esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Field and Laboratory Tests for Soil Suitability Assessment in Raw Earth Construction
## 3 Results and Discussions
### Field Test Results and Analysis
Soils with longer cigar lengths (soil 1 with 37 cm, soil 3 with 26.5 cm, and soil 8 with 15.7 cm) consistently scored higher in the ring and ribbon tests, suggesting that soil cohesion, as measured by the cigar test, is closely related to clay-sized particle content and plasticity. Conversely, shorter cigar lengths (soil 4 with 4.8 cm, soil 9 with 3.6 cm) corresponded to poorer performance in other tests. The dropping ball test shows high consistency (mostly scoring 5), making it less discriminating for soil characterization. However, soils with very low scores in other tests (like soils 4 and 6) also showed reduced performance in the dropping ball test, indicating it may be useful for identifying particularly unsuitable soils. The pellet test results, combining both shrinkage and resistance measurements, provide complementary information. Soils with high resistance (4-5) generally performed well in other tests, particularly those with longer cigar lengths. However, shrinkage values show more complex relationships, suggesting this parameter might be capturing different soil properties than other tests. The jar test results (number of visible layers) do not show strong direct correlations with other test results, suggesting it might be measuring different soil characteristics. This test appears to be more useful for understanding particle size distribution rather than predicting soil behavior in other tests. These correlations suggest that while each test provides unique information, there is significant redundancy in what they measure. The cigar test emerges as a potentially efficient indicator of overall soil suitability, as its results consistently align with other test outcomes. This finding could be valuable for streamlining field testing procedures, though further statistical analysis would be needed to confirm these observed relationships. Geotechnical tests provide more information and precision, particularly concerning the particle size distribution as well as the nature and proportion of the active phase, i.e., the clay, but they also have limits as they require more time, equipment, and are not applicable in the field.
# Spintronic RF-Direct on-off Keying Modulation Using a Frequency Division Multiplex
## 2 Wireless Spintronic Communication
To overcome challenges to communicate with the STNO, we used the OOK modulation method when directly modulating a signal of one STNO with data, as OOK modulation supports communication regardless of the signal purity. It also allows wireless communication without a PLL circuit, and with a simple structure for direct modulation. To date, there have been no reports on a realization of the frequency PLL for an STNO signal for communication. OOK can be used for communication by exploiting the advantages of the STNO for low power consumption and nano-meter scale size. Receiving of data can be easily realized simply with the function of envelope detection in the receiver. Figure 2: Spintronic oscillation characteristics of the fabricated STNO depending on (**a**) the magnetic field and (**b**) the rotation field angle at the condition of IDC = 1.5 mA. (1 Oe (Oersted) = 1 Gauss in air = 0.1 millitelsa). Consequently, we target the realization of wireless communication with low power consumption under the constraint of application in near field communication due to the low level of the output signal. Each STNO has a different frequency in the array. On this basis, a new spintronic RF-direct OOK modulation using a frequency division multiplex (FDM) technique is realized for wireless communication without interference. The new modulation concept provides frequency modulation with an FDM by assigning each frequency at every STNO on an array structure. Amplitude is simultaneously modulated directly in the RF signal generated from the STNO with OOK on each STNO. By simultaneously applying OOK modulation to all STNOs while generating different frequencies for multiple STNOs on an array structure, we present a new spin RF-direct OOK modulation using an FDM that modulates data together with amplitude and frequency signals for wireless communication using an STNO array.
