esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Calculation Model and Method  
In this work, the Italian 80 MW LBE-cooled reactor is chosen as the research object to study differences of the safety characteristics of the ADS and CNES [19]. On the one hand, we adopt this reactor because it is sufficiently representative for the various ADS conceptual designs [25]. On the other hand, compared with other ADS conceptual types we can obtain the detailed composition and geometric distribution of all materials (fuel, cladding, coolant, spallation target, reflector, control rod, etc.) for this reactor through the existing literature and technical reports. After obtaining these basic data and empirical correlation, the calculation of spallation neutron distribution between the accelerator proton beam and the metal target, the processing of macroscopic cross sections in the reactor core, and the use of material thermophysical properties for the thermal-hydraulics analysis could be carried out during the reactor modeling process.  
The ADS device consists of a subcritical core, a high-energy proton accelerator and a heavy metal spallation target. The CNES reactor is a critical core; its operational process is implemented by the control rods. The configuration of the LBE-cooled ADS and CENS is shown in Figure 1. It can be seen from Figure 1 that the accelerator proton beam is introduced into the target and then hits the metal target to initiate the spallation reaction. As a result of the spallation reaction process, neutrons will be supplied to the subcritical reactor core, which serves as an external neutron source to maintain fission chain reactions. On the one hand, the power level could be changed by adjusting the beam intensity during the transient operating conditions. On the other hand, the external neutron source in the core could be interrupted by quickly cutting off the accelerator beam so as to achieve emergency shutdown protection under accident conditions. The heat produced in the core will be carried out by the coolant of lead bismuth eutectic (LBE). In the heat exchanger (IHX), the coolant of the primary side is high temperature LBE, and the coolant of the secondary side is an organic diathermic fluid. As shown in Figure 1, the CNES core itself could sustain a chain fission reaction without external neutron source, and the power level could be adjusted by the control rods during the transient process. The secondary circuit of CNES is similar to that of ADS.  
Figure 1: Configuration of the LBE-cooled ADS and CNES.
# Radial Structure of OAM-Carrying Fundamental X-Waves
###### Abstract  
We investigate the spectral degree of freedom of OAM-carrying localized waves and its influence on their transverse intensity distribution. In particular, we focus our attention on exponentially decaying spectra, which are very tightly connected to fundamental X-waves; we then show how it is possible to structure their transverse intensity distribution, thus creating a radial structure similar to that of Bessel beams.  
localized waves; angular momentum of light; spectral control +
Footnote †: journal: Applied Sciences  
**Citation**: [PERSON]; [PERSON] [PERSON].  
Radial Structure of OAM-Carrying Fundamental X-Waves. _Appl. Sci._**2021**, _11_, 169. [[http://dx.doi.org/10.3390/app11010169](http://dx.doi.org/10.3390/app11010169)]([http://dx.doi.org/10.3390/app11010169](http://dx.doi.org/10.3390/app11010169))  
Received: 2 December 2020  
Accepted: 23 December 2020  
Published: 27 December 2020  
**Published: 27 December 2020**  
**Vol. 2020**  
essentially polychromatic superpositions of Bessel beams, one possible way to overcome this problem would be to directly generalize the results concerning Bessel beams with two indices [30] to the domain of optical pulses. In doing so, however, one would have to deal with superposition of X-waves, rather than single X-waves, as Bessel beams with two indices exist only as superposition of Bessel beams.  
Alternatively, one could investigate the possibility to exploit the peculiar structure of nondiffracting beams, i.e., their spatio-temporal correlation, to induce a radial structure into them, by simply shaping their frequency spectrum. This is the approach we will follow in this work. In particular, we aim at introducing a new degree of freedom, which we call _spectral index_, which indices, as we will show, a radial structure into X-waves similar of that of Laguerre-Gaussian beams. Here, we focus our attention on so-called fundamental X-waves, which posses exponentially decaying spectra, and investigate how their spectral properties can shape their transverse structure  
This work is organized as follows: in Section 2, we first present a brief review of scalar localized pulses, and, in particular, fundamental X-waves and their spectrum. Then, we show how changing their spectral order results in the appearance of a transverse spatial structure, similar to that of Laguerre-Gaussian beams. Section 3 is then dedicated to the analysis and discussion of these results. Materials and methods used for this work are briefly discussed in Section 4, and, finally, conclusions are drawn in Section 5.
