esa-sceva/satcom-chunk-collection huggingface dataset:
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 2 Related Work
### Studies on the Impact of Population Density  
A link between population density and investment in infrastructure by telecommunications companies has been noted in the past. Indeed, there is a consensus that a higher population density is linked to more developed infrastructure. In developed countries, the population correlates with cellular network density [PERSON] et al. (2018). Data from the Organization for Economic Cooperation and Development (OECD) have shown that, among the developed countries, those with large urban populations such as South Korea, Japan, France, and the Netherlands exhibit higher broadband penetration than those with a significant rural area, such as the United States and Canada [PERSON] et al. (2016).  
One study that tested the link between population density and mobile infrastructure is by [PERSON] et al. [PERSON] et al. (2018). They found that population density is not the whole story. They argue that there is also a correlation with the cost of investment. When financial markets demand higher interest on borrowing funds, telecommunications firms invest less in capital-intensive infrastructure. The study [PERSON] et al. (2018) conclusion is that both higher population density and favorable interest rates are required to drive investment capital expenditure (CAPEX). The cases considered in this study are those with a combination of low population density and/or a high cost of capital.
## 3 Proposed Method  
Taking the IP dataset as an example, the overall framework of the proposed SSAMMA is shown in Figure 1. The basic process was as follows. Firstly, the SLIC algorithm was used to segment the HSI to obtain \(k\) adaptively sized super-pixel image blocks \(\{S_{1},S_{2},\cdots,S_{k}\}\). The samples in the spatial region block were adaptively weighted in each image block to obtain a new image to show that samples of the same category had more similar spectral lines. Secondly, the HSIs were randomly divided into training samples and test samples. For each category of ground objects in the training samples, a single-manifold map was constructed. The optimal mapping matrix for each sub-manifold was found by combining the spatial-spectral intra-manifold map and the spatial-spectral inter-manifold map. Finally, the pixels in each sub-manifold are downscaled. The feature representation of the entire hyperspectral data in the low-dimensional subspace is obtained by combining all of the sample features in the low-dimensional subspace. The obtained low-dimensional data cube is fed into the classifier to obtain the final classification results.
## 1 Introduction
In order to complete the ground evaluation requirements of IS for the Taiji technology verification satellite, a type of sensitive structure used in the experiment is designed and a dedicated torsion pendulum measurement system is developed to complement the high-voltage levitation test and comprehensively evaluate the IS. In the direction of the measurement axis, the resolution of the current system can reach \(9.5\times 10^{-7}\) m/s\({}^{2}\)/Hz\({}^{1/2}\). In addition, it is found that the main factor limiting the system resolution on the ground is the seismic noise, which points out the direction for further system performance upgrades to meet the future evaluation requirements of the Taiji project [35]. The research contents of this paper are as follows: The second section briefly introduces the principles of the space IS measurement and electrostatic force control. A multi-degree-of-freedom electrode division is also achieved. Section 3 describes the detailed design of the sensitive structure, front-end electronics, and torsion pendulum ground measurement system. In Section 4, the designed system is used to measure and evaluate the control quality and accelerometer resolution of the test mass (TM) in the non-sensitive axis direction, and the experimental results are analyzed and discussed. Finally, a comprehensive summary of the work in this paper is concluded, and the future upgrade work is expected.
