esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] [13] tilted the blades of a fan at a certain angle so that the shape of the blade channel was changed, the flow noise between the blades decreased, and the outer-shell radiation noise decreased more significantly in the middle- and low-frequency bands. In recent years, the application of optimization methods has promoted the development of fluid machinery, and scholars have conducted many studies on optimization objectives and optimization algorithms. [PERSON] [14] optimized the design of centrifugal fans for refrigerators based on the response surface method to modify the blade angle, size, and other parameters in order to lower the noise. [PERSON] [15] built a prediction model for a centrifugal fan with sample points obtained through experimental methods, and they used the noise and casing quality as optimization objectives, which resulted in a 7.3 dB noise reduction of the fan. [PERSON] [16] obtained a performance dataset for centrifugal fans by numerical means and trained a high-precision artificial neural network, which accelerated the optimization progress of the fans. [PERSON] [17] used the response surface method to select the inlet and outlet angles of the blades, and the simulation analysis results showed that the flow separation in the blade channel was improved, the interference of the blade wake on the airflow was weakened, and the efficiency of the fan was improved by 4.2%. [PERSON] [18] optimized a particle swarm algorithm using a limit state machine to redesign the blade size with the pneumatic properties in mind, and the results showed that the total pressure rose by about 11 Pa. [PERSON] [19] used a multi-objective genetic algorithm based on data obtained from the Kriging prediction model to optimize the aerodynamic performance of the impeller for cooling, increasing the static pressure of the fan. Many studies focus on the improvement in single parameters of the volute or impeller, while because of the compact structure of the multi-blade centrifugal fan, the matching of the volute and impeller is decisive for the comprehensive performance of the fan. In addition, when selecting the optimization objective of the fan, usually only the aerodynamic or noise performance is considered, and the multi-objective optimization method is less frequently used. In this paper, both volute and impeller parameters are considered and a prediction model named wPSO-BP is constructed for fast optimization, extracting samples based on the Latin hypercube method. The NSGA-III-LBWO algorithm is proposed and applied to the multi-objective optimization of the total pressure, efficiency, and sound pressure level of the fan. The optimization effect is demonstrated, comparing the prototype fan with the optimized fan, which provides an idea for the optimization of centrifugal fans.
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 3 The Architecture of Software-Defined Vehicular Cloud
### The Components of SDVC
#### 3.1.3 Components of the Multi-Access Edge Cloud-Enabled SDN for VM Migration
The optimal placement of the controllers, routing overhead between them (active VM, communication infrastructure, and SDN OpenFlow controllers) challenge the management of VM migration service. In fact, the inter-controller communication throughput between the adjacent SDN controllers would be maximized through optimum algorithm pertaining to the main SDN controller's placement with active VM and SDN communication infrastructure controllers. The SDN OpenFlow controller controls and forwards routing rules for, e.g., deploying any ITS services initiated from cloud computing. These rules are pushed to the SDN communication and infrastructure controllers through the northbound interface. The core architecture of the SDVC is adopted to logically centralize data forwarding and computation resources. The ETSI framework defines a reference for their deployment of V2X services that require ultra-low latency access [39]. The access of the V2X application is done through the MEC server. MEC servers provide mobile edge (ME) computing resources such as computation, storage, and network. Furthermore, MEC is an entity that contains the ME platform.
# Research on Path and Sequence Planning for Multi-Cabin Onboard Transportation of Large Cruise Ships
## 2 Mathematical Model of Multi-Cabin Transportation Path Planning
### Objective Function  
The objective function for cabin module transportation primarily aims to minimize the overall loading time for all cabins, which is the sum of the transportation time and installation time for each cabin. In the optimization of the transportation paths for each cabin, a grid map is used, allowing the transportation time for each cabin to be equated to the length of the transportation path. Therefore, the objective function for the transportation of cabin modules can be expressed as follows:  
\[\min\left\{T_{all}=\sum_{i=1}^{n}T_{i}\right\} \tag{1}\]  
\[\min\left\{T_{p}=L_{j},\ \ j=1,2,\cdots,n\right\} \tag{2}\]  
where Tall represents the total duration of the loading process for all cabin modules; Ti = Tpi + Tfi is the loading time of the i-th cabin module, which is the sum of the transportation time Tp and the installation time Tf; n is the number of cabin modules; Tf is a constant determined by the shipyard's modular installation process for the cruise ship; and Lj represents the transportation path length of the j-th cabin.
