esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review
### Classification of Saline-Alkaline Soil  
Based on the formation period and transformation characteristics of saline-alkaline land in China, these soils can be classified into three categories. One such category is potentially salinized soils [10], which make up approximately 50% of the total land area. These soils were previously affected by salt accumulation, but the process has since halted. However, due to extreme drought conditions, the residual salts remain in the soil, as natural leaching is difficult. A notable example is salinized ash desert soil [11], which is widely distributed in arid regions with high terrain and specific bioclimatic conditions. A typical occurrence of potentially salinized soil can be observed in terraces formed in Piedmont sloping plains and old river (lake) beds. In these regions, salt accumulation was interrupted due to changes in topography, yet significant amounts of salt remained in the soil over time. Although these soils do not currently display significant salinization, they constitute about 20% of the total land area, and have the potential to transform into saline-alkaline soils under specific environmental conditions or improper land management practices [12].  
Saline-alkaline soils are generally categorized into three levels based on their salinity: mild, moderate, and severe. Mild saline-alkaline soils contain less than 3% salt, and have a pH between 7.1 and 8.5. Under these conditions, the negative impact on crops is relatively low, allowing seedling emergence rates to remain between 70% and 80%. This type of land is more conducive to agricultural activities, and can be cultivated with minimal soil improvement efforts. On the other hand, severe saline-alkaline soils have a salt content exceeding 6% and a pH above 9.5, resulting in a highly alkaline environment. These conditions significantly hinder plant growth, causing seedling emergence rates to drop below 50%. Due to the extreme levels of salinity and alkalinity, direct crop cultivation is usually impractical, making soil improvement a necessary step before agricultural use. Moderate saline-alkaline soils lie between these two extremes, displaying intermediate salt concentrations and alkalinity levels. Although they still present challenges for farming, their productivity can be improved with appropriate management techniques.  
There is an inverse relationship between salinity and seedling emergence, meaning that as salinity levels rise, seedling emergence declines. Over time, the spatial distribution of saline-alkaline soils shifts, causing the central points of high, moderate, and low saline-alkaline stress zones to change. In coastal areas, regions experiencing severe salinity stress often remain close to the shoreline, while moderately saline-alkaline lands gradually migrate inland, shifting from the southwest coastline toward the northwest [13; 14].
## 2 Methods
### Case 2: Low-Latitude Region
#### 3.2.2 FR and VTEC Estimations  
The FR and VTEC maps are estimated over Thailand with the same procedure that was used for Alaska. Figure 10a,b represents the spatial distributions of FR on 26 March 2010 and 29 March 2011, respectively. It is found that both maps show the spatially homogenous FR angles, where the standard deviations are 0.23\({}^{\circ}\) and 0.20\({}^{\circ}\) on 26 March 2010 and 29 March 2011, respectively. This observation is different from the last experiment, where the prominent stripe-shaped FR distribution is found. However, careful inspection suggests that there is a clear difference in FR magnitude between Figure 10a,b: Figure 10b illustrates larger FR values than Figure 10a. Because the system-dependent terms and noise have been calibrated and mitigated, this difference is considered to be primarily introduced by the ionosphere. Figure 10c,d displays the spatial distributions of VTEC on 26 March 2010 and 29 March 2011, respectively. It is observed that both Figure 10c,d show a similar pattern with Figure 10a,b. The maximum magnitudes of Figure 10c,d are 31 and 45 TECU, respectively. The mean values are 25 TECU for Figure 10c and 41 TECU for Figure 10d, showing the comparable results with Figure 9b,c. Due to the different VTEC magnitude, the additional ionospheric phase error may be produced when the interferogram is formed from these collected SAR images.  
Figure 9: Experimental region and the International Global Navigation Satellite System (GNSS) Service (IGS) VTEC maps. (**a**) Experimental area; (**b,c**) IGS VTEC maps at 16:00 (UT) on 26 March 2010 and 29 March 2011, respectively. The black rectangle denotes the spatial coverage of SAR images.
