esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results and Discussion
### Deviant Filter Performance Conditions  
We found that the filter is unable to find edges for some binary images. Figure 7 shows an example that illustrates this.  
We expected that the FCCF would generate a cross-like shape; however, it was not the case. This sets a constraint in achievable applications of the FCCF.  
For this reason, we do not recommend using the FCCF for computing edge maps of binary images.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 3 Experimental Design
### Measurement of Voltage, Current, and Power of PD
#### 3.1.3 Input Power  
* Considering the internal capacitor is in parallel with the PD, the power generated by the PD varies with time. Measuring the constantly changing voltage and current waveforms for evaluation of the AC power is challenging, as Equation (7) is not applicable. Thus, the mean power is a better representation of PD's power. When PD is subjected to excitation, the voltage and current waveforms are shown in Figure 9.  
The power generated by the PD, which is the input power (P\({}_{\text{in}}\)) to the rectifier circuit, is calculated as follows:  
\[\text{P}_{\text{in}}=\text{V}_{\text{RMS}}\cdot\text{I}_{\text{RMS}}\cdot\cos\theta \tag{7}\]  
where \(\theta\) is the phase angle between the voltage and the current of PD.
## 3 Experiment
### Results for the Cross-View Setting
#### 3.2.1 Number of Components Selection for Joint Bayesian  
As we know, the dimension of DeepGait is 4096, and high dimension means that more training data are needed for model learning when Joint Bayesian [28] is used for gait recognition. In fact, number of training samples is often limited in gait recognition, therefore, the dimension of DeepGait needs to be reduced. Due to the powerful discrimination of our proposed DeepGait, we can achieve a competitive performance even in a low dimension after PCA. Experiments of different number of components were performed with Joint Bayesian, so that we could choose the right \(K\) components, where \(K\) is the number of components, to strike a balance between recognition performance and computational complexity. Figure 3 shows the results of different \(K\) components under different combinations of Gallery and Probe views.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Rank-1/Rank-5** & **Dataset** & **\#Subjects** & **DeepGait** & **GEI** & **MGEI** & **GEnI** & **FDF** & **GFI** \\ \hline \multirow{4}{*}{**rank-1**} & **View-55** & 3,706 & **90.6** & 85.3 & 79.3 & 75.1 & 83.1 & 61.9 \\  & **View-65** & 3,770 & **91.2** & 85.6 & 83.2 & 77.3 & 84.7 & 66.6 \\  & **View-75** & 3,751 & **91.2** & 86.1 & 84.6 & 79.1 & 86.0 & 69.3 \\  & **View-85** & 3,249 & **92.0** & 85.3 & 83.9 & 80.7 & 85.6 & 69.8 \\ \cline{2-10}  & **Mean** & **92.3** & 85.6 & 82.8 & 78.1 & 84.9 & 66.9 \\ \hline \multirow{4}{*}{**rank-5**} & **View-55** & 3,706 & **96.0** & 91.8 & 89.3 & 85.5 & 91.0 & 75.5 \\  & **View-65** & 3,770 & **96.0** & 92.3 & 91.5 & 87.7 & 92.3 & 79.5 \\ \cline{1-1}  & **View-75** & 3,751 & **96.1** & 92.2 & 92.0 & 88.8 & 92.5 & 81.3 \\ \cline{1-1}  & **View-85** & 3,249 & **96.5** & 92.6 & 91.9 & 89.3 & 92.3 & 81.9 \\ \cline{1-1} \cline{2-10}  & **Mean** & **96.2** & 92.2 & 91.2 & 87.8 & 92.0 & 79.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of rank-1 (%) and rank-5 (%) identification rates with different gait representations on the whole dataset (NN). GEI: Gait Energy Image; MGEI: Masked GEI based on GEnI; GEnI: Gait Entropy Image; FDF: Frequency-Domain Feature; GFI: Gait Flow Image.  
We can see that \(K=2048\), achieved the worst performance due to under-fitting. The training samples are insufficient when Joint Bayesian was used with high dimension. When dealing with the lowest dimension (\(K=64\)), our proposed method still achieved competitive performances among three cross-view combinations (55:65, 65:75, 75:85). Further, we found that '\(K=256\)' achieved almost the same result with '\(K=512\)' under all the cross-view conditions while '\(K=256\)' has half the number of components. For the best balance of performance and computing cost, we finally set \(K=256\) when Joint Bayesian is used in the following experiments.
