esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Modern Chladni Plates to Analogously Manifest the Point Interaction
## 4 Applications  
More importantly, the point-interaction model can be exploited to precisely determine the dispersion relation between the frequency and the wave number for the flexural wave. From the Kirchhoff-Love plate theory, the dispersion relation between the frequency and the wave number is given by [38]  
\[f(k)=\frac{C}{2\pi}\,k^{2}, \tag{16}\]  
where \(C=\sqrt{D/\rho h}\), \(D\) is the flexural rigidity given by  
\[D=\frac{Eh^{3}}{12(1-v^{2})}, \tag{17}\]  
where \(E\) is the Young modulus, \(v\) is the Poisson ratio, \(\rho\) is the mass density, and \(h\) is the thickness of the plate. The material properties for aluminum are as follows: \(E=70\) GPa, \(v=0.33\), and \(\rho=2700\) kg/m\({}^{3}\)[38]. Using these material properties and \(h=1\) mm, the theoretical coefficient \(C/2\pi\) can be calculated to be 0.248. We employed the parabolic formula in Equation (16) to make the best fit to the experimental results obtained in Figures 5 and 6. The fitting coefficient \(C/2\pi\) is approximately 0.228, as shown in Figure 8. The experimentally determined coefficient \(C/2\pi\) can be found to agree with the theoretical value very well. This good agreement validates that the point-interaction model can be utilized not only to explore the formation of modern Chladni patterns, but also to determine the dispersion relation between the resonant frequency and the wave number for the vibration of plates. As the dispersion relation in Equation (16) includes the information of the flexural rigidity \(D\) as well as the Young modulus \(E\), the proposed theoretical model is believed to be practically useful for measuring the acoustic properties of plates.  
Figure 7: Dependence of the coupling strength on the resonant frequency by means of the best reconstruction of experimental patterns.
## 1 Introduction
Preparing mushrooms as a flour substitute involves drying them using various methods [14; 27; 28] and then grinding the dried mushrooms into powder. Some aspects of using mushroom powder as a flour replacement are describe in an earlier article [14]. The base flour most often replaced with mushroom powder is wheat flour. Mushrooms have been studied as a substitute for flour in pasta production [29] and baked goods such as bread [14; 30], cakes [31], cookies [22; 24; 27], and breadsticks [32]. Enriching these products with mushrooms contributed to obtaining products with an increased content of fiber [24; 29], B vitamins, and vitamin D [14] or with a higher content of polyphenols [6; 14; 22]; however, the products were usually darker [14; 21; 22] and characterized by lower specific volume and higher hardness [14]. The presence of mushroom flour also resulted in a lower glycemic index of final products [29]. Mushroom powders have also been used in gluten-free formulas [33]. Mushrooms are a raw material rich in many major and trace elements [11] and may be an important source of these in the human diet. For adult Poles, the recommended dietary allowance (RDA) for various major elements is for calcium (Ca), 1000 mg/day; for magnesium (Mg), 310-400 mg/day; for iron (Fe), 10-18 mg/day. In turn, the RDA for trace elements is in the case of zinc (Zn), 8-11 mg/day; copper (Cu), 0.9 mg/day; selenium (Se), 55 mg/day; and manganese (Mn), 1.8-2.3 mg/day [34]. In the European population, the average dietary intakes of selected elements in adults during the day are Ca from 598 mg [35] to 1374 mg [36], Mg from 232 mg to 439 mg [37], Fe from 9.4 mg to 17.9 mg [38], Zn from 8-14 mg [39], Cu from 1.15-2.07 mg [40], Se from 31-65.6 \(\upmu\)g [41], and Mn from 2 mg to 6 mg [42]. Mushrooms, compared with most vegetables, contain a larger amount of phosphorus (P) and potassium (K), and a relatively high content of Mg and Se [43]. However, mushrooms also can bind lead (Pb) or cadmium (Cd) and may constitute nutritional hazards [44]. Therefore, it is important to monitor the amount of these heavy metals in the mushroom biomass. The maximum permitted content of Cd and Pb in three species of mushrooms cultivated by producers, i.e., button mushrooms, oyster mushrooms, and shitiake (_Lentiumula edodes_) is 0.15-0.2 mg Cd and 0.3 mg Pb per 1 kg of fresh weight [45]. With an average 90% water content in mushrooms, the amounts of these elements in dry weight should not exceed 1.5-2 mg Cd and 3 mg Pb on a dry weight basis [44]. The aim of the present research was to estimate the impact of using lyophilistates from white button mushrooms and oyster mushrooms as partial wheat flour substitutes (2%, 4%, 6% \(w/w\)) on the functional properties of the obtained blended flours as well as on physical parameters, basic nutritional composition, mineral content, selected health-promoting properties, and sensory evaluation of shorthead cookies prepared from these composite flours.
