esa-sceva/satcom-chunk-collection huggingface dataset:
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 4 Selected Case Studies
### Description of the Dataset  
The Envisat IEs have been reprocessed by ESA, using the RAIES processor [21] and delivered as L1B products. One L1B file is a collection of all IE bursts recorded in one satellite orbit (or pass). One IE record consists of a 2D array of complex values, containing both in-phase (I) and quadrature (Q) components of a complex signal \(z(n,r)=\) I + jQ, indexed by (\(n,r\)), where \(n=\) 1,...,1984 denotes the pulse number along the satellite track and \(r=\) 1,..., 128 is the index of the concentric range annuli. A collection of L1B files has been provided by the European Space Agency, and all the data made available by ESA are now catalogued on disk. The available IE data cover all sorts of water targets, including open ocean, coastal zone and inland water. For the purpose of this paper, the attention has been concentrated on inland water targets.  
Figure 8: Example of two-bin range solution.
## 4 Results
### Pattern of the Departing Ship Trajectory
#### 4.2.4 Departure Phase  
The departure phase is the process by which the ship passes through the waterway and fully exits the port, as is shown in Figure 25a. The SOG of this phase is 203 counts, with the most at 11-12 knots, as is shown in Figure 36a, and the average SOG of the ship being 12.31 knots, which is considered a fast speed in this setting. The distribution of the COG is shown in Figure 36b, and the various courses are set according to the position of the next port of each ship.  
The time series boxplot of the departure phase has no significant change compared to the other phases. Figure 37 shows the time series of the SOG and COG, with the SOG usually being about 12 knots, which increases slightly. The ship's COG is maintained in the same direction as the waterway and then changes in accordance with the destination of each ship. The summarized descriptive statistics are shown in Table 14.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scaled**} & \multicolumn{5}{c}{**Speed Over Ground**} & \multicolumn{5}{c}{**Course Over Ground**} \\ \cline{2-10}  & **Mean** & **Std.** & **Min** & **Median** & **Max** & **Mean** & **Std.** & **Min** & **Median** & **Max** \\ \hline
0 & 9.24 & 1.07 & 6.90 & 9.20 & 13.80 & 166.41 & 3.04 & 161.50 & 166.30 & 173.50 \\
1 & 8.85 & 1.39 & 6.30 & 8.90 & 14.20 & 164.97 & 3.26 & 158.80 & 164.50 & 174.60 \\
2 & 8.30 & 1.21 & 5.20 & 8.40 & 14.40 & 164.41 & 2.94 & 155.80 & 164.55 & 172.60 \\
3 & 8.12 & 1.54 & 4.80 & 8.00 & 14.60 & 164.33 & 3.12 & 153.20 & 164.30 & 172.80 \\
4 & 8.42 & 1.49 & 4.30 & 8.60 & 14.60 & 164.30 & 2.58 & 159.70 & 163.85 & 173.70 \\
5 & 9.11 & 1.70 & 5.20 & 9.20 & 14.60 & 163.42 & 2.35 & 158.60 & 163.15 & 172.40 \\
6 & 9.93 & 1.60 & 5.80 & 10.30 & 14.50 & 163.40 & 1.80 & 158.60 & 163.25 & 167.80 \\
7 & 10.39 & 1.44 & 6.30 & 10.90 & 14.50 & 163.34 & 1.98 & 158.60 & 163.30 & 168.40 \\
8 & 10.99 & 1.44 & 7.20 & 11.30 & 14.50 & 163.43 & 2.10 & 157.30 & 164.60 & 167.70 \\
9 & 11.11 & 1.34 & 8.10 & 11.60 & 14.40 & 163.56 & 2.11 & 159.20 & 165.20 & 167.60 \\
10 & 11.38 & 1.12 & 8.40 & 11.50 & 14.50 & 163.03 & 2.02 & 158.10 & 164.60 & 167.40 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Descriptive statistics for the departure waterway phase of the scaled time series.  
Figure 36: Frequency analysis of the departure phase: (a) SOG; (b) COG.
