esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
In this study, MLP-ANN method is applied to map forest vertical structure in Gongju-si, which is located near the center of South Korea. Fourteen input neurons generated from Sentinel-2 optical satellite images, existing DTM and DSM data were used as input layers for the MLP-ANN approach.
## 3 Results
### Investigating VOC Signalling Using Volatile Metabolite and Transcript Analyses
#### 3.2.1 Constitutive VOC Emissions
Gene expression values were calculated by applying the 2-\(\Delta\)ACI algorithm. Estimated relative quantities were normalised for the expression value of the biosynthesis-related genes of (**E**) MeJA; AOS (allene oxide synthase), (**F**) MeSA; CHORS (chorismate synthase), (**G**) (Z)-3-hexen-1-ol; LOXD (lipoxygenase D), and (**H**) \(\alpha\)-pinene; VvPNaPin1 (\(\alpha\)-pinene synthase) during drought initiation, peak drought, and recovery. Different letters indicate significant differences between the treatments, with lower case letters used for the comparison of treatments within a particular time point and Greek letters used for comparing a treatment across different time points. Significance was set at \(\alpha\) = 0.05, Tukey test. Error bars indicate the standard error of mean (SEM, \(n\) = 3). No significant differences in \(\alpha\)-pinene concentration were noted at DI among the treatments, except in the WW\({}_{\rm r}\) (15.9 \(\mu\)g L\({}^{-1}\)) which was significantly lower than the WW\({}_{\rm c}\) (21.4 \(\mu\)g L\({}^{-1}\)) (\(p<0.03\)) (Figure 3D). The concentration of \(\alpha\)-pinene continued to decrease significantly at PD in the WW\({}_{\rm r}\), yielding 9.3 \(\mu\)g L\({}^{-1}\) (\(p<0.0001\)). The DS\({}_{\rm c}\) vines had significantly higher levels of \(\alpha\)-pinene (14.5 \(\mu\)g L\({}^{-1}\)) as compared to 9.2 \(\mu\)g L\({}^{-1}\) in DS\({}_{\rm e}\) at PD (\(p=0.003\)). \(\alpha\)-Pinene levels in the co-located treatments did not recover to their original levels after re-watering (on Day 9) but remained lower than the DS\({}_{\rm c}\) and WW\({}_{\rm c}\). However, the expression of \(\alpha\)-pinene synthase, VvPNaPin1, did not correspond to our metabolite data (Figure 3H). There were no significant differences between treatments at any timepoint.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 3 Corner Node Algorithm  
The computational time and type of error depend on the heuristic value used with A*. Figure 2 shows an example of a distortion that may occur when using the straight-line distance from the selected node to the destination as the heuristic value. Distortion arises because the evaluation function \(\mathbf{f}(\mathbf{n})\) assigns a low score to the wrong path compared with the ideal path, even though there is an obstacle between the origin and destination. The A* algorithm's performance also decreases when a high-resolution image is provided and there are many obstacles on the map. This is because each pixel must be used as a node in the absence of a topology map.  
Figure 2: Distortion of the route when a topology map is not provided.  
Figure 3 shows the difference in the pathfinding process when an algorithm processes an image-based map with versus without a topology map. In the case of Figure 3a, because a topology map is not provided, all of the pixels are required for the process, resulting in performance degradation. However, Figure 3b shows that the number of nodes required for the path search is significantly reduced with the use of a topology map.  
If the topology map is provided through the CNA, it is possible to predefine efficient node connections during preprocessing compared with the existing method, in which obstacles cannot be determined until the selected node approaches the obstacle. Therefore, the addition of the CNA reduces the computation required after encountering an obstacle.  
This paper proposes using the CNA to generate topology maps. Before processing the map data, an obstacle such as a wall is stored in a list. The algorithm then generates a topology map in a two-step process involving the creation and connection of nodes. A description of the process is provided in the following.  