## 3 Parametric Study  
This section investigates the effects of key antenna parameters on the RLBW and ARBW of the inverted L-shaped patch antenna with a corner-truncated partial ground plane diagonally adjoined with a square branch. The key antenna parameters included branch dimension (\(b\)), corner-truncated size (\(t\)), vertical length (\(L_{1}\)) and horizontal length (\(L_{4}\)) of the inverted L-shaped radiating patch, and the distance between the radiating patch and the edge of the substrate (\(d\)). The parametric studies were designed to investigate the possible effects of varying the antenna structures. Therefore, it can provide only proper trends for initiation and tuning the design but cannot be used to visualize the full range of antenna behavior.
## 2 Materials and Methods
### Irradiation and Exposure Settings  
While the x-ray generator was in the non-AEC mode, the strips were irradiated in three exposure sets. In each exposure set, only one of the kVp, mAs, and surface-image-distance (SID) parameters was varied as shown in Table 2. The kVp, mAs and SID values are x-ray tube parameter settings usually adjusted to acquire an optimum quality of radiographs in diagnostic radiology, while ensuring minimum radiation exposure to patients. Kilovoltage peak (kVp) is a high voltage (in kV) applied between the anode and the cathode of the tube to accelerate electrons. The kVp value is directly proportional to the energy of the x-ray photons in the beam [41]. Milliamperage second (mAs) is a small current (mA) and time (s) product. Milliamperage is the low current (in mA) that flows in the  
Figure 2: **(a)** Experimental setup for measuring the current–voltage (C–V) signal response and diagnostic x-ray dose. Each tested LED strip contained 12 LED chips which were connected in parallel by the manufacturer. The strip was positioned on the beam’s central axis to minimize x-ray flux inhomogeneity dependency that arises due to the heel effect. **(b)** Unfors base unit that is connected to a detector/probe with a detachable cable. **(c)** Handheld digital multimeter connected to an LED strip for taking radiation-induced signal readings.  
tube's tungsten cathode filament and hence consists in thermionically emitting electrons [42]. The time (s) is the x-ray beam exposure time. The mAs value is therefore directly proportional to the number of x-ray photons in the x-ray beam. Source-to-image distance (SID) is the distance between the image receptor (detector) and the tube's x-ray source point. The SID is inversely proportional to the radiation exposure of the dosimeter probe/strips-if the kVp and mAs are kept constant i.e., shorter SIDs imply more exposure of the dosimeter probe/strips to radiation, and longer SIDs imply less exposure. In our study, the kVp, mAs, and SID parameter settings (values) for each exposure were referenced from [PERSON]'s work [43].  
In the first exposure set, the mAs and SID were constant at 50 and 60 cm respectively. Readings were then obtained when the tube voltage was varied from 40 to 117 kVp. Similarly, in the second exposure set, the kVp and SID values were constant at 70 and 60 cm respectively. Readings were then taken when the tube current-time product was adjusted from 0.5 to 250 mAs. In the last exposure set, while maintaining the mAs and kVp at 50 and 70 respectively, readings were taken when the SID range was 30-100 cm, as illustrated in Table 2.
