esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Preparation of the Samples  
Meat samples (3 whole vacuum-packed pork chops (longissimus thoracis and lumborum muscles (LTL)) were purchased from a local wholesale market. The pork chops weighed m\({}_{1}\) = 3.74 kg, m\({}_{2}\) = 3.88 kg, and m\({}_{3}\) = 3.69 kg. Besides the breed (Hungahybrid AB, Beta Fresh Kft., Hajdunanas, Hungary), other biological characteristics of the meat were unknown. The main focus of this research was the impacts of various treatment combinations on randomly selected raw materials rather than the effects of gender or age. Samples were prepared by cutting the meat crosswise in the direction of muscle fibers after removing surface fat, ligament, and connective tissue. A total of 126 samples were prepared for analysis. Two sample sets were prepared (one set: two sample/treatment/pork chop) one for physical and chemical analysis while the other set was used for microbiological and challenge tests. The average sample size was 43.53 \(\pm\) 3.27 g.  
Meat pieces were vacuum-packed using a Multivac C100 vacuum sealing machine (MULTIVAC Sepp Haggenmuller SE & Co. KG, Wolfertschwenden, Germany) in a 90 \(\upmu\)m PA/PE pouch (20 \(\upmu\)m PA--70 \(\upmu\)m PE, AMCO Kft., Budapest, Hungary); the pouch had an oxygen permeability of 50 cm\({}^{3}\)/m\({}^{2}\) per 24 h at 23 \({}^{\circ}\)C/75% HR and a water/steam permeability of 2.6 g/m\({}^{2}\) per 24 h at 23 \({}^{\circ}\)C/85% HR.  
The moisture, pH, protein, and fat content of the raw meat were measured in triplicate using the procedures reported by AOAC [42]. Additionally, the color, texture, TBARS, DSC, and microbiological analyses were also carried out on the untreated meat. Table 1 shows the measured parameters of the raw material.  
For conducting challenge experiments, fresh overnight cultures of _Listeria monocytogenes_ H3 isolated from meat in BH broth (Merck 110493, Darmstadt, Germany) were washed three times and re-suspended in a sterile saline solution. The viable count of the washed cell suspension in Maximal Recovery Diluent (MRD) (Merck 112535, Darmstadt, Germany) was determined by spread-plating 0.1 mL of the suspensions on Palcam Agar Base (Merck 111755, Darmstadt, Germany) supplemented with Palcam Listeria Selective Supplement (Merck 112122, Darmstadt, Germany). The plates were incubated at 37 \({}^{\circ}\)C for 24 h.  
The washed suspension of _L. monocytogenes_ was used to inoculate the meat samples to reach an approximate initial count of 10\({}^{5}\) CFU/g. Uninoculated meat samples were also prepared as a control.
###### Abstract  
An accurate state of charge (SOC) estimation is vital for the safe operation and efficient management of lithium-ion batteries. At present, the extended Kalman filter (EKF) can accurately estimate the SOC under the condition of a precise battery model and deterministic noise statistics. However, in practical applications, the battery characteristics change with different operating conditions and the measurement noise statistics may vary with time, resulting in nonoptimal and even unreliable estimation of SOC by EKF. To improve the SOC estimation accuracy under uncertain measurement noise statistics, a variational Bayesian approximation-based adaptive dual extended Kalman filter (VB-ADEKF) is proposed in this paper. The variational Bayesian inference is integrated with the dual EKF (DEKF) to jointly estimate the lithium-ion battery parameters and SOC. Meanwhile, the measurement noise variances are simultaneously estimated in the SOC estimation process to compensate for the model uncertainties, so that the adaptability of the proposed algorithm to dynamic changes in battery characteristics is greatly improved. A constant current discharge test, a pulse current discharge test, and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the DEKF algorithm. The experimental results show that the proposed VB-ADEKF algorithm outperforms the traditional DEKF algorithm in terms of SOC estimation accuracy, convergence rate, and robustness.  
Keywords:state of charge (SOC); joint estimation; lithium-ion battery; variational Bayesian approximation; dual extended Kalman filter (DEKF); measurement statistic uncertainty
## 3 Results and Discussion
### Model Evaluation and Visualization  
Figure 8 shows the model's performance after tuning the hyper-parameters on the datasets with \(K_{\text{c}}\), \(K_{\text{h}}\), \(K_{\text{m}}\), and \(K_{\text{v}}\) as target variables, respectively. The R and Evar values of both the training and testing sets were higher than 0.9, and the MSE and MAE maintained low values. In addition, the R values on four datasets were all higher than those before hyper-parameter tuning (Figure S1). The above analysis not only showed that the PSO-DT model had excellent prediction and generalization ability, but it also confirmed the role of PSO tuning [37, 38].  
