esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Verification of the Composite Main Reflector
### Verification of the Performance of the Composite Main Reflector  
In order to verify the performance of the reflector based on the manufacturing process, a modal test of a single reflector was performed. The modal test was performed using accelerometer-type Dewetron equipment. After attaching an accelerometer to the end of the reflector, the reflector was excited using an impact hammer to measure the stiffness of each of the four types of reflectors when in deployed conditions. In order to simulate the deployment shape of the actual antenna during the test, a deployment structure was attached to the top, middle, and bottom of the antenna, as shown on the left side of Figure 13, and the test was performed at the same deployment angle as the actual operating conditions.  
The test results are shown on the right side of Figure 13. All of the manufactured reflectors satisfied the stiffness requirement (2.5 Hz or more) when the antenna was deployed. But, the first natural frequency was measured to be relatively high because the weight of the composite reflectors in Cases 3 and 4 excluding the adhesive film was lighter than the others. In the case of the reflector in Case 4, which was additionally laminated at the fastening interface, the weight increased slightly compared to Case 3, but as the number of laminates at the location supporting the reflector under the deployment condition increased, the stiffness of the restraint increased, and the stiffness when the reflector was deployed also tended to increase.
## 6 Existing Routing Protocols
### TEEN (Threshold sensitive Energy Efficient sensor Network)  
TEEN [8] is a cluster based hierarchical routing protocol based on LEACH. This protocol is used for time-critical applications. It has two assumptions [14]:  
* The BS and the sensor nodes have same initial energy
* The BS can transmit data to all nodes in the network directly.  
In this protocol, nodes sense the medium continuously, but the data transmission is done less frequently. The network consists of simple nodes, first-level cluster heads and second-level cluster heads. TEEN uses LEACH's strategy to form cluster. First level CHs are formed away from the BS and second level cluster heads are formed near to the BS.  
A CH sends two types of data to its neighbors--one is the hard threshold (HT) and other is soft threshold (ST). In the hard threshold, the nodes transmit data if the sensed attribute is in the range of interest and thus it reduces the number of transmissions. On the other hand, in soft threshold mode, any small change in the value of the sensed attribute is transmitted. The nodes sense their environment continuously and store the sensed value for transmission. Thereafter the node transmits the sensed value if one of the following conditions satisfied:  
* Sensed value \(>\) hard threshold (HT).
* Sensed value \(\sim\) hard threshold \(>=\) soft threshold (ST).  
TEEN has the following drawbacks:  
* A node may wait for their time slot for data transmission. Again time slot may be wasted if a node has no data for transmission.
* Cluster heads always wait for data from nodes by keeping its transmitter on.
# GIS-Based Wind and Solar Power Assessment in Central Mexico
###### Abstract  
In Mexico, the economic and industrial development is in the center and north; this represents more than 50% of the country's total consumption. Data on population and energy consumption will be obtained from the following sources: the National Institute of Geography and Statistics (INEGI), and the Energy Information System. Regarding meteorological data, two databases are used: the Automatic Weather Stations (AWS) (for solar irradiance data) and the MERRA-2 reanalysis data (for wind data). These data will be analyzed for use in a geographic information system (GIS) using kriging interpolation to create maps of solar and wind energy. The area studied includes the following states: Mexico City, Puebla, State of Mexico, Hidalgo, Morelos, Zacatecas, Queretaro, San Luis Potosi, Guanajuato, Aguascalientes and Tlaxcala. The results showed that the areas with the highest solar potential are Hidalgo, Estado de Mexico, Morelos, northern Puebla, southern Queretaro, northwestern Guanajuato, and northern Zacatecas, with 5.89 kWh/m\({}^{2}\)/day, and the months with the highest solar potential are March, April, May, and June. Regarding wind potential, the maximum wind power density is in Puebla, with 517 W/m\({}^{2}\), and the windy season in central Mexico spans June, July, August, September, October, and November.  
AWS; GIS; kriging; MERRA-2; Mexico; solar; wind; wind power density
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 4 Case Study  
To verify the feasibility of laser scanning-based technology for the geometric accuracy evaluation of subway stations, we applied the proposed method to evaluate the geometric accuracy of the Hongqi Road subway station. The Hongqi Road Station is the eighth station in the Suzhou Subway Line S1, it has an east-west layout and is located on the west side of the intersection of Qianjin West Road, as shown in Figure 8. The main body of the station is an underground two-story single-column, double-span closed frame structure constructed by the open excavation method. It has a length of 313.6 m, a width of 32.2 m, and a bottom slab burial depth of approximately 16.6 m.
