esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
A total of 16 patients were involved in this study. All subjects were treated with the Leksell Gamma Knife Model C and they underwent PET/CT Discovery 690 with TOF (GE Medical Systems) before and after the treatment. For basal studies, tumor size ranged from 0.25 to 10.56 cm\({}^{3}\) (mean \(\pm\) standard deviation: 2.83 \(\pm\) 2.41 cm\({}^{3}\)) with a SUV\({}_{\text{max}}\) between 1.6 and 6.84 (mean \(\pm\) standard deviation: 3.91 \(\pm\) 1.57). In follow-up studies, tumor size ranged from 0 (complete response) to 12.02 cm\({}^{3}\) (mean \(\pm\) standard deviation: 2.40 \(\pm\) 2.96 cm\({}^{3}\)) with a SUV\({}_{\text{max}}\) between 0 (complete response) and 4.6 (mean \(\pm\) standard deviation: 2.81 \(\pm\) 1.17). Changes (\(\Delta\)) in AUC, SUV\({}_{\text{max}}\), SUV\({}_{\text{mean}}\), MTV and TLG between baseline and follow-up scans and medical reports performed by the three nuclear medicine physicians are shown in Table 1 for each patient.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Patient N.** & \(\Delta\)**AUC** & \(\Delta\)**MTV** & \(\Delta\)**SUV\({}_{\text{max}}\)** & \(\Delta\)**SUV\({}_{\text{mean}}\)** & \(\Delta\)**TLG** & **Physician Report** \\ \hline \#1 & 2.33 & 13.87 & \(-12.63\) & \(-12.18\) & 0.05 & Stable \\ \hline \#2 & \(-38.62\) & 18.80 & \(-32.72\) & \(-25.73\) & \(-11.77\) & Improvement \\ \hline \#3 & \(-59.05\) & 0.12 & \(-47.66\) & \(-33.56\)  
Starting from an exploratory analysis of PET parameters to understand if \(\Delta\)AUC could actually provide further information to other PET parameters, Pearson correlation coefficients (\(r\)) between \(\Delta\)AUC and the aforementioned prognostic indices were computed. \(\Delta\)MTV, \(\Delta\)SUVmax, \(\Delta\)SUVmean, and \(\Delta\)TLG were not highly correlated with \(\Delta\)AUC (see Figure 4). The determination coefficients (\(R^{2}\)) were low, demonstrating a low correlation between considered measures. As a result, it can be affirmed that \(\Delta\)AUC provides additional information than other PET parameters. The paired \(t\)-test showed a \(p\)-value greater than 0.05 in all cases, so there is no evidence of an association between \(\Delta\)AUC and the aforementioned indices. Finally, three outcome classes were identified based on the variation of AUC and on the shifting of the CSH curve: positive response, stable response, and negative response, as shown in Figure 5.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 2 The NAND Memory Array
### Array Operation
#### 2.2.1 Read  
Reading the state of a cell means discriminating one \(V_{T}\) level against another and irrespective of the \(V_{T}\) levels of all other cells in the string. To accomplish this, the string has first to be connected to the sensing circuit, i.e., the select lines are high and the BL is biased; therefore, a current can flow to the (grounded) sourceline. Unselected cells are then placed in a pass-transistor mode, biasing their WLs at a _pass voltage_ larger than the maximum \(V_{T}\). The actual read operation is then performed, sensing the cell current and comparing it against fixed reference values to assess the corresponding binary code [38]. The operation is performed more than one time for MLC and TLC cells. To increase parallelism, strings are read in parallel across a page (typically 8-16 kB), resulting in a throughput higher than 150 MB s\({}^{-1}\).
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 3 Test Case Modeling
### Test Case Shading Scenarios Model  
The simulated dynamic partial shading scenarios take into account the passage of a moving object close to the PV module, obstructing direct radiation and most of the diffuse radiation. In the model, it has been assumed that the irradiance is nil on the shaded area of the PV module. Four shading shapes are taken into account: a rectangular shape and three trapezoidal shapes. For each shape, three shading objects that are equal in height and differ only in length are considered, as summarized in Table 2. Taking the length of the solar cell edge as the unit of measurement, the dimensions of each shading object are 2 \(\times\) 1, 3 \(\times\) 1, and 4 \(\times\) 1. The shading scenarios with rectangular shapes were also tested during the preliminary test campaign.  
