esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 3 Fundamentals and Basic Definitions
### Types of GANs
#### 3.3.1 cGAN  
Conditional GAN (cGAN), proposed by [PERSON] and [PERSON] [41], are an extension of the traditional GAN architecture, where both the generator and the discriminator are conditioned on auxiliary information, such as class labels or more detailed tags associated with the data. This conditional input, denoted as \(c\), is provided to both networks as an additional input to guide the data generation process. As a result, the generator learns to produce samples that not only resemble real data but also conform to the specified condition, rather than generating generic samples from an unknown noise distribution. Equation (2) formally represents the loss function of a cGAN:  
\[\underset{G}{\text{min}}\underset{D}{\text{max}}V(D,G)=\mathbb{E}_{x\sim p_{ data}(x)}[\log D(x|c)]+\mathbb{E}_{z\sim p_{z}(z)}[\log(1-D(G(z|c)))] \tag{2}\]  
where \(D(x\mid c)\) and \(G(z\mid c)\) represent the discriminator and generator conditioned on the auxiliary information \(c\), respectively; \(x\) denotes the real data sample, and \(p_{z}(z)\) refers to the prior noise distribution used as input to the generator [20].
# Securing Relay Networks with Artificial Noise: An Error Performance-Based Approach +
###### Abstract
By applying an additional processing unit to generate AN, the studied scheme can be easily embedded in a practical system to secure wireless communications, e.g., key transmission or control signaling. Moreover, our study provides exact SER expressions of the QAM signals at the relay, which can be used as benchmarks for future extensions, e.g., deriving SER expressions for other modulation schemes, and designing AN signaling in other scenarios. _Notation:_ Throughout this paper, we use \(\mathcal{CN}(b_{1},b_{2})\) to represent a complex circularly symmetric Gaussian distribution with mean \(b_{1}\) and variance \(b_{2}\). We use \(\mathcal{E}\left\{\cdot\right\}\) and \(\Pr(\cdot)\) to denote statistical expectation and probability. Moreover, \(|b|\) and \(b^{*}\) represent absolute value and complex conjugate of \(b\), respectively.
# Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method
## 2 Problem Formulation
### UAV Dynamics Model  
The dynamic of a fixed-wing UAV is represented by the following equations of motion:  
\[\dot{\mathbf{p}}_{UAV}^{n}=R_{b}^{n}\mathbf{v}_{UAV}^{b} \tag{2}\]  
\[m\dot{\mathbf{v}}_{UAV}^{b}=F_{ag}^{b}\left(\mathbf{v}_{UAV}^{b}\right)-mS\left(\mathbf{w} _{nb}^{b}\right)\mathbf{v}_{UAV}^{b}+m\mathbf{u}_{1} \tag{3}\]  
\[J\ddot{\mathbf{w}}_{nb}^{b}=M_{at}^{b}\left(\mathbf{w}_{nb}^{b},\mathbf{v}_{UAV}^{b}\right) -S\left(\mathbf{w}_{nb}^{b}\right)\mathbf{l}\mathbf{w}_{nb}^{b}+A\mathbf{u}_{2} \tag{4}\]  
In the Equation (2), \(\mathbf{p}_{UAV}^{n}\) represents the position of the UAV relative to the \(n\)-frame, and \(\mathbf{v}_{UAV}^{b}=\left[\begin{array}{cc}v_{x}^{b}&v_{y}^{b}&v_{z}^{b}\end{array} \right]^{\text{T}}\) is the velocity of the UAV in \(b\)-frame. In the Equation (3), \(m\) is the quality of the UAV, \(F_{ag}^{b}\) is the combined force of the aerodynamic and gravity forces of the UAV in \(b\)-frame, which is a nonlinear function of \(\mathbf{v}_{UAV}^{b}\). \(\mathbf{u}_{1}=\left[\begin{array}{cc}T_{x}^{b}&0&0\end{array}\right]^{\text{T}}\) is the control input of the translation loop, and only includes a thrust of the body. In the Equation (4), \(\mathbf{I}\) is the inertia matrix, \(M_{at}^{b}\) is the resultant moment of the aerodynamic and thrust moments of the UAV, which is a nonlinear function of \(\mathbf{w}_{nb}^{b},\mathbf{v}_{UAV}^{b}\), and \(\mathbf{u}_{2}=\left[\begin{array}{cc}\varpi_{a}&\varpi_{e}&\varpi_{r}\end{array} \right]^{\text{T}}\) is the control input of the rotation loop, denoting the rudder angle.  
