esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Cities in Russia: Current Situation and Insights for Future Development
## 4 Smart City Components
### Smart Communication  
Good communication between the people and the city government makes a city a better place to live. The problems are solved faster and citizens get a better environment as a result. There are several ways to enhance communication in the city.  
1. **Government portals:** The government should provide certain services for its citizens. Such services include creating a safe environment for the mind and body and issuing various documents and certificates. To get some particular document, one often has to visit a government agency several times. In a smart city, this problem has to be solved using information technology. Allocation of papers should happen remotely and automatically. Another common problem is tax payments. In the smart city, a tax payment should be as convenient as possible. This can be solved by digitalizing the entire process. For example, there is a UK government portal which helps to manage taxes [25].
2. **City problem trackers:** Many problems arise in daily life in a cityL road accidents, fires, power outages, and so on. In a smart city, citizens should be able to easily track such problems. That could be done via the centralized governmental source of information. The aim of this source should be to provide relevant and accurate information. In this way, citizens will be able to make adjustments to their plans according to the state of the problems.
3. **Emergency notifications:** A system for notifications about earthquakes, fires, floods, and other natural disasters could be implemented with the use of information technology. For example, in the article [26] the authors proposed an application (android base) which can send warning alarms to people via SMS or voice call before cyclones and floods strike, or other types of natural disasters, and also can tell them about the optimal routes to the nearest shelter. Another possible solution is to use Bluetooth technology to pass instructions between people. That can be helpful if the communication systems may be disabled. The use of such systems can make the city a safer place for people.
# Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms
## 3 Methodologies  
### ABC Algorithm  
As a typical swarm intelligence algorithm, ABC simulates the foraging behaviors of natural bees, where a food source represents a solution and its fitness is measured in terms of nectar amount.  
The algorithm begins with a randomly distributed initial population generation and evaluation [11]. Then, repeated search cycles are executed to update the optimal solution. During the cycles, as shown in Equation (7), employed bee probabilistically produces neighbor food source \(x^{\prime}_{ik}\) around the current optimal solution \(x_{ik}\).  
\[x^{\prime}_{ik}=x_{ik}+rand(-1,1)*\Bigl{(}x_{ik}-x_{jk}\Bigr{)} \tag{7}\]  
Here, \(i\) and \(j\) represent the numbers of the solutions, \(i,j\in\{1,2,\ldots,\) N\(\}\), \(i\
eq j\). \(k\) is the dimension of the solution, which is selected randomly, \(k\in\{1,2,\ldots,\) D\(\}\).  
Subsequently, the \(mth\) onlooker bee randomly chooses to exploit or not around corresponding employed bee with the probability \(P_{m}\) defined as Equation (8).  
\[P_{m}=\frac{fitness_{m}}{\sum_{m=1}^{SN}fitness_{m}} \tag{8}\]  
If the current solution to be exploited cannot be improved for several iterations, it will be abandoned, and a randomly produced scout bee will replace it.
###### Abstract  
Scarabaeoidea and Chrysomeloidea insects are agriculture-destructive coleopteran pests. Few effective _Bacillus thuringiensis_ (Bt) insecticidal proteins against these species have been described. Bt isolate BtSU4 was found to be active against coleopteran insects. Genome sequencing revealed two new _cry8_ genes in BtSU4, designated as _cry8 Ha1_ and _cry8 la1_. Both genes expressed a 135 kDa protoxin forming irregular shape crystals. Bioassays performed with Cry8 Ha1 protoxin showed that it was toxic to both larvae and adult stages of _Holotrichia parallels_, also to _Holotrichia oblita_ adults and to _Anoplophora glabripemis_ larvae, but was not toxic to larval stages of _H. oblita_ or _Colaphellus bowringi_. The Cry8 Ha1 protoxin only showed toxicity against _H. parallela_ larvae. After activation with chymotrypsin, the Cry8 Ha1 activated toxin lost its insecticidal activity against _H. oblita_ adults and reduced its activity on _H. parallela_ adults, but gained toxicity against _C. bowringi_ larvae, a Chrysomeloidea insect pest that feeds on crucifer crops. The chymotrypsin activated Cry8 Ha1 toxin did not show toxicity to any one of these insects. These data show that Cry8 Ha1 and Cry8 Ha1 protoxin and activated toxin proteins have differential toxicity to diverse coleopteran species, and that protoxin is a more robust protein for the control of coleopteran insects.  
