esa-sceva/satcom-chunk-collection huggingface dataset:
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 3 BRR-Based UEP Algorithm and Scalable Video Layer-Switching
### Proposed FEC Assignment Algorithm  
Based on the performance metric model (\(R_{avg}\)) in (3), we propose a FEC assignment algorithm which allocates the amount of FEC packets to each block in order to maximize the average block recovery rate (\(R_{avg}\)). Similar to the simple local hill climbing method, we find the \(B(t,\,q)\) that can achieve the largest value of \(R_{avg}\) for a given FEC assignment.  
Based on (3), we define \(R_{avg}(F(t,q))\) as the average block recovery rate when FEC packets are assigned into \(B(t,\,q)\). If the amount of the increased average block recovery rate by the allocated number of FEC packet in \(B(t,q)\) is defined as \(\delta(t,q)\), it is calculated as  
\[\delta(t,q)=R_{avg}(F(t,q)+1)-R_{avg}(F(t,q)) \tag{4}\]  
Using (4), the \(B(t,\,q)\) which can achieve the largest increased value of average block recovery rate is selected as  
\[B(\hat{t},\hat{q})=\underset{t,q}{\arg\max}\;\delta(t,q) \tag{5}\]  
The number of FEC packets of the selected \(B(\hat{t},\hat{q})\) is updated by 1. The accumulated value of \(F(t,q)\) is represented as \(F_{sum}(t,\,q)\) and is updated by 1. If the overall number of assigned FEC packets (\(F_{sum}(t,\,q)\)) exceeds the limited channel resource (\(T_{pkt}\)), the proposed algorithm is terminated.
# Measurement of Visible Radiation through a _Sansevieria cylindrica_-Based \"Living Sensor\"
## 3 Discussion
### Selectivity Achievement of the Living Sensor  
This section is devoted to studying the selectivity of the living sensor in order to investigate physical quantities, different with respect to the Vis-light, which could affect the output voltage and can lead to distortions of the measured value. In fact, the analysis of the influence, external parameters, and signal varying randomly, different with respect to the measurand, which act on the system, arouse interest for the exhaustive characterization of the sensor. Among these factors, the change of temperature and relative humidity has a significant effect on the sensor, affecting directly or indirectly the measurement accuracy and the robustness of the entire system. To study the selectivity of the _Sausweieria cylindrica_, the thermal/relative humidity-controlled chamber, Espec SH-242, presented in Section 2, was used. Figure 6 shows the output voltage coming from the electrodes as a function of the applied temperature inside the chamber and for fixed values of illuminance and relative humidity (50%). The temperature was increased from 5 \({}^{\circ}\)C to 65 \({}^{\circ}\)C with a step of 10 \({}^{\circ}\)C over a period of time of 5000 s. The procedure was pursued by increasing and decreasing the temperature in order to investigate nonlinearities and hysteretic behaviours also related to root exudate production and the consequent modification of the soil redox potential.  
Focusing our attention on the relative humidity variation, Figure 7 shows the output voltage coming from the electrodes as a function of the applied relative humidity inside the chamber and for fixed values of illuminance and temperature (20 \({}^{\circ}\)C). The relative humidity was increased from 30% to 90% with a step of 10% over a period of time of 5000 s. As with temperature, the measurements were performed in order to study the effect of hysteresis. Tables 2 and 3 show the effect of temperature and relative humidity, respectively, on the output voltage of the living sensor. The results evince a maximum variation of output voltage, estimated as the difference between the voltage during the increment (V\({}_{\text{I}}\)) and the voltage during the decrement (V\({}_{\text{D}}\)), of about 10.3 mV and 2.2 mV for the temperature and relative humidity analysis, respectively. The next section will be devoted to the metrological  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**d [cm]** & **Min [lux] @ (\(-\)15 cm, \(-\)20 cm)** & **Max [lux] @ (0 cm, 0 cm)** \\ \hline
10 & 4700 & 10,270 \\
20 & 3080 & 5220 \\
30 & 2411 & 3730 \\
40 & 1722 & 2395 \\
50 & 1369 & 1769 \\
60 & 1080 & 1438 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured minimum and maximum illuminance values for each distance.  
