esa-sceva/satcom-chunk-collection huggingface dataset:
# Ant Colony Optimization with Warm-Up
## 2 Literature Review  
The Ant Colony Optimization (ACO) was introduced by [1] as a novel nature-inspired metaheuristic for the solution of hard combinatorial optimization problems. ACO belongs to the class of metaheuristics, which are approximate algorithms used to obtain good enough solutions to NP-hard problems in a reasonable amount of time. When searching for food, some species of ants initially explore the area surrounding the nest randomly. As soon as an ant finds a food source, it evaluates the quantity and the quality of the food. On the way back, the ant deposits a chemical pheromone trail on the ground. The quantity of pheromone deposited depends on the quantity and quality of the food and guides other ants to the food source. As shown by [5], the communication via pheromone between the ants enables them to find the shortest paths between their nest and food sources, and the same consideration also applies in ant colony optimization algorithms for solving combinatorial optimization problems. Even if the first proof-of-concept application for the ACO was a traveling salesman problem (TSP), up to now the above algorithm was applied to many combinatorial optimization problems. For instance, it was applied to assignment problems [6; 7; 8], routing problems [9; 10; 11], scheduling problems [12; 13]. Less known but equally efficient applications concern the resource-constrained project scheduling problem [14; 15], flow shop scheduling [16], sequential ordering problem [17], and open shop scheduling problem [18]. The scientific community also proposed many applications for nonindustrial environments such as solutions for DNA sequencing or web page ranking [19]. The various variants of the ACO generally differ from each other in the pheromone update rules. In particular, most applications belong to one of these two categories: the iteration-best-update or the best-so-far-update. Basically, in the first case, the update of pheromone takes place at every iteration, while in the second case it takes place only when a new best solution is found, introducing in this way a much stronger bias towards the good solutions found. The most successful ACO variants are the Ant Colony System [20] and the Min-Max Ant System [21], which also are the most used in practice. Since this claims to be just a brief overview of the key points concerning ACO, for a deeper analysis of the scientific contributions on this algorithm the literature reviews by [4; 22] are suggested.
## 4 Experiment
### Performance Comparisons
#### 4.3.3 Comparison of Loss Functions and Anomaly Score Calculation  
To verify the effectiveness of ArcFace Loss, CrossEntropy Loss (CE) and ArcFace Loss were used to train the classifier, extract the hidden feature vectors of the sound spectrogram, and take the negative maximum likelihood function of GMM as the anomaly score. The detection results are shown in Table 5 and Figure 13. ArcFace Loss had a better effect. The average AUC of the five measuring point positions increased by 2.87%. This is because it can aggregate the data into different arc-shaped spaces according to the classes, reducing the distance within the same class and increasing the distance between different classes.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 4 Waveform Selection
### Candidate Waveforms
#### 4.1.1 OFDM  
Figure 3 shows the block diagram of massive MIMO system with OFDM waveform in both the uplink and the downlink. In the uplink, each user passes their data through an IFFT block and adds cyclic prefix (CP) to its data block and then sends it through the channel. CP is part of the end of the block which is added to the beginning of the block. At the BS, after removing the CP, the received signal of each BS antenna is passed through an FFT block and then the resulting signals are given to the detection block. This block separates the signal of each user and therefore each user's signal is detected. In the downlink the data that has to be sent to each user is precoded in the BS and then the resulting signal of each BS antenna is passed through an IFFT block. After adding the CP, the signals are transmitted through the channel. At the receiver side, the CP is removed and each user passes their received signal through an FFT block and therefore detects its desired signal.
