esa-sceva/satcom-chunk-collection huggingface dataset:
# Bioremediation Methods for the Recovery of Lead-Contaminated Soils: A Review
## 3 Phytoremediation
### Phytoremediation Capability of Spontaneously Grown Plants
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Extractant** & **Conditions** \\ \hline Bidistilled water & BDW & - \\ \hline \multirow{6}{*}{Neutral salt solutions} & Ca(NO\({}_{3}\))\({}_{2}\) & 0.1 M \\ \cline{2-3}  & MgCl\({}_{2}\) & 1 M \\ \cline{2-3}  & NH\({}_{4}\)NO\({}_{3}\) & 1 M \\ \cline{2-3}  & NaNO\({}_{3}\) & 0.1 M \\ \cline{2-3}  & Mg(NO\({}_{3}\))\({}_{2}\) & 0.5 M \\ \cline{2-3}  & CaCl\({}_{2}\) (no ac) * & 0.01 M \\ \cline{2-3}  & CaCl\({}_{2}\) (ac) ** & 0.01 M with HCl (0.1 M) \\ \hline \multirow{6}{*}{Organic acids} & \multirow{2}{*}{LMWOA (low molecular weight organic acids)} & acetic, lactic, citric, malic, and formic acids (ratio 4:2:1:1:1) \\  & & organic acids) & solution with a total concentration \\ \cline{2-3}  & & 10 mM \\ \cline{2-3}  & HOAc & 0.11 M \\ \hline \multirow{2}{*}{Chelating agents} & EDTA & Na\({}_{2}\)-EDTA 0.01 M + \\  & & CH\({}_{3}\)COOH\({}_{4}\) 1 M \\ \cline{2-3}  & DTPA & 0.005 M DTPA + 0.1 M TEA + 0.01 \\ \cline{2-3}  & DITA & M CaCl\({}_{2}\) \\ \hline \multirow{2}{*}{Diluted acids} & HCl & 0.1 M \\ \cline{2-3}  & HNO\({}_{3}\) & 0.5 M \\ \hline \end{tabular}  
* no ac: not acidified; ** ac: acidified.  
\end{table} 
Table 1: Reagents used for the extraction of the bioavailable fraction of Pb in the soil. (TF \(>\) 1 or shoot/root quotient \(>\) 1) and 36 as excluders (TF \(<\) 1 or shoot/root quotient \(<\) 1); most of these (fourteen species) belonged to the grass family (_Poaceae_). Eleven plant species belonging to seven different families were classified as hyperaccumulators on the basis of three criteria: (1) accumulation in shoots 10-500 times more than usual plants (lead 5 mg/kg), (2) shoot accumulation \(>\)1000 mg/kg, and (3) shoot/root quotient \(>\)1. Among these, three herbs (_S. arreensis_, _Co. sumatrenis_, and _Cyperus_ sp.) and two shrubs (_C. odoratum_ and _B. asiatic_) could be considered as possible phytoremediators for lead-contaminated soil. This can justify their observed broad presence in lead-contaminated sites supported by high propagation rates. _C. odoratum_ and _B. asiatic_ can reach the height of 34 m and are perennial shrubs, generally preferable in phytoremediation treatment, preventing the need for annual planting.
# Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
## 4 Experiments  
### Experimental Setup  
The experimental environment and network parameter settings are as follows: Intel Xeon Silver 4210 CPU, GeForce RTX 2080 Ti graphics card, 11 GB video memory, 31 GB RAM, Ubuntu 20.04.3 LTS operating system, Python 3.7.0, PyTorch 1.11.0 and Cuda 11.6. The initial learning rate was set to 0.001, batch_size was set to 16, momentum was set to 0.9, an Adam optimizer was used, the initial decay rate was 0.5 and the number of training rounds was experimentally set to 200.  
In this paper, we conduct point cloud classification and segmentation to evaluate the performance of the proposed method. For the point cloud classification, the ModellNet40 dataset [31] provided by Princeton University was used for the point cloud classification. It contains about 40 object categories (such as aircraft, tables, plants, etc.) and 12,311 CAD models represented by triangular grids. The dataset has 40 categories and 12,311 models. The data were divided into 9843 training samples and 2468 test samples. Each model sampled 1024 points as the initial data points for the experiment. For the part segmentation of the point cloud, the ShapeNet dataset [32] was used for the experiment. This dataset contains 16,881 shapes and 50 part segmentation category labels for 16 object classes. For a given input 3D point cloud, the objective of segmentation is to classify each point in the point cloud to give a category label, e.g., for chair shapes, the components such as chair legs, chair back, etc., need to be segmented. For the point cloud semantic segmentation, the experiment was conducted by using the S3 DIS dataset [33]. The S3 DIS dataset contains 6 areas of 3 different buildings divided into 271 individual rooms, each point in the scene corresponds to a fixed label and the labels belong to 13 categories (ceiling, floor, wall, door, etc.). In this paper, the spatial coordinates of the scene points and their RGB information were used as the input features of the network, and the rooms were partitioned into 1 m \(\times\) 1 m \(\times\) height (m) cubes during training. A total of 4096 points were randomly selected from each cube to generate the training data. A standard 6-fold cross-validation was used in the experiments.