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 4 Results
### Differences between the Assessment Levels  
Comparisons are performed using the susceptibility assessments of landslides in the study area from different periods. Figure 9 and Table 6 compare the differences between the assessment results for 1992 and 2000 and between those for 2000 and 2009, showing that the area where the level difference is zero is the largest area. Meanwhile, the area with increased susceptibility is much larger than the area with decreased susceptibility in the period ranging from 1992 to 2000, which is contrary to the results for the period ranging from 2000 to 2009.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Level Difference**} & \multicolumn{2}{c}{**1992 and 2000**} & \multicolumn{2}{c}{**2000 and 2009**} \\ \cline{2-5}  & **Area (10\({}^{3}\) km\({}^{2}\))** & **Percentage (\%)** & **Area (10\({}^{3}\) km\({}^{2}\))** & **Percentage (\%)** \\ \hline \(-3\) & 0.041 & 0.053 & 0.516 & 0.667 \\ \(-2\) & 0.633 & 0.819 & 1.232 & 1.592 \\ \(-1\) & 5.433 & 7.030 & 13.308 & 17.193 \\ \(0\) & 41.923 & 54.244 & 48.466 & 62.614 \\ \(1\) & 26.954 & 34.876 & 12.799 & 16.535 \\ \(2\) & 1.928 & 2.495 & 0.893 & 1.154 \\ \(3\) & 0.373 & 0.483 & 0.190 & 0.245 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Differences in the assessment levels.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Susceptibility Level**} & \multicolumn{2}{c}{**1992**} & \multicolumn{2}{c}{**2000**} & \multicolumn{2}{c}{**2009**} \\ \cline{2-7}  & \multirow{2}{*}{**Number**} & **RNA \({}^{*}\) (10\({}^{3}\)/km\({}^{2}\))** & **Number** & **RNA (10\({}^{3}\)/km\({}^{2}\))** & **Number** & **RNA (10\({}^{3}\)/km\({}^{2}\))** \\ \hline Level I & 33 & 1.25 & 34 & 1.52 & 26 & 1.11 \\ Level II & 79 & 3.02 & 44 & 2.45 & 38 & 2.41 \\ Level III & 53 & 3.90 & 60 & 3.50 & 59 & 2.84 \\ Level IV & 53 & 4.77 & 80 & 4.03 & 95 & 5.44 \\ \hline \hline \end{tabular} \({}^{*}\)RNA denotes the ratio of number of landslides to area.  
\end{table}
Table 5: Historical landslide event levels.
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## 4 Experimental Results and Comparison Analysis
### Performance of Tamper Detection and Self-Recovery
#### 4.2.1 Text Addition Attack  
Figure 6 shows the performance of tamper detection and recovery for text addition attacks. A text logo \"LENA\" is added to the watermarked Lena image, which is shown in Figure 6c. The tamper localization maps of these three methods are presented in Figure 6d-f. From the detection results, it can be seen that the detection results in [13; 25] have obvious missed detection problem around the tampered regions, especially in [13]. This is because it only has one-level detection in [13], which cannot provide a reliable detection result. Figure 6g-i gives the corresponding reconstructed images. From the figures, we can learn that there leaves some tampering traces on the images restored by [13; 25], which are caused by the poor tamper localization result. These traces cannot be restored during image recovery process. Compared to references [13; 25], the recovered Lena image in the proposed method  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Images** & **Smooth Blocks** & **Texture Blocks** & **Watermark Capacity (bpp)** & **PSNR (dB)** \\ \hline Car1 & 10,516 & 5868 & 2.36 & 40.41 \\ Car2 & 13,572 & 2812 & 2.17 & 42.20 \\ Clock & 12,366 & 4018 & 2.25 & 41.22 \\ Airplane & 10,946 & 5438 & 2.33 & 40.56 \\ Cameraman & 10,535 & 5849 & 2.36 & 40.65 \\ Lena & 8561 & 7823 & 2.48 & 39.82 \\ Barbara & 7321 & 9063 & 2.55 & 39.52 \\ Venice & 6062 & 10,322 & 2.63 & 39.15 \\ Boat & 6029 & 10,355 & 2.63 & 38.87 \\ Goldhill & 5038 & 11,346 & 2.69 & 38.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The analysis of watermark imperceptibility.  
has good subjective effect. It is almost the same as the original watermarked image. To make a better comparison, Table 3 lists the FPR, FNR, and the PSNR of recovered images. Due to the use of the three-level detection strategy, the FPR of the proposed method is slightly higher that [13; 25]. However, the proposed method has the lowest FNR among these three methods. This good tamper detection result also contributes a better image quality for the restored image. The PSNR value of the restored image in the proposed method is more than 45 dB.