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 4 Experimental Analysis
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Case** & **Forklift Speed \(v\) [m/s]** & **Path-Length \(L\) [m]** & **Path Shape** & \([x_{0},y_{0}]^{T}\) [m] & \(\theta_{0}\) [rad] \\ \hline I & 0.75 & \(\sim\)100 & Closed-loop path & \([11.5,74.4]^{T}\) & \(-\pi/2\) \\ II & 0.75 & \(\sim\)100 & Closed-loop path & \([11.5,74.4]^{T}\) & \(-\pi/2\) \\ III & 1.6 & \(\sim\)100 & Closed-loop path & \([11.5,74.4]^{T}\) & \(-\pi/2\) \\ IV & 1.6 & \(\sim\)100 & Closed-loop path & \([11.5,74.4]^{T}\) & \(-\pi/2\) \\ V & 2.75 & \(\sim\)100 & Closed-loop path & \([11.5,74.4]^{T}\) & \(-\pi/2\) \\ VI & 2.75 & \(\sim\)100 & Closed-loop path & \([11.5,74.4]^{T}\) & \(-\pi/2\) \\ VII & 1.2 & \(\sim\)137 & Rectilinear path with U-turn & \([41.7,74.4]^{T}\) & \(-\pi/2\) \\ VIII & 1.2 & \(\sim\)137 & Rectilinear path with U-turn & \([41.7,74.4]^{T}\) & \(-\pi/2\) \\ IX & 1.28 & \(\sim\)289 & Closed-loop path & \([11.5,74.4]^{T}\) & \(-\pi/2\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main features of the performed test case trajectories. Figure 12: Top view of the estimated trajectories along with the UWB anchors (gray squared markers). Ground truth trajectory (continuous gray line), UWB (continuous blue line), IMU + OFS (dashed red line), and UKF (pointed green line) measured trajectories of the forklift are represented, too: (**a**) Test Case IV, (**b**) Test Case IX by referring to Table 2.  
To better highlight the tracking performance improvement given by the employment of the proposed UKF algorithm in this two test cases, Figure 13 shows the boxplot of the errors along the \(x\)-coordinate (Figure 13a for test case IV and Figure 13b for test case IX), the \(y\)-coordinate (Figure 13c for test case IV and Figure 13d for test case IX), the distance error (Figure 13e for test case IV and Figure 13f for test case IX), and the orientation error \(\epsilon_{\theta}\) (Figure 13g for test case IV and Figure 13h for test case IX). Figure 13: Boxplots of the errors by considering the three different tracking systems (UWB, IMU + OFS, and UKF) (**a**) of the \(x\)-coordinate for test case IV, (**b**) of the \(x\)-coordinate for test case IX, (**c**) of the \(y\)-coordinate for test case IV, (**d**) of the \(y\)-coordinate for test case IX, (**e**) of the distance error for test case IV, (**f**) of the distance error for test case IX, (**g**) of the orientation error for test case IV, and (**h**) of the orientation error for test case IX. From the boxplots, it is possible to retrieve the mean localization errors in terms of distance error. Such errors are reported in Table 3. As it can be seen, the UKF is able to reduce both localization and orientation errors.
