esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Linear Regression Algorithm against Device Diversity Problem
### Automatic Device-Transparent Algorithm for Crowdsourcing Indoor Localization System  
Based on the LR method, the device diversity problem can be solved. However, the LR method is applied to the premise that the coordinates of the RSS values are same. In offline training phase, the RSS values used to build the radio map have been labeled, so these RSS values meet the prerequisites for the LR method and the device diversity problem could be solved automatically. In online localization phase, the coordinates of the RSS values are unknown, which means the LR method cannot be  
Figure 5: Linear correlation between Lenovo and Huawei.  
Figure 6: Comparison of signal distribution.  
used directly. Therefore, we use the correlation ratio computed from the Pearson Product-moment correlation coefficient to roughly label the RSS values collected by an unknown device.  
\[t(r_{\mathcal{Y}},r_{\mathcal{X}})=\frac{\sum\limits_{k=1}^{m}{(r_{\mathcal{Y}k }-\bar{r}_{\mathcal{Y}})(r_{\mathcal{X}k}-\bar{r}_{\mathcal{X}})}}{\sqrt{\sum \limits_{k=1}^{m}{(r_{\mathcal{Y}k}-\bar{r}_{\mathcal{Y}})^{2}\sum\limits_{k=1 }^{m}{(r_{\mathcal{X}k}-\bar{r}_{\mathcal{X}})^{2}}}}} \tag{11}\]  
where \(m\) is the number of APs, \(r_{\mathcal{Y}k}\) and \(r_{\mathcal{X}k}\) are the RSS values measured from the \(k\)-th AP, \(\bar{r}_{\mathcal{Y}}=\frac{1}{m}\sum_{k=1}^{m}{r_{\mathcal{Y}k}}\) is the average of the RSS values from the tracking device and \(\bar{r}_{\mathcal{X}}=\frac{1}{m}\sum_{k=1}^{m}{r_{\mathcal{X}k}}\) is the mean of RSS values measured by the training device in a fingerprint.  
The range of the absolute value of Pearson correlation ratio is \((0,1)\) where \(1\) indicates the highest linear correlation between RSS values and \(0\) indicates the least similarity. In the online phase, when the RSS vector \(r_{\mathcal{Y}}\) is acquired, the similarity between the online point and all fingerprints \(r_{\mathcal{X}}\) in \(\mathbf{X}\) can be obtained by \(t\). Given a threshold \(t_{th}\), we can get the set of nearest neighbor fingerprints in radio map \(\mathbf{X}\) for \(r_{\mathcal{Y}}\).  
\[\mathbb{A}=\{r_{\mathcal{X}}\in\mathbf{X}|t(r_{\mathcal{X}},r_{\mathcal{Y}})> t_{th},0\leq t_{th}\leq 1\} \tag{12}\]  
Based on the nearest neighbors in Equation (12), the RSS data collected in the online phase can be labeled