esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Lafun Gruel Rheological Properties  
Frequency sweep studies (a dynamic test) were conducted to characterise the rheological properties of the experimentally produced lafun samples (Table 2). The storage modulus G' and the loss modulus G'' were obtained from a rheological test in the linear viscoelastic range. G' is an amount of the deformation energy stored in the sample during the shear process (elastic behaviour of the sample), and G'' indicates the deformation energy capped during shear but lost to the sample afterward (viscous behaviour of the sample) [8]. The plots of frequency against both G' and G'' produced a linear relationship in all the samples (G', R\({}^{2}\geq\) 0.80 and G'', R\({}^{2}\geq\) 0.90). Regardless of the treatment used, G' was always higher than G'', signifying that lafun gruel exhibited more solid-like behaviour [8]. Also, the values of the two moduli increased with increasing frequency from 0.1 Hz to 10 Hz. To further understand the inherent attribute of the bonds within each sample, tan \(\delta\) (G''/G'') was plotted against frequency. Figure 1 shows the impact of cassava variety on the tan \(\delta\) of the lafun sample prepared using _L. mesenteroides_ as the starter culture, and a similar trend was observed for all other samples. Tan \(\delta\) values for all the samples were hardly affected by frequency. However, tan \(\delta\) values for both bitter and sweet cassava were higher, which indicates that they are more viscous than the fortified variety and have a greater number of weak-bond interactions within the gruel (2017). The more elastic the samples are, the higher the consumer acceptability, as farint is a food product expected to behave like an elastic material based on the characteristics mentioned by ([PERSON], 1993). Thus, the behaviour of the laboratory lafun samples was compared with that of the market sample. The G' values obtained for the market sample were higher only in the lafun samples produced with bitter cassava and _W. koreensis-2_ and a combination of both _W. koreensis_ strains, as well as the sample produced with sweet cassava and _L. mesenteroides_. It can, therefore, be inferred that 88% of the laboratory samples had higher deformability and thus exhibited elastic-like behaviour (Table 2). These results suggest that the majority of the laboratory samples have better quality regarding rheological properties than the market samples studied. This suggestion is based on the standard rheological quality of hydrated flour, which is, high deformability. Laboratory lafun flour showed better lafun gruel-making behaviour and resulted in superior lafun gruel quality.
# Simulation Analysis of Nanosecond Laser Processing of Titanium Alloy Based on Helical Trepanning
## 3 Results and Discussion
### Simulation Results  
#### 3.2.1 Titanium Alloy Micro Hole Interface Evolution Process Simulation  
The changing process of the titanium alloy phase transition interface with time is presented in Figure 2, where the blue and red areas represent the gas phase and metal phase, respectively. The color interface between the red and blue areas denotes a two-phase mixing interface. The green line shows the isotherm of the melting temperature (1670 K).  
When the laser started processing (Figure 2a), a small pit appeared in the center of the material. With the laser processing time and laser position shift, the material gasified, and the depth of the micro hole increased rapidly. At the same time, the pit gradually moved to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Symbols** & **Value (Unit)** & **Physical Meaning** \\ \hline \(T_{s}\) & 1670 (K) & melting point \\ \(T_{l}\) & 3200 (K) & evaporation point \\ \(C_{ps}\) & 780 (J/kg/K) & specific heat capacity of solid \\ \(C_{pl}\) & 700 (J/kg/K) & specific heat capacity of liquid \\ \(L_{l}\) & 247 (J/g) & latent heat of evaporation \\ \(L_{s}\) & 6340 (J/g) & latent heat of melting \\ mu & 6 (g/m/s) & dynamic viscosity \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermodynamic parameters of TC4.  
Figure 1: Scanning path of nanosecond laser processing.  
Figure 2: Phase transition process of the free interface of the titanium alloy micro hole with the laser processing time \(t\). (**a**) \(t\) = 0.058755 ms; (**b**) \(t\) = 0.11994 ms; (**c**) \(t\) = 0.16412 ms; (**d**) \(t\) = 0.23016 ms.  
the left and right ends. In addition, changes in the pit's width and depth were inconsistent, which could be observed from the energy distribution of the Gaussian light. The laser energy decreased exponentially from the center of the beam to the boundary of the spot. Since the Gaussian energy distribution characteristics of the light source on the spot point produced high temperature, which led to fast gasification of the near point of material and produced much heat, the effect of thermal radiation was not obvious. Moreover, it could be seen in the isotherm distribution that the heat change was concentrated near the light source point, and the generated heat radiation did not reach the phase transition temperature of the material after a few microns.