## 3 Results and Discussions
### Volatile Compounds of Raw and Roasted Flaxseed
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Volatile Compounds** & **RT (Retention Time, min)** & **Raw Flaxseeds (\%) * & **Roasted Flaxseed (\%) * & **Aroma Characteristics ** \\ \hline \multicolumn{5}{c}{Alcohols} \\ \hline
3-Methylbutan-1-ol & 3.904 & \(2.75\pm 0.05\) * & 4.48 \(\pm\) 0.03 * & Whiskey, malt, burnt \\
2-Methylbutan-1-ol & 3.983 & \(3.81\pm 0.21\) * & 3.64 \(\pm\) 0.032 * & Malt \\ Pentan-1-ol & 4.843 & \(3.97\pm 0.11\) * & 1.38 \(\pm\) 0.21 * & Fruit, baslamic \\ Hexan-1-ol & 9.943 & \(23.06\pm 0.03\) * & 3.74 \(\pm\) 0.11 * & Resin, flower, green \\ Phenol & 15.682 & \(3.63\pm 0.03\) * & 2.99 \(\pm\) 0.61 * & Whiskey, malt, burnt \\ \hline \multicolumn{5}{c}{Aldehydes} \\ \hline Hexanal & 6.059 & N.D. & 24.24 \(\pm\) 0.21 & Grass, tallow, fat, \\ Benzaldehyde & 14.758 & \(5.76\pm 0.11\) * & 10.42 \(\pm\) 0.32 * & Almond, burnt sugar \\ Nonanal & 19.753 & \(1.55\pm 0.08\) * & 2.85 \(\pm\) 0.11 * & Fat, citrus, green \\ Decanal & 22.41 & \(1.82\pm 0.03\) * & 1.25 \(\pm\) 0.04 * & Orange peel, tallow \\ \hline \multicolumn{5}{c}{Ketones} \\ \hline Acetophenone & 18.532 & \(13.22\pm 0.21\) * & 17.28 \(\pm\) 0.21 * & Must, flower, almond \\ Benzophenone & 31.22 & \(3.18\pm 0.03\) * & 1.85 \(\pm\) 0.11 * & Balsamic \\ \hline \multicolumn{5}{c}{Terpenes and terpenoids} \\ \hline \(\alpha\)-Pinene & 13.358 & \(12.70\pm 0.21\) * & 6.81 \(\pm\) 0.30 * & Pine \\ \(\beta\)-Pinene & 15.343 & \(4.80\pm 0.21\) * & 1.99 \(\pm\) 0.03 * & Pine, resin \\ D-limonene & 17.32 & \(10.72\pm 0.10\) * & 9.85 \(\pm\) 0.03 * & Citrus, mint \\ Camphene & 14.117 & \(2.48\pm 0.21\) & N.D. & Camphor \\ \hline \multicolumn{5}{c}{Acids} \\ \hline Benzoic acid & 21.15 & \(1.19\pm 0.21\) & N.D. & Balsamic \\ \hline \multicolumn{5}{c}{Others} \\ \hline
2-Methyloctane & 9.503 & \(4.51\pm 0.02\) * & 2.80 \(\pm\) 0.02 * & Alkane \\ \hline
2-Pentylfuran & 15.97 & N.D. & 2.95 & Fruity \\ \hline \hline \end{tabular}  
* Each value was the mean of triplicate measurements; N.D.—not detected. ** drawn from [46,47]; Note: *b different superscripts in a row indicate significant differences within samples (\(p<0.05\)) according to Duncan comparison test.  
\end{table} 
Table 3: Volatile compounds of raw flaxseeds and roasted flaxseed.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Benefits
#### 8.1.1 Enhanced Automation and Intelligence  
KDN typically uses ML to learn from data, making it more intelligent and efficient. This intelligence allows for higher automation, reducing human intervention in the management of networks as much as possible. Management of conventional networks has been achieved using a manual approach, where network administrators define static configurations for the network devices using a CLI. Such manual approaches are error-prone,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Group** & **Sub-Group** & **Name** & **Purpose** & **Controller** & **Performance** \\ \hline \multirow{9}{*}{Cloud computing} & \multirow{3}{*}{NFV-C [533]} & \multirow{3}{*}{NFV in cloud computing} & \multirow{3}{*}{—} & \multicolumn{2}{c}{Reduce server response time, improve QoE} \\ \cline{3-5}  & & & & & 2.5–5 s \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \multirow{9}{*}{Business} & \multirow{9}{*}{—} & \multirow{9}{*}{HPC-BE [546]} & \multirow{9}{*}{HCP-BE [547]} & \multirow{9}{*}{HCP-BE [548]} & \multirow{9}{*}{HCP-BE [549]} & \multirow{9}{*}{HCP-BE [548]} & \multirow{9}{*}{HCP-BE [548]} \\  & & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \cline{3-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._consume a lot of time, and cannot dynamically adapt to network changes. SDN provides a centralized platform for network management and makes it more easier than a manual approach; however, SDN network management is based on static application policies. KDN can automate network management by dynamically updating application configuration policies based on knowledge of network status to automatically configure the network without human intervention. KDN learns from large amounts of data to identify patterns and anomalies, allowing the network to automatically adapt and optimize network operations. For instance, the KDN can automatically adjust network resource allocation based on traffic patterns to ensure that critical applications receive more resources. Maintenance can also be automated using the concept of predictive maintenance, which uses the data collected about device statistics, event logs, etc. as input to the machine learning models to predict whether maintenance is required or not. If maintenance is required, network administrators can take proactive steps for maintenance before the issues become problematic. Repetitive network management tasks such as device configuration and software updating can be automated using KDN, where the network will be monitored using the data collected from the devices and configured when required without human intervention.