The shading object moves at a constant speed (4 cm/s) along the short edge of the PV module, as shown in Figure 6. The motion path is set between two points, A and B, respectively; the PV module is placed in the middle of the path. A motion cycle consists of a forward motion from A to B, a waiting time, and a backward motion from B to A.  
The shapes and size of the shading objects have been defined to consider different dynamic partial shading cases, in terms of the number of completely and partially shaded cells at the same time, the rate of variation of the shaded area of PV cells, and the duration of the shading process. In a real world scenario, the shading process of a photovoltaic generator can be either deterministic or random. A deterministic partial shading process occurs in fixed PV installations; it is related to the motion of the sun in the sky and the fixed objects nearby to the PV generator [27], like trees, buildings, poles, etc., that can cause partial occlusion from the sun in some hours of the day. A deterministic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size** & **Rectangular** & **Trapezoidal-A** & **Trapezoidal-B** & **Trapezoidal-C** \\ \hline \hline
2 \(\times\) 1 & & & \\ \hline
3 \(\times\) 1 & & & \\ \hline
4 \(\times\) 1 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Shading objects.  
Figure 6: Diagram of the motion cycle: the shading object is moved forward and backward in one cycle.  
partial shading process is usually a very slow process. A random partial shading process occurs both in fixed PV installations, due to the passage of clouds, and in stand-alone and moving applications, (e.g., cars or boats) because of motion. Fast dynamics characterize these types of shading. A random partial shading process is usually quite a fast process. The moving objects and their motion speed taken into account in this work allows us to simulate the shading processes that fall in the last case.
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 3 Methods
### Parallelized BPA on GPU  
To optimize the processing process, a heterogeneous processing architecture was designed. Figure 5 shows the system block diagram of the proposed GNSS-R BSAR signal processing. In the heterogeneous framework, the GPU and the CPU process the data serially. The pre-processing part is mainly executed in CPU, the BPA is executed in GPU, finally, the image is sent back to CPU for output and storage.
## 3 The Next Video Frame Prediction Techniques Using SA-ConvLSTM and Encoder-Decoder Structures  
### Model Structure  
The proposed architecture structure consists of two main components: an encoder and decoder as depicted in Figure 4. Given an input video frame of length \(T\), the objective is to learn the mapping from the input frame to \(K\) actual future video frames. The ground truth video frame at time \(t\) is denoted by \(X_{t}\), and the sequence of input video frames is denoted by \(X_{1:T}\). The predicted output frame at time \(t\) is represented as \(\hat{x}_{t}\), and the aim is to generate a sequence \(\hat{x}_{t+1:T+K}\) that closely approximates \(X_{T+1:T+K}\).  
The encoder processes the input sequence using an encoding consisting of multiple ConvLSTM layers. These layers are designed to extract salient features from the data and learn sequential dependencies. The final hidden state of the encoder encapsulates the context and a summary of the entire input sequence before passing this output to the decoder. The decoder generates future frames based on the compact representation provided by the encoder. It captures the temporal and spatial details of the video to yield accurate and consistent predictions, with the final output being the predicted frame of the target sequence.  
Figure 4: SA-ConvLSTM-based encoder–decoder structure.  
The input video is divided into frames which are then fed into the encoder-decoder model equipped with SA-ConvLSTM. After processing using the encoder and decoder, the output is the predicted frame for the next sequence in the video.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 7 Conclusions  
The internal calibration design of the BiSAR system plays an important role in the LT-1 mission, determining the precision of interferometric measurements. Multiple calibration loops were designed based on the composition of the LT-1 internal calibration system. The echo compensation formula was derived for the bistatic mode of LT-1, leading to the proposal of an innovative internal calibration strategy. Temperature compensation tests were conducted for calibration equipment, based on the internal calibrator and antenna calibration networks hardware. Through these tests, effective temperature compensation curves and compensation error evaluation values were obtained. Additionally, a BiSAR ground verification system was established, and the BiSAR synchronization performance was analyzed through system integration experiments. The experimental results have verified the effectiveness and accuracy of the system internal calibration. Additionally, LT-1 in-orbit testing has obtained helpful DSM products with an average error of 1.3 m. Furthermore, the internal calibration strategy proposed in this study provides a valuable reference for future spaceborne bistatic and multistatic SAR systems.  