The guidance system computes a desired quaternion, a desired angular velocity, and a desired angular acceleration. The translation loop and rotation loop solve the control inputs \(\mathbf{u}_{1}\), \(\mathbf{u}_{2}\) to track the desired speed and attitude, respectively. The structure of the UAV controller is shown in Figure 1.  
Figure 1: Controller structure.
## 3 Proposed Algorithm
### Implementation Steps  
#### 3.2.1 Hilbert Transform  
The analysis presented above is conducted in the complex domain, which considers the demodulator's wave height changes from the beat signal phase to the real signal. However, this strategy does not allow the improved RELAX algorithm to estimate the parameters, and therefore the complex signals are transformed utilizing the Hilbert transform, affording the WSAW parameter estimation.  
It should be noted that the signal is converted from a real signal to a complex signal, and thus, when the improved algorithm is used for spectrum analysis, only the positive-axis spectrum is retained. However, to preserve the energy constantly, the amplitude becomes twice the original value, and the final amplitude estimation result is half the original.
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 2 Materials and Methods
### Methods
#### 2.2.2 Statistical Analysis and Mapping Atmospheric Exposure
The 98 th percentile defines the maximum land mask detected by S1. In addition, the DEM was binary classified into altitudes above and below 50 cm. Specifically, 50 cm was chosen as it is the general predicted sea-level rise for Norway until the year 2100 [7]. These percentile-based binary land masks were then summed into a percentage of atmospheric exposure classification product ITZ_AtmExp with the legend defined in Table 3. Figure 8 demonstrates how the backscatter threshold contour lines (in white) limiting the land masks for the fifth, 50 th, and 95 th percentile images correspond to the GPS tracks and add up in the final ITZ_AtmExp product. Pixels in SAR shadow and SAR overlay regions were masked out in the final product. As we used both ascending and descending S1 paths, masked (no data) pixels only occurred in a few places, i.e., east or west of very steep mountains. \begin{table}
\begin{tabular}{c c c} \hline
**Class** & **Color Code** & **Pixel Values** \\ \hline No data & (255,255,255) & 255 \\ Land (DEM \textgreater{} 50 cm) & (0.00) & 8 \\ Land (mask from S1) & (1.99,0.1) & 7 \\ \textgreater{}95\% & (218,165,32) & 6 \\
50-75\% & (255,255,0) & 4 \\
25-50\% & (173,255,47) & 3 \\
5-25\% & (0.255,0) & 2 \\ \textless{}5\% & (0.255,255) & 1 \\ Water & (0.01,255) & 0 \\ \hline \end{tabular}
\end{table} 
Table 3: Legend of intertidal zone atmospheric exposure maps. Figure 7: Land–water threshold vs. percentile backscatter image of both polarizations: VV and VH.
# The Deep Deterministic Policy Gradient Algorithm Based on RIS Technology in a Coal Mine Tunnel
###### Abstract  
Reconfigurable Intelligent Surface (RIS) technology relies on its reconfigurable electromagnetic properties and offers an efficient solution for enhancing signal quality in coal mine communications. RIS technology significantly enhances signal coverage and transmission quality in complex, confined environments. This paper proposes a channel propagation optimization scheme for coal mine RIS communication systems, using the Deep Deterministic Policy Gradient (DDPG) algorithm. By jointly optimizing base station power allocation and RIS phase shift, this paper comparatively analyzes RIS reflection performance under ideal and non-ideal conditions, focusing on its impact on system propagation rates. A comparison of system stability and convergence rates among the DDPG, A3C, and DQN algorithms reveals that, under the DDPG optimization scheme, the average link rate reaches 6.6 bps/Hz with ideal RIS reflection and 4.6 bps/Hz with non-ideal conditions when the base station transmit power is defined as 38 dBm. Furthermore, increasing the number of RIS units from 8 to 32 results in a system link rate improvement from 5 bps/Hz to 6.8 bps/Hz. The research results provide new design ideas for optimizing coal mine RIS communication systems and open up new solutions for the use of artificial intelligence in complex coal mine tunnel environments.  
