esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation and Design of the Transonic Laminar Flow Characteristics in a Laminar Aircraft
## 2 Computational Approach
### Grid Independence Verification  
The grid is generated by ANSYS ICEM. The specific grid generation process is as follows: Firstly, the 3D grid block of the aircraft wall was cut out, as shown in Figure 1(a). Then the external O-BLOCK function was used to generate the boundary layer mesh of the aircraft. In the boundary layer, the two adjacent grid nodes' size growth rate was 1.15. The grid structure of the aircraft is shown in Figure 1(b). Finally, the surface and volume grids, as shown in Figure 1(c), were generated. A characteristic inflow and outflow boundary condition was applied. All solid surfaces were set as the no-slip adiabatic wall. The symmetry boundary condition was used to generate only a half-model grid. All calculation domains are calculated by RANS equations. A grid convergence study was applied to ensure the reliability of the numerical solution. Since the aircraft model is symmetric, the calculation was performed by half-mode to improve the computational efficiency.  
The experimental aircraft with a laminar flow wing was calculated using six levels of the meshes shown in Figure 3 with the boundary condition in Table 1. It can be seen from Figure 2(b) that the main 2D grid size of the central experimental wing surface is  
Figure 2: Grid generation process.  
0.032 m \(\times\) 0.028 m. \(\gamma-\text{Re}_{\theta}\) represents the transition model. _Ma_ represents the Mach Number. \(\alpha\) represents the angle of attack. _Re_ represents the Reynolds Number. _FSTI_ represents the free stream turbulence intensity. The first cell height condition is shown in Table 2. The growth ratio near the boundary layer, such as the ratio between Mesh 1 and Mesh 2, was 2, from 3.1 million volume grids to 120 million volume grids. The aerodynamic forces coefficient results are shown in Figure 4, in which the mesh number represents the mesh number. Mesh 6 is the finest grid, Mesh 1 is the coarsest grid, \(C_{L}\) represents the lift coefficient, \(C_{D}\) represents the drag coefficient and \(C_{M}\) represents the pitch moment coefficient. The results indicated that the aerodynamic characteristics of the six grids have little difference, so the grid independence is verified. When the number of volume cells was approximately 13.0 million (Mesh 3), the y+ is closest to 1, shown in Figure 5b. Y+ is a non-dimensional number similar to the local Reynolds number, determining whether the influences in the wall-adjacent cells are laminar or turbulent [37]. It is well known that Y \({}^{+}\approx 1\) should be guaranteed when studying the boundary layer flow. Thus we chose Mesh 3 for subsequent calculation.
## 4 Experimental Setup
### Corpora
#### 4.1.2 Casia  
CASIA is a Chinese emotion corpus recorded by the Institute of Automation of the Chinese Academy of Sciences, performed by four professional actors (i.e., two females, two males) in a pure recording environment. It covers 9600 utterances including six emotional states: anger, fear, happiness, neutral, sadness, and surprise. Each speaker has 300 utterances of the same text and 100 different texts for each emotion. Since only part of the voice data were publicly released, we used publicly available 50 identical texts recorded by four actors with four basic emotions to reduce the effect of text content on emotion recognition, composing a total corpus of 800 sentences.
# Coal and Gangue Recognition Method Based on Local Texture Classification Network for Robot Picking
## 4 Experiment
### Study of Parameters  
As introduced in Section 3, the scale of the local texture images is controlled by the value of the hyper-parameter \(n\).  
By introducing \(n\), LTC-Net aims at zooming into a smaller scale and recognizing the local texture images, and therefore, when conducting the local sampling process, the value of \(n\) actually represents the level of \"local\". An overlarge \(n\) cuts the images into fragments, causing the similarity problem again, while smaller \(n\) eventually degenerates into the CNN (VGG16) method.  
With a reasonable assumption that the local texture images could be classified most effectively at a certain scale, controlled trials are conducted using Dataset2. As shown in Figure 8, the peak of performance, considering the accuracy and recall rates of both coals and gangues combined, occurs when \(n\) equals 5. A gradual decreasing accuracy is also shown towards a total failure of recognition when \(n\) reaches 11.