Conceptualization, K.L., Y.J. and H.Z.; methodology, Y.J., K.L. and F.Z.; software, H.Y.; validation, F.Z. and Y.J.; writing--original draft preparation, Y.J. and H.Y.; writing--review and editing, Y.J., K.L. and H.Y.; visualization, Y.J.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Medium- and Long-Term Development Plan for China's Civil Space Infrastructure, LuTan-1.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Tool Application to Evaluate Pollutant Emissions
### Carbon Balance Method
#### 4.2.2 Carbon Balance Quantities  
The quantities in the Carbon Balance method are the following:  
* \(\dot{m}_{fuel}\;[\rm kg/h]:fuel\) flowrate;
* \(f_{C}:\) emission carbon factor. It is a coefficient that allows converting activity data into GHG emissions and is calculated as follows: \[\begin{array}{c}f_{C}=0.5441(CO_{2\;concentration}-CO_{2\;amb\;air\; concentration})\;\;\;+\frac{CO_{magnutation}}{18522}\\ +\frac{HC_{concentrate}}{17355}\end{array}\] (30) where the dry quantities are the \(CO_{2\;amb\;air\;concentration}\) [%] (concentration of CO\({}_{2}\) in the ambient air), \(CO_{2\;concentration}\) [%] and \(CO_{concentration}\) [ppm] (concentration of CO\({}_{2}\) and CO in the exhaust gas, respectively), while \(HC_{concentration}\) [ppm] (concentration of HC in the exhaust gas) is a wet quantity. Since the carbon dioxide can be found in larger quantities than the other components, it is usually measured in percentage;
* \(H_{2}\)[g\({}_{H_{2}O}\)/kg\({}_{\rm air,dry}\)]: absolute moisture in the aspiration air, defined as the ratio between steam density \(\rho_{s}\) and dry air density \(\rho_{air,dry}\) at the same temperature;
* \(Fuel_{mix\;cont\;C}\) [% mass] : carbon content in the fuel mixture;* \(\bullet\)\(Fuel_{mix\ cont\ H}\) [% mass] : hydrogen content in the fuel mixture;
* \(\bullet\)\(FSC\) : fuel dry specific constant for exhaust gases, calculated as follows: \[FSC=-0.055593 Fuel_{mix\ cont\ H}+0.008002 Fuel_{mix\ cont\ N}+0.0070046 Fuel_{mix\ cont\ O}\] (31)  
The basic assumption of the Carbon Balance method is flawed: indeed, since it considers only the emissions based on carbon (CO, CO\({}_{2}\) and HC), it eliminates all the other components based on nitrogen and oxygen (e.g., nitrogen oxides NO\({}_{\text{X}}\) and the oxygen itself). Consequently, corrective coefficients able to consider all the other fuel components are necessary:  
* \(Fuel_{mix\ cont\ N}\) [% mass] : nitrogen content in the fuel mixture;
* \(Fuel_{mix\ cont\ O}\) [% mass] : oxygen content in the fuel mixture.
## 3 Information-Theoretic Results
### Coding Theorems  
Within the RDP framework, two scenarios, the one-shot and the asymptotic, have been extensively studied by [PERSON] and [PERSON] [47] and [PERSON] et al. [26]. The proofs for the one-shot scenario mainly rely on the strong functional representation lemma [48], whereas the proofs for the asymptotic scenario predominantly utilize techniques in channel synthesis [39; 41]. Coding theorems in information theory typically involve both achievability and converse results.  
