esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 3 Proposed Methodology
### Network Building and Nodes Positioning
The fitness value for the EECP-MFO algorithm is calculated using the following Equation (1):  
\[\text{Fitness Function}=\frac{\text{W1}\times\text{Residual}_{\text{energy}}}{ \left(\text{W2}\times\text{Avg}_{\text{Distance}}\right)\text{W3}\times\text{ Delta}_{\text{Difference}}} \tag{1}\]  
\[\text{Residual}_{\text{energy}}=\text{Initial energy}-\text{Consumed energy} \tag{2}\]  
\(\text{Residual}_{\text{energy}}\) represents the FANETS nodes' remaining energy calculated by using Equation (2), \(\text{Avg}_{\text{Distance}}\) is the average distance between nearby nodes, and Delta_Difference represents the load balancing factor (LBF). Clusters with equal numbers of members are only possible if everything goes according to plan. However, it is challenging to achieve in a real-world environment since sensor nodes move around and modify their properties. The load balancing factor is calculated using the delta difference. Weights for energy, average distance, and delta difference are represented by the variables W1, W2 and W3. In an ideal world, all CHs would have an equal number of nodes. However, this is not realistic in real-world settings since nodes change their position regularly and change their neighborhood association degree. When a node's degree of the neighborhood varies from the ideal degree, this is called the delta difference. The exemplary nodes of SNs from their neighbors are calculated using the following formula:  
\[\text{Delta\_Difference}=\text{ABS}(\text{Ideal\_degree}-\text{Node\_degree}) \tag{3}\]  
Suppose the selection criteria for CH are static, and a single parameter has the potential to bias the fitness function. In that case, it is possible to make an incorrect choice of CH. EECP-MFO assigns weight to parameters dynamically depending on the circumstance to mitigate the skewing problem and reduce the negative influence on fitness values due to the skewing problem. First, each parameter value was normalized between 0 and 10. According to Equation (3), the negative impact of each parameter variation is determined for each parameter deviation. \[\text{dev(i)}=\text{ABS}(\text{mean}-\text{parameters(i)}) \tag{4}\]  
To account for deviation from the mean, penalized outlier parameters are employed in Equation (3), and these are used to derive updated values for parameters. Another Equation (5) is used to penalize the outlier penalty, and it has the following formula:  
\[\text{w(i)}=1/\text{dev(i)} \tag{5}\]  
The sum of all weights is equal to \"1.\" Equation may be used to compute the fitness of each node for all Sensor nodes using Equation (1).
## 2 Named Data Networking: Nuts and Bolts
### Security Principles in NDN
#### 2.1.2 NDN Certificates  
To ensure secure communication in NDN, data producers need to possess at least one cryptographic key pair. Producers use their private keys to sign _Data_ packets, and consumers verify them using public keys. Each public key is encoded in an X509 format digital  
Figure 1: Example of a _Data_ packet and its signing certificate.  
certificate signed by an issuer [15]. The NDN certificate's name follows a specific naming convention: /<IdentityName>/KEY/<KeyId>/<IssuerId>/<Version>. IdentityName represents the namespace in which the key can operate, followed by the keyword KEY. The KeyId identifies an instance of the public key. Its value can be an 8-byte-long random value, an SHA-256 digest of the public key, a timestamp, or a numerical identifier. The IssuerId part identifies the issuer of the certificate. Its value is identical to the KeyId. Finally, the version of the certificate is included. In addition to its name, a certificate _Data_ packet contains other fields: the FreshnessPeriod and the Content, which embeds the bits of the DER-encoded public key. Like any NDN Data packet, certificates contain a signature field, which is the signature of the issuer.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 6 Simulation Results
### Simulation Results for MMCDR
When the DRA scheme is adopted, the available resources of each system are limited, which leads to the lower CDRs of the satellite terminals and the terrestrial systems. By allocating an appropriate number of time slots to the satellite terminals and the terrestrial BSs, the proposed algorithms can keep all the CDRs within a desirable range, which ensures fairness among all the satellite terminals and the terrestrial systems. Compared with the optimal algorithm, the greedy algorithm results in more satellite terminals with high CDR. This is because the greedy algorithm continuously allocates each time slot to the candidate satellite terminals until the candidate set is empty or \(N_{b}\) satellite terminals have been allocated. Figure 10: CDF of the CDR of the satellite terminal. Figure 9: Minimum CDR of the terrestrial systems with varying traffic demand of the satellite system.