# Research on Risk Assessment Method for Land Subsidence in Tangshan Based on Vulnerability Zoning
## 4 Conclusions  
The results obtained in this study are as follows:  
1. Utilizing ArcGIS, a susceptibility zoning map of Tangshan City was generated by analyzing six key factors: the lithology, rainfall, population density, road network density, seismic acceleration, and river network density. A single-factor analysis was conducted, followed by spatial-overlay processing using ArcGIS to assign values and to classify each factor. The study area was determined based on the susceptibility zoning results, in conjunction with the distribution of historical subsidence areas and goaf areas in Tangshan City.
2. The indicator system for evaluating the ground subsidence hazard encompasses three levels: the evaluation objective, evaluation criteria, and evaluation indicators. The evaluation objective is the assessment of the urban ground subsidence hazard. The evaluation criteria are divided into three categories: natural factors, engineering factors, and triggering factors. The natural factors include the lithology, quaternary groundwater, geological faults, and distance-to-water systems. The engineering factors encompass the road loads, distribution of subsidence areas, distribution of goaf areas, and road network density. The triggering factors involve the rainfall, underground pipeline leakage, seismic liquefaction, and seismic acceleration. These twelve indicators constitute the evaluation indicator level.
3. The evaluation model for the ground subsidence hazard employed the analytic hierarchy process (AHP) in conjunction with the information value evaluation model. The AHP was utilized to determine the weights of individual hazard evaluation indicators, and the information value of each indicator was calculated. The products of the weights and information values were summed to obtain the total information value for the study area. The study area was subsequently classified into four levels of risk: high, moderate, relatively low, and low.
## 3 Datasets and Methods
### Hybrid ANN  
The CNN features on hand edges as described in Section 3.2.1 rely on an approximate masking operation to segment the hand edges. However, this technique is prone to retaining background edges as well, most specifically if the background encompasses hues within the skin mask used in this operation. To mitigate this issue, a hybrid ANN is proposed which is illustrated in Figure 6.  
The hybrid ANN takes two sets of inputs--convolutional features on the original image at the left of the neural network and CNN features on the edge image, i.e., one containing hand edges at the right of the neural network--both of which are explained in detail in Section 3.2.1. Both of these inputs are passed through fully connected blocks followed by a concatenation operation and a final fully connected block at the end of which the predicted class label is obtained.  
The fully connected blocks correspond to a series of dense layers, whereas the concatenate block gives a concatenated feature vector obtained by passing both the inputs through the fully connected blocks. The inception of this hybrid ANN stems from the hypothesis that if the original image is passed in parallel along with the edge image, the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Neural Network** & **Implementation Steps** \\ \hline ANN, hybrid ANN, CNN & **1:** Construct the neural network as per the architectures \\  & specified in Section 3.2.4; \\  & **2:** Split the dataset into training and testing sets; \\  & **3:** Compile the neural network using a categorical cross \\  & entropy loss function and an Adam optimizer [29]; \\  & **4:** Perform one-hot encoding on the dependent variable of \\  & the training and testing sets \\  & **5:** Train the neural network on the training set with the \\  & testing set as the validation data. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Deep-learning techniques.  
neural network would be able to differentiate between the hand edges and the background edges, if any, because the original image features provide additional information to the ANN to capture the location of both the background and the hand region.  
Table 8 outlines the architecture of the hybrid ANN for the ISL digits, the Cambridge hand gesture dataset, the ASL alphabet and the ASL finger-spelling dataset.  
The hybrid ANN utilizes he-uniform [30] as an initialization scheme for the ReLU layers.
# An In-Network Cooperative Storage Schema Based on Neighbor Offloading in a Programmable Data Plane
## 5 Evaluation
### Performance Results
#### 5.2.4 The Accuracy of Traffic Split Ratio  
Given a limited rule capacity at the switch, we need to make a tradeoff between the accuracy of the traffic split ratio and the number of flow entries. We measured the throughput under different lengths of the ID's suffix (M) under an expected split ratio of 4:1. We adjusted the bandwidth ratio between PATH1 and PATH2 to 1:4 through the three-layer router.  