Figure 9 presents a visual comparison of the actual and estimated dimensionless elastic stiffness coefficients. The diagonal line in the figure represents the best prediction curve, where the estimated output equals the actual output. As illustrated, the points were distributed close to the diagonal line, indicating that an excellent prediction was achieved.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyper-Parameters** & \(K_{\text{c}}\) & \(K_{\text{h}}\) & \(K_{\text{m}}\) & \(K_{\text{v}}\) \\ \hline max\_depth & 10 & 13 & 12 & 14 \\ min\_samples\_split & 3 & 2 & 2 & 2 \\ Other hyper-parameters & & min\_samples\_leaf = 1, max\_features = 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The optimal DT hyper-parameters after PSO tuning.  
Figure 8: Performance evaluation of the DT-PSO model on four datasets.  
The MSE values of the training and testing sets were small (less than 0.05), and the R-square (R\({}^{2}\)) was higher than 0.99, which further confirmed the accuracy of the prediction.  
Figure 10 presents a visualization of a DT for the \(K_{\rm c}\) dataset. To maintain the interpretation of the DT, the max_depth was restrained to 4 as an example. It should be noted that good predictive performance (CV training performance = 0.96) was also achieved under this condition. The decision rule, MSE value, the number of samples, and the mean value can be identified in Figure 10. Only \(T/2R\) and \(G_{\rm sand}/G_{\rm clay}\) were employed during the construction of the decision rules, implying the significance of these two input variables. The variable importance will be further discussed in the following section.
## 3 Transmission Parameters of Shallow-Water Channel
The coherence time \(T_{c}\), specifying a time interval at which TVIR remains constant, is calculated in the same manner on the basis of \(R(\Delta t)\). During the inland-water experiment, both the coherence bandwidth \(B_{c}\) and coherence time \(T_{c}\) were calculated at threshold levels of 0.5, 0.7, and 0.9 of the maximum value of the corresponding correlation function. The coherence bandwidth values obtained were equal to 124.84 Hz, 68.54 Hz, and 36.74 Hz, respectively. These values are typical for shallow-water channels with multipath propagation. Usually, the coherence bandwidth of such a channel is in the order of several dozen Hz. The coherence time \(T_{c}\) was equal to 3.03 s, 1.16 s, and 0.36 s, respectively, for all three cases of threshold levels. The values of all transmission parameters are shown in Table 1. \begin{table}
\begin{tabular}{l c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(\tau_{M}\), \(T_{r}=0.1\) & 8.67 ms & \(v_{M}\), \(T_{r}=0.1\) & 2.23 Hz \\ \(\tau_{M}\), \(T_{r}=0.01\) & 27.13 ms & \(v_{M}\), \(T_{r}=0.01\) & 3.5 Hz \\ \(\tau_{rms}\) & 17.12 ms & \(v_{rms}\) & 0.83 Hz \\ \hline \(T_{c}\), \(T_{r}=0.5\) & 3.03 s & \(B_{c}\), \(T_{r}=0.5\) & 124.84 Hz \\ \(T_{c}\), \(T_{r}=0.7\) & 1.16 s & \(B_{c}\), \(T_{r}=0.7\) & 68.54 Hz \\ \(T_{c}\), \(T_{r}=0.9\) & 0.36 s & \(B_{c}\), \(T_{r}=0.9\) & 36.74 Hz \\ \hline \end{tabular}
\end{table} 
Table 1: Transmission parameters of the measured UAC channel at a distance of 1035 m.  
Figure 5: Power Delay Profile (**a**) and Doppler Power Spectral Density (**b**) of the measured UAC channel at a distance of 1035 m.  