**Definition 4** (Achievablility).: _The tuple \((R,R_{c},R_{d},D,P)\in\mathbb{R}_{\geq 0}\times\overline{\mathbb{R}}_{\geq 0}^{4}\) is achievable if, for any \(\epsilon>0\), there exists a sequence of \((n,2^{n(R+\epsilon)},2^{n(R_{c}+\epsilon)},2^{n(R_{d}+\epsilon)})\) codes \((f_{n},g_{n})\) such that the distortion constraint_  
\[\mathrm{E}_{P}[\Delta(X^{n},Y^{n})]\leq D+\epsilon,\] ( **D** )  
_and the perceptual constraint(s) **(P)** are both satisfied, where \(Y^{n}=g_{n}(f_{n}(X^{n},Z^{n},K),Z^{n},K)\)._  
**Definition 5**.: _The rate region \(\mathcal{R}_{D,P}\) is the closure of the set of achievable rates, i.e.,_  
\[\mathcal{R}_{D,P}=\mathrm{cl}\Big{(}\{(R,R_{c},R_{d})\in\mathbb{R}_{\geq 0} \times\overline{\mathbb{R}}_{\geq 0}^{2}:(R,R_{c},R_{d},D,P)\text{ is achievable}\}\Big{)}.\]  
Given a rate region \(\mathcal{R}_{D,P}\) induced by a coding scheme, the converse theorem asserts that any rate tuples \((R,R_{c},R_{d})\) outside \(\mathcal{R}_{D,P}\) are unachievable. Proofs of the converse often leverage the time-sharing technique (e.g., see [25; 31; 43]), which relies on the assumption that the output sequence is i.i.d. However, the single-letter characterization can also be derived under tensorizable and decomposable conditions [26], which implicitly imply the optimality of an i.i.d. output sequence.
# Array Gain of a Linear Array in an Ocean Waveguide
## 5 Conclusions  
The degradation of \(AG\) in the ocean waveguide is investigated in the paper from the fluctuant acoustic channels that can be depicted with the fluctuant propagation loss and the acoustic channel coherence degradation. A general formula of \(AG\) for a linear array in the ocean waveguide is derived as Equation (18) under the assumption that the ambient noise is isotropic. The formula takes full account of the influence of acoustic channels and can be applied to shallow water, deep sea, and continental slope area. Furthermore, we prove that the traditional formula of \(AG\) (by [PERSON]) is only a special case of the formula presented in this paper. An analysis of \(AG\) for a uniform linear array is presented that provides some insight into the physics of the problem of the array performance in the ocean waveguide. We have found that the obtained \(AG\) in the ocean waveguide is reduced compared to that calculated by the traditional method. The numerical simulations compare AGs obtained by the traditional method and the proposed method in four scenarios, including the acoustic channel coherence decreasing exponentially, shallow water, deep sea, and upslope waveguide. Numerical results show that (1) the stronger the acoustic channel  
Figure 8: Fluctuant acoustic channels and AGs for the receiving depth of 120 m and distance of 48 km in the upslope waveguide: (**a**) \(ATL\) on the elements; (**b**) Correlation coefficients; (**c**) \(AG_{\text{U}}\) (blue solid line), \(AG_{\text{W}}\) (red dash line) and the ideal value of \(AG\) (black solid line) varying with the element number.  
coherence, the greater AG; (2) \(AG_{\rm U}\) is almost equal to \(AG_{\rm W}\) when the fluctuation of \(ATL\)s is small; (3) \(AG_{\rm U}\) will be always be larger than \(AG_{\rm W}\) if the fluctuation of the propagation losses is ignored. It is inferred that both acoustic channel coherence and transmission loss fluctuation should be considered when calculating \(AG\) in ocean waveguide.  
Conceptualization, Z.-W.L.; methodology, Z.-W.L. and C.-M.Y.; software, Y.J.; validation, L.X. and J.-Y.D.; formal analysis, Y.J.; investigation and resources, L.-G.L.; data curation, C.-M.Y.; writing--original draft preparation, L.X.; writing--review and editing, Z.-W.L. and J.-Y.D.; supervision, L.-G.L.; project administration, Z.-W.L.; funding acquisition, Z.-W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2016 YFC1400200, National Natural Science Foundation of China, grant number 41706113, and Open Foundation of Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, grant number 2017A04.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Measurements and Experimental Results
### Summary  
As shown in Table 6, deep learning has the highest accuracy at 89.77%, and the lowest algorithm (PHOG) is at 53.4%. In terms of the time spent on the training and testing data, the PHOG is the quickest algorithm and the BOW is the slowest algorithm, which require 8232.574 and 447,958.95 s, respectively.