## 6 Performance Evaluation
### Simulation Model
#### 6.1.4 The Simulation  
The parameter for our simulation will be three variables: period range (T), number of queries (Q), and the number of nodes (N). Given a tuple of (T, N, Q), Q queries whose periods are uniformlydistributed from 1 up to T are generated and executed for 10,000 time units. We will analyze the sensitivity of the AEC, EER, network lifetime and NLR to (1) the number of queries and (2) the period range.
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
## 2 Hierarchical Visual Place Recognition Based on Semantic-Aggregation
### Fine Matching
#### 2.2.1 Semantic Edges Extraction and Description  
Given an image, we can obtain its semantic segmentation through coarse matching. We firstly detect and extract its edges based on Canny, outputting a list of semantic edges and corresponding semantic labels. Figure 4 shows the semantic segmentation and its semantic edges.  
There are many existing methods to describe edges. Among the existing edge descriptors, we prefer the wavelet descriptor [39]. Wavelet transform can generate a unique representation for a signal. More importantly, the multi-scale decomposition of wavelet descriptors makes the edge descriptors more compact and better discriminative.  
For the semantic edges extracted above, we subsampled them and collected \(P\) pixels. Their \((x,y)\) locations in the image are connected into a two-dimensional vector to outputting an original sequence. Then, we separately computed the discrete Harr-wavelet transform  
Figure 3: Illustration of fine matching process. Given an image (**a**), we first get its semantic segmentation (**b**) through coarse matching. Then, we extract its semantic edges, and describe these semantic edges (**c**) with wavelet transform. After that, we associate the query semantic edge descriptor and the Candidates semantic edge descriptors with semantic labels (**d**). Finally, we match them with cosine distance to find the best match (**e**).  
over each row and column and normalized them by L2-normalization, outputting a semantic wavelet descriptor, which has translation, scaling, and rotation invariance.
## 6 Security and Efficiency Analysis
### Efficiency Analysis
#### 6.2.1 Latency  
The following procedure is followed to obtain the latency values. After the initialization and registration processes, message sending is started. The time counter is started in the first message-sending process of the nodes and the time counter is stopped at the 100 th message sending. Then, the number 100 is divided by the time difference obtained. This process is repeated 5 times, the average value is found and the latency value is obtained. The results are given in Table 2.  
Since the solutions of the proposed protocol are added to the existing 802.15.4 protocol, there will inevitably be a delay. While WiSeN nodes spend 5.16 ms for a message under normal conditions over 802.15.4 protocol, this value increases to 6.27 ms when the proposed protocol is implemented. When the results are normalized and the traditional situation is accepted as 1.00, the proposed protocol becomes 1.21.
# HSVNet: Reconstructing HDR Image from a Single Exposure LDR Image with CNN
###### Abstract  
Most photographs are low dynamic range (LDR) images that might not perfectly describe the scene as perceived by humans due to the difference in dynamic ranges between photography and natural scenes. High dynamic range (HDR) images have been used widely to depict the natural scene as accurately as possible. Even though HDR images can be generated by an exposure bracketing method or HDR-supported cameras, most photos are still taken as LDR due to annoyance. In this paper, we propose a method that can produce an HDR image from a single arbitrary exposure LDR image. The proposed method, _HSVNet_, is a deep learning architecture using a Convolutional Neural Networks (CNN) based U-net. Our model uses the HSV color space that enables the network to identify saturated regions and adaptively focus on crucial components. We generated a paired LDR-HDR image dataset of diverse scenes including under/oversaturated regions for training and testing. We also show the effectiveness of our method through experiments, compared to existing methods.  
high dynamic range; low dynamic range; HSV color space; convolutional neural networks; U-net; supervised learning +
Footnote †: journal: Applied
# Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels
## 3 Experimental Results and Analysis
### Effects of Different Panel Design Parameters  
#### 3.3.1 Effect of Nailing Angle  
Specimens KWG30070235240 and KWG33080235240 were used as comparative test specimens to explore the effect of the nailing angle, as shown in Figure 9a,b. From analysis of the failure mode, the NCLT panels having \(0^{\circ}\) nailing angle incurred more serious damage than that of NCLT panels  
Figure 8: Full view of specimen deformation.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Specimen Label**} & \multicolumn{3}{c}{**Maximum Mid-Span Displacement/mm**} & \multicolumn{3}{c}{**Bearing Capacity/kN**} \\ \cline{2-7}  & **No. 3 Measuring Point** & **No. 4 Measuring Point** & **Average Value** & **F** & \(F_{max}\) & \(F/F_{max}\) \\ \hline KWL30070235480 & 126.44 & 121.74 & 124.09 & 44.9 & 52.0 & 0.85 \\ KWG30070235240 & 106.48 & 100.61 & 103.55 & 39.0 & 45.8 & 0.85 \\ KWG33080235240 & 124.58 & 129.67 & 127.13 & 49.9 & 54.4 & 0.92 \\ KWS30070235240 & 104.22 & 105.95 & 105.09 & 37.4 & 43.3 & 0.86 \\ KWL30070235240 & 133.54 & 127.84 & 130.69 & 52.7 & 55.2 & 0.95 \\ KWL50070235480 & 133.24 & 131.70 & 132.47 & 83.9 & 85.7 & 0.98 \\ \hline \hline \end{tabular} \(F\) is the theoretical bearing capacity; \(F_{max}\) is the maximum bearing capacity from the experiments.  