## 2 Governing Equations
### \(\gamma\)-\(\widetilde{\mathrm{Re}_{\theta,t}}\)-\(S\) Transition Model
#### 2.1.1 Empirical Correlations in the Model  
Like other \(\gamma\widetilde{\mathrm{Re}_{\theta,t}}\) approaches available in the literature, the present model contains three empirical correlations needed to compute \(\mathrm{Re}_{\theta,t}\), \(\mathrm{Re}_{\theta,c}\), and \(\mathrm{F_{length}}\). In this paper, the correlation developed by [PERSON] et al. [2] for \(\mathrm{Re}_{\theta,t}\) is adopted:  
\[\mathrm{Re}_{\theta,t}=\left\{\begin{array}{cc}\left(1173.51-589.428\cdot Tu+ 0.2196/Tu^{2}\right)F(\lambda_{\theta})&Tu\leq 1.3\\ 331.5(Tu-0.5668)^{-0.671}F(\lambda_{\theta})&Tu>1.3\end{array}\right., \tag{18}\]  
\[F(\lambda_{\theta})=\left\{\begin{array}{cc}1+\left[12.986\lambda_{\theta} +123.66\lambda_{\theta}^{2}+405.689\lambda_{\theta}^{3}\right]\exp\biggl{(}- \left(\frac{Tu}{15}\right)^{1.5}\biggr{)}&\lambda_{\theta}\leq 0\\ 1+0.275[1-\exp(-35\lambda_{\theta})]\exp\biggl{(}-\frac{Tu}{0.5}\biggr{)}& \lambda_{\theta}>0\end{array}\right.. \tag{19}\]  
It is important to note that the correlations in Equations (18) and (19) contain the turbulence intensity \(Tu\). In the framework of the \(k\)-\(\omega\) model, \(Tu\) can be computed using the solution to the \(k\) equation. Here, we adopt the approach introduced in [8]. Specifically, \(Tu=Tu_{\infty}\) is set for all the points of the flow field.  
Moreover, \(\mathrm{Re}_{\theta,t}\) is computed by iterating on the value of \(\theta_{t}\), since \(\mathrm{Re}_{\theta,t}\) is a function of \(\theta_{t}\) itself because of the presence of \(\lambda_{\theta}\). Differently, for \(\mathrm{Re}_{\theta,c}\) and \(\mathrm{F_{length}}\), we use the correlations introduced by [PERSON] et al. [17]:  
\[\mathrm{Re}_{\theta,c}=\min\left(0.615\widetilde{\mathrm{Re}_{\theta,t}}+61.5,\widetilde{\mathrm{Re}_{\theta,t}}\right), \tag{20}\]  
\[\mathrm{F_{length}}=\min\left(\exp\left(7.168-0.01173\widetilde{\mathrm{Re}_{ \theta,t}}\right)+0.5,300\right). \tag{21}\]2.1.2 \(\tilde{v}\) Equation Coupling with the \(\gamma\)-\(\widetilde{\text{Re}_{\theta,t}}\) Model  
The production and destruction terms that appear in the \(\tilde{v}\) transport equation are suitably defined as follows:  
\[\begin{split}&\text{P}_{\tilde{v}}=\gamma_{\text{eff}}c_{b1} \tilde{S}\tilde{v},\\ &\text{D}_{\tilde{v}}=c_{w1}f_{w}\left(\frac{\tilde{v}}{d} \right)^{2}.\end{split} \tag{22}\]  
The term \(\gamma_{\text{eff}}\) in Equation (22) is devoted to modeling the separation-induced transition, and it is defined as follows:  
\[\gamma_{\text{eff}}=\text{max}\big{(}\gamma,\gamma_{\text{sep}}\big{)} \tag{23}\]  
with:  
\[\gamma_{\text{sep}}=\text{min}\left(2.0\cdot\text{max}\bigg{[}0,\bigg{(} \frac{\text{Re}_{v}}{3.235\text{Re}_{\theta,c}}\bigg{)}-1\bigg{]}\text{F}_{ neuttach},2.0\right)\text{F}_{\theta,t} \tag{24}\]  
and:  
\[\text{F}_{neutral}=\exp\left(-\frac{\text{R}_{T}}{20}\right)^{4}. \tag{25}\]
# Optimal Design of Small-Aperture Optical Terminals for Free-Space Links
###### Abstract  
We present the generalised design of low-complexity, small-aperture optical terminals intended for kilometre-scale, terrestrial, free-space laser links between fixed and dynamic targets. The design features single-mode fibre coupling of the free-space beam, assisted by a fast-steering, tip/tilt mirror that enables first-order turbulence suppression and fine target tracking. The total power throughput over the free-space link and the scintillation index in fibre are optimised. The optimal tip/tilt correction bandwidth and range, aperture size, and focal length for a given link are derived using analytical atmospheric turbulence modelling and numerical simulations.  
