esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### Encapsulation
#### 5.1.2 Single-Molecule Chemistry  
Nanoreactors are confinements of selected molecules such that there is some level of deliberate control over their reactions. Nanoreactors require the property of an impermeable encapsulation to be paramount in order to tailor environmental conditions for only specific reactions to occur and thus should be able to proficiently shield molecules from unfavorable reactants. The [PERSON] group demonstrated a DNA origami nanoreactor \"vault\" that could capture single enzymes within its cavity and could impose specific conditions on its locking mechanism [99] (Figure 6C) to further restrict reaction conditions. In doing so, the responsiveness of the enzyme to substrates can be reduced to desired situations in the interest of study or for inactivating unwanted pathways. Alternatively, enzymes can be spatially localized. This has been shown for 2D surfaces to measure the effect of distance separation on reactions and investigate biophysical mechanisms of target proteins [100; 101], but also within the cavity of a DNA origami reactor [102; 103; 104]. This localization is similar to how natural enzymatic pathways achieve rapid reaction rates. Such a system will allow scientists to compose and study specific pathways under highly specific concentrations and relative distances. DNA nanostructures can also aid in isolating single membrane proteins by simulating a small lipid bilayer within a constructed cavity.  
Figure 6: Applications of encapsulating DNA nanostructures. (**A**) A DNA nanorobot that selectively delivers thrombin to a tumor site to induce thrombosis of the tumor to destroy it (reprinted with permission from Springer Nature [97]); (**B**) A DNA nanopore with a locking mechanism that can restrict a flow of molecules through the channel (reprinted with permission from American Chemical Society [98]); (**C**) A DNA origami nanovault functions as a nanoreactor by capturing a single enzyme within its cavity and controlling its reactions via a latch mechanism (reprinted with permission from Springer Nature [99]); (**D**) A DNA origami mold that is used for nanocasting of gold nanoparticles (reprinted with permission from AAAS [92]).  
Such studies are otherwise challenging due to the poor stability and activity of membrane proteins in any other environment than upon the cellular membrane [105], thus calling for strategies that can artificially simulate that environment.
## 2 Channel Model
### Transmission Loss  
The transmission loss is the reduction of acoustic intensity from a source to a destination [37]. When acoustic waves propagate underwater, their intensity drops. The transmission loss consists of spreading loss and absorption loss [38]. Transmission loss can be expressed as  
\[TL=-10 log\left(\frac{I(r,d)}{I_{o}}\right) \tag{1}\]  
\[TL=-20 log\left(\frac{P(r,d)}{P_{o}}\right)dB, \tag{2}\]  
where \(I_{o}\) is the intensity at a source, \(I\) is the intensity at a distance \(r\) and depth \(d\). The variable \(P\) and \(P_{o}\) are the acoustic pressure at the distance and source point, respectively. Figure 1 shows the transmission loss of acoustic waves.
## 4 The Non-Convex Approximate RPCA Algorithm
### Derivation of the Overall Formula for NCA-RPCA Algorithm
 \tag{28}\]Second step, with \(S\) and \(K\) fixed, update \(L\):  
\[\mathbf{L}^{(k+1)}=\underset{L}{\text{argmin}}\ \sum_{j}\bigtriangledown g(\sigma_{j}^{(k )})\sigma_{j}+\frac{\rho^{(k)}}{2}\bigg{\|}(\mathbf{M}-\mathbf{S}^{(k+1)}+\frac{K^{( \mathbf{k})}}{\rho^{(k)}})-\mathbf{L}\bigg{\|}_{F}^{2} \tag{29}\]  
Let \(\mathbf{Z}^{(k)}=\mathbf{M}-\mathbf{S}^{(k+1)}+\frac{\mathbf{k}^{(k)}}{\rho^{(k)}}\), \(\xi_{j}^{(k)}=\bigtriangledown g(\sigma_{j}^{(k)})/\rho^{(k)}\), then the above equation becomes  
\[\mathbf{L}^{(k+1)}=\underset{L}{\text{argmin}}\ \sum_{j}\xi_{j}^{(k)}\sigma_{j}+ \frac{1}{2}\Big{\|}\mathbf{Z}^{(k)}-\mathbf{L}\Big{\|}_{F}^{2} \tag{30}\]  
