esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Time Series Prediction Using LSTM and ARIMA
### Lstm  
The drawbacks of RNNs are overcome with the use of LSTMs. Essentially, an LSTM is a type of RNN, which includes additional features, in order to have memory on the sequence of the data. An LSTM is a set of system modules, wherein the streams of the data are obtained and stored. The modules appear to be similar to a transport line, which connects out of one module to a different one accommodating data from precedent and amassing them for the current one. The utilisation of gates in one system module enforces data to be disposed of, filtered, or integrated for the next modules. Thus, the gates predicated on the sigmoidal neural network layer sanction the system modules, to sanction the data to go through or get disposed of.  
Three types of gates are used in an LSTM with the target of controlling each system module's state:  
* Forget gate, which produces a number between 0 and 1, where 1 is used to completely keep the information from the previous timestamp, and 0 implies to completely ignore it.
* Memory gate selects the new data that needs to be stored in the system module. Initially, the input door layer selects the values to be altered. Thereafter, a layer makes a vector of new potential values which could be added to a state.
* Output gate presents the decision on what will be output by each system module. The output value will be based on the state of the system module in conjunction with the filtered and newly added data.  
In this paper, we used a univariate LSTM with three batch numbers, namely 100, 1000, and 7000, to test and locate the best generalisation.
# Full-Scale Model Test of Subway Contact Channel under Mechanical Construction +
## 2 Test Scheme
### Test Platform  
The full-scale test relied on the comprehensive test platform of Ningbo University. The basic components of the platform included a modular soil pressure simulation device, modular support base, foundation simulation device, tunnel segment longitudinal loading simulation device, multi-ring segment tunnel test unit assembly and transportation device, multi-ring segment tunnel test unit lifting device, and connecting channel shield-tunneling machine receiving simulation device. The platform can conduct 1:1 physical model tests of shield-tunneling construction, which truly simulate and test the bearing capacity of the segment lining structures under normal load and ultimate load, the mechanical transmission between rings, and other mechanical properties. Additionally, the platform can also simulate the construction progress of mechanical connection channels to study the influence of the cutting process on the main channel structures.  
Figure 1: Full-scale model test object. (**a**) Front view. (**b**) Lateral view (the 4 th ring).  
The loading system of the test platform adopted a steel counter-stress frame connected by a seven-ring flange as the main body, and the loading was assisted by 24 oil cylinders per ring. There were 168 oil cylinders in total, as shown in Figure 2. The displacement accuracy of the oil cylinder is 0.1 mm, which can realize three control modes: load control, displacement control, and load-displacement control. From this, the actual resistance of soil layers can be simulated accurately.
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 4 Results
### Hydrocarbon Sealing Faults (F1-F6)
#### 4.1.3 Fault-Sealing Capacity  
To assess the capillary sealing capacity of the faults F1-F6, the SGR (Shale Gouge Ratio) values were calculated (Figure 6). The SGR values provide an indication of the sealing potential of the fault surfaces. In general, the F1, F2, and F3 faults exhibit relatively weak heterogeneities in the SGR distribution along their surfaces. The majority of these fault surfaces have SGR values between 35 and 50%, with some smaller sections ranging between 25 and 35% or even exceeding 50% (Figure 6a-c). This suggests that the capillary sealing capacity of these faults may be relatively consistent along their surfaces. In contrast, the F5 fault displays stronger heterogeneities in the SGR distribution along its surface. The upper part of the fault, between the T60 and T61 interfaces (on the football side), and a narrow section along the T62 interface (on the footwall side) exhibit low SGR values. However,other parts of the F5 fault surface show high SGR values (Figure 6d). This indicates that the sealing capacity of the F5 fault may vary along its surface, with certain sections potentially having better capillary sealing properties than others.  
Figure 5: The average dip-slip fault displacement rate of the F1–F10 during different deposition periods in the southern Wenchang A subbasin.  
Figure 6: Figure illustrating the SGR value distribution along the F1 (**a**), F2 (**c**), F3, (**e**) and F5 (**g**) faults and the associated Allan diagrams for F1 (**b**), F2 (**d**), F3 (**f**), F5 (**h**). View from the side of the hanging wall. For fault locations, see Figure 3.  
