or sink) node immediately [13]. Keeping in view the system requirements of SGs, parameters like energy consumption, mobility, network lifetime, delay and bandwidth can be treated as performance metrics for effective use of the WSN in SGs. Many routing techniques specifically designed for WSNs have been proposed in [23; 24; 25]. Likewise, the multipath routing protocols according to [26] have been proposed for WSNs. These multipath routing protocols use multiple paths for data delivery, and thus improve the network reliability and robustness. In this paper, we propose an energy efficient and multipath opportunistic node connection routing protocol for WSNs in NANs to achieve load balancing through splitting up traffic in terms of real-time and non-real-time across multi-disjoint paths and energy consumption balance through asynchronous working-sleeping cycle of sensor nodes. Figure 1 demonstrates the wireless sensor networks deployed in the NAN of SGs in which our NAN gateway is actually the mobile sink. The opportunistic node connections between mobile sink sensor nodes and between adjacent sensor nodes can be clearly seen in Figure 1.  
As a reflection of opportunistic node connectivity caused by asynchronous working-sleeping cycle strategy, OCRG is constructed in which a probe message forwarding mechanism is used for each node to forward the information to the mobile sink node [27]. Additionally, EMOR utilizes the residual energy, availability of the node's buffer size, working-sleeping cycle of sensor nodes and link quality factor to calculate the optimal link and path connectivity for both real-time and non-real-time traffic and assigns multi-disjoint paths to them accordingly. The rest of the paper is organized as follows: Section 2 describes the related research work conducted for WSN routing protocols in SGs and various other applications. System modeling is presented in Section 3. EMOR protocol design for WSNs in NAN is presented in Section 4. Performance evaluation and simulation results can be depicted from Section 5. Finally, Sections 6 and 7 briefly discuss the paper and provide some future research directions.
## 3 Assessment of the Developed Algorithm Efficiency in Some Case Studies
### White Sea (southern bays)  
The White Sea, morphologically a shelf marine basin is located on the periphery of the Arctic Ocean to which it is connected through the Barents Sea. It is essentially a mediterranean sea, being semi-enclosed by the northern European landmass. Both climate change, specific landscape features of the watershed and intensification of agricultural/industrial activities in the area brought about some significant ecological changes in the sea, notably in its southern bays, which trophic status turned to mesotrophic.  
Concentrations of _chl_, _sm_ and _doc_ were measured _in situ_ in the southern bays of the White Sea (recipients of full-flowing riverine fresh waters) in July 2001. One SeaWiFS image taken synchronously with shipborne measurements on July 10, 2001 (time gaps between _in situ_ measurements and satellite overpass were less than 24 hrs) was processed by the LM-algorithm. In the absence of a dedicated hydro-optical model, the Lake Ladoga model has been employed for reasons given in section 2.5.  
The CPA concentrations measured _in situ_ are compared in Table 4 with the concentrations retrieved by the LM-based algorithm described above.
###### Abstract
Recently, researchers have used satellite-based images to accurately measure and review land use alteration, evaluate land degradation, and monitor land use [30; 31; 32]. Landsat data are also utilized to investigate land degradation issues such as desertification and deforestation. In three distinct zones in the Swat District, including agro-forest, alpine forest, and scrub forest, yearly deforestation rates of 1.85 percent, 1.28 percent, and 0.80 percent were recorded [33; 34]. The northern region of Malam Jabba, Swat District, KPK, Pakistan, was chosen as a case study since it is the most severely affected by vegetation loss caused by anthropogenic and natural sources at the regional level. This land cover classification is performed on Landsat satellite images as it has the advantage of long-term data availability compared with some recent satellite missions. Assessing the urban and agricultural land cover impact on forest land cover is critical. We must evaluate the existing patterns of land use as well as how and why land should be managed and reviewed in order to have a sustainable management practice [33]. In this research, the Malam Jabba region was considered because it contains the maximum cover of confiferous forest in the region. The primary objectives within the context of the current study are (i) to investigate the efficacy of hybrid models for the LULC and its impact on the forest cover area in the research region, and (ii) to create spatial maps utilizing the presented methodologies to pinpoint the most pressing hotspots in need of urgent action. We used the random forest procedure using feature collection because of its appealing abilities, including high precision and robustness over clustering the training data. These findings confirmed the importance of spectral bands, vegetation indices, and DEM in LULC mapping. For data sets with a large number of variables, it is vital to choose the most relevant ones [35; 36]. The spatial and temporal data analysis was conducted from 1980 to 2020 with a five-year gap. Each Landsat image was classified as forest, woodland, and other land cover types using the random forest classifier. The training and validation points were created and used for the kappa coefficient and error matrix to assess the accuracy of image classification. In order to offer a current overview of the state of forest resources in the region and the dynamics in deforestation rates, we quantified the rate and potential differences and contrasted them with the other research results and statistics. The information presented in this research will be a great help in the future for guiding future efforts to sustainably manage the forest ecosystem.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 5 Conclusions  
In this paper, a reversible data hiding scheme for encrypted color halftone images is proposed. As less information redundancy is available for halftone images, we adopted two stego images to add redundancy artificially. This can increase the embedding capacity of the scheme, and, more importantly, the original image can be restored. Moreover, we combine the four stego images generated in embedding procedure into one image for easy transmission. By using two secret keys, the cover image can be recovered losslessly after the watermarks are extracted. Experimental results demonstrate the advantages of our methods. The proposed scheme can realize real reversibility with high embedding capacity for encrypted color halftone images.  