# Onboard Processing in Satellite Communications Using AI Accelerators
## 3 Onboard AI Applications
### Use Cases Assessment  
Figure 10 and Table 2 present the analysis and comparison of the different scenarios presented, including the identification of the inputs and outputs of the AI model; the values in the \"Total Scores\" column represent the sum of each KPI score, as presented in Figure 10. Regarding the applicability on board, the use cases with higher scores are interference detection and antenna beamforming, because the regenerative level remains at RF and antenna level, respectively, as explained in Table 1. Meanwhile, the flexible payload use case is the one that has a lower score concerning onboard applicability because it requires a regenerative payload at the low layer level (DU-RAN) due to the fact that a return channel demodulator would be required to analyze the traffic demand in real time; an additional ML block for onboard traffic prediction is also feasible. Nevertheless, the response time to changing traffic demand is expected to be drastically reduced due to the response time of inference compared to other decision-making techniques.  
However, the flexible payload use case is one of those expected to have a higher AI gain due to the complexity of optimal radio resource management with conventional techniques. Therefore, it is expected that with AI-based techniques, an increase in performance of up to 30% and a reduction in power consumption of up to 50% can be obtained [2].  
Based on the three KPIs explained in Table 1, the most promising use cases for onboard processing are interference detection, antenna beamforming, and flexible payload.  
Figure 9: Azimuth cut of 16 \(\times\) 16 subarrays in a multibeam scenario with nulling between beams.
## 4 Results and Discussion
In [PERSON] and [PERSON]'s work [5], the ANN (3-6-6-1) model surpassed all the other models by attaining 85.19% and 86.42% based on the 5-fold and 10-fold cross-validation, respectively. Our proposed Hyperparameter-tuned DNN model outperformed all other ML models by achieving 87.72% and 87.64% with stratified 5-fold and 10-fold cross-validation, respectively. Our proposed work improves the accuracy by 2.53% and 1.22%, respectively, with the same dataset used in both studies. Compared to ANN, the numerous layers in DNN enable models to learn complicated features and carry out more demanding computational tasks more efficiently. Furthermore, because DNNs can learn faster, they are more effective in training time. In comparison to other methods presented in this work and the literature, the DNN model is the best ideal for the diagnosis of Kyphosis. This is because DL algorithms can eventually learn from their own mistakes. It can check the accuracy of its estimations or results and make the required corrections. Traditional machine learning algorithms, on the other hand, require varying levels of human input to assess output accuracy.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
###### Abstract  
For energy-limited wireless sensor networks (WSNs), we propose a multiparameter fusion decision routing (MPFDR) algorithm in this study. This algorithm gives a comprehensive account of the residual energy and forward distance, single-hop transmission ratio, cache queue, and energy equilibrium degree. It calculates the routing evaluation parameters of the forward neighbors, realizing a multidirectional reflection of the network status. Simultaneously, combined with the defined routing selection strategy based on the parameter contribution degree and fuzzy contribution degree, the fusion contribution degree of each forward neighbor is obtained. Then, the node with the most considerable fusion contribution degree is selected as the next hop. Finally, the performance of the MPFDR algorithm is simulated and compared with other algorithms. Simulation results indicate that our algorithm has good congestion control ability in energy-limited wireless sensor networks and can significantly reduce the packet loss rate and average hops.  
Wireless sensor networks; multiparameter fusion decision; routing algorithm; routing optimization parameter; fusion contribution degree
## 6 Security
### Data Integrity and Sender Authentication  
We now prove that our scheme guarantees aggregated data integrity and smart meter authentication for those that participate in the aggregation.  
