esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Deployment and Evolution of Heterogeneous Smart Public Lighting Systems
## 6 Deployment and Configuration of a Smart Lighting Infrastructure
### Step 2. In-Field Verification and Light Fixtures Upgrade  
**Phase 2.1. In-field verification and gateway installation.** If a third party network is chosen to provide the connectivity to streetlights, the network provider must be selected, either Sigfox or one of the available cellular providers, considering both the service coverage and the yearly fees.  
If, on the contrary, the deployment of a proprietary network is chosen, an in-field verification phase is needed to check whether the locations chosen for the gateways, either LoRa, IEEE 802.15.4 or BMN, actually provide the expected link quality. In the LoRa case this is a critical phase as, in principle, one should first make tentative choices of gateways locations, prioritizing the highest sites, install them and check whether all light fixtures experience good connectivity conditions. Clearly, when thousands of smart fixtures are to be installed, only a subset of communication links can be checked, which means that 100% coverage might not be reached when the network is fully deployed. The deployment of IEEE802.15.4 and Bluetooth gateways is much less critical, as each of them needs to be connected only to a few streetlights, which usually experience line-of-sight conditions. Based on the outcomes of the verification phase, the gateways are finally installed.  
**Phase 2.2. Light fixtures upgrade.** Upgrading each street light with a smart LC is not only a matter of replacing the old hardware with the new one. A fundamental step of this phase is the georeferentiation of each device, whose unique MAC address must be associated to a specific location. As a matter of fact, the installation might well involve thousands of streetlights and in case a failure is reported to the Control Center by the LC, along with the its MAC address, the corresponding position should be immediately known for a timely maintenance.  
Usually, smart lighting devices come with a set of identical (for each device) barcode stickers, which are attached to the fixture and to the installation sheet that has to be filled in by the installer. Such sheets, that contains also the positions of devices, provide the link between the MAC address of each device and its location. Clearly, this is an error prone procedure, with many weakness points (transcription errors, stickers swapping,...).  
More advanced instruments are at disposal, such as portable devices that scan barcodes and communicate the streetlight coordinates to the Control Center. The most straightforward solution is to equip the LC with a GPS receiver, so that its position is automatically acquired and sent to the Control Center each time a report is transmitted.
## 2 Materials and Methods
### Measurement of DPPH Radical Scavenging Activity  
To measure the DPPH radical scavenging activity of the SF and SSF extracts, the reductive activity of the sample based on the electron donor effect on DPPH was measured. To 0.98 mL of 150 \(\upmu\)M DPPH solution, 0.02 mL of 20 mg/mL SF and SSF extracts were added, and the mixture was left in the dark for 30 min. Thereafter, the absorbance was measured at 517 nm [30]. A synthetic antioxidant, BHT, was used as the positive control. The difference in absorbance between the sample and the negative control was estimated as percentage (%), and the DPPH radical scavenging activity was calculated as follows:  
\[\text{DPPH radical scavenging activity (\%)}=\text{[1}-\text{(A/B)]}\times 100 \tag{2}\]  
A: Absorbance of the sample group  
B: Absorbance of the negative control
## 3 A Proposed Approach
### Planning Space Representation  
Safety is always one of the priority issues for ships in path planning problems. In order to generate safe trajectories, it is necessary to correctly represent the environment in which the path planning algorithm executes, which is especially crucial for ship navigation in complex environments. A suitable safety distance between the OS and obstacles (static and dynamic) during the entire voyage should always be maintained. This section describes the FM-based planning space representation for static and dynamic obstacles.
