esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Overview of Optimization Methods in Food Science: Linear, Non-Linear, and Evolutionary Approaches
### Non-Linear Methods  
Non-linear optimization methods extend the capabilities of linear approaches by addressing problems with non-linear relationships among variables. These relationships, which may involve curved or exponential interactions, are prevalent in food science due to the intricate physical, chemical, and biological processes involved in production systems. Non-linear methods are indispensable for modeling and optimizing such complex systems, as they provide a rigorous mathematical framework for capturing non-linearities [41].  
Applications of non-linear methods are diverse and include optimizing heat transfer processes, enzymatic reactions, and fluid dynamics. These methods are particularly valuable when high accuracy is required in modeling system behavior. However, their computational intensity can be a drawback, especially for problems with a large number of variables or constraints. Additionally, non-linear methods are less suitable for scenarios requiring rapid, approximate solutions, as their complexity often demands significant computational resources [39; 42].
###### Abstract  
Although various topologies of multi-terminal high voltage direct current (MT-HVdc) transmission systems are available in the literature, most of them are prone to loss of flexibility, reliability, stability, and redundancy in the events of grid contingencies. In this research, two new wind farms and substation ring topology (2 WF-SSRT) are designed and proposed to address the aforementioned shortcomings. The objective of this paper is to investigate MT-HVdc grid topologies for integrating large offshore wind farms with an emphasis on power loss in the event of a dc grid fault or mainland alternating current (ac)grid abnormality. Standards and control of voltage source converter (VSC) based MT-HVdc grids are defined and discussed. High voltage dc switch-gear and dc circuit topologies are appraised based on the necessity of dc cables, HVdc circuit breakers, and extra offshore platforms. In this paper, the proposed topology is analyzed and compared with the formers for number and ratings of offshore substations, dc breakers, ultra-fast mechanical actuators, dc circuits, cost, flexibility, utilization, and redundancy of HVdc links. Coordinated operation of various topologies is assessed and compared with respect to the designed control scheme via a developed EMTDC/PSCAD simulation platform considering three fault scenarios: dc fault on transmission link connecting the wind farm to mainland power converters, dc fault within substation ring of VSC-HVdc stations, and ultimate disconnection of grid side VSC station. Results show that 2 WF-SSRT is a promising topology for future MT-HVdc grids.  
Keywords:HVdc transmission systems; MT-HVdc grid topologies for offshore wind farms; 2 WF-SSRT topology; comparison of MT-HVdc grid topologies +
Footnote †: journal: Applied Sciences
# Subspace Detours Meet Gromov-Wasserstein
## 2 Background
### Classical Optimal Transport
#### 2.1.2. Knothe-Rosenblatt Rearrangement  
Another interesting coupling is the Knothe-Rosenblatt (KR) rearrangement, which takes advantage of the increasing rearrangement in one dimension by iterating over the dimension and using the disintegration of the measures. Concatenating all the increasing rearrangements between the conditional probabilities, the KR rearrangement produces a nondecreasing triangular map (i.e., \(T:\mathbb{R}^{d}\to\mathbb{R}^{d}\), for all \(x\in\mathbb{R}^{d}\), \(T(x)=(T_{1}(x_{1}),\ldots,T_{j}(x_{1},\ldots,x_{j}),\ldots,T_{d}(x))\), and for all \(j\), \(T_{j}\) is nondecreasing with respect to \(x_{j}\)), and a deterministic coupling (i.e., \(T_{\pi}\mu=\
u\)) [18; 19; 20].  