Theorem 2 implies that the aggregate signature scheme is secure against active attacks such as message forgery or false data injection. Thus, smart meters can attest to their own encrypted meteringdata. The concentrator can verify the integrity of each ciphertext generated from smart meters on the aggregation path, and assure the identities of the meters participating in the aggregation.  
Now, we prove that if each ciphertext is authentic and not forged during the aggregation (by Theorem 2), the integrity of the final aggregation result is also guaranteed. Without a loss of generality, we suppose smart meters \(s_{1},\ldots,s_{n}\) participate in the aggregation and report \(c_{1}(=\ Enc_{k_{1}}(m_{1})\pmod{M}),\ldots,\)\(c_{n}(=\ Enc_{k_{2}}(m_{n})\pmod{M})\) to the concentrator. Let the path key obtained by the concentrator be \(PK=k_{1}+\cdots+k_{n}\) for the aggregation path, and \(C=c_{1}+\cdots+c_{n}\). If \(\forall i\), \(c_{i}\) is not forged, \(m_{i}=c_{i}-k_{i}\) is also authentic for a given \(k_{i}\). Then, we can observe that  
\[\begin{array}{rcl}Dec_{PK}(C)&=&\sum_{i=1}^{n}c_{i}-PK=\sum_{i=1}^{n}(c_{i}- k_{i})\\ &=&m_{1}+\cdots+m_{n}\end{array}\]  
is also authentic for a given \(PK\). If any adversaries manipulate the distributed encryption key after the initial path key setup phase and alter a metering data (or inject false data) during the aggregation procedure, it may affect the path key and result in different ciphertext (from the authentic one), which can be detected during the verification procedure by the concentrator. Therefore, aggregated data integrity and sender authentication properties can be achieved in the proposed scheme.  
However, if some authenticated meters are compromised subsequently and then launch the same attack using the valid encryption keys and identities (that is, inside adversary model), the proposed scheme cannot detect such an active attack by the compromised meters, because the attack could be done using the valid encryption keys and valid identities. Thus, dealing with the inside attack requires additional security techniques, which is one of the challenging future research issues.
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 3 Dam Crack Detection Based on Underwater Robots  
### Underwater Robots  
As the main tool for humans to explore the deep-sea environment, underwater robots have always been valued by various countries. In recent years, they have played an important role in the development of marine resources and deep-sea exploration [54]. At present, according to the operating space, control mode, and power characteristics, underwater robots can be divided into human-occupied vehicles (HOV) and unmanned underwater vehicles (UUV). In recent years, HOVs and UUVs have played an increasingly important role with the demands of deep and distant ocean surveys. Unmanned submersibles can be further divided into cable remote-operated underwater vehicles (ROV) and autonomous underwater vehicles (AUV) [35]. The classification of underwater robots is shown in Figure 3.  
HOVs can carry researchers into the deep sea for direct observation, analysis, and evaluation on the seabed, and can operate manipulators to carry out exploration tasks. Since HOVs involve personal safety issues and require a complete and complex life support system, HOVs have a large volume and weight and need to be equipped with complex carrying, lowering, and rescue systems; they are currently mainly used for scientific research and deep-sea investigations [55]. The symbol of the ROV is that it has an umbilical cable, which is the link of the signal and data transmission. The power, video, data, and control signals are connected to the control equipment through the umbilical cable, and are remotely operated by the inspectors to complete specific underwater tasks. ROVs have the characteristics of safety, economy, high efficiency, and large operating depth [56; 57]. The AUVs do not need cables to control the underwater robot. The robot has a certain self-judgment ability and carries its own power energy. Since the AUV gets rid of the umbilical cable, the operation is more flexible and the operating range is larger [35]. Underwater robots are shown in Figure 4.
