esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Biochar Effects on Soil Water Availability  
Biochar addition has been shown to alter soil moisture content, and soil physical and hydraulic properties [20; 21]. We found from in situ sensor measurements (one soil moisture probe in each plot), that the soil moisture content (\(\theta\)) was higher in both biochar plots but differences across treatments were not significant due to the large within-group variability. Using the UAV-derived \(\theta\) that covered the whole area of each plot to better account for the variability within each plot, we found significant increases in \(\theta\) in biochar-amended plots (Figure 4). This demonstrated the advantage of high-resolution drone-based remote sensing approach over ground point measurements in cropland and agricultural assessment.  
The significantly higher \(\theta\) in biochar groups than the control was probably due to the increase in water holding capacity in biochar amended soil. The higher water holding capacity is likely linked to the porous biochar structure that increases the particle surface area, so that more water can be retained by the soil. The increased water retention with biochar addition can also reduce nutrient leaching [57] and result in more efficient plant nutrient uptake [24]. This might partly explain the higher leaf nitrogen concentration observed in BC1.  
It should be noted that the \(\theta\) derived from temperature-vegetation relationships reflects the water availability in the whole root zone [43], and the rice rooting depth can reach 0.5-1.0 m [45], far deeper than the depth of 15 cm at which the in-situ sensors were placed in this study. The shallower the measurement location, the stronger the influence from atmospheric forcing and consequently the larger the fluctuations of the measurements, explaining the higher variability of the in-situ measurements compared to the remote sensing estimates. Besides, the groundwater level was reported 0.8 m below the surface during the dry period [29] and the capillary rise in clay loam soil of the site might dampen the fluctuations of root zone soil moisture. The mismatch of in-situ sensor-measured and UAV-derived \(\theta\) makes the two sets of data not readily comparable.  
The soil matrix potential presented a different picture for the two biochar applications, increasing in BC1 and decreasing in BC2, contrary to the 0 that increased in both biochar applications. The lower soil matric potential found in BC2 despite higher soil moisture is due to the different water retention properties in BC1 and BC2. In fact, at given soil moisture, the matric potential in BC1 is lower than in BC2 (Supplementary Figure S5). Along with the decreased soil matric potential in BC2, the BC2 plots also exhibited lower leaf nitrogen concentration, LAI, GPP, and WUE. Apart from the biochar feedstock difference between BC1 and BC2, [29] attributed the lower plant performance to the shorter time of BC2 settlement in soil. The BC1 had been applied six months earlier and might be better established in soil.
## 2 Materials and Methods  
### System Model  
Figure 1 shows a multi-UAV-assisted MEC system with \(M\) mobile user devices, \(N\) unmanned aerial vehicles (UAVs), and \(K\) edge clouds (ECs). The set of mobile user devices is denoted as \(E=\{E_{1},\ \ldots,\ E_{m}\}\), the set of UAVs is represented by \(U=\{U_{1},\ \ldots,\ U_{n}\}\), and the set of edge servers is denoted as \(S=\{S_{1},\ldots,S_{k}\}\). The system time is uniformly divided into time slots, specifically represented as \(T=\{T_{1},\ \ldots,\ T_{t}\}\). Each user device needs to periodically process computationally intensive and latency-sensitive tasks. In time slot \(T_{i}\), the computational task generated by user device m can be expressed as \(W_{m}=\{D_{m},\ C_{m},\ A_{m}\}\), where \(D_{m}\) denotes the task size, \(C_{m}\) represents the number of CPU cycles required to process the task, and \(\lambda_{m}\) is the task arrival rate, with one computational task generated by each mobile device every second. Mobile device computational tasks are fine-grained and divisible; therefore, a partial offloading model is adopted. The generated  
computational tasks can be offloaded to UAVs, and the UAVs then offload part of the tasks to edge clouds based on their own state, achieving collaborative processing between UAVs and ECs.