\end{table}
Table 3: Load test results.  
with 30\({}^{\circ}\) nailing angle. The 30\({}^{\circ}\) nailing could better bite the vertically and horizontally textured solid wood panels, which limited cocking of the wood slabs during the progress of damage. It also limited the development of rupture cracks along the slanting direction, maximizing structural integrity of the NCLT panels. From Figure 9a,b, the ultimate bending moment capacity increased by 18.8% when testing specimen KWG33080235240. In addition, the maximum displacement at mid-span increased by 22.8%, and the ultimate bearing capacity and mid-span displacement were significantly improved, indicating that 30\({}^{\circ}\) nailing could indeed improve the bending resistance of NCLT panels. The slanted wood at the bottom tension zone was deformed faster than that of the 0\({}^{\circ}\) NCLT panels when deploying 30\({}^{\circ}\) nailing, and the difference of strain in the pure bending and bending-shear zone increased with loading. This caused the wood in the bottom tension zone to deform at a faster rate, which was detrimental to the wood material.
## 2 Proposed Scheme and RF Transmission Algorithm
### Chaotic Encryption and n-QAM  
Algorithm 1 performs the chaotic encryption by executing an additive combination of the biomedical signal (\(ECG\)) with the chaotic signal (\(cS\)) from the master system (13) [38; 39]. The purpose of this additive combination is to mask the original ECG data by allowing the chaotic signal to dominate, making the original signal unrecognizable and thus encrypted. In this stage, the encryption process itself does not add complexity; it arises during the modulation stage.  
```
1:Biomedical signal \(ECG\), Chaotic signal \(cS\), Modulation order \(M\)
2:Modulated signal \(mS\)
3:procedurePreprocess_Encrypt_Signal
4:def\(M=n\)-QAM
5:\(eS\gets ECG+cS\)\(\triangleright\) ECG signal combined with chaotic signal
6:\(sN\leftarrow[\;\;]\)\(\triangleright\) Initialize the normalized signal array according to the n-QAM order
7:for each \(eS_{i}\) in \(eS\)do
8:\(sN\leftarrow\frac{eS_{i}-min(eS)}{max(eS)-min(eS)}\)
9:endfor
10:return sN
11:\(sD\leftarrow[\;\;]\)\(\triangleright\) Initialize the discretized signal array
12:for each \(sN_{i}\) in \(sN\)do
13:\(sD\leftarrow\) floor\((sN_{i}\cdot(M-1)^{2})\)\(\triangleright\) Signal rounding and conversion to complex signal
14:endfor
15:return\(sD\)
16:ECG_ds \(\leftarrow\) {real(sD), imag(sD)}
17:\(mS\leftarrow\) n-QAM\((ECG\_ds,M)\)\(\triangleright\) Modulation of the encrypted signal based on the n-QAM order
18:return\(mS\)
19:endprocedure
```  
**Algorithm 1** Modulation and Encryption of ECG Signal  
Before modulation, each value of the encrypted signal is normalized by subtracting its minimum value and dividing by the difference between the maximum and minimum values, yielding the normalized signal (\(sN\)). The proposed Algorithm 1 continues determining the discrete value for each normalized value that falls within the range of values of the modulation order \(M\). The n-QAM technique then modulates the signal using two channels: I (in-phase) and Q (quadrature). The \"n\" represents the number of symbols corresponding to specific amplitude and phase combinations. The process begins with digitizing the normalized signal (\(sN\)) into a serial bit-stream, which is then split into phase shift and amplitude change stages. This creates a new signal (\(sD\)) with a 2.45 GHz carrier frequency. The transmitted signal is complex due to these variations, as each transmitted symbol is represented by a vector sent by RF and is graphed as an n-QAM constellation; this is depicted in the complex variable \(ECG\_ds\). The final output of the proposed algorithm is a complex modulated signal (\(mS\)) prepared for secure transmission.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 2 Literature Review  
A search was carried out via Google Scholar and Scopus using the keywords \"data fusion\" and \"agriculture\". Almost 400 articles were originally selected, but this number was reduced to 119 after we removed low quality documents or documents outside of the scope of this work. From this total, 50 explore proximally collected data, 45 explore satellite data, 8 explore aerial (UAV) data, and 16 use data collected at multiple scales (Figure 2). Each scale is treated separately in this section, as they have some peculiarities that require more focused descriptions and analyses. To improve the legibility of the tables containing the full list of references, many acronyms are used, all of which are defined in Table 1.  