When the length of the matched ID's suffix is 2, we built four flow entries for every interface. The mask of the corresponding field is \(0\times 11\), and the IDs are split in the ratio of 3:1. If the length of the matched ID's suffix is 3, the IDs are split in the ratio of 3:1, and the IDs are split in the ratio of 4:3:1 when the length of the matched ID's suffix is 4. As shown in Figure 10, the measured traffic ratio (4.4:1) is the most accurate when the length of the ID's suffix is 4. However, the number of flow entries is also the greatest. If the split ratio needs to be updated frequently, the cost of maintaining the accuracy of the traffic split ratio and the resulting traffic fluctuation cannot be neglected.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experiments** & **Time(s)** \\ \hline ECMP (no constraint) & 18 \\ ECMP (1:3 split ratio) & 28.5 \\ Ours (no constraint) & 18.3 \\ Ours (1:3 split ratio) & 17.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the transfer time between ECMP and our mechanism.  
Figure 9: Results of the influence of the load balancing schema.
## 4 Experimental Results
### Embedded Devices  
The embedded devices utilized in the experiments are shown in Figure 7. Table 2 provides detailed specifications of all the embedded devices employed in this study. The Rockchip RK3566 served as the primary embedded platform in the experiments to validate the precision of the proposed model and to measure its inference latency with different computing cores engaged. Comparative experiments are also conducted using the Nvidia Jetson Nano, which boasts abundant computing resources, and the AllWinner H618, known for its limited computing capabilities. These experiments underscore the adaptability of the model and its potential across diverse computational resource environments.  
* Nvidia Jetson Nano: Developed by Nvidia, the Nvidia Jetson Nano is a compact embedded single-board computer tailored for accelerating machine learning and artificial intelligence [40; 41; 42]. It features a quad-core ARM Cortex-A57 processor and is equipped with 4 GB of memory.
* Rockchip RK3566: The RK3566 is a 64-bit high-performance embedded processor from Rockchip, comprising 4 ARM Cortex-A55 cores that can operate at a maximum frequency of 1.8 GHz [43; 44; 45]. The specific RK3566 development board utilized in the study is equipped with 2 GB of memory.
* AllWinner H618: Engineered by Zhuhai AllWinner Technology, the H618 is an embedded processor designed for applications in security surveillance and smart connectivity in the internet of things [46; 47; 48]. It operates on the ARM Cortex-A53 architecture and includes 4 processor cores with a peak frequency of 1.5 GHz. The H618 development board featured in this study is equipped with 1 GB of memory.
# Time Series Classification with Shapelet and Canonical Features
###### Abstract  
Shapelet-based time series classification methods are widely adopted models for time series classification tasks. However, the high computational cost greatly limits the practicability of the Shapelet-based methods. What is more, traditional Shapelet can only describe the overall shape characteristics of subsequences under the Euclidean distance metric, so it is vulnerable to noise. Other than Shapelet, there are other types of discriminative information contained in the subsequences. To deal with the aforementioned problems, an accurate and efficient time series classification algorithm, named Shapelet with Canonical Time Series Features, is proposed in this paper. The proposed algorithm is based on the following three key strategies: (1) randomly selecting Shapelet and limiting the scope of Shapelet to improve efficiency; (2) embedding multiple canonical time series features in Shapelet to improve the adaptability of the algorithm to different classification problems and make up for the accuracy loss caused by the random selection of Shapelet; and (3) building a random forest classifier based on the new feature representations to ensure the generalization ability of the algorithm. Experimental results on 112 UCR time series datasets show that the proposed algorithm is more accurate than the STC algorithm which is based on Shapelet exact search and the Shapelet transform technique, as well as many other types of state-of-the-art time series classification algorithms. Moreover, extensive experimental comparisons verify the significant advantages of the proposed algorithm in terms of efficiency.  