**Keywords:**_Bacillus thuringiensis; cry8_ genes; Coleopteran; insecticidal activity; Cry protoxin; dual mode of action  
**Key Contribution:** The study shows that Cry8 Ha1 protein has a broad insecticidal spectrum against Chrysomeloidea and Scarabaeoidea pests.
# A Review of the Potential Climate Change Impacts and Adaptation Options for European Viticulture
## 5 Climate Change Adaptation Strategies
### Short-Term Adaptation Strategies
#### 5.1.2 Protection against Extreme Heat and Sunburns  
The negative impacts of extreme heat, water scarcity, and high irradiance in vineyards urge short-term adaptation strategies, such as the application of exogenous compounds that could maintain, or even improve, plant growth and development under these environmental stresses. Several sunscreenmaterials that form an inert particle film upon the leaves, such as calcium carbonate (CaCO\({}_{3}\)), kaolin (Al\({}_{2}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\)), and potassium silicate (K\({}_{2}\)SiO\({}_{3}\)), or the use of shade nets, have been studied to increase canopy and fruit zone reflective capacity [171; 172]. Currently, one of the most commonly used compounds in viticulture is kaolin, a white clay, which is chemically inert and has excellent reflective properties. [PERSON] et al. [173] were the first to test the effects of this mineral on apple productivity. More recently, several studies developed for grapevines grown under severe summer stress corroborated that the use of kaolin has a positive impact on leaf cooling, minimizing the scorching of leaves and clusters during the hottest summer days [139; 140]. Additionally, structure and function of the photosynthetic machinery and water relations were significantly improved [174; 140; 175], in part as a response to altered hormonal relationships (abscisic acid (ABA) decreased; indoleacetic acid (IAA) increased) [176]. In terms of yield and quality attributes, it was also shown for cv. Touriga-Nacional that kaolin application improved the antioxidant capacity in berries, as well as the amounts of some secondary metabolites, such as total phenols, flavonoids, and anthocyanins [174; 175]. In another study, it was found that these compounds, qualitatively associated with the increase of colour, flavour, aroma, texture, astringency, antioxidant capacity, and stabilization of wines, were synthesized in greater quantity due to a more favourable stimulus of biosynthetic pathways (especially the phenylpropanol and flavonoid synthetic pathways) and associated gene expression [47; 177].
# Towards a Universal Semantic Dictionary
## 2 Related Work
### Multilingual Word Embeddings  
In 2013, [PERSON] et al. [3] published a simple two-step procedure for creating universal embeddings. In the first step, they built monolingual models of languages using huge corpora,and, in the second step, a small bilingual dictionary was used to learn linear projection between the languages. The optimization problem was the following:  
\[\min_{W}\sum_{i=1}^{n}||Wx_{i}-z_{i}||^{2} \tag{1}\]  
where \(W\) denotes the transformation matrix, and \(\{x_{i},z_{i}\}_{i=1}^{n}\) are the continuous vector representations of word translation pairs, with \(x_{i}\) being in the source language space and \(z_{i}\) in the target language space.  
