esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 4 Predicting Signal Strength
### Signal Power in Line-Of-Sight Conditions  
In a LOS situation, the signal attenuation obeys the Friis transmission equation [42],  
\[P_{r}/P_{t}=A_{t}A_{r}/d^{2}\lambda^{2}=G_{t}G_{r}\bigg{(}\frac{\lambda}{4\pi R_{ txrx}}\bigg{)}^{2}, \tag{1}\]  
where \(P_{t}\) is the power fed into the transmitting antenna which is 27 watt (or 14.3 dB W) which is mentioned in the GPS specifications, while in the real-world, GPS space vehicles transmit up to 4 dB more power [43]. The power transmitted by the satellites varies by different space vehicles and decreases with age, which can be, to some extent, compensated by examining the current maximum signal strength of each satellite [44]. \(P_{r}\) is the available power at the output terminals of the receiving antenna. \(G_{t}\) and \(G_{r}\) are the gains of the transmitting and receiving antennas, respectively. The wavelength of the signal is denoted by \(\lambda\) and \(R_{txrx}\) is the distance between satellite and receiver antennas. The antennas should have the same polarization and the signal travels in unobstructed free line of sight between the antennas.  
The term \(A_{los}=(\lambda/4\pi R_{txrx})^{2}\) is called free space loss. In Equation (1), the atmospheric loss and other possible losses, e.g., cable loss, are ignored. Atmospheric loss is in the order of 0.035 dB for zenith angle and increasing with an order of ten times for low elevation satellites [45].  
The GPS signals are right-hand circularly polarized [46]. Circular polarization can be interpreted as a combination of horizontal and vertical linear polarizations. Different approaches exist to show the polarization of the signal, among them the _Jones_ vector is an easy-to-implement representation [47]. The Jones vector for the receiving antenna is defined as  
\[\mathbf{e}_{rx}=\frac{1}{\sqrt{2}}\begin{bmatrix}&G_{hcp_{AA}}^{rx}+G_{hcp_{AA }}^{rx}\\ &-JG_{hcp_{AA}}^{rx}-G_{hcp_{AA}}^{rx}\end{bmatrix}, \tag{2}\]  
where hcp is the right-hand circular polarization and lhcp is the left-hand circular polarization for the angle of arrival (AoA), respectively. Analogously, the Jones vector for the transmitting antenna for the angle of departure (AoD) can be defined. Therefore, the signal received in an LOS situation is  
\[S_{LOS}=P_{t}\mathbf{e}_{rx}^{H}\mathbf{e}_{tx}A_{los}e^{-j\mathbf{a}}, \tag{3}\]  
and thus  
\[P_{LOS}=|S_{LOS}|. \tag{4}\]
# Deep Learning within a DICOM WSI Viewer for Histopathology
## 3 Results
### Deep Learning Model Integration  
As previously mentioned, the framework integrated within the DICOM Inference WSI Web Viewer to execute the inference processes is Pytorch. Because of this, it was necessary to have the models trained in.pth format using this framework, or to transform them to it.  
In the case of the models trained for the H&E and Ki67 datasets, Pytorch was used; therefore, they were integrated directly into the viewer, while the HER2 model was developed using Caffe, a previous step being necessary using the _MMdnn_ to transform them into the required format. This procedure was performed by first transforming the original model into an intermediate representation, and then converting it into the required framework.  
In addition, each model can generate different types of outputs, requiring the implementation of different types of views that enable us to handle them in a particular manner. Specifically, the views implemented for each of the models are contained in the following JSON file, _pacvolume/views/views.json_, which includes an entry for each of the possible views. The contained key is the view identifier, which must be added to the view parameter listing of the _params.json_ file. The value of each view includes another key-value dictionary in which the only mandatory key is the description, whose value is the word that appears in the list of available views when a model is selected.  
The description and identifier of the views cannot be modified _a priori_, since they are the identifiers used to guide the execution of the inference server. The views currently available in the viewer are the following.  
* _Text_: It displays the outcome in the result text box. The processing is limited to the zoomed-in region selected by the user, performing a single inference and classifying it as appropriate. This view is available for the HER2 model. Some examples are illustrated in Figure 6.
* _Heatmap_: The classification of each of the analyzed patches is depicted on a heatmap. The processing is localized to the user-marked augmented region, but, in this case, a tiling process is carried out in which each patch has the input size of the neural network. Each of the individually obtained patches is subjected to the inference procedure, resulting in the generation of a map that shows the classification of each patch. This view is offered for the H&E and Ki67 models. Some examples are illustrated in Figure 7.  