First, the algorithm searches the data corresponding to an obstacle, and a node is created in the free space adjacent to the data corresponding to a corner. In other words, this step creates a node after referencing all of the previously stored obstacle data lists, searching for adjacent data again, and determining whether the corresponding data correspond to a corner.  
Second, the algorithm creates a connection between the nodes when the nodes can be connected in a straight line. Once determined, the algorithm performs vector subtraction by converting the position of each node into a 2D vector. If there is no obstacle between the generated vectors, the algorithm determines that the node can be connected in a straight line and makes the connection between the two vectors.  
After completing the two-step preprocessing step, the path search can proceed with a pathfinding algorithm such as A*.
## 3 Results and Discussion  
### Modes of Failure and Crack Pattern  
Figure 7 shows the cracking responses of the UHPC beams to flexural testing (0). In the elastic stage of this test (Figure 8), no cracks were detected. Microcracks were typically initiated at the beam's softit surface and propagated vertically toward its top surface. The pullout of fibers from the concrete caused these microcracks [20]. When the load on a single fiber is greater than the fiber-matrix bonding resistance, fiber pullout ensues, which increases the load that the other fibers must overcome [1]. This mechanism made the fibers undergo a progressive pullout (collapse) mode. Figure 7 presents the enhanced deformability of the bar-reinforced UHPC (B-F) beams compared to the control beam (A); however, all beams (except for E1) failed due to one major flexural crack formation. This cracked section developed when the fiber-pullout phenomenon explicitly occurred. This mechanism typically took place when the beam approached its ultimate load, followed by steel bar yielding.  
In the E1 case, two major flexural cracks extending from the beam's bottom surface to the loading points caused the failure. The best deformability was observed for E2, where tightly spread flexural cracks diffused along the beam's pure flexural zone. However, no clear-cut relationship between the reinforcement depth and cracking intensity and distribution was observed. The observed discrepancy in the failure patterns of the beam replicas could be attributed to their variable fiber distribution (dispersion). Therefore, the combined mechanism of fiber pullout and bar yielding likely precipitated the failure of the UHPC beams.
## 2 Methods
### Ground Data Processing
#### 2.3.2. Hyperspectral Data Processing  
A total of 21 VIs were computed using the hyperspectral data collected in the field, 13 VIs were narrow band indices while 8 were broad band indices (Table 1). Hyperspectral bands employed were 445, 531, 550, 570, 670, 680, 681, 700, 705, 708, 715, 720, 726, 734, 740, 747, 750, 753, and 800 nm. Spectral bands used to compute broad band VIs were: Blue (436-528), green (512-610), red (625-691), and NIR (829-900 nm). Broad band VIs were computed using the Landsat 8 spectral response function available in the Landsat Science website ([[http://landsat.gsfc.nasa.gov/](http://landsat.gsfc.nasa.gov/)]([http://landsat.gsfc.nasa.gov/](http://landsat.gsfc.nasa.gov/))). Narrow and broad band indices were then used to establish the relationship between CC and the spectral characteristics of mangrove leaves. Therefore, the selection of these indices was based upon their tested ability to predict CC.
###### Abstract  
In the ocean environment, the minimum variance distortionless response beamformer usually has the problem of signal self-cancellation, that is, the acoustic signal of interest is erroneously suppressed as interference. By exploring the useful information behind the signal self-cancellation phenomenon, a high-precision direction estimation method for underwater acoustic sources is proposed. First, a pseudo spatial power spectrum is obtained by performing unit circle mapping on the beam response in the direction interval. Second, the online calculation process is given for reducing the computational complexity. The computer simulation results show that the proposed algorithm can obtain satisfactory direction estimation accuracy under the conditions of low intensity of acoustic source, strong interference and noise, and less array snapshot data.  