Performing the singular value decomposition on \(\mathbf{Z}^{(k)}\), we have \(\mathbf{Z}^{(k)}=\mathbf{U}\mathbf{\Delta}^{(k)}\mathbf{V}^{\text{T}}\), where the diagonal matrix of singular values is represented by \(\mathbf{\Delta}^{(k)}=diag(\delta_{1}^{(k)},\delta_{2}^{(k)},\cdots,\delta_{r}^{( k)})\). The jth singular value of \(\mathbf{Z}^{(k)}\) is denoted as \(\delta_{j}^{(k)}\), and the jth singular value of \(\mathbf{L}^{(k+1)}\) is  
\[\sigma_{j}^{(k+1)}=\underset{\sigma_{j}}{\text{argmin}}\ \frac{1}{2}(\sigma_{j}- \delta_{j}^{(k)})^{2}+\xi_{j}\sigma_{j} \tag{31}\]  
According to the singular value soft-thresholding operator [43], the optimal solution for \(\sigma_{j}^{(k+1)}\) is given by  
\[\sigma_{j}^{(k+1)}:=(\delta_{j}^{(k)}-\xi_{j})_{+} \tag{32}\]  
Therefore,  
\[\mathbf{L}^{(k+1)}=\mathbf{U}[diag(\mathbf{\Delta}^{(k)}-\xi)_{+}]\mathbf{V}^{\text{T}} \tag{33}\]  
where \(\xi_{j}\) is the column vector composed of \(\xi\), and \(\mathbf{U}\) and \(\mathbf{V}\) are matrices obtained from the singular value decomposition of \(Z^{(k)}\). Finally, update the Lagrange multiplier \(K\) and the penalty parameter \(\rho\). \[ \begin{array}{l}\mathbf{K}^{(k+1)}=\mathbf{K}^{(k)}+\rho^{(k)}(\mathbf{M}-\mathbf{L}^{(k+1) }-\mathbf{S}^{(k+1)})\\ \rho^{(k+1)}=\phi\rho^{(k)}\end{array}  \tag{34}\]  
If the convergence condition is satisfied in the \(k+1\) iteration, meaning that the difference between the observed matrix and the reconstruction of the low-rank and sparse matrices is smaller than a given threshold, then at this point, \(L\) and \(S\) are very close to the actual low-rank and sparse components. It can be considered that the decomposition results are relatively stable. Therefore, output \(\mathbf{L}^{(k+1)}\) and \(\mathbf{S}^{(k+1)}\), and the entire algorithm process concludes. Based on the algorithm procedure described above, create a flowchart. Assuming the input original data matrix is \(M\) and the outputs \(L\) and \(S\) are the low-rank and sparse components of \(M\), the flowchart of the NCA-RPCA algorithm is illustrated in Figure 7.
## 5 Factors That Influence the Effectiveness of In-Vehicle Messaging
### Characteristics of the Environment  
The external environment also plays a role in influencing driver's processing of information from an in-vehicle display and how attending to an-in-vehicle display may impact their performance in operating a highly automated vehicle. For example, environmental characteristics, such as lane width, quality of roadway markings, and the presence of trees or buildings near the road can influence driver performance in speed maintenance and maneuvering [110, 111, 112, 113]. Thus, depending on the location where a takeover is taking place, a driver's performance at the maneuvering/control interaction level can be negatively impacted by environmental features [110]. Specific structural features of the roadway such as lane width, road markings, and the presence of trees or buildings near the road all affect driver vehicle control, including speed [113]. Time of day has also been shown to be a significant factor in the rate and severity of automobile crashes with nighttime driving being more dangerous than daytime driving due to several factors such as fatigue [114, 115]. In nighttime driving, there is lower luminance which can affect visual processing of stimuli on the roadway and increase processing times [116]. Depending on the time of the day, the relevance of certain information also changes (e.g., lodging information may be more relevant in the evening). However, it remains unclear how these variations in road environment conditions can affect driver interaction with automation; specifically, the transfer of control between the automation and the human driver as well as interpretation of in-vehicle messaging.  
From an engineering standpoint, these environmental factors need to be considered when providing messaging that might result in a takeover scenario for the driver. In-vehicle messaging displays need to smartly respond to roadway configuration, the time of day and luminance levels to promote ease of information processing for the driver. Roadway structure may not be as easily addressed in terms of messaging content; for example, drivers should be informed of inadequate lane width markings for automated vehicle control. Such messaging needs to be timely and salient to ensure vehicle and driver safety.