## 5 Results and Discussion  
In order to evaluate our implementation, a set of performance criteria has been adopted, namely, the gain in cubic metric reduction, the Bit Error Rate (BER) degradation and the resources' consumption.  
In a communication system, the BER is a critical parameter; thus, some experiments have been conducted to evaluate it. For this purpose, the physical layer of the ECMA-368 standard [19] has been used. It describes the physical layer of an Ultra Wideband (UWB) communication system intended for Wireless Personal Area Network (WPAN), using a band of frequencies not subject to a license between 3.1 GHz and 10.6 GHz. It supports different bit rates: 53.3 Mbps, 80 Mbps, 106.7 Mbps, 160 Mbps, 200 Mbps, 320 Mbps, 400 Mbps and 480 Mbps. This standard adopts Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) technology [19].  
In addition to OFDM, it has a frequency hopping provided by a Time-Frequency Coding (TFC). Each ECMA-368 symbol consists of 128 subcarriers, which span a bandwidth of 528 MHz.
## 5 Formulation of the LMI Stability Criterion
### Design of the LMI Condition
#### 5.2.1 LMI Approach with Fixed Parameters \(\bar{\delta}\) and \(\epsilon\)  
After formulating the LMI condition (76) and adjusting its expression to incorporate the matrices \(\mathcal{J}\), \(\mathcal{M}\), \(\mathcal{Q}\), and \(\mathcal{H}\) along with the scalar \(\beta\), the following theorem is derived. In the first approach to creating the LMI condition for stabilizing the gain \(\mathcal{K}\), the developed LMI is presented in this theorem.  
**Theorem 1**.: _Assuming that there exist matrices \(\mathcal{S}>0\), \(\mathcal{R}\), \(\mathcal{M}_{1}>0\), and \(\mathcal{M}_{3}>0\), as well as scalar variables \(\gamma>0\) and \(m_{2}>0\), satisfying the following LMI condition for a fixed scalar \(\zeta\geq 0\) and considering certain positive constant parameters \(\epsilon\) and \(\bar{\delta}\):_  
\[\left[\begin{array}{ccc}\Xi_{11}&\mathcal{S}&\mathcal{O}&\mu_{2}\mathcal{S} \mathcal{C}_{1}^{\mathrm{T}}&\mathcal{O}\\ \star&-\gamma\mathcal{I}&\mathcal{O}&\mathcal{O}&\mathcal{O}\\ \star&\star&\frac{\gamma}{\epsilon^{2}}\mathcal{D}_{1}\mathcal{D}_{1}^{ \mathrm{T}}-\mathcal{M}_{1}&\frac{\gamma}{\epsilon^{2}}\mathcal{D}_{1}&\frac {\gamma}{\epsilon^{2}}\mathcal{D}_{1}\mathcal{B}^{\mathrm{T}}\\ \star&\star&\star&\frac{\gamma}{\epsilon^{2}}\mathcal{I}-\left(\sigma\Big{(} \frac{1}{\mu_{3}}-1\Big{)}^{2}+\frac{1}{\mu_{3}}\Big{)}m_{2}&\frac{\gamma}{ \epsilon^{2}}\mathcal{B}^{\mathrm{T}}\\ \star&\star&\star&\star&\Xi_{55}\\ \end{array}\right]<0 \tag{77}\]  
_where the quantity \(\Xi_{11}\) is defined by expression (70), and \(\Xi_{55}=\frac{\gamma}{\epsilon^{2}}\mathcal{B}\mathcal{B}^{\mathrm{T}}-\left( \sigma\Big{(}\frac{1}{\bar{\delta}^{2}}-1\Big{)}^{2}+\frac{1}{\bar{\delta}^{2}} \right)\mathcal{M}_{3}\)._  
_Next, the knee exoskeleton robot's nonlinear dynamics, as described by (5), are stabilized by the proposed controller specified in (25). Additionally, the following equation can be employed to calculate the gain \(\mathcal{K}\) of the implemented controller:_  
\[\mathcal{K}=\mathcal{RS}^{-1} \tag{78}\]  
Notice that the stability condition expressed by (77) represents an LMI formulated regarding unknown variables \(\mathcal{S}\), \(\mathcal{R}\), \(\mathcal{M}_{1}\), \(m_{2}\), \(\mathcal{M}_{3}\), and \(\gamma\).  