## 4 Experiment
### Comparison of the HAM10000 and ISIC 2019 Datasets
#### 4.4.2 Comparison with Segmentation Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Method** & **ACC** & **Sensitivity** & **Specificity** \\ \hline \multirow{8}{*}{Experiment 1} & Original Otsu + wide-ShuffleNet & 79.62\% & 79.62\% & 96.60\% \\  & Otsu Momentum [57] + wide-ShuffleNet & 81.51\% & 81.51\% & 96.92\% \\  & Image Entropy [56] + wide-ShuffleNet & 83.91\% & 83.91\% & 97.32\% \\  & U-Net + wide-ShuffleNet & 85.10\% & 85.10\% & 97.52\% \\  & U-Net + EfficientNet-B0 [54] & 85.65\% & 85.65\% & 97.61\% \\ (80\% training, 20\% testing) & FrCN + Inception-ResNet-v2 [55] & 87.74\% & - & - \\  & FrCN + Inception-v3 [55] & 88.05\% & - & - \\  & FrCN + DenseNet-201 [55] & 88.70\% & - & - \\ \cline{2-5}  & EW-FCM + wide-ShuffleNet & 84.80\% & 84.80\% & 97.48\% \\ \hline \multirow{8}{*}{Experiment 2} & Original Otsu + wide-ShuffleNet & 80.14\% & 80.14\% & 96.69\% \\  & Otsu Momentum [57] + wide-ShuffleNet & 82.54\% & 82.54\% & 97.09\% \\ \cline{1-1}  & Image Entropy [56] + wide-ShuffleNet & 84.83\% & 84.83\% & 97.47\% \\ \cline{1-1}  & EW-FCM + wide-ShuffleNet & 86.33\% & 86.33\% & 97.72\% \\ \hline \multirow{8}{*}{Experiment 3} & Original Otsu + wide-ShuffleNet & 78.55\% & 78.55\% & 96.94\% \\ \cline{1-1}  & Otsu Momentum [57] + wide-ShuffleNet & 80.34\% & 80.34\% & 97.19\% \\ \cline{1-1}  & Image Entropy [56] + wide-ShuffleNet & 81.20\% & 81.20\% & 97.31\% \\ \cline{1-1} \cline{2-5}  & EW-FCM + wide-ShuffleNet & 82.56\% & 82.56\% & 97.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with segmentation methods on the HAM10000 and ISIC2019 datasets (micro-average). Figure 8: Segmentation results of various segmentation methods. analysis with the EW-FCM segmentation technique (such as using the Jaccard index). Second, the complexity increases when using DL for segmentation and classification. For instance, the total parameters of the DL segmentation U-Net and DL classification Efficient-B0 are 12.7 M (7.7 M + 5 M = 12.7 M). EW-FCM uses DL only for the classification state, thus decreasing the complexity and being suitable with a portable system. Next, we evaluate the performance of the skin classification with various classifiers and present the results in Table 8. The proposed method improves the skin classification for two reasons: wide-ShuffleNet is better than ShuffleNet, and the new segmentation technique is better than the original image. Efficient-B0 has the highest accuracy, but its parameters increase approximately three times compared to wide-ShuffleNet. In future studies, we will investigate the performance of EW-FCM with ShuffleNet V2 (2.4 M parameters) and compare it with the proposed wide-ShuffleNet (1.8 M parameters).
# Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection
## 2 Basic Definitions  
In the non-binary \((N,K)\) LDPC codes, \(N\) is the code word length and \(K\) is the information bits length. The non-binary LDPC codes are determined by the sparse non-binary parity check matrix \(\mathbf{H}\), while the check matrix \(\mathbf{H}\) has \(M\geq N-K\) rows and \(N\) columns. Each row represents a check equation and each column represents a code word. For any hard decision vector \(\mathbf{y}\), the check equation is \(\mathbf{s}_{h}^{(k)}=\mathbf{y}^{(k)}H^{T}\), where the real numbers involved are carried out under the Galois field. \(h\) represents the elements in the checksum matrix. \(k\) represents the number of iterations. The number of non-zero elements in each row and each column in the check matrix is equal, which is called the rule code, while the others are called the irregular codes. The row weight of the check matrix \(H\) is \(d_{c}\) and the column weight is \(d_{v}\). A non-\"0\" element on the nth column of the Galois field is represented by \(M(n)\) in the check matrix \(H\). A non-\"0\" element on the \(m\)th row of the Galois field is represented by \(N(m)\) in the check matrix \(H\). \(M(n):=\{m:h_{mn}\
eq 0\}\), \(N(m):=\{n:h_{mn}\
eq 0\}\).  