[PERSON] et al. [21] made a connection between this coupling and optimal transport by showing that it can be obtained as the limit of optimal transport plans for a degenerated cost:  
\[c_{t}(x,y)=\sum_{i=1}^{d}\lambda_{i}(t)(x_{i}-y_{i})^{2},\]  
where for all \(i\in\{1,\ldots,d\}\), \(t>0\), \(\lambda_{i}(t)>0\), and for all \(i\geq 2\), \(\frac{\lambda_{i}(t)}{\lambda_{i-1}(t)}\underset{t\to 0}{\longrightarrow}0\). This cost can be recast as in [22] as \(c_{t}(x,y)=(x-y)^{T}A_{t}(x-y)\), where \(A_{t}=\text{diag}(\lambda_{1}(t),\ldots,\lambda_{d}(t))\). This formalizes into the following Theorem:  
**Theorem 1** ([19; 21]).: _Let \(\mu\) and \(\
u\) be two absolutely continuous measures on \(\mathbb{R}^{d}\), with compact supports. Let \(\gamma_{t}\) be an optimal transport plan for the cost \(c_{t}\), let \(T_{K}\) be the Knothe-Rosenblatt map between \(\mu\) and \(\
u\), and \(\gamma_{K}=(Id\times T_{K})_{\mu}\mu\) the associated transport plan. Then, we have \(\gamma_{t}\underset{t\to 0}{\longrightarrow}\gamma_{K}\). Moreover, if \(\gamma_{t}\) are induced by transport maps \(T_{t}\), then \(T_{t}\) converges in \(L^{2}(\mu)\) when \(t\) tends to zero to the Knothe-Rosenblatt rearrangement._
## 4 Simulation Experiment and Result Analysis
### Complexity Analysis  
In this section, the computational complexity of the algorithm is analyzed and the time complexity of the key operational steps is now calculated for the FD-CBSS algorithm based on Fast IVA. Let the number of frequency points of STFT be \(K\) and the total number of frames of data be \(B\). Assume that the number of source signals \(N\) is the same as the number  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **DTW [42]** & **Fast IVA** \\ \hline PESQ & 3.17 & 4.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PESQ averages with different algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PESQ** & **s1** & **s2** \\ \hline y\({}_{1}\) & 0.3681 & 3.9754 \\ y\({}_{2}\) & 3.9232 & 0.3563 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PESQ value between each separated speech and source speech signal.  
of observed signals \(M\). For convenience, only multiplication operations are considered when calculating the complexity, and the complex-valued multiplication is a four-fold relation of the real-valued multiplication [43]. The real-valued multiplication operations required for the main procedure are shown in Table 5, where \(N_{p}\) represents the number of iterations of the Fast IVA algorithm.  
Combining the above analysis, the time complexity of the main steps of the Fast IVA algorithm is calculated. Therefore, the time complexity of the overall process of the algorithm is the sum of above processes, which is  
\[T=O\big{(}N_{p}NBK\big{)}. \tag{16}\]  
In order to compare the computational complexity of various algorithms, the running time of the frequency domain permutation algorithm in the literature [43] is given. Choose the same equipment and operating operating environment, the number of speech signals is \(N=4\), the length of the speech signal is 10 s and the sampling frequency is \(f_{s}=8000\) Hz. In Table 5, the running time required for the completion of each algorithm is compared to illustrate the time complexity of the Fast IVA algorithm.  
As shown in Table 6, the running times of the algorithm designed in [43] and in this paper are 13.7 s and 7.9 s, respectively. The significantly smaller running times indicate the low complexity of the algorithms. The biggest advantage of this algorithm is that the signal permutation is done during the signal separation process, eliminating the need for subsignal permutation algorithm design. Based on the computational complexity analysis of the above algorithms and the comparative analysis of the experimental results, the CBSS algorithm designed in this paper cannot only achieve high quality extraction of speech signals, but it can also complete the separation of signals quickly with low time complexity.
## 3 Results and Discussion
### Influence of Speed on the Volume Fraction of Gas in the Gear Cavity  
The gear pump with the number of teeth six and seven is set at a speed of 7000 to 10,000 RPM, while the number of teeth eight and nine is set at a speed of 7000 to 11,000 RPM. The effect of speed on the volume fraction of gas in the gear cavity was analyzed.  
The significant effect of speed on the gas volume fraction in the gear cavity while keeping the gear outer diameter the same can be seen in Figure 7. As the speed increases, the peak gas volume fraction values for different numbers of teeth show an increasing trend. Taking 10,000 RPM as an example, the peak gas volume fraction of the six-teeth pump is the highest, reaching 11.51%. The peak values of seven-teeth, eight-teeth, and nine-teeth pumps are 10.35%, 9.67%, and 9.31%, respectively. Under other speed conditions, a similar trend is also shown; that is, as the number of teeth increases, the peak gas volume fraction gradually reduces.  