atmospheric turbulence; free-space optical; single-mode fibre Article
## 2 Methodology and Materials
### Cost Savings  
To maximize cost savings, classical optimization model in CPLEX solver software environment is applied in this research. The optimization method minimizes the grid-operational cost (\(C_{opt}\)) by Equation (10) and calculates maximum cost savings (\(S_{cost}\)) using Equation (11),  
\[min\ C_{opt}(t)=\sum_{i=1}^{t}\Bigl{(}C_{grid}(i)\times P_{grid}(i)\Bigr{)}- \sum_{i=1}^{t}\bigl{(}C_{FiT}(i)\times P_{export}(i)\bigr{)} \tag{10}\]  
\[s.t.\ P_{PV}(t)+P_{bat}(t)+P_{grid}(t)-P_{load}(t)=0\]  
\[S_{cost}(t)=\sum_{i=1}^{t}\bigl{(}C_{int}(i)-C_{\text{opt}}(i) \tag{11}\]  
Here, \(P_{grid}(t)\), \(P_{export}(t)\), \(P_{load}(t)\) indicate power imported from grid, exported to grid and load power at time \(t\) respectively. With this, \(C_{grid}(t)\), \(C_{FiT}(t)\) and \(C_{int}(t)\) symbolize for grid tariff, feed-in-tariff and grid's initial cost before optimization correspondingly. The data information regarding the projection are collected from reference [35].
## 3 Implementation Method of Proposed Macrodiversity System for Maritime Wireless Sensor Networks
### Identifying Coded Frame ID and Transmission Package ID  
Considering that the sensor data should be recovered from the coded frame, the coded frame ID and transmission package ID both need to be identified and aligned from different receivers.  
The identification scheme of the coded frame ID and transmission package ID is implemented using majority-voting decision algorithm, which classifies all candidate IDs and determines the ID with the most candidate IDs as the current ID. When local convergence module receives the transmission packages, processor needs to extract the header of the received package and identify the coded frame ID and transmission package ID. The specific steps of identifying coded frame ID are as follows:  
1. Extract the information sequence of the first 16 bits in the information header to obtain the coded frame ID sequence;
2. Generate a coded frame ID every two bits using the unmapping rule {00\(\rightarrow\)0, 01\(\rightarrow\)1, 10\(\rightarrow\)2, 11\(\rightarrow\)3} and count the occurrences of each coded frame ID;
3. Set the coded frame ID with the most occurrences to the current coded frame ID.  
The sequence containing transmission package ID is located in the last 16 bits of the information header, and the identification process is the same as that of the coded frame ID.  
Figure 3: The frame or package formats for macrodiversity reception.
## 4 Results and Discussion  
Optimization using GA took about 2 h while running a total of 33,208 iterations. Each result value calculated with the optimization design parameters is presented in [11] and the results of the model optimized in this paper are shown in Table 5. The detailed shape of the optimized LFHX is shown in Figure 5.  
As shown in Table 5, the heat transfer rate of the starting model produced from the optimization process is lower than the comparable value of the reference model, while the heat transfer rate of the final model after optimization is increased by about 5% compared to the reference model. Furthermore, effectiveness increased by about 1.6%, and overall weight decreased by about 5.8%; however, fin efficiency decreased by 1%. As a result, it is expected that the heat dissipation performance can be improved by roughly 5% by considering effectiveness and fin efficiency, which were not included in Ref. [11].  