## 1 Introduction
In addition, it should be noted that the development of low-carbon and energy-saving alternatives to cement has attracted widespread interest [20]. In recent years, alkali-activated (AA) cementitious materials based on sludges and slag, which reuse the waste, have received considerable attention [21; 22]. Compared with traditional OPC, AA materials show a variety of obvious advantages, such as low carbon emission, rapid hardening, excellent durability, and early strength development [23]. Therefore, AA materials are important for improving the sustainable development of construction materials. Among them, the research on alkali-activated slag (AAS) cementitious materials is relatively extensive. Nevertheless, the drying shrinkage of AAS concrete, which is an important engineering characteristic that affects the development of cracking, is twice as high or even higher than that of OPC concrete [24]. Meanwhile, LWA usually absorbs water during the preparation process of concrete due to its high porosity, which increases the drying shrinkage of AAS concrete [25]. Thus, compared with OPC concrete, AAS lightweight aggregate concrete is more easily cracked, which has a negative influence on the bearing capacity and durability of the concrete. However, relevant research has shown that pre-treatment of LWA is an effective strategy to reduce the drying shrinkage of concrete [26; 27; 28]. Therefore, it is significantly valuable that the effect of different pre-treatment methods for NSLA on the shrinkage of AAS concrete can be systematically evaluated. The main purpose of this study is to investigate the influence of the particle gradation (particularly particle size) of NSLA on the mechanical properties, especially the axial compressive performance, of alkali-activated slag non-sintered lightweight aggregate concrete (abbreviated as AN-LAC). Based on the above-mentioned analysis, AN-LAC with the best aggregate gradation was selected to further explore the effect of different pre-treatment methods for aggregate on the performance of concrete. Additionally, a scanning electron microscope (SEM) test was also performed to analyze the microstructure of AN-LAC after the pre-treatment of aggregate. Through the above-mentioned tests, this study can fill the gap in the current literature on the effect of characteristics of NSLA, such as particle gradation and water absorption, on the performance of LWAC, to a certain extent. In addition, this paper can also establish the modified elastic modulus prediction model of AN-LAC considering the effect of the particle size of NSLA, which provides theoretical support for the relevant research.
## 4 Data Transformation Impact on Gray Values Compression
### Burrows-Wheeler Transformation  
The Burrows-Wheeler transformation, or BWT for short, was described in a 1994 article by [PERSON] [49]. In BWT, the input data are not treated as a string, but the input stream is divided into blocks and each block is encoded independently. This transformation is most efficient when the input data are processed as one block. The basic idea is to reorder a current stream \(S\) with \(N\) symbols into another stream \(L\), and the mathematical term for the reordering is _permutation_.  
The encoding algorithm adopts a stream \(S\) consisting of \(N\) symbols \(S[0],\ldots,S[N-1]\) selected from the sorted alphabet \(X\). The encoder creates an \(N\times N\) matrix \(M\) and stores \(S\) in its first row followed by \(N-1\) copies of the stream \(S\) cyclically shifted one symbol to the left. The matrix is sorted lexicographically by row, and the output of the BWT algorithm is the last symbol in each permuted stream and the row number where the original stream is found.
# Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach +
## 4 Results
### Simulation Results  
In order to validate the accuracy of the proposed strategy, in the first simulation scenario, we carried out Monte Carlo simulations with 10,000 trials, i.e., 100 trials over 10 different trajectories and under 10 random deployment of the \(M\) RFID tags and assuming no constraints on the tag placement. The different trajectories are synthesised automatically using sequences of randomly generated via-points. All of the simulations are conducted in a simulated warehouse environment using Robotics System Toolbox in Matlab (see Figure 4).  
Figure 4: Trajectory samples generated by different velocities for the same path in a realistic warehouse scenario. (**a**) Ground truth trajectories, (**b**) estimated trajectories by the SEKF. A possible configuration of the four RFID is reported with red circle markers and denoted with numbers.  
Figure 3: Experimental setup. (**a**) Room map, with the two areas. Vertical tags are depicted as blue triangular markers, Horizontal tags are depicted as red square markers. (**b**) A picture of the robot taken during the experimental campaign.  