Specifically, parameters such as pH, temperature, partial pressure, and hydraulic retention time (HRT) significantly influence microbial activity, thereby affecting the total yield of hydrogen and VFA [21; 22]. However, high-temperatures and acid pretreatments can adversely affect dark fermentation processes by degrading the sugars released during pretreatment, converting them into inhibitory compounds such as furfural and 5-hydroxymethylfurfurral (5-HMF) [23]. Studies have indicated that when furfural or HMF concentrations exceed 1 g/L, the hydrogen yield is reduced by approximately 50%. Similarly, high polyphenols concentrations can also inhibit the hydrogen production [24; 25]. In summary, autoclave-based pretreatment effectively enhances lignocellulosic biomass conversion by combining heat and pressure to achieve high solubilization efficiency. Its scalability, minimal sugar loss, and reduced inhibitor formation make it a viable and sustainable strategy for bioresource utilization [7; 8]. This study is the first and only one in the literature to investigate the valorization of four lignocellulosic biomasses through an enhanced biorefinery approach, utilizing the following two-stage process: hydrothermal pretreatment, followed by DF. The objective of this study was to evaluate the solubilization and extraction of major bioproducts, including total reducing sugars (TRS), proteins (PR), and volatile fatty acids (VFA), under economically and environmentally durable conditions. In fact, pretreatment of biomass with a duration less than 30 min is very usual. Subsequently, acidogenic anaerobic digestion, using the pretreated biomass, was conducted to evaluate the effect on hydrogen and VFAs productions.
## 5 Theory and Field Test Verification
### Field Application Studies
#### 5.2.2 Solving the Optimal Solution  
Due to a large number of candidate points, if the exhaustive method is used, there are a total of 3.5352 \(\times\) 10\({}^{18}\) solutions, which is not feasible in terms of time. However, using IPSO to solve the problem, it is found that the algorithm has a fast convergence speed (see Figure 10), and the running time is 244.86 s, the individual population has converged to the optimal solution when it has evolved to the 50 th generations. The algorithm parameters are set as in Section 5.1. According to the field test, the expected velocity of the P wave is taken as 4200 m/s. According to the above setting parameters and models, the optimal layout scheme is shown in Figure 9.  
Combined with the on-site installation conditions, the actual installation position of the microseismic sensor is shown in Figure 9, which is dragged 200 m forward along the incision position. On the one hand, this adjustment is beneficial to the recovery of the on-site sensors, and on the other hand, it can effectively monitor the ore body at the working face behind the stopped mining line.
## 4 Stability Analysis  
[PERSON] et al. (2014) and [PERSON] (2016) have shown that the lower branch solutions (second solutions) are unstable (not realizable physically), while the upper branch solutions (first solutions) are stable (physically realizable) by considering the unsteady Equations (2)-(4). Thus, the new dimensionless time variable \(\tau=ct\) was introduced. The use of \(\tau\) was associated with an initial value problem and was consistent with the question of which solution will be obtained in practice (physically realizable). Using the variables \(\tau\) and (7), we have  
\[ \begin{split}\psi&=\sqrt{cv}xf(\eta,\tau),\quad \theta(\eta,\tau)=\frac{T-T_{\text{on}}}{T_{w}-T_{\text{on}}},\quad\phi(\eta, \tau)=\frac{C-C_{\text{on}}}{C_{w}-C_{\text{on}}},\\ \eta&=\sqrt{\xi}y,\quad\tau=ct\end{split}  \tag{16}\]  
Equations (2) and (3) can be written as  
\[\frac{\partial^{3}f}{\partial\eta^{3}}+f\frac{\partial^{2}f}{\partial\eta^{2} }-\left(\frac{\partial f}{\partial\eta}\right)^{2}+1-\frac{\partial^{2}f}{ \partial\eta\sigma}=0 \tag{17}\]  