# Research on a Real-Time Control System for Discrete Factories Based on Digital Twin Technology
## 2 Methods
### Modeling of Digital Twin Factory  
Referring to the current research status of twin factories [21] and combining the operation characteristics of gear factories [22], a model for the digital twin gear factory is designed as a three-dimensional framework based on a database, as shown in Figure 1. The model consists of a database and the three following parts: a physical factory, a virtual factory, and a management system. The physical factory is constructed based on the process characteristics of the products. The virtual factory is generated through mapping, fusion, computation, and reasoning. The management system is established through sensor collection and human-computer interaction, and all parts of the digital twin factory are organically interconnected. The model is used to realize the integration of elements and the fusion of processes for the physical factory, virtual factory, and management system through the entity mapping and real-time interaction of the physical factory and the virtual factory. The physical factory has perceptual access capabilities and can be considered as a type of factory, adding multisource heterogeneous real-time data on the basis of the initial factory, which consists of machinery equipment, personnel, materials, and other components. The virtual factory is a virtual mapping of the physical factory using geometry, behavior, rules, and other models. And it is a highly restored virtual model of the physical factory simulation software. The management system is responsible for the control and optimization of the production elements, production plan, production process, and other aspects, with the aim of improving the production efficiency of the factory. The data of the physical factory, virtual factory, and management system, and the data of their fusion constitute the digital twin data, which eliminates the information isolated island. The real-time prediction, feedback, and monitoring of the production process are achieved via realizing the interactive fusion of the physical space and information space of the process.  
The model of the digital twin gear factory is improved based on previous research findings [21]. To achieve real-time control and efficient management, computer networks, camera monitoring, and data analysis should also be included in the digital twin factory. The digital twin factory will be more extensive and advanced than the initial factory.
## 4 Discussion
For instance, other possible application types include active vision control for surveillance purposes, such as mobile exploring robots that perform visual inspection of historical places and objects [34], navigation tasks in different environments or guiding motions of robotic manipulators [35].
## 2 Background
### Avoiding Flicker and Keeping the Light White  
Human eyes perceive objects around them by projecting images onto the retina. The brain needs a certain amount of time to process the received pictures, and our visual system cannot respond to the stimulus of the changes in front of the eyes, causing a delay in the observation of brightness or color changes. If intermittent stimuli appear below a certain frequency, our visual system can perceive changes (the effect is called flicker). Above the frequency at which the flicker effect ceases is called the flicker fusion threshold [21]. The flicker frequency of our LEDs are above 100 Hz, greater than the threshold, so the naked eye cannot see the flicker.  
The most common and universal light in our lives is white light. The LEDs we are discussing here are used for both communication and general lighting in many cases. In general lighting, people generally have difficulty in accepting colored lights. Therefore, it is necessary to discuss the maintenance of white light in color modulation.  
In the process of perceiving objects around us, our eyes go through a process called the sum of time. In the process, the eye accumulates photons until it is saturated. This period is called the \(t_{c}\):  
\[\zeta=I*t(t\leq t_{c}) \tag{1}\]  
\(I\) is the intensity of the stimulus (LED project's color stimulus in our case), and \(t_{c}\) is the critical duration. With the increase of \(t\), once the threshold is reached, additional stimulus light will not be perceived by the visual system. The perception of color by the human eye is the average of the stimulus within the critical duration. According to [PERSON]'s law, the color \(\Psi\) is  
\[\psi=\frac{\int I_{R}(t)dt+\int I_{G}(t)dt+\int I_{B}(t)dt}{t} \tag{2}\]  
where \(I_{R}(t)\), \(I_{G}(t)\), and \(I_{B}(t)\) are the intensity functions of red, green, and blue light, respectively. For RGB-LED, if the three colors are emitted at the same ratio, the human eye will perceive the white light in the critical duration for the sum of the time. This is illustrated in Figure 2.
## 2 Named Data Networking and Its Role in IoT Smart Environments
### NDN in a Nutshell
#### 2.1.2 Forwarding Fabric  
Only interests are routed in NDN [16]. Specifically, at the interest reception (from the application layer or from the underlying access network layer), each node runs the following processing:  
* First, the node looks in the CS. If a matching is found, the data packet is immediately sent over the same interface that the interest arrives from.
* Otherwise, the node looks in the PIT. If a match is found, it means that the same request has already been forwarded, the node is waiting for the data, and there is no need to transmit it again. As a result, the interest is discarded and the node only updates the PIT entry with the incoming interface of the received interest. Such a feature, typically referred to as request aggregation, reduces the number of accesses to the original content source.