In Figure 8, we can finally observe the results that would be obtained by applying the system to an entire WSI, and how the results would compare with the original labeling performed by a specialized pathologist.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Class** & **ACC** \\ \hline Background & 0.99 \\
0+ : & [0–10\%] & 0.91 \\
10+: & [10–20\%] & 0.88 \\
20+: & [20–45\%] & 0.78 \\
45+: & [45–85\%] & 0.82 \\
85+: & [85–100\%] & 0.85 \\ \hline \hline \multicolumn{2}{c}{Average} & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Kief7 model results (accuracy).
## 3 Results and Discussion
Figure 5: Relative densities, volume densities, and microwave dielectric properties of the MLLC ceramics. (**a**) Relative densities, (**b**) volume densities, (**c**) dielectric constants, (**d**) \(Q\)\(\times\)\(f\)s, and (**e**) \(\tau_{f}\)s of all the samples sintered at 800 \({}^{\circ}\)C.  
As seen from the XRD pattern of MLLC11.5-40 wt%Ag powders sintered at 800 \({}^{\circ}\)C in Figure 6a, Ag powders could not react with MLLC11.5 powders and mainly existed in the form of the secondary phase. Figure 6b shows a SEM micrograph of the cross-section of the MLLC11.5 ceramic, whose surface was coated with silver slurry. The ceramic was co-fired at 800 \({}^{\circ}\)C for 4 h and displayed a clear interface between the solidified silver slurry and the ceramic, marked as a and b, respectively. Figure 6c,d show the elemental analysis results of areas a and b by EDS, which displayed that areas a and b belonged to Ag and the MLLC11.5 ceramic, respectively. This result indicates that MLLC11.5 ceramics could be suitable for LTCC application. The MLLC11.5 composition was also employed to prepare a GPS antenna composed of a ground plane, radiation layer, and dielectric substrate. The designed sample is shown in Figure 7a. The dielectric substrate was the MLLC11.5 ceramic, about 1.5 mm thick and 21 mm in diameter. The radiation layer was a camber line with a 1 mm width, whose length could be modified to satisfy the GPS frequency (\(\sim\)1.575 GHz). Another part of the front face was the ground plane, which also included a part of the side face and half of the back face of the dielectric substrate. Considering the size limit of the dielectric substrate, the radiation layer could connect with the ground plane through a metal wire with 50 \(\Omega\) impedance, which could increase the inductance of the antenna to reduce the resonant frequency. The GPS antenna was designed with HFSS software and prepared with the screen-printing process according to the designed size. The front and back face of the GPS antenna is shown in Figure 7b,c, respectively. The metal pattern of the side face was handmade. The soldering was guaranteed by thickening and widening the silver slurry of the solder joint position. Figure 7d shows the return loss (S11) of the GPS antenna. Both the simulated and experimental data of the S11 were less than \(-\)10 dB at the resonant frequency. By modifying the length of the radiation layer, the resonant frequency was improved and nearly identical to the simulated data. The GPS signal test was carried out for the antenna by using a hardware system and displayed in the software, as shown in Figure 7e. A total of 11 or 12 PCs GPS satellites were detected at the same time and displayed more than a 28 dBHz carrier-to-noise ratio (C/N0) on average, indicating antenna application [40]. Figure 6: (a) XRD pattern of MLLC11.5-40 wt% Ag powders calcined at 800 \({}^{\circ}\)C, (b) SEM micrograph of the cross-section of MLLC11.5 ceramic coated with silver slurry and sintered at 800 \({}^{\circ}\)C, (c) EDS result of area a, and (d) EDS result of area b.
## 3 Finite Element Analysis
### Analytical Results
#### 3.2.1 Planes of Maximum Normal and Shear Stress Ranges  
First, the maximum tangential stress range was investigated to elucidate the effect of \(\delta\) on crack branching direction [11]. The analysis was suggested to be based on an elasto-plastic stress field [21]. The normal stress range (\(\Delta\sigma\)) and the shear stress range (\(\Delta\tau\)) level on every plane were investigated. Such a plane was expressed by \(\theta\), \(\varphi\), and \(\psi\) that are the rotation angles around the axes of the local cylindrical coordinate system \(R\), \(T\), and \(Z\) whose origin coincides with the center of the crack tip element (see Figure 18). Underlying assumption is that if the cracks grow by a tensile mode, the growth direction should be determined by the \(\Delta\sigma\), whereas when the cracks grow by a shear mode, the direction is determined by the \(\Delta\tau\) near the crack tip. Considering that \(\Delta\tau_{\text{ZR}}\) was very small under the applied loading cycles, \(\Delta\tau\) was represented by a range of \(\Delta\tau_{\text{ZT}}\) values.  