# Design and Evaluation of a Shaping and Pruning Machine for Dwarf and Densely Planted Jujube Trees
## 2 Materials and Methods
### Design of Shaping Frame
#### 2.5.2 Design of Shaping Adjustment Frame
**(a)** Structure of adjustment cylinder. **(b)** Geometric relationship for adjustment cylinder. Note: \(l_{c1d1}\) is maximum expansion of cylinder, \(l_{c2d2}\) is the length of cylinder without expansion, \(x\) is the expansion of adjustment cylinder, \(l_{c1d1}\), \(l_{c1d1}\), \(l_{c1d1}\) and \(l_{c1d1}\) are auxiliary lines. The corresponding values are as follows: \(l_{ad}=l_{a1d1}=l_{a2d2}=200\) mm, \(l_{bc}=l_{b1c1}=l_{b2c2}=215\) mm, \(l_{ab}=l_{a1b1}=l_{a2b2}\), \(l_{c1d1}=l_{c2d2}+x\), \(l_{d1e1}=l_{b1f1}\), \(l_{b1e1}=l_{d1f1}\), \(l_{d2c2}=l_{b2f2}\), \(l_{b2c2}=l_{d2f2}\). Figure 9: Geometric diagram of adjustment frame. Note: \(OA\) stands for the adjustment range of vertical beam height, which is 1450–2250 mm; \(AB\) represents the beam, which is 1100 mm in length; \(OG\) is the height between the hinge point of the outer sleeve for the vertical beam and the ground, 350 mm; \(OC\) is the distance from the outer edge of the tractor near the side pruning assembly to the hinge point of the outer sleeve for the vertical beam, and its length is 900 mm; \(CD\) is the outer edge of the tractor; \(GH\) is ground; \(EF\) is the outer contour of the maximum pruning position at which the side pruning assembly is located (i.e., the crown radius of the trim is 700 mm); \(\alpha\) is the maximum adjustment angle of the adjustment frame. \[\left\{ \begin{array}{c}l_{l2/2}=l_{l2\ell 2}=l_{a2d2}\cdot\sin(180^{\circ}- \alpha)\\ l_{c2/2}=l_{b2c2}-l_{l2/2}\\ l_{a2\ell 2}=l_{a2d2}\cdot\cos(180^{\circ}-\alpha)\\ l_{d2/2}=l_{b2c2}=l_{ab}-l_{a2\ell 2}\\ l^{2}=l_{c2/2}^{2}+l_{d2/2}\end{array} \right. \tag{12}\]  
After simplified arrangement, the following results can be obtained:  
\[\left\{ \begin{array}{c}l=\sqrt{l_{ab}^{2}-114l_{ab}+778}\\ x=\sqrt{\left(l_{ab}+57\right)^{2}+529}-\sqrt{l_{ab}^{2}-114l_{ab}+778}\end{array} \right. \tag{13}\]  
Formula (13) is the relationship between the original length \(l\) of the adjustment cylinder, the expansion of the cylinder \(x\) and the installation distance of the cylinder \(l_{ab}\). Considering the rationality of the space layout, the designed length of \(l_{ab}\) is 400 mm, the original length \(l\) of the cylinder is 340 mm, and the expansion of the cylinder \(x\) is 118 mm.
## 3 Results  
### Nitro-Oxidative Stress Status  
Reactive oxygen species (ROS) synthesis was evaluated by TOS levels, OSI and MDA concentration. As shown in Figure 1, experimental gonadotoxicity induced significant oxidative stress, by increasing TOS (\(p\) < 0.05), OSI (\(p\) < 0.05), and MDA (\(p\) < 0.05) as compared with the control group. The inhibitory effects were most effective for the second dose, P2 (50 mg dw/mL), both for TOS (\(p\) < 0.05) and OSI (\(p\) < 0.001) and for dose P3 (100 mg dw/mL), the oxidative stress level was significantly reduced (\(p\) < 0.05) (Figure 1c). Regarding the MDA concentrations after _P. sempervirens_ pretreatment, in Figure 1d there is an obvious decrease in the peroxidation byproducts, but our statistical tests could not confirm this, probably because of the small number of animals used per group.  