In all the trials, four RFID tags, with random locations (see an example in Figure 4) are considered. The system evolution along each trajectory is simulated with a sampling time \(T_{s}=200\) ms. In each trajectory, the desired linear velocity \(v_{k}\) and the maximum angular velocity \(\omega_{k}\) of the mobile platform moving on the plane (and explicitly reported in (2)) are set equal to 2.8 m/s while the controlled angular velocity depends on the shape of the trajectory and ranges in the set \((-0.5,0.5)\) rad/s. All the uncertainties (i.e., \(\gamma_{k}\) acting on the vehicle velocities in (10), and \(\varepsilon_{k}\) acting on the offset-affected RFID ranges in (12)) are assumed normally distributed, zero-mean and white. Therefore, \(\gamma_{k}\sim\mathcal{N}(0,Q_{k})\), where \(Q_{k}=\text{diag}(\sigma_{\
u}^{2},\sigma_{\
u}^{2})\) and \(\sigma_{\
u}=0.08\) m/s and \(\sigma_{\omega}=0.09\) rad/s, which are a conservative assumption with respect to an actual industrial vehicle. Similarly, \(\varepsilon_{k}\sim\mathcal{N}(0,R_{k})\), where \(R_{k}=2\sigma_{\tau}^{2}I_{M}\) and, hence, \(\sigma_{\tau}=0.1\) m is the range reading uncertainty. We assume here that \(I_{M}\) is the identity matrix of dimension \(M\) (i.e., of the same dimension of the number of available RFID tags). As reported in (2), the vehicle state is identified with its Cartesian coordinates \((x_{k},y_{k})\) and its orientation \(\theta_{k}\). Therefore, the covariance matrix expressing the vehicle initial location uncertainty is \(P_{0}=\text{diag}(0.1^{2}\text{ m}^{2},0.1^{2}\text{ m}^{2},0.1^{2}\text{ rad}^{2}\). By denoting with \(s_{k}=[x_{k},y_{k},\theta_{k}]^{T}\) the vehicle state at time \(kT_{s}\), the initial state estimate is assumed to be \(\hat{s}_{0}=s_{0}+\eta\), where \(\eta\sim\mathcal{N}(0,P_{0})\). Notice that we denote with the hat \(\hat{}\) the quantities that are estimated.
## 3 Results
### Scanning Electron Microscopy (SEM)  
Figure 7 shows the surface structures and morphological characteristics of sesame stalk feedstock (SF) corresponding to the biochar (SB-1, SB-2, and SB-3) that were identified by SEM images. SEM magnified at 500X represents the morphological appearances of sesame biochar produced by different pyrolysis conditions. The raw sesame stalk (Figure 7a) had a smaller pore size with relatively lower porosity and blocked tubular structure compared to its biochar. It was clear that an evident porous tubular structure and channels occurred with different pyrolysis conditions, and the grid structure was detected at high magnification, which was 2000X for feedstock and 1000X for biochar. The parabolic dish collector method increased the porous numbers and diameters of the different biochar types (SB-1 and SB-2), which in turn developed the fibrous channels with a highly complex network of pores. The pores of SB-3 were relatively different in size and shape compared to those of SB-1 and SB-2. On the other hand, the SEM images of SB-2 and SB-3 (1000X) were relatively close and presented a variety of shapes and sizes of microspheres and macrospores compared to SB-1.  
Figure 7: SEM images of sesame stalk feedstock (SF) and its different biochar types produced at 300 \({}^{\circ}\)C for 3 h: (**a**) sesame stalk feedstock (SF); (**b**) SB-1 biochar formed around the inner sidewalls of the internal chamber of the parabolic dish collector); (**c**) SB-2 biochar formed on the upper part of the internal chamber; and (**d**) SB-3 biochar of conventional method (muffle production).
# Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings
## 3 Results
The addition of 20% noise resulted in additional accuracy drop of 2.89%, 11.6%, 14.45% for the proposed method, MSE-based template matching, and correlation-based template matching method, respectively, but showed increase in accuracy of 3.12% for PCA + K-means. Our method showed the best performance in in-vivo dataset as well. Figure 4 shows the classification results of spikes from in-vivo in 3-dimensional PCA feature domain. Compared to the manual classification results, our method showed the highest accuracy of 99.39%, and MSE-based template matching, correlation-based template matching, and PCA + K-means resulted in accuracy of 97.73%, 94.90%, and 83.02%, respectively. As seen in Figure 4, all four methods were successful at classifying the red and the blue clusters. However, for the yellow cluster, compared to our method, the other models were highly inaccurate in classifying the spikes on the boundary. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Noise** & **Ours** & **MSE-Based** & **Correlation-Based** & **PCA + K-Means** \\  & & & **Template Matching** & **Template Matching** & **Clustering** \\ \hline Easy2 & 0.05 & 100 (98.68) & 100 (98.41) & 100 (98.79) & 99.96 (98.21) \\  & 0.1 & 99.85 (98.49) & 95.54 (97.31) & 94.91 (98.89) & 99.96 (98.18) \\  & 0.15 & 97.99 (97.19) & 89.53 (88.82) & 94.86 (94.25) & 98.79 (96.77) \\  & 0.2 & 96.50 (95.20) & 93.50 (87.44) & 93.37 (90.35) & 95.46 (93.84) \\ \hline Difficult1 & 0.05 & 100 (98.78) & 100 (98.80) & 100 (98.98) & 100 (98.84) \\  & 0.1 & 99.96 (98.93) & 99.34 (95.12) & 99.48 (98.84) & 100 (98.93) \\  & 0.15 & 99.77 (97.55) & 90.15 (84.82) & 98.55 (96.83) & 99.77 (97.32) \\  & 0.2 & 98.08 (96.62) & 81.99 (92.10) & 97.22 (93.75) & 95.69 (92.95) \\ \hline Difficult2 & 0.05 & 99.61 (98.49) & 98.08 (97.52) & 91.62 (93.76) & 86.04 (87.28) \\  & 0.1 & 96.62 (94.66) & 92.34 (68.51) & 90.56 (88.54) & 84.11 (83.90) \\  & 0.15 & 83.23 (82.20) & 83.03 (73.61) & 80.82 (74.36) & 74.43 (72.63) \\  & 0.2 & 71.15 (51.55) & 62.72 (32.00) & 67.75 (33.24) & 65.92 (32.22) \\ \hline Total & & 96.42 (94.09) & 92.89 (87.96) & 94.32 (91.06) & 93.76 (90.57) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# F127/Cisplatin Microemulsions: _In Vitro_, _In Vivo_ and Computational Studies
## 3 Results and Discussion
### Biochemical Analysis  
As shown in Table 1, the group treated with the bulk form of Cis had higher serum levels of AST, ALT, and ALP compared to the control group (\(p<0.001\)). Serum levels of BUN and creatinine were statistically significantly higher in rats treated with the bulk form  
Figure 4: **(A)**: Photomicrograph of the kidney of a control rat; normal glomerulus (G) and normal proximal tubules (PT). (**B**) Photomicrograph of the kidney of a rat treated with a bulk form of Cis (7 mg/kg); arrow shows narrowing of proximal tubules. (**C**) Photomicrograph of the kidney of a rat treated with F127/Cis microemulsion (7 mg/kg) with normal Bowman’s capsule and normal distal tubules. (**D**)Light micrograph of a rat’s kidney treated with 14 mg/kg of F127/Cis microemulsion showing swelling of proximal tubules (arrow). (H & E stain. Mic. Mag. \(\times\) 40).  
of Cis than in the control rats (\(p<0.05\) and \(p<0.001\), respectively). Serum LDH levels were also higher in rats treated with the bulk form of Cis compared to the controls (\(p<0.05\)) (Table 1). The rats that received the 7 mg/kg dose of F127/Cis microemulsion had higher serum levels of LDH, BUN, and creatinine than the healthy control group; however, these changes were not statistically significant (\(p>0.05\)). The group that received the 14 mg/kg dose of F127/Cis microemulsion showed a significant increase in serum liver enzymes than the normal control group (\(p<0.01\)). There was also a significant increase in serum BUN and creatinine levels in this group compared to the normal control group (\(p<0.05\) and \(p<0.01\), respectively). Statistical analysis revealed a significant difference in liver malondialdehydedehyde content of rats who received the high dose (14 mg/kg) of F127/Cis microemulsion compared to the normal control rats (\(p<0.05\)). On the other hand, serum LDH levels did not change in rats receiving the 14 mg/kg dose of F127/Cis microemulsion compared to the untreated controls (\(p>0.05\)). The group treated with the low dose (7 mg/kg) of F127/Cis microemulsion showed elevated levels of serum AST and ALT (\(p<0.05\)); however, the other parameters remained unchanged.  
Previous experiments have shown that the nanomecilles forms of nanoparticles and drugs have better biocompatibility and biodegradability than the bulk forms of chemicals [37]. As established earlier, nanoformulations of Cis have less systemic toxicity and provide tumor-specific effects as compared with the bulk forms of Cis [38; 3]. The anti-cancer and hepatotoxicity of different nanoformulations of drugs have been investigated as molecular nano-theranostic agents [39; 40; 41]. Cis encapsulation in micelles or nanoemulsions has resulted in increased therapeutic efficacy and anti-cancer efficiency [38].