## 4 Implementation of the Static Fuzzy Logic Controller (Static FLC)
### Fuzzification Process of the Static Fuzzy Logic Controller  
Figure 3 shows the experimental setup created to test the performance of the proposed static FLC. The proposed static FLC calculates the error \(E_{k}\) and the rate of change of the error \(dE_{k}\) for every discrete sample instant \(k\)[105]. Here, \(E_{k}\) and \(dE_{k}\) were computed by the static FLC according to (1) and (2):  
\[E_{k}=(R_{k}-Y_{k}) \tag{1}\]  
\[dE_{k}=E_{k}-E_{(k-1)} \tag{2}\]  
where \(R_{k}\) is the desired input trajectory or the reference signal that is proportional to the desired velocity and \(Y_{k}\) is the actual output (actual angular velocity of the motor). These \(E_{k}\) and \(dE_{k}\) crisp values are mapped into the non-singleton fuzzy sets through the fuzzification process. The singleton fuzzy sets are not suitable due to the non-linearity behavior of the DC motor, because of a lack of information about the surroundings. When taking into account a non-singleton fuzzy set, this can be expressed as an elliptic function \(\wp\), which means the linguistic variable belonging to that fuzzy set is periodic in two directions (according to \(E_{k}\) and \(dE_{k}\), the linguistic variables can be used to obtain values, either positive or negative). In this case, it contains information about the surroundings (the \(E_{k}\) and \(dE_{k}\) crisp values are affected by uncertainty) and is capable of handling non-linear situations compared to singleton fuzzy sets. This non-singleton fuzzifier for the fuzzy set error \(E\) can be expressed as (3):  
\[nsg:E\rightarrow\wp\
i\forall e\in E\rightarrow\mu_{e}(\cdot)=nsg[e]:\mu_{e }(e)=1\wedge Support[\mu_{e}(\cdot)]\supset\{e\} \tag{3}\]  
where \(nsg\) is the non-singleton set, \(\forall e\) is the logical operator with a predicate variable \(e\) that belongs to the fuzzy set error, \(E\rightarrow\) is the implication (the if and then condition) and \(Support[\mu_{e}(\cdot)]\supset\{e\}\) is the degree of truth that the implication relation belongs to \"\(e\)\". Here, \(e\) is an element of \(E\). When discrete sampling instants are taken, then (3) can be expressed as for every sampling instant \"\(k\)\" as (4):  
\[nsg:E_{k}\rightarrow\wp\
i\forall e_{k}\in E_{k}\rightarrow\mu_{e_{k}}( \cdot)=nsg[e_{k}]:\mu_{e_{k}}(e_{k})=1\wedge Support\big{[}\mu_{e_{k}}(\cdot) \big{]}\supset\{e_{k}\} \tag{4}\]  
Figure 4 shows that the non-singleton fuzzy sets of the static FLC utilize seven linguistic variables. These variables can be expressed as \"\(NB\)\", \"\(NM\)\", \"\(NS\)\", \"\(ZE\)\", \"\(PS\)\", \"\(PM\)\" and \"\(PB\)\", which mean \"_Negative Big_\", \"_Negative Medium_\", \"_Negative Small_\", \"_Zero_\", \"_Positive Small_\", \"_Positive Medium_\" and \"_Positive Big_\", respectively.
