esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Scale Implementations for Twitter Sentiment Classification
## 4 Sentiment Analysis Classification Framework  
In the beginning of this section, we define some notation used throughout this paper and then provide a formal definition of the confronted problem. After that, we introduce the features that we use to build the feature vector. Finally, we describe our Spark algorithm using pseudo-code and proceed to a step by step explanation. Table 1 lists the symbols and their meanings.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(H\) & set of hashtags \\ \(E\) & set of emoticons \\ \(T\) & training set \\ \(TT\) & test set \\ \(L\) & set of sentiment labels of \(T\) \\ \(p\) & set of sentiment polarities of \(TT\) \\ \(C\) & A\(k\)NN classifier \\ \(w_{f}\) & weight of feature \(f\) \\ \(N_{f}\) & number of times feature \(f\) appears in a tweet \\ \(count(f)\) & count of feature \(f\) in corpus \\ \(f_{rf}\) & frequency of feature \(f\) in corpus \\ \(F_{C}\) & upper bound for content words \\ \(F_{H}\) & lower bound for high frequency words \\ \(M_{f}\) & maximal observed value of feature \(f\) in corpus \\ \(hf_{i}\) & \(i\)-th hash function \\ \(F_{T}\) & feature vector of \(T\) \\ \(F_{TT}\) & feature vector of \(TT\) \\ \(V\) & set of matching vectors \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols and their meanings.  
Figure 2: The Spark stack.  
Assume a set of hashtags \(H=\{h_{1},h_{2},\ldots,h_{n}\}\) and a set of emoticons \(E=\{em_{1},em_{2},\ldots,em_{m}\}\) associated with a set of tweets \(T=\{t_{1},t_{2},\ldots,t_{l}\}\) (training set). Each \(t\in T\) carries only one sentiment label from \(L=H\cup E\). This means that tweets containing more than one label from \(L\) are not candidates for \(T\), since their sentiment tendency may be vague. However, there is no limitation on the number of hashtags or emoticons a tweet can contain, as long as they are non-conflicting with \(L\). Given a set of unlabelled tweets \(TT=\{tt_{1},tt_{2},\ldots,tt_{k}\}\) (test set), we aim to infer the sentiment polarities \(p=\{p_{1},p_{2},\ldots,p_{k}\}\) for \(TT\), where \(p_{i}\in L\cup\{neu\}\) and \(neu\) means that the tweet carries no sentiment information. We build a tweet-level classifier \(C\) and adopt a \(k\)NN strategy to decide the sentiment tendency \(\forall tt\in TT\). We implement \(C\) by adapting an existing MapReduce classification algorithm based on AkNN queries [46], as described in Subsection 4.3.
# Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network
## 3 Application of Recurrent Network
### Conventional Angle Recognition Method  
In our previous study, we used the following relationship between the curvature and the voltage of the piezoelectric sensor [22]:  
\[q(t) =Cv(t)+\epsilon\bar{k}(t), \tag{2}\] \[\dot{q}(t) =-\frac{v(t)}{R_{load}}, \tag{3}\]  
where \(q\) is the charge stored in the piezoelectric sensor, \(C\) is capacitance, \(v\) is the piezoelectric voltage, \(\epsilon\) is electromechanical coupling coefficient, \(\bar{k}\) is the mean curvature, and \(R_{load}\) is the load resistance.  
Applying the Laplace transformation, Equations (2) and (3) can be written as:  
\[Q(s) =CV(s)+\epsilon\bar{K}(s), \tag{4}\] \[sQ(s) =-\frac{V(s)}{R_{load}}, \tag{5}\]  
in the \(s\) domain.  
Inserting Equation (4) into Equation (5) by substituting for \(Q(s)\), we have:  
\[s\cdot(CV(s)+\epsilon\bar{K}(s))=-\frac{V(s)}{R_{load}}. \tag{6}\]  
By reorganizing Equation (6) as the relation between \(K(s)\) and \(V(s)\), we have:  
\[\epsilon\bar{K}(s)=-\frac{1+s\cdot C\cdot R_{load}}{s\cdot R_{load}}V(s). \tag{7}\]  
When the piezoelectric sensor has low capacitance value and load resistance value (\(1>>C\cdot R_{load}\)), Equation (7) reduces to:  
\[\epsilon\bar{K}(s)\simeq-\frac{1}{s\cdot R_{load}}V(s). \tag{8}\]  
Equation (8) can be expressed as follows in the time domain after implementing the inverse-Laplace transform:  
\[\epsilon\bar{K}(t)\simeq-\frac{1}{R_{load}}\cdot\int_{0}^{t}v(\tau)d\tau. \tag{9}\]  
In the beam structure, the value of the mean curvature is related to the change of the slope between the start and end of the beam, and the slope can be expressed as the angle of the sensor [22]. From these relations, we derive the following equation using the angular change of the sensor and piezoelectric voltage [18]:  
\[\theta(t_{n}) =a\times\sum_{m=1}^{n}\left[(v\left(t_{m}\right)-O\left(t_{m} \right))\times\Delta t_{m}\right] \tag{10}\] \[n =1,\ 2,\...\]  
where \(\theta\) is the angle variation of the piezoelectric sensor, \(A\) is the gain including all proportional terms, \(O\) is the offset, \(\Delta t_{m}\) is the time interval, and \(t_{n}=\sum_{m=1}^{n}\Delta t_{m}\).
# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
## 3 Results and Discussion  
To verify the effectiveness of the proposed approach under the influence of light and the complex background of the dial, a series of pressure gauges with damping fluid with a size of 480*480 were selected for the experiment, including MSRCR image enhancement, meter center extraction, pointer positioning and recognition reading experiments. The hardware configuration of the computer used in this experiment is Intel i5-5200U and 2.7 GHz. The programming environment is the Microsoft Visual Studio 2015, using C++ language for programming. Table 2 lists the primary parameters of the camera in the inspection robot. Figure 6 shows the experimental results of each step.  
Figure 6a is the original pointer gauge image. From Figure 6a, it can be seen that the contrast of the image taken under natural light is not high, especially the blurred pointer of the scale line leads to a position inaccurately. For the experiment of center extraction and pointer positioning, the numbers and words in the dial interfere with the next detection. Therefore, this paper proposed a recognition reading algorithm based on coordinate positioning. Firstly, the MSRCR algorithm was used to reduce the influence of light and improve the contrast of the scale line. Then, the center and pointer were directly extracted based on the image information, which greatly reduced the influence of other useless information in the background. Figure 6b is an enhanced image of MSRCR. It can be clearly found that the scale line is clearer, which is beneficial to the next pointer extraction experiment. Figure 6c shows the result of the circle center extraction. Although the extraction result obtained the inner and outer contours of the dial, it did not affect the location of the center of the circle. Figure 6d-f shows the pointer obtained by using the difference between the template and the original image, and then refined the pointer  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name** & **Type** & **Performance** \\ \hline
1 & CCD camera & MV-CA050-10 GM & Resolution: 2448*2048 \\
2 & Lens & M0824-MPW2 & Focal length: 8 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of the camera.  
Figure 5: The principle of the angle method.  
to draw a circle with an appropriate radius. The coordinates of the positioning pointer were determined by the intersection of the specified circle and the refined pointer. When drawing a circle, in order to reduce the deviation caused by incomplete pointer extraction, the radius of the circle should be increased as much as possible under the premise of ensuring that it can intersect with the refined pointer. This is because the radius of the selected circle is proportional to the distance from the intersection to the scale line. The larger the radius of the circle was, the higher the accuracy of the reading was. Figure 6 shows the obtained coordinates of the initial scale value. Finally, the pointer reading was obtained using the angle method.  
Figure 6: The algorithm detection result.
## 4 Numerical Tests on Mechanical Performance Following Secondary Pre-Tightening of Supports
### Summary and Engineering Suggestions  
Applying secondary pre-tightening torque to the cable-clip joint can significantly enhance the bearing capacity of the support. However, in this study, when the initial torque was greater than 250 N-m and the same secondary pre-tightening torque was applied, the degrees of influence on the bearing capacity and the yield-pressure performance of the support were within 5% and 10%, respectively. In practical engineering, it is recommended to perform secondary pre-tightening on the cable-clip joints of yield-type supports. Taking the cable-clip joint of the U29 steel support as an example, the initial pre-tightening torque is preferably 250 N-m.  
The larger the secondary pre-tightening torque, the more significant were the influence on the bearing capacity of the support and the yield-pressure performance of the joint. When the secondary pre-tightening torque was less than the initial torque, the increase in the support bearing capacity was less than 5%. For practical engineering applications, taking the cable-clip joint of the U29 steel support as an example, it is recommended that the secondary pre-tightening torque should be greater than the initial pre-tightening torque.  
When secondary pre-tightening was applied before the support joint has undergone sufficient slippage, the enhancing effect on the support bearing capacity was limited; when the joint slippage exceeded 50% of the ultimate slippage, applying the secondary pre-tightening torque had a significant enhancing effect on the support bearing capacity. For practical engineering purposes, taking the cable-clip joint of the U29 steel support as an example, it is recommended that secondary pre-tightening should be applied when the amount of joint slippage is not less than 200 mm.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 2 5G Testbed Overview
### RF Front-End Overview  
With both ADC/DAC integration in FPGA and OFDM engine working seamlessly, this work next step was to develop the mmWave RF front-ends. The used front-end is a fully custom-made solution composed by several analog components connectorized, as illustrated in Figures 12 and 13.  