## 3 Proposed Strongest AoA Components Estimation Algorithm
### Approximated 1-Bit CRLB with AQNM  
In this subsection, we detail the corresponding approximated CRLB of hybrid architecture with 1-bit A/Ds, which can be used as a reference benchmark [30; 40; 41].  
For hybrid mmWave communication system equipped with ideal A/Ds, the received signal follows a circularly complex Gaussian distribution, i.e., \(\mathbf{\hat{y}_{i}}\sim\mathcal{CN}(\mathbf{a}(\theta)s_{k},\sigma^{2} \mathbf{I})\). In addition, we can obtain the array response for the \(m\)-th element of the ULA as follows  
\[\dot{a}_{m}(\theta)=e^{j2\pi\frac{d}{\lambda}m\cos(\theta)}i2\pi\frac{d}{ \lambda}m\sin(\theta), \tag{13}\]  
and  
\[||\dot{\mathbf{a}}(\theta)||^{2} =\sum\limits_{m=0}^{M-1}\left|\dot{a}_{m}(\theta)\right|^{2}= \left(2\pi\frac{d}{\lambda}\sin(\theta)\right)^{2}\sum\limits_{m=1}^{M-1}m^{2}\] \[=\left(2\pi\frac{d}{\lambda}\sin(\theta)\right)^{2}\frac{M(M-1) (2M-1)}{6}. \tag{14}\]Hence, we can further calculate the Fisher Information Matrix (FIM) as  
\[\text{FIM} =\frac{2}{\sigma^{2}}\Re\left\{\frac{\partial\mathbf{a}(\theta)s_{k} }{\partial\theta}\left(\frac{\partial\mathbf{a}(\theta)s_{k}}{\partial\theta} \right)^{H}\right\}=2\frac{p}{\sigma^{2}}||\dot{\mathbf{a}}(\theta)||^{2}\] \[=2\text{SNR}\left(2\pi\frac{d}{\lambda}\sin(\theta)\right)^{2} \frac{M(M\!\!-\!1)(2M\!\!-\!1)}{6}, \tag{15}\]  
where \(\text{SNR}=\frac{p}{\sigma^{2}}\), and \(P=1\).  
Based on the obtained FIM, the corresponding \(\text{CRLB}=\frac{1}{L\text{-FIM}}\) expression for ideal quantization can be expressed as  
\[\text{CRLB}=\frac{3\lambda^{2}}{4\pi^{2}d^{2}L\sin^{2}(\theta)M(M\!\!-\!\!1)(2 M\!\!-\!\!1)\text{SNR}}. \tag{16}\]  
Utilizing AQNM, we can further derive the approximation of CRLB for hybrid architecture equipped with 1-bit A/Ds at transceivers [41], which is given by  
\[\text{CRLB}_{\text{1b}}= \frac{1+(1-\eta)\text{SNR}}{\eta}\text{CRLB}\] \[= \frac{3\lambda^{2}+1.0902\lambda^{2}\text{SNR}}{2.5464\pi^{2}d^{2 }L\sin^{2}(\theta)M(M-1)(2M-1)\text{SNR}}, \tag{17}\]  
where \(1-\eta\) is the inverse of signal-to-quantization-noise ratio (SQNR) (approximately \(\eta=0.6366\) for 1-bit quantization [42]). Hence, the obtained approximated 1-bit CRLB of hybrid architecture can be used as a RMSE performance benchmark in simulations.
# A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS
## 1 Introduction
Besides, the entropy value is introduced into the setting of the fusion weight, which can effectively extract the detailed information from the enhanced images, improve the fusion effect, and achieve HDRI for complex scenes with high contrast. The remainder of this paper is organized as follows. Section 2 provides a comprehensive analysis of the digital TDI imaging model. According to its particularity, we build the proper experimental system and carry out a preliminary experiment to discover the change laws of image features as the TDI stages increase. All of these form the foundation for further studies. In Section 3, we put forward the EMAHE algorithm for maximizing the image information content, which is proved by relevant experiments. In Section 4, an image fusion method based on gradient pyramid decomposition and entropy weighting is proposed. The comprehensive experimental results are delivered in Section 5. Consequently, conclusions are drawn in Section 6.