## 3 Results and Discussions
### Isothermal Adsorption and Its Kinetics  
The Langmuir equation was used to describe the adsorption equilibrium of coal in this paper. The \(V_{L}\) and \(P_{L}\) changed from 31.55 to 38.63 cm\({}^{3}\)/g (mean 36.51 cm\({}^{3}\)/g) and from 0.70 to 1.29 MPa (mean 1.01 MPa), respectively. According to Figure 9A, the adsorption curves were typical isothermal adsorption curve of high-rank coal, which is characterized by larger \(V_{L}\) and smaller \(P_{L}\) values compared with semi-anthracites and low- and medium-rank coals [16; 18; 48].  
The calculation method of \(D_{e}\) is shown in Figure 9B and given by Equation (4). The results are summarized in Table 3. The value of \(D_{e}\) in Hudi coal mine ranged from 1.42 to \(2.88\times 10^{-5}\) s\({}^{-1}\) (mean 2.09 \(\times\) 10\({}^{-5}\) s\({}^{-1}\)). These results conform with the report of [PERSON] et al., whose research showed an average effective diffusion coefficient of \(2.92\times 10^{-5}\) s\({}^{-1}\)[42].
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 6 Discussion
### Image Processing Techniques' Roles to Address Security for IoTSs  
Security of Networks  
Image processing has proposed various methods [68; 69; 32] to address concerns about the safety of the communication channel that runs between the image processing and the final destination. It is said that image processing techniques have a role in strengthening the security of communication channels [68].  
The technique of matching images [29; 55] has been used in cloud-stored images, where it has been shown to be effective in maintaining the confidentiality of data storage and communication networks. It has helped to ensure the security and confidentiality of the image that was retrieved from its storage in the cloud. As a result of the matching procedure, communications have been made more secure [29].  
Numerous IoTSs have established a connection between image encryption, image transfer, and cloud storage [67; 70]. There has been an increase in the level of security provided to the proposed communication channels [50].  
Image processing techniques have greatly improved the security of contents and channels required for transmission. Image processing techniques have contributed to the development of a variety of security procedures for IoTSs, which we examine.  
Zigzag image encryption may be more resistant to data attacks [66];
2. As a result of advancements in image processing, IoT devices and networks are now able to transmit data safely. The current conventional security scheme, in particular the two-step authentication, has been proposed to be replaced by a biometric encryption scheme that uses image processing [48];
3. IoTSs stored on a server and hence subject to attack at the server level benefit from image block splitting as a security measure. Increased security and decreased attack likelihood are two benefits of block-based image encryption schemes [68];
4. Encryption of images may improve the safety of the contents of images. However, improvements to this method are currently being made to enhance the functionality of privacy protection for contents of images connected to IoTSs [50];
5. The matching procedure helps to increase the safety of image data associated with the IoT [29].
# Study of Green Synthesis of Ultrasmall Gold Nanoparticles Using _Citrus Sinnensis_ [PERSON]
## 3 Results and Discussion  
### Formation of AuNPs Using Fresh C. sinensis Peel Extract at Different pHs  
In this study, we aimed to investigate the pH effect on the production of AuNPs when pHs (from 6 to 11) adjusted before the reaction started. We were particularly interested in investigating the appearance characteristics (such as shape, size, etc.) and distribution of the formed AuNPs. The AuNPs which synthesized at different pHs were analyzed using an UV-vis spectrophotometer. The absorbance curves of the AuNPs obtained are shown in Figure 1. The maximum absorption wavelength (\(\uplambda_{\text{max}}=545\) nm) in Figure 1 is found in the control group (pH = 3), and shifts from 536 nm to 529 nm (blue shift) along with the solution pH increases from 3 to 11. It means that increasing the pH of a solution decreases the AuNPs size. Additionally, inverse correlation between the absorbance intensityof AuNPs and the solution pH occurs. The absorbance intensity of all the AuNPs groups decreases, accompanied by the increase in pH (pH = 6, 8 and 11). Compared to the absorbance intensity of 1.33 at \(\lambda_{\text{max}}\) = 545 nm (control), only 0.67 is observed at \(\lambda_{\text{max}}\) = 536 when pH is 8.