[PERSON] and [PERSON] [7] proposed a procedure to obtain multilingual word embeddings by concatenating the two word vectors coming from the two languages, applying Canonical Correlation Analysis. [PERSON] et al. [14] found that bilingual translation can be largely improved by normalizing the embeddings and by restricting the transformation matrices into orthogonal ones. [PERSON] et al. [6] showed that the neighborhoods of the mapped vectors are strongly polluted by hubs, which are vectors that tend to be near a high proportion of items. They proposed a method that computes hubness scores for target space vectors and penalizes those vectors that are close to many words, i.e., hubs are down-ranked in the neighboring lists. [PERSON] et al. [15] studied some theoretical and empirical properties of a general cross-space mapping function, and tested them on cross-linguistic (word translation) and cross-modal (image labeling) tasks. They also introduced the use of negative samples during the learning process. [PERSON] et al. [16] proposed methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Since English usually offers the largest corpora and bilingual dictionaries, they used the English embeddings to serve as the shared embedding space. [PERSON] et al. [17] built a generic framework that generalizes previous works made on cross-linguistic embeddings and they concluded that the best systems were the ones with orthogonality constraint and a global pre-processing with length normalization and dimension-wise mean centering. [PERSON] et al. [4] also proved that translation matrices should be orthogonal, for which they applied Singular Value Decomposition (SVD) on the transformation matrices. Besides, they also introduced a novel \"inverted softmax\" method for identifying translation pairs. All these works listed above applied supervised learning. However, in 2018, [PERSON] et al. [5] introduced an unsupervised way for aligning monolingual word embedding spaces between two languages without using any parallel corpora. Their method consisted of an adversarial approach for aligning two monolingual word embedding spaces, followed by a refinement step using frequent aligned words (according to the adversarial mapping). This unsupervised procedure holds the current state-of-the-art results on Dinu's benchmark word translation task.
# The Bearing Characteristics of Sand Anchors under Vibrating Load
## 2 Test Details  
### Materials  
The sandy soil in this test was taken from the typical sandy soil in the Inner Mongolia region; the soil has a depth of 0.5-1 m. The soil was air-dried, crushed, and stored in a cool, dry place. The basic physical and mechanical properties of the soil samples were tested according to the Standard for Geotechnical Test Methods (GB/T 50123-2019) [36]. The tests included a specific gravity test (GB/T 7.1-7.2), sieve test (GB/T 8.1-8.2), and straight shear test (GB/T 21.1-21.4). The basic physical properties of the soil are shown in the following charts (Table 1 and Figures 1 and 2). The sand soils were prepared with 10%, 12%, and 14% moisture content according to the test requirements, and their densities were 1.72 g/cm\({}^{3}\), 1.75 g/cm\({}^{3}\), and 1.73 g/cm\({}^{3}\), respectively. The anchor hole grouting material was standard sand, ordinary silicate cement, and water at a ratio of 1:1:0.43, and ordinary silicate cement with strength class 42.5 was used, the basic properties of which are shown in Table 2. The anchors are made of common ribbed steel bars with a
# Data-Driven Health Assessment in Flight Control System
## 4 Health Assessment Method
### Relative Deterioration Degree  
Each characteristic parameter has its special physical meaning and dimension, the relative deterioration degree method [18] is used for normalization, and then the membership function is constructed in this subsection. The relative deterioration degree refers to the similarity between the current state of the characteristic parameter and fault state; the value range is set as [0,1]. The value 1 indicates the fault state, and the value 0 is the healthy state. For characteristic parameters analysis of the flight control system, the intermediate type calculation method is adopted to calculate the relative deterioration degree, and its degradation degree function parameters include the maximum \(x_{\text{max}}\), minimum \(x_{\text{min}}\), and optimal range \([x_{a},x_{b}]\), which is shown as:  
\[d(x)\ =\ \begin{cases}1&x<x_{\text{min}}\\ \frac{x-x_{\text{min}}}{x_{a}-x_{\text{min}}}&x_{\text{min}}\leq x\leq x_{a}\\ 0&x_{a}\leq x\leq x_{b}\\ \frac{x-x_{b}}{x_{\text{max}}-x_{b}}&x_{b}\leq x\leq x_{\text{max}}\\ 1&x>x_{\text{max}}\end{cases}, \tag{11}\]
## 4 Experimental Results and Analysis
### Comparative Analysis of Experimental Results
#### 4.4.1 Contrast Experiments
The combination of the CNN and LSTM increases the complexity of the model. CNN-LSTM combines the spatial feature extraction ability of convolutional neural networks and the time series processing ability of LSTMs, which is suitable for complex data. The CNN: R-Squared is higher, indicating that the model has a certain ability to fit the data. Each indicator has its own advantages and disadvantages, indicating that the CNN may have advantages in dealing with certain specific tasks. The CNN is good at capturing the spatial characteristics of data, which is suitable for image processing and some time series analysis tasks. CNN-BiLSTM-GRU-Attention: The overall performance is excellent. Due to the complex structure of