esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
### Node-Level-Focused Energy Conservation Schemes
#### 4.1.2 Data-Driven Techniques
This allows for further energy savings by reducing unnecessary sampling activities during periods of stability [79]. Model-based active sampling aims to minimize the number of data samples by leveraging computational models. This approach involves the utilization of forecasting models to create an abstraction of the sensed phenomenon. Initially, the model is constructed using a set of sampled data, after which it is employed to predict future data points rather than continuously sampling in the field. By relying on these predictive models, the energy expended on data sensing and transmission can be conserved, leading to significant energy savings [80]. Data reduction schemes like data compression, data prediction, and edge computing are effective in LWSNs as they can greatly reduce the number of multi-hop communications. However, the gain of data aggregation in LWSNs is less effective compared to that of the traditional WSNs. This is because the linear topology prevents the creation of large clusters. Also, node redundancy in LWSNs is minimized by the linear topology. For energy-efficient data acquisition, hierarchical sensing and model-based active sampling have the same effect in LWSNs as in traditional WSNs, as they do not affect the number of multi-hop communications and are not affected by the linear structure of the network. The impact of adaptive sampling will be less effective in event-driven LWSN applications due to the linear topology, which does not benefit from spatio-temporal correlations.
## 3 Results and Discussion
### Reusability of MPHAC  
Reuse of catalysts is an important factor to take into consideration for their practical application. The reusability of the MPHAC was assessed through five adsorption-desorption cycles. Desorption cycle was carried out using 5 mL of acetonitrile/methanol (1/1, \(v\)/\(v\)), vibrated under 180 rpm for 2 h at room temperature. The adsorbent was subsequently filtered and dried overnight for subsequent use. Figure 8 shows the removal factor of 4-CP by the MPHAC/PS system used in five consecutive cycles under the defined optimum conditions (i.e., initial 4-CP concentration: 100 mg/L, MPHAC dose: 1250 mg/L, PS dose: 350 mg/L, with a solution pH of 5, and a contact reaction time of 60 min). As can be seen, the 4-CP removal factor was gradually decreased for each progressive number of cycles (i.e., 99.3%, 98.4%, 97.0%, 93.2% and 89.5%, for the cycle 1, 2, 3, 4 and 5, respectively). This decline of the 4-CP removal factor of the MPHAC/PS system is due to the reduction of both adsorption and degradation processes.  
As can be seen from Figure 8, the used catalyst exhibits good stability and high efficiency towards 4-CP removal even after five cycles. This indicates its high reusability and economic viability. The slight decrease of the 4-CP removal factor observed after five cycles (<10%) can be due to the blockage of the active sites of the catalyst by intermediate products in the solution (e.g., phenol, hydroquinone, benzoquinone, malonic acid, as will be detailed in the following section), therefore hindering the binding of 4-CP molecules to the surface of the catalyst and reducing the removal of 4-CP through the adsorption process [44]. In addition, as reported by [PERSON] et al. [46], the reduction of 4-CP removal efficiency can be attributed partly to the oxidation of divalent iron ions, which in turn reduces the production of sulfate radicals and the degradation of 4-CP.  
Figure 7: Effect of 4-CP concentration on 4-CP removal by the MPHAC/PS system (MPHAC dose: 1250 mg/L; PS dose: 350 mg/L; and a solution pH of 5).