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model
### Watermark Embedding Module  
SACT system embeds watermark information by controlling the network data flow and capturing the required packet flow. TCP protocol is a reliable communication protocol for connection. The application layer protocols that it supports include: HTTP/HTTPS, FTP, Telnet, SMTP, POP3. UDP protocol is a connectionless and unreliable communication protocol, which can provide excellent instant information processing. The application layer protocols that it supports include: DHCP, NTP, DNS, NFS, SNMP. To reduce the load of SDN control, our system only writes watermark information through TCP/UDP protocol.  
The workflow of the module is as follows:  
1. [leftmargin=*]
2. Setting the sending of flow table in SDN switch. After receiving TCP/UDP protocol data packet, the switch uploads the key information of data packet and switch port and other information to the controller for further processing through _Packet-In_ message. If the switch receives other protocol packets, it directly forwards it according to the flow table.
3. After receiving the message, the SDN controller converts the switch port and other information into the binary bit stream \(B\) according to the coding mapping relationship as the initial watermark information.
4. The binary bit stream \(B\) is extended by convolutional code to obtain sequence \(E\), which improves its self error correcting ability. We use the _L-Bits to N-Packets_ coding model to transform it into the interval arrival delay sequence \(M\). The controller notifies the switch to send packets according to the delay sequence by _Packet-Out_ message.
## 3 Materials and Methods
### Split City Port Study Case--Ferry Fleet Energy Demand Analysis
The average load value refers to the power value in the case of charging the electrical ESS on ships with constant power and is defined by the expression:  
\[P_{AVG}=\frac{\textit{Daily energy needs}}{24\textit{-hour interval}} \tag{7}\]  
The amount of peak or maximum power \(P_{MAX}\) is defined by the expression:  
\[P_{MAX}=\int_{t_{\pi 1}}^{t_{\pi 2}}u_{MAX}(t)\cdot i_{MAX}(t)dt \tag{8}\]  
where \(P_{MAX}\) indicates the maximum power with which the group of consumers loads the network, \(u_{MAX}\) is the maximum voltage of the network, and \(i_{MAX}\) is the maximum current of the group of consumers, while \(t\) represents time. Considering the energy needs of each individual ship in accordance with the available charging time, maximum charging power is estimated. The peak power required to charge a group of ferries in the most demanding moment amounts to 2.594 MW. Compared to the average annual energy needs of 0.477 MW, this represents an increase of almost 5.5 times, which significantly exceeds the capacity of the port infrastructure. Therefore, it is concluded that the energy needs of electric ferries in this particular case are extremely non-seasonal, i.e., non-periodic.
## 1 Introduction
### Research Gap and Significance  
Based on the preceding works, many of the proposed schemes that consider predicting the channel parameters task are mainly focused on implementing several deep neural networks (DNN) while applying RL approaches, which in turn leads to an increase in the number of hidden layers with a massive number of neurons in each layer. The significance of this study is to illuminate that we can eliminate the need for such DNN approaches, and instead, we can adopt the RL based developed Q-learning algorithm to predict the channel coefficients for each user device in MISO-NOMA cell, and at the same time, a notable improvement in system performance and network convergence is realized. The most prominent gain of the developed channel estimator scheme is that it can enhance the system performance without the need for hidden layers or an external training set.  
In addition, several RL algorithms have been proposed to explicitly address the issues associated with channel state information (CSI), beamforming, and power allocation. To the best of the authors' knowledge, there is no study that explores the incorporation between Q-learning algorithm for channel prediction and the power allocation policy as an integrated scheme for multiuser detection in downlink MISO-NOMA system in fading channels.  
Furthermore, it is worth mentioning that unlike deep learning algorithms, that mainly depend on learning from a training data set, the proposed Q-learning algorithm in our study is developed to dynamically enhance the system performance and adjust to the variations in the channel based on the feedback from the environment.