On the transmitter side, it was considered a typical direct-conversion (to avoid IQ imbalance effects on transmitted signals) also known as homodyne architecture (see Figure 12). It is composed of a PLL operating at 15 GHz, a multiplier by 4, which up-converts the LO signal to 60 GHz, and an up-conversion mixer. In addition, an external 10 MHz reference clock is required for the PLL. In addition, to avoid the non-linearities introduced by the Power Amplifier (PA), the amplification stage at RF (60 GHz) has deliberatively not been included in this study. Otherwise, it would have required the study of mitigation techniques such as Digital Pre-Distortion (DPD).  
Figure 11: Block diagram of the implemented digital IF RF transceiver architecture.  
At the receiver, a two-stage heterodyne architecture is employed. A Low-Noise Amplifier (LNA) at RF is coupled to the antenna, followed by a down-conversion to a 6 GHz IF stage, performed by a first mixing stage process with a 54 GHz LO signal. In a second down-conversion stage, received signals are shifted to IF frequencies. Therefore, unlike the transmission architecture, two PLLs operating at 13.5 and 6 GHz are therefore required, as can be seen in Figure 13. Furthermore, the external clock references for both TX/RX PLLs are generated by two independent PRS10M rubidium oscillator. Such reference clock signals are characterized by very low phase noise and very high frequency stability (\(\approx\)0.05 ppb), being, therefore, very reliable clock sources.
## 2 Structure Design and Principle  
Figure 1a shows schematic view of the designed narrowband PA. The structure consisted of a silicon grating on a stack of a gold film and a quartz substrate. The period of the structure is \(\Lambda_{1}\). The incident beam was a traverse-magnetic (TM) polarized plane wave, whose polarization was perpendicular to the silicon strips. Figure 1b shows a single unit cell of the PA. In each unit cell, there were two periodically arranged silicon strips, with a period of \(\Lambda_{2}\) and a filling-factor of \(f_{2}\). Thus, the silicon grating had an overall-period of \(\Lambda_{1}\) and a sub-period of \(\Lambda_{2}\). The width of each silicon strip was \(f_{2}\Lambda_{2}\). The thickness of the gold film was \(h_{1}\). The height of the silicon grating was \(h_{2}\).  
The silicon grating could diffract the incident light into different orders. The in-plane wavevectors of various diffraction-orders were determined by [31]  
\[k_{\parallel}^{(i)}=k\sin\alpha+\frac{2\pi i}{\Lambda_{1}} \tag{1}\]  
where \(k\) is the wavevector of the incident light, \(\alpha\) is the incident angle, \(k_{\parallel}^{(i)}\) is the in-plane wavevector of the \(i\)th diffraction-order, and \(i=0\), \(\pm 1\), \(\pm 2\),..., \(\pm\)N. By choosing proper grating parameters, the (+1, -1) orders could be coupled with planar propagated plasmonic modes. Under this condition, the resonant  
Figure 1: (**a**) Schematic view of the narrowband perfect absorber (PA). \(I_{0}\) and \(I_{r}\) stand for the intensities of the incidence and reflection. \(\Lambda_{1}\) stands for the structure period. (**b**) Cross-sectional view of the unit cell.  
coupling between the plasmonic mode and the lattice mode induced a strong modulation of the optical response. According to the wavevector matching condition, the coupling processes satisfied  
\[\frac{2\pi n_{\text{eff}}^{(+1)}}{\lambda^{(+1)}}=\beta_{\parallel}^{(+1)}=k\sin \alpha+\frac{2\pi}{\Lambda_{1}} \tag{2}\]  
\[\frac{2\pi n_{\text{eff}}^{(-1)}}{\lambda^{(-1)}}=\beta_{\parallel}^{(-1)}=k \sin\alpha-\frac{2\pi}{\Lambda_{1}} \tag{3}\]  
where \(\beta_{\parallel}\) and \(n_{\text{eff}}\) are the in-plane wavevector and effective index of the planar plasmonic mode, \(\lambda^{(+1)}\) and \(\lambda^{(-1)}\) stand for the resonance wavelengths. These resonances resulted in absorption peaks at \(\lambda^{(+1)}\) and \(\lambda^{(-1)}\). Under the normal incidence condition (\(\alpha=0\)), there was only a single absorption peak because \(\lambda^{(+1)}=\lambda^{(-1)}\).  