* If also the PIT matching fails, the node looks in the FIB to forward the interest over an outgoing interface. In case of failure, a NACK can be sent back and the interest is discarded.  
Data packets are forwarded over the same interfaces that the interests were received, according to the PIT entry.  
Figure 1: Named Data Networking (NDN) node’s data plane.
## 2 Experimental Setup  
### Sample Preparation and Curing  
In this work, six types of cement mortars have been analysed. On one hand, mortars made with a commercial ordinary Portland cement, designated CEM I 42.5 R (CEM I hereafter), according to the Spanish and European standard UNE-EN 197-1 [39], were prepared. On the other hand, mortars prepared with a commercial Portland cement with fly ash, CEM II/B-V 42.5 R [39] (CEM II from now on), which has a fly ash content from 21% to 35% (by weight), were also studied.  
In addition to this, mortars with different contents of red mud were tested. Those mortars were prepared with ordinary Portland cement, CEM I 42.5 R [39], which was partially replaced by 5%, 10%, 15% and 20% of red mud. They have been named as RM5, RM10, RM15 and RM20 respectively hereafter. After drying, the ground red mud was passed through a sieve of 125 \(\upmu\)m and more than 60% of the material also passed through asieve of 63 \(\upmu\)m. The red mud was provided by the company Alcoa, from its factory at San Cibrao, Spain, and its chemical composition can be observed in Table 1.  
All the specimens were prepared with a water to binder ratio of 0.5 and with an aggregate to cement ratio of 3:1. The particle size distribution of the aggregate used is shown in Table 2. Both kinds of samples (cylindrical and prismatic) were made. The dimensions of the cylindrical specimens were 15 cm height and 10 cm diameter, whereas the prismatic samples had measurements of 4 cm \(\times\) 4 cm \(\times\) 16 cm [41]. Once set, both kinds of specimens were maintained in a chamber at 20 \({}^{\circ}\)C and 95% relative humidity (RH) for the first 24 h. After that time, the samples were de-moulded and the cylindrical ones were cut to obtain slices of 1 cm and 5 cm height. Lastly, all the specimens were kept under optimum hardening conditions (20 \({}^{\circ}\)C and 100% RH) until the testing ages.  
\begin{table}
\begin{tabular}{c c} \hline
**Composition** & **Red Mud** \\ \hline SiO\({}_{2}\) & 9\% \\ Al\({}_{2}\)O\({}_{3}\) & 12\% \\ CaO & 6\% \\ Fe\({}_{2}\)O\({}_{3}\) & 37\% \\ Na\({}_{2}\)O & 5\% \\ TiO\({}_{2}\) & 20\% \\ H\({}_{2}\)O (1000 \({}^{\circ}\)C) & 11\% \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of red mud (weight basis) [40].  
\begin{table}
\begin{tabular}{c c} \hline
**Sieve (mm)** & **Cummulative Passing** \\ \hline
4 & 100\% \\
2 & 99.97\% \\
1 & 67.69\% \\
0.5 & 33.23\% \\
0.25 & 23.08\% \\
0.125 & 2.26\% \\
0.063 & 0.23\% \\ \hline \end{tabular}
\end{table}
Table 2: Particle size distribution of the aggregate used.
## 2 Specimens and Experiment Setup
### The Flow Rate and Flow Velocity Measurement  
To verify the pumping efficiency of the anti-phase and in-phase motion of the two-bimorph structure, the experiment set-up is designed as shown in Figure 6. Figure 6a shows the flow rate testing layout. A flow induced by water level is designed with the bionic piezoelectric valveless pump. The silicone tube straight alignment must be confirmed in order to avoid the resistance caused by flow in the bending tube. The water is poured into the water tank, so that the water flows naturally into the cavity and discharges the air. A cup placed on the electronic scale is set at the end of the outlet to measure the output water weight. The steady-state flow rate is measured by dividing the water volume by the time it takes to flow through the tube without using the pump. The gain of flow rate is obtained by dividing the flow rate increment when the pump is used by the steady-state flow rate. Figure 6b shows the flow velocity testing set-up, which is similar to the flow rate measuring test. The colored droplets are squeezed, and the time the colored liquid flows out of the silicone tube is measured. The flow velocity is computed by dividing the displacement of the tube length by the time it takes to finish flowing through it.  
Figure 5: Finite element model of proposed piezoelectric pumping element: (**a**) immersed region as shown in transparent, and (**b**) element types.  