time series; Shapelet; canonical time series features; classification Article  
Time Series Classification with Shapelet and Canonical Features  
[PERSON], [PERSON], [PERSON] and [PERSON]
# Spatiotemporal Image Fusion in Remote Sensing
## 2 Fusion Methods to Increase Spatiotemporal Resolution of Satellite Images
### Unmixing-Based Spatiotemporal Image Fusion Methods
Additional unmixing-based methods dedicated to fusing Landsat and Medium Resolution Imaging Spectrometer (MERIS) are described by [53; 65]. [PERSON] et al. [53] described a linear mixing method to downscale MERIS data from 300 m to 25 m resolution using the Dutch land use database to derive the fractional composition of input pixels. Besides the above presented methods, we also have the so called hybrid methods that rely on different technologies to perform the fusion. [PERSON] et al. [12], for example, developed a spatiotemporal image fusion method capable to predicting pixel values at fine resolution in challenging situation such as heterogeneous areas and where land cover change occurs during the period between the input and prediction dates. First, this method assumes that all pixels of coarse resolution images are mixed pixels. The reflectance of these pixels can be described using a linear mixture model:  
\[C_{m}=\sum_{i=1}^{M}f_{i}\left(\frac{1}{a}F_{im}-\frac{a}{b}\right)+\varepsilon \tag{8}\]  
\[C_{n}=\sum_{i=1}^{M}f_{i}\left(\frac{1}{a}F_{in}-\frac{a}{b}\right)+\varepsilon \tag{9}\]  
where \(C_{m}\) and \(C_{n}\) represent the reflectance of the mixing pixel at date \(t_{m}\) and \(t_{n}\), \(f_{i}\) is the fraction of the \(i\)-th land cover, \(F_{im}\) and \(F_{in}\) represent the reflectance of the \(i\)-th land cover at date, and \(t_{m}\), \(t_{n}\), \(a\) and \(b\) represent the coefficients of the linear regression model developed for relative calibration between fine resolution and coarse resolution images. Second, the changes of coarse-resolution reflectance from \(t_{m}\) and \(t_{n}\) is calculated as follows:  
\[C_{n}-C_{m}=\sum_{i=1}^{M}\frac{f_{i}}{a}(F_{in}-F_{im}) \tag{10}\]  
Similar to [PERSON] et al. [12], [PERSON] et al. [54] developed a method for fusing coarse-spatial, fine-temporal and fine-spatial, coarse-temporal images that assumes that surface reflectance values of coarse resolution pixels are mixed. The method relies on predicting the fraction map of fine resolution images from the available coarse resolution fraction maps by making use of images acquired before and after the prediction dates. The fraction maps can be obtained using any available spectral unmixing model such as a linear spectral mixture model or multiple endmember spectral mixture analysis model.
## 2 Materials and Methods
**Sessions 3-5:** Participants performed three weight training sessions on separate days, each at the same time of day, which consisted of the bent-over row, deadlift, and lunge exercises at 60% of 1 RM, with three sets of 10 repetitions performed at a cadence of 2 s up and 2 s down using an audible metronome. An audible cadence was used to control for potential variations in the lifting cadence of the participants. Between each set, there was 3 min rest period, and a 10 min rest between each type of exercise was included [21]. A minimum of a 24-48 h rest interval between resistance training sessions was included. Oxygen consumption (VO\({}_{2}\)) was recorded during each training session through a breath-by-breath analysis using a Cortex Metalyzer 3B (Cortex, Leipzig, Germany) while the participant was wearing a mask. The flow and gas sensors were calibrated before every test. The temperature and the humidity of the room were respectively set to 22-27 \({}^{\circ}\)C and 52-64%. Before beginning training, participants were provided with an explanation of the Borg Rate of Perceived Exertion Scale (6-20; RPE). The VO\({}_{2}\), RER, and HR were recorded for a total of 49 min for each training session, including the resting, exercise, and recovery periods. The blood pressure and RPE were recorded before the start of training and immediately after each exercise using a portable Omron sphygmomanometer and the RPE scale. **Outcome measures:** The recorded variables were VO\({}_{2}\) (mL/kg/min), VO\({}_{2}\) (L/min), METs, RER, HR (bpm), SBP (mmHg), DBP (mmHg), and RPE score. For each exercise of three sessions, the MET intensity (Figure 2), RER, HR, blood pressure, and RPE are presented as average values. For MET, RER, and HR, only exercise data were included, while for blood pressure and RPE, values recorded after each exercise were included. The summed EE in kilocalories (kcal) for each exercise included the exercise and rest period after the exercise set. EE was computed from VO\({}_{2}\) (mL/kg/min) as VO\({}_{2}\) (mL/kg/min) \(\times\) body weight/1000 and multiplied by 5 kcal for exercise EE [18] and 4.7 kcal for the rest period after exercise [23]. **Statistical analysis:** Raw data from the cortex metalyzer were exported and processed in Excel software. For statistical analysis, SPSS software (IBM SPSS Statistics version 19, Armonk, NY, USA) was used. Study demographics are presented in the form of descriptive statistics (Table 1). Variables for the statistical analysis included RER, HR, SBP, DBP, RPE, and calculated EE average values, which are presented in Tables 2 and 3. For all three training sessions, RER, HR, SBP, DBP, and RPE were compared between the two groups using one-way ANOVA (Table 2). The significance level was set to \(p<0.050\). Energy costs through summed values of EE of each set of three exercises for both groups are presented for each training session (Table 3).