Comparing the curves in Figure 7, it can be seen that at 7000 RPM, the gas content in the pump of all six to nine teeth show erratic fluctuation curves. However, as the speed continues to increase after the gear pump runs steadily, the fluctuation of the gas volume fraction becomes more stable.  
Figure 7: Gas volume fraction curves corresponding to different speeds. The values of T for different gear speeds (7000–11,000 RPM) are 0.100857 s, 0.0075 s, 0.00667 s, 0.006 s, and 0.005454 s, respectively. (**a**) \(Z=6\); (**b**) \(Z=7\); (**c**) \(Z=8\); (**d**) \(Z=9\). (**a,b**) at 7000–10,000 rpm; (**c,d**) at 7000–11,000 rpm.  
As is clear from the data in Table 2, when other gear parameters are kept unchanged, the number of teeth has relatively little influence on the cavitation phenomenon at 7000 RPM, 8000 RPM, and 9000 RPM, respectively, and the average values of the cavitation phenomenon for each number of teeth at different speeds are 1.9%, 2.46%, and 3.15%, respectively. Taking the six-teeth model as an example, the growth rate of the air content in the gear cavity shows a marked unevenness as the speed increases from 7000 RPM to 10,000 RPM, which is 31.4%, 33.5%, and 158% successively. The air content of the pump increases abruptly, especially as the speed increases from 9000 RPM to 10,000 RPM. This indicates that there is a critical speed under this working condition, and when the speed exceeds 9000 RPM, the gear cavity will experience a serious cavitation phenomenon. A similar trend is observed for models with other tooth numbers, with critical speeds of seven to nine teeth being 9000 RPM, 10,000 RPM, and 10,000 RPM, respectively.
## 3 Results and Discussion
### Films Characterization
#### 3.1.4 Mechanical Properties  
Film integrity is required in food packaging in order to withstand the stress that may occur during shipping, handling and storage. No significant differences (\(p>0.05\)) in mechanical properties were observed with ASE incorporation at 3 and 6 wt.% compared to neat PCL (Table 1). These results are in accordance with the previous characterization performed by SEM and OTR, suggesting that no significant interactions between the polymer matrix and the natural extract compounds in ASE were produced [51]. It is known that materials with thermoplastic behaviour, such as PCL, can be processed into films by the application of different thermal/mechanical dry processing techniques. Therefore, it is essential to study the influence of the selected dry processing on the thermoplastic behaviour of the obtained materials to select adequate processing parameters. In this work, the selected method carried out for the production of PCL-based active films did not significantly affect or modify the final properties of the obtained materials.  
On the other hand, variations in mechanical properties of composite films have been directly related to the composition and diversity of chemical compounds present in plant extracts and the biopolymers used. The concentration of the added extract is also an important factor to be considered in order to study possible changes in the physical properties of the composite films [50]. [PERSON] and [PERSON] [53] prepared chitosan-based films incorporated with green tea extract (GTE) and glycerol as plasticizer reporting no significant differences in mechanical properties of the resulting films compared to the neat polymer at low GTE concentrations (0-5 wt.%). However, tensile strength and elongation at break significantly increased up to 16% and 11% compared to the unfortified controls, respectively, upon the incorporation of GTE from 5 up to 20 wt.%.
## 8 Related Work
Our protocol overcomes those shortcomings by considering security and routing protocol at the design time. The scheme is based on GAF, along with a hexagonal deployment model to achieve better security and efficiency. It securely disseminates 97% data packets to the mobile sinks successfully and even more efficiently than non-secure approaches like TTDD and DD.