Figure 1: Categorization of data fusion approaches adopted in this work. Figure 2: Number of references selected in this review. To better organize the literature presented in this section and the subsequent discussion, both the data fusion techniques and the data being fused were categorized in a few more general classes (Table 2).
## 3 Test Results and Discussion  
### Ultimate Load-Bearing Capacity  
The ultimate load is defined as the maximum load achieved by the beam. The loads being supported by concrete beams that were reinforced with GFRP bars markedly exceeded those reinforced with GFRP and steel bars. The concrete beams that were reinforced with GFRP showcased a superior load-bearing capacity compared to those that were reinforced with steel bars. Despite steel-reinforced concrete beams enduring more load before the onset of initial cracks, they ultimately failed sooner. GFRP-reinforced concrete beams manifested the early initiation of cracks when contrasted with steel-reinforced concrete beams but exhibited a higher ultimate load-bearing capacity, trailing only behind GFRP-reinforced concrete beams. The ultimate load-carrying capacity of the beam specimens before failure is depicted in Figure 3 below.  
Figure 3: Ultimate load-bearing capacity of beam specimens.  
Figure 2: Sample specimen under initial and final loading states: (**a**) test sample under initial applied load; (**b**) test sample post ultimate load failure.  
Compared to concrete beams reinforced with steel bars, the average ultimate load-bearing capacity of the concrete beams reinforced with GFRP bars and BFRP bars increased by 76.5% and 35.8%, respectively. This increase can be attributed to the higher tensile strength of FRP bars compared to steel bars. One reason for the significant variation in the ultimate load-bearing capacity of beams reinforced with FRPs is the difference in the cross-sectional area of the reinforcements.
# Three-Dimensional High-Energy Electron Radiography Method for Static Mesoscale Samples Diagnostics
## 2 The TDHEER Method and General Layout  
HEER benefits from the magnetic lens optics, which improves the spatial resolution dramatically compared to projection radiography. The principle of HEER is shown in Figure 1: The electron beams pass through the target and then are imaged point to point from the target plane to the image plane by the imaging lens. There are two primary requirements of the imaging lens system. First, the lens must provide point-to-point focus from the object to the image. Second, it must form a Fourier plane, where particles are radially sorted by the magnitude of the scattering within the object. With this correlation, particles scattered to large angles by multiple Coulomb scattering can be removed through collimation at the Fourier plane. The remaining parameters of a particular imaging lens system design are determined by the radiographic applications. The beam energy must be high enough to penetrate the areal density of the object to be radiographed, and the aperture of the lens system must be chosen to provide sufficient angular acceptance throughout the required field of view. An additional strong design requirement is the resolution of the radiography system. This resolution is typically dominated by chromatic aberrations due to the energy spread of the injected beam in combination with the spread of energy loss through the object due to areal density variations of the object.  
The TDHEER is combined with HEER and a tomography reconstruction method. Its principle is also shown in Figure 1. The sample is placed on a rotatable platform, which can be rotated around the \(y\)-axis (\(\pm\)90 degrees) with a step of 1 degree and imaged with HEER at the detectors for each rotation angle. Afterward, a total of 180 images of the sample are collected with different rotation angles. Then we use the three-dimensional reconstruction algorithms [11] to get the sample's three-dimensional slice images of each axis. From these slices, we can get the detailed internal structures of the sample. The method of TDHEER is studied and confirmed by start-to-end simulation. The electron beam parameters used in the simulation are the following: Beam energy 50 MeV, energy spread 0.1%, normalized emittance 1.0 mm mrad, bunch charge 1 nC, bunch length rms 10 ps, and parallel beam on the samples, as shown in Figure 1.  
Figure 1: The sketch map of high-energy electron radiography (HEER) and three-dimensional high-energy electron radiography (TDHEER) with a parallel beam.
# Use of Total Least Squares Adjustment in Geodetic Applications
## 2 Solution of the Predetermined Model
### Method of Taking into Account the Presence of Errors in Variables (EIV)
#### 2.2.1 Computational Algorithm  
This algorithm is an iterative procedure according to [7]:  
Step 1: Set the initial value for the unknowns.  