Because the current heat exchanger is optimized and based on one specific operating point of the vehicle, it is necessary to investigate performance variations over the whole driving range of the vehicle. Changes in the mass flow rate, temperature, and pressure of coolant and air, which are the heat exchanger's input boundary conditions, affect the heat transfer rate, effectiveness, and pressure drop, and may cause changes in design parameters to satisfy the required performance during the optimization process.
## 2 Engineering Background  
The Laoshishan Tunnel, located in Kunming, Yunnan Province, China, poses considerable engineering challenges due to the extensive distribution of karst formations and the highly developed karst features, as illustrated in Figure 1. These conditions are exacerbated during the rainy season, which brings substantial groundwater inflows, complicating the geological environment even further. The surrounding rock mass, primarily consisting of shale with interbedded argillaceous sandstone, is of notably poor quality, contributing to the complex and demanding construction conditions.  
The stratum of the tunnel site area mainly includes Quaternary Holocene alluvial-diluvial (Q4 al+pl) silty clay, pebble soil, gravel soil, residual slope (Q4 dl+el) silty clay, and breccia soil; the Paleogene Relu Formation (E2-3r) purple conglomerate, calcareous lithic sandstone, and glutenite with different particle sizes; the Middle Triassic Lieyi Formation (T2 ly) sericite-quartz phyllite intercalated with sericite phyllite; and the Lower Triassic Dangen Formation (T1d) gray-black carbonaceous slate, sericite slate, phyllite, metamorphic argillaceous siltstone intercalated with crystalline limestone, and dolomitic solitic limestone. The design parameters for tunnel support are illustrated in Figure 2.  
This section is characterized by numerous faults. Influenced by the regional tectonic framework, the rock mass exhibits highly developed joints and fissures, resulting in a generally fractured surrounding rock mass. Excavation of the large deformation section has revealed that the surrounding rock comprises steeply inclined, extremely thin slate and carbonaceous slate. Additionally, due to tectonic compression, portions of the surrounding rock strata are distorted and display wavy structures, as illustrated in Figure 3.  
Figure 1: Geographic location map of the tunnel.  
Figure 3: Typical rock strata of tunnel face.  
Figure 2: Design schematic diagram of tunnel support parameters (units: cm).  
Due to the highly fractured nature of the rock mass, the uniaxial compressive strength measured using point load tests in the vertical direction is less than 5 MPa, classifying it as extremely soft rock. The weak interlayer bonding results in the formation of smooth surfaces between layers, commonly referred to as the \"mirror phenomenon\", as illustrated in Figure 4, rendering it impossible to measure the strength along these layers. Following tunnel excavation, the surrounding rock undergoes relaxation, resulting in localized micro-creep. The combined effect of these factors leads to progressive deformation, which, in turn, triggers the rupture of deeper rock masses. This process causes the deformation of the surrounding rock to manifest gradually. In the case of the tunnel, the soft rock has exhibited persistent deformation over a significant length of 190 m, accounting for 11.57% of the total tunnel length, all of which is categorized as large deformation.
## 5 Conclusions  
In this paper, a novel subspace clustering method based on kernel block diagonal representation and similarity-preserving strategy is proposed. The proposed KBDSP method has three steps: (1) capture the nonlinear structure of input data by introducing the kernel self-expressing frame to our model; (2) generate a similarity matrix with block diagonal structure by introducing the block diagonal representation term to our model; (3) capture the pairwise similarity information between data points by introducing the similarity-preserving term to our model. In this study, we conducted experiments on nine benchmark datasets. Experimental results have well demonstrated the effectiveness and superiority of the proposed KBDSP method. In future work, we will consider extending KBDSP to the deep framework to further improve its performance utilizing nonlinear information. In the proposed KBDSP, we only use single kernel methods to conduct the algorithm. Therefore, in the future, we will also research multiple kernel learning methods to improve the KBDSP method.  