Conceptualization, Y.-X.S. and B.Y.; software, Y.-X.S. and B.Y.; Formal analysis, Y.-X.S., B.Y., and J.-S.P.; Methodology, Y.-X.S., B.Y., and H.-M.Y.; Writing--original draft, Y.-X.S., B.Y., J.-S.P., and N.C.; Writing--review and editing, B.Y., J.-S.P., H.-M.Y., and N.C.  
This work was funded by the National Natural Science Foundation of China (NSFC) (No. 61272432), Shandong Provincial Natural Science Foundation (No. ZR2014 JL044), and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No. 18 YJAZH110).  
The authors declare no conflicts of interest.
# Opportunities for Integrating Underground Railways into Low Carbon Urban Energy Networks: A Review
## 3 Urban Waste Heat Sources
### Data Centers  
The potential of recovering waste heat from data centers was investigated by the authors of [18; 19; 20], who highlighted the high energy consumption associated with cooling systems in data centers and indicated how recovering low-grade waste heat from these systems can increase the operating efficiency of data centers. The authors of [19] showed how a data center can be cooled by air, liquid or a combination of the two (hybrid cooling). Air cooling involves the deployment of computer room air conditioners (CRACs) or computer room air handlers (CRAHs), which circulate cold air through the racks. Most existing data centers operate with air-cooled systems, as shown by the authors of [18], who also described the typical operation of these systems, where the heat picked up by the air is rejected to chilled water loops, which then dump the heat to the atmosphere, typically through chillers or cooling towers. As air has a low heat carrying capacity, it needs to be supplied at lower temperatures to the racks, so waste heat can be harvested from the CRAC or CRAH return air at temperatures varying from 25 to 45 \({}^{\circ}\)C. There is also the possibility to recover waste heat from the chilled water return stream, usually at temperatures between 10 and 20 \({}^{\circ}\)C. As for liquid cooling, which includes both hybrid and all liquid systems, the heat is removed by direct contact between the coolant liquid and the electric parts of the server. As liquids have higher heat carrying capacities than air, liquid cooling systems operate with lower temperature differences, which leads to the possibility of extracting waste heat at higher temperatures, normally varying from 50 to 60 \({}^{\circ}\)C [19]. Data centers can be located either in rural or urban areas, so their availability as a heat source will vary depending on local conditions. As they are required to run constantly for reliability purposes, data centers generate waste heat that could be exploited all year round. The main restriction is also related to the continuous operation; as downtime and service disruption need to be minimized for data reliability and security reasons, retrofitting waste heat recovery systems into existing data centers is often not feasible.