\[\frac{1}{\text{Pr}}\frac{\partial^{2}\theta}{\partial\eta^{2}}+f\frac{ \partial\theta}{\partial\eta}+Nb\frac{\partial\theta}{\partial\eta}\frac{ \partial\phi}{\partial\eta}+Nt\left(\frac{\partial\theta}{\partial\eta}\right) ^{2}+Du\frac{\partial^{2}\phi}{\partial\eta^{2}}-\frac{\partial\theta}{ \partial\tau}=0 \tag{18}\]  
\[\frac{\partial^{2}\phi}{\partial\eta^{2}}+Lef\frac{\partial\phi}{\partial\eta }+\frac{Nt}{Nb}\frac{\partial^{2}\theta}{\partial\eta^{2}}+LeS_{T}\frac{ \partial^{2}\theta}{\partial\eta^{2}}-Le\frac{\partial\phi}{\partial\tau}=0 \tag{19}\]  
subject to the boundary conditions  
\[ \begin{split} f(0,\tau)=0,\quad\frac{\partial f}{\partial\eta}(0,\tau)=\varepsilon+\sigma\frac{\partial^{2}f}{\partial\eta^{2}}+\delta\frac{ \partial^{3}f}{\partial\eta^{3}},\\ \theta(0,\tau)=1,\quad Nb\frac{\partial\phi}{\partial\eta}(0, \tau)+Nt\frac{\partial\phi}{\partial\eta}(0,\tau)=0,\\ \frac{\partial f}{\partial\eta}(\eta,\tau)\to 1,\quad\theta(\eta, \tau)\to 0,\quad\phi(\eta,\tau)=0\quad\text{as}\quad\eta\to\infty\end{split}  \tag{20}\]  
To determine the stability of the solution, with \(f=f_{0}(\eta)\), \(\theta=\theta_{0}(\eta)\), and \(\phi=\phi_{0}(\eta)\) satisfying the boundary value problem (17)-(20), we have (see [PERSON] et al. (2014) or [PERSON], (2016))  
\[ \begin{split} f(\eta,\tau)&=f_{0}(\eta)+e^{-\gamma \tau}F(\eta),\\ \theta(\eta,\tau)&=\theta_{0}(\eta)+e^{-\gamma\tau}G (\eta),\\ \phi(\eta,\tau)&=\phi_{0}(\eta)+e^{-\gamma\tau}H( \eta),\end{split}  \tag{21}\]  
where \(\gamma\) is an unknown eigenvalue parameter and \(F(\eta)\), \(G(\eta)\) and \(H(\eta)\) are small relative to \(f=f_{0}(\eta)\), \(\theta=\theta_{0}(\eta)\), and \(\phi=\phi_{0}(\eta)\). The solution of the eigenvalue problem (17)-(20) gives an infinite set of eigenvalues \(\gamma_{1}<\gamma_{2}<\gamma_{3}\ldots\); if \(\gamma_{1}\) is negative, there is an initial growth of disturbances, and the flow is unstable, but when \(\gamma_{1}\) is positive, there is an initial decay, and the flow is stable.
## 4 Results and Discussion
### VR Shopping Model Improvement
#### 4.2.3 Statistical Analysis  
Figure 14 illustrates that the scores for most participants increased following the improvements, indicating a positive impact of the enhancements made to the VR model. Notably, the most significant improvements were observed in vision (C8) and accuracy of packaging function realization (C15). Additionally, enhancements were recorded in the consistency of operational behavior (C1), the accuracy of information transmission (C2), rationalization of the control module (C3), hearing (C7), and touch (C9). However, the scores for smell (C10) and taste (C11) remained the lowest, with both receiving a score of only 3.  
Smell and taste represented significant limitations in our research. While focusing on visual, auditory, and tactile experiences addressed the core aspects of sensory perception, it did not encompass the full spectrum of multi-sensory interactions. Incorporating smell and taste could offer a more nuanced and comprehensive understanding of user experience. The primary challenge in integrating these senses lies in the technical and logistical difficulties associated with accurately simulating and measuring these sensory inputs within a controlled experimental setting. Future research should aim to develop methodologies for effectively incorporating all five senses, thereby enhancing the comprehensiveness and efficacy of sensory research.  
The results indicate that VR shopping systems hold substantial promise for enhancing marketing and shopping experiences but also present significant ethical challenges. On the one hand, virtual reality systems often gather extensive data regarding user behavior, preferences, and interactions within the virtual environment. These data may encompass sensitive information that, if inadequately managed, could result in privacy breaches or unauthorized use. On the other hand, the integration of VR technology into the shopping experience can markedly alter consumer behavior. While such changes may enrich the  
Figure 14: Before and after improvement.  