## 3 Results  
### Patient Demographics  
Patient demographic information is presented in Table 1. Of the 57 patients in the prediction group, two were lost to follow-up and one patient expired before undergoing TACE. Five additional patients were excluded from the analysis because tumor segmentation was not possible. As a result, 49 patients were included in the prediction phase of the analysis. Of the 100 patients in the training group, two were excluded from the analysis because tumor segmentation was not possible. As a result, 98 patients were included in the training phase of the analysis (Figure 1).  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Prediction Set (\(n\) = 49)** & **Training Set (\(n\) = 98)** \\ \hline Age (years), mean (range) & 65.9 (10.4) & 63.5 (9.8) \\ Sex, male/female & 36/13 & 77/21 \\ \multicolumn{3}{l}{Etiology of liver cirrhosis, n (\%)} \\ \multicolumn{3}{l}{Hepatitis B virus} & 30 (61) & 63 (63) \\ \multicolumn{3}{l}{Hepatitis C virus} & 7 (14) & 12 (12) \\ \multicolumn{3}{l}{Alcoholic} & 9 (19) & 16 (16) \\ \multicolumn{3}{l}{Hepatitis B virus + Alcoholic} & 2 (4) & 6 (6) \\ \multicolumn{3}{l}{Hepatitis C virus + Alcoholic} & 1 (2) & 3 (3) \\ \multicolumn{3}{l}{HCC} \\ \multicolumn{3}{l}{Number} & & \\ \multicolumn{3}{l}{1} & 46 (94) & 81 (82) \\ \multicolumn{3}{l}{2} & 3 (6) & 17 (18) \\ \multicolumn{3}{l}{Tumor size} \\ \multicolumn{3}{l}{\textless{2 cm}} & 17 (34) & 34 (35) \\ \multicolumn{3}{l}{\textless{2 }} & 27 (55) & 46 (47) \\ \multicolumn{3}{l}{\textless{5 cm}} & 5 (11) & 18 (18) \\ \multicolumn{3}{l}{Child–Pugh score, n (\%)} & \\ \multicolumn{3}{l}{A} & 41 (84) & 84 (86) \\ \multicolumn{3}{l}{B} & 8 (16) & 14 (14) \\ \multicolumn{3}{l}{Survival} \\ \multicolumn{3}{l}{\textless{2 Progression-free survival (months)}} & 16.2 (12.2) & 16.1 (17.9) \\ \multicolumn{3}{l}{Overall survival (months)} & 31.4 (13.3) & 50.7 (26.7) \\ \hline \end{tabular}
\end{table}
Table 1: Baseline Patient Characteristics of Prediction and Training Set.  
The mean age of the prediction and training groups were 65.9 \(\pm\) 10.4 and 63.5 \(\pm\) 9.8 (mean \(\pm\) standard deviation [SD]) years, respectively. Etiology of liver cirrhosis in the prediction and training groups included hepatitis B virus (61% and 63%), hepatitis C virus (14% and 12%), and alcoholic (19% and 16%). Some viral carriers in the prediction and training groups were also chronic alcoholics; hepatitis B virus (4% and 6%); and hepatitis C virus (2% and 3%), respectively. In the prediction and training groups, Child-Pugh Class A was 84% and 86% and Child-Pugh Class B was 16% and 14%, respectively. The number of HCCs in the prediction and validation group included single (94% and 82%) and two (6% and 18%) HCCs, respectively. The HCC tumor sizes in the prediction and validation groups included <2 cm (34% and 35%), 2 \(\leq\) x < 5 cm (55% and 47%) and \(\geq\)5 cm (11% and 18%), respectively. Progression-free survival (PFS) of the prediction and validation groups were 16.2 and 16.1 months and overall survival (OS) was 31.4 and 50.7 months, respectively (Table 1).
# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 2 Background  
Hardware accelerators have been a topic of interest in recent years, especially with the possibility of designing DL architectures that can be implemented on these devices. In the field of communications, the interest is mainly focused on the possibility of deploying DL algorithms as close as possible to the radio. For example, Figure 1 shows an SDR radio system's schematic with different processing stages in which an FPGA is used as a hardware accelerator.  
The FPGA is flexible enough to accommodate different DL models due to its reprogrammable architecture; that is, the FPGA can be reprogrammed according to the problem to be solved. However, the available computing resources on an FPGA are limited, and, therefore, DL algorithms have to be designed with those constraints in mind. More precisely, the traditional approach of training DL models on the cloud and directly deploying them on the radio is not suitable anymore. The main reason is that DL models are large, i.e., they usually contain millions of single-precision (FLOAT32) or double-precision (FLOAT64) floating-point trainable parameters and use a large amount of training data to achieve high performance in terms of accuracy. In order to remove such limitation, it is necessary to adapt such large DL models so that they can be deployed on the resource-constrained FPGA to perform the inference.