In this section, the two parameters were taken to be fixed _a priori_, for which \(\epsilon\) should be small enough according to the definition of the adopted controller (25)-(26), and \(\bar{\delta}\) should be high since it defines the allowable maximum level of the lumped disturbance as noted in Assumption 1. Thus, these two parameters should be optimized, which is the objective of the subsequent three sections. We start by presenting the LMI with an unknown parameter \(\epsilon\) while \(\bar{\delta}\) is fixed. After that, we establish the LMI with an unknown parameter \(\bar{\delta}\) after fixing \(\epsilon\). In the third and final section, we formulate the LMI in which we need to optimize two unknown parameters, \(\epsilon\) and \(\bar{\delta}\).
## 3 Preliminaries and Problem Formulation  
In this section, the basic definitions are first described. Second, according to these definitions, we formulate the problem of wind speed prediction.  
**Definition 1**.: _Turbine network: In this paper, the turbine network is denoted as an undirected graph \(G=(V,E)\), where \(V\) is indicated as the node set. \(V\) contains \(N=|V|\) turbines. \(E\) is the set of edges in the graph._  
**Definition 2**.: _Wind speed representations: At timestep \(t_{s}\), the wind speed representations of the whole turbine network are defined as \(X^{t_{s}}=\{x_{1,t_{s}},x_{2,t_{s}},\ldots,x_{N,t_{s}}\}\in\mathbb{R}^{N\times K}\), where \(x_{N,t_{s}}\) indicates the wind speed representations in the \(N\)th turbine. \(K\) stands for the dimensions of wind speed representations, such as wind speed and wind direction._  
**Problem formulation. Wind speed prediction:** _Given the turbine network \(G=(V,E)\), the wind speed representations of historical \(P\) timesteps are \(\mathcal{X}=\left\{X^{t_{1}},X^{t_{2}},\ldots,X^{t_{p}}\right\}\in\mathbb{R}^ {P\times N\times K}\). The target is to learn a mapping function \(\psi(\cdot)\) that forecasts wind speed at future \(Q\) timesteps for \(N\) turbines:_  
\[\mathcal{X}\underset{\psi(\cdot)}{\rightarrow}\overline{\mathcal{Y}}_{WP}, \tag{1}\]  
_where \(\overline{\mathcal{Y}}_{WP}=\left\{\tilde{Y}_{WP}^{t_{p+1}},\tilde{Y}_{WP}^{ t_{p+2}},\ldots,\tilde{Y}_{WP}^{t_{p+Q}}\right\}\in\mathbb{R}^{Q\times N\times K}\) is the predicted wind speed series in the next \(Q\) timesteps. Because the target variable is wind speed, we set \(K\) as one._
# Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment
## 5 Experimentation
### Performance Evaluation
#### 5.2.2 Comparison with Similar Approaches  
To validate our scheme, we investigated our proposal against the few approaches that offer similar solutions, including [PERSON] et al. [14] (LM+TFIDF ), [PERSON] et al. [13] (LBP+SVM) and [PERSON] et al. [44] (EOC+CTAF). We compared LaGMO with LM+IDF based on the approach utilizing facial landmark points and the TF-IDF term weighting scheme. Regardless of LM+TFIDF focusing on age estimation in general, Table 3 and Figure 5 illustrate that LaGMO out-performed LM+IDF. We further compared our scheme with LBP+SVM. Although the LBP+SVM proposal focuses on detecting juveniles from adults, only texture-based features obtained from the coordinates of the facial landmark points were considered for feature representation. Regardless, it remains the only proposal that is fully dedicated to juvenile detection. As illustrated in Table 3, LaGMO performed better than LBP+SVM. Regarding EOC+CTAF, the authors proposed it for age estimation in general. However, the approach incorporated a channel for child age estimation and reported an impressive CS of 95.0 and a MAE of 2.69. We assumed the performance was due to the age threshold being 21 years for EOC+CTAF but 17 years for LaGMO. Therefore, we took on the challenging task of adjusting the threshold in EOC+CTAF to 17 years. Interestingly, we observed degradation in the performance of EOC+CTAF. Finally, with a CS of 89.86% and a MAE of 4.42, LaGMO demonstrated performance that was state-of-the-art for juvenile age estimation.  
Figure 4: The effect of reduced TF.  