Figure 6: Temperature effects on the output voltage. The graph includes the increment and decrement evolution as a function of the applied influence.  
characterization of the device. Performance considerations and features, also taking into the account the results obtained here, will also be conducted.
# Development of Folded Expanded Metal Mesh with Sound Absorption Performance
###### Abstract
Regarding the shape of the cavity in a sound-absorbing structure, some studies have pointed out that an incomplete cavity could effectively improve sound-absorption performance [11; 20; 21]. Furthermore, after an unshaped cavity structure is divided into several independent cavities, the results show that the low-frequency sound-absorption ability was improved [22; 23; 24]. Meanwhile, the multi-layer structure with a folding panel has been used as a support structure for the surface panel. Expanded metal mesh (EMM) is very different from the common perforated metal plate. In the manufacturing process, EMM is formed by equidistant slitting and stretching, which can reduce material waste. In addition, the tiny holes on the EMM plate generate the potential for sound-absorbing. In this study, we focused on the acoustic resonance structure of the metal panel based on the manufacturing cost; therefore, the object in this study is EMM with a lower cost. In order to obtain higher sound-absorbing performance while considering the structure of the material, this research proposed sound-absorption structures composed of EMM based on the height of the specimen, the thickness of the cavity, and the folded shape, followed by the development of the folded expanded metal mesh (FEMM). Furthermore, we used the light steel joint ceiling of an office as the application target. After installing the FEMM with better sound-absorption performance in the space, we then carried out field verification.
# Vibration Analysis of Piezoelectric Cantilever Beams with Bimodular Functionally-Graded Properties
###### Abstract  
Piezoelectric materials have been found to have many electromechanical applications in intelligent devices, generally in the form of the flexible cantilever element; thus, the analysis to the corresponding cantilever is of importance, especially when advanced mechanical properties of piezoelectric materials should be taken into account. In this study, the vibration problem of a piezoelectric cantilever beam with bimodular functionally-graded properties is solved via analytical and numerical methods. First, based on the equivalent modulus of elasticity, the analytical solution for vibration of the cantilever beam is easily derived. By the simplified mechanical model based on subarea in tension and compression, as well as on the layer-wise theory, the bimodular functionally-graded materials are numerically simulated; thus, the numerical solution of the problem studied is obtained. The comparison between the theoretical solution and numerical study is carried out, showing that the result is reliable. This study shows that the bimodular functionally-graded properties may change, to some extent, the dynamic response of the piezoelectric cantilever beam; however, the influence could be relatively small and unobvious.  
piezoelectric effect; bimodular model; functionally-graded materials; cantilever; vibration +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
The cells were grown in the incubator for 6 days, then the growth medium was removed, cell colonies were fixed with 70% ethanol for 10 min and stained using crystal violet solution. Afterwards, the cell colonies were scanned and counted using open-source imaging software ImageJ (National Institute of Health, Bethesda, MD, USA). The cell viability of control sample (cells not treated with electric pulses) was evaluated as 100%, and the viability of the experimental samples were compared to that of the control. Cell size measurements: Chinese hamster ovary (CHO) cells were grown at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) humidified incubator for 24 h prior to the measurement. Afterwards, cells were trypsinized, centrifuged (300 RCF) and resuspended in PBS at concentration of 2 \(\times\) 10\({}^{6}\) cells/mL and placed in Neubauer chamber. After 5 min incubation, cells were imaged using an inverted microscope (Nikon eclipse TS 100) with mounted camera (Moticam 2300/3.0M Pixel). Cell diameters were assessed by image]. At least 100 cells were counted to evaluate the cell size. Calculation of the distance between electroporated cell membrane and the nearest plasmid: The distance between the cell membrane and the nearest plasmid was evaluated as follows. First, the volume of all cells in solution was calculated. For this, the average radius of a cell (_R_) was estimated to be 9.47 \(\pm\) 0.09 \(\upmu\)m by imaging under inverted light microscope (Nikon eclipse TS 100) using Moticam 2300 camera and processing the images using open source software ImageJ (National Institute of Health, Bethesda, MD, USA). Then the volume of the cells in the sample was calculated by calculating the volume of a single spherical cell (_Vsphere_) and multiplying it by the number of cells in sample (_Ncells_) (Equation (2)):  