The initial values for the unknowns are taken from the common weighted LS solution (WLS) with the measurement precision \(\mathbf{P}=\mathbf{\Sigma}_{\mathrm{b}}^{-1}\) (this refers to observations that are treated as observations in ordinary LS and this is all explained through the regression line in the following Section 3):  
\[\hat{\mathbf{x}}^{0}=(\mathbf{A}^{\mathrm{T}}\mathbf{P}\mathbf{A})^{-1} \mathbf{A}^{\mathrm{T}}\mathbf{P}\mathbf{b}. \tag{29}\]  
Step 2: Set \(i=0\) (\(i\)--iteration).  
\[\mathbf{v}^{i}=\mathbf{A}\hat{\mathbf{x}}^{i}-\mathbf{b}, \tag{30}\]  
\[\mathbf{Q}_{\mathrm{b}}^{-i}=\mathbf{Q}_{\mathrm{b}}+((\hat{\mathbf{x}}^{i})^ {\mathrm{T}}\bigotimes\mathbf{I}_{n})\mathbf{Q}_{\mathrm{A}}(\hat{\mathbf{x}}^ {i}\bigotimes\mathbf{I}_{n}), \tag{31}\]  
where matrix \(\mathbf{Q}_{\mathrm{A}}\) depends on the precision of measurements which are included in the adjustment and can be computed as [6; 7]:  
\[\mathbf{Q}_{\mathrm{A}}=\sum_{i=1}^{u}\sum_{j=1}^{u}\mathbf{c}_{i}\mathbf{c}_{ j}^{\mathrm{T}}\bigotimes\mathbf{Q}_{ij}, \tag{32}\]  
with \(\mathbf{c}_{i}\) and \(\mathbf{c}_{j}\) as unit vectors with \(1\) in the position that belongs to the variable and \(0\) otherwise. Matrix \(\mathbf{Q}_{ij}\) represents a cofactor matrix of measurements that appear alongside unknowns with the dimensions of \(n\times n\). We compute vector \(\mathbf{e}_{\mathrm{A}}^{i}\), which belongs to matrix \(\mathbf{E}_{\mathrm{A}}^{i}\) in the \(i\)-th iteration:  
\[\mathbf{e}_{\mathrm{A}}^{i}=-\mathbf{Q}_{\mathrm{A}}(\hat{\mathbf{x}}^{i} \bigotimes\mathbf{I}_{n})(\mathbf{Q}_{\mathrm{b}}^{-i})^{-1}\mathbf{v  
From Equations (39) and (40), the cofactor matrices are:  
\[\mathbf{Q}_{\hat{b}}=\mathbf{P}_{\hat{\Lambda}}\mathbf{Q}_{\hat{b}}^{\sim}= \widetilde{\mathbf{A}}\mathbf{Q}_{\hat{\kappa}}\widetilde{\mathbf{A}}^{\mathsf{ T}}, \tag{41}\]  
\[\mathbf{Q}_{\hat{v}}=\big{(}\mathbf{I}-\mathbf{P}_{\hat{\Lambda}}\big{)}\mathbf{ Q}_{\hat{b}}^{\sim}=\mathbf{Q}_{\hat{b}}^{\sim}-\mathbf{Q}_{\hat{b}}. \tag{42}\]  
The a posteriori reference variance is:  
\[\hat{\sigma}_{0}^{2}=\frac{\hat{\mathbf{v}}^{\mathsf{T}}\mathbf{Q}_{\hat{b}}^{ \sim}{}^{-1}\hat{v}}{n-u}. \tag{43}\]
# Error Resilience for Block Compressed Sensing with Multiple-Channel Transmission
###### Abstract  
Compressed sensing is well known for its superior compression performance, in existing schemes, in lossy compression. Conventional research aims to reach a larger compression ratio at the encoder, with acceptable quality reconstructed images at the decoder. This implies looking for compression performance with error-free transmission between the encoder and the decoder. Besides looking at compression performance, we applied block compressed sensing to digital images for robust transmission. For transmission over lossy channels, error propagation or data loss can be expected, and protection mechanisms for compressed sensing signals are required for guaranteed quality of the reconstructed images. We propose transmitting compressed sensing signals over multiple independent channels for robust transmission. By introducing correlations with multiple-description coding, which is an effective means for error resilient coding, errors induced in the lossy channels can effectively be alleviated. Simulation results presented the applicability and superiority of performance, depicting the effectiveness of protection of compressed sensing signals.  
Keywords:block compressed sensing; error resilience; reconstruction +
Footnote †: journal: Applied