## 3 RS Applications in Ocean
### Ocean Surface Current (OSC)
#### 3.2.5 Altimeter  
Altimeters with Ku-band (13.8 GHz) also provide information about the large-scale dynamic topography of the ocean, particularly OWH and OSW speed, which can be directly used to estimate the global OSC and its variability at coarse spatial resolutions (e.g., 10 km) [112; 135; 142]. Moreover, integrating altimetry data with tide-gauge data and hydrodynamic models increases the knowledge about global OT and OSC variability [136; 142]. However, altimeters cannot measure the non-geostrophic components of the OSC due to non-geostrophic and local winds [136]. Several efforts have been made so far to derive OSC from altimetry data. For example, the NRT Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) products, as geostrophic OSCs, have been generated by integrating the satellite altimetry and precise satellite position data and are available at a global scale (see [PERSON] and [PERSON] [90] for more information). Moreover, the OSCAR project, developed by the National Oceanic and Atmospheric Administration (NOAA) [112; 113; 114], and the GlobCurrent project, developed by European Space Agency (ESA) [90], provide the complementary information for AVISO products in reliable estimating of the Ekman component of the OSCs [118].
# Context-Aware Enhanced Application-Specific Handover in 5G V2X Networks
## 3 Connection-Aware Policy Mechanism (CAP)
### Small-Cell Performance Index (PI) Measurement Using TOPSIS
This approach significantly improves the QoS in the UDN network by reducing needless handovers, signaling overhead, and potential changeover failures. The following is given:  
* \(\mathbf{v}\): velocity of the UE;
* \(\mathbf{d}\): distance between previous and current UE movements;
* \(\mathbf{C}_{i}\): PI of \(\mathrm{SC}_{i}\);
* \(\mathbf{R}_{i}\): rank of \(\mathrm{SC}_{i}\). * The Stay Time Assigned (STA) for the network sector is calculated as follows: \[\mathrm{STA}=\frac{d}{v}\] (4)
* The ST for each \(\mathrm{SC}_{i}\) in the network sector is calculated as follows: \[ST_{i}=STA-(C_{i}+R_{i})\] (5)  
The handover process's initiation depends on achieving the handover decision criteria, which consists of the highest PI, the assigned ST, and the availability of resources. The top-ranked SC is subject to load checking. The load ratio is based on the (available connection/total connection). Based on this, the ST threshold is checked, and subsequently, the handover process is initiated.
# Novel Service Efficiency Evaluation and Management Model
## 3 Upper Confidence Limit of Service Efficiency Index and Minimum Value
Let \(y_{h1},\ldots,y_{hj},\ldots,y_{lm}\) be the observed value of \(Y_{h1},\ldots,Y_{hj},\ldots,Y_{lm}\) for the \(h\)th workstation, then the observed values of \(S_{\textit{II}}^{*}\) and \(US_{\textit{II}}\) are:  
\[S_{\textit{II}0}^{*}=\frac{1-\delta_{h0}^{*}}{\gamma_{h0}^{*}}\text{ and }US_{\textit{II}0}\!=\bigg{(}S_{\textit{II}0}^{*}+\frac{t_{\alpha/2n-1}}{\sqrt{n }}\bigg{)}\sqrt{\frac{\chi^{2}_{1-\alpha/2n-1}}{n}}, \tag{10}\]  
where  
\[\delta_{h0}^{*}\!\!=\frac{1}{n}\!\sum_{j=1}^{n}y_{h,j}\text{ and }\gamma_{h0}^{*} \!=\sqrt{\frac{1}{n}\!\sum_{j=1}^{n}\!\Big{(}y_{h,j}-\delta_{h0}^{*}\Big{)}^{ 2}}.\]  
If the upper confidence limit of the service efficiency index is used as a statistical test tool, it can be judged whether the service performance meets the requirements as long as the value of the upper confidence limit is greater than the required value of the index. Although the point estimate of the indicator is usually the most direct and easy-to-use, the disadvantage is that it will cause the risk of misjudgment due to sampling errors. Therefore, through the relationship that the value of the upper confidence limit is greater than the required value of the indicator, the point estimate converted to the indicator is greater than the MV value, which will help practical applications and at the same time will not lead to the risk of misjudgment due to sampling errors. Obviously, when \(US_{\textit{II}0}\geq k\), then  
\[\bigg{(}S_{\textit{II}0}^{*}+\frac{t_{\alpha/2n-1}}{\sqrt{n}}\bigg{)}\sqrt{ \frac{\chi^{2}_{1-\alpha/2;n-1}}{n}}\geq k\]  
equivalent to \(S_{\textit{II}0}^{*}\geq MV\), where \(MV\) is the minimum value as follows:  
\[MV=\sqrt{\frac{n}{\chi^{2}_{1-\alpha/2n-1}}}\times k-\frac{t_{\alpha/2n-1}}{ \sqrt{n}}.\]Therefore, the statistical hypothesis testing rules are defined below:  
1. If \(S_{Ith}^{*}\geq MV\), then do not reject \(H_{0}\) and conclude that \(S_{Ith}\geq k\). 2. If \(S_{Ith}^{*}<MV\), then reject \(H_{0}\) and conclude that \(S_{Ith}<k\).