minimum variance distortionless response; signal self-cancellation; direction estimation; underwater acoustic source; spatial power spectrum +
Footnote †: journal: Applied Sciences
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 3 Proposed Algorithm
### Evaluation Parameters
#### 3.1.2 Awareness of Load  
As the number of data packets sent by nodes in the network increases, the receiving end nodes are unable to process them in a timely manner, making for an increase in the workload of the sending nodes. The escalation in data packet transmission within the network inherently amplifies the likelihood of collisions among packets from neighboring nodes. Such collisions culminate in congestion at the Media Access Control (MAC) layer, which in turn substantially degrades transmission efficiency. Furthermore, when the buffer queue reaches capacity, nodes must discard incoming data packets, exacerbating the packet loss rate. This scenario not only underscores the challenges of efficiently managing network traffic but also emphasizes the critical need for sophisticated mechanisms to alleviate congestion and optimize packet handling, thereby ensuring network reliability and performance.  
1. Load of Node  
To measure the load situation of the current node, we propose the concept of _load of node_ (LN), which is defined as follows:  
\[LN_{i}=\frac{Load_{i}(t)}{Load_{max}} \tag{9}\]  
Among them, \(Load_{i}(t)\) represents the length of data packet frames waiting to be processed in the MAC layer buffer queue of node \(i\) at time \(t\), and \(Load_{max}\) represents the maximum frame length that the MAC layer of node \(i\) can accommodate. It indicates that the larger the value of \(LN_{i}\), the higher the utilization rate of the MAC layer buffer queue of node \(i\) at the current time, in other words, the greater the load on node \(i\).  
2. Load of Link  
The load situation of a link is determined jointly by the load situation of the sender and receiver at both ends of the link. Therefore, in combination with load of node mentioned before, _load of link_ (LL) is introduced to reflect the link load condition between two nodes, defined as follows:  
\[LL_{ij}=e^{LN_{i}}\cdot e^{LN_{j}} \tag{10}\]  
In the above equation, \(LL_{ij}\) represents the load of link between node \(i\) and node \(j\), while \(LN_{i}\) and \(LN_{j}\) respectively represent the load of node \(i\) and node \(j\). It reflects that the greater the load on the nodes at both ends of the link, the greater the load on the link.  
3. Load of Neighbor Set  
The load situation of a node is not only affected by the node itself and a certain link, but also by the neighbors around it. Therefore, we propose _load of neighbor set_ (LNS) in order that reflecting the load impact of the neighbors around the node.  
\[LNS_{i}=\prod_{N_{i}}e^{LN_{i}} \tag{11}\]  
Among them, \(LN_{i}\) represents the load of neighboring nodes for node \(i\), and \(N_{i}\) represents the number of neighboring nodes for node \(i\) at time \(t\).
# [PERSON] on Korean Block Ciphers
## 2 Related Works
### Target Block Ciphers
#### 2.2.4. Speck  
In 2013, NSA released two lightweight block ciphers, SPECK and SIMON, for the low-end devices [10]. SPECK has been implemented in order to perform efficiently in the software environment. The SIMON, on the other hand, has been implemented and optimized for hardware. Although these two ciphers are considered sisters, SIMON has already been designed as quantum circuit [9]. For this reason, this paper only covers quantum circuit implementation and optimization for SPECK.  
SPECK takes the form of block cipher and is implemented with an ARX structure that consists of simple operations such as addition, rotation, and bit-wise exclusive operation. SPECK is also implemented in various parameters to provide different levels of security, which are shown in Table 5.  
Figure 4: Key schedule of LEA-128.  
Figure 5: \(i\)-th round function of LEA.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Block Size (bits)** & **Key Size (bits)** & **Word Size (bits)** & **Keywords** & **Rounds** \\ \hline
32 & 64 & 16 & 4 & 22 \\ \hline
48 & 72, 96 & 24 & 3, 4 & 22, 23 \\ \hline
64 & 96, 128 & 32 & 3, 4 & 26, 27 \\ \hline
96 & 96, 144 & 48 & 2, 3 & 28, 29 \\ \hline
128 & 128, 192, 256 & 64 & 2, 3, 4 & 32, 33, 34 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of SPECK.  