The antioxidant capacity (TAC), the antioxidant enzymes (GPx and CAT) as shown in Figure 1a,d,e, and SH levels (Figure 2a) are the most reliable parameters for evaluating the antioxidant defense mechanism in an in vivo experimental model. Therefore, a single dose of chemotherapy (200 mg/kg BW) is enough to activate the antioxidant barriers as seen is GPx activity (\(p\) < 0.05). On the other side, CAT was significantly depleted (\(p\) < 0.05), as well as SH groups (\(p\) < 0.05). _P. sempervirens_, in turn, was able to le  
Figure 1: The in vivo antioxidant response of _P. sempervirens_ extract seen in (**a**) TAC, (**b**) TOS, (**c**) OSI, (**d**) MDA levels, (**e**) GPX and (**f**) CAT activities in serum of Control, CPA, and CPA + P1/P2/P3 exposed animals. Data represent mean \(\pm\) SEM; One-way ANOVA followed by [PERSON]’s Multiple comparison test; * Significant at \(p\) < 0.05; *** Significant at \(p\) < 0.001—compared with Control group; * Significant at \(p\) < 0.05; *** Significant at \(p\) < 0.001—compared with CPA group.  
returning the enzymatic activities to near-normal and increasing the glutathione level to greater values than control, in a dose dependent manner (\(p<0.001\)) (Figure 2a).  
ROS react with nitrogen oxide (NO) creating reactive nitrogen species (RNS), which are known for being strong oxidants [24]. Indirectly, the RNS level was measured by determining the concentrations of NO and 3 NT, as seen in Figure 2b,c. Therefore, both parameters were significantly increased (\(p<0.05\)) following CPA administration. The _P. sempervirens_ pretreatment significantly reduced the NO levels (\(p<0.001\)), (\(p<0.01\)) and (\(p<0.05\)) in an inversely proportional mode with respect to the dose.
## 4 Conclusions  
In this study, electric arc furnace slag (EAFS) was studied as a potential full replacement for cement in the production of alkali-activated mortars. The results obtained in this study allowed a conclusion that EAFS as the sole precursor will result in mixes with relatively low performance. According to the XRD results, this is most likely due to the lower number of amorphous phases compared to other common aluminosilicate pozzolans.  
The SiO\({}_{2}\)/Na\({}_{2}\)O ratio and compressive strength are generally directly proportional. The compressive strength was also affected by curing time. The continued reaction was observed after the 24 h thermal curing stage. The specimens in this study experienced 7 days, 28 days and 91 days of curing in a dry chamber at 23 \(\pm\) 2 \({}^{\circ}\)C and RH of 65%. The maximum recorded compressive strength was obtained from specimens tested on day 91 corresponding to 4% and 2.5 of Na\({}_{2}\)O/binder and SiO\({}_{2}\)/Na\({}_{2}\)O ratios, respectively.  
Despite the shortcomings of EAFS as a sole precursor, the mechanical performance increased significantly after subjecting the specimens to an accelerated carbonation stage. After subjecting the specimens to a CO\({}_{2}\)-enriched environment for 28 days, AAEAFS showed an average compressive strength increase of \(\sim\)500%, with one case reaching 800% (i.e., from an initial 3.9 MPa in uncarbonated mixes to 31 MPa for carbonated ones). The maximum performance was observed in mixes with 12% and 1.0 for Na\({}_{2}\)O/binder and SiO\({}_{2}\)/Na\({}_{2}\)O ratios, respectively, thus showing a shift in the optimal alkaline activator contents.  
The sealed shrinkage test showed considerable dimensional variability over time. Great autogenous shrinkage may have occurred due to the continuous alkali activation reaction. Nevertheless, this phenomenon is still widely unknown and must be further researched.  
The complete replacement of cement with alkali-activated aluminosilicate waste may translate into significant reductions in cost and minimal environmental impacts, especially with incorporating a forced carbonation curing stage using industrial CO\({}_{2}\)-rich flue gases. Therefore, greater focus should be given to this curing technique in future research.  