## 2 IEEE 1588 (PTP)
### Time-Synchronization Mechanism of PTP
#### 2.2.2 Peer-To-Peer Mechanism  
In the E2E mechanism, the synchronization accuracy may decline if the paths connecting the master and slaves are composed of numerous devices. However, the P2P mechanism addresses this by performing synchronization between all neighboring devices, such as switches or routers. Although this technique requires that all neighboring devices support the P2P mechanism, it can ensure more accurate time synchronization. Moreover, unlike the E2E mechanism, the delay and offset can be calculated through three messages in the P2P mechanism. Figure 1b demonstrates the synchronization mechanism of the P2P method. First, the requester node transmits a pdelay-request message at time t\({}_{1}\), and the responder node receives it at time t\({}_{2}\). Next, the responder node stores time t\({}_{2}\) in the pdelay-response message and transmits it to the requester node, and the requester receives it at time t\({}_{4}\). Finally, the responder stores time t\({}_{3}\) in a pdelay-request follow-up message and transmits it to the requester. Through this process, the requester acquires four timestamps and calculates the propagation delay and offset using Equations (1) and (2). The P2P mechanism is also based on a link with a symmetrical structure in which the transmission rates in both directions are identical, and the time-synchronization accuracy is determined according to the measurement position of the timestamp.
## 4 Discussion

Table 2: Comparison of manual methods for predicting the required femoral implant size in terms of their accuracy. Bold values indicate the best performance per metric. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Absolute Accuracy** & **+1/\(-\)1 Size Accuracy** & **Modality** \\ \hline [PERSON] et al. 2020 [37] & 19.2\% & 51.2\% & 2D: X-ray \\ [PERSON] et al. 2018 [11] & 56\% & 99\% & Shoe size \\ [PERSON] et al. 2017 [17] & - & 85–95\% & Demographics \\ [PERSON] et al. 2018 [19] & - & 94\% & Demographics \\ [PERSON] et al. 2019 [18] & - & 76\% & Demographics \\ [PERSON] et al. 2020 [22] & - & 94.4\% & Demographics \\ [PERSON] et al. 2020 [2] & 43.7\% & 90.1\% & Demographics \\ [PERSON] et al. 2021 [16] & 48.4\% & 95\% & Demographics \\ [PERSON] et al. 2022 [21] & - & 83.09\% & Demographics \\ [PERSON] et al. 2022 [23] & **82.2\%** & - & 3D: MRI \\ Manufacturer’s default plan & 23.1\% & **99.11\%** & 3D: MRI \\ Shape coefficient regression & 58.93\% & 98.21\% & 3D: MRI \\ Hypergraph regularized group lasso & 70.08\% & **99.11\%** & 3D: MRI \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of automatic methods for predicting femoral implant size in terms of their accuracy. Bold values indicate the best performance per metric. method yields equal improvement for predicting tibial implant sizes. Tibial and femoral implant sizes are correlated and, thus, a method combining both bones could help increase the accuracy.
## 5 Performance Evaluation and Analysis
### Key Performance Indicators (KPIs)
#### 5.1.3 Radio Resource Utilization (RRU)  
Figure 7 compares the RRU across different vehicular densities--low, medium, and high--using various scheduling methods in a 5G-NR-V2X Mode 2 environment with aperiodic traffic and variable data sizes. It evaluates the performance of DS, SB-SPS, and the proposed SEAC with an N-step approach, where the value of N is set to 10.  
The SEAC method shows consistently higher RRU, with utilization rates of 75.2%, 70.21%, and 64.36% for low, medium, and high vehicular densities, respectively. This indicates superior efficiency in resource management compared with DS and SB-SPS, which exhibit a noticeable decline in RRU as the vehicular density increases. The SEAC's use of GRUs for predictive modeling allows for real-time adjustments in resource allocation, effectively optimizing bandwidth usage and managing the variability in data traffic demands. This ensures reliable and efficient communication in V2X networks, particularly in dynamic and complex scenarios.  
Figure 6: Probability of reception (PoR) vs. the number of vehicles per mile/lane.  
Figure 7: Radio resource utilization (RRU) across varying vehicular densities.