\[V_{cells}=V_{sphere}\times N_{cells}=4\pi\frac{R^{3}}{3}\times 90000 \tag{2}\]Then, the number of plasmids in the sample for each plasmid concentration was calculated using Equation (3):  
\[N_{plasmids}=c_{malor}\times N_{A}\times V_{solution} \tag{3}\]  
where \(N_{plasmids}\) is the number of plasmids in the solution, \(c_{malor}\) is the molar concentration of plasmid DNA (taking into the account that the molecular weight of pGFP plasmid is ~2,154,743 g/mol), NA is the Avogadro's number (equal to \(6.022\times 1023\)), and \(V_{solution}\) is the volume of the whole solution (50 \(\mu\)L in all of the experiments). Finally, assuming that the cells are evenly distributed in the solution and the plasmids are evenly distributed in the solution outside the cells, the distance (\(D\)) between the plasmid and its nearest object (cell membrane or another plasmid) can be calculated by Equation (4):  
\[D=\sqrt[3]{\frac{V_{solution}-V_{cells}}{N_{plasmids}}} \tag{4}\]  
Statistical analysis: Each experimental point was repeated at least 6 separate times. The error bars in graphs are represented as standard error of mean \(\pm\) SEM. The statistical significance between samples was calculated using one-way ANOVA.
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
## 3 Experiment Analysis
### Data Collection and Processing Configuration  
The experiment data is collected from 25 GPS-only stations of IGS network [27] and 75 GPS + BDS stations of Multi-GNSS Experiment network (MGEX) [28], which are illustrated in Figure 3. The data length is from the day of year (DOY) 121 to 152, 2017, with the sampling interval of 30 s. During this experiment period, two GPS satellites and five BDS GEO satellites experienced orbit maneuvers. The proposed algorithm has been implemented in Positioning and Navigation Data Analyst (PANDA) software for experiment data processing [29; 30].  
In POD processing, the code and phase observations on GPS L1/L2 and BDS B1/B2 are used to form the respective ionosphere-free combinations that are processed jointly. The reduced CODE five-parameter solar radiation pressure (SRP) model [31] is applied to all GPS and BDS satellites. For the antenna PCO and PCV of GPS and BDS satellites, we adopted the values provided by IGS. Table 1 lists some of the important configurations of dynamic models and observation models used in the POD experiment.
# Strategies for Enhancing Extracellular Electron Transfer in Environmental Biotechnology: A Review
## 2 Mechanism of Extracellular Electron Transport in Microorganisms
### Factors Affecting EET Efficiency  
EET is subject to the influence of multiple factors, encompassing temperature, pH levels, the existence of electron acceptors and donors, redox potential, as well as the properties of the electrode. Temperature affects the rate of chemical reactions and the availability of electron transfer [30]. High temperatures can enhance EET efficiency by increasing the rate of enzymatic reactions involved in electron transfer. The pH level is an essential determinant that affects both the activity of enzymes involved in EET and the proton gradient across cellular membranes. Enzymes involved in extracellular electron transfer often exhibit pH-dependent activity, and an increase in pH can disrupt the proton gradient and impede EET efficiency [31]. Electron acceptors and donors are also critical factors in EET efficiency. Oxygen is a highly favorable electron recipient because of its strong reduction potential and abundance in many natural environments [32]. In environments with a limited availability of electron acceptors, microorganisms may employ alternative  
Figure 1: Elucidating the extracellular electron transport pathways that are commonly observed in electroactive microorganisms. (**a**) DET facilitated by cytochrome proteins, (**b**) the process of DET facilitated through nanowires, (**c**) the mode of IET facilitated by electron shuttles, (**d**) the method of indirect electron transfer facilitated through exterior vesicles of the membrane (OMVs). Outer membrane (OM), interior membrane (IM), and periplasm (PP).  