The variations of \(\Delta\sigma\) and \(\Delta\tau\) due to \(\theta\) for the different values of \(\delta\) for the RP case were indicated on the \(\varphi=\psi=0\) plane in Figure 19. \(\Delta\sigma\) and \(\Delta\tau\) at each \(\theta\) were divided by the maximum of \(\Delta\tau\) (\(\Delta\tau_{\text{max}}\)) because the absolute values depend on the distance from the crack tip and are not important. When \(\delta\) increases, the relative \(\Delta\sigma\) value also increases and the maximum of \(\Delta\sigma\) (\(\Delta\sigma_{\text{max}}\)) plane turns toward the branch direction, whereas the \(\Delta\tau_{\text{max}}\) plane remains on the coplanar plane. In the calculation results, both \(\Delta\sigma_{\text{max}}\) and \(\Delta\tau_{\text{max}}\) planes were slightly oriented toward the \(\varphi\) direction within \(7^{\circ}\) for all cases.
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 4 Proposed Protocol Methodology for E-Commerce
### Implicated Symmetric and Asymmetric Cryptography
#### 4.1.1 Utilizing ElGamal  
In our protocol, we need an algorithm that generates large and random keys suitable for encrypting sensitive information in e-commerce applications. There are many asymmetric encryption algorithms, the most prominent of which are ElGamal and RSA. However, we chose ElGamal because it provides faster encryption and decryption processing than RSA, and it consumes less power and provides better scalability than RSA (as shown in Table 1). Our protocol uses the ElGamal algorithm only for generating and calculating public and private keys, and not for encryption and decryption. Encryption and decryption processes are very expensive in asymmetric algorithms such as ElGamal; thus, we use ElGamal only to generate large random keys. ElGamal provides keys with lengths of 1024, 2048, 4096, etc. Our protocol generates a public key of length 1024 and a private key using CRT technology (as shown in the following algorithm).  
* Generating a large random prime number (\(p\));
* Choosing a generator number (\(a\));
* Using CRT to generate an random integer (\(P_{r}\)), as the secret number and less than (\(p-2\));
* Computing (\(d\)), where \(d=a.P_{r}\)_mod_\(p\);
* The private key is \(P_{r}\), and the public key is (\(P_{u}\)), which includes \(p\), \(a\), \(d\).  
Then, our protocol divides the public key into four parts, with each part of size 256, and performs the XoR processes for the four parts to obtain a secret and random key ready for use in the AES algorithm. The purpose of using ElGamal is to generate random and different keys for each user of e-commerce applications.
## Preliminaries
### The GLV-GLS Method  
We will now describe the GLV method to explain the GLV-GLS method. Let \(E\) be an elliptic curve defined over a finite field \(\mathbb{F}_{q}\). An endomorphism \(\phi\) of \(E\) over \(\mathbb{F}_{q}\) is a rational map \(\phi:E\to E\) such that \(\phi(\mathcal{O})=\mathcal{O}\) and \(\phi(P)=(g(P),h(P))\) for all points \(P\in E\), where \(g\) and \(h\) are rational functions and \(\mathcal{O}\) is a point at infinity. An endomorphism \(\phi\) is a group homomorphism, defined as  
\[\phi(P_{1}+P_{2})=\phi(P_{1})+\phi(P_{2})\ \ \text{for all}\ \ P_{1},P_{2}\in E.\]  
Suppose that \(\#E(\mathbb{F}_{q})\) contains a subgroup of order \(r\) and let \(\phi\) be an efficiently computable endomorphism on \(E\) such that \(\phi(P)=\lambda P\) for some \(1\leq\lambda\leq r-1\). The GLV method computes the integers \(k_{0}\) and \(k_{1}\) such that \(k=k_{0}+k_{1}\lambda\) mod \(r\) for scalar multiplication \(kP\). Because  
\[kP= k_{0}P+k_{1}\lambda P\] \[= k_{0}P+k_{1}\phi(P),\]  
scalar multiplication \(kP\) can be computed by computing \(\phi(P)\) and then using multiple scalar multiplications [31]. This is because the multi-scalars \(k_{0}\) and \(k_{1}\) have approximately half the bit-length of the scalar \(k\). The efficiency of the GLV method depends on scalar decomposition and the efficiency of computing endomorphism \(\phi\).  