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 3 Attitude Determination Methods
### Tri-Axial Attitude Determination Method (TRIAD)
#### 3.1.2 Optimized TRIAD  
One of the limitations of the TRIAD method presented in Section 3.1.1 is the dependence on the choice of the first direction. This direction, called the anchor, remains untouched through the TRIAD process. However, in reality, neither of the two vectors used are perfectly aligned with the model. Therefore, in [25], the authors proposed to improve the method by taking the relative accuracy of the two measurements into account. The idea is to weight the two attitude matrices corresponding to the choice of using either of the two vectors as the first direction. This algorithm is called the optimized TRIAD algorithm. We define \(\sigma_{5}\) and \(\sigma_{B}\) as the standard deviations of the error of the LOS vector and the magnetic field vector, respectively. The attitude matrices \(A_{S}\) and \(A_{B}\) computed via the TRIAD method are for using the Sun LOS vector and magnetic field vector, respectively, as the first direction of the TRIAD frame. The weighting process is done as follows (Equation (13)).  
\[A^{*}=\frac{\sigma_{S}^{2}}{\sigma_{S}^{2}+\sigma_{B}^{2}}A_{S}+\frac{\sigma_{ B}^{2}}{\sigma_{S}^{2}+\sigma_{B}^{2}}A_{B} \tag{13}\]  
In order to obtain an attitude matrix, the resulting matrix must be orthogonal; therefore, the final attitude matrix is obtained in Equation (14). According to [26], one orthogonalization cycle is needed as \(A^{*}\) is close to being orthogonal.  
\[A=\frac{1}{2}[A^{*}+(A^{*-1})^{T}] \tag{14}\]  
Coarse Sun sensors are obviously not as accurate as Sun sensors. Therefore, it is quite legitimate to take the relative uncertainties of the two components into account via the presented method.  
As for the simple TRIAD algorithm, no noise correction is applied. In case of large noise or, for example, high variability of the magnetic field, the computed attitude would not be accurate.
# An Application of Manifold Learning in Global Shape Descriptors
## 4 Experiments
### Multi-Class Classification Results  
In this section, we corroborate the findings of Section 4.2 by training a linear multi-class SVM classifier to assess the accuracy of LESI compared to other shape descriptors. For this experiment, we utilized the McGill dataset. In addition to the shape descriptors evaluated in Section 4.2, we computed another normalized version of Shape-DNA by dividing the feature vector by its first element (similar to what LESI offers) as suggested in [20]. This way we can compare the effect of the exponential weighting scheme without the influence of the normalization method or compactness (offered by cShape-DNA). Using 10-fold cross-validation and repeating the experiment three times, we report the average accuracy for each method in Table 2.  
The new LESI approach significantly outperforms all other methods when using a two-tailed paired t-test \((p<0.05)\). The t-test was performed on one set of 10 folds in order to avoid violating the independence assumption of the t-test. There is a significant improvement in accuracy when comparing the Shape-DNA (Normalized) to other variants of the Shape-DNA, which is due in part to the normalization method. However, the average accuracy of the LESI descriptor is noticeably higher (95%) when compared to 90% of the Shape-DNA (Normalized).  
Finally, Figure 6 shows the confusion matrix obtained from the linear multi-class SVM using LESI descriptor. The number of correct classifications made for each class (indicated by the green diagonal), confirms that our method captures the discriminative features of the shapes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Method** & **NN** & **FT** & **ST** & **E** & **DCG** \\ \hline \multirow{4}{*}{TOSCA} & ShapeDNA & **1.0000** & 0.8091 & 0.9391 & 0.4486 & **0.9584** \\  & cShapeDNA & 0.9474 & 0.7748 & 0.8984 & 0.4748 & 0.9241 \\  & GPS & 0.4868 & 0.4244 & 0.6320 & 0.3614 & 0.6787 \\  & LESI & 0.8684 & **0.8456** & **0.9430** & **0.4860** & 0.9244 \\ \hline \multirow{4}{*}{McGill} & ShapeDNA & 0.7922 & 0.3452 & 0.4977 & 0.3411 & 0.7192 \\  & cShapeDNA & 0.7882 & 0.3943 & 0.5483 & 0.3852 & 0.7470 \\ \cline{1-1}  & GPS & 0.3843 & 0.2508 & 0.4066 & 0.2588 & 0.6020 \\ \cline{1-1}  & LESI & **0.9647** & **0.7046** & **0.8739** & **0.6644** & **0.9251** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shape retrieval performance using TOSCA and McGill datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Average Accuracy** \\ \hline Shape-DNA & 21.02\% \\ Shape-DNA (Normalized) & 90.60\% \\ cShape-DNA & 71.37\% \\ GPS & 50.11\% \\