Figure 6: (**a**) The flow rate measurement set-up, and (**b**) the flow velocity measurement set-up of the pumping element.  
Figure 7 shows the electrical connection of dual piezoelectric bimorphs to drive the anti-phase and in-phase motions. The various electrical fields are applied on the upper-layer and lower-layer piezoelectric ceramics with different polarization to produce larger bending deformation, since the shrinkage and extension were caused respectively by the same and the opposite directions between the electrical field and the polarization. The deformation mechanism on the piezoelectric bimorph was discussed in detail by [PERSON] and [PERSON] [24].
## 1 Introduction
### Background and Previous Works
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Scientific Studies** & **Key Contributions** & **Data Selection and Evaluation Method** & **Artificial Intelligence Concept Used for Predictive Maintenance of Electrolyser** \\ \hline Authors’ Proposal & Input data pre-processing. Predictive maintenance based on predicted data using artificial intelligence. Open circuit fault prediction in the power converter of electrolyser. Prediction of different parameters of the electrolytic cell design using input parameters: hydrogen production rate, cathode area, anode area, and the type of cell design. Prognosis and health management model (PHM) to predict the load voltage of the electrolyser for the state of health information. The voltage is used as a state of health indicator which increases according to the time passed, and it is caused by the degradation of the electrolyser. & n/a \\ [PERSON] et al. [15] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Authors’ contribution and related studies. The remainder of the paper is organised as follows. Section 2 establishes the fundamentals of maintenance in electrolysers as well as the materials and methods applied by authors to develop a novel algorithm for feature selection using DRL. An LSTM neural network was then designed to use the selected feature for intelligent maintenance of a PEM electrolyser. Simulation results are shown in Section 3. Discussion of the results in Sections 4 and 5 provides the conclusions and future works.
## 2 Materials and Methods
### Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) Analysis  
Total cellular RNA was extracted from the cells using an RNeasy Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. Complementary DNA was synthesized using a SuperScript(tm) VILOT(tm) cDNA Synthesis Kit (Invitrogen, Thermo Fisher Scientific Inc., Waltham, MA, USA). The TaqMan probe was used for real-time PCR monitoring in the Step One(tm) Plus RT-PCR System (Thermo Fisher Scientific). The 2\({}^{-\Delta\Delta\text{ct}}\) method was utilized to quantify expression levels; glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as a housekeeping gene to obtain a relative mRNA level. The accession numbers for the TaqMan gene expression assay are as follows: tumor necrosis factor-\(\alpha\) (_Tnf-\(\alpha\)_): Rn9999017_m1; interleukin-6 (_Il-6_): Rn014103330_m1; transforming growth factor-\(\beta\) (_Tgf-\(\beta\)_): Rn00572010_m1; interferon-\(\gamma\) (_Ifn-\(\gamma\)_): Rn00594078_m1; cytidine/uridine monophosphate kinase 2 (_Cmpk2_): Rn01473059_m1; and GAPDH: Rn01775763_g1.
# Effect of Dune Sand on Drying Shrinkage Cracking of Fly Ash Concrete
## 2 Experimental Plan and Method
### Shrinkage Strain of Inner Ring and Shrinkage Stress at Cracking  
Figure 10 shows the shrinkage strain of the inner ring immediately before cracking for each mixture.  
The shrinkage strains of the W170 mixtures range from 43 to 71 \(\times\) 10\({}^{-6}\), and the D40 mixture shows the maximum shrinkage strain. The D60 mixture exhibits the minimum shrinkage strain, which is approximately 60% of the shrinkage strain of the D40 mixture. The shrinkage strains of the W160 mixtures range from 53 to 66 \(\times\) 10\({}^{-6}\), and the D10 mixture exhibits the maximum shrinkage strain. The D20 mixture shows the minimum shrinkage strain, which is approximately 80% of the maximum shrinkage strain.  
Figure 11 presents the shrinkage stress of each mixture before cracking was calculated using Equation (3). The shrinkage stress results show the same tendency as the shrinkage strain results. The shrinkage stresses of the W170 and W160 mixtures range from 3.3 to 5.6 MPa and 4.2 to 5.2 MPa, respectively. The shrinkage stress and net time to cracking were compared, and it was confirmed that there was no close relationship between them.  