To investigate the resonance properties, finite-difference time-domain (FDTD) simulations were carried out. In the simulations, optical parameters of Au and Si in [32] were used.
## 3 Results
### M-GRCT Financial Control in Both Municipalities  
Table 4 records the cash flow inputs and reference values in each scenario used to provide the economic projections considered by the M-GRCT model. The results of the financial evaluation of each municipality, when operating in the conventional way (linear model) and when implementing a business model based on the management and use of recyclable inorganic waste (circular model), are shown in Table 4.  
The results show that the highest NPV is found for a linear scenario in Tibasosa (EUR 325,415), while for a circular economy scenario, the NPV is lower (EUR 109,398). However, for the municipality of Arbelaez, the highest NPV is found for a circular economy scenario (EUR 180,494), and this value is lower under a linear management model (EUR 110,172). Figure 5: Methane emissions in sanitary landfill reductions (**left**) Scenario 1: Tibasosa; (**right**) Scenario 2: Arbeláez. Figure 6: Carbon footprint annual reduction (**left**) Scenario 1: Tibasosa; (**right**) Scenario 2: Arbeláez. It was observed that the highest recycling rates in both municipalities were found in plastic. Recycling efficiencies of 17% in Arbelaez and 10.8% in Tibasosa were determined by the M-GRCT model. Moreover, both municipalities showed a similar annual amount of recyclable waste disposed in landfalls (407.6 t/year in Arbelaez and 439.3 t/year in Tibasosa). However, there was a notable difference in the total incomes for cleaning services when comparing the values in both municipalities (COP 13,160,986 in Arbelaez and COP 143,886,022 in Tibasosa), as shown in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Municipality** & \multicolumn{2}{c}{**Arbeláez**} & \multicolumn{2}{c}{**Tibasosa**} \\ \hline Model & Linear & Circular & Linear & Circular \\ Scenario & Scenario 1 & Scenario 2 & Scenario 3 & Scenario 4 \\ \hline \multicolumn{4}{c}{**Evaluation Criteria**} \\ \hline NPV & EUR 110,172 & EUR 180,494 & EUR 325,415 & EUR 109,398 \\ IRR & 34.2\% & 33.4\% & 77.6\% & 26.7\% \\ B/C & EUR 0.00035 & EUR 0.00054 & EUR 0.00038 & EUR 0.00045 \\ Payback (years) & 6.7 & 8.6 & 2.6 & 9.8 \\ \hline \multicolumn{4}{c}{Note: NPV and B/C in EUR; NPV: Net Present Value; IRR: Internal Return Rate; B/C: Benefit-Cost Ratio; Payback: Return Period.} \\ \end{tabular}
\end{table} 
Table 4: Evaluation criteria of the linear and circular management models (_M-GRCT_).
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 3 QoSS Applied to a Multi-Hop Network
### Calculation of Multi-Hop Network QoSSS
#### 3.4.1 Step 1  
The initial step in reducing complexity is to assess how the two end-points are connected, if there are multiple intermediary nodes, and whether the network utilizes multiple channels. In the reduction and simplification process, the primary goal is to derive an equivalent point-to-point network by reducing the number of hops or multi-channel connections between end-points thus leading to an equivalent point-to-point link. To begin, the QoS metrics, specifically \(P(C)\) and \(P(I)\), must be calculated for all intermediary connections between nodes. This is accomplished by applying Equations (3) and (4) and solving for \(P(C)\) and \(P(I)\), respectively. Using the example of Figure 2, this would require calculating \(P(C)\) and \(P(I)\) for the eight individual links.