reconfigurable intelligent surface; coal mine tunnel; deep deterministic policy gradient; link rate +
Footnote †: journal: applied sciences  
1
# Encapsidation of Staufen-2 Enhances Infectivity of HIV-1
## 3 Results
### Staufen-2 Is Packaged into HIV-1 through Its Interaction with Gag  
To investigate if Staufen-2 interacts with HIV-1 Gag for encapsidation into virions, we performed a GST pull-down assay by transfecting expression plasmids Gag-GST and Staufen-2-FLAG. Staufen-1 FLAG was used as a positive control for interaction with HIV-1 Gag. Total cell lysates were extracted 48 h post-transfection, and the GST pull-down products were probed with an anti-FLAG antibody to confirm the interaction between Staufen and HIV-1 Gag (Figure 3A). The immunoblotid analyses showed an interaction of both Staufen-1 and -2 with Gag-GST, but no interaction was detected with GST alone (Figure 3A). To evaluate an RNA dependence of the Staufen-2 GAG interaction, we treated the GST pull-down products with RNase A (Figure 3B). The interaction between Staufen-2 and HIV-1 Gag and Staufen-1 and HIV-1 Gag was not disrupted upon RNase A treatment, indicating that both Staufen-1-Gag and Staufen-2-Gag interactions are RNA-independent (Figure 3B).  
Additionally, we monitored the localization of both HIV-1 Gag (EGFP tagged) and Staufen-2 (FLAG-tagged) upon co-transfection, followed by confocal microscopy. The co-localization of HIV-1 Gag and Staufen-2 was observed as orange/yellow rings (Figure 3C, upper panel) in the cytoplasm as in the positive control Staufen-1 (Figure 3C, middle panel). Vector control cells (FLAG) transfected with EGFP tagged HIV-1 Gag was used as a negative control, where the merged images clearly showed no overlap (Figure 3C lower panel).  
Next, the Staufen-2 over-expressing cells were co-transfected with HIV-1 proviral vector, and RNPs were isolated using sucrose step-density gradient centrifugation after 36 h post-transfection. We observed that Staufen-2 was associated with HIV-1 Gag containing RNPs (Figure 3D). The association of Staufen-2 with these RNPs was not considerably affected in the presence of RNase A, corroborating our immunoprecipitation results, wherein the interaction between Staufen-2 and HIV-1 Gag was found to be RNA-independent. Ribosomal protein S6 was used as an RNP marker, the association of which is known to be RNA dependent [57; 58]. These observations suggest that Staufen-2 interacts with HIV-1 Gag and is incorporated into HIV-1 virions.  
Figure 2: Impact of encapsidated Staufen proteins on replication-competent NL4-3 HIV-1 infectivity: (**A**) NL4.3-based HIV-1 viral particles were produced from HEK293T cells in the presence of Staufen-1 FLAG or Staufen-2 FLAG, or vector alone. (**B**) An equal amount of p24 normalized viral particles were used for the TZM-bl infectivity assay. Viral infectivity was quantified relative to the virus prepared with vector control. The presented values represent mean \(\pm\) standard deviations (error bars) for three independent experiments. Asterisks (**) indicates \(p\)-value \(\leq\) 0.0026 and (***) indicates \(p\)-value \(\leq\) 0.0001.
## 4 Performance Evaluation  
In this section, we evaluate the overall performance of HMC-MAC and we compare it to other methods. The metrics used for the evaluation is the aggregate throughput (which is the number of received packets per seconds at the sink), packet delivery ratio, number of frame repetitions and number of dropped packets. We used NS-2 simulator. We implemented the beacon propagation process where nodes send beacons in a TDMA fashion as explained in part 3. Each node includes its 1-hop and 2-hop neighbors in the beacon frame enabling a 3-hop neighborhood discovery. The network dimensions are \(100\times 100\)\(m^{2}\) with a communication range of \(20\)\(m\). Our topologies correspond to hierarchical topologies of \(50\) nodes with a maximum depth of \(7\) (\(Lm\) = 7), and \(3\) children by coordinator (\(Cm\) = 3), there are no end-devices in the network. We used the ZigBee hierarchical routing protocol in all simulations [22]. We also assumed that number of available channels is \(16\). We fixed the packet queue size to 200. Each point in the graph is the mean over 50 iterations. These parameters are the same for all the evaluated methods.  
In this paper, we do not study the effect of the overhead of the synchronization period nor the build-up phase of the network. Moreover, all the presented results are obtained from a 20 s observation window during \([T_{1};T_{2}]\) activity period. During this window, we have \(80\) alternations between group intervals (each interval being \(125\)\(ms\) long). This evaluation is done after the network has reached a stable state and all nodes have joined in. In this paper we do not study the impact of the duration of group intervals on the network performance.  
In this version of HMC-MAC, we considered only 3 interfaces for the sink and two children for each interface. There is no rational for the number of interfaces, \(3\) is just a case study. It is clear that with more interfaces we can achieve better throughput. We limited the number of children per interface to \(2\) in order to avoid competition between these nodes and thus avoid collisions on the sink on each interface. The simulation is carried out by varying the number of packets generated by each node, the topologies and the type of traffic production. Also note that we do not do any packet or frame aggregation in the network.
