esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance in Selective Fading Conditions
### AWGN Performance Analysis  
In the current study, applying attenuation to the demodulated signal controls SNR in the communication system. In the receiver, thermal noise of a constant level is added to the demodulated signal, thus allowing SNR change. The scheme provides an opportunity for sensitivity measurements, yet the SNR stays an implicit parameter. However, the research aims to determine the performance degradation in multipath fading conditions, so it is sufficient to demonstrate relative SNR change. The QCPSK and FM-CSK communication systems' performance in the AWGN channel is presented in Figure 10. For both cases, the received signal level corresponding to BER = \(10^{-3}\) is assumed to be equal to the noise level, i.e., for the AWGN channel, a normalized SNR is 0 dB for this BER level.  
For normalized SNR values below \(-2.7\) dB, the QCPSK communication system performs better than the FM-CSK data transmission system. If the normalized SNR is between \(-2.7\) dB and \(0\) dB, FM-CSK shows almost the same performance in the AWGN channel as the QCPSK system. However, further SNR increase results in better QCPSK communication system performance compared to FM-CSK data transmission system.
## 2 Materials and Methods
### Fruit Harvest and Measurements of Fruit Color, Size, Soluble Solids Contents, and Acidity  
Fruits were harvested on 8 January 2020 and 7 January 2021. One fruit was harvested per tree, and eight fruits per replicate were collected. Fruits were washed using vegetable detergent, rinsed with deionized water, and air dried at room temperature for 30 min. The color was measured at three points around the fruit equator using a Chromameter (Minolta CR-400, Tokyo, Japan) calibrated to a white plate using the CIELAB system, and a*, b*, a*/b*, and hue values were reported [23]. A higher a*/b* value and/or lower hue value indicates a more orange or red color [6; 24; 25]. Individual fruit was measured for weight and size. Data reported are an average of eight individual fruits x three replicates.  
The flavedo tissue (1-2 mm from the fruit surface) was manually taken using a sharp stainless-steel knife for peel oil extraction. The flavedo-removed fruits were then halved, and juice was gently extracted using a kitchen juicer with an automatic self-reversing reamer (Oster Model 3183, Household Appliance Sales and Service, Niles, IL, USA). Juice samples were centrifuged at 10,000\(\times\)\(g\) for 15 min. The supernatant was used for soluble solids content (SSC) and titratable acidity (TA) measurements. SSC was determined by refractive index using a digital refracometer (Atago RX-5000 cx, Tokyo, Japan). TA was measured by titration of 10 mL of supernatant with 0.1N NaOH to pH 8.1 using an autotitrator (808 Titrando, Metrohm, Riverview, FL, USA).
_Applied_  
_sciences_  
_TDCCN: A Two-Phase Deep Color Correction_  
_Network for Traditional Chinese Medicine_  
_Tongue Images_  
**[PERSON] 1,2, [PERSON] 1,2,* (c), [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2**  
Beijing Key Laboratory of Computational Intelligence and Intelligent System,  
Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (Y.L.);  
[EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (H.Z.)  
College of Microelectronics, Faculty of Information Technology, Beijing University of Technology,  
Beijing 100124, China  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-10-6739-2799  
**Abstract:** In Traditional Chinese Medicine (TCM), tongue diagnosis is an indispensable diagnostic method. Due to the limitations of acquisition devices and variations of the illumination, there is a significant color distortion between the captured tongue image and the human visual perceived image. In this paper, we proposed a Two-phase Deep Color Correction Network (TDCCN) for TCM tongue images. In the first phase, a deep color correction network was designed to model the mapping between the captured image and the target objective chromatic values under a standard lighting condition. The first phase provides consistent color tongue images by using different cameras and capture devices. The output tongue images at this phase can be used for further automatic quantitative analysis. The second phase provides flexible color adjusting options to adapt to different work environments and the subjective preference of doctors for visually perceived color appearance. Only three additional parameters are used to describe the adjustment operation. Experimental results show that our method achieved state-of-the-art performance in objective color correction and obtained satisfactory perceptual adaptation.  
**Keywords:** tongue image analysis; color correction; perceptual distortions; traditional Chinese medicine
#### 1.1.1.1 Opcode Layout  
The Csound API mandates that opcodes should have the following components:  
* A data structure declared with the following format. It always contains an OPDS member as its first component. This holds a set of elements common to all opcodes. structNAME { OPDS h; //outputargumentaddresses //inputargumentaddresses //dataspacemembers }; where we need to declare one pointer variable for each output and input argument (in the order they should occur in the Csound language code). When the opcode is called, the output and input argument addresses are passed to Csound through these pointers. The C variable types for different Csound argument types are:
* MYFLT*: pointers to the internal floating-point data type (MYFLT is either defined as a 64 or a 32-bit float, depending on the engine version and platform) are used for all basic numeric arguments. (i, k, or a).