## 3 Results
### Description of Sauerkraut Juice and Dehydrated Sauerkraut Juice
#### 3.1.2 Aromatic Volatiles in Sauerkraut Juice and Dehydrated Sauerkraut Juice  
Profiles for aromatic volatiles was determined for sauerkraut juice and dehydrated sauerkraut juice and are presented in Figure 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **SJ** & **DSJ** \\ \hline TPC, mg 100 g GAE, dw * & 713.7 \(\pm\) 43.2 a ** & 359.5 \(\pm\) 7.7 b \\ ABTS, mg TE 100\({}^{-1}\), dw & 15.50 \(\pm\) 1.84 a & 28.62 \(\pm\) 2.03 b \\ \hline \hline \end{tabular}
* mg GAE-gallic acid equivalent, dry weight (dw). ** Values with different letters are significantly different (\(p\leq\) 0.05).  
\end{table}
Table 2: Total phenol content and antiradical activity by ABTS+ in sauerkraut juice (SJ) and dehydrated sauerkraut juice (DSJ).  
Figure 1: Bioaccessibility index (BAC) of the sauerkraut juice (SJ) and dehydrated sauerkraut juice (DSJ) based on TPC and ABTS+ scavenging activity after in vitro digestion.  
There were two common compounds in sauerkraut juice before and after spray-drying: acetic acid with a sour taste, and allyl isothiamate with a pungent taste of mustard, horseradish, and wasabi (mean values and standard deviations in Table S1, Supplementary Materials). The metabolized products of glucosinolates-isothiocyanates are the prime sources of the characteristic flavor of the Brassica vegetables [3; 28]. The highest peak area in sauerkraut juice was carvone with a caraway and spearmint-like odor, while for dehydrated sauerkraut juice it was benzoic acid with a faint, pleasant odor. Other compounds are characteristic to each of the raw material.
# Study of the Preparation and Dielectric Property of PP/SMA/PVDF Blend Material
###### Abstract  
Polypropylene (PP) and polyvinylidene fluoride (PVDF) polymer materials have been widely used because PP has excellent piezoelectricity and PVDF has a robust tensile strength. However, few studies have been reported about PP/PVDF blends, and the main issue has proven to be the interfacial compatibility of blends. This research investigated the microstructure of blends to characterize their compatibility. X-ray diffraction (XRD) and scanning electron microscope (SEM) analysis showed the poor interactions between PP and PVDF interface, and thus this study adopted styrene maleic anhydride (SMA) copolymer as compatibilizer to modify the properties of blends. The XRD and SEM investigations showed an enhancement in the interactions between PP and PVDF after the addition of SMA. The dielectric properties of PP/SMA/PVDF blends showed a significant increase when PP content was 40%-60% and the dielectric constant (\(\varepsilon\)) was up to 13; meanwhile, the dielectric loss (tan\(\delta\)) of PP/SMA/PVDF was lower than PP/PVDF blends.  
polymer blends; PP/PVDF; compatibilizer; dielectric property; microstructure +
Footnote †: journal: Applied  
Article
# IoT-Enabled Smart Cities: Evolution and Outlook
## 2 IoT-Enabled Smart Cities Landscape
### Rio de Janeiro  
Rio's Smart City strategy was originally bound to the celebration of the Olympics in 2016, but a fatal landslide in 2010 accelerated the construction of a Centre of Operations to tackle natural disasters and coordinate relevant emergency responses. The solution adopted was based on IBM's proprietary platform for smart cities. The initiative is already largely centred on safety and security, both in terms of disaster prevention and management, and freedom of information. Regarding the main services that the city is providing, they are as follows:  
* **Emergency monitoring and response.** The video feeds from the surveillance cameras installed in the city are the core of the Intelligent Operations Centre (IOC) focusing on controlling traffic and weather to ensure smooth functioning of day-to-day operations.
* **Local government data sharing.** Departments from the local government hosted at the IOC premises exchange data amongst themselves using IBM's platform to increase the efficiency of their services.