\begin{table}
\begin{tabular}{c c c} \hline
**Criteria Layer** & **Sub-Criteria Layer** & **Score** \\ \hline \multirow{3}{*}{Packaging function (B5)} & Accuracy of packaging function realization (C15) & 8 \\  & Matching degree of packaging function (C16) & 7 \\  & Packaging model and workmanship (C17) & 7 \\ \hline  & Total score & 7.36 \\ \hline \end{tabular}
\end{table}
Table 13: _Cont._shopping experience, they also raise concerns about immersive technology's long-term psychological and social effects. Prolonged exposure to virtual environments may impact users' perception of reality, consumption patterns, and social interactions. To address these issues responsibly, it is crucial to tackle privacy concerns, assess the impact on consumer behavior, ensure accessibility, and secure informed consent.
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 6 Results
### Accuracy Evaluation  
In this subsection, the accuracy of the proposed tracking technique is evaluated by simulations. Three distributed sensors are deployed on the ground, with the positions at \(\left[100,-10,0\right]^{T}\), \(\left[-10,-10,0\right]^{T}\), and \(\left[-50,100,0\right]^{T}\), respectively. Such a deployment is only based on practical considerations. Actually, the optimal sensor location cannot be guaranteed due to the mobility of the target, as in (65) and (66). The FG iteration number of the detector is fixed at 15, and 100 snapshots are assumed for the detection of each time index.  
First of all, the real path of the target and tracking trajectories are shown in Figure 4. Note that the standard deviations of AOAs from both the azimuth and elevation angles are set equal in the simulations, denoted by \(\sigma_{\phi,\theta}\), in order to evaluate the general tendency of the system behavior. However, in practice, \(\sigma_{\phi}\) and \(\sigma_{\theta}\) can be different. The tracking results with \(\sigma_{\phi}=5^{\circ}\) can be seen in Figure 4a. In order to evaluate the accuracy of the proposed tracking technique, RMSEs are calculated. Specifically, the observer, realized by the 3D location detector described in Section 3, achieves \(\text{RMSE}=1.6\) meters, while this value is reduced to 1.4 with the proposed EKF. The RMSE values of the observer and the EKF are 6.15 and 4.6 in Figure 4b, which are higher than Figure 4a, because of a larger \(\sigma_{\phi,\theta}=20^{\circ}\) is assumed in the simulation.  
Moreover, another simulation was conducted to evaluate whether the 2D tracking performance can be improved with the help of the measured elevation angles. Actually, in many applications, the importance of 2D location accuracy is more than that of 3D. Therefore, it will be very beneficial to see if the 2D detection can be enhanced with additional assistance from elevation measurement. As shown in Figure 5, the RMSE values achieved by using only the azimuth angle \(\phi\) for 2D detection are plotted, versus the standard deviation \(\sigma_{\phi}\). Interestingly, considering both \(\phi\) and \(\theta\) with 3D detection, the RMSEs in 2D are found smaller than the case where only \(\phi\) is used, which are closer to the theoretical CRB bound. This finding proves the fact that the information carried by the elevation angle \(\theta\) also contributes to the detection in the 2D plane, which was ignored in the previous works [16]. Therefore, the introduction of the elevation angle is not only for realizing 3D detection, but also for enhancing the entire system performance. The mathematical proof of such improvement in terms of a lower CRB is left as future work.  
Figure 4: Trajectories of 3D tracking with the proposed technique.  
Figure 5: Improvement of 2D location with elevation measurement.
## 5 Discussion
### Data
As follows from Figure 3a-c, \((\Delta\eta/\lambda)_{max}\) and \(\tau\) are all correlated: the higher the field, the higher the induced birefringence, the short is the time needed to reach the final equilibrium as a consequence of the stronger magnetic driving force. The facts that (i) in Figure 2b the signal returns to zero values when the field is switched off and (ii) in Figure 3c the points collected after the switching on (solid symbols) and off (open symbols) almost coincide within the experimental error, are indications of absence of signal drift and constancy of baseline. The trend of \((\Delta\eta/\lambda)_{max}\) as a function of magnetic field is even more interesting. In addition to a reasonable monotonic increase as a function of B, the trend shows saturation for high B values. This suggests that there is a limit of induced directional ordering that can be achieved with a relatively weak magnetic field. The obtained sigmoidal shape is qualitatively similar to that often observed in liquid dispersions where induced birefringence results from particles being oriented with their magnetic dipole along the field direction [21; 22; 23; 24; 28]. Below, we will model the dependence presented in Figure 3c, assuming that our system behaves like a liquid dispersion of dipolar particles.