# Transparent Redirections in Content Delivery Networks
## 2 State of the Art
### CDN Redirections
#### 2.2.1 DNS Based Request Routing  
DNS bases request routing is common in CDNs due to the ubiquity of the DNS system [9]. The DNS server is responsible for translating domain names to IP addresses. DNS maintains a large distributed database, which means not all the domain names are stored on every DNS server. To deal with this fact, the DNS server might recursively query the specific domain name by pursuing the query for the client at other servers, starting at the root zone through Top Level Domain servers towards the authoritative name server of the specific domain.  
When DNS based request routing is used, a specialized DNS server is inserted in the DNS resolution process acting as a request router. The server is capable of returning a different set of A, AAAA or CNAME records based on the user's location, defined policies or other metrics. The returned record points directly to a CDN cache node also referred to as a surrogate server. The redirection procedure is depicted in Figure 1.  
DNS is simple and does not scale well. RFC 1034 [9] states the following:  
* \"The sheer size of the database and frequency of updates suggest that it must be maintained in a distributed manner, with local caching to improve performance. Approaches that attempt to collect a consistent copy of the entire database will become more and more expensive and difficult, and hence should be avoided.\"
* \"Tradeoffs between the cost of acquiring data, the speed of updates, and the accuracy of caches, the source of data should control the tradeoff.\"  
Due to the large scale of the distributed database, it is slow to populate updates and changes in cases of changes in the CDN or during downtimes and unexpected situations. This limitation of the DNS is also reflected in the DNS based redirection. [PERSON] et al. summarize these reflected issues and others in RFC3568 [8]:  
* \"DNS only allows resolution at the domain level. However, an ideal request resolution system should service request per object level.\"
* \"In DNS based Request-Routing systems servers may be required to return DNS entries with a short time-to-live (TTL) values. This may be needed in order to be able to react quickly in the face of outages. This in return may increase the volume of requests to DNS servers.\"  
DNS based redirection is fast and simple; however, as only the domain name is taken into consideration a non-optimal surrogate server might be returned to a client's request. The adaptation process might take a while in case of downtimes or changes in the CDN which might result in service unavailability decreasing the QoE (Quality of Experience) for users.
# Linking Off-Road Points to Routing Networks
## 4 Implementation
### Outlook  
One may argue that the here-proposed edge linkage is still a naive implementation because it neglects possible detours. Fortunately, by the introduction of both vertex and edge linkage, it is straight-forward to construct a valid linkage that creates a feed-link [14] from the non-connected point. Another complaint may arise that the complete use of \(M\) is quite naive. Let us imagine that the edge created to link an off-road point to the routing network leads over a river, lake, mountain gorge, etc. Pedestrians or travelers with common means of transportation are not able to overcome these obstacles and have to seek an alternate route. Real datasets such as OSM represent obstacles by a polygon in which the actual obstacle is contained, such as in Figure 7. If we stipulate that every chart of \(M\) is large enough such that every obstacle can be rendered in at least one chart's image, any lower-bounding metric can be modified to respect the presence of obstacles. The additional computation can be conducted, for example, by [PERSON]'s algorithm [44] which solves the shortest path problem on a plane with obstacles. Lastly, we could adapt our introduced concept to more detailed routing networks. New models enrich network information, for instance, with affordance [45] to describe possible physical actions.  
\begin{table}
\begin{tabular}{c c c c} \hline \(|V|\) & **Linkage** & **Routing** & **Ratio** \\ \hline
100 & 0.40 s & 8.06 s & 4.96\% \\
200 & 0.74 s & 15.21 s & 4.87\% \\
400 & 1.33 s & 32.26 s & 4.12\% \\
800 & 2.47 s & 59.69 s & 4.14\% \\ \hline \end{tabular}
\end{table}
Table 1: Running time for linkage and routing on the setting described in Section 4.1. The ratio of execution time between linkage and routing is almost constant with variance in the number of nodes \(|V|\).