## 4 Numerical Simulation and Result Analysis
### Simulation Results and Analysis  
1. The simulation results of ping application, the distance between the source and destination of each ping, are shown in Figure 13.
2. Change the number of nodes in the awareness combat network. The ping success rate of a node when the number of nodes is different is shown in Figure 14.
3. The simulation results of video information used for fire control are shown in Figure 15.  
Figure 14: Ping success rate of a node when the number of nodes is different.  
Figure 13: Distance between source and destination nodes.  
It can be seen from the above simulation results that when the number of nodes is small, the network control overhead of the routing algorithm designed in this paper is slightly smaller than AODV, and the difference is not significant. As the number of nodes increases, the control overhead of AODV grows significantly faster than the routing designed in this paper. When the number of nodes is 70, the control overhead of AODV reaches 0.75, namely, 75% of the bandwidth is used for network routing control, and the data transmission efficiency is low. In this case, the control overhead of the routing algorithm designed in this paper is only 0.48, indicating that nearly half of the bandwidth is used for video data transmission and the efficiency is significantly higher than that of AODV.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 4 Proposed Mobility Model
It is worth noting that when the mobile sink visits node 1 in V\({}_{1}\), it will be a neighbor of some or all the nodes in V\({}_{2}\). In this case, the mobile sink neighbors nodes 2, 3, and 5 from V\({}_{2}\). As a result, data can be collected from the nodes being visited, their neighbors who are members of the other set, and so on. After pausing for a specified period at node 1, the mobile sink will move towards another node in V\({}_{1}\), node 4. Thus, it will neighbor nodes 2, 3, and 5. Therefore, the mobile sink will be able to collect information from node 4 and its neighbors. Upon visiting all the nodes in V\({}_{1}\), the mobile sink will move to the nearest node in V\({}_{2}\). For example, the mobile sink will visit node 5 in V\({}_{2}\), a neighbor to node one, and node four from V\({}_{1}\). When the mobile sink arrives at the new location, i.e., near node 5, the mobile sink can collect information from node 5, node 1, and node 4 using single-hop communication. Consequently, by adopting this approach, the mobile sink will be able to visit nodes that are not neighbors to each other and collect data from the neighbors of the node being visited. It can also be observed that static sensor nodes will have multiple chances to report their data to the mobile sink, especially with multiple neighbors. As a result, up-to-date data will be collected from sensor nodes promptly. The chances of sensor nodes suffering from buffer overflow are reduced because static sensor nodes do not have to store the collected information for long, waiting to be visited by the mobile sink. Figure 3 shows a message flow diagram presenting the communication between the sink and the sensor nodes. Figure 2: A flowchart of the proposed model.
# Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System
## 4 Simulation and Experimental Results
### The Case of a Larger Number of Satellites  
To further specify the selection performance of proposed methods in the case of more transmitters, further simulation have been conducted. The simulation parameters are set most practically, using the ephemeris of all the four GNSS constellations (BDS, GPS, GLONASS, and Galileo). In the simulation, the positions of 55 satellites used are shown in Figure 13.  
The bistatic range variance of each T/R pair is expressed as \(\Delta_{i}^{2}=\left(Q/cos\left(\frac{\phi_{i}}{2}\right)\right)^{2}\), where Q is the coefficient. The selection results of the CHO method are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **The Measured RMS** & **The Theoretical Results** \\ \hline Results without selecting/m & 65.7517 & 55.3471 \\ Results with CMF method/m & 57.3979 & 46.3356 \\ Results with CHO method/m & 50.8885 & 41.4248 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Positioning error analysis for target by the CHO method (6 satellites).  
Figure 13: Satellite location diagram.  
From Table 4, 31 satellites are selected from 55 satellites using CHO method, which greatly reduces the amount of calculation for multistatic satellite signal processing. At the same time, the positioning error of this method is equivalent to that of 55 satellites, which is significantly smaller than the error of randomly selecting 31 satellites, and it further illustrates the effectiveness of this method for selecting satellites. However, it also shows that changing the range measurement error does not change the satellite selection results. This is because when the level of the range measurement error is equivalent, once the multistatic geometry and the system parameters are certain, the number of satellites filtered out are determined.  