# Dynamic Parameters Identification of Serial Robot Based on Dual Quaternion
## 3 Materials and Methods
### Identification Method of Dynamic Parameters of Linkages
\(\mathbf{p}_{n}^{I}\) is the representation of the position of the nth connected linkage coordinate system with respect to the \(i\)-th connected linkage coordinate system, \(m_{I}\) is the mass of the \(i\)-th connected linkage, \(\hat{\mathbf{v}}_{ig}^{I}\) is the representation of the gravitational acceleration in the \(i\)-th linkage coordinate system, \(\mathbf{q}_{ci}^{I}\) is the representation of the quaternion of the position of the \(i\)-th center of mass of the connected linkage with respect to the \(i\)-th connected linkage coordinate system, \(\mathbf{I}_{I}\) is the inertia matrix of the \(i\)-th connected linkage, \(\hat{\mathbf{W}}_{I}\) is the acceleration matrix of the \(i\)-th connected linkage, \(\mathbf{p}_{ci}^{I}\) is the representation of the quaternion of the position of the \(i\)-th connected linkage center of mass with respect to the \(i\)-th connected linkage coordinate system, \(\hat{\mathbf{v}}_{ci}^{I}\) is the representation of the quaternion of the velocity of the \(i\)-th center of mass of the connected linkage with respect to the \(i\)-th connected linkage coordinate system. Furthermore, the velocity process actually includes uniform motion and accelerated motion. The acceleration includes positive acceleration and negative acceleration, and the negative acceleration is deceleration. It can be obtained that the equation for the force and torque is:  
\[\mathbf{f}_{I}^{l}+\mathbf{\tau}_{I}^{I}= \begin{bmatrix}\mathbf{V_{gt}}\mathbf{W_{I}}\end{bmatrix} \begin{bmatrix}\mathbf{D}\mathbf{A}_{I}\\ \mathbf{D}\mathbf{B}_{I}\end{bmatrix} + \begin{pmatrix}\mathbf{q}_{i+1}^{I}{}^{*}\\ \end{pmatrix} ^{-} \begin{pmatrix}\mathbf{q}_{i+1}^{I}\end{pmatrix} ^{+}M(\dot{\mathbf{W} }_{I})M(\mathbf{I}_{I}) \tag{24}\]  
\[\mathbf{D}\mathbf{A}_{I}=m_{I}\overline{p}_{I}^{I} \tag{25}\]  
\[\mathbf{D}\mathbf{B}_{I}= \begin{bmatrix}m_{I}p_{ix}p_{iy}\;m_{I}p_{ix}p_{iz}\;m_{I}p_{ iy}p_{iz}\;m_{I}p_{ix}{}^{2}\;m_{I}p_{iy}{}^{2}\;m_{I}p_{iz}{}^{2}\end{bmatrix}  \tag{26}\]  
\[M(\mathbf{I}_{I})= \begin{bmatrix}M(\mathbf{I}_{I}\mathbf{A})\;M(\mathbf{I}_{I}\mathbf{B})\end{bmatrix}  ^{T} \tag{27}\]  
\[M(\mathbf{I}_{I}\mathbf{B})= \begin{bmatrix}I_{ixx}\;I_{ixy}\;I_{ixz}\;I_{iyy}\;I_{iyz} \;I_{zz}\end{bmatrix}  \tag{28}\]  
where \(\mathbf{V_{gt}}\) is the combination matrix of the linear acceleration of the \(i\)-th connected linkage, \(\mathbf{W_{I}}\) is the combination matrix of the angular velocity and angular acceleration of the \(i\)-th connected linkage, \( \begin{pmatrix}\mathbf{q}_{i+1}^{I}{}^{*}\\ \end{pmatrix} ^{-}\) is the right multiplied quaternion matrix form of \(\mathbf{q}_{i+1}^{I}{}^{*}\), \( \begin{pmatrix}\mathbf{q}_{i+1}^{I}\\ \end{pmatrix} ^{+}\) is the left multiplied quaternion matrix form of \(\mathbf{q}_{i+1}^{I}\), \(M(\dot{\mathbf{W}}_{I})\) is the angular velocity matrix of the \(i\)-th connected linkage, \(M(\mathbf{I}_{I})\) is the inertia parameter matrix of the \(i\)-th connected linkage, \(M(\mathbf{I}_{I}\mathbf{A})\) is the identity matrix.
## 2 Multiprecision Hardware
### Novel Data Types  
A software solution is not meant to be efficient for real time execution, as it has to emulate nonstandard numbers and arithmetic on the IEEE-754 floating-point arithmetic units. To address this issue, novel floating-point data types were developed to replace the common floating-point arithmetic on a hardware level. Significance arithmetic relies on the concept of significant digits that carry meaning, contributing to a number, and insignificant digits used for the approximation of numerical errors. Significance arithmetic was extended by [30] to unums and posits which are based on a variable-width storage format for the significand and exponent potentially help to minimize the number of bits used. However, there are several obstacles for the computation in the posit space, including the change of bit length of the number structure and concerns about the numerical stability of algorithms. Another approach is the floating-point adaptive noise reduction (FP-ANR) format suggested by [31]. FP-ANR is compatible with the existing floating-point representation format as it replaces the uninformative bits of the mantissa by a self-detectable pattern. Scanning the mantissa from right to left, one can deduce the number of cancelled bits without using extra memory fields. To conclude, there is a wide range of software solutions emulating the new formats and a short list of implementations on FPGA [32].
## 3 Results and Discussion
### Network Construction and Module Identification  
The T-P-D network of the Group I and Group II diseases was constructed using Gephi, which consisted of 196 nodes (i.e., 122 targets, 36 pathways, and 38 diseases) and 392 edges (Figure 6A). As shown in Figure 6A, a target in the network was connected to either one or multiple pathways and a pathway was connected to either one or multiple diseases. To extract the relationship among the targets, the T-T network was constructed through the corresponding relationships between targets and pathways, where the edges of the network represent the common pathways between every two targets. A functional module of targets was identified through a set of shared pathways using Gephi with the Louvain algorithm (Figure 6B).  