## 5 Experimental Investigation  
The issues of impedance matching of the sub-surface antenna with the sensor and antenna radiation pattern are addressed experimentally in a 3.3 m-wide, 5.4 m-long and 1.2 m-deep sandbox assembled specifically for these experiments (Figure 4). The fine sand (left) and gravel (right) were selected as homogeneous, well-characterized media: sensors are inserted through 40-mm diameter Poly(Vinyl Chloride) (PVC) pipes positioned at various depths, ranging from 7 to 103 cm from the surface. During all experiments, the sensors are inserted by at least 1 m in the tubes to make sure the environment is homogeneous along the whole length of the antenna attached to the acoustic transducer.  
In all recorded charts, the time axis starts at the measurement window beginning time (offset by about 1 \(\upmu\)s), which was positioned to focus on the echoes returned by the delay line and does not include the emitted pulse. Our custom software allows for defining two time-windows, one positioned close to the emitted pulse for shallow interface echo recording (not shown here), and a second focusing on the sensor response (used here). Measurement durations typically last 850 ns to 1200 ns, or 4096 samples recorded at 4556 to 3417 MHz.  
Figure 4: Experimental setup of the 3.3 m-wide, 5.4 m-long and 1.2 m-deep sandbox, with two media for assessing the dependence of the medium permittivity on the link budget. Fine sand (left) and gravel (right) provide environments representative of sub-surface measurement environments. The coordinate framework with \(Z\) along the depth, X along the RADAR scan direction and Y along the longest length of the antenna dipoles is used in the text to refer to the varying parameter during the measurement.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 4 Experiments
### Ablation Experiments
#### 4.3.3 Tracking Module Effectiveness Analysis  
In order to further objectively analyze the effectiveness of our proposed tracking algorithm, we designed three sets of comparative experiments: (a) HMMATrack (w/o multilevel cooperative matching): does not use multilevel cooperative matching but only uses the classic Kalman filter matching algorithm other structures remain unchanged; (b) HMMATrack (w/o automatic trajectory correction): the automatic trajectory correction module is not used, and other structures remain unchanged; and (c) HMMATrack: the complete HMMATrack framework. It can be seen from Figure 10 that the ball trajectory obtained by the tracking model using the complete trajectory correction algorithm has better continuity.  
Figure 8: Heuristic composite sampling ablation analysis on soccer, basketball, and volleyball test data sets.  
Figure 9: MNet backbone ablation analysis on soccer, basketball, and volleyball test data sets.  
Table 4 objectively evaluates the effectiveness of each functional module design from the three indicators of AUC, \(P\), and \(P_{Norm}\). Table 4 shows the average results of different network structures on the three indicators of AUC, \(P\), and \(P_{Norm}\) for three ball objects. It can be seen from the table that the algorithm that only performs random cropping and random sampling without any other sampling strategies performs the worst in terms of AUC, \(P\), and \(P_{Norm}\). Compared with the MNet network, the algorithm using ResNet-18 as the backbone network has lower AUC, \(P\), and \(P_{Norm}\) indicators. On the contrary, the network that adds a complete heuristic composite sampling strategy, MNet detection module and multilevel collaboration, and automatic matching and tracking function modules performs the best in the three indicators of AUC, \(P\), and \(P_{Norm}\).
## 4 Behavior of Distributed versus Concentrated Local Control Strategy in Different Real Grids
### Behavior of Concentrated versus Distributed Control Strategy  
The operation conditions of a sunny day at 12:00 are simulated for all cases. Therefore, PV facilities connected on both real grids have injected their maximal active power of 5 kW. The urban grid loads have consumed 0.75 kW, while the rural grid ones have consumed 0.685 kW. Loads on both real grids have been simulated with a \(\cos(\varphi)\) = 0.95.  