The calculated maximum column heights for both Reservoir I and II, determined using the SGR-capillary pressure relationships proposed by [PERSON] et al. [39] and Yielding [61] (Equations (3)-(5)), reveal a congruence with the measured column heights, as indicated in Table 1. Notably, the measured column heights generally align closely with the upper limits of column heights projected by the [PERSON] et al. [39] algorithm, constituting approximately 50% of the predicted values on average (Table 1). Remarkably, even SGR values as modest as 15.8 are shown to sustain significant oil column heights, notably up to 55.8 m (Figure 7). Conversely, the lowest observed SGR value that accommodates gas columns is 25, corresponding to a column height of 17.4 m. Note that for higher (>30) SGR values, supported column heights seem to vary drastically. These findings underscore the potential of fault sections in the study area with SGR values exceeding 15 to serve as effective hydrocarbon seals, while fault sections with SGR values lower than 15 serve as hydrocarbon leakage pathways. This further supports the notion that certain fault segments, characterized by SGR values beyond the threshold, can effectively contain hydrocarbon migration.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 6 Performance Evaluation
### Energy Consumption  
Since the smart meter resources in the AMI are very limited and usually rely on limited power supply to operate, energy consumption is also the key to performance analysis. Here, we analyze the energy consumption of smart meters in sending/receiving messages. According to [15], the energy consumed by the smart meter to send and receive 1 bit of data is 0.72 \(\upmu\)J and 0.81 \(\upmu\)J, respectively, so the energy consumption of the proposed scheme is about 804.96 \(\upmu\)J. Through the above analysis of computing and communication  
Figure 5: DC’s computational cost comparison of [2, 13, 17, 18, 20].  
Figure 6: Communication costs comparison of [2, 13, 17, 18, 20].  
overheads, it can be seen that this scheme also has advantages in energy consumption. Combined with Table 3, it is easy to calculate the corresponding energy consumption. Figure 7 shows the comparison of the proposed scheme with other related schemes in terms of energy consumption.  
In summary, the proposed scheme achieves communication security of AMI with the minimum computation, communication cost and energy consumption, greatly improving performance and reducing the burden on smart meters. The improvement of performance means that the scheme can accommodate more access to smart meters, thereby reducing the cost of software and hardware iteration, which is of great significance in practical applications.
## 3 Results and Discussion
### Heat Preservation  
The fiber/PVA and PE films were used to culture Chinese cabbage; the control used no film. The results in Figure 1 show that the ability of the films to preserve temperature followed the following order: PE film > fiber/PVA film > no film. The ability of the fiber/polymer film to preserve heat was poorer than that of the PE film; however, our field test demonstrated that the fiber/polymer film could be beneficial under certain conditions, such as during the summer season or in high-temperature regions.  
Figure 1: Temperatures measured under soil (at depths of 0, 10, and 20 cm) covered by different mulching films (fiber/PVA and PE) and no film.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type of Film** & **Machine** & **Percentage** & **Cross-Machine** & **Percentage** \\  & **(average, MPa)** & **Elongation (\%)** & **(Average, MPa)** & **Elongation (\%)** \\ \hline Fiber/ST film & 2.92b & 24.77b & 3.52a & 9.06b \\ Fiber/PVA film & 3.58a,b & 23.71b & 2.62b & 10.92b \\ Fiber/PA film & 3.69a & 10.92b & 3.74a & 18.31b \\ PE film & 12.96c & 132.3a & 12.96c & 132.3a \\ \hline \end{tabular}
\end{table}
Table 1: Tensile strength at break of the different mulching films. ST: Starch; PVA: Poly(vinyl alcohol); PA: Polyacrylate; PE: Polyethylene \({}^{\mathrm{a}}\).
## 4 Experiments and Analysis
### Comparative Algorithm  
For the experimentation, a selection was made that encompassed one foundational swarm intelligence algorithm and two enhanced versions of the initial algorithm. Unique surrogate model management methodologies are embodied by each of these algorithms, which are particle swarm optimization (PSO), a Gaussian process surrogate model-assisted evolutionary algorithm (GPEME) [18], and committee-based active learning for surrogate-assisted PSO (CALSAPSO). This curated selection aimed to facilitate comprehensive comparative scrutiny among these algorithms.  
(1) Particle Swarm Optimization (PSO). PSO is a computational approach that emulates the collective behavior observed in the social phenomena of bird flocks or fish schools. Introduced by [PERSON] and [PERSON] in 1995 [34; 35], scholars have proposed these variations to either accelerate convergence [36] or amplify population diversity, the latter being a strategy to diminish the likelihood of confinement to local optima.  