Conceptualization, R.V.S.; methodology, R.V.S.; formal analysis, D.K.; investigation, D.K.; resources, R.V.S.; writing--original draft preparation, D.K. and G.L.; writing--review and editing, R.V.S. and J.d.B.; supervision, R.V.S. and J.d.B.; project administration, R.V.S.; funding acquisition, R.V.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FCT--Foundation for Science and Technology, through research projects PTDC/ECI-CON/29196/2017 (RInoPolyCrete) and EXPL/ECI-ECC/0288/2021 (ECO\({}_{2}\)Alkrete).  
Not applicable.  
The authors acknowledge the support of the CERIS Research Institute, IST, University of Lisbon. The authors would also like to acknowledge the support of HARSCO and SIKA for part of the materials provided for this experimental research.  
The authors declare no conflict of interest.
## 4 Experiments and Results
### Analysis with Baseline Methods  
We compared our proposed method with several state-of-the-art and well-established image restoration and enhancement techniques, including conventional approaches and deep learning frameworks, on standard benchmark datasets SIDD [70] and DND [71]. Figures 4 and 5 show denoising and super-resolution results in terms of PSNR and SSIM on  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Components**} & \multicolumn{1}{c}{**SSIM Dataset**} & \multicolumn{1}{c}{**DND Dataset**} \\ \hline Source & Smartphone cameras & Consumer cameras \\ \hline Number of Original Images & 16,000 & 150 \\ \hline Noise Level & High & Relatively low \\ \hline Data Provided & 320 image pairs (training), & 1000 image patches BREAK \\  & 1280 image pairs (validation) & (512 \(\times\) 512) for testing \\ \hline Ground Truth & Available & Available \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components of benchmark datasets.  
the SIDD benchmark dataset for MIRNet [72], RIDNet [73], CBDNet [74], and our proposed ELEF mechanism. It is clear from the results in Figures 4 and 5 that CBDNet still has issues with edge preservation and detail preservation, whereas RIDNet and MIRNet preserve edges but lack detail preservation. Our proposed model outperforms other competing mechanisms in edge preservation as well as other detail preservation, with better PSNR and SSIM values.  
Figure 6 displays denoising and super-resolution results in terms of PSNR and SSIM on the DND benchmark dataset for MIRNet [72], RIDNet [73], CBDNet [74], VDN [75] and our proposed ELEF mechanism. It can be observed from the results in Figure 6 that MIRNet, RIDNet, VDN, and CBDNet have some blurry effects and smooth out some parts of edges, whereas our proposed model gives excellent results in terms of detail preservation and edge preservation without any deterioration. Furthermore, the proposed filter gives excellent PSNR and SSIM values and outperforms other models.  