A key schedule gets processed in order to generate a round key \(RK\), which will be used in each round. The initial key words are \(K=\{K[0],I[0],\ldots,I[m-2]\}\), and the following key schedule is performed for \(0\leq i<r\), and the new key value \(RK=\{K[0],K[1],\ldots,K[r-1]\}\) is used as the round key. \(\alpha\) and \(\beta\) are 7 and 2 when the size of the block is 32-bit, respectively. For the rest of the block size, \(\alpha\) and \(\beta\) are 8 and 3, respectively:  
\[\begin{array}{c}l[i+m-1]=(K[i]\boxplus\mathrm{ROR}_{\alpha}(I[i]))\oplus i \\ K[i+1]=\mathrm{ROL}_{\beta}(K[i])\oplus l[i+m-1]\end{array} \tag{15}\]  
During the round function of SPECK with the block size of \(2n\), for \(0\leq i<r\), a \(2n\)-bit ciphertext word is generated using an \(n\)-bit round key for a \(2n\)-bit (\(X[0]\), \(X[1]\)) input word:  
\[\begin{array}{c}X[1]=(\mathrm{ROR}_{\alpha}(X[1])\boxplus X[0])\oplus RK[i ]\\ X[0]=\mathrm{ROL}_{\beta}(X[0])\oplus X[1]\end{array} \tag{16}\]  
Key schedule and encryption of SPECK in case of \(m=2\) are shown in Figure 6.
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
## 3 Results
### Parameter Analysis for \(s\)  
The parameter \(s\) describes the iteration stepsize for processing the subcubes of LRMR's local image restoration  
PSNR. The computational times in MATLAB are shown in Figure 9. The computational times are smaller for larger stepsizes \(s\). However, the CPU times decrease significantly for stepsizes larger than 4. The computational tests were computed on the Indian Pines data set.  
In the following, we present the computational times in seconds in MATLAB and Python in Table 2. Also, the gradient values for the computational times in MATLAB \(\
abla t_{Matlab}\) are presented. The result is that a balance between smaller stepsizes that produce better PSNRs and larger stepsizes that are better in terms of computational time and effort has to be achieved. However, note that the difference in PSNR values is not significant, as Figure 8 shows. Figure 9 and the gradient values for the CPU times in Table 2 show clearly that a stepsize \(s\geq 4\) provides significant improvement regarding computational times.  
Figure 8: PSNR plot for different values of \(s\).  
Figure 9: CPU time plot for different values of \(s\).  
As for the rank value \(r\), the computational effort is significantly better in MATLAB than in Python.
# Fungi-Templated Silver Nanoparticle Composite: Synthesis, Characterization, and Its Applications
###### Abstract  
The self-assembly of nanoparticles on living bio-templates is a promising synthetic methodology adopted for synthesizing nano/microstructures with high efficiency. Therefore, the method of bio-templating offers various advantages in controlling the geometries of nano/microstructures, thereby increasing the efficiency of the synthesized material towards various functional applications. Herein, we utilized a filamentous fungus (_Sclerotium rolfsi_) as a soft bio-template to generate silver nanoparticle (AgNP) microtubules adhering to the fungal hyphae. The resulting composite combines the unique properties of silver nanoparticles with the biological activity of the fungi. The 3D fungal hyphae-silver nanoparticle (FH-AgNP) composite was characterized using SEM, elemental analysis, and the X-ray diffraction technique. Additionally, to highlight the functional application of the synthesized composite, dye degradation studies of methylene blue under visible light was effectuated, and a percentage degradation of 67.86% was obtained within 60 min, which highlights the potent catalytic activity of FH-AgNPs in dye degradation. Further, the antibacterial study of the composite was carried out against the bacterium _Escherichia coli_, and it was found that 200 \(\mathrm{\SIUnitSymbolMicro}\) of the composite exhibited maximum antibacterial properties against Gram positive (_Staphylococcus aureus_) and Gram negative (_Escherichia coli_) bacteria. Overall, fungi-templated silver nanoparticle composites are a promising area of research due to their combination of biological activity and unique physical and chemical properties.  
bio-templates; microorganisms; _Sclerotium rolfsi_; methylene blue; _Escherichia coli_2023132158.