(2) GPEME. This represents a Kriging-based SAEA that incorporates the lower confidence bound as its pivotal criterion for the selection of solutions worthy of evaluation, specifically tailored for median scale problems.  
(3) CALSAPSO. Within the domain of the SAEA, the strategy of active learning is integrated using a triad of models. An integrated surrogate model arises from the evaluation of the root mean square error. Moreover, a localized surrogate model is crafted by scrutinizing the samples marked by the highest uncertainty, harmonized with the identification of potential optimal solutions as inferred from the integrated surrogate model. It is noteworthy that PSO is the driving force behind the optimization, and the algorithm delineated in this paper functions in harmony with the PSO framework. The parameter configuration for the comparative algorithms is illustrated in Table 1.
# Arithmetic Optimization AOMDV Routing Protocol for FANETs
## 3 AO-AOMDV Routing Protocol
### Route Process  
As shown in Figure 1, the AO-AOMDV protocol achieves route discovery and route maintenance by propagating RREQ messages from the source node S to the destination node D and returning RREP messages from intermediate nodes or the destination node through pre-established reverse paths. Multiple disjoint paths are established between the source and destination nodes, resulting in the discovery of N non-overlapping paths. Assume that we first calculate the fitness value of path \(p1\) (e.g., L1, L2, L3), where \(HT(p_{1})\), \(E(p_{1})\), and \(C(p_{1})\) are the maximum or minimum values among L1, L2, and L3, respectively. This yields \(F(p_{1})\). Similarly, we compute the fitness values for other paths, and finally, using AO, we select the highest fitness value among all valid paths. Let us now understand the process of route discovery and route maintenance.
## 4 Conclusions  
Overall, the additives had the greatest effect on the bulk ionic conductivity of the semicrystalline phase of the electrolytes. Addition of minor fractions of C18 MS to C18 LiTFSI resulted in increases in conductivity of over two orders of magnitude at room temperature, while addition of C18 ES or C18 IPS resulted in decreases in ionic conductivity over the entire composition space and temperature range investigated. It is noted that the ionic conductivities observed for the C18 MS0.05 and C18 MS0.3 semicrystalline blends are orders of magnitude higher than those reported previously for a crystalline lithiated telechelic alkyl sulfonate salt, C48(SO3 Li)2 (C18 MS0.05: 9.15 \(\times\) 10\({}^{-7}\) S/cm at 25 \({}^{\circ}\)C, C18 MS0.3: 8.01 \(\times\) 10\({}^{-7}\) S/cm at 25 \({}^{\circ}\)C, C48(SO3 Li)2: \(\sim\)10\({}^{-9}\) S/cm at 136 \({}^{\circ}\)C) [33]. The neutral additives did not significantly increase conductivity for the liquid crystalline phase of the electrolyte. Use of additives also decreased the temperature window for liquid crystalline phase behavior. The fundamental origin for the conductivity enhancement in only the case of select crystallized electrolytes is not yet understood and will be the subject of future investigations.  
\begin{table}
\begin{tabular}{c c c} \hline
**Material** & \(\sigma_{0}\) (S/cm) & \(E_{a}\) (kJ/mol) \\ \hline C18 LiTFSI & \(6.31\times 10^{16}\pm 1.1\times 10^{15}\) & \(141\pm 2\) \\ C18 MS0.05 & \(5.17\times 10^{2}\pm 2.9\times 10^{1}\) & \(49.6\pm 0.8\) \\ C18 MS0.3 & \(1.22\times 10^{11}\pm 2\times 10^{9}\) & \(98.2\pm 0.6\) \\ C18 MS1.0 & \(1.62\times 10^{30}\pm 3\times 10^{28}\) & \(225\pm 3\) \\ C18 ES0.05 & \(8.03\times 10^{25}\pm 3.6\times 10^{24}\) & \(204\pm 7\) \\ C18 ES0.3 & \(3.44\times 10^{38}\pm 1.7\times 10^{32}\) & \(250\pm 10\) \\ \hline \end{tabular}
\end{table}
Table 6: Values of \(\sigma_{0}\) (S/cm) and \(E_{a}\) (J) obtained from the Arrhenius fits of conductivity for several electrolyte compositions.
## 2 System Model  
### Theoretical Background  
The most common method to set up a biological nanonetwork is to use signaling molecules in order to encode the information under an on-off keying (OOK) concentration-based modulation, and rely on the Brownian motion for the message propagation (we exclude chemotactic effect because it is out of the scope of our work). In this model, communicating entities release information molecules into the intercellular medium and through the ligand-receptor binding process; each party measures the particle concentration in its vicinity and reconstructs the transmitted message from the received pulses. Fundamental to the BN is the fact that the molecular channel has an inherent memory and the diffused substance remains in the channel, requiring either low or high concentrations for the transmitted message [1,15].  