Figure 12 compares the splitting tensile strength development history with the shrinkage stress at cracking. Based on the measured data, the splitting tensile strength development history was derived using Equation (4), which is presented in ACI 209 [28].  
\[f_{\text{fs}}(t)=\left[\frac{t}{a+bt}\right]f_{\text{fs}}28 \tag{4}\]  
where \(f_{\text{fs}}\) is the tensile strength at age t (MPa), \(t\) is the age (days), \(f_{\text{fs}}28\) is the tensile strength at age 28 days (MPa), and \(a\) and \(b\) are constants.  
Figure 11: Drying shrinkage stress at cracking.  
Figure 10: Drying shrinkage strain at cracking.  
The comparison results show that the shrinkage stress at cracking is higher than the splitting tensile strength of concrete developed at the same time. Thus, predicting the cracking of concrete simply based on the splitting tensile strength and shrinkage stress of concrete may yield inaccurate results. This inaccuracy may occur because the cracking of restrained concrete is not caused by shrinkage stress alone but, rather, is attributable to the complex actions of various factors, such as the shrinkage rate, tensile creep, and tensile strength [29].
## 5 Approximately Optimal Steiner Tree Algorithm  
### Algorithm Overview  
We present a high-level overview of the proposed algorithm to produce approximately optimal Steiner trees, divided into four steps.  
**Step 1--APSP and routing tables:**: Solve the APSP problem, as in [7], and add an efficient routing table scheme via triangle finding in \(\tilde{\mathcal{O}}(n^{1/4})\) rounds, with success probability \((1-1/poly(n))\) (this step determines the algorithm's overall success probability).
**Step 2--shortest-path forest:**: Construct a shortest-path forest (SPF), where each tree consists of exactly one source terminal and the shortest paths to the vertices whose closest terminal is that source terminal. This step can be completed in one round and \(n\) messages, per ([2] Section 3.1). The messages can be in classical bits.
**Step 3--weight modifications:**: Modify the edge weights depending on whether they belong to a tree (set to \(0\)), connect nodes in the same tree (set to \(\infty\)), or connect nodes from different trees (set to the shortest-path distance between root terminals of the trees that use the edge). This uses one round and \(n\) messages.
**Step 4--minimum spanning tree:**: Construct a minimum spanning tree (MST) on the modified graph in \(\tilde{\mathcal{O}}(1)\) rounds as in [6], and prune leaves of the MST that do not connect terminal nodes since these are not needed for the Steiner tree.  
The correctness of the algorithm follows from the correctness of each step together with the analysis of the classical results of [13], which uses the same algorithmic steps of constructing a shortest-path forest and building it into an approximately optimal Steiner tree.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation
### Experiment Setup and Settings  
All results were averaged over 1000 runs. In each run, data points were first randomly shuffled to avoid any bias related to their collection order; each data point was then randomly labeled as RP, with probability \(p_{RP}=0.7\) or TP, with probability \(p_{TP}=1-p_{RP}=0.3\). The position of each TP was then estimated by applying the selected combination of the strategy and RF parameter, and the positioning error was measured as the distance between estimated and real positions. The error was then averaged over all the TPs and all the runs, leading to the average positioning error, which was adopted as the first performance indicator in the subsequent subsections. The average positioning error was measured for \(k\) varying in the range [1; 40]; the minimum of the average positioning error as a function of \(k\) is referred to in the following as minimum average positioning error.
## Materials and Methods
### Two-Leaf PRI Model  
Treating the canopy as two big leaves, i.e., sunlit and shaded leaves, and considering their distinct difference of absorbed radiation and PRI, a two-leaf approach was developed to derive PRI\({}_{t}\) using multi-angle observations.  
To derive fractions of observed sunlit and shaded leaves, observed canopy reflectance (R\({}_{can}\)) for each view angle was separated to four components on the basis of a four-scale bidirectional reflectance model [70], as following:  
\[R_{can}=R_{T}\times P_{T}+R_{G}\times P_{G}+R_{S}\times P_{S}+R_{Z}\times P_{Z} \tag{8}\]  
where \(P_{T}\) and \(P_{S}\) are the fractions of sunlit and shaded leaves, respectively; \(P_{G}\) and \(P_{Z}\) are the fractions of sunlit and shaded background, respectively; \(R_{T}\) and \(R_{S}\) are the reflectance of sunlit and shaded leaves, respectively; \(R_{G}\) and \(R_{Z}\) are the reflectance of sunlit and shaded background, respectively. The fractions of four components are irrelevant with respect to wavelength, whereas reflectance of these four elements is wavelength dependent.