strategies for EET. Organic substances like sugars, fatty acids, alcohols, and hydrogen gas are typical electron donors [33]. Redox potential is another crucial factor in EET processes. Microorganisms can utilize various electron acceptors with different redox potentials, such as solid metal oxides and metals like gold and platinum. The redox potential of these metal oxides influences their ability to accept electrons from microbial donors [34]. Electrode properties also play a significant role in EET efficiency. Conductive materials like graphite, carbon nanotubes, and metals like gold and platinum are often employed as electrodes because they possess strong electrical conductivity [35; 36]. Biochar, a representative carbon-based material, has also been shown to serve as an ES to mediate the DIET process of _Methanosarcina_ and _Geobacter_[37]. Thus, temperature, pH level, the presence of electron acceptors and donors, redox potential, and the characteristics of the electrodes are a few factors that generally affect the efficiency of EET.
## 5 Ultra-Wide Ionospheric Monitoring  
Oblique backscatter sounding is a powerful tool for large-area ionospheric detection. The WIOBSS-AA applies the backscatter technology to monitor the northwest ionosphere. The oblique backscatter swept-frequency ionograms recorded by the WIOBSS-AA provides the amplitudes of backscatter echoes with respect to group distance against operating frequency. Applications of the inversion algorithms to the backscatter ionograms can extract the useful information regarding the ionospheric electron density distribution along the propagation paths. Some oblique backscatter sounding results of WIOBSS-AA are shown in this section.
## 4 Analytical Framework for Heterogeneous Service Provisioning
### Problem Overview and Healthcare Service Requirements
#### 4.1.3 Approximation and Convex Transformation  
The utility-based optimization problem, as formulated in the previous subsections, includes a non-differentiable maximum function in the computation of the per-link service delivery rate:  
\[g_{s}^{t}=\max_{\{i|t\in T_{s,i}\}}g_{s,i}. \tag{10}\]  
This formulation poses a challenge for conventional convex optimization techniques due to the discontinuity in the max operator. To address this, we use a smooth approximation of the maximum function based on the \(n\)-norm, which is differentiable and converges to the maximum as \(n\rightarrow\infty\).  
We approximate Equation (10) as follows:  
\[g_{s}^{t}\approx\left(\sum_{\{i|t\in T_{s,i}\}}g_{s,i}^{n}\right)^{\frac{1}{n }}, \tag{11}\]  
where \(n\) is a large positive integer. This approximation retains smoothness while closely matching the original maximum value when \(n\) is sufficiently large. Substituting this into the constraint set, we redefine the link capacity constraint as:  
\[\sum_{s\in S}\left(\sum_{\{i|t\in T_{s,i}\}}g_{s,i}^{n}\right)^{\frac{1}{n}} \leq c_{t},\quad\forall t\in T. \tag{12}\]  
With this transformation, the optimization problem becomes convex, and standard solvers or learning-augmented techniques (e.g., primal-dual updates or neural approximators) can be employed to find optimal or near-optimal solutions.  
The updated delay-aware and smoothed optimization problem is now expressed as:  
\[\text{P4:}\quad\underset{g\geq 0}{\text{maximize}}\quad\sum_{s\in S}\sum_{i=1}^{n_ {s}}\left(\mathcal{U}_{\text{s}}(g_{s,i})-\varphi_{s}\sum_{t\in T_{s,i}}d_{s}(g_ {s,i}^{t})\right), \tag{13}\]  
\[\text{subject to}\quad\sum_{s\in S}\left(\sum_{\{i\in T_{s,i}\}}g_{s,i}^{n} \right)^{\frac{1}{2}}\leq c_{t},\quad\forall t\in T. \tag{14}\]  
This convex transformation enables efficient convergence and supports scalable implementation in dynamic and resource-constrained healthcare environments. The formulation is also amenable to integration with ML models, particularly for delay estimation and adaptive parameter tuning, which are discussed in the next subsection.