Figure 5: (**A**) The top 10 items of GO biological function enrichment; (**B**) the top 10 items of KEGG pathway enrichment; (**C**) the categories of the pathways enriched; and (**D**) the disease categories of KEGG enrichment.  
As shown inFigure 6B, four functional modules (Modules 1-4) were identified, where Module 1 consisted of 28 targets (22.95% of total targets) that were connected by eight pathways, such as amyotrophic lateral sclerosis (hsa05014), hepatocellular carcinoma (hsa05225), prostate cancer (hsa05215), and so on. Module 2 consisted of 43 targets (35.25%) that were connected by 27 pathways, such as [PERSON] disease (hsa05016), Parkinson's disease (hsa05012), [PERSON]'s disease (hsa05010), and so on. Module 3 consisted of 20 targets (16.39%) that were connected by 17 pathways, such as human T-cell leukemia virus 1 infection (hsa05166), type II diabetes mellitus (hsa04930), inflammatory bowel disease (hsa05321), and so on. Module 4 consisted of 31 targets (25.41%) that were connected by 17 pathways, such as alcohol dependence (hsa05034), nicotine addiction (hsa05033), cocaine addiction (hsa05030), and so on.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 3 The Proposed Algorithm
### Illumination Map Enhancement Based on Gaussian Laplacian Pyramid Fusion
#### 3.2.3 Design of Fusion Weights  
In order to combine the advantages of the two images, the Gaussian-Laplacian pyramid algorithm is used to fuse the two input images obtained above, and the fusion weights are composed of brightness weight and detail weight. This section mainly introduces two kinds of weights.  
In order to have a natural brightness, we adopt a global brightness weight for the \(k\)-th input image as in [27]:  
\[W_{k,1}=\exp\left(-\frac{(I_{k}-0.5)^{2}}{2\times 0.25^{2}}\right), \tag{16}\]  
As pointed out in Section 2, the gradient and variance are important for image details. In order for the enhanced image to contain rich content, the detail weight is designed as follows:  
\[W_{k,2}=\lambda\frac{\ln(1+10\times a_{k})}{\ln 10}, \tag{17}\]  
\(\lambda\) is the normalization factor, where  
\[a_{k}=\sigma_{k}+grad_{k}, \tag{18}\]  
\[grad_{k}=|\
abla_{x}I_{k}|+|\
abla_{y}I_{k}|, \tag{19}\]  
where \(\
abla_{x}\) and \(\
abla_{y}\) are the gradients of the local patch in \(x\) direction and \(y\) direction, respectively, and \(\sigma_{k}\) is the variance of the local region. \(a_{k}\) is the summation of the variance and the gradient, indicating the amount of information in the local area. To ensure that the  
Figure 9: Results of the two algorithms for denoising and contrast enhancement. (**a**) Original image; (**b**) images enhanced by Equation (14); (**c**) images enhanced by Equation (15).  
value of \(W_{k,2}\) is between \([0,1]\), \(a_{k}\) needs to be normalized. So, the larger the \(a_{k}\), the higher the weight value and information contained in the image.  
The final weight is obtained by:  
\[\overline{W}_{k}=\frac{W_{k}}{\sum_{k}W_{k}}, \tag{20}\]  
where  
\[W_{k}=W_{k,1}+W_{k,2} \tag{21}\]
## 2 Methods
### Evaluation Metrics
The closer the value of this index is to 1, the better the model performance, and the closer the value is to 0, the worse the model performance. In Equation (7), \(P\) stands for Precision and \(R\) stands for Recall. These four indicators can convert the results in the confusion matrix to a ratio between 0 and 1, to better measure the effectiveness of the model. \[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[P=\frac{TP}{TP+FP} \tag{5}\]  
\[R=\frac{TP}{TP+FN} \tag{6}\]  
\[\text{F1 Score}=\frac{2 PR}{P+R} \tag{7}\]  
Metrics such as PSNR, SSIM, and BER are very important for assessing the watermark robustness and its imperceptibility. PSNR indicates how nearly identical the original image and watermarked image are; higher values thus indicate less distortion and better imperceptibility. SSIM quantifies the structural image similarity and indicates the perceptual quality of the watermarked image in comparison; values of SSIM close to 1 indicate high visual fidelity. BER is a measure of the error rate in rescuing the watermark, where lower values mean greater strength against attacks. The metrics are thus credibly comprehensive in evaluating the balance between invisibility and resilience.