## 3 Voltage Transfer Gain Calculation
### Voltage Transfer Gain in CCM  
The proposed converter can operate in CCM when the converter supplies a high load current. The volt-second balance equation in a DC-DC converter is a fundamental principle based on the conservation of energy for an inductor over a switching cycle. It is expressed as in Equation (7). This equation ensures that the total energy transferred during one switching cycle is conserved, balancing the volt-seconds on the input and output sides of the converter. Therefore, the voltage gain of the proposed converter is derived in CCM, as shown in the equations below.  
\[\frac{1}{Ts}(\int_{0}^{Ts}(V)dt=0 \tag{7}\]  
\[\frac{1}{Ts}(\int_{0}^{DTs}(2 Vg)dt+\int_{D}^{Ts}(Vs+Vc_{1}-Vc_{2})dt=0 \tag{8}\]  
\[\frac{1}{Ts}(\int_{0}^{DTs}(Vc_{2}+Vc_{3})dt+\int_{0}^{DTs}(Vc_{4})dt+\int_{D}^ {Ts}(Vc_{2}+Vc_{3}-V_{o})dt=0 \tag{9}\]  
\[iL_{1}=iL_{2}=\frac{VgDTs}{L_{2}}(L_{2}=L_{1}) \tag{10}\]  
\[iL_{3}=iL_{4}=\frac{4 VgDTs}{(1-D)L_{3}}(L_{3}=L_{4}) \tag{11}\]  
\[Vc_{1}=Vg \tag{12}\]  
\[Vc_{2}=\frac{2 Vg}{(1-D)}=Vc_{3} \tag{13}\]  
\[V_{o}=\frac{(D+1)(Vc_{2}+Vc_{3})}{(1-D)} \tag{14}\]  
\[Mdc(CCM)=\frac{V_{o}}{Vg}=\frac{4(1+D)}{(1-D)^{2}} \tag{15}\]  
Equations (1) and (4) form the foundation, with the application of volt-second balance to inductors L\({}_{1}\), L\({}_{2}\), L\({}_{3}\), and L\({}_{4}\) leading to the derivation of Equations (8) and (9). The solutions to these equations are presented in (13) and (14). The peak current of the inductors is expressed by Equations (10) and (11) for each of the four inductors. Equation (12) describes voltage across C\({}_{1}\), which is equivalent to the input voltage source. Equation (15) presents the voltage gain equation (Mdc) of the proposed converter in CCM. It is worth highlighting that, as evident from Equation (15), the proposed converter demonstrates superior voltage gain compared to previous converters.