Figure 4: Denoising and super-resolution results on the SIDD benchmark dataset.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**METHODOS** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) \\ \hline SADNet [80] & 39.41 & 0.954 \\ DeamNet [81] & 39.42 & 0.951 \\ CycleISP [82] & 39.52 & 0.953 \\ MIRNet-v2 [72] & 39.83 & 0.951 \\ ELEF (Ours) & 42.99 & 0.9889 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: _Cont._
## 3. Sacrificial Anode CP
### Magnesium-Based Alloy Anode Material  
The magnesium-based alloy anode material is commonly used in corrosive environments with high resistivity, such as soil and freshwater. Ions in solution affect the protective properties of the surface film, the pH, and the intermediate process of magnesium anode dissolution, thus changing the anodic efficiency of magnesium. Therefore, in the seawater environment, the microstructure of magnesium alloy easily forms corrosion couples, making the protective film dissolve easily, leading to heavy self-corrosion. Meanwhile, the occurrence of hydrogen precipitation corrosion and a negative differential result in low current efficiency, leading to a short service life [26]. Magnesium-based sacrificial anode materials mainly consist of pure magnesium anode and magnesium alloy anode, similar to the zinc-based anode. Pure magnesium anode is affected by impurity elements; however, relying on reducing the impurity content alone is not very advantageous. Therefore, alloying is often used to reduce the adverse effects of impurity elements. Commonly used magnesium alloy anodes are the Mg-Mn system and the Mg-Al-Zn-Mn system. The Mg-Al-Zn-Mn system material is a low-potential sacrificial anode, which can protect steel in soil, freshwater, and seawater environments. Several commonly used Mg-Al-Zn-Mn materials are AZ31, AZ63, AZ91, etc. AZ31 magnesium alloy is mainly used for extruded rod magnesium anodes, and it is widely used in complex environments, especially for the protection of steel structures such as ship hulls, wharves, coastal tide gates, etc. The AZ63 magnesium anode has a large power generation per unit mass, stable working potential, and uniform surface dissolution after corrosion, and its current efficiency is generally greater than 50%, making it more suitable for soil and freshwater environments [27]. The use of AZ91 alloy in the marine environment lacks research. [PERSON] [28] found that AZ91 alloy produced corrosion products that were non-uniform and not easily dislodged automatically under the corrosive effect of simulated seawater. The crack density on the surface of the corrosion products increased significantly after the addition of the Sn element, and there were obvious signs of detachment from the surface. The open-circuit potential, discharge potential, and current efficiency of AZ91 alloy were \(-1.557\) V (v.s.SCE), \(-1.328\) V (v.s.SCE), and \(49.82\)%, respectively, while those of AZ91 + 2% Sn were \(-1.592\) V (v.s.SCE), \(-1.402\) V (v.s.SCE), and \(58.51\)%, respectively. By combining these results with the open-circuit potential change analysis, it was found that the open-circuit potential of AZ91-Sn series alloys was smoother than that of AZ91 alloy, which may be related to the fact that the corrosion products generated on the surface of the alloy were more easily shed after the addition of Sn elements.
# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 2 Methods  
The block diagram of the proposed method for ECG beat classification is depicted in Figure 1. The procedure is divided into five steps: (1) ECG signal pre-processing, (2) bispectrum analysis, (3) 2D-GFT, (4) feature extraction and (5) classification by the SVM-RBF.
# Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections +
## 2 Materials and Methods
### Problem Formulation and System Model
#### 2.1.1 Virtual Tunnel Modeling
 \tag{6}\]  
From the equations above result that the sequence \(\left\{L_{j,k}^{d}\right\}_{k=0}^{\infty}\) can be represented as a Markov chain, the transition probabilities between the states of this chain being given by:  
\[p_{i,l}=P\left(L_{j,k+1}^{d}=l|L_{j,k}^{d}=i\right) \tag{7}\]  
By \(\alpha_{n_{j}}\) we denote the probability that during the transmission of a packet exactly \(n_{j}\) packets arrive. Considering that during the service time, \(t\), of a packet the number of new packets that are stored in the queue is Poisson distributed with parameter \(\lambda_{A^{j}}\cdot t\) we can express \(\alpha_{n_{j}}\) as:  
\[\alpha_{n_{j}}=\int_{t=0}^{\infty}\frac{\left(\lambda_{A^{j}}t\right)^{n_{j}} }{n_{j}!}e^{-\lambda_{A^{j}}\cdot t}f_{B_{j}}(t)dt \tag{8}\]  