## 3 Test Results and Analysis
### Analysis of Orthogonal Test Results
#### 3.1.2 Analysis of Variance
From the bar data in Figure 5, it can be seen that with the decrease in water content, the strength of the 3d cured soil increased from 0.050 MPa to 0.178 MPa and 0.038 MPa to 0.204 MPa for Ratio I and Ratio II, respectively; the strength of the 7d cured soil increased from 0.092 MPa to 0.305 MPa and 0.087 to 0.317 MPa, respectively; and the strength of the 28d cured soil increased from 0.184 MPa to 0.550 MPa and 0.173 MPa to 0.586 MPa, respectively. The reduction in the water content dramatically increased the strength of the cured soil, but the flowability also decreased dramatically with the reduction in the water content. The compressive strength at 3d and 7d was slightly higher for Ratio I than it was for Ratio II. However, the compressive strength at 28d was higher for Ratio II than it was for Ratio I, which is in line with the expected results of the orthogonal test design. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Source of Variance**} & **Sum of** & \multirow{2}{*}{**Degrees of** & \multirow{2}{*}{**Mean Square**} & \multirow{2}{*}{**F-Ratio**} & \multirow{2}{*}{**F-Critical Value** (a = 0.05)**} & \multirow{2}{*}{**Significance**} \\  & **Squared** & & & & & \\  & **Deviations** & & & & & \\ \hline Cement & 79,221.38 & 3 & 26,407.13 & 16.11 & 9.28 & * \\ Fly ash & 664.51 & 3 & 221.50 & 0.14 & 9.28 & \\ Blast furnace slag & 28,456.23 & 3 & 9485.41 & 5.79 & 9.28 & * \\ Phosphogypsum & 4357.13 & 3 & 1452.38 & 0.89 & 9.28 & \\ Inaccuracies & 4918.38 & 3 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Variance analysis table of 28d compressive strength. Reduced water content leads to the easier formation of hydration products (e.g., C-S-H gels) in dense mesh structures between particles, reinforcing the soil skeleton. Macroscopically, it promotes an increase in compressive strength. On the other hand, increased water content leads to an imbalance in the water-cement ratio. Excess water dilutes the concentration of the curing agent, yielding a slower reaction rate and delayed or incomplete cementation, resulting in reduced strength. On the basis that (1) the strength met the specification requirements, but the fluidity did not (water content: 140%), and (2) the flowability met the requirements, but the compressive strength did not (water content: 180%), two of the water content rates were selected to test the performance enhancement in the fluid-cured soil.
## 2 Constitutive Model
### Strain-Based Formula  
The strain energy function is described as a component of a suitable strain tensor, such as the Green strain tensor:  
\[U=U\big{(}\epsilon^{G}\big{)} \tag{1}\]  
where \(\epsilon^{G}=\frac{1}{2}(\mathbf{C}-\mathbf{I})\) is the Green strain tensor, \(\mathbf{C}=\mathbf{F}^{T}\mathbf{F}\) is the right Cauchy-Green deformation tensor, \(\mathbf{F}\) is the deformation gradient, which is expressed in terms of the displacement vector, \(\mathbf{u}\) (\(\mathbf{F}=\
abla\mathbf{u}+\mathbf{I}\)), and \(\mathbf{I}\) is the identity matrix. Without loss of generality, the strain energy function can be written as follows:  
\[U=U\big{(}\overline{\epsilon}^{G},\ J\big{)} \tag{2}\]  
where \(\overline{\epsilon}^{G}=\frac{1}{2}\big{(}\overline{\mathbf{C}}-\mathbf{I} \big{)}\) is the modified Green strain tensor, \(\overline{C}=J^{-\frac{3}{3}}\mathbf{C}\) is the modified right Cauchy-Green strain, and \(J=\det\mathbf{F}\). The fundamental hypothesis in the above models based on the strain-based formula is that the preferred material directions are initially arranged with an orthogonal coordinate system in the reference stress-free configuration. These directions may become non-orthogonal only after deformation [20].  
From Equation (2), the variation in \(U\) is given as follows:  
\[\delta U=\frac{\partial U}{\partial\overline{\epsilon}^{G}}:\delta\overline{ \epsilon}^{G}+\frac{\partial U}{\partial\overline{J}}\delta J=\overline{ \mathbf{F}}\frac{\partial U}{\partial\overline{\epsilon}^{G}}\overline{ \mathbf{F}}^{T}:\delta\mathbf{e}+\frac{\partial U}{\partial J}\delta J \tag{3}\]  
Using the principle of virtual work, the variation in the strain energy potential can be written as follows:  
\[\delta W_{I}=\int_{V0}J(\mathbf{S}:\partial\mathbf{e}-p\delta\varepsilon^{V0 })dV^{0}=\int_{V0}\delta UdV^{0} \tag{4}\]  
For a compressible material, the strain variations are arbitrary; hence, this equation defines the stress components for such a material as follows:  
\[\mathbf{S}=\frac{1}{J}\mathrm{DEV}\bigg{[}\overline{\mathbf{F}}\frac{\partial U }{\partial\overline{\epsilon}^{G}}\overline{\mathbf{F}}^{T}\bigg{]} \tag{5}\]  
\[p=-\frac{\partial U}{\partial J} \tag{6}\]  
With the pure displacement formulation from which the strain invariants are calculated, the kinematic variables of a finite element (FE) model could behave insufficiently if the material is incompressible. The problem is that the stiffness matrix is almost singular from a numerical perspective since the effective bulk modulus of a material is larger than the effective shear modulus. Hence, this causes difficulties in obtaining the solution of the discretized equilibrium equation.