Conceptualization, Y.Y.; Methodology, Y.Y.; Formal analysis, F.L.; Writing--original draft, F.L.; Visualization, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Basic Research Plan in Shaanxi Province (No. 2020 JM-543), and the Key Research and Development Project of Shaanxi Province (No. 2021 GY-083).  
Not applicable.  
The codes and data used in this study are available on request from the corresponding author.  
The authors declare that there are no conflicts of interests; we do not have any possible conflicts of interest.  
Figure 6: The convergence curve of KBDSP on four datasets.
# Practical V2I Secure Communication Schemes for Heterogeneous VANETs
## 4 Four Heterogeneous Signcryption Schemes for V2I Communication Scenarios
### PKI\(\rightarrow\)IBC Many-to-One Signcryption (MOHSC-I)  
**Setup**: Input a security parameter \(l\), PKG selects the additive/multiplicative group \(G_{1}\)/\(G_{2}\) of prime order \(q\) (\(P\) be a generator of \(G_{1}\)), a bilinear map \(e:G_{1}\times G_{1}\to G_{2}\), and three cryptographic hash functions \(H_{1}:\{0,1\}^{*}\to G_{1}\), \(H_{2}:G_{2}\to\{0,1\}^{n},H_{3}:G_{1}^{3}\times\{0,1\}^{n}\to Z_{q}^{*}\). Then, it selects \(s\in Z_{q}^{*}\) randomly as the master secret key and computes the master public key \(P_{pub}=sP\). Finally, PKG will publish **params** = \(\{q\), \(G_{1}\), \(G_{2}\), \(e\), \(P\), \(P_{pub}\), \(H_{1}\), \(H_{2}\), \(H_{3}\}\) while keeping \(s\) secret.  
**PKI-KG**: A vehicle \(V\) in the PKI selects \(x_{V}\in Z_{q}^{*}\) randomly as the secret key \(sk_{V}\) and calculates \(pk_{V}=x_{V}P\) as the public key. Let \(V_{i}\)'s public/secret key is \(pk_{i}=x_{i}P\)/\(sk_{i}=x_{i}\) below.  
**IBC-KG**: An RSU in an IBC first sends its identity \(ID_{r}\) to the PKG, then PKG calculates the secret key \(sk_{ID_{r}}=sh_{1}(ID_{r})=sQ_{ID_{r}}\), and transmits \(sk_{ID_{r}}\) to RSU via a secure channel. Let the identity of RSU be \(ID_{r}\) and the public/secret key \(pk_{r}=H_{1}(ID_{r})=Q_{r}\)/\(sk_{r}=sQ_{r}\) below.  
**Signcrypt**: Taking **params**, a random message \(m_{i}\), the sender \(V_{i}\)'s secret key \(sk_{i}\), the receiver RSU's public key \(pk_{r}\) as inputs, the algorithm performs the following steps:  
1. Randomly choose \(r_{i}\in Z_{q}^{*}\) and calculate \(R_{i}=r_{i}pk_{i}\);
2. Calculate \(k_{i}=e(r_{i}sk_{i}P_{pub},pk_{r})\), \(c_{i}=m_{i}\oplus H_{2}(k_{i})\);
3. Calculate \(h_{i}=H_{3}(pk_{i},pk_{r},R_{i},c_{i})\), \(S_{i}=sk_{i}h_{i}P_{pub}\);
4. Output the signcryption ciphertext \(\sigma_{i}=(R_{i},c_{i},S_{i})\) to a nearby RSU.  
**Aggregate-Verify**: A receiver RSU firstly act as an aggregate signcryption generator to save the verification costs. After receiving multiple ciphertexts \(\sigma_{i}=(R_{i},c_{i},S_{i})\) (\(i=1,2,\ldots,n\)), the RSU computes \(S=\sum_{i=1}^{n}S_{i}\), and get the final aggregate signcryption ciphertext \(\sigma=(R_{1},\ldots,R_{n},c_{1},\ldots,c_{n},S)\). Then, the RSU performs the following procedures to verify the ciphertext \(\sigma\) by using \(n\) sender vehicles' public keys \(\{pk_{i}\}_{i=1}^{n}\), the aggregate signcryption ciphertext \(\sigma\) and the receiver RSU's public key \(pk_{r}\).  