The main driving force for binding is the unbound ligand concentration, meaning that the model is based on the law of mass action, just like diffusion does. Once the diffusion process transfers the information molecules to the receiver, the latter counts the number of occupied binding sites to measure the concentration, as shown in Figure 1, and reacts accordingly by releasing signaling molecules as the response to the received message. Each one of these sites can trap only one molecule at a time for a period of time equal to the residence time (RT). These molecules are bound to only one signaling molecule [16,17]. As the concentration of the signaling molecules near the receiver's surface periphery increases, the probability of a successful connection also increases. It should be noted here that the receptor concentration is held constant for simulation purposes, and the ligand is varied.
## 2 Basic Equations  
The perturbed singular value decomposition of \(A\) (2) can be written as  
\[\widehat{U}^{T}\widehat{A}\widehat{V}=\widehat{\Sigma}, \tag{4}\]where  
\[\widetilde{U} = U+\delta U:=[\widetilde{u}_{1},\widetilde{u}_{2},\ldots,\widetilde {u}_{m}],\ \widetilde{u}_{j}\in\mathbb{R}^{m},\] \[\delta U := [\delta u_{1},\delta u_{2},\ldots,\delta u_{m}],\ \delta u_{j}\in\mathbb{R}^{m},\] \[\widetilde{u}_{j} = u_{j}+\delta u_{j},\ j=1,2,\ldots,m,\] \[\widetilde{V} = V+\delta V:=[\widetilde{v}_{1},\widetilde{v}_{2},\ldots, \widetilde{v}_{n}],\ \widetilde{v}_{j}\in\mathbb{R}^{n},\] \[\delta V := [\delta v_{1},\delta v_{2},\ldots,\delta v_{n}],\ \delta v_{j}\in\mathbb{R}^{n},\] \[\widetilde{v}_{j} = v_{j}+\delta v_{j},\ j=1,2,\ldots,n,\]  
and  
\[\widetilde{\Sigma} = \Sigma+\delta\Sigma=\left[\begin{array}{c}\widetilde{\Sigma}_ {n}\\ 0\end{array}\right],\] \[\widetilde{\Sigma}_{n} = \Sigma_{n}+\delta\Sigma_{n}=\text{diag}(\widetilde{v}_{1}, \widetilde{v}_{2},\ldots,\widetilde{v}_{n}),\] \[\delta\Sigma_{n} = \text{diag}(\delta\sigma_{1},\delta\sigma_{2},\ldots,\delta \sigma_{n}),\] \[\widetilde{\sigma}_{i} = \sigma_{i}+\delta\sigma_{i}.\]  
Equation (4) is rewritten as  
\[\delta U^{T}AV+U^{T}A\delta V+\delta F=\left[\begin{array}{c}\delta\Sigma_{n }\\ 0_{(m-n)\times n}\end{array}\right]+\Delta_{0}, \tag{5}\]  
where \(\delta F=U^{T}\delta AV\) and the matrix \(\Delta_{0}\)  
\[\Delta_{0}=-\delta U^{T}A\delta V-U^{T}\delta A\delta V-\delta U^{T}\delta AV -\delta U^{T}\delta A\delta V\in\mathbb{R}^{m\times n}\]  
contains only higher-order terms in the elements of \(\delta A\), \(\delta U\), and \(\delta V\).  
Let the matrices \(U\) and \(\delta U\) be separated as \(U=[U_{1},U_{2}],\ \ U_{1}\in\mathbb{R}^{m\times n}\) and \(\delta U=[\delta U_{1},\delta U_{2}]\), \(\delta U_{1}\in\mathbb{R}^{m\times n}\), respectively. Since the matrix \(A\) can be represented as \(A=U_{1}\Sigma_{n}V^{T}\), the matrix \(U_{2}\) is not well determined but should satisfy the orthogonality condition \([U_{1},U_{2}]^{T}[U_{1},U_{2}]=I_{m}\). The perturbation \(\delta U_{2}\) is also undefined so that we can bound only the perturbations of the entries in the first \(n\) columns of \(U\), i.e., the entries of \(\delta U_{1}\). Further on, we shall use (5) to determine componentwise bounds on \(\delta U_{1}\), \(\delta V\) and \(\delta\Sigma_{n}\).