# Nutrition, Flavor, and Microbial Communities of Two Traditional Bacterial Douchi from Gansu, China
## 3 Results and Analysis
### Analysis of Nutritional Components of Longnan and Qingyang Douchi
#### 3.1.3 Analysis of FAAs in the Two Types of Douchi
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**FAAs**} & & \multicolumn{2}{c}{**Content (g/100 g)**} \\ \cline{3-4}  & & **LN Douchi** & **QY Douchi** \\ \hline \multirow{6}{*}{Essential amino acids} & Threonine (Thr) & 0.58 \(\pm\) 0.02 a  & 0.24 \(\pm\) 0.02 b  \\  & Lysine (Lys) & 0.82 \(\pm\) 0.00 a  & 0.60 \(\pm\) 0.02 b  \\  & Isoleucine (Ile) & 0.80 \(\pm\) 0.01 a  & 0.43 \(\pm\) 0.00 b  \\  & Leucine (Leu) & 1.72 \(\pm\) 0.02 a  & 0.91 \(\pm\) 0.02 b  \\  & Phenylalanine (Phe) & 1.19 \(\pm\) 0.01 a  & 0.66 \(\pm\) 0.00 b  \\  & Valine (Val) & 0.74 \(\pm\) 0.01 a  & 0.45 \(\pm\) 0.01 b  \\ \hline \multirow{6}{*}{Non-essential amino acids} & Alanine (Ala) & 1.15 \(\pm\) 0.08 a  & 0.65 \(\pm\) 0.01 b  \\  & Histidine (His) & 0.56 \(\pm\) 0.02 a  & 0.41 \(\pm\) 0.00 b  \\  & Proline (Pro) & 2.11 \(\pm\) 0.08 a  & 1.68 \(\pm\) 0.02 b  \\  & Glutamate (Glu) & 6.13 \(\pm\) 0.11 a  & 4.81 \(\pm\) 0.02 b  \\  & Tyrosine (Tyr) & 0.22 \(\pm\) 0.02 b  & 0.35 \(\pm\) 0.01 a  \\  & Aspartate (Asp) & 0.84 \(\pm\) 0.02 a  & 0.64 \(\pm\) 0.01 b  \\  & Cysteine (Cys) & 0.32 \(\pm\) 0.02 a  & 0.11 \(\pm\) 0.01 b  \\  & Serine (Ser) & 0.62 \(\pm\) 0.02 a  & 0.24 \(\pm\) 0.01 b  \\  & Glycine (Gly) & 0.63 \(\pm\) 0.02 a  & 0.21 \(\pm\) 0.01 b  \\  & Arginine (Arg) & 0.86 \(\pm\) 0.01 a  & 0.47 \(\pm\) 0.01 b  \\ \hline \hline \multicolumn{2}{c}{Total} & 19.31 \(\pm\) 0.86 & 12.58 \(\pm\) 0.18 \\ \hline \end{tabular} Note: Letters indicate Duncan’s pairwise differences among different samples (\(p<0.05\)).  
\end{table} 
Table 3: Composition and content of FAAs in Longnan and Qingyang douchi (g/100 g). shown in Table S2, the amino acid with the minimum RC in the two types of douchi was threonine, which was the first limiting amino acid for both types of douchi. The SRC of Qingyang and Longnan douchi were 78.51 and 79.51, respectively, indicating that both types of douchi had high nutritional value, and maybe Longnan douchi had higher protein nutritional value than Qingyang douchi.