## 2 System Model of MC-MDCSK-MCS  
### Generator of Fractional-Order Chaotic Signals  
The application of the fractional-order chaos system leads to high privacy in secure communication systems [26; 27; 28]. The fractional-order Chen chaos system is used for generating chaotic signals for the proposed MC-MDCSK-MCS system. The definition of the fractional-order Chen chaos system is given as follows [29]:  
\[D^{\alpha}\mathbf{X}=\left\{\begin{array}{l}35(x_{2}(t)-x_{1}(t))\\ -7x_{1}(t)-x_{1}(t)\cdot x_{3}(t)+28x_{2}(t)\\ x_{1}(t)\cdot x_{2}(t)-3x_{3}(t)\end{array}\right. \tag{1}\]  
where \(\mathbf{X}=[x_{1}(t)\;\;x_{2}(t)\;\;x_{3}(t)]^{T}\) is the state vector. \(\alpha\) is the number of fractional orders with \(0.83\leq\alpha<1\). The chaotic behavior of fractional-order Chen chaos system is presented in Figure 1 with \(\alpha=0.95\). The initial conditions are set as \(x_{10}=1,\;x_{20}=2,\;x_{30}=3\).
# Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks
## 2 Background and Motivation
A great challenge can be encountered when capturing a pivoted threat: samples from different but interlinked origins can lead to a large occurrence of outliers due to dissimilar measurements, sizes, and types of data, incrementing the chances of high dispersion, as some records could only be observed under the presence of well-configured sandboxed cryptojacking instance. As described in Section 3, there are two important gaps in the state-of-the-art regarding cryptojacking detection: 1. In the first instance, it has already been proven that traditional scopes are outdated and not sufficient, therefore SA and DA techniques can serve only as a basis for congregating sample-features 2. The source and selection of the features, either at the network or host level, may be merged to achieve a desired level of detection, but not to be considered separately. This raises an important research question:  
**Research question**: How to optimize web-based cryptojacking detection by a procedure that takes the best of network and host features and merges them? This work concentrates on a new form of cryptojacking feature representation technique, recently adopted for ML-oriented cybersecurity scenarios, called autencoding (AE) [25], which is a specific structure of neural network (NN) focused on compressively capturing the latent information from unstructured data, in virtue of dissimilaring the effects of noise, high dimensionality, and non-linearity. Then, the outputs are subjected to a deep dense neural network (DDNN) [26], a type of deep neural network, which has demonstrated nice effectiveness in the treatment of a vast number of malware-related scenarios. As a sign of a successful performance, the resulting model is compared with different methodologies presented in the state-of-the-art for cryptojacking detection.
## 3 Results and Discussion
### Overall Performance of Pilot-Scale CW Units
adsorbents (such as activated carbon or modified zeolite) in order to avoid toxicity effects on microbial communities [46]. Several issues that affect treatment efficiency and system sustainability, such as clogging, plant death, and media aging, may affect the long-term operation of CWs. Reduced hydraulic conductivity and flow short-circuiting are the results of clogging, a major problem caused by excessive organic matter accumulation, biofilm overgrowth, and fine particle deposition [36; 47]. Clogging can be mitigated by maintaining appropriate hydraulic loading rates, routinely checking flow patterns, and pretreating the leachate in an anaerobic tank or in an Imhoff settling tank [41; 48]. The treatment's effectiveness may also be lowered by plant death brought on by harsh weather, hazardous pollutants, or inadequate oxygen transmission [49]. A stable system performance can be guaranteed by choosing hardy plant species like _Typha latifolia_ and _Phragmites australis_ and by replanting the area on a regular basis.
## 3 Results and Discussion
### Performance Evaluation Results
#### 3.2.4 Wet Surface Adhesion  
The result of the wet surface adhesion tests showed that, as shown in Figure 10, samples A and B displayed adhesiveness for more than 60 s. In the case of sample A, a more stable performance was observed compared to samples B and C under conditions where contact with moisture lasted, but in the case of sample C, it was confirmed that the adhesion capacity could not be maintained for a long time due to the relatively low viscosity value.
## 4 Results and Discussion
### VOSK Monolingual ASR Toolkit
#### 4.1.1 Operational Workflow  
The core task in speech recognition involves converting speech into text. Upon submitting speech data for conversion, a request is transmitted to the API, which processes it. The VOSK server initiates speech transcription to derive text results, which are then relayed back to the API. The API formats the results appropriately before returning them to the user, delineating the operational workflow of the API.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 2 Related Work
### Ad Hoc Networking  
Ad hoc networks are primarily used in changing environments and can be used to address the limitations of infrastructure networks [18]. They are suitable for cases in which it is difficult to construct a wired network or can be used for a short period after the network is constructed. Ad hoc networks have the advantage of fast network configuration and low costs, because there is no restriction on host movement and there is no need for wired networks and base stations [19].  