When the total number of satellites is very large, the number of satellites selected by CHO method is still a lot. At this time, we can use the CMF method to select again, and can also select the specified number of satellites according to the actual computing power. When Q = 15, the selection result of the combination of CHO and CMF is shown in Figure 14.
# A Full Technological Traceability System for Extra Virgin Olive Oil
## Materials and Methods
### RFID Technology  
The open source prototype system based on RFID technology was created adopting different wireless communication protocols for each phase along the Infotracing flow. The tags were read during each of the above mentioned phases through the RFID antennas and in addition, being in an outdoor in-field context, through a portable customized reader. The codes were subsequently sent to a smartphone via Bluetooth that, using a specifically programmed Android app (Infoliva, see description below), stores the code and the related additional information entered by the operator. The customized portable reader was developed using open source technologies consisting of an Arduino(r) micro controller unit (MCU) with an integrated Bluetooth (Bluno Mega 2560) and an RFID antenna interfaced through a serial communication port. A detailed description of the antenna is reported in [PERSON] et al. (2016). The RFID standard used was UHF at 860 MHz. The tags used were Class 1 Gen 2 that, compared to the predecessors Class 1 Gen 1 that did not contain any kind of support for the  
Figure 1: Extra virgin olive oil (EVOO) traceability scheme and phases including relative multi-actors, multi-tags and multi-devices. The standing olive tree phase included application of the first radio frequency identification (RFID1); cultivar identification; first tag association and information storage in the database. During the olive harvesting the second RFID (RFID2) was applied on the crate, and other information related to each crate were added. During olive pressing, the information related to processing and yield were added. The nutritional information of the olive oil related to fats, carbohydrates, etc., was assessed and stored On the final product was printed and applied to the bottle a label with a QR code tag protected by a “scratch and win” system. Finally, once scratched, the QR code was scanned by consumers, and the information tracked by the infotracing system was read on the web through a dedicated link that was generated.  
processed data security, support two systems useful for the processed data security such as the TID code (the serial tag ID) and the access/kill passwords.
## 4 FDTD Algorithm
### Stability  
In the IO case of the FDTD method (\(\alpha=1\)), the time-step size is limited by the Courant condition [9]  
\[\Delta t\leq\frac{1}{c_{0}\sqrt{\frac{1}{(\Delta x)^{2}}+\frac{1}{(\Delta y) ^{2}}+\frac{1}{(\Delta z)^{2}}}} \tag{35}\]  
where \(c_{0}\) denotes the light speed in a vacuum. Meeting this condition is required to ensure the stability of simulation. However, the condition (35) is not sufficient in the case of FDTD in the media described by the TF constitutive relations.  
Following the standard derivation of the IO FDTD stability condition [9], the time-step limit is derived for FDTD in the media described by the TF constitutive relations based on (25)-(30)  
\[\Delta t\leq 2^{1-\frac{1}{\alpha}}\Bigg{(}\frac{\sqrt{\mu_{\alpha}\varepsilon _{\alpha}}}{\sqrt{\frac{1}{(\Delta x)^{2}}+\frac{1}{(\Delta y)^{2}}+\frac{1}{ (\Delta z)^{2}}}}\Bigg{)}^{\frac{1}{\alpha}}. \tag{36}\]  
The detailed derivation of (36) is presented in Appendices A-C. This new time-step limit is obtained through analysis of the numerical-dispersion relation. When \(\alpha=1\) is assumed, the condition (36) simplifies to the IO case (35). In the considered one-dimensional simulation, the stability condition simplifies to  
\[\Delta t\leq 2^{1-\frac{1}{2}}(\sqrt{\varepsilon_{\alpha}\mu_{\alpha}}\Delta z )^{\frac{1}{2}}. \tag{37}\]  
Let us verify numerically the condition (37). For this purpose, let us consider the simulation with an initial condition \(E_{x}^{0}(k_{s})=1\), while the field values at other spatial and temporal localizations are equal to zero. For a varying parameter \(\frac{1}{2}<\alpha<1\), values of \(\Delta t\) around the analytical stability limit are considered. For each value of the time-step size, a short simulation is performed. In the case of the considered FO model, a decay in field amplitude is expected, which is further discussed in Section 5. Therefore, if the maximum value of the electric field at the end of the simulation is larger than one, it is considered as unstable. The results of numerical verification are presented in Figure 1, where the minimal unstable value of \(\Delta t\) is plotted along with the analytically obtained stability limit.  
For each considered value of \(\alpha\), all the verified values of the time-step size below the limit (37) resulted in stable simulations, while all the values above this limit resulted in unstable ones. This confirms the correctness of the derived stability condition.