* STRINGDAT*: used for string arguments.
* PVSDAT*: holds an fsig argument *ARRAYDAT*: for array arguments (of any fundamental type).
* A function with the signature int func(CSOUND *, void *); for each one of the required action times (init, control, and/or audio). The first argument is a pointer to the Csound engine that instantiated this opcode. The second argument receives a pointer to the allocated dataspace for a given instance of the opcode.  
New opcodes are registered with the engine using the Csound C API function csoundAppendOpcode(), int csoundAppendOpcode(CSOUND *csound,constchar*opname, int dsblksiz,intflags,intthread, constchar*outypes, constchar*intypes, int(*iopadr)(CSOUND *,void *), int(*kopadr)(CSOUND *,void *), int(*aopadr)(CSOUND *,void *));  
This takes in the opcode name opname; the size of the dataspace dsblksiz in bytes; multithreading flags (normally 0); a thread code for the action times, which determines whether it should be active on i-time (1), k-rate (2), and/or a-rate (4); and the functions for i-time, k-rate, and a-rate (NULL if not needed), for an engine given by csound. Two strings, outtypes and intypes, define the output and input argument types expected by the opcode. Valid values are the characters i, k, a, S, f, with an added [] to indicate array arguments.
## 1 Introduction
This study proposes an approach that applies social media data for earthquake damage assessment in different spatial units using different classification algorithms. This cause is enhancing situational awareness in disaster response and relief activities. For this purpose, damage assessment in three spatial units, including county, city, and 10 \(\times\) 10 km grids, was performed. In this research, an attempt has been made to use both official spatial units based on which government information is usually provided (county and city) and spatial units that are homogeneous in terms of spatial coverage (10 \(\times\) 10 km grids). The reason for choosing 10 km for its grid size is that it is not so small that it is difficult to determine the damage based on social network data, and it is not so large that it causes the aggregation of information in the wrong spatial units. In addition, the performance of Naive Bayes, SVM, and deep learning algorithms were compared.
## 3 Results and Discussion
### Von Mises Stress Distribution  
After verifying the modeling procedure, the wall models were simulated, and structural responses were monitored and investigated. Figure 9 shows the maximum von Mises stress distribution in the studied wall models under blast loading. Table 4 also shows the maximum stresses in each model. It can be observed that with a reduction in the stand-off distance, the stress level was increased and distributed over a wider area of the wall. For example, in model-1 with a stand-off distance of 2 m, the maximum stress was 118.56 kN/m\({}^{2}\) higher than that in model-2 with a stand-off distance of 5 m. As the amount of TNT increased, the stress also increased, and more significant damages were observed in the models. For example, in model-3, with an explosive charge weight of 5 kg, the maximum stress was 29.32 kN/m\({}^{2}\) lower than that in model-6, with an explosive charge weight of 7 kg. In addition, as the thickness of the walls increased, the stress generally decreased. For example, in model-13 with a thickness of 25 cm, the maximum stress was 73.55 kN/m\({}^{2}\) lower compared to model-7 with the wall thickness of 20 cm.  
Since the considered boundary conditions in these models were fixed at both ends (Figure 6a), the one-way behavior of the wall is obvious in Figure 9. According to Figure 9, shear failure was the dominant failure mode, since the grout shear strength was less than its compressive strength.  
Figure 8: Pressure–time diagram obtained in the present study for validation process.  
Figure 7: FE modeling of the slab: (**a**) modeling in Abaqus for validation; (**b**) blast loading in Conwep.  
Figure 9: The von Mises stress distribution in some of the simulated wall models.
## 3 Advancements and Alternatives to Modal-Based Damage Identification
### Advancements to Environmental & Operational Challenges
#### 3.1.2 Pattern Recognition Methods
They combined time-series analysis with an Autoregressive Neural Network (ANN). The frequency/temperature relationship for the first seven modes was analyzed using an auto-correlation function and a cross-correlation function, which showed that all seven modes attain very similar relationships with temperature. This allowed the authors to reduce the number of input variables required to train the ANN for the purposes of structural damage detection. Comparative results with a multi-linear regression model showed that the ANN prediction method produced fewer error values overall than the multi-linear regression model. [PERSON] et al. [121] conducted further work on the Meriden Bridge in a study that aimed to model the frequency-temperature relationship using an extended Kalman filter for NN learning, which attains a superior convergence speed and success than other common NN training algorithms, such as back propagation. The extended Kalman filter works as a second-order algorithm for recursive state estimation in nonlinear dynamic systems, and as NN training can be considered as a nonlinear estimation problem, the two work together in synergy. Comparison results against a multi-linear regression model produced fewer error vales and demonstrated the potential for real-time monitoring applications.