## 4 Results and Analysis
### Cognitive Workload Monitoring System
The latest recorded latency for gateway-003 is 196 ms, which is notably lower than that of gateway-002, indicating a more efficient data transmission process and enhancing the responsiveness of applications relying on this gateway. The classification processing time for gateway-003 is measured at 3849.74 ms, which is substantially shorter compared to gateway-002. This suggests that gateway-003 may have more efficient processing capabilities or less computational load at the time of measurement. The workload levels graph for gateway-003 shows the distribution of cognitive workload levels, providing a visual representation of the virtual user's cognitive load. Figure 14: Gateway-002 cognitive workload monitoring data graphs. Figure 16 represents the monitoring data from gateway-004 on Thing Board, showcasing its unique performance metrics. The latest latency recorded for gateway-004 is 132 ms: the lowest among all four gateways. This minimal latency suggests that gateway-004 provides the most efficient data transmission, which is critical for applications requiring real-time data processing. The classification processing time for gateway-004 stands at 2866.82 ms, which is also the shortest among the four gateways. This efficiency in processing indicates a robust performance in cognitive workload assessment, potentially due to better hardware or lower concurrent computational demands. The third graph in Figure 16 illustrates the workload levels for gateway-004, continuing the pattern of distinguishing between low, average, and high workload levels. The table in this figure offers a clear and concise representation of the workload classifications, aiding in performance evaluation and decision-making. We have proposed a system that enables continuous data transmission, real-time classification, and effective monitoring of results by integrating the DGNN model with Raspberry Pi, CoAP, and Things Board. While we have utilized the STEW dataset in this study, validating our model with real human participants is crucial. We plan to recruit healthy participants from the university, adhering to ethical guidelines and obtaining informed consent. EEG data will be collected during cognitive tasks, and the system's performance will be evaluated based on classification accuracy, sensitivity, specificity, and response time. Figure 15: Gateway-003 cognitive workload monitoring data graphs.
# Dynamic Characteristics of Urban Rail Train in Multivehicle Marshaling under Traction Conditions
## 4 Analysis and Discussion
### Vibration and Comfort Analysis of Urban Rail Trains
#### 4.2.2 Stability Analysis  
The evaluation of the train stability mainly refers to the vibration acceleration, vibration amplitude, vibration frequency and vibration duration of the train body. The running quality is determined by the parameters of the vehicle itself, which is the most widely used dynamic performance of trains in the world. Sperling index is one of the indicators to evaluate the train stability, so this index is also the running stability evaluation Chinese standard specified in GB/T 5599-2019.  
The sperling index \(W_{\rm s}\) can be expressed as  
\[W_{\rm s}=0.896\sqrt[10]{\frac{a^{3}}{f}F(f)} \tag{2}\]  
where \(a\) is the vibration acceleration, cm/s\({}^{2}\); \(f\) is the vibration frequency, Hz; and \(F(f)\) is a weighting factor that considers the sensitivity of the human body to various frequencies of vibration. Here, \(W_{\rm s}\leq 2.5\) is excellent grade, \(W_{\rm s}\leq 2.75\) is good grade and \(W_{\rm s}\leq 3.0\) is a pass grade.  
Figure 13 shows the vertical sperling index and the lateral sperling index of each car of the urban rail train during different time periods. In terms of the vertical sperling index, each carriage monotonically increases within 0-15 s and 20-40 s, and the value of the sperling index decreases within 15-20 s because the working condition of the traction motor changes from constant rotation during this time period. The torque area becomes a constant power area, and the torque output by the motor is reduced to optimize the sperling index of each carriage. With increasing vehicle speed, the influence caused by the traction motor gradually decreases, and the value of the stability index gradually increases. The change rate of the four motor cars within 15-20 s is greater than that of the two trailers, indicating that the traction motor has a greater impact on the EMU's stability index. The value of the vertical sperling index of M1 is the largest, followed by M2, with variation ranges of 1.11-2.25 and 1.09-2.23, respectively. In terms of the lateral sperling index, the values of the lateral sperling index of all carriages monotonically increase, and the difference in the lateral sperling index values of the six carriages is small. The vertical sperling index and lateral sperling index of each car of the urban rail train are both less than 2.5, so the stability level of the urban rail train is excellent.