###### Abstract  
To investigate the susceptibility of drifting snow along the Ya'an-Qamdo section of the railway, which is located in a high-altitude and cold plateau in Southwest China with scarce meteorological information, the Weather Research and Forecasting Model (WRF) is used in this paper to simulate the spatio-temporal distribution of meteorological data. According to the varying terrain, the railway section from Ya'an to Qamdo is divided into two regions along 100.8\({}^{\circ}\) E for double-layer nested simulation. The original land use data of the WRF model are used in region 1. Due to the increased number of mountains in region 2, the original data are replaced by the MCD12 Qlv006 land use data, and the vertical direction layers are densified near the ground to increase simulation accuracy. The simulated results are compared with the observation data. It is found that after densification, the results have been significantly improved. The results obtained by the WRF model can accurately simulate the change trends of temperature, rainfall, and wind speed, and the correlation coefficients are relatively high, which verifies the accuracy of WRF for simulating complex terrain regions. The simulation results further indicate that approximately 300 km of the Ya'an-Qamdo railway may experience drifting snow. Among them, no drifting snow events occur in Ya'an County, and the areas with higher probability are located at the border between Luding County and Tianquan County, followed by Kangding area. The remaining areas have a probability of less than 10%. The WRF model demonstrates its capability in the drifting snow protection of railways with limited meteorological data.  
drifting snow; southwest railway; WRF; susceptibility; numerical simulation +
Footnote †: journal: _Applied_  
0000-0002-1447-3399][PERSON]  
1 School of Civil Engineering, Shijiazhuang Thedao University, Shijiazhuang 050043, China; [EMAIL_ADDRESS](X.Z.); [EMAIL_ADDRESS](Z.Z.); [EMAIL_ADDRESS](W.Y)
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.9 ESRVR and Co-ESRVR [53]  
In the paper [53], the author proposed two protocols, namely energy-aware scaleable reliable and void-hole mitigation routing (ESRVR) and cooperative energy-aware scaleable reliable and void-hole mitigation routing (Co-ESRVR). The authors considered sparse node deployment over a large area and focused on reliability and void avoidance. The whole process is compiled of four steps. Firstly, the author simulated the environment as a cube (3D) with uniform density, where each node has its ID and pressure sensor to dint the depth. In the next step, the sender broadcasts the HP, including a timestamp, depth, ID, and expiry time of the HP. At the end of exchanging HP, each node knows the location of its two-hop neighbors. For transmitting data, a node first checks if the next node is two-hop communication and the depth of the next node. If multiple nodes meet the criteria, the sender can randomly choose a node to forward the data. Otherwise, the system recognizes the node with no neighbor as a void zone and refrains from sending data through that node. Other nodes update their routing table accordingly through the exchange of periodical HP. To ensure data transmission reliability, nodes use backward transmission, which works such that the sender can choose a lower in-depth node but has two-hop path connectivity. Two-hop communication detects void zones and avoids the void zone with the help of a holding time mechanism.  
For Co-ESRVR, the author used a relay node to transmit data from source to destination. Here, the sender broadcasts a symbol x, which both the relay node and the destination node receive. The destination node is closer to the sea surface, and the relay node is below. These nodes are selected by the source node using routing table information. The id of the destination and relay are embedded in the symbol x by the source node. The destination waits for a set interval of time to receive the packet from the relay; if the relay node fails to deliver the packet, the destination sends the partial message from the source; otherwise, it combines and sends it to the sink. The authors simulated the protocols using MATLAB and compared ESRVR and Co-ESRVR with the existing protocol reliable and interference-aware routing protocol (RIAR). The simulation backs their claim of showing a higher delivery ratio for both protocols over RIAR. Co-ESRVR shows the highest delivery ratio, followed by ESRVR and RIAR. While ESRVR has a minor delay, closely followed by Co-ESRVR and RIAR. These protocols are beneficial for military applications or where the delivery is the priority. It consumes more energy than RIAR and drains out faster than RIAR.