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 2 The System Model
### Remote State Estimation
1:while\(\Delta>\varepsilon\)or \(t\leq t_{\max}\)do
2: Calculate \(P_{i,g}^{t}\) according to formulas (5)-(9)
3: Update \(\Delta=\mathbf{P}_{n}^{k|k}-\mathbf{P}_{n}^{k|k-1}\)
4: Update \(k=k+1\)
5:endwhile
6:Obtain \(\mathbf{P}_{n}^{k}=\mathbf{\bar{P}}_{n}^{k|k}\)
7:for\(k=1\) to \(K\)do
8:for\(n=1\) to \(N\)do
9:for\(g=1\) to \(G\)do
10: Set \(b_{n,g}^{k}\in\{0,1\}\)
11:if\(b_{n,g}^{k}=1\)then
12:\(\mathbf{p}_{n}^{k}=\mathbf{p}_{n}^{k|k}\)
13:else
14:\(\mathbf{P}_{n}^{k}=\mathbf{A}_{n}\Big{(}\mathbf{P}_{n}^{k-1}\Big{)}(\mathbf{A} _{n})^{\prime}+\mathbf{Q}_{n}\)
15:endif
16:endfor
17:endfor
18:endfor
```  
**Algorithm 1** Iterative Algorithm Based on the KF Method. Based on the well-known properties of the standard KF (see Lemma 2.3 in [17]), it follows that \(u_{n}\left(\mathbf{P}_{n}^{k-1}\right)\geq\bar{\mathbf{P}}_{n}\), where \(\bar{\mathbf{P}}_{n}\) is a fixed constant. Consequently, by applying the natural logarithm function to (19), the expression can be expressed as  
\[D_{n}^{k}=\ln(J_{n}^{k})\approx\mathrm{Tr}\Big{\{}-\gamma_{n}^{k}+\alpha\ln \Big{[}u_{n}\left(\mathbf{P}_{n}^{k-1}\right)-\bar{\mathbf{P}}_{n}\Big{]}\Big{\}}. \tag{21}\]  
Therefore, Problem 1 can be transformed into  
\[\text{Problem 2:}\min_{\bar{\mathbf{\theta}}_{n}^{k},\eta_{n}^{k},\hat{\mathbf{ \lambda}}_{n,g}^{k}}\frac{1}{K}\sum_{k=1}^{K}\sum_{n=1}^{N}\left(D_{n}^{k} \right)\text{, s.t. (\ref{eq:1}), (\ref{eq:1}), (\ref{eq:1}), (\ref{eq:1}), (\ref{eq:1}), (\ref{eq:1}).} \tag{22}\]
## 3 Description of the Maritime Data Transfer Protocol
\begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline \hline
**Name of the Proposal** & **Disadvantages** & **Action Taken in MDTP to Deal with the Disadvantage** \\ \hline Deadline-Constrained 802.11 MAC & Works at a lower level (physical and network access layers rather than middleware or data-based ones). & MDTP is used at the data level for data-based information transfers. \\ \hline Constrained Application Protocol & Conceived for the application layer rather than a level below. & Used at the data level (as PDUs used by a middleware solution) rather than one level above (application) or levels below (transport, network). \\ \hline Advanced Message Queuing Protocol & Conceived for reliable transmission mediums rather than an underwater channel. Adding security can be challenging. & Designed for underwater transmissions (the ones deemed as more difficult) from scratch. Security is also offered as an option. \\ \hline MACA-based power control & Focused on bit transmission, rather than data level with higher level information. & Used at the data level (as PDUs used by a middleware solution) rather than one level above (application) or levels below (transport, network). \\ \hline Power-efficient routing protocol & It is not a suitable solution for transmissions at the data level. & Used at the data level (as PDUs used by a middleware solution) rather than one level above (application) or levels below (transport, network). \\ \hline Message Queue Telemetry Transport & This proposal has not been conceived for underwater environments. & Designed for underwater transmissions (constrains and unreliability of the transmission medium are taken into account). \\ \hline MQTT-SN & It has been developed for sensor networks instead of underwater environments. & Designed for underwater transmissions (constrains and unreliability of the transmission medium are taken into account). \\ \hline Embedded binary HTTP & Solution conceived for the application layer rather than lower ones. Power demanded may be too much for constrained devices. It relies on a layered protocol architecture likely not to be present in an underwater environment. & MDPT can be used on top of regular IP networks or separately over underwater, acoustic-based deployments. \\ \hline Extensible Messaging and Presence Protocol & Lack of Quality of Service capabilities. XML might be troublesome for underwater data transmissions. & Non-verbose format is used for data transmissions. QoS is guaranteed by its inclusion of MDTP in a DDS development. \\ \hline Lightweight M2M & It is built on top of CoAP, so it is unsuitable for underwater data transmissions. & Designed for underwater transmissions (constrains and unreliability of the transmission medium are taken into account). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Actions taken by MDTP.