We selected any code word \(\mathbf{c}=[c_{0},c_{1},\cdots,c_{N-1}]\in\{\mathrm{GF}(q)\}^{N}\), where \(q=2^{b}\). Before sending, the code word is mapped to the bipolar vector \(\mathbf{t}=[t_{0},t_{1},\cdots,t_{N-1}]\) according to the transmitted signal constellation \(\Omega\), where \(t_{n}=\left[t_{nb},t_{nb+1},\cdots,t_{(n+1)b-1}\right],0\leq n<N\). Using Binary Phase Shift Keying (BPSK) modulation, \(\phi:\mathrm{GF}(q)\rightarrow\Omega^{b}\), where \(\Omega=\{-1,1\}\). The specific mapping rule is \(\phi(c_{n})=t_{n}=\left[t_{nb},\cdots,t_{(n+1)b-1}\right]\), where \(t_{i}\in\Omega,0\leq i<Nb\). After the transmission of the Additive White Gaussian Noise (AWGN), the output is:  
\[r=t+n \tag{1}\]  
where \(n\) is the independent white noise real vector with noise power \(\sigma^{2}=(2R_{c}E_{b}/N_{0})^{-1}\); \(E_{b}/N_{0}\) is the signal-to-noise ratio of each information bit; and \(R_{c}=K/N\) is the bit rate.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 5 Known Issues and Limitations  
We applied Basemap and Planet Fusion imagery for a one-year analysis. More research is necessary to assess the performance of these datasets for a longer study period (e.g., including periods of prolonged droughts \(\sim\)3-5 years) and in other study regions--for example, in Southern India, where OFRs are common [4], and where there is a monsoon climate in which there could be weeks without a clear-sky image [64]. In addition, the validation of this study was conducted using cloud-free SkySat imagery; therefore, there is still a need to further evaluate the performance of both datasets under cloudy conditions. However, this will require extensive field work, including visiting multiple OFRs on cloudy days, which imposes several challenges, as most of the OFRs in eastern Arkansas are located on private properties. Furthermore, we assumed that OFR surface area would vary within known and limited boundaries (i.e., OFR shapefile buffered to 20 m). However, different results might be obtained if the Basemap and Planet Fusion images are used to monitor water bodies that frequently change their boundaries--water impoundments that are located close to water streams and rivers that flood frequently, impacting the edges of water bodies. Lastly, although we calculated the uncertainties introduced by Basemap and Planet Fusion, when using these datasets for monitoring purposes, it would be helpful to have an estimated uncertainty accompanying every surface area observation. For instance, whenever there are repeated observations by the Basemap or continuous synthetic observations from Planet Fusion, the uncertainties from these images will be higher; however, as of now, we cannot estimate an observation based uncertainty.
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 2 Background
### Distributed Storage  
Existing data storage services, such as HDFS [12], GFS [13], and CEPH [14], are mostly clustering systems in data centers. Due to monitoring of the load status of each storage node, data requests are routed to storage nodes by centralized scheduling to support load balancing. When load balancing is performed, generally only the storage space of the node is considered, and the network link usage between the requester and the node is ignored. In a CDN network, contents are spread based on requests and the popularity of contents. It dispatches requests to the nearest content replica via central scheduling, and resolves download bottlenecks through caching, and is not suitable for real-time data injecting. The cloud storage system is a core cloud-based service offered by most cloud providers, such as Google, Amazon, and Microsoft. However, cloud storage system is located far from users, resulting in a high response delay for applications. Moreover, a massive amount of data generated by IoT devices places significant pressure on the core network for its transmission to remote clouds. For this purpose, providing storage as close to users as possible or at the edge of the network is beneficial.
## 5 Frequency Regulation by DFIGs Integrated by VSC-HVDC
### Correction to Inertia of DFIG Based on Kinetic Energy of Rotor  
The DFIGs initially operate under the derated mode, then the power reserve is released together with the reducing kinetic energy to prevent frequency drop. It is clear that the kinetic energy reduces when the rotor speed reduces, thus the existing fixed inertia corresponding to the initial rotor speed overestimates the regulation capability.  
The application of the over speed/pitch angle control depends on the wind speed range, and the rotor speed of the WT is adjusted based on the control strategies, which contributes to the inertia of the DFIG.  
1. With high wind speed, the rotor speed is fixed at the rated value. The pitch angle control is applied to regulate the frequency. No kinetic energy is released, thus \(H_{\text{DFIG}}\) is 0.
2. With medium wind speed, the over-speed and pitch angle control may be applied, with \(\omega_{\text{wt}}\) approximately changing in a linear manner during frequency regulation, \[\omega_{\text{wt}}=\omega_{\text{wt,opt}}+\frac{P_{\text{wt,opt}}-P_{\text{ wt}}(\beta)}{P_{\text{wt,opt}}-P_{\text{wt,del}}}(\omega_{\text{wt,max}}- \omega_{\text{wt,opt}})\] (16) where \(\beta\) is the pitch angle. \(H_{\text{DFIG}}\) is then solved similarly to that under low wind speed in the following.