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 3 Methodology
### Determination of SPC Confidence Intervals  
The SPC parameter depends on the sound level values in both the source and receiving rooms, as well as on the ambient noise level in the receiving room. Based on this, it is concluded that the confidence interval (_CI_) for the _SPC_ parameter values will depend on the mentioned levels and their corresponding confidence intervals [42]. The 95% confidence interval (\(CI\_value\)) indicates that, for multiple repeated measurements, there is a 95% probability that the _SPC_ parameter value will fall within the range of \(SPC\pm CI\_value\). This interval can be calculated for each measurement of the sound reduction index of the partition at each location. The 95% confidence interval for the sound level in the source room can be calculated as follows [42]:  
\[\Delta L_{s}\approx\frac{1.96}{\sqrt{n}}\sqrt{\frac{1}{n-1}\sum_{i=1}^{n} \left(\sum_{f=160}^{5000}L_{si}(f)/16-L_{s}(avg)\right)^{2}}, \tag{6}\]  
where \(n\) is the number of microphone positions in the source room, \(L_{si}\) is the sound level for each microphone position in the source room, and \(L_{s}(\text{avg})\) is the average level value for all microphone positions and all \(1/3\) octave frequency bands between 160 Hz and 5000 Hz. Similarly, it is possible to determine the 95% confidence interval for the sound level in the receiving room,  
\[\Delta L_{r}\approx\frac{1.96}{\sqrt{n}}\sqrt{\frac{1}{n-1}\sum_{i=1}^{n} \left(\sum_{f=160}^{5000}L_{ri}(f)/16-L_{r}(avg)\right)^{2}}, \tag{7}\]  
and for the ambient noise level,  
\[\Delta L_{b}\approx\frac{1.96}{\sqrt{n}}\sqrt{\frac{1}{n-1}\sum_{i=1}^{n} \left(\sum_{f=160}^{5000}L_{bi}(f)/16-L_{b}(avg)\right)^{2}}. \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline
**SPC** & **Privacy States** \\ \hline \(<\)60 & Speech is almost always audible and often understood \\ \hline
60–65 & Speech mostly audible; short spoken phrases occasionally understood \\ \hline
65–70 & Speech is often heard; short spoken phrases are less often understood \\ \hline
70–75 & Speech is rarely heard; speech is essentially unintelligible (short phrases \\  & heard no more than once every 15 min) \\ \hline
75–80 & Speech is seldom audible, and when heard, it is incomprehensible. \\ \hline
80–85 & Speech possibly heard every 15 min; speech unintelligible \\ \hline \(>\)85 & Speech imperceptible \\ \hline \end{tabular}
\end{table}
Table 1: Descriptions of privacy states for different values of the SPC parameter [25].  
Finally, the value of the 95% confidence interval for the _SPC_ is given as follows:  
\[\Delta SPC=\sqrt{\left[\Delta L_{b}(avg)\right]^{2}+\left[\Delta L_{s}(avg)\right] ^{2}+\left[\Delta L_{r}(avg)\right]^{2}}. \tag{9}\]