## 5 Experimentation, Results, and Analysis
### Limitations and Challenges  
While the DCAT model excels in various aspects, it also presents several limitations and challenges that need to be considered for practical applications:  
* **Computational Complexity:** The model's dynamic nature makes it computationally intensive, limiting its applicability in low-resource settings.
* **Scalability Issues:** The need for high-quality annotations restricts the model's scalability across diverse datasets.
* **Overfitting Risk:** Due to its complexity, the model is susceptible to overfitting when trained on limited or less diverse data.
* **Data Bias:** The model can inherit biases present in the training data, posing ethical challenges.
# A 3D-Printed Enclosed Twist Dielectric Resonator Antenna with Circular Polarization
## 5 Measurement Results  
The measured reflection coefficient and axial ratio values are presented in Figure 13. These results indicate that the antenna is well matched to the design frequency of 5.8 GHz, showing good agreement with the simulation results. From Figure 13, we can see that the axial ratio remains below 3 dB. However, there was an increment in the dB value of the axial ratio, reaching a value of 2.6 dB at the operational frequency. The measurement of the normalized gain pattern is presented in Figure 14. We can see a deformation of the radiation pattern, while the maximum gain is reduced 2.4 dB.  
One of the challenges of working with sensitive structures such as DRAs is that any small disturbance affects their operation. Therefore, a study was carried out to evaluate the sensitivity of the antenna in relation to its position with respect to the feed slot. For this, in Figure 15, we set the variable \"\(a\)\", which will represent the movement of the position with respect to the feed. In Figure 16a, the axial ratio shows how sensitive the antenna is to a small leveling movement relative to the slot. The approximate distance the antenna moved from the central axis was measured with a vernier caliper, in this case with a value of 2 mm, which has a great influence on the axial ratio parameter and therefore on the radiation pattern.  
Figure 12: The 3D-printed antenna. (**a**) CURA implementation with the infill percentages. (**b**) Implemented antenna. From left to right: twist alone, twist in the enclosure with the exposed half, and full antenna.  
Figure 13: Measurement results for the LHCP implementation. (**a**) Reflection coefficient \(|S_{11}|\) as a function of the frequency. (**b**) Axial ratio as a function of the frequency.  
Another explanation for the differences between simulation and measurement is what the printer understands when working with different infill percentages. Since the box as shown above has a very low infill, it is likely that the printer does not differentiate between a 15% infill and a 10% infill, thus affecting the permittivity of the box. For this, Figure 16b shows a sensitivity study of slight variations in the permittivity of the box, showing that the antenna is not sensitive to those variations.
# Transitioning Broadcast to Cloud
## 4 Transitioning Broadcast to Cloud
### Cloud-Based OTT Systems  
In Figure 9, we show a hybrid architecture where cloud is used only to implement OTT/streaming services. Everything else stays on prem. This is the easiest possible example of integration.  
To route streams to cloud, broadcast workflow produces contribution streams, one for each channel, and then sends them over IP (e.g., using RTP+FEC or SRT) to cloud.  
The cloud platform receives such streams, performs necessary conversions, transcodes them, and distributes them over CDNs to clients. As shown in Figure 9, the cloud platform may also be used to implement DVR or time-shift TV-type functionality, DRM protection, SSAI, analytics, etc. All standard techniques for optimizing multi-format/multi-screen streaming delivery (dynamic packaging, dynamic manifest generation, optimized profiles, etc. [23]) can also be employed in this case.  
To make such system work well, the main technologies/improvements that are needed, include:  
* reliable real-time ingest, e.g., using RTP+FEC, SRT, RIST, or Zixi-type of protocols and/or a dedicated link, such as AWS Direct connect [81];
* improvements in signal processing stack--achieving artifact-free conversion of broadcast formats to ones used in OTT/streaming;
* improvements in metadata handling, including full pass-through of SCTE-35 and compliant implementation of SSAI and CSAI functionality based on it.  
Generally however, hybrid architectures of this kind have already been deployed and proven to be effective in practice. Some of the above-mentioned close-gap technologies have also been implemented. For instance, cloud ingest using RTP, SMPTE 2022-1, SMPTE  
Figure 9: Hybrid architecture with Over the Top (OTT)/streaming workflow offloaded to cloud.  
2022-2, or SRT, improvements in support of SCTE-35 for ad-insertions, and improvements in encoding stack were among recent updates in Brightcove VideoCloud system [90].