# Low-Complexity GSM Detection Based on Maximum Ratio Combining
## 3 MRC-RS Detection Algorithm
### Sort TACs  
In the proposed detector, an ordering algorithm is firstly developed to sort the TACs. \(\mathbf{k}=\left[k_{1},k_{2},\cdots,k_{N_{\text{C}}}\right]^{T}\) with length of \(N_{\text{C}}\) denotes the weight vector and the MRC value \(k_{i}\) corresponding to \(i\)-th TAC can be represented as  
\[k_{i}=\frac{\left\|\left(\bar{\mathbf{h}}_{i}\right)^{H}\mathbf{y}\right\|^{2}}{\left\| \bar{\mathbf{h}}_{i}\right\|^{2}}, \tag{5}\]  
where \(i\in\{1,2,\cdots,N_{\text{C}}\}\), and the symbol \(\bar{\mathbf{h}}_{i}\) can be expressed as  
\[\bar{\mathbf{h}}_{i}=\mathbf{h}_{i(1)}+\mathbf{h}_{i(2)}+\cdots+\mathbf{h}_{i(N_{p})}=\sum_{l= 1}^{N_{p}}\mathbf{h}_{i(l)}, \tag{6}\]  
where \(i\in I\), \(\bar{\mathbf{h}}_{i}\) is the sum of the column vectors of \(\mathbf{H}\) corresponding to the \(i\)-th TAC.  
Then sort the weight vector \(\mathbf{k}=\left[k_{1},k_{2},\cdots,k_{N_{\text{C}}}\right]^{T}\) in descending order and obtain the ordered TACs as  
\[\left[w_{1},w_{2},\cdots,w_{N_{\text{C}}}\right]=\arg sort(\mathbf{k}), \tag{7}\]  
where \(sort(\cdot)\) is defined as a sorting function for reordering the elements of the input vector in descending order, and \(w_{1}\), \(w_{N_{\text{C}}}\) are the indices of the maximum and minimum value in vector \(\mathbf{k}\), respectively.  
In the next, we select the two most likely TACs corresponding to \(w_{1}\) and \(w_{2}\), and delete the duplicate antenna indices to obtain antenna set \(\Gamma\). We also obtain \(L\) new TACs by deleting the TACs that do not include any antenna in set \(\Gamma\). Then the MRC algorithm is used to calculate the MRC value of new TACs and get a new weight vector \(\mathbf{r}=\left[r_{1},r_{2},\cdots,r_{L}\right]^{T}\) with a length of \(L\). The MRC value \(r_{j}\) of \(j\)-th TAC in \(L\) new TACs can be calculated as  
\[r_{j}=\frac{\left\|\left(\bar{\mathbf{h}}_{j}\right)^{H}\mathbf{y}\right\|^{2}}{\left\| \bar{\mathbf{h}}_{j}\right\|^{2}}, \tag{8}\]  
where \(j\in\{1,2,\cdots,L\}\), \(\bar{\mathbf{h}}_{j}\) represents the sum of channel state vectors corresponding to the \(j\)-th TAC, that is,  
\[\bar{\mathbf{h}}_{j}=\mathbf{h}_{j(1)}+\mathbf{h}_{j(2)}+\cdots+\mathbf{h}_{j(N_{p})}=\sum_{l= 1}^{N_{p}}\mathbf{h}_{j(l)} \tag{9}\]  
Meanwhile, the new weight vector \(\mathbf{r}=\left[r_{1},r_{2},\cdots,r_{L}\right]^{T}\) is resorted in descending order, and we obtain the ordered TACs as  
\[\left[n_{1},n_{2},\cdots,n_{L}\right]=\arg sort(\mathbf{r}), \tag{10}\]  
where \(n_{1}\) and \(n_{L}\) are the indices of the maximum and minimum values in \(\mathbf{r}\), respectively. Then we only reserved \(round(L/2)\) most likely TACs to estimate the modulation symbols.