## 2 Proposed Architecture  
### RSP16T Cell  
The RSP16T cell is shown in Figure 1, with its corresponding layout in Figure 2. This design features two storage nodes (Q and QB) and two internal nodes (S1 and S0). In the operation of this cell, when the word line (WL) is set to VDD and the write word line (WWL) is set to GND, the storage and internal nodes are accessed through their respective transistors (N7 and N8 for the bit line BL; P5 and P6 for the bit line bar, BLB). It is assumed for both the proposed and comparison cells that a state where Q = 1 (QB = 0, S1 = 1, S0 = 0) is held. This assumption is valid due to their symmetry in operations and SEU recovery behavior.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 3 Estimation Methods for DRR, CDC Error, and Phase Inconsistency Errors
### Estimation for Phase Inconsistency Errors
Then \(U_{a}(m+n\cdot f_{prf};l)\) can be re-expressed as  
\[U_{a}\left(m+n\cdot f_{prf};l\right)=\mathbf{g}^{H}diag\left\{\mathbf{P}_{n} \left(m\right)\right\}\mathbf{\tilde{S}}_{a}\left(m,l\right). \tag{42}\]  
The image entropy involving phase inconsistency error is then given as  
\[ \begin{split}\Phi&=-\sum_{l}\sum_{m}\sum_{n=1}^{N} \frac{U_{a}\left(m+n\cdot f_{prf};r\right)\cdot U_{a}^{H}\left(m+n\cdot f_{ prf};r\right)}{E_{2}}\ln\frac{U_{a}\left(m+n\cdot f_{prf};r\right)\cdot U _{a}^{H}\left(m+n\cdot f_{prf};r\right)}{E_{2}}\\ &=-\sum_{l}\sum_{f}\sum_{n=1}^{N}\frac{\mathbf{g}^{H}\cdot\Omega \cdot\mathbf{g}}{E_{2}}\ln\frac{\mathbf{g}^{H}\cdot\Omega\cdot\mathbf{g}}{E_{ 2}}\end{split} \, \tag{43}\]  
where \(\left[\bullet\right]^{H}\) represents conjugate-transpose and  
\[\Omega=diag\left\{\mathbf{P}_{n}\left(m\right)\right\}\mathbf{\tilde{S}}_{a} \left(m,l\right)\mathbf{\tilde{S}}_{a}\left(m,l\right)^{H}diag\left\{\mathbf{ P}_{n}(m)^{*}\right\}, \tag{44}\]  
\[E_{2}=\sum_{l,m,n}\mathbf{g}^{H}\Omega\mathbf{g}. \tag{45}\]  
The optimization can be expressed as  
\[\overset{\wedge}{\mathbf{g}}=\arg\min_{\mathbf{g}}\ \ -\sum_{l}\sum_{m}\sum_{n=1}^{N} \frac{\mathbf{g}^{H}\Omega\mathbf{g}}{E_{2}}\ln\frac{\mathbf{g}^{H}\Omega \mathbf{g}}{E_{2}}. \tag{46}\]  
To solve the optimization problem, the cyclic coordinate iteration is needed, in which we minimize the objective function on one channel error at a time while holding all other channels fixed. In each iteration, the optimum algorithms, such as gradient descent and Newton-like algorithm, can be used. Reference [29; 35] give a fast convergence algorithm that can also be introduced here. A kind of closed-form solution in each iteration can be obtained by constructing a surrogate function, which has high convergence rate. The termination threshold for iterations is set as  
\[\Delta\Phi=\Phi_{q+1}-\Phi_{q}<\Delta\Phi_{thr}, \tag{47}\]where \(\Phi_{q}\) is the image entropy after \(q\)-th iteration and \(\Delta\Phi_{th}\) denotes the threshold for the precision. The small \(\Delta\Phi_{th}\), the higher the precision and the greater the computational load. Since the estimation method is based on the minimum entropy, it can be named as minimum entropy (ME) method. The flowchart for this estimation method is given in Figure 8. Because the errors usually interact with each other in practice, we perform the estimations in Figures 6 and 8 alternately to give high estimation precision. Notice that the proposed ME method in our manuscript is different from conventional minimum-entropy-based autofocusing. Because the proposed method is performed in Range-Doppler domain before the imaging processing and focuses on the phase inconsistency errors between different channels while MEBA is targeted at azimuth phase error in image domain.