## 3 Results  
### Dissipation Study Results  
The dissipation study aimed to determine the DT50 (time for the concentration to reduce to 50% of its initial value, half life time) and DT90 (time for the concentration to reduce to 10% of its initial value) for pinochemri dihydrochalcone. Results obtained in two different soils, Biostrateg and Laskowice, indicated that the dissipation of dihydrochalcone follows a single first-order (SFO) kinetic model with the determination coefficient (R\({}^{2}\)) 0.97. For Biostrateg, models DT50 and DT90 reached 1.3 and 5.46 days, respectively, while in the case of Laskowice, soil relevant values were 0.75 and 4.55 days, respectively [Figures 2 and 3].  
These findings align with those of [PERSON] et al., who reported a dihydrohalcone dissipation rate of 2.3 to 9.85 days (for DT50 and DT90) across three soil types, also described by the SFO model [11].  
Figure 2: Biostrateg soil dissipation model.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Methods Used** & **Input Variables** & **Energy Savings** & \begin{tabular}{c} **Fuzzy** \\ **Rules** \\ \end{tabular} & \
\begin{tabular}{c} **Study** \\ **Period** \\ \end{tabular} &
\begin{tabular}{c} **Other** \\ **Techniques Used** \\ \end{tabular} \\ \hline
[16] & Fuzzy logic model & \
\begin{tabular}{c} Indoor \\ temperature, user \\ location \\ \end{tabular} & — & — & — &
\begin{tabular}{c} Real-time \\ monitoring \\ \end{tabular} \\
[17] & Fuzzy Mamdani, Random Forest & \
\begin{tabular}{c} Indoor–outdoor \\ difference \\ \end{tabular} & — & — & — &
\begin{tabular}{c} Machine learning \\ for air quality \\ \end{tabular} \\
[24] & Fuzzy logic, mobile app & \
\begin{tabular}{c} Temperature, \\ humidity \\ \end{tabular} & 7.4–13.2\% & — & — &
\begin{tabular}{c} Mobile app for \\ feedback \\ \end{tabular} \\
[23] & Fuzzy logic control & \
\begin{tabular}{c} Temperature, \\ humidity \\ \end{tabular} & 0.25 & — & — &
\begin{tabular}{c} Comparison \\ with LQR \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research papers on general applications of fuzzy logic in HVAC control.
# Data-Driven Formation Control for Multi-Vehicle Systems Induced by Leader Motion
## 4 Sampled-Data Leader Motion Mechanism Design
### Leader Motion Mechanism Architecture  
We now describe the architecture of the proposed leader motion influence input by extending the protocol adopted in [18] to second-order systems. In the following, without loss of generality, we assume that the leader node is node \(1\).  
We assume that the input generation mechanism is based on a dynamic elaboration of local data, namely past sampled motion as in (19) and (20), and exploits them through an internal ARMA model with auxiliary variables \(\varphi_{i}\), \(i=1,\ldots,\mu\), \(\mu\in\mathbb{N}\), with the rules:\[\begin{array}{rcl}\varphi_{1}(k+1)&=&a_{1}r_{1_{k}}-b_{1}u_{k},\\ \varphi_{i}(k+1)&=&\varphi_{i-1}(k)+a_{i}r_{1_{k}}-b_{i}u_{k},\end{array} \tag{30}\]  
with coefficients \(b_{i}\) and \(a_{i}\in\mathbb{R}\) as design parameters. Input \(u_{k}\) is then computed as:  
\[u_{k}=\varphi_{\mu}(k)+a_{0}r_{1k}. \tag{31}\]  
Equations (30) and (31) have the structure of a generic ARMA model (see, e.g., [29]), with design parameters \(b_{i}\) and \(a_{i}\in\mathbb{R}\). The initial conditions \(\varphi_{i}(0)\) are set as:  
\[\varphi_{i}(0)=0\qquad\text{ for all }i=1,\ldots,\mu. \tag{32}\]  
It is worth remarking that Equation (31), together with (32), implies that in the initial time instants, the input takes values equal to \(u_{0}=a_{0}r_{1_{0}}\) and \(u_{1}=a_{1}r_{1_{0}}-b_{\mu-1}u_{0}+a_{0}r_{1_{1}}=(a_{1}-b_{\mu-1}a_{0})r_{1_ {0}}-a_{0}r_{1_{1}}\).  