# SPARQL2 Flink: Evaluation of SPARQL Queries on Apache Flink
## 4 Implementation  
This section presents our last contribution. We implemented the transformations described in Section 3 as a Java library [35]. According to Apache Flink [10], a Flink program usually consists on four basic stages: (i) loading/creating the initial data, (ii) specifying the transformations of the data, (iii) specifying where to put the results of the computations, and (iv) triggering the program execution. The SPARQL2 Flink [35] library--available on Github under the MIT license, is focused on the first three stages of a Flink program, and it is composed of two modules, called: _Mapper_ and _Runner_, as shown in Figure 1. Apache Jena ARQ and Apache Flink libraries are shared among both modules. The _Mapper_ module transforms a declarative SPARQL query into an Apache Flink program (Flink program), and it is composed of three submodules:  
Load SPARQL Query File: this submodule loads the declarative SPARQL query from a file with a.rq extension. Listing 1 shows an example of a SPARQL query that retrieves the names of all the people with their email if they have it. ```
PREFIXfoaf:<[[http://xmlns.com/foaf/0.1/](http://xmlns.com/foaf/0.1/)]([http://xmlns.com/foaf/0.1/](http://xmlns.com/foaf/0.1/))> SELECT?person?name?mbox MHERE{?personfoaf:name?name. OPTIONAL[?personfoaf:mbox?mbox] }
```  
Listing 1: SPARQL query example. Translate Query To a Logical Query Plan: this submodule uses the Jena ARQ library to translate the SPARQL query into a Logical Query Plan (LQP) expressed with SPARQL Algebra operators. The LQP is represented with an RDF-centric syntax provided by Jena, which is called SPARQL Syntax Expression (SSE) [36]. Listing 2 shows an LQP of the SPARQL query example. ```
((project(?person?name?mbox) (Iefjoin ((bgp(triple?person<[[http://xmlns.com/foaf/0.1/name?name](http://xmlns.com/foaf/0.1/name?name)]([http://xmlns.com/foaf/0.1/name?name](http://xmlns.com/foaf/0.1/name?name)))) (bgp(triple?person<[[http://xmlns.com/foaf/0.1/mbox?mbox](http://xmlns.com/foaf/0.1/mbox?mbox)]([http://xmlns.com/foaf/0.1/mbox?mbox](http://xmlns.com/foaf/0.1/mbox?mbox)))) )) ) }
```  
Listing 2: SPARQL Syntax Expression of the SPARQL query example. _Convert Logical Query Plan into Flink program:_ this submodule converts each SPARQL Algebra operator in the query to a transformation from the DataSet API of Apache Flink, according to the correspondence described in Section 3. For instance, each _triple pattern_ within a _Basic Graph Pattern_ (_BGP_) is encoded as a combination of _filter_ and _map_ transformations, the _leftjoin_ operator is encoded as a _leftOuterJoin_ transformation, whereas the _project_ operator is expressed as a _map_ transformation. Listing 3 shows the Java Flink program corresponding to the SPARQL query example. Figure 1: SPARQL2 Flink conceptual architecture.
## 2 Materials and Methods
### Continuous Relative Phase and Its Variability Calculation  
The continuous relative phase (CRP) and the CRP variability (CRPV) were used to calculate segment coordination and its variability, respectively, in line with previous literature [17; 24]. CRP and CRP were calculated for foot, shank, thigh, and hip segments. A phase plane for each segment throughout a pedaling cycle was constructed by plotting normalized angular positions (\(\theta\): \(x\)-axis) versus normalized angular velocities (\(\omega\): \(y\)-axis). Phase angle (\(\Phi\)) was calculated as \(\Phi=\tan^{-1}(\omega/\theta)\)  
Figure 1: MyoMotion sensors placements on segments.  
along each data point of the pedaling cycle. Normalized angular position (\(\theta\)_inorm_) and angular velocity (\(\omega\)_inorm_) profiles were calculated using Equations (2) and (3), respectively.  
\[\theta\textit{inorm}=\left(\frac{2\left[\theta i-\min(\theta i)\right]}{\max( \theta i)-\min(\theta i)}\right)-1 \tag{2}\]  
\[\omega\textit{inorm}=\left(\frac{\omega i}{\max\{\left|\omega i\right|\}}\right) \tag{3}\]  
where \(i\) denotes each data point of the pedaling cycle. The minimum (min) and maximum (max) angles for the series of ten pedaling cycles in each condition were used to normalize the angular position (\(\theta\)) for each of the 101 data points \(i\). The angular velocity (\(\omega\)) data were normalized to the maximal (\(\max\{\omega\}\)) velocity within the ten pedaling cycles. Regarding each of the oscillators, the phase angle (\(\emptyset\)) was obtained by calculating the four-quadrant arctangent angle relative to the right horizontal axis at each instant in the pedaling cycle [17; 24].  