The main concept of the GLS curves is described as follows: Let \(E^{\prime}/\mathbb{F}_{q^{2}}\) be the quadratic twists of \(E/\mathbb{F}_{q^{2}}\)[12]. Let \(\psi\) be the quadratic twist map and \(\pi\) be the \(q\)-th Frobenius endomorphism. Then, we can obtain the efficiently computable endomorphism \(\phi=\psi\circ\pi\circ\psi^{-1}\), which satisfies the equation \(X^{2}+1=0\) if \(p\equiv 5\pmod{8}\). However, GLS curves only work for elliptic curves over \(\mathbb{F}_{q^{m}}\) with \(m>1\).  
As mentioned in the introduction, the GLV-GLS method is the generalized method of the GLV method and GLS curves. Let \(\phi\) and \(\psi\) be two efficiently computable endomorphisms over \(\mathbb{F}_{p^{2}}\) and be a point of prime order \(r\). Then, the four-dimensional scalar multiplication \(kP\) for any scalar \(k\in[1,r]\) can be computed as follows:  
\[kP= k_{0}P+k_{1}\phi(P)+k_{2}\psi(P)+k_{3}\psi(\phi(P)),\]  
where \(\max_{i}(|k_{i}|)<Cr^{1/4}\) for \(0\leq i\leq 3\) and \(C\) is some explicit constant. The details of internal algorithms of the four-dimensional scalar multiplication can be found in Sections 4 and 5.
## 4 Trends and Challenges
### Hrws  
Japan launched the Advanced Land Observing Satellite-4 (ALOS-4) on 1 July 2024. ALOS-4 in-orbit configuration as shown in Figure 30. The Japan Aerospace Exploration Agency (JAXA) released the first observation images captured by the L-band synthetic aperture radar (PASAR-3) aboard the satellite from 15 to 17 July 2024 (Japan Standard Time). \"ALOS-4\" is the first radar satellite to implement digital beamforming technology on an artificial satellite. While maintaining the high spatial resolution of the previous \"ALOS-2\", it has expanded the observation range by up to four times (200 km wide at a 3 m resolution) [123]. It is expected to contribute to disaster situation understanding, environmental observations such as forest distribution, and marine observations such as sea ice, together with \"ALOS-2\".  
Figure 30: ALOS-4 satellite [image credit: JAXA].  
Figure 29: AIRSAT-08 low-earth-orbit SAR satellite [image credit: AIR].  
In China, we are actively developing our own high-resolution wide-swath SAR systems. [PERSON] et al. designed and implemented an airborne X-band SAR system with 16 receiver channels in the elevation direction, supporting digital beamforming (DBF) to meet future HRMS imaging demands. The system operates with a 500 MHz bandwidth and adopts a Scan-On-Receive (SCORE) mode to expand the imaging swath while maintaining high resolution [124]. Through real flight experiments, the imaging performance was validated under realistic terrain conditions. The study addressed channel gain and phase mismatches by modeling and calibrating system errors. Results showed that the DBF technique significantly improved signal-to-noise ratio and ensured image consistency over a wide field of view. This work provides a technical foundation for future airborne and spaceborne HRMS SAR systems and offers an effective solution to the traditional trade-off between resolution and swath width.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 5 Experiments
### Effect of Sampling Size  
The proposed approach leverages a sampling rollout to facilitate the exploration of the whole solution space, thereby leading to more satisfactory results. To evaluate the influence of sampling size, we apply the same sampling rollout to AM and POMO and report the comparison results on both the conventional and improved VRP-PDs with 100 nodes, as illustrated in Tables 6 and 7. Specifically, we set the sampling sizes to 320, 640, 1280, and 2560 using the respective trained policy networks. As for the conventional VRP-PD, the proposed GDRL with a sampling width of 1280 attains an average tour length of 17.64, outperforming AM and POMO by 194.06% and 2.95%, respectively. Pertaining to the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**GA**} & \multirow{2}{*}{**SA**} & \multirow{2}{*}{**ACO**} & \multicolumn{4}{c}{**Conventional VRP-PD Benchmarks**} \\  & & & & **ALNS** & **AM (Greedy)** & **AM (Sampling)** & **POMO** & **GDRL** \\ \hline Length & 63,066.28 & 44,081.06 & 41,092.51 & 37,356.83 & 60,495.22 & 54,237.10 & 29,094.36 & 26,857.70 \\ \hline Time (s) & 98.87 & 88.98 & 77.12 & 13.24 & 7.38 & 885.49 & 15.52 & 17.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Routing results on benchmark instances in conventional VRP-PDs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**GA**} & \multirow{2}{*}{**SA**} & \multirow{2}{*}{**ACO**} & \multicolumn{4}{c}{**Lproved VRP-PD Benchmarks**} \\  & & & & **ALNS** & **AM (Greedy)** & **AM (Sampling)** & **POMO** & **GDRL** \\ \hline Length & 70,677.72 & 49,401.19 & 46,051.95 & 41,865.41 & 67,796.36 & 60,783.23 & 32,605.75 & 28,259.75 \\ \hline Time (s) & 103.61 & 93.25 & 80.82 & 13.88 & 7.73 & 927.98 & 16.26 & 18.81 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Routing results on benchmark instances of improved VRP-PDs.  