Figure 6: Space-frequency Correlation Function (**a**) and Space-Time Correlation Function (**b**) of the measured UAC channel at a distance of 1035 m.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 3 Filter Design for Obtaining Time Domain Spherical Coefficients
### Practical Considerations of Filter Implementation  
In (26), \(a_{nm}(\
u)\) is obtained by filtering \(b_{nm}(\
u,R_{Q})\) with \(\rho_{n}(\
u,R_{Q})\), where we get  
\[b_{nm}(\
u,R_{Q})*\rho_{n}(\
u,R_{Q})=a_{nm}(\
u)*z_{n}(\
u)\approx a_{nm}(\
u). \tag{27}\]  
Naturally \(a_{nm}(\
u)\) at time index \(\
u\) is only influenced by \([b_{nm}(\
u-L_{p},R_{Q}),b_{nm}(\
u-L_{p}+1,R_{Q}),\cdots,b_{nm}(\
u+L_{p},R_ {Q})]\) because of the Legendre function in \(p_{n}(\
u,R_{Q})\). However, with the influence of sinc functions in \(z_{n}(\
u)\) in our proposed filters \(\rho_{n}(\
u,R_{Q})\), we now need the past \(L\) samples and the future \(L\) samples of \(b_{nm}(\
u,R_{Q})\) to obtain \(a_{nm}(\
u)\) at time index \(\
u\). For offline signal processing, \(L\) samples of zeros should be added both in the beginning and the end of the vector of \(b_{nm}\) before filtering it with pre-designed \(\rho_{n}(\
u,R_{Q})\). Moreover, an overlap of \(2L+1\) samples is needed for frame based signal processing. For on-line real time signal processing, we cannot obtain future samples of \(b_{nm}(\
u,R_{Q})\). As a result, we add \(L\) samples of zeros in front of the filter \(\rho_{n}(\
u,R_{Q})\), and create a buffer of the past \(2L+1\) samples of \(b_{nm}(\
u,R_{Q})\). At time index \(\
u\), we obtain \(a_{nm}(\
u-L)\) with the buffer of \([b_{nm}(\
u-2L,R_{Q}),\cdots,b_{nm}(\
u,R_{Q})]\). Thus, there is a \(L\) samples of group delay of the system. We further discuss and compare the group delay with frequency domain method in Section 5.5.
# Quantum Antenna as an Open System: Strong Antenna Coupling with Photonic Reservoir
## 2 Model of Quantum Antenna as an Open System
\begin{array}{rl}\hat{\Im}(t)=-&\frac{1}{2}\int\limits_{-l/2}^{l/2}\int \limits_{-l/2}^{l/2}dxdx^{\prime}f(x)f^{*}(x^{\prime})\times\\ &\left\{\underset{k}{\sum}\frac{g_{\mathbf{k}}^{2}}{\hbar}e^{ik\cdot\epsilon(x -x^{\prime})}\int\limits_{0}^{t}\big{[}\hat{\sigma}^{+}\hat{\sigma}^{-}\rho(t ^{\prime})-\hat{\sigma}^{-}\rho(t^{\prime})\hat{\sigma}^{+}\big{]}e^{i(\omega -\omega_{k})(t-t^{\prime})}dt^{\prime}+H.c.\right\}\end{array}  \tag{11}\]  
The next step of simplification consists in the standard transition from summation over \(\mathbf{k}\) to the frequency integration [1] using the replacement  
\[\underset{\mathbf{k}}{\sum}(\ldots)\Rightarrow 2\frac{V}{(2\pi)^{3}}\int \limits_{0}^{2\pi}d\varphi\int\limits_{0}^{\infty}dk\cdot k^{2}\int\limits_{0 }^{\pi}d\theta\sin\theta(\ldots) \tag{12}\]
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 4 Pipe Routing Scheme
### Pipe Formation  
Since the UAV network topology changes frequently, the quality of the current route may deteriorate over time and new, better quality routes may become available. Ideally, the target UAV (source node) should proactively switch routes when the current route deteriorates. In practice, we accomplish this by switching to a new route when the quality of the current route drops below a certain threshold. This proactive switching can improve throughput, reduce data flow interruptions, and prevent packet drops due to route breaks.  
Since network topology information is not centralized and no node knows the entire topology, the nodes must periodically broadcast their information via the protocol's Hello packets. As in [17], the target UAV (source node) collects and builds limited network topology information around the current route to form a \"pipe\" and enables proactive route switching. When a target is discovered, the target UAV performs route discovery and selects the best route to BS. In addition, a pipe (see Figure 4) is established along the current route from the target UAV to the BS. The pipe comprises the nodes along the route (red nodes) and their 2-hop neighbors (green nodes). The topology information (including the LLT, En, and IL values) of the nodes within the pipe is periodically transmitted to the source node as discussed in [17]. A pipe width of 2-hop neighbors around the current route appears to give a good trade-off between the usefulness of the neighborhood information and its incurred overhead [17]. Increasing the pipe width correspondingly increases the amount of known network topology; while this may help identify more alternate routes, it comes at the cost of more control packets and computational overhead.