The CRP for data point \(i\) was calculated by subtracting the phase angle of the distal segment from that of the proximal segment and used as the coordination of two segments [17; 24] according to Equation (4):  
\[\textit{CRP}_{i}=\varnothing_{i}^{\textit{distal}}-\varnothing_{i}^{\textit{ proximal}} \tag{4}\]  
where \(\varnothing_{i}^{\textit{distal}}\) and \(\varnothing_{i}^{\textit{proximal}}\) are the phase angle of the distal and proximal oscillators, respectively, at data point \(i\) in the pedaling cycle. CRP angles were calculated for ten couplings according to the Table 1. The CRPV was calculated as the between-cycling cycle standard deviation of the CRP data points within all trials for all participants.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements
### Security  
Security is one of the prime challenges in WHNCS deployment in the process control industry. Irrespective of critical and non-critical processes, they are always prone to security threats [97]. To achieve secure transmission, the network should be aware of denial-of-service (DoS) attacks and cyber-attacks from outside networks [98]. Both active and passive attacks may take place, such as snooping on transmission signals, the modification of signal information, signal interruption, data flooding, and re-routing the network paths. These must all be considered in the design phase [3; 99]. Due to resource limitations, security protocols have to be balanced against other quality of service (QoS) performance requirements [100]. Data encryption, data authentication, and cluster-based private data aggregation (CDDA) techniques will minimize security attacks in the network. Sensor network encryption protocol (SNEP), localized encryption and authentication protocol (LEAP), and random key pre-distribution (RKP) are some of the most effective techniques to block malicious attacks such as data flooding, information spoofing, and data transit attacks [101].
## 8 Application Case
Evaporation temperatures of 43.5 \({}^{\circ}\)C and 53.8 \({}^{\circ}\)C are registered using hot air at 50 \({}^{\circ}\)C and 60 \({}^{\circ}\)C. The condensation temperature is 17.1 \({}^{\circ}\)C for all cases. The maximum saturation pressures that the system can handle limit these temperatures. This detail is in Figure 5b, where tests have determined the maximum evaporation pressure to be 1.4 bar and 3.2 bar for water and 1.3 bar and 1.8 bar for air. These values are associated with pressure ratios of 2.8-6.4 (adopting hot water) and 2.6-3.6 (adopting hot air), respectively, which have a lower limit associated with the minimum temperature of the natural water, which, in turn, determines the condensation pressure. Consequently, the net electric efficiencies at the system level are 3.9% and 7.1% (for water), and 3.4% and 4.7% (for air), respectively. Figure 5: Performance for pentane with different hot fluids at various temperatures (water: 50 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, air: 50 \({}^{\circ}\)C and 60 \({}^{\circ}\)C). Thermodynamic cycles (**a**); electric efficiency and pressure (**b**); net energy and temperature at evaporator and condenser (**c**); mass flow rates (**d**). The net electric energy delivery ranges from 31.1 kJ/kg to 58.7 kJ/kg, with hot water as a heat source, and moves from 29.5 kJ/kg to 40.1 kJ/kg using air (Figure 5c). The final chart (Figure 5d) shows that to evaporate 1 kg/s of pentane, a mass flow rate of 25 and 19 kg/s is necessary using hot liquid water, while 67-70 kg/s is required using hot air. Similarly, the temperature range of cold water derived for ORC condensation is about 14-15 kg/s. The same considerations apply to isopentane as the working fluid, with results detailed in Figure 6. Without delving into the specifics, the net electric efficiency at the system level ranges from 3.7% in the worst case to 7.0% in the best case. The pressure ratios vary from 2.7 to 6.0, with a maximum operating pressure of 3.6 bar. The results align closely with the existing literature. The efficiency also accounts for the auxiliary power due to the water pump. Figure 6: Performance for isopentane with different hot fluids at various temperatures (water: 50 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, air: 50 \({}^{\circ}\)C and 60 \({}^{\circ}\)C). Thermodynamic cycles (**a**); electric efficiency and pressure (**b**); net energy and temperature at evaporator and condenser (**c**); mass flow rates (**d**).