System evolution (15) under the first agent as the driving node adopting (30) and (31) can be expressed as:  
\[\left\{\begin{array}{rcl}\mathbf{r}(k+1)&=&\Phi_{r}\mathbf{r}(k)+\Phi_{r0} \mathbf{v}(k)+a_{0}\Psi_{r}r_{1_{k}}+\Psi_{r}\varphi_{\mu}(k),\\ \mathbf{v}(k+1)&=&\Phi_{r0}\mathbf{r}(k)+a_{0}\Psi_{r}r_{1_{k}}+\Psi_{r0} \varphi_{\mu}(k),\\ \boldsymbol{\varphi}(k+1)&=&(\mathbf{a}-\mathbf{b}a_{0})r_{1_{k}}+F\boldsymbol {\varphi}(k)-\mathbf{b}\varphi_{\mu}(k),\\ \mathbf{r}(0)=\mathbf{r}_{0},&\mathbf{v}(0)=\mathbf{v}_{0},&\boldsymbol{ \varphi}(0)=\mathbf{0}_{\mu},\end{array}\right. \tag{33}\]  
with \(\boldsymbol{\varphi}(k):=\begin{bmatrix}\varphi_{1}(k)&\ldots&\varphi_{\mu}( k)\end{bmatrix}^{\top}\), \(\mathbf{a}\), \(\mathbf{b}\in\mathbb{R}^{n}\) compactly encoding the internal mechanism set of parameters, namely \((\mathbf{a})_{i}=a_{i}\), \((\mathbf{b})_{i}=b_{i}\), and finally \(F:=\begin{bmatrix}0&0&0&\ldots&0\\ 1&0&0&\ldots&0\\ \vdots&\ddots&\ddots&&\vdots\\ 0&&\ddots&0&0\\ 0&0&\ldots&1&0\end{bmatrix}.\)  
In the next parts of this section, we study how to set parameters \(a_{i}\), \(b_{i}\) to achieve the desired goals described in the Problem Statement, both guaranteeing the desired steady state formation and the finite time transient behavior.
## 4 Results and Discussion
### OP Pricing and Profit  
In order to study the Game I results we show the optimal price \(p^{*}\) and the OP profit \(\Pi_{OP}\), varying the maximum system load \(L\) and the parameter \(c\).  
Figure 8 shows the OP optimal price as a function of \(L\) for different values of \(c\). When \(c\) increases, the optimal price increases as expected, given that \(c\) acts as a conversion factor. More interesting is the behavior of the price when it is analyzed in terms of the maximum system load \(L\). When the maximum system load (eq. L) increases the utility of the sinks decreases given the growing mean transmission time. When \(L<\frac{1}{2}\) the OP decreases its price and thanks to it all the sinks decide to subscribe. Nevertheless, when the generated traffic is more than the half of the network capacity is more profitable for the OP to keep constant the price and decrease the percentage of subscribed sinks as shown in Figures 8 and 9. In terms of real system load \(L_{R}=x_{1}L\), it is equivalent to the maximum system load while \(L\leq 1/2\), but when \(L>1/2\) the real load remains constant in \(L_{R}=1/2\), which means that real system load never exceeds the 50% of the capacity. Another approximation studied in [33] where different priorities were used in the OP wireless network obtained a better efficiency. In order to implement this improvement in our model a sensing data differentiation in delay requirements is needed, where priority traffic has a more restrictive utility function, while non-priority traffic utility function is more relaxed. This would allow us to obtain a better efficiency in the OP network and, in addition, allows the IoT-SPs to offer new services using the sensing data with lower requirements.  
Figure 8: OP optimal price as a function of \(L\) for different values of \(c\).  
Figure 9: Social state as a function of \(L\) for different values of \(c\).  
Figure 10 shows the OP profit as a function of \(L\) for different values of \(c\). The figure shows how the OP profit increases when the system load increases until \(L=\frac{1}{2}\). After this point, the profit remains constant with the system load. In addition OP profit also experiments an increase with \(c\) for any value of \(L\).