## 5 Case Study for Mitigating Security Attacks in Smart Healthcare System
### Performance Evaluation of the Proposed Architecture
Moreover, the proposed architecture uses malware analysis, i.e., dynamic analysis, to discard malicious malware from the regular operations of the smart healthcare system. However, most of the previous studies do not conduct malware analysis in their proposed solutions. Furthermore, the authors of [11] used a  
Figure 8: Evaluation of the intelligence layer where different AI-based algorithms are trained using a standard dataset and validated using a generated dataset from the malware samples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Precision** & **Recall** & **Log-Loss Score** & **F1 Score** \\ \hline Random Forest & 93.24\% & 92.99\% & 6.34\% & 93.67\% \\ \hline Support Vector & 82.13\% & 82.78\% & 14.19\% & 82.90\% \\ Machine & 66.89\% & 65.13\% & 32.84\% & 67.12\% \\ \hline Logistic & 42.56\% & 43.60\% & 48.67\% & 44.02\% \\ \hline Perceptron & 32.90\% & 33.53\% & 69.89\% & 33.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance parameters of the proposed architecture. private blockchain to store their data, although most of the private blockchain has a central repository to maintain and control the private data of the healthcare ecosystem. The private blockchains are severely affected by network-related attacks; therefore, the blockchain is useful only for critical applications, such as covert military applications, where they can integrate more sophisticated solutions at the cost of latency to strengthen the security of the central repository of private blockchain. However, in smart healthcare systems, maintaining the latency is challenging; hence, it is advisable not to use it. In our proposed work, we have two security filters, i.e., malware and network-related attacks; both malware and intelligence layers discard the malicious data from the proposed architecture. Only non-malicious data are forwarded to the public blockchain; further, to improve the latency of the smart healthcare system, we utilize the significant benefits of IPFS that improve the response time of the blockchain network, resulting in greater scalability. As a result, the proposed work is better and more robust than the existing work [11] in terms of accuracy, scalability, and security.
## 2 Related Research
### Intelligent Transportation System
#### 2.1.2 V2X Communication Environments  
Based on the edge computing environment, it is possible to control traffic flow via communication with vehicles traveling on the road and all surrounding infrastructure. In particular, future vehicles will only be able to maintain a fast, safe, and comfortable operating environment through communication with everything in the vicinity. In this way, in order to ensure mutual compatibility between cars and surrounding infrastructure devices in smart transportation networks, the 3 GPP standards body for mobile communications proposed in 2016 a V2X standard that utilizes mobile communications networks.  
Figure 2 shows the communication environment of the smart transportation network proposed by the 3 GPP.  
It was designed so that the surrounding infrastructure can actively communicate with smart cars and other cars in the vicinity using V2X based on IEEE802.11P in the Internet of Things, and even autonomous driving is possible. This protocol allows the infrastructure facilities, smart vehicles, and infrastructure equipment in the vicinity to autonomously control traffic conditions via periodic communication using Wi-Fi.  
However, even in the above V2X communication environment, there are still many risks, such as forgery of the identity of the smart car itself, attacks via illegal remote connections, and tampering with surrounding cars and infrastructure.  
Figure 1: German traffic forecast information system.
## 3 Method
### Overall Pipeline
#### 3.1.4 Dual Attention Unit (DU)  
Channel Attention (CA)  
The CA [68] module can calculate channel-focused attention weights to obtain the informative features and denoise the noisy or irrelevant information by focusing on global statistics. Mathematically, the channel attention mechanism can be represented as follows:  
\[CA(x)=\sigma(W_{2}\delta(W_{1}x)) \tag{3}\]  
where x represents the input feature map, \(\delta\) denotes the ReLU activation function, W\({}_{1}\) and W\({}_{2}\) are learnable weights, and \(\sigma\) represents the sigmoid function.  
Spatial Attention (SA)  
The SA mechanism extracts a spatial attention map, which identifies locally important spatial regions, from the feature maps and emphasizes the interactions among the neighboring spatial locations. The SA [69] mechanism models spatial dependencies. Mathematically, the spatial attention mechanism can be represented as follows:  
\[SA(x)=\sigma(W_{3}\delta(W_{4}x)) \tag{4}\]where x represents the input feature map, \(\delta\) denotes the ReLU activation function, W3 and W4 are learnable weights, and \(\sigma\) represents the sigmoid function. Our model uses adjustable weights W1, W2, W3 and W4, in the range [0.5-1.5]. Any value below or above significantly affects the model's performance on denoising and super-resolution tasks. The best performance was observed when all weights were set to 1.0, indicating that balanced attention mechanisms work well for the said tasks.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 2 Separation Formulation Method
### Track Prediction Method
#### 2.1.2 LSTM Prediction  
This study employs LSTM networks to predict UAV tracks. Track prediction involves processing long data sequences, including multiple continuous time points of the UAVs' position, speed, heading, etc. In comparison, back-propagation neural networks and CNN models may struggle with such long sequence data. One of the strengths of LSTM networks lies in their ability to effectively avoid the problem of vanishing gradients. The structure of LSTM networks endows them with strong performance in handling complex operation data of UAV logistics.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### Mesh Tests
#### 5.3.3. Quarry  
In this experiment, the nodes were put around a quarry, as seen in Figure 12. This configuration was chosen because it has nodes further apart (about 400 m) and uses spreading factor 10.  