# Chitin and Chitosan in the Alcoholic and Non-Alcoholic Beverage Industry: An Overview
## 3 Chitosan Applications in Beverages
### Extending the Shelf-Life
In orange juice, the application of chitosan showed a quality improvement in reducing enzymatic and non-enzymatic browning and controlling the spoilage during the storage period, avoiding the application of the traditional thermal treatments (pasteurization), which produces a negative impact on the nutritional value [66]. These authors showed that it is a necessary equilibrium between quality and nutritional values, as greater chitosan content enhanced quality; but it decreases vitamin C due to chitosan application related to the flocculation ability of chitosan, suggesting the application of lower chitosan doses in the orange juice. Moreover, [PERSON] [109] has already previously studied the effect of chitosan application on apple and pear juice enzymatic browning. The authors indicated that browning could be prevented in apple juice, for example, by the addition of at least 200 mg/L chitosan, independently of the type of chitosan applied (low or high molecular weight). The effectiveness of chitosan treatments to control enzymatic browning in apple and pear juices may be due to the capacity of the positively charged polymer to coagulate suspended solids to which polyphenol oxidase (PPO) is bound [109]. Apple juice research data has indicated that chitosan showed antifungal properties in juice [107,110], and as mentioned previously, has been shown that in apple juice, low-molecular-weight chitosan exhibited higher antioxidant and free radical scavenging effects than high-molecular-weight chitosan [88].
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 3 Proposed Scheme
### System Model  
This paper considers a downlink MU-MIMO broadcast system which consists of one BS and \(K\) users as shown in Figure 4. BS is equipped with \(N_{t}\) transmitting antennas and each user has \(N_{r}\) receiving antennas. The MIMO channel of each user is assumed to be flat fading, since frequency selective fading channel can be easily overcome by using orthogonal frequency division multiplexing (OFDM) modulation. The system model can be further extended to frequency selective fading MIMO channel considering all subcarriers. In this system model, the transmit signal for the \(k\)-th user can be denoted as follows,  
\[\mathbf{x}_{k}=\mathbf{W}_{k}\mathbf{s}_{k}. \tag{1}\]  
The received signal at the \(k\)-th user is given by  
\[\mathbf{y}_{k}=\underbrace{\mathbf{H}_{k}\mathbf{W}_{k}\mathbf{s}_{k}}_{\text {desired signal}}\ +\underbrace{\sum_{j=1,j\
eq k}^{K}\mathbf{H}_{k}\mathbf{W}_{j}\mathbf{s}_{j} }_{\text{undesired signals}}+\mathbf{n}_{k},k=1,\cdots,K, \tag{2}\]  
where \(k\) and \(j\) are user indices, \(\mathbf{W}_{k}\) is \(N_{t}\times N_{r}\) precoding matrix for user \(k\), \(\mathbf{s}_{k}\) is a \(N_{r}\times 1\) data symbol vector, \(\mathbf{x}_{k}\) is a \(N_{t}\times 1\) precoded signal vector for the \(k\)-th user. \(\mathbf{y}_{k}\) is a received signal vector for the \(k\)-th user and \(\mathbf{n}_{k}\) is \(N_{r}\times 1\) zero-mean additive white Gaussian noise (AWGN) vector with variance \(\sigma^{2}\). In the Equation (2), the first term denotes signal in the intended direction (desired user \(\mathbf{s}_{k}\)) and the second term denotes multi-user interference caused due to undesired signals (undesired users \(\mathbf{s}_{j}\))  
Figure 3: The downlink single-cell MU-MIMO system.
## 2 Fast Deploying Monitoring System  
### Traditional Monitor System  
The structure of a traditional landslide monitoring system is shown in Figure 1. In Figure 1, all kinds of monitoring sensors are connected with the data transfer unit (DTU) by wires, and they communicate with each other in accordance with the Modbus protocol or SDI-12 protocol. The DTU, GPRS/3G/4G communication module, and power supply system constitute a remote measurement unit (RTU). The monitoring data are sent to the mobile communication network by the communication module and transmitted to the control center through the public network. In this way, the system robustness is poor. If the GPRS/3G/4G communication module of one monitoring point breaks down, all the data from the sensors under this monitoring point will not be submitted, which means a partial paralysis of the monitor system. Therefore, a more flexible and stable networking structure is required to improve the robustness of the traditional landslide monitoring system. This is the adaptive landslide monitoring system.