improved VRP-PD, GDRL outstrips POMO with gap reductions of 2.52%, 3.00%, 3.14%, and 3.17% when the sampling size is set to 320, 640, 1280, and 2560, respectively.
## 2 Radio Propagation Measurement Method  
### Radio Propagation Measurement Setup  
As the 2.4 GHz band is widely used for UAV control, we measured the received power from the UAV to the ground at 2.5 GHz and evaluated the propagation characteristics. We introduce the configuration of the propagation measurement equipment in A2G communication using the UAV. Figure 1a shows the small UAV (DJI Mavic Mini 3) [42] used in this study. In this study, this UAV was used as a transmitter station and was equipped with a transmitter, a transmitter battery, and a half-wavelength dipole antenna as the transmitting antenna. To ensure the safety of the surrounding area, the UAV flew while tethered to a fishing line.  
Figure 1b shows the receiver system on the ground. A half-wavelength dipole antenna was used as the receiving antenna, and a standard spectrum analyzer (SA) was used to measure the received power. The results measured by the SA were imported into a personal computer (PC), and the radio wave propagation characteristics, such as the cumulative distribution function (CDF) characteristics of measured instantaneous response and the Rician K-factor, were analyzed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **[GHz]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Sampling** \\ **Interval of** \\ **Each Snapshot** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **UAV Speed** \\ **[m/s]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Altitude** \\ **[m]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Channel** \\ **Modeling** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Channel** \\ **Characteristics** \\ \end{tabular} } \\ \hline
[32] & 27/38 & 500 ms & — & 16, 19, 50 & — & Path loss, direction \\
[33] & 1.2/4.2 & 100 ms & 0.7, 1.2 & 0–100 & FE2R & Path loss, Rician K-factor \\
[34] & 3.95 & 100 ns & 6.1 & 10, 20, 30 & Ray tracing & Rician K-factor, direction \\
[35] & 2.5 & 40 ns & 5.6 & 15, 30, 50, & SAGE & Path loss, Rician K-factor \\
[36] & 1.2–6 & 12.5 μs & — & 80 & — & Path loss, Path loss \\
[37] & 2.4/3.5 & 10.24 μs & 2 & 0–60 & SAGE & Rician K-factor \\
[38] & 3.9 & 2 μs & — & 0–40 & Ray tracing & Path loss, \\
[39] & 2.5 & — & 5 & 15–105 & — & Rician K-factor \\
[40] & 6.5 & — & — & 0–30 & SAGE & Rician K-factor \\ Our work & 2.5 & 10 ms & 0.3 & 10, 15, 20, 25, 30 & Ray tracing & Rician K-factor \\ \hline \hline \end{tabular}
\end{table}
Table 1: A2G UAV channel measurement campaigns.
# QoE Optimization in a Live Cellular Network through RLC Parameter Tuning
###### Abstract  
The mobile communication networks sector has experienced a great evolution during the last few years. The emergence of new services as well as the growth in the number of subscribers have motivated the search for new ways to optimize mobile networks. In this way, the objective pursued by optimization techniques has been evolving, shifting from the traditional optimization of radio parameters to the improvement of the quality perceived by users, known as quality of experience (QoE). In mobile networks, the radio link control (RLC) layer provides a reliable link between both ends of the communication and has a great impact on the QoE. In this paper, the optimization of the QoE for users based on the adjustment of the RLC layer is proposed. For this purpose, two typical services demanded by the users of mobile networks have been selected: the real-time video streaming service and file transfer service. For a broader view of the behavior of the QoE in relation to RLC, optimization tests have been carried out in scenarios with different system bandwidths. In this way, the relationship between the QoE and the optimal configuration of RLC in different network load situations has been analyzed. A proof of concept has been carried out to show the capability of this optimization. To that end, both a cellular network simulator and a live cellular network devised for research purposes have been used.  
end-to-end (E2E) optimization; quality of experience (QoE); radio link control (RLC) IEEEexample:BSTcontrol