Table 5 contains the data gathered from the test. The data from the shortest connection (about 100 m) was omitted as it would have skewed the RSSI and SNR values due to the nodes being closer.  
This test simulates nodes used to carry information around a potentially dangerous working environment and along higher distances. While LoRa may not be suitable for moving around highly time-critical information, especially with the high spreading factor and its increased sending times, it could still be used for other, less critical purposes, like notifying workers with new data and information gathered from sensors around the work environment.
# ScienceIoT: Evolution of the Wireless Infrastructure of KREONET
###### Abstract  
Here, we introduce the current stage and future directions of the wireless infrastructure of the Korea Research Environment Open NETwork (KREONET), a representative national research and education network in Korea. In 2018, ScienceLoRa, a pioneering wireless network infrastructure for scientific applications based on low-power wide-area network technology, was launched. Existing in-service applications in monitoring regions, research facilities, and universities prove the effectiveness of using wireless infrastructure in scientific areas. Furthermore, to support the more stringent requirements of various scientific scenarios, ScienceLoRa is evolving toward ScienceIoT by employing high-performance wireless technology and distributed computing capability. Specifically, by accommodating a private 5G network and an integrated edge computing platform, ScienceIoT is expected to support cutting-edge scientific applications requiring high-throughput and distributed data processing.  
ScienceLoRa; ScienceIoT; KREONET; Internet of Things; low-power wide-area network (LPWAN); LoRa/LoRaWAN; private 5G; edge computing +
Footnote †: journal: _Sensors_
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 2 Accelerator Neutrino Beams
### Neutrino Production and Hadronic Cross-Sections  
As mentioned above, conventional beams are mainly a source of \(\
u_{\mu}\) with a relatively small contamination of \(\
u_{e}\) from kaon decays or decay-in-flight of muons. The relative weight of \(\
u_{e}\) from kaons and muons depends on the hadron energy, the length of the secondary hadron beamline and the decay volume. The smaller the energy, the higher the contribution of DIF because the kaon yield is suppressed. A long decay volume also enhances the DIF contribution due to the large difference between the muon and kaon lifetime.  
If the energy of the incoming proton beam is large, it is possible to have a small amount of \(\
u_{\tau}\) coming from the tauonic decays of charmed mesons (\(D_{s}\)). In long-baseline experiments, the \(\
u_{\tau}\) contamination is always negligible (\(10^{-4}\)) and can be enhanced only in beam dump experiments, where the neutrinos are detected at a very short distance from the beam dump [24]. In conclusion, conventional neutrino beams are powerful sources of \(\
u_{\mu}\) and \(\bar{\
u}_{\mu}\) but the production of other flavors is strongly suppressed. They are particularly effective to study \(\
u_{\mu}\to\
u_{e}\) and \(\overline{\
u}_{\mu}\to\overline{\
u}_{e}\) oscillations but must be complemented by natural sources or non-conventional facilities if flavors different from \(\
u_{\mu}\) are needed.  
Neutrino experiments require quite precise information on the production of the \(\pi^{+}\),\(\pi^{-}\),\(K^{+}\), \(K^{-}\) and \(K^{0}_{L}\) mesons. This is mandatory since the originating hadron's momentum is directly correlated with the resulting neutrino energy by relativistic kinematics [25]. Uncertainties in the hadron yields lead to the largest uncertainties in the neutrino fluxes. This crucial item will be discussed in Section 5.