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 5 Simulation Results and Discussions
### Simulation 2
With the increase in trajectory, iteration count, and clutter rate, the time consumption of the IMM-TPMB-GBP-ASCKF filter is only slightly higher than that of the IMM-TPMB-GBP-CKF filter, which is reasonable due to the additional noise estimation process of the proposed filter. As the noise increases, the running time of the four filters significantly increases. Due to the improved ASCKF filter's good adaptability to noise, it can effectively filter out clutter, and the time consumption does not change significantly compared to CKF. Additionally, under high clutter density conditions, the IMM-TPMB-GBP-ASCKF filter demonstrates higher accuracy. The results indicate that the proposed IMM-TPMB-GBP-ASCKF filter maintains robust tracking performance for highly maneuverable targets and under the impact of unknown noise, while incurring a moderate computational cost. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Particle Number** & **Clutter Rate** & **TPMB-BP** & **TPMB-GBP** & **IMM-TPMB-** & **IMM-TPMB-** \\  & low & 20.635 & 19.229 & 171.960 & **176.263** \\  & high & 34.871 & 30.615 & 179.284 & **184.736** \\ \hline  & low & 29.256 & 27.836 & 174.642 & **178.918** \\  & high & 50.697 & 47.698 & 193.856 & **197.604** \\ \hline  & low & 39.198 & 33.651 & 181.284 & **182.684** \\  & high & 59.989 & 54.873 & 205.207 & **208.837** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of averaged running time under different clutter levels and particle counts in Simulation 2. Figure 7: Number of false targets for four filters under low and high clutter rates.
## 4 The Bassnet Model
### Extraction of Audio Descriptors  
The sonification of CQT log magnitude spectrogram outputs generated by the model is not straight-forward, because this data representation is not lossless. First of all, the phase information is discarded and, furthermore, the CQT transform is not invertible, like, for instance, the Fast Fourier Transform. However, because we need to sonify only data describing a single source type (bass guitar), and assuming the bass guitar tracks are monophonic, it is often possible to create a rough approximation of the target sound while using some form of additive synthesis based on a small set of audio descriptors.  
In the current work, we use pitch (or fundamental frequency, denoted \(f_{0}\)), level (denoted \(l\)), and onset strength (denoted \(o\)) as descriptors for sonification. Here, we define level as the instantaneous power of the STFT spectrogram, expressed in dB. These descriptors capture aspects of the sound that can be estimated from the CQT spectrogram relatively accurately and, at the same time, provide--in the case of monophonic bass guitar--sufficient information to synthesize a bass sound.  
We need ground truth data to train the model to predict these descriptors from the generated CQT spectrogram \(\hat{y}\). To extract instantaneous pitch values from the bass tracks in the training data we the convolutional neural network (CNN) pitch estimation system crepe [57]. For onset strength, we use the CNN onset detector from madmom [58; 59]. Both are publicly available state-of-the-art methods. To estimate sound level we use the _stft_ and _power_to_db_ functions from _librosa_[60].
# Adsorption of Metolachlor and Its Transformation Products, ESA and OXA, on Activated Carbons
## 3 Results
### Adsorption
#### 3.2.2 Transformation Products ESA and OXA Adsorption
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbent**} & \multirow{2}{*}{**Metabolite**} & \multirow{2}{*}{
\begin{tabular}{c} **qmax, exp** \\ **(mg/g)** \\ \end{tabular} } & \multicolumn{3}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} & \\ \cline{4-9}  & & **qmax** & **b** & **k\({}_{\text{f}}\)** & **k\({}_{\text{f}}\)** & **k\({}^{2}\)** \\  & & **(mg/g)** & **(L/mg)** & **R\({}^{2}\)** & **(mg/g)**(L/mg)**\({}^{1/\text{Inf}}\)** & **n\({}_{\text{f}}\)** & **R\({}^{2}\)** \\ \hline S-KC & & 218 & 258 & 0.291 & 0.957 & 65 & 2.5 & 0.807 \\ R-KC & ESA & 273 & 278 & 0.668 & 0.888 & 118 & 4.0 & 0.824 \\ AQ630 & & 93 & 104 & 0.383 & 0.571 & 38 & 3.4 & 0.502 \\ \hline S-KC & & 434 & 479 & 0.297 & 0.993 & 182 & 3.9 & 0.851 \\ R-KC & OXA & 554 & 644 & 0.311 & 0.989 & 182 & 2.6 & 0.931 \\ AQ630 & & 126 & 380 & 0.280 & 0.789 & 52 & 3.2 & 0.922 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fitting parameters of the equilibrium adsorption isotherms to the Langmuir and Freundlich models. the micropores (already adsorbed) and the molecules remaining in the solution, making the driving force to overcome the mass transfer resistance between the aqueous and the solid phase weaker; however, further investigation of this phenomenon should be performed. Nevertheless, the removal of the metabolites from water remains quite efficient for both studied adsorbents, being close to 50% for R-KC activated carbon. These results demonstrate the ability of the synthesized porous material to be employed for environmental remediation, it being able to adsorb in its pores both metolachlor metabolites, even when the amount of them is low.