Figure 5: Comparison with similar approaches.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Correlative Methods
#### 2.5.3 Prediction Based on Correlation to within-Season Attributes  
Attempts have been made to correlate tree fruit load to within-season estimates of tree structural parameters, such as crown area or volume, and/or reflectance indices related to canopy chlorophyll or health. These parameters can be assessed manually or by using tools such as LiDAR on ground or aerial platforms, or imagers on ground, aerial or satellite platforms. Satellite-derived data are desirable for its potential to provide yield predictions at a regional scale.
# Blockchain Architecture for Lightweight Storage
## 4 Consensus Process
### Block on Chain  
Newly generated blocks by agent nodes in DPoS must be validated by 2/3 of the witnesses [25], thereby significantly prolonging the transaction confirmation time. To address this issue, a verification node has been introduced into RP-DPoS to verify the legitimacy of the block, thereby reducing the verification delay.  
In Figure 6, the dotted line shows the set of verification nodes and indicates the sequential steps of the consensus with numbers. The agent node packages and signs the transaction and transmits the block to the client node. To mitigate the risk of malicious verification nodes, the PBFT algorithm is employed. The number of selected verification nodes, denoted as N, must satisfy the condition N \(\geq\) 3f + 1, where f represents the number of malicious nodes. The block verification steps are as follows:  
The client will temporarily save the block data and send them to the authentication node N0 by appending it to the request message <Request, t, c, BlockData> \(\sigma_{c}\). Here, cRequest represents the type of message sent, t is the timestamp, c indicates the client's address, BlockData is the serialized block data, and \(\sigma_{c}\) is the client's signature on the message.  
After N0 receives the request and confirms that the signature is correct, it sends a pre-prepare message to broadcast the request to the slave node, structured as <cPrePrepare, m, d, s> \(\sigma_{p}\). In this structure, cPrePrepare represents the type of message being sent, m contains the client's request, d is the message digest, s indicates the message's serial number, and \(\sigma_{p}\) is the signature of the node.  
After receiving the pre-prepare message, the verification node confirms whether the signature, the message summary, and the serial number match, and if no errors are found, it verifies the legality of the block. The verification rules are as follows:  
Figure 5: Node voting.  
Figure 6: Block on chain.  
* Verify whether the block height matches the height of the agent node.
* Verify whether the hash of the block is correctly pointing.  
Upon satisfying the aforementioned conditions, the node broadcasts a prepare message to the other verification nodes in the form of \(<\)cPrepare, v, d, s; \(\vartriangle\sigma_{i}\), where v represents the view number and i denotes the node number.  
The node receives the prepare message, verifies the signature, and ensures the consistency of v, d, and s. It then broadcasts the commit message \(<\)cCommit, v, d, s, i\(>\sigma_{i}\) to the other verifying nodes after receiving a total of \(2\)f + 1 messages, including its own.  
The node receives the commit message and validates it according to the same rules as in step 4. When the number of messages received reaches \(2\)f + 1, the node sends a reply message to the client.  
The client receives f + 1 commit messages, indicating that verification has passed and the block is legal. The client node broadcasts the previously saved block to other nodes and then deletes the block data to initiate the next verification round.
## 7 Applications in SDN-based VANETs
### Bandwidth Management  
Radical augmentation in the utilizations of SDN-based VANETs expands the interest of data transfer capacity necessity. The current system's engineering and highlights do not bolster the dynamic data transfer capacity request. SDN worldview makes accessible the practical transfer speed on-request bandwidth on demand (BWoD) in the profoundly altering condition of the SDN-based VANETs. The OpenFlow based SDN enables buyers to profit the data transmission on-request benefits enabling vehicles to determine their transfer speed prerequisites powerfully. This adaptiveness can be executed utilizing system gadgets, for example, RSUs, switches, and data transmission scheduler introduced on an SDN controller. However, effective designation of data transfer capacity is likewise a test [148].
# The Role of Platelet-Rich Plasma in the Management of Ankle Osteoarthritis: A Systematic Review
## 1 Introduction  
Osteoarthritis (OA) has been considered the most common form of joint disease worldwide, and its functional and clinical impairments place a major burden on both communities and healthcare systems. As a highly correlated condition with aging, its impact is expected to increase in a way that is directly proportional to global life expectancy. Additionally, the increasing appearance of OA risk factors, such as obesity, physical inactivity, and joint injury, is contributing to a propagation phenomenon [1].  