###### Abstract
The performance of our proposed resource allocation method is evaluated through simulation experiments, and the results demonstrate that it can improve the communication performance of the NR-V2X vehicle networking system. The remainder of this paper is structured as follows: Section 2 provides a review of the related literature. In Section 3, we introduce the system model and formulate the optimization problem. Section 4 simplifies the formulated optimization problem and presents a near-optimal solution using DRL. We conduct simulations to demonstrate the effectiveness of our proposed method in Section 5, followed by concluding remarks in Section 6.
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 3 QoS Guarantee within Internet of Things Environment
### QoS Motivation and Challenges in IoT
In [28], the authors presented a three-layer architecture for the IoT environment (perception layer, network layer, and application and service layer). The architecture includes also a cross-layer QoS management facility as well as QoS brokers for supporting end-to-end QoS operations. In fact, the broker is responsible for resolving QoS requirements received from the upper layer and translating them to QoS requests on the local layer. The QoS requirements are a set of performance parameters to be respected. In this work, the authors had grouped the IoT applications into different types, and assigned a QoS level to each application type. Indeed, control applications need a guaranteed service, query applications need a guaranteed or a differentiated service, real-time monitoring applications need a differentiated service, while the non-real-time monitoring applications need best effort service. The research work conducted in [29], specifies different queues and a scheduler within an object in order to ensure a specific priority while processing QoS constrained flows. Moreover, some research works tried to adapt the slotted CSMA/CA algorithm to ensure a QoS guarantee. The research conducted in [30] had adapted the IEEE 802.15.4 standard and specifically the slotted CSMA/CA algorithm by integrating a QoS mechanism. This approach is called SDA-CSMA/CA and is based on the specification of different values of CSMA/CA variables (contention window, minimal and maximal BE) enabling different priorities according to traffic types. Thus, three priority levels are considered while using SDA-CSMA/CA. In this context, smaller CWs and BE intervals (minimal BE and maximal BE) are assigned to high-priority traffics. Consequently, important messages have higher chances for channel access with shorter backoff time.
# A Portable Non-Motorized Smart IoT Weather Station Platform for Urban Thermal Comfort Studies
## 2 Proposed Weather Station Platform
### Housing and Shielding Design for the Edge Device  
Shielding and ventilation of the sensors, especially temperature and humidity sensors, are very important to minimize measurement errors due to direct solar radiation, atmospheric radiation, wind, rain, fog, snow, dust, dirt, and pollutants. According to the World Meteorological Organization (WMO) guidelines, the standard weather shield for the sensors should be of low thermal absorption and high solar reflectance material, and the shield must allow air passage over the sensor [46]. Hence, housing and shielding designs for the AT sensor (see Figure 2) were explored for the edge device, and its performance was evaluated using Davis Vantage Pro 2.  
The housing design is a 190 mm \(\times\) 57 mm \(\times\) 114 mm plastic enclosure, with the anemometer screwed onto the enclosure lid. On the two shorter sides of the enclosure, the 114 mm sides, the AT, GT, RH sensors, and a GPS antenna were fixed. The RH sensor was kept inside a hollow pipe, with one end fixed to the box and the other open for air to pass over the sensor. The AT sensor was kept inside a semi-circular  
Figure 1: The portable weather station device setup and real-time monitoring interface.  
Figure 2: (**Left**) image—Housing design (1—WS, 2—AT, 3—RH, 4—GT, 5—GPS antenna, 6—device housing). (**Right**) image—Shielding design of AT and RH (1—WS, 2—AT, 3—RH).  
painted white. A white color was chosen for the shields to minimize thermal absorption and was enclosed by aluminium foil for high solar reflection.
# Model Test on Grouting Properties of Alluvial Filler Soil
## 6 Engineering Characteristic Analysis of Grouting Soil
### Test Verification and Analysis  
In order to verify the developed model, the geotechnical tests before and after grouting were carried out in the laboratory. The moisture content of soil is determined by the drying method, the natural density of soil is measured by the ring method, and the shear strength index of soil is obtained by the direct shear test.  