Figure 20 shows an overview of the voltage profiles of two typical real grids, urban and rural, when no-control, distributed \(cos\varphi(P)\), distributed \(Q(U)\), and concentrated \(L(U)\) local control strategies are exercised. Figure 20a,b correspond to the no-control condition for the urban and rural grid case, respectively. Five feeders in the urban grid case and two feeders in the rural one violate the upper voltage limit. The use of a distributed \(cos\varphi(P)\) or \(Q(U)\) local control strategy eliminates all of the voltage limit violations in urban grids, as shown in Figure 20c,e. Meanwhile for rural grids, they do not eliminate all of them, as indicated in Figure 20d,f. In contrast, the concentrated \(L(U)\) local control strategy eliminates all of the voltage limit violations for both grids, Figure 20g,h.  
Figure 19: Different inverter control characteristics: (**a**) \(cos\varphi(P)\) and (**b**) \(Q(U)\).  
Table 2 shows the \(Q^{xx}\) and the distribution transformer loading for different control strategies. The results show that in both grids, the \(Q^{xx}\) is higher for the distributed control strategies than for the concentrated one. Transformer loading follows the same trend as \(Q^{xx}\), thus being higher for the distributed \(cos\varphi(P)\) or \(Q(U)\) than for the concentrated \(L(U)\) local control strategy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Distributed**} & \multicolumn{2}{c}{**Concentrated**} \\ \hline \multicolumn{4}{c}{_cosp(P)_} & \multicolumn{2}{c}{**Q(U)**} & \multicolumn{2}{c}{**_L(U)_**} \\ \hline  & \(Q^{xx}\) (\(\mathbf{kvar}\)) & \(\mathbf{Tr^{L}}\) (\%) & \(Q^{xx}\) (\(\mathbf{kvar}\)) & \(\mathbf{Tr^{L}}\) (\%) & \(Q^{xx}\) (\(\mathbf{kvar}\)) & \(\mathbf{Tr^{L}}\) (\%) \\ \hline Urban & 491.68 & 130.00 & 436.03 & 125.11 & 305.02 & 114.52 \\ Rural & 176.44 & 181.65 & 176.44 & 181.65 & 115.15 & 158.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reactive power consumption, loss, and exchange for different control strategies in real LVGs.  
Figure 20: Overview of voltage profiles of two typical real grids, urban and rural, when no-control, distributed \(cos\varphi(P)\), distributed \(Q(U)\), and concentrated \(L(U)\) local control strategies are exercised.
## 3 Role of Lipids for Membrane Function and Intracellular Transport
### Binding Sites for Gb3 on Shiga Toxin  
The binding of Shiga toxin to the glycosphingolipid Gb3 has been studied with a variety of test systems, including cells, liposomes, and solid phase, as discussed thoroughly in an earlier review [6]. Each of the five B-chains have three binding sites for Gb3 resulting in a total of 15 theoretical binding sites at Shiga toxin for Gb3. Two of the three binding sites at each B-chain bind to Gb3 when the carbohydrate structure is oriented close to perpendicular to the membrane surface, whereas one site binds to the carbohydrate part when it is oriented nearly parallel with the membrane surface (Figure 8). This is very interesting since, in the absence of cholesterol, glycosphingolipids have the carbohydrate structure sticking out almost perpendicular to the membrane surface, whereas the presence of cholesterol close to the glycosphingolipid makes the carbohydrate moiety bend and become almost parallel to the membrane surface [145; 146]. It is not known how many of these sites, or which sites, that Shiga toxin must bind to in order to be endocytosed. It is also not known how various Gb3 species contribute to binding and uptake of Shiga toxin; we have discussed this in detail earlier [6]. As mentioned above, the dominating Gb3 species in cells are those with N-amidated C16:0, C24:0 and C24:1. Whereas the C16:0 species are expected to penetrate approximately to the midplane of the membrane, the C24:0 and C24:1 species should be able to reach almost halfway into the opposing leaflet. Although the amounts of Gb3 species vary between cell lines and may include minor amounts of other species than the three mentioned, we find it remarkable that C24:1 is the only species containing a double bond and present in high amounts. In the HEp-2 cells we used for most of our studies, the C24:1 species constitutes approximately 50% of the total Gb3 [8]. The importance of the various Gb3 species to the binding and uptake of Shiga toxin, as well as why cells have so much of the N-amidated C24:1 species where the double bond is found almost exactly in the midplane of the membrane (C24:1 is nervonic acid where the double bond is between C15 and C16), are issues which need further investigation.