The limiting probability \(d_{n_{j}}\), defined according to (5), satisfies the equilibrium equations of the Markov chain attached to the queuing process [34]:  
\[ \begin{array}{l}d_{n_{j}}=d_{n_{j}+1}\alpha_{0}+d_{n_{j}}\alpha_{1}+\cdots+ d_{1}\alpha_{n_{j}}+d_{0}\alpha_{n_{j}}=\\ =\sum\limits_{k=0}^{n_{j}}d_{n_{j}+1-k}\alpha_{k}+d_{0}\alpha_{n_{j}},\quad n_ {j}=0,1,\ldots\end{array}  \tag{9}\]  
If the queue is in equilibrium the maximum value of \(n_{j}\) will be limited because the fraction of the packets leaving behind in the queue \(n_{j}\) packets, after their transmission, is decreasing with the value of \(n_{j}\) and will approach zero for \(n_{j}\) large. We denote the total time spent in the system by a packet with the random variable \(S\) having a probability distribution function \(F_{S_{j}}(\cdot)\) and probability density function \(f_{S_{j}}(\cdot)\). It is considered that the system is in equilibrium when a new packet arrives in the queue. The distribution of the number of packets stored in the buffer after the transmission of this (new) packet is equal to \(\left\{d_{n_{j}}\right\}_{n_{j}=0}^{\infty}\). Considering a FIFO system we have, similar to Equation (8):  
\[d_{n_{j}}=\int_{t=0}^{\infty}\frac{\left(\lambda_{A^{j}}t\right)^{n_{j}}}{n_{ j}!}e^{-\lambda_{A^{j}}\cdot t}f_{S_{j}}(t)dt \tag{10}\]The time spent in the system by a packet (also called sojourn time), is the sum of \(W\) (its waiting time in the queue) and \(B\) (its transmission time on the channel), where \(W\) and \(B\) are independent random variables. More precisely a newly arriving packet in the queue first must wait for the service time of the packet that is in the transmission chain (residual service time), and then it continues to wait for the transmission of all packets which were already in the queue on its arrival. Based on PASTA (Poisson Arrivals See Time Averages) the server of the queue is busy on the arrival of a new packet with probability \(\rho_{j}\), which in our case is the occupation rate of the tunnel.
## Data and Methods
### Other Data  
#### 2.2.1 HY-1C CCD Coastal Zone Imager (CZI)  
**Data source:** The National Satellite Ocean Application Service (NSOAS). The HY-1C has five main payloads onboard, namely, a Chinese Ocean Color and Temperature Scanner (COCTS), Coastal Zone Imager, ultraviolet imager, calibration spectrometer, and an AIS system for ship tracking. The NSOAS provides the L1A level data of the HY-1C CZI, and these data were downloaded from the website: [[https://osdds.nsoas.org.cr/#/](https://osdds.nsoas.org.cr/#/)]([https://osdds.nsoas.org.cr/#/](https://osdds.nsoas.org.cr/#/)). The spatial resolution was set as 50 m.
## 3 Methodology
### Word Embedding Methods
#### 3.2.4 Doc2 Vec  
Doc2 Vec generates a vector representation of a document to predict the target word [37]. In doing so, document length is not considered. Doc2 Vec can be divided into the Distributed Memory Model of Paragraph Vectors (PV-DM), and the Distributed Bag of Words version of Paragraph Vector (PV-DBOW). The architectures of these models are illustrated in Figure 3.  
In the PV-DM method, a single word is considered from each paragraph, and each paragraph has a unique identity. PV-DBOW uses a paragraph vector to classify the words in the document.  
While both the PV-DM and PV-DBOW methods are used for generating distributed representations of words, there are some differences between them. PV-DM, as illustrated in Figure 3,b, predicts four words from a single input. Another key difference is that PV-DBOW tends to require less storage space, as it only stores SoftMax weights, whereas PV-DM requires more data storage. The third difference is that the PV-DM target pulls the words around it, while PV-DBOW extracts one word from the entire paragraph [44].  
These word embedding methods are summarized in terms of their advantages and disadvantages in Table 1, below.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Word Embedding Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{6}{*}{**BoW**} & Characterized by ease of implementation. & It does have a few limitations and drawbacks. \\  & It can be used to create an initial draft model before proceeding to more sophisticated word embeddings. & As the size of the vocabulary increases, the size of the BoW vector representation grows \\ \cline{1-1}  & &  of the BoW vector representation grows \\ \cline{1-1}  & &  accordingly. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of word embedding methods.  
Figure 3: Architecture of Doc2 Vec [44]. **(a)** PV-DM architecture; **(b)** PV-DBOW architecture.  
Figure 2: Architecture of Word2 Vec models: CBOW and skip-gram.