**LESI** & **95.69\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification accuracy using McGill dataset.
## 1 Energy and QoS  
The power consumption of mobile services will depend on the load. Clearly quality of service (QoS) will also depend on load because a more heavily loaded computational or communication resource will quite naturally increase response times. However such issues are somewhat more complex, because the server clusters hosting the services may turn off some of the resources under lighter loads, so that when load is higher although power consumption will obviously increase, QoS can also improve.  
A simple but quite realistic power consumption relation for current processing units is \(\Pi=A+B\rho\), where \(A\) is the power consumption of the processing unit when it is idle, and \(B\) is the rate at which it increases as a function of the load factor \(\rho\)[1]. Thus, a very efficient processor might have a very small value of \(A\), and \(B\) would correspond to the rate of increase in power consumption as more and more cores are turned on as the load increases. Unfortunately, for much of the current equipment \(A\) is still a significant part of the total processor power consumption. This includes the fact that the memory system and the peripheral equipment and network connections need to be powered even when no jobs are being processed, and that the operating system can remain active (and hence contributes to the energy consumption) even when there are no external jobs that need to be processed. We can also obtain the expression for the energy consumption per job:  
\[E_{job}=\frac{A}{\lambda}+BE[S] \tag{1}\]  
where \(\lambda\) is the average arrival rate of jobs and \(E[S]\) is the average job service time. The equation supports the principle of concentrating computation on a small number of processing units in order to minimise the power consumption per job.  
However just power consumption on its own is not the only important fact: quality of service (QoS) is also primordial. In [1] we discuss how we can achieve optimum energy consumption to QoS trade-offs by adjusting system load in the context of a computing cloud. In this paper we discuss the much broader question: suppose that a mobile community could access services from both a local server within the operator provider (the \"local server\") and from remote service providers (\"remote server\"), then what fraction of their workload should they send remotely if they wish to optimise both QoS and energy consumption.  
Of course, the decision to use a remote service will depend on a variety of considerations based on security, cost, data and software protection and resilience. Nevertheless, there will also be technical considerations based on QoS and energy consumption per job. Thus this paper only focuses on the technical choice between a local or remote cluster service, and shows that this choice can be formulated as an optimisation problem. In the sequel, we first review the literature, and then provide some experimental measurements regarding the energy consumption and performance of servers. Next we formulate the optimisation problem, describe its solution and present some numerical examples.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Enhancing the EPR Effect by Targeting the Tumor microenvironment (TME)
#### 5.1.4 Targeting EGFR and CD44 in Combination  
The Epidermal Growth Factor Receptor (EGFR) is another critical target in cancer therapy due to its overexpression in various cancer types. Strategies to exploit EGFR for enhanced drug delivery include the use of monoclonal antibodies, EGF ligands, and EGF-conjugated liposomes, all of which improve the specificity of drug delivery to EGFR-overexpressing tumors. Combining EGFR targeting with CD44 targeting has been shown to further enhance the EPR effect. Additionally, PEGylated recombinant human hyaluron (PEGPH20) has emerged as a promising therapeutic agent. By degrading HA in the TME, PEGPH20 improves the efficacy of chemotherapeutic agents such as gemcitabine, leading to enhanced tumor growth inhibition in preclinical models [172; 173].
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 4 Dataset Description and Analysis  
The experimental data used in this work are part of two large-scale measurement campaigns that took place in the cities of Oslo, Norway, in the summer of 2019 and Rome, Italy, in January 2021. Data were collected using the same measurement setup described in Section 4.1 and were stored using the same structure in described in Section 4.2, that also provides information on how to obtain the data. A comparison between the two datasets is provided in Section 4.3; this is followed by a description of the preprocessing strategies adopted to compensate for their differences in Section 4.4.