the model, the training and prediction time is the longest. It combines the advantages of multiple neural networks and can deal with complex data structures and time series relationships. CNN-BiLSTM: R-Squared is the highest, reaching 0.777, indicating that the model has an excellent data fitting effect and a long training and prediction time, but it has a higher prediction accuracy. It combines the advantages of convolutional neural networks and bidirectional long short-term memory networks and is suitable for processing data with complex spatial and temporal characteristics. LSTM-Attention: The attention mechanism improves the prediction accuracy, MAPE and other indicators perform well, and the training and prediction time is relatively short, indicating that the model has achieved a balance between efficiency and accuracy. The attention mechanism improves the ability to capture key information and is suitable for sequence tasks that need to be focused on. The GRU: The GRU performs well in MAE and other indicators, and the training and prediction time is relatively short. As a variant of the LSTM, the GRU has a similar performance but a simpler structure, which is suitable for scenarios with limited computing resources. CNN-Attention: The MAPE value is 20.21, which is excellent, and it has a long training time, but the prediction time is relatively short. It combines a convolutional neural network and attention mechanism to efficiently capture important features in the data. The FINM: This performs well in indicators such as Loss Value, MAE, MSE, and RMSE, and R-Squared is the highest, reaching 0.7788. Although the training and prediction time is not the shortest, the overall performance of the FINM is excellent. Combined with a variety of advanced technologies and algorithms, it can efficiently and accurately handle complex data structures and time series relationships and is suitable for a variety of prediction tasks.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 7 Open Challenges
### Real-World Deployment  
Many challenges arise when ASC models are deployed in smart city [90, 91] or industrial sound analysis [92] scenarios. The first challenge is the model complexity, which is limited if data privacy concerns require the classification to be performed directly on mobile sensor devices. Real-time processing requirements often demand for fast model prediction with low latency. In a related study, [PERSON] et al. contrasted the performance of different audio event detection methods with the respective computational costs [93]. In comparison to traditional methods such as support vector machines (SVM) and Gaussian mixture models (GMM), fully-connected neural networks achieve the best performance while requiring the lowest number of operations.  
This often requires a model compression step, where trained classification models are reduced in size and redundant components need to be identified. Several attempts were made to make networks more compact by decreasing the number of operations and increasing the memory efficiency. For instance, the MobileNetV2 architecture is based on a novel layer module, which mainly uses convolutional operations to avoid large intermediate tensors [94]. A similar approach was followed by [PERSON] et al. for AED [95]. The authors replaced the common CNN based front-end by depthwise separable convolutions and the RNN backend with dilated convolutions to reduce the number of model parameters and required training time. In the MorphNet approach proposed by [PERSON] et al., network layers were shrunk and expanded in an iterative procedure to optimize network architectures in order to match given resource constraints [96]. Finally, [PERSON] and [PERSON] showed in the EfficientNet approach that a uniform scaling of the network dimensions depth, width, and resolution of a convolutional neural network led to highly effective networks [97].  
A second challenge arises from the audio recording devices in mobile sensor units. Due to space constraints, microelectro-mechanical system (MEMS) microphones are often used. However, scientific datasets used for training ASC models are usually recorded with high-quality electret microphones [98]. As discussed in Section 7.1, changed recording conditions affect the input data distribution. Achieving robust classification systems in such a scenario requires the application of domain adaptation strategies.
## 6 Conclusions  
The FSI response characteristics of aircraft hydraulic pipe with complex constraints and boundary conditions are studied in this paper. The main work and research conclusions are as follows.  
1. The FSI fourteen-equation theoretical model of pipe conveying fluid is developed. The modified friction coupling model is contained in the axial motion equations, and the impacts of gravity, centrifugal force, Coriolis force and the moment of inertia caused by the fluid within the pipe, takes into account in the method that is applicable for describing the FSI of pipe conveying fluid in wide pressure and Reynolds number range.
2. The external excitation model, boundary condition model and middle constraint are developed for solving the FSI fourteen-equation model. These models contain the flow pulsation excitation of axial piston pump, the velocity-inlet boundary condition, pressure-outlet boundary condition and the middle constraint (elastic clamp), which can be applied to solve the complex hydraulic pipeline system with various fluid and structural excitation, when complex constraints are contained.