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 2 GNSS Attitude Determination  
In this section, we will introduce a single-baseline model for GNSS attitude determination, a classic GNSS multi-baseline model for vector determination, and how to use the baseline vector to obtain attitude information by the direct method.
## 3 Collaboration Performance Evaluation Based on Combination of AHP and EW
### Determining the Comprehensive Weight Based on Combination of AHP and EW
#### 3.2.1 Determining the Subjective Weight Based on AHP  
The indicators in each layer have different degrees of importance to the indicators of the previous layer. A comparison matrix is a numerical representation of the relative importance of the related indicators of this level to a certain factor at the previous level. For example, the comparison matrix of criterion layer \(B=(b_{1},b_{2},\ldots,b_{n})\) regarding target layer \(A\) is as follows:  
\[B=\begin{pmatrix}b_{11}&b_{12}&\cdots&b_{1n}\\ b_{21}&b_{22}&\cdots&b_{2n}\\ \cdots&\cdots&\cdots&\cdots\\ b_{n1}&b_{n2}&\cdots&b_{nn}\end{pmatrix}=\begin{pmatrix}b_{ij}\end{pmatrix}_{nn} \tag{1}\]  
where \(n\) is the number of elements in the criterion layer, \(b_{ij}\) represents the relative importance value of \(b_{i}\) to \(b_{j}\) relative to the target layer, and [PERSON]'s 1-9 scale method is used to quantify the important relationships among different elements.  
According to the value of the comparison matrix \(B\), calculate the product \(m_{i}\) of the elements of each row in the comparison matrix, and calculate the \(n\)-th root of \(m_{i}\).  
\[m_{i}=\sqrt[n]{\prod_{j=1}^{n}b_{ij}}\;,(i,j=1,2,\ldots,n) \tag{2}\]  
The vector \(M=[m_{1},m_{2},\ldots,m_{n}]^{T}\) is normalized.  
\[z_{i}=\frac{m_{i}}{\sum_{i=1}^{n}m_{i}}\;,(i=1,2,\ldots,n) \tag{3}\]  
where \(Z=[z_{1},z_{2},\ldots,z_{n}]^{T}\) is the feature vector. After the indicator weights are obtained, a consistency check is required. The largest eigenvalue of the comparison matrix \(B\) is calculated, where \((BZ)_{i}\) represents the i-th element of the vector \(BZ\).  
\[\lambda_{max}=\frac{1}{n}\sum_{i=1}^{n}\frac{(BZ)_{i}}{z_{i}} \tag{4}\]  
Finally, the consistency index test is carried out on the obtained weight coefficients. If \(CR<0.10\), the comparison matrix has a satisfactory consistency, that is, the weights are calculated correctly. The consistency ratio (\(CR\)) is calculated as follows:  
\[CR=\frac{\lambda_{max}-n}{RI(n-1)} \tag{5}\]  
where \(RI\) is the average random consistency index.
## 3 Experiment  
The method mentioned above was used to monitor the function describing the stresses produced by K\({}^{+}\)\(\leftrightarrow\)Na\({}^{+}\) ion exchange in BK7 glass (from Schott AG, Mainz, Germany). Flat glass plates with dimensions of \(12\times 30\times 1.5\) mm were used. The source of K\({}^{+}\) ions was KNO\({}_{3}\) sodium nitrate (pure for analysis). The volume of salt melted in the ceramic crucible was \(\sim\)250 cm\({}^{3}\). The glass plates were introduced into the crucible in a special holder made of fused quartz [14]. Before immersion in the molten salt they were gradually heated and after removal they were slowly cooled down and washed in deionized water. The measurements of propagation constants of waveguide modes were made using the m-line method by measuring synchronous angles [14]. These measurements were carried out using a goniometer with an angular resolution of 1.8\({}^{\prime\prime}\). The light source was a laser diode (\(\lambda\) = 677 nm). A prismatic coupler made of PSK-3 glass (from Schott AG, Mainz, Germany) was used (refractive index \(n_{677}\) = 1.5491, breaking angle \(\delta\) = 70.0963\({}^{\circ}\)). The uncertainties of the determined effective refractive indexes of the waveguide modes were of the order of \(\sim\)10\({}^{-4}\)[15]. The refractive index profiles were calculated using a procedure based on the mode characteristic equation [14; 19]. In this case, the number of waveguide modes determines the number of points of the refractive index profile. The function describing the \(n_{TE}(x)\) or \(n_{TM}(x)\) refractive index profiles was obtained by fitting the solution of the diffusion equation to the points of the profile [14]. Then, on the basis of Equation (8), \(n_{0}(x)\) was calculated. An example of such calculations is shown in Figure 4.