## 2 Related Work  
The importance of communication energy consumption of underwater sensor nodes has been highlighted in several research papers:  
[PERSON] et al. [13] introduced an autonomous underwater vehicle (AUV)-aided underwater acoustic sensor network (UWSN). It utilized AUVs to assist data collection in UWSNs to reduce the number of data collection tasks of sensor nodes, thereby reducing the energy consumption of UWSN. Similarly, in [14], [PERSON] et al. proposed an AUVs-assisted energy-efficient clustering (AEC) mechanism. It introduced the wake-up sleep cycle for the underwater nodes. Although [13; 14] can effectively reduce the energy consumption of a UWSN, the delay for this data acquisition and processing scheme is excessive for some data with heightened importance. For transmission tasks that have requirements on transmission and the processing delay, they can only be transmitted to the BS through sensor nodes or sinking nodes.  
Therefore, [PERSON] et al. [15] proposed two novel lightweight sensor data compression algorithms, i.e., the lossy data compression algorithm (LCA) and the lossless data compression algorithm (NLCA). In these algorithms, the sensors can transmit a smaller amount of data, reducing the energy consumption of sensor nodes. An energy-efficient routing protocol for selecting relay nodes in UWSMs based on the fuzzy analytical hierarchy process was presented in [16]. To some extent, the average energy consumption of each node in the UWSN receiving and transmitting data was reduced. Meanwhile, [PERSON] et al. [17] proposed energy-efficient probabilistic depth-based routing (EEPDBR) for UWSNs, which is improved from the traditional depth-based routing (DBR) algorithm. It takes a node's depth, residual energy, and forwarding number within its 2-hop neighborhood into account. The algorithm can achieve a higher packet delivery ratio and lower average delivery time, while saving energy consumption effectively. The studies in [15; 16; 17] reduced the energy consumption of underwater nodes to a certain extent and extend the service life of the UWSN.  
Existing studies have improved the energy efficiency of nodes in a UWSN to some extent. To the best of our knowledge, few existing studies, however, considered an essential concern, i.e., the communication energy consumption of nodes located in different water depths varies in UWSNs. This is because the transmission energy consumption is different due to the different transmission paths of various nodes to ensure the quality of service (QoS). Therefore, if the influence of water depth is not considered, the existing research can reduce the overall energy consumption of underwater sensor networks. Yet, some sensors are potentially more energy-hungry. As a result, nodes need to be replaced frequently in different time periods, resulting in long-term service interruption.
###### Abstract  
Up to half of the global fruit and vegetable production is wasted or lost along the supply chain, causing wastage of resources and economic losses. Ambient parameters strongly influence quality and shelf life of fresh fruit and vegetables. Monitoring these parameters by using Internet of Things (IoT)-enabled sensor and communication technology in supply chains can help to optimize product qualities and hence reduce product rejections and losses. Various corresponding technical solutions are available, but the diverse characteristics of fresh plant-based produce impede establishing valuable applications. Therefore, the aim of this review is to give an overview of IoT-enabled sensor and communication technology in relation to the specific quality and spoilage characteristics of fresh fruit and vegetables. Temperature, relative humidity (RH), O\({}_{2}\), CO\({}_{2}\) and vibration/shock are ambient parameters that provide most added value regarding product quality optimization, and can be monitored by current IoT-enabled sensor technology. Several wireless communication technologies are available for real-time data exchange and subsequent data processing and usage. Although many studies investigate the general possibility of monitoring systems using IoT-enabled technology, large-scale implementation in fresh fruit and vegetable supply chains is still hindered by unsolved challenges.  
wireless sensing; Internet of Things; cold chain management; fruit and vegetables; postharvest quality; shelf life; food waste