# An 8 kb RRAM-Based Nonvolatile SRAM with Pre-Decoding and Fast Storage/Restoration Time
###### Abstract  
Combining the advantages of low-power consumption of static random access memory (SRAM) with high stability and nonvolatile of resistive memory (RRAM), an improved 8T2R nonvolatile SRAM (nvSRAM) memory cell was proposed in this paper. In order to solve the problem that data cannot be stored when SRAM is powered off, RRAM technology was introduced into SRAM to realize an SRAM with nonvolatile function. The differential mode was adopted to improve the data restoration speed. Meanwhile, a pre-decoding technology was proposed to realize fast address decoding, and a voltage-mode sensitive amplifier was used to achieve fast amplification of two bit lines, so as to improve the reading speed of the memory. An 8 kb nvSRAM was implemented with a CMOS 28 nm 1P9M process. The simulation results show that when the power supply voltage was 0.9 V, the static/read/write noise margin was 0.35 V, 0.16 V and 0.41 V, respectively. The data storage time was 0.21 ns, and restoration time was 0.18 ns. The time for the whole system to read 1 bit of data was 5.2 ns.  
static random access memory; resistive memory; nonvolatile; noise margin; restoration time
## 4 Results and Discussion
### Comparison of EEMD-Based Standard RNN, LSTM and GRU  
As shown in Figure 10, LSTM has higher prediction accuracy than GRU, both of which are better than standard RNN. For the standard RNN, LSTM, and GRU models, the MAE values are 16.935, 5.357, and 9.8425, respectively, and the NRMSE values are 6.1, 11.3, and 17, respectively. The evaluation metrics in Figure 10 illustrate that the prediction accuracy of the LSTM and GRU models is better than that of the standard RNN model. The problem of gradient disappearance in standard RNN is the primary cause for this performance distinction. The LSTM and GRU approaches are more practicable for landslide displacement prediction because of the gated unit structures.  
Figure 10: Comparison of model performance in terms of MAE, MSE, MAPE, NRMSE, R\({}^{2}\), and KGE.  
In this study, the GRU model consumes 47.66 s to train, while the LSTM model consumes 201.75s, an increase of nearly three times the computational cost due to the complex network structure. The comparative analysis shows that the LSTM and GRU models provide equally satisfactory performance for landslide displacement prediction, but GRU is more efficient because of its simpler network structure.
## 2 Materials and Methods
### Volatile Free Aroma Compounds  
#### 2.5.1 Free Aroma Extraction and Quantification  
The extraction and quantification of free aroma compounds were performed using a headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC-MS). Grape berries (100 g) were randomly selected and ground using an electric grinder. Grape juice was separated from the pomace by centrifugation and filtration. Extraction and GC/MS analyses of the volatile aroma compounds were performed under pre-established conditions. For the extraction of volatile aroma compounds, 10 mL grape juice was added to a 20 mL volume headspace vial containing 3 g NaCl and acetonitrile as an internal standard, and the sample vial was incubated at 50 \({}^{\circ}\)C with constant stirring for 1 h. After incubation, an SPME fiber (50/30 \(\upmu\)m DVB/CAR/PDMS; Supelco, Bellefonte, PA, USA) was introduced into the headspace of the sample vial for 20 min to adsorb the volatile aroma compounds. The SPME fiber was directly injected into the injection port of a gas chromatograph (6890N, Agilent Technologies, Santa Clara, CA, USA) equipped with an Agilent 5975 Series Mass Selective Detector. An HP-INNOWax capillary column (30 mm \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m; Agilent Technologies) was used to separate the volatile aroma compounds, and purified helium gas was used as a carrier gas at a constant flow rate of 2 mL/min. The injection port temperature was set to 250 \({}^{\circ}\)C, and the purge flow to the split vent was set to 144.9 mL/min for 1 min. The oven temperature was initially held at 40 \({}^{\circ}\)C for 5 min, then increased to 250 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and finally held at 250 \({}^{\circ}\)C for 5 min. The mass detector was operated in the positive-ion electron-impact ionization mode at 70 eV with a scan range of 50-700 m/z. Volatile aroma compounds were identified using their mass spectra from the NIST 11 (National Institute of Standards and Technology)mass spectral library and Kovat retention indices. The concentrations of the volatile aroma compounds were estimated using the following Equations [12]:  
\[RF_{SC}=\frac{A_{IS}\times C_{SSC}}{C_{IS}\times A_{SC}} \tag{1}\]  
\[C_{SC}=\frac{C_{IS}\times A_{SC}\times RF_{SC}}{A_{IS}} \tag{2}\]  
where \(RF_{SC}\) is the response factor for a specific compound, \(A_{IS}\) is the peak area of the internal standard, \(C_{SSC}\) is the concentration of the authentic standard of a specific compound, \(C_{IS}\) is the concentration of the internal standard, \(A_{SC}\) is the peak area of a specific compound, and \(C_{SC}\) is the concentration of a specific compound.