1. Compute \(h_{i}=H_{3}(pk_{i},pk_{r},R_{i},c_{i})\),\(i=1,2,\ldots,n\);
2. Verify \[e(S,P)=e(\sum_{i=1}^{n}h_{i}pk_{i},P_{pub})\text{;}\] (1)
3. If (1) is held, emits true, which means \(\sigma\) is valid. Otherwise, emits false and abort.  
**Unsigncrypt**: If **Aggregate-Verify** algorithm emits _true_, the receiver RSU's performs the following steps based on the aggregate signcryption \(\sigma\) and its secret key \(sk_{r}\).  
1. Calculate \[k_{i}^{\prime}=e(R_{i},sk_{r});\] (2)
2. Calculate \(m_{i}=c_{i}\oplus H_{2}(k_{i}^{\prime})\), and get the message \(m_{i}\).
# Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
## 4 Empirical Analysis  
### Construction of the US Air Traffic Network  
As the flight data of China is not open to the public, it is difficult to obtain flight data for the whole of China. The flight data used in this work were obtained from the Transportation Statistics Bureau of the US Department of Transportation [42]. The information of the historical flight data consists of detailed data for individual flights by aircraft and airline identification code (tail number and airline ID, respectively), date of flight, real and scheduled departure (arrival) times, origin and destination, flight time, and whether the flight was canceled or diverted.  
We took the departure and arrival flight data from 100 major airports in the United States from September 2015 to February 2016 as an example for empirical analysis. The US flight network we established contains 100 nodes and 14,621 directed lines that connect most major cities in the US.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Gelatin Films  
Gelatin is a tasteless, water-soluble, transparent high protein with a faint yellow color that is produced as a result of the hydrolysis of the fibrous protein collagen [37]. Pork, fish, and bovine are sources of collagen for gelatin production [36]. Depending on its method of synthesis, gelatin can be categorized as (I) Type A--derived from acid-treated collagen--and (II) Type B--obtained from alkali-treated collagen [42; 76]. Gelatin is utilized in the adjustment, gelling, texturization, and emulsification of bakery, confectionary, beverages, and dairy items in the food industry. However, its limited mechanical and thermal stability has prevented it from being used in many applications [76]. The gelatin-based film shows low O\({}_{2}\) permeability and good mechanical properties but is highly moisture-sensitive and water vapor permeable because of its hygroscopic nature [42].  
Zein is a prolamin protein present in corn that is insoluble in water but soluble in 60-90% ethanol solutions and also in alkaline solutions with pH greater than 11 [77; 78]. It has thermoplastic behavior and hydrophobic, antioxidant, and antibacterial properties [79]. Zein-based films are thermally stable, smooth, and have selective barriers to O\({}_{2}\), CO\({}_{2}\), and oils. Hence, it has good film-forming properties due to its hydrophobic nature and barrier properties. Despite this, zein-based films exhibit fragility and poor mechanical properties. However, the addition of plasticizers or combining them with other polymers as composite films can improve their structural properties [30; 42]. [PERSON] et al. [80] developed zein-chitosan-based blend systems enhanced with cinnamon, thymol, thyme, and oregano and studied their physicochemical properties and antimicrobial efficiency with a specific focus on their potential application in the active packaging of fresh strawberries. The result showed that the zein-chitosan blend is appropriate as a bioactive compound carrier to prevent moisture loss, make barriers, ensure microbial food quality, and prolong the shelf life of fruits. These systems can be used for sustainable active food packaging.