## 4 Results  
After selecting the hyperparameter values, the two classifiers are trained. The training and test ratios were chosen as 80:20. A 5-fold cross-validation approach was utilized to validate the trained models. The overall process was carried out with the non-filtered data, and the parameters mentioned in Equations (23)-(27) were determined, as provided in Table 6.  
In the next step, the filtered signal from a notch filter is obtained, and the process is repeated. In this approach with the filtered signal, greater than 99% accuracy was achieved for both classifiers. The performance evaluation parameters for the filtered signal are listed in Table 7 for the two ensemble classifiers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**XGB**} \\ \cline{2-7}  & **db4** & **Sym4** & **Haar** & **db4** & **Sym4** & **Haar** \\ \hline Precision & 0.96 & 0.94 & 0.96 & 0.97 & 0.97 & 0.97 \\ Sensitivity & 0.96 & 0.94 & 0.96 & 0.97 & 0.97 & 0.97 \\ F1\_score & 0.96 & 0.94 & 0.96 & 0.97 & 0.97 & 0.97 \\ Specificity & 0.96 & 0.94 & 0.96 & 0.97 & 0.97 & 0.97 \\ Accuracy & 0.96 & 0.93 & 0.95 & 0.97 & 0.97 & 0.96 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The five evaluation parameters of two classifiers for three wavelets executed on the raw signal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Random Forest**} & \multicolumn{2}{c}{**Xtreme Gradient Boosting**} \\ \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline max\_depth & 90 & max\_depth & 20 \\ max\_features & 3 & learning rate & 0.1 \\ min\_samples\_leaf & 3 & n\_estimator & 500 \\ min\_samples\_split & 8 & min\_child\_weight & 1 \\ n\_estimators & 200 & gamma & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimum parameters.  
Finally, Table 8 presents a comparison of the other models investigated with the same dataset in different works. Wavelet packet decomposition up to three levels, along with the special SVM approach named SVM-PSO (SVM--particle swarm optimization), was implemented by [PERSON] et al. [50] and achieved an accuracy of 86.03%. Information fusion (IF) and DL approaches were used [51] on a motor current signal, which result almost 98.3% accuracy. Another study on the same dataset applied an  
Figure 16: ROC curves for the RF and XGBoost classifiers.  
Figure 14: Confusion matrix of the sym4 wavelet for (**a**) RF and (**b**) XGBoost classifiers.  
Figure 15: Confusion matrix of the Haar wavelet for (**a**) RF and (**b**) XGBoost classifiers.  
empirical wavelet transform and CNN for fault classification, showing 97.37% accuracy [52]. Therefore, by comparison with the recent research on the motor current signal, our approach provides a better result, with greater than 99% accuracy for ensemble classifiers.
## 2 Project Overview  
In order to study the stability of the slope under freeze-thaw conditions, a slope was selected as our research object. The proposed cutting slope is located at the K105+700-800 section of the National Highway 332 line of the Elunchun Autonomous Banner, lying at the east foot of the middle section of the Greater Xing'an Mountains, belonging to a high-latitude and low-altitude permafrost area. The area has large daily and annual temperature differences, the amount of sunshine is 2500-3100 h/year, and the sunshine rate is 65-71%. The annual average temperature is \(-2.7\)\({}^{\circ}\)C to \(-1\)\({}^{\circ}\)C, the annual average rainfall is 450-500 mm, and the freezing period is from October to May of the following year. The maximum frozen soil depth is 3.5 m. The slope is 10 m high and the slope angle is 45\({}^{\circ}\). The surface layer of the slope is filled with gravel and fine-grained soil, and the lower layer is fully weathered volcanic breccia. In addition, the bedrock fissure ice and interlayer ice are extremely developed in the slope, and soil-bearing ice layers and pure ice layers are developed five meters below the excavation surface. Moreover, the skeleton of the rock mass is broken, the rock mass is low in hardness, and the remaining solid particulate matter after melting is less than 20%. After thawing, there are many collapsed veins on the slope surface. When the surface layer is disturbed, obvious traces of ice veins can be seen, and the frozen soil in this location has strong thawing or sinking properties. The stability of the slope in this location will inevitably decrease due to the above characteristics. In order to quantify the reduction in slope stability, on-site core sampling and indoor freeze-thaw tests were conducted. Furthermore, the compressive strength and elastic modulus of specimens after different freeze-thaw cycles were measured. Then, using experimental data and a numerical simulation model, the stability of the slope under various design conditions was analyzed.