## 3 Materials and Methods
### Data Refinement
#### 3.2.2 Quantization  
The reason behind the data quantization is it enables additional limitations of resource usage. Data are quantized in three ways using custom quantizers:  
1. **Selection bit(s).** Here, all data values that have passed the previously applied selection steps have a value of 1. Using this approach, FPGA implementation requires just 1 bit for each value in the input data layer presentation.
2. **Uniform quantizer.** Min-max normalization is applied on capped data, multiplied by \(2^{n}-1\), where n is a chosen number of bit width. The result is rounded to the nearest integer. The proposed quantizer function is presented in Figure 8(a). \[Q(x)=round(\frac{x-min}{max-min}\cdot(2^{n}-1))\] (1)
3. **Non-uniform quantizer.** The quantizer is defined as follows \[Q(x)=\begin{cases}round(log_{2}(x)),&\text{if }x>0\\ -2,&\text{otherwise}\end{cases}\] (2) The next step is to rescale data (using a min-max scaler), so it does not contain negative values. The non-uniform quantizer function is presented in Figure 8(b).  
An overview of data refinement methods described in this section is given in Table 1.  
Figure 8: Distribution of pt across HGCAL layers for EM, PU and QCD showers used in 3 Ef+Hf approach (**a**) 3 CE-E + CE-H layers split (**b**) summed pt in grouped layers.  
Figure 9: Quantizer function y = Q(x): (**a**) in the case of a uniform quantizer, (**b**) in the case of non-uniform quantizer.
# Multi-Device to Multi-Device (MD2 MD) Content-Centric Networking Based on Multi-RAT Device
## 2 Related Works and Problems
### Problems with Existing Environments  
In an MD2 MD CCN environment, mobile devices actively search for and connect to nearby devices, configure a network, and then connect to the device with the content such that that content can be retrieved. In such an environment, the main issues relate to searching for and connecting to the nearby device. Without the assistance of any infrastructure, it should be possible for a device to search for a nearby device itself and establish a connection to a device with the specific content. In an MD2 MD CCN environment, it is necessary to perceive information such as the possession of desired content and whether the device supports CCN. Additionally, there can be situations in which it is necessary to connect to devices satisfying certain conditions depending on the user's requirements. For example, if the user desires to connect to the Internet through a connection with another device, information such as whether there is a backhaul is also necessary. Therefore, recognition of the device's information is essential when establishing a connection between devices.  
IEEE 802.11 states that it is possible to determine the Access Point (AP) information through the Beacon frame and the Prove Request/Response frame that includes the information required for establishing an AP connection. Also, it is possible to add optional fields in addition to the fields defined in the standard for these Beacon and Probe Request/Response frames. However, many restrictions are imposed on additions and corrections since the optional field is not standard. A problem arises in that it may be erroneously recognized in a device.  
With typical search methods, the device obtains the information on the other device by sending and receiving messages after a connection between the devices has been established. Therefore, it is impossible to obtain detailed information on a device prior to establishing a connection. As such, the establishment of a connection is delayed due to the repetition of unnecessary processes such as the connections and disconnections that occur until a device satisfying the user requirements is found.  
The connection process of a CCNx device for connecting to a device through a WLAN is illustrated in Figure 4. After scanning the nearby APs, the user device obtains detailed information on the device by establishing a direct connection to the AP. Therefore, if the connected device does not satisfy the user requirements, the device must first disconnect and then reconnect to another device.