The Optimized Link State Routing (OLSR) protocol is a routing method that maintains routing information for all destinations based on the link state and distance vector, calculates routes in advance, generates routing tables, and updates the routing tables after determined periods of time [20]. It is suitable for an ad hoc network with a large amount of location movement. However, it is not suitable for real-time communication because it requires a search of the entire network when a route is requested, and this may generate a large amount of control traffic. The Ad Hoc On-Demand Distance Vector (AODV) records the addresses and hops of the nodes passed by because the source node floods the packet to find a destination, floods it back to the neighboring nodes that received it, and then responds to the source node [21]. This routing protocol is a method that transmits data as a representative reactive routing protocol that sets a routing path only when the topology changes, and it does not set a routing path if data are not delivered. Ad Hoc Multicast Routing (AMRoute) and the Ad Hoc Multicast Routing Protocol Utilizing Increasing ID NumberS (AMRIS) are protocols in which the unique shortest path is determined from the source side to each destination and data are transmitted through it [22; 23]. The On-Demand Multicast Routing Protocol (ODMRP) and Core-Assisted Mesh Protocol (CAMP) are protocols in which data are transmitted through one or more paths [24; 25].  
The Mobile Ad Hoc Network (MANET) and Flying Ad Hoc Network (FANET) consider mobility, which is the most important point for UAV communication [26; 27]. MANET has the advantage that the network is decentralized and the devices are mobile. FANET is a protocol optimized to track the fast speeds of nodes and rapid network topology changes by using the concept of a routing protocol in MANET. The ad hoc network has a mobility feature because it uses mobile hosts, and this makes communication link instability worse.
## 2 System Description
### First Time Slot  
In the first time slot, \(S\) transmits wireless information to \(R\) by transmitting power \(P_{\text{S}}\) and \(R\) receives the following signals:  
\[y_{\text{GR}}=H_{\text{GR}}\sqrt{P_{S}}X_{S}+n_{\text{GR}}, \tag{1}\]  
where the noise \(n_{\text{GR}}\) satisfies the normal distribution \(N(0,N_{\text{W}})\), and \(H_{\text{GR}}\) is the wireless fading coefficient that satisfies the following Nakagami-m distribution.  
\[f(H_{\text{GR}};m_{\text{R}},\Omega_{\text{R}})=\frac{2}{\Gamma(m_{\text{R}})} (\frac{m_{\text{R}}}{\Omega_{\text{R}}})m_{\text{R}}H_{\text{GR}}^{2m_{\text{R }}-1}\exp(-\frac{m_{\text{R}}\big{|}H_{\text{GR}}\big{|}^{2}}{\Omega_{\text{R} }}), \tag{2}\]  
where \(m_{\text{R}}\geq 0.5\) is the Nakagami-m fading parameter, \(\Gamma(\text{x})\) is the gamma function, and \(\Omega_{\text{R}}=\text{E}(|H_{\text{GR}}|^{2})\) is the variance of the fading amplitude that is normalized to ensure that the fading does not change the average power of the received signal. Let \(\Omega_{\text{R}}=1\).  
According to the system model, let \(\Delta_{\text{GR}}=P_{S}/N_{\text{W}}\) denote the channel average SNR. Then, according to Equation (1), the instantaneous SNR \(\gamma_{\text{GR}}\) of the wireless channel \(R\) can be obtained as follows:  
\[\gamma_{\text{GR}}=H_{\text{GR}}^{2}\Delta_{\text{GR}}=\frac{P_{S}\big{|}H_{ \text{GR}}\big{|}^{2}}{N_{\text{W}}}. \tag{3}\]  
Let \(H_{2,\text{GI}}=\big{|}H_{\text{GI}}\big{|}^{2}\), \((\text{I}=\text{R},\text{D})\), \(H_{2,\text{GR}}\) satisfies the Gamma distribution and is defined as follows:  
\[G(H_{2,\text{GR}};\alpha_{\text{R}},\beta_{\text{R}})=\frac{(H_{2,\text{GR}})^ {\alpha_{\text{R}}-1}}{\beta_{\text{R}}^{\alpha_{\text{R}}}\Gamma(\alpha_{ \text{R}})}\exp(-\frac{H_{2,\text{GR}}}{\beta_{\text{R}}}), \tag{4}\]  
where the parameter relationship between the two distributions of Gamma and Nakagami-m satisfies \(\alpha_{\text{R}}=m_{\text{R}},\beta_{\text{R}}=\Omega_{\text{R}}/m_{\text{R}}\).  
Based on the properties of the Gamma function under the same average SNR, when \(\Delta_{\text{GR}}\) is a constant, \(\big{|}H_{\text{GR}}\big{|}^{2}\Delta_{\text{GR}}\sim G(\gamma_{\text{GR}};m_{ \text{R}},\Delta_{\text{GR}}\Omega_{\text{R}}/m_{\text{R}})\).