## 2 Unburied Pipe Tests
### Experimental Setup and Tests
The non-leaking state (referred to as _NL_), set by stopping all the leaks with the repair clamps;
2. Water leaking from the _j_-th active leak (_Lk-j_), a condition set by temporarily removing the corresponding repair clamp (Figure 2a);
3. All the leaks activated simultaneously (_Lk-123_). The first condition provided data on the environmental noise. The pressure gauge installed in the pressure tank did not reveal any pressure drop occurring over time, thus confirming that the non-leaking state had been correctly restored. The second and the third conditions permitted the assessment of AE events related to active leaks. Three acquisitions were collected for each condition, separated by proper time intervals, for the sake of statistical confidence. The terminal valve was kept closed in all tests. Consequently, on one hand, the flow rate inside the pipe was entirely ascribable to the leaking flow of an active leak, in order to simulate a condition of null water consumption by the customers. On the other hand, no flow was present at the sensor location. All the relevant parameters characterizing AE were investigated to find possible correlations with the leaking conditions. In particular, the analysis focused on the trend of three parameters that appeared more sensitive to the presence of leaks, namely:  
1. Total amount of Hits per acquisition, which coincide with the number of AE events for the adopted setup, where only one sensor is installed for measurements;  
Figure 3: Acoustic Emission (AE) sensor close up. 2. Cumulative Counts, computed by summing the Counts of all Hits related to one acquisition;
3. Cumulative Amplitude, computed by adding together the Amplitude values of all Hits related to one acquisition. These quantities were computed for each test condition, by averaging (with arithmetic mean) the values provided by the three corresponding acquisitions (in order to achieve a better accuracy). The analysis also examined the distribution of the Average Frequency values (ratio of the Counts of an AE event to the duration of the event, referred to as AF hereafter) over the RA values (ratio of the rise time of an AE event to the event Amplitude). The pattern of AF over RA values was proposed in [17] as a possible tool to detect leaks. In particular, AE events related to active leaks are expected to exhibit higher values of the AF to RA ratio. Such AE events are referred to as _tensile-type_ emissions, since a similar behavior is observed for AE signals associated with the tensile mode of crack propagation. Conversely, AE detected in a non-leaking state (possibly due to a non-zero water flow inside the pipe) should feature lower ratios, hence being referred to as _shear-type_ emissions (consistently with AE signals characterizing shear crack growth). This peculiar pattern (i.e., a larger percentage of _tensile-type_ AE events) should permit leak detection. Differing from the previous one, this analysis considered all the detected AE events, without averaging the acquisitions.
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 4 Experimental Verification
### Longchi Tunnel Scene Location Experiment Results
Due to the continuity of the observation equation, the radius of curvature is small, but the position error with frequent changes is the smallest, which can be below the meter level.
## 5 Implementations  
### The PRINCE Accelerator Core  
The integrated PRINCE cipher accelerator core in the SoC was designed using Verilog HDL. Figure 7 presents the overall internal architecture of the implementation with a simple but flexible interface, and the connection diagram with the Configuration/Status Register (CSR) peripheral bus of the proposed SoC.  
Accessing and communicating with the hardware core is achieved through the process of reading and writing data values to registers with specified addresses in the peripheral bus, represented and connected to the data ports of the accelerator. The PRINCE accelerator core possesses the base address of 0x3000 in the CSR peripheral bus, assigned by the SoC builder. By implementing this simple \"register-controlled interface\" scheme, two dedicated functions to control the accelerator are formed:  
* \"prince_write_to_address(address, data)\": This function prepares the \"write_data\" and \"address\" for the accelerator core and then simulates the core with \"cs\" and \"we\" for the writing process. This function writes 32-bit data or configuration values for the 8-bit address in the PRINCE accelerator core.
* \"prince_read_from_address(address)\": The function simulates the core with \"cs\" and \"we\" for the reading process and reads the output from the accelerator core after preparing the \"address\" and ignoring the \"write_data\". This function reads 32-bit data from an 8-bit address in the PRINCE accelerator core.  
The internal register address map of the accelerator used in the software interface is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Address** & **Description** \\ \hline Key Input & 0x10–0x13 & 128-bit key input registers \\ Block Input & 0x20–0x21 & 64-bit message input registers \\ Result & 0x30–0x31 & 64-bit cipher output registers \\ Control & 0x08 & Accelerator control bit \\ Status & 0x09 & Accelerator status bit \\ Configuration & 0x0A & Accelerator configuration bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Internal register address mapping for the PRINCE accelerator.  
Figure 7: The PRINCE accelerator architecture with bus interface.