# Classification of Potato Varieties Drought Stress Tolerance Using Supervised Learning
## 3 Results
### The Climatic Water Balance Determination
Figure 2: Relative yield decrease in years (**a**) according to the climatic water balance: very dry, dry, normal, and wet; (**b**) for different cultivar groups with a tolerance to soil drought; and (**c**) both splits simultaneously. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**(c) Tolerance to soil**} & \multicolumn{2}{c}{**Climatic water balance**} & \multicolumn{1}{c}{**Min**} & \multicolumn{1}{c}{**Max**} & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**Std**} & \multicolumn{1}{c}{**Q.25**} & \multicolumn{1}{c}{**Q.5**} & \multicolumn{1}{c}{**Q.75**} \\ \hline Group I & very dry & \(-3.49\) & 34.25 & 19.64 & 9.95 & 13.91 & 20.54 & 27.03 \\  & dry & 2.64 & 37.92 & 22.79 & 14.18 & 12.14 & 29.22 & 32.75 \\  & optimal & 1.02 & 43.16 & 30.43 & 8.97 & 27.61 & 28.97 & 35.34 \\  & wet & 0.50 & 24.87 & 17.03 & 5.77 & 13.10 & 16.92 & 22.42 \\ Group II & very dry & 11.70 & 36.79 & 28.36 & 6.77 & 24.40 & 29.11 & 33.42 \\  & dry & 12.48 & 48.76 & 27.97 & 10.11 & 21.66 & 24.96 & 33.90 \\  & optimal & 8.85 & 56.83 & 34.78 & 11.17 & 31.15 & 35.24 & 42.17 \\  & wet & 24.90 & 35.82 & 29.85 & 5.28 & 25.68 & 29.33 & 33.50 \\ Group III & very dry & 26.08 & 48.06 & 39.29 & 5.98 & 37.82 & 40.41 & 42.62 \\  & dry & 23.76 & 61.43 & 45.70 & 10.27 & 40.70 & 47.33 & 52.71 \\  & optimal & 27.31 & 56.16 & 42.69 & 7.98 & 39.46 & 42.45 & 48.37 \\  & wet & 25.74 & 61.64 & 38.32 & 15.21 & 27.47 & 31.12 & 45.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The Cultivars Differentiation into Groups with Different Tolerance to Soil Drought Based on the Yield Decrease  
Based on the statistical analysis, three new groups of varieties with different tolerance to soil drought were distinguished:  
* Group I: resistant varieties with the lowest yield decrease, i.e., up to 30%;
* Group II: cultivars with medium tolerance to soil drought, where the yield decrease was in the range of 30-40%;
* Group III: susceptible cultivars where the yield drop exceeded 40%. In the first group, there were 19 varieties; in the second group, there was also 19 varieties, and in the third group: 12 varieties. In all three groups, there were varieties from different groups of earliness, i.e., from very early to late. The greatest range of variability was recorded in the group of resistant cultivars. This range was from 3% for the _Laskara_ variety to 30% for the _Tafiun_ variety. In the group of cultivars with medium resistance, the differentiation was much smaller. In the group of cultivars with the lowest resistance, the range of variability ranged from 43% in cv. _Danusia_ to 55% in cv. _Owacja_.
## 3 Results  
### Direct Treatment of Astrocytes with NTP (20-90 mJ) Led to Increased Astrocyte Infiltration  
Planar astrocytes were subjected to a scratch assay and immediately followed with NTP treatment. Regrowth assessment was performed at one, two, and three days post-treatment. Astrocyte cultures subjected to 140 mJ of plasma treatment were completely ablated, resulting in no measurable cells present. Therefore, these were excluded from further analysis. Across all other groups, significant gap reduction was observed over time. Astrocyte ingrowth into the scratch region was observed such that gap length was significantly reduced at each time point relative to previous time points (\(p<0.05\)) (Figure 2). Astrocytes treated with \(\leq\)90 mJ plasma exhibited healthy morphology with no signs of distress. At one day post-treatment, cultures treated with both 50 mJ and 90 mJ plasma displayed a statistically significant reduction in mean gap area when compared to the untreated group (\(p<0.05\) and \(p<0.01\), respectively). At two days following treatment, cultures treated with 20 mJ plasma along with 50 mJ and 90 mJ exhibited a significant reduction in the mean gap distance (\(p<0.05\), \(p<0.01\), and \(p<0.05\), respectively). Lastly, at three days post-treatment, only cultures treated with 50 mJ plasma resulted in significantly decreased mean gap distance as compared to the control groups (\(p<0.05\)) (Figure 2).  
Figure 2: NTP treatment of astrocytes resulted in increased cell/process infiltration. (**A**) Phase-contrast images showing astrocyte infiltration into the gap area 0–3 days post-treatment with 0 mJ, 20 mJ, 50 mJ, and 90 mJ plasma. Dashed lines in images on day 0 and day 1 were added to assist visualization of scratch boundaries. Scale: 250 μm. (**B**) Astrocyte infiltration was significantly increased over time, as expected. Interestingly, one day post-treatment with 50 mJ and 90 mJ of plasma led to significant astrocyte infiltration into the gap area, two days following treatment, 20 mJ, 50 mJ, and 90 mJ plasma treatment all led to significant reduction in mean gap distance, and three days post-treatment, only 50 mJ plasma resulted in reduced mean gap distance when compared to time-matched untreated control cultures (bars represent standard error of the mean (SEM); \({}^{*}\)\(p<0.05\); \({}^{**}\)\(p<0.01\)).