## 1 Introduction
At present, although there have been some SRB automatic recognition algorithms, most of the methods utilize radio telescopes to obtain observation data and then classify SRBs according to the spectra. As radio telescopes are expensive and sparsely distributed, we urgently need a method to efficiently detect SRBs in real time without utilizing radio telescopes. Since 2005, many studies have focused on the impact of SRBs on GNSS receiver observation data. However, there is a lack of specific algorithms or experimental verification to detect SRBs using its impact on GNSS receiver observation data. Despite the impact of SRBs on GNSS cannot be completely eliminated, some measures can be taken to reduce or alleviate the interference caused by SRBs. Furthermore, real-time and accurate identification of SRBs is an essential prerequisite for suppressing the interference. In this paper, intense L-band SRB events are detected using the fact that SRBs lead to a decrease in the \(C/N_{0}\) of several GNSS satellites over a large area of the Earth's surface. Other interference, such as ionospheric scintillation and multipath, may also cause a decrease in the \(C/N_{0}\), but this phenomenon does not simultaneously occur on multiple satellites over a large area of the sunlit hemisphere, which is a significant feature that distinguishes SRBs from other types of interference. During the detection process, the \"falling moments\" and \"rising moments\" are first selected to determine the valley period of a single satellite at a single monitoring station. Then, the detection of a single station is obtained by comparing multiple satellites over the station. Finally, the detection of intense L-band SRBs is calculated by comparing multiple stations. The proposed method, which does not rely on radio telescopes, has a high recognition rate and low false alarm rate, is low-cost, and offers all-weather real-time monitoring.
## 3 Interoperability Issues  
Among the various _Levels of Conceptual Interoperability_[36], in this study we analyze the technical and semantic interoperability (L1 and L3) because they are the most relevant for the purposes of domain integration in the geographical data field. In fact, the pragmatic, dynamic and fully conceptual interoperability reach a higher level of abstraction for our purposes and could be investigated for future developments.  
Moreover, to properly study the integration between the two standards, an in-depth analysis of their data structure is necessary.
## 4 Simulation Verification
### False Bit Rate and False Packet Rate
\[P_{p,i}=\sum_{i=1}^{N}\prod_{0}(1-\big{\{}1-P_{s}(\gamma_{i,d})P_{d}(2\gamma_ {s,i})\big{\}})f_{\gamma_{i}}(\gamma_{i,d})\cdot\prod_{j=1,j=i}^{N}F_{\gamma_{ j,d}}(\gamma_{i,d})d\gamma_{i,d} \tag{15}\]  
In the formula, \(Ppi\) is the packet loss rate of the ith channel for which the solution It is the sum of multiple links formed by multiple relay nodes. \(i\) = (1, 2, 3, \(\cdots\), n). An experiment to calculate the parameter values according to the above formula was conducted. A comparative experiment on signal-to-noise ratio and BER was organized. The comparison algorithm contains LCARQ, RACRQ, ORCARQ and MOARQ. The simulation results are shown in Figure 1.  
Figure 4 shows the BER simulation comparison of the four algorithms. The false bit rates of the four algorithms are simulated and calculated under different sig-nal-to-noise conditions. The four curves gradually decrease with increasing SNR, and the decrease values gradually increase for different SNRs. It can be seen that the BER of the signal  
is nonlinearly and negatively correlated with the BER during the signal retransmission. Because the coordinate system scales are different, the three numerical points are amplified. The difference between the signal-to-noise ratios of LCARQ and RARQ is 0.44 (0.106-0.066) can be seen in Figure 4a. The difference between the two decreases to 0.12 (0.046-0.0.024), seen in Figure 4b. The difference between the two de-creases to 0.06 (0.009-0.003) can be seen in Figure 4c. The three sets of data comparison differ in the comparison of the variability of different algorithms with the change of the signal-to-noise ratio (0.066-0.004) \(<\) (0.106-0.010). The results reflect that the change of the BER of the LCARQ algorithm is not significantly affected by the change of the signal-to-noise ratio. (0.06 \(<\) 0.09). Another finding of the experiment is that the interference between signals is enhanced. This finding is different from the experimental results of the existing literature [12; 15; 21]. For example, when the SNR is 20 db, the BER values of the four algorithms are between \(10^{-2}\) or \(10^{-3}\), which is hardly sufficient for real communication. This result is caused by the fact that the signals of the collaborative communication are mainly sent in broadcast mode. The effect of interference between signals is enhanced and the probability of signal distortion becomes larger. Broadcast communication is a major drawback of radio communication and the variability of experimental results cannot be avoided.