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 2 Materials and Methods
### Encoder with Residual Network  
HRS images are rich in data, and DL networks have the capability to capture more information and richer features from HRS image data. However, during training, deep networks often face challenges such as gradient explosion and gradient disappearance due to the increasing number of network layers, which can hinder effective training of the network and affect the segmentation effect. ResNet50 [26] effectively addresses these issues, allowing the construction of very deep networks. Resnet50 uses three convolutional layers of 1 \(\times\) 1, 3 \(\times\) 3, 1 \(\times\) 1, respectively, while introducing a branch to add with the convolutional layers and then outputs the final result. Therefore, this article uses ResNet50  
Figure 1: MAFANet structure.  
as the backbone for multi-level feature extraction, forming the encoding part. Resnet50 residual structure is shown in Figure 2.
# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 2 Implicit-Explicit Coupling-Enhancement-Based 3D Reconstruction
### Implicit Synthetic Co-Visibility-Cluster-Guided Dense 3D Reconstruction
#### 2.1.3 Dense Point Cloud Reconstruction of UAV Large-Scale Scenes  
In our algorithm, each co-visibility cluster corresponds to a series of multi-view depth images. However, the original depth maps from the input images may have their own missing depth information due to the use of the dense reconstruction [35] method for prediction. The depth information generated based on NeRF is dense. To address this, we refer to the depth maps of the new viewpoints to further supplement the missing information in the original image's depth maps. Ultimately, we generate complete depth maps for each corresponding image frame within a co-visibility cluster. Next, we perform depth optimization on all generated depth maps to ensure depth consistency for the purpose of merging information from multi-view depth images. For each pixel in an image, we use its depth information \(\lambda\) and camera parameters to project it back into three-dimensional space:  
\[X=\lambda R_{i}^{T}K_{i}^{-1}p+C_{i} \tag{5}\]  
where \(p\) is the pixel's homogeneous coordinate, and \(X\) is the three-dimensional point in the world coordinate system. Then, we further project \(X\) into the neighboring images \(N(i)\) of \(I_{i}\). Assuming \(N_{k}\) is the \(k\)th neighboring image in the set of adjacent depth images \(N(i)\), here we use \(d(X,N_{k})\) as the depth information of the image \(N_{k}\), and use \(\lambda(X,N_{k})\) to represent the reprojected depth value, as mentioned above. If \(\lambda(X,N_{k})\) is close enough to \(d(X,N_{k})\), we can determine that \(X\) is consistent in both \(I_{i}\) and \(N_{k}\), which means  
\[\frac{|d(X,N_{k})-\lambda(X,N_{k})|}{\lambda(X,N_{k})}<\tau_{2} \tag{6}\]  
where \(\tau_{2}\) is a threshold. If \(X\) is consistent in at least two neighboring images in the set \(N(i)\), it is considered a reliable scene point, and the depth value for the corresponding pixel \(p\) in \(I_{i}\) is retained. Otherwise, it is removed. Finally, all the depth maps are reprojected into a 3D form and merged into a single complete point cloud.
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 5 Experimental Evaluation  
To assess the effectiveness of our proposed model for clustering Arabic text, we implemented it to evaluate its accuracy and efficiency. The full source code of our implementation is publicly available to ensure the reproducibility of our results [56]. In this section, we address the following research questions:  
* RQ1: What is the effectiveness of GWO-optimized SOM in clustering Arabic text compared to other models?
* RQ2: How efficient are GWO-optimized SOM in clustering Arabic text compared to other models?
* RQ3: What is the impact of data representation techniques on the effectiveness and efficiency of clustering Arabic text?  
To answer these research questions, we carried out an empirical evaluation on two Arabic datasets, the MSA dataset [25] and NADA dataset [57], and compared our results with two other models for clustering Arabic text, K-Means and standard non-optimized SOM. Figure 3 shows an illustration of the experiment process we used in this paper. The experiment starts with collecting data sources for Arabic text. Then, each of these sources is preprocessed and represented using two different representation techniques. After that, the outcome of the representation is trained using three clustering techniques, K-Means, SOM, and GWO-optimized SOM (our model). The final step is to evaluate the clustering in terms of the accuracy, F1-score, precision, recall, and training time for each experiment. All the experiments are executed using a MacBook Pro with a 2.3 GHz Intel Core i5 processor and 8 GB 2133 MHz LPDDR3 RAM. The following subsections show the details of each of these steps.