According to the test results, the initial cohesion of soil \(c_{0}\) was 28.70 kPa; the initial internal friction angle of soil \(\Phi_{0}\) was 21.50\({}^{\circ}\); the injection rate of the slurry \(\eta\) was 0.431; the cohesion of slurry \(c_{g}\) was 126.10 kPa; the internal friction angle of slurry \(\Phi_{g}\) was 34.60\({}^{\circ}\); the coefficient of variation of cohesion \(k_{c}\) was 0.469; and the coefficient of variation of variation of internal friction angle \(k_{\Phi}\) was 0.135. \(\Delta c\) and \(\Delta\Phi\) are the incremental values of the cohesion force and the internal friction of the soil.  
By substituting the experimental data above into Equations (7) and (10), we get the following theoretical data: The value of \(\Delta c\) was 13.75 kPa and the value of \(\Delta\Phi\) was 0.532\({}^{\circ}\); the cohesion of grouting soil \(c\) was 42.45 kPa; and the internal friction angle of grouting soil \(\Phi\) was 22.032\({}^{\circ}\).  
According to the theoretical calculation and laboratory test results, the experimental values and theoretical prediction values of the shear strength parameters of the soil before and after grouting are shown in Table 2. It can be seen from Table 2 that the theory of this study can effectively predict the shear strength parameters of soil mass after grouting and has a certain engineering application value.
# Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System
## 2 Problem Formulation and Preliminaries
### WUNT TR Algorithm
#### 2.3.2 Newton-Cotes Quadrature Formulas  
Applying the Newton-Cotes formula, the definite integral of \(f(x)\) in the interval \([a,\,b]\) can be estimated by using a limited number of sampling points. This provides a mathematical basis for the WTNT method through systematic sampling.  
**Definition 1**. (Numerical Integration) Suppose that \(a=x_{0}<x_{1}<\ldots<x_{n}=b\), then the following form of formula\[Q(f)=\sum_{k=0}^{m}\omega_{k}f(x_{k})=\omega_{0}f(w_{0})+\omega_{1}f(w_{1})+ \ldots+\omega_{m}f(w_{m}), \tag{17}\]  
Which satisfies:  
\[\int_{a}^{b}f(x)dx=Q(f)+E(f) \tag{18}\]  
Then the above equation is called numerical integration or quadrature formula, where \(x_{k}\) is the integration node, \(\omega_{k}\) is the weight coefficient, and \(E(f)\) is the truncation error, that is, the error term of the integration formula.  
**Theorem 2**.: _(Compound Trapezoidal Rule) Consider the function \(y=f(x)\) on the closed interval \([a,b]\), and divide \([a,b]\) into \(m\) subintervals with a width of \(h=\frac{b-a}{m}\), where the equidistant node is \(x_{k}=x_{0}+kh(k=0,1,\ldots,m)\), then the numerical estimation of definite integral with the composite trapezoidal rule is as_  
\[\int_{a}^{b}f(x)dx=\frac{h}{2}\Bigg{(}f(a)+f(b)+2\sum_{k=1}^{m-1}f(x_{k}) \Bigg{)}+E_{T}(f,h) \tag{19}\]  
If the function \(f\) is second-order continuous and differentiable on \([a,b]\), then the remainder  
\[E_{T}(f,h)=-\frac{(b-a)f^{\prime\prime}(\xi)}{12}h^{2}=O\Big{(}h^{2}\Big{)} \tag{20}\]  
where \(a<\zeta<b\). \(O\big{(}h^{2}\big{)}\) means that when the calculation step size is reduced by \(1/2\), the error should be reduced to \((1/2)^{2}=0.25\) times. That means that when the sampling step is smaller, the estimation error is smaller, which is sufficient to meet the accuracy requirements.
# Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm
## 1 Introduction
The use of the FxLMS algorithm sometimes causes high pressure in the primary noise, chaotic behavior in the noise and causality constraint violation. To overcome these problems, the Volterra Filtered-x LMS (VFxLMS) algorithm has been developed [19]. In the broadband feedforward noise measurement, it has been measured directly from the reference sensor, but in the narrowband feedforward system, noise is generated from the information provided by the reference sensor [20]. The primary application of the LMS algorithms is the minimization of the error in the original signal generated by the primary source [21]. The FxLMS algorithm is better than the FxLMS algorithm to eliminate the noise source presented in the system; which can be observed from the reduction of computational complexity [22]. Optimization algorithms are also used in the design of ANC systems. The non-linear approach to noise elimination is the efficient one when compared to the linear approach of ANC [23]. The widely used optimization algorithm is a Genetic Algorithm (GA). It has the advantage that it does not require the information about the secondary path. In recent days, the Particle Swarm Optimization (PSO) algorithm is also used for ANC systems to get the coefficient of the filter bank while tuning [24; 25].