Clinical evidence has suggested that the prevalence of ankle OA within the general population is significantly lower than OA in other joints [2]. With the hands, knees, and hips being the most common areas affected by OA [3], ankle OA in particular ranges from 9% to 15% of all OA cases in general adult populations [4]. However, ankle OA is more commonly found in the young, active population, predominantly in women, due to their tendency to experience repeated trauma throughout a longer lifetime period [5]. Quality of life and physical functioning in patients with ankle OA have been found to be equivalent to those perceived in patients with musculoskeletal comorbidities like hip OA, but also compared with systemic conditions, such as end-stage kidney disease or congestive heart failure [6].  
Surgery methods like joint replacement, in contrast with their effect on hip/knee OA, are associated with important functional restrictions in ankle OA patients. Regarding nonoperative modalities, there is a wide variety of options, from pharmacological to physical therapies, but these are not without side effects or produce only short-termresults over time [7; 8]. Furthermore, a lack of evidence from high-quality studies has been suggested by recent systematic reviews on intra-articular injection therapy in large joint OA [9; 10]. Recent advances in biological research have highlighted the importance of growth factors in cartilage pathology [11]. Platelet-rich plasma (PRP) arises as an effective treatment in the management of the symptoms of OA. However, despite its increasing popularity and potential biological benefits, the clinical improvements related to current PRP injectables are still uncertain [12]. The reasons for this include the lack of standardized protocols for preparation and administration, the existence of different types of PRP products, or the use of different activating methods for platelet stimulation, among others. Therefore, the effectiveness of PRP is still being debated, as the current literature reports contradictory results from different methodological approaches [11; 12; 13]. Despite all this incongruruce, the use of PRP in treating ankle OA has become more prevalent in recent times, and several studies have demonstrated both positive and neutral outcomes. Due to these mixed results, we aimed to systematically synthesize the available scientific literature on PRP as a therapeutic intervention in managing ankle OA.
# IoT-Based Implementation of Field Area Network Using Smart Grid Communication Infrastructure
## 2 Hierarchy-Based Smart Grid Communication Infrastructure for Smart Farming Applications
### Wide Area Network (WAN)  
The wide area network (WAN) unites various NANs. Data are collected by various collection points and are sent to a central controller. The area covered by the WAN is around thousands of square miles. The data rates required by WAN are around 10-100 Mbps [39]. A wide area necessitates a large bandwidth for the management of a smart grid network. WAN is appropriate for supervisory control and data acquisition (SCADA) systems for monitoring, control, data acquisition, and the management of  
Figure 2: Field area network.  
a smart power grid [39; 40; 41; 42]. IEEE 802.16 (Wimax) and cellular technologies, such as LTE, 3G, 4G, 5G, EDGE, and GPRS are appropriate adoptions for wide area network applications. Geographic information systems (GIS), remote sensing, and the Global Positioning System (GPS) can be used for the field-specific management of smart farming parameters. WAN is applicable for the IoT applications [39; 40; 41; 42]. IoT facilitates \"machine-to-machine\" or \"device-to-device\" communication. The web of things can benefit smart farming in terms of contemporary techniques for higher crop production.
# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
## 3 Autonomous Robot System Design
LIDAR systems can produce even higher resolution data [20], but these sensors generally attract a high cost, both computationally and financially [21]. In our system, we primarily use a laser range finder (Hokuyo, Osaka, Japan URG-04 LX-UG01) for obstacle detection, with contact/touch sensors as a failsafe in the event that the laser range finder fails. Figure 2: (a) System block diagram of the autonomous robot and (b) Prototype implementation on a Pioneer 3-DX research robot (© 2017 IEEE from [10]). Since we assume that GPS is unavailable in the targeted indoor factory automation context, there is no global object tracking system available to keep track of the position of the robot, so we must use another form of robot localisation [2]. As the Pioneer robot used in our research has wheel-based locomotion, we can use wheel encoders to provide odometry data for this purpose. Finally, we require some form of actuation so that we can move the wheels in a way that allows the robot to make progress towards the target destination. This is provided by two electric motors inside the robot, driving the wheels on each side. This system has been implemented on a Pioneer 3-DX research robot, which is shown in Figure 2b with four radio receivers and parabolic reflectors. The robot has an HP Mini 110-3627 TU N570 netbook with a 1.66 GHz processor and 1 GB of RAM on board with Ubuntu 14.04.1 LTS, which runs the software for this robot. Robot Operating System (ROS) [22] was used to interface with the robot itself, which runs as a series of nodes that communicate using messages. The robot itself and each sensor were abstracted into ROS nodes, and each of the four software modules exists as an individual node to allow for greater modularity and reduced coupling.