## 3 Numerical Simulation Methods and Validation
### Calculation Model of Multi-Box Structures  
According to the study by [PERSON] [22], the area of the ship's compartment is generally around 10 m\({}^{2}\), the height between decks is generally 2.4-2.8 m, and the surrounding wall thickness is about 6 mm. According to the geometric similarity rule, 3 \(\times\) 3 \(\times\) 3 multi-cabin structures with extended boundaries were designed, each with a length-width -height dimension of 120 cm and a wall thickness of 3 mm. A comparative study of the damage characteristics under the implosion of multi-cabin structures with different structural dimensions was conducted. Since the quasi-static pressure is related to the volume of the structure [23], the kinetic energy of the wall plate subjected to the blast is related to its mass, and the mass of the wall plates is mainly determined by the area when the thickness of the wall plate material is the same. Therefore, four groups of 3 \(\times\) 3 \(\times\) 3 multi-box structures with the same volume and chamber wall aspect ratios \(\gamma\) of 1, 2, 3, and 4 were designed. One set of walls has an equal area and aspect ratio \(\gamma\). The other two sets of walls have equal side lengths of 120 cm on one side and a = 120 \(\times\)\(\sqrt{\gamma}\) cm and b = 120/\(\sqrt{\gamma}\) cm on the other two sides. The whole chamber structure has a length to width ratio \(\gamma\) and length to height ratio \(\sqrt{\gamma}\) with the dimensions shown in Table 6.  
Using the numerical simulation method described in the previous section, the finite element model is shown in Figure 2. As shown in Figure 3, the A-profile, B-profile, and C-profile are made along parallel x-y, x-z, and y-z axes, respectively, with the center of the blast-loaded cabin. The length, width, and height of the single cabin are \(a\), \(b\), and 120 cm, respectively.  
Figure 3: Multi-box structural section and structural dimensions (Unit: cm).  
Figure 2: Finite element model. (**a**) Box structure and coordinate system; (**b**) Air domain.
## 1 Introduction
While the usefulness of \(\rho\) for detecting water on bare soils has been demonstrated in several studies [33; 34], first results of a successful exploitation of this feature, derived from VHR SAR data for detecting water in urban areas, have only recently been reported [21; 22; 28]. This paper presents an urban floodwater mapping experiment that considers for the first time 20 m S-1 images and is largely built upon recent findings concerning urban flood detection using VHR SAR data. The experiment aims to introduce and test a novel approach that is based on two fundamental processing steps: the first one exploits a multi-pass technique to detect areas of double-bounce in an urban environment; the second one uses the change of \(\rho\) within those areas to detect the inundated pixels. The novel contributions that this paper brings, with respect to current literature on floodwater mapping, are the following. First, the approach is totally self-consistent, that is, it does not need sources of data other than SAR, such as the land cover maps and/or VHR optical data that are generally used to identify urban areas. This is important for operational purposes, since ancillary data cannot always be available. Secondly, and for the first time, the approach exploits the S-1 SAR data to map urban floods. These pave the way for future operational floodwater mapping algorithms broadened to urban areas (typically masked in current approaches), despite the moderate spatial resolution of S-1 images. To verify to what extent the moderate resolution (20 m) of the default Interferometric Wide Swath (IW) S-1 products could represent a critical point for the proposed approach, the \(\rho\) feature was also extracted at 5 m, taking advantage of the availability of Strip Map (SM) data. The high sensitivity of the coherence to the presence of water within building areas is investigated in the framework of Hurricane Harvey, which hit the metropolitan area of Houston, Texas in August 2017. The paper is organized as follows: Section 2 describes the proposed methodology and the importance of SAR double-bounce effects for detecting flooded buildings. Details about the dataset are given in Section 3. Section 4 presents the results, while Section 5 contains the discussion. Finally, Section 6 gives the conclusions.