* **Rio Agora.** A platform which combines an online social platform with the already existing portal called Central 1746 [32] which allows citizens to propose and debate public policy with municipal departments and agencies in different themes as well as to request a number of services from the city's government such as waste removal, repair of damaged roads and walkways or reporting of illegal activity, the requests posted on the website are integrated in the IOC and its lifecycle managed through the system.
## 4 RMNCE-Based Adaptive UKF
### Application in UKF  
The aforementioned adaptive strategy is employed to the UKF to solve the nonlinear estimation problem. Omitting the well-known details of the UKF, the main procedure of calculating the Kalman gain in the proposed RMNCE-UKF is shown from Equation (30) to Equation (33):  
\[\mathbf{K}_{k}=\mathbf{P}_{xz,k}\mathbf{P}_{zz,k}^{-1} \tag{30}\]  
\[\mathbf{P}_{xz,k}=\sum_{j=1}^{2N+1}w_{j}^{(c)}\Big{(}\mathbf{\chi}_{k,j}^{*}\ -\ \mathbf{\hat{\chi}}_{k}^{-}\Big{)}\Big{(}\mathbf{\xi}_{k,j}^{*}\ -\ \mathbf{\hat{\chi}}_{k}^{-}\Big{)}^{T} \tag{31}\]  
\[\mathbf{P}_{zz,k}=\mathbf{P}_{\hat{z}^{-},k}\ +\ \mathbf{\hat{\rho}}\mathbf{ \hat{R}}_{k}^{RMNCE} \tag{32}\]  
\[\mathbf{P}_{\hat{z}^{-},k+1}=\sum_{j=1}^{2N+1}w_{j}^{(c)}\Big{(}\mathbf{\xi}_ {k,j}^{*-}\ -\ \mathbf{\hat{\chi}}_{k}^{-}\Big{)}\Big{(}\mathbf{\xi}_{k,j}^{*}\ -\ \mathbf{\hat{\chi}}_{k}^{-}\Big{)}^{T} \tag{33}\]  
where \(\Big{\{}\mathbf{\chi}_{k-1,j}^{*}\Big{\}}\) and \(\Big{\{}\mathbf{\xi}_{k+1,j}^{*-}\Big{\}}\) are the unscented transformations of the sigma points regarding to the state equation and measurement equation respectively, \(\mathbf{\hat{\chi}}_{k}^{-}\) is the state prediction and \(\mathbf{\hat{\chi}}_{k}^{-}\) is the measurement prediction. The detailed time updating and measurement updating can be found in [31]. Figure 2 presents the flow chart of the RMNCE-UKF.  
Figure 2: The flow chart of the proposed adaptive RMNCE-UKF.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 2 Principle and Framework of the ASPF
### Framework of the ASPF in SDM and NDM  
In order to show the principle of ASPF more clearly, we explain it from the following two aspects, namely, the operation of ASPF with similar days in SDM, and without similar day in NDM. The block diagrams are shown in Figure 2(a),2(b), respectively.  
Figure 2(a) shows the case when ASPF is working in SDM. \(x\) is compared with every data in _group_\(k\) until the closest data is found, which will be used as the revised PV power. Later, the actual solar power \(P_{act}\) will be recorded in _EMS_ when the whole day of operation ends. In order to testify the effectiveness  
Figure 3: The ASPF schematic in different mode.  
Figure 2: The principle of the _Identify similar days_.  
of ASPF in SDM, \(P_{sdm}\) and \(P_{act}\) are compared in _Updating judgment_. If the largest difference between these two vectors at any time slot is less than a certain value, \(P_{sdm}\) is chosen as \(P_{rev}\), otherwise the network will be updated.  
Figure 3b shows the case when ASPF is working in NDM. Because no similar day is found in the current database, ASPF learns the new data through BPNN and decides if the BPNN is updated or not. Different from SDM, \(P_{rev}\) is acquired by the BPNN, and the difference between \(P_{rev}\) and \(P_{act}\) determines whether the network is updated in NDM. In this way, ASPF absorbs more useful data and thus becomes better in predictions.