3. A discrete time transfer matrix method (DTTMM) for solving the FSI fourteen-equation model in time domain is presented. The excellent feature of the present method is that the whole solution procedure can be independently described by a unified matrix expression. It means that there is not any modification to the solution procedures from one analysis model to another, which makes a stylization solution method and further comprehensive investigation easier compared to most existing solution methods.
4. The numerical solution and experiment of an ARJ21-700 aircraft hydraulic pipe with complex constraints are carried out under four working conditions to prove the theoretical model and solution method presented in this work. The results calculated by the DTTMM method are in good agreement with the experimental data, and the research shows that the pulsating amplitude of fluid increases with the increasing flow velocity and fluid pressure. As for the pipe, the vibration response under the flow pulsation excitation shows a forced vibration with periodic characteristics, and the flow pulsation excitation will cause large amplitude radial vibration of pipe. Moreover, the vibration amplitude increases with the increasing flow velocity, and the higher the flow velocity is, the earlier the vibration velocity reaches the response peak, but the fluid pressure has relatively less influence on the vibration response.  
The proposed methodology can be successfully used for the analysis of the FSI response characteristics of the aircraft hydraulic pipe with complex constraints and boundary conditions, which can also provide theoretical references and engineering technology guidance for the FSI analysis of pipe conveying fluid. Further work will involve the frequency domain analyses and the investigation of a complete aircraft hydraulic pipeline system.
## 5 Numeric Results and Discussion
These reasons may be the explanation for the higher MAE and RMSE metrics  
Afterward, continuing with the analysis of the behavior of the models according to the type of day, Table 7 shows the R\({}^{2}\) values for five sunny days and five cloudy days. It is clearly noticeable that on sunny days the R\({}^{2}\) is around 0.95, whereas when the day is cloudy, regardless of the model, the R\({}^{2}\) values are around 0.8. In this case, also, the SVR model stands out for obtaining the best results. Figure 12: Forecast of PV generation for a week of July with NARX model. Figure 13: Forecast of PV generation for a week of July with SVR model. Finally, in order to analyze what happens on each day of July on a more detailed basis, the error made in the prediction of each day has been calculated. Since the results presented in Table 6 may not be very detailed due to the fact that a punctual error of one day can ruin the average of the month. For that reason, Figure 15 shows in a pie diagram form the daily relative error made by ranges. Summarising Figure 15, we can see how, as on previous occasions, the SVR model stands out from the others; in 58% of the cases, it commits an error of less than 4%. In the case of the FFNN model, this occurs in 52% of the cases. In the NARX model, 48%, and, finally, with the analytical model, it is in 26% of the cases that errors of less than 4% occur. As for the maximum errors, it is the analytical model that obtains the highest errors, 32%, compared to 19%, 16%, and 13% for the NARX, FFNN, and SVR models, respectively. Table 8 summarises the mean errors for each model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Day** & **Date** & **FFNN R\({}^{2}\)** & **NARX R\({}^{2}\)** & **SVR R\({}^{2}\)** & **MODELICA R\({}^{2}\)** \\ \hline \multirow{6}{*}{Sunny days} & 5 July 2021 & 0.963 & 0.943 & 0.962 & 0.897 \\  & 9 July 2021 & 0.980 & 0.927 & 0.968 & 0.881 \\  & 10 July 2021 & 0.980 & 0.971 & 0.978 & 0.915 \\  & 19 July 2021 & 0.981 & 0.973 & 0.982 & 0.894 \\  & 26 July 2021 & 0.971 & 0.953 & 0.977 & 0.906 \\  & 6 July 2021 & 0.780 & 0.779 & 0.871 & 0.758 \\  & 7 July 2021 & 0.676 & 0.670 & 0.813 & 0.726 \\ \multirow{2}{*}{Cloudy days} & 8 July 2021 & 0.662 & 0.670 & 0.802 & 0.728 \\  & 13 July 2021 & 0.824 & 0.839 & 0.902 & 0.834 \\  & 6 July 2021 & 0.780 & 0.779 & 0.871 & 0.758 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the four models for five sunny days and five cloudy days. Figure 14: Forecast of PV generation for a week of July with OpenModelica model.