3. With low wind speed, only the rotor speed control is applied, and the nonlinear relationship between the output of the DFIG and \(\omega_{\text{wt}}\) based on the \(c_{\text{p}}\) function is given by, \[c_{1}\left(\frac{2c_{2}\upsilon_{\text{w}}}{\omega_{\text{wt}}D}-c_{2}c_{9}- c_{6}\right)e^{-\frac{2\gamma_{\text{T}}\upsilon_{\text{wt}}D}{\omega_{\text{wt}}D}+c \gamma_{9}}=(1-k_{\text{der}})c_{\text{p,opt}}-\frac{2S_{\text{B}}K_{\text{DFIG }}\Delta f_{\text{wt}}}{\rho Av_{\text{w}}^{3}}\] (17) where \(c_{\text{p}}\) is the power utilization coefficient of WT; \(c_{1}\)-\(c_{9}\) are the parameters to describe \(c_{\text{p}}\); \(D\) is the diameter of the blade; \(A\) is the sweeping area; \(\rho\) is the air density; \(k_{\text{der}}\) is the derating coefficient. The subscript opt denotes the MPPT mode.  
By transforming \(\omega_{\text{wt}}\) to the rotor speed of the DFIG, i.e., \(\omega_{\text{r}}\), the kinetic energy of DFIG is given by,  
\[\left\{\begin{array}{l}\omega_{\text{r}}=p\eta\omega_{\text{wt}}\\ \Delta E_{\text{DFIG}}=\frac{1}{2p^{2}}J(\omega_{\text{r}}^{2}-\omega_{\text{ r,opt}}^{2})\end{array}\right. \tag{18}\]  
where \(p\) is the pole pair numbers, \(\eta\) is the gear ratio, and \(J\) is the total inertia moment of the DFIG.  
With the base power \(S_{\text{B}}\), the inertia \(H_{\text{DFIG}}\) of the DFIG is given by,  
\[H_{\text{DFIG}}=\frac{\Delta E_{\text{DFIG}}}{S_{\text{B}}}=\frac{1}{2p^{2}S_{ \text{B}}}J(\omega_{\text{r}}^{2}-\omega_{\text{r,opt}}^{2}) \tag{19}\]
## 2 Related Work
### Gaussian Embedding  
Gaussian embedding [18] is a method to embed word types into the space of Gaussian distributions, and learn the embeddings directly in that space, which represents words not as low-dimensional vectors, but as densities over a latent space, directly representing notions of uncertainty and enabling a richer geometry in the embedded space.  
In word representation, embedding an object as a single point can not naturally express uncertainty about the target concepts with which the input may be associated, and the relationships between points are normally measured by distances required to obey the triangle inequality. Point vectors are typically compared via their dot products, cosine-distance, or Euclean distance, none of which provide asymmetric comparisons between objects. To overcome the limitations in representing objects as points, Gaussian embedding is proposed to learn representations in the space of Gaussian distributions, advocating for density-based distributed embeddings.  
In Gaussian embedding, we learn both means and variances from data, representing them as densities over a latent space instead of low-dimensional vectors. As Gaussians innately represent uncertainty and have a geometric interpretation as an inclusion between families of ellipses, our method adopts KL divergence between Gaussian distributions to measure the relationship between objects, which is straightforward to calculate.  
Mapping to a density provides many advantages, including better capturing uncertainty about a representation and its relationships, providing asymmetric comparisons between objects, which is more effective than dot product or cosine similarity, and which enables more expressive parameterization of decision boundaries.
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## Validation and Analysis
### Kinematic Experiment
#### 3.2.3 Positioning Results of Different Reference Receivers
From the analysis of the RMS and STD values, we can conclude that it is no statistically significant difference in the positioning errors for scheme-five and scheme-six, which can achieve a similar accuracy of 3.0 cm, 1.5 cm, and 4.5 cm level in N, E, and U components. Compared with the RMS values of scheme-one and scheme-two with SBRTK model, the RMS values of scheme-five and scheme-six with MBRTK model have a performance improvement by approximately (9%, 0%, and 6%) and (9%, 16%, and 16%) in N, E, and U components, respectively. Meanwhile, the STD values of scheme-five and scheme-six illustrate a better consistency than those of scheme-one and scheme-two. Besides, the success rate of four different schemes is 84.5%, 86.0%, 94.4% and 96.8%, respectively. Compared with scheme-one and scheme-two, the scheme-five and scheme-six have a similar availability improvement by approximately 10% in the success rate. However, it should be noted that scheme-six with three reference receivers does not present better performance than scheme-five with two reference receivers in both positioning accuracy and availability. The possible reason is that the reference receiver SDWB is close to the SDWA, which cannot further enhance the strength of the control network in the kinematic positioning. Figure 13 presents L1/B1 carrier residuals for four schemes with different reference receivers. The DD residuals can be derived with the fixed coordinate parameters for MBRTK to compare with the residuals of SBRTK. It can be seen that the residuals of four schemes are randomly distributed along the zero \(Y\)-axis, and the mean values of the residuals are approximately 0 cm. The RMS and STD values of the residuals for scheme-one are worse than that of scheme-two. The possible reason is that there is a mountain behind the SDWA station, which leads to a worse satellite geometry than that of SOTH station. Although the residuals of scheme-six are slightly worse than that of scheme-five, the RMS and STD values of both schemes with multiple reference receivers have a good consistency, which are obviously smaller than that derived from scheme-one and scheme-two. Figure 12: Positioning errors for scheme-one, scheme-two, and scheme-six in N, E, and U components.
## 4 Numerical Results and Discussion
A numerical comparison of the performance in terms of SD between the conventional cluster of three circular sensors and the designed cluster is given in Table 1, assuming that measurements  
Figure 6: Estimated ratio of differences in time of arrival (DToAs) (\(\Delta t_{12 est}/\Delta t_{13 est}\)) with respect to the actual DoA (\(\theta\)) for the case of A\({}_{0}\) mode propagating. are affected by AWGN with zero mean. The simulation of the A0 mode propagation was run when considering r = 0.4 [m] as impacts distance and Fs = 1 [MHz]. In order to assess the SD of DoA numerically, 1000 simulations were performed. The comparison is provided with the same maximum absolute value for the DToAs (i.e., \(d/v\)), so with the same memory requirement and same computational cost to estimate the time delays. As can be seen from Table 1, the SD value that is obtained with the cluster of shaped sensors is considerably reduced for all PSNR values. \begin{table}
\begin{tabular}{c c c} \hline \hline
**PSNR** & **Circular** & **Sensors** & **Shaped Sensors** \\ \hline
26 dB & 1.61 & 0.95 \\
22 dB & 1.98 & 1.34 \\
20 dB & 2.35 & 1.76 \\
19 dB & 2.63 & 2.01 \\
18 dB & 3.08 & 2.78 \\
17 dB & 3.98 & 3.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Standard Deviation (SD) for the conventional cluster and proposed cluster of shaped sensors for noise-affected measurements at different Peak Signal to Noise Ratio (PSNR) values (SD Values are in [Deg]). Figure 7: Estimated DoA (\(\theta_{est}\)) with respect to the actual DoA (\(\theta\)) at different sampling frequency values (indicated in the title of subplots, with the computed value of standard deviation of DoA) for the case of A\({}_{0}\) mode propagating (the upper and lower bound limits computed according to the Propagation of Uncertainty theory on the basis of the ideal sensors geometries). Figure 8: Estimated DoA (\(\theta_{est}\)) with respect to the actual DoA (\(\theta\)) for noise-affected measurements (additive white Gaussian noise) at different PSNR values (indicated in the title of subplots, with the computed value of standard deviation of DoA). The plotted results were computed by simulating the A\({}_{0}\) mode propagation, with Fs = 1 MHz.
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 5 Results and Discussions
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Sheeting Type** & **Color** & **Orientation Degrees** & **Observation Angle** & **Age (Years)** & **True \(R_{A}\)** & **Predicted \(R_{A}\)** \\ \hline Engineering Grade & Blue & 90 & 0.2 & 0 & 44 & 42.81 \\ Engineering Grade & Blue & 90 & 0.2 & 2 & 37 & 39.25 \\ Engineering Grade & Blue & 0 & 0.5 & 10 & 5 & 2.08 \\ Engineering Grade & Green & 0 & 0.2 & 5 & 65 & 55.02 \\ Diamond & Blue & 135 & 0.2 & 0 & 176 & 201.84 \\ Diamond & Yellow & 45 & 1 & 2 & 86 & 96.82 \\ Diamond & Blue & 180 & 1 & 10 & 5 & 8.96 \\ Diamond & White & 45 & 1 & 5 & 92 & 91.76 \\ Diamond & Green & 0 & 1 & 0 & 22 & 20.89 \\ Diamond & Green & 180 & 0.5 & 10 & 30 & 54.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Samples from the test sets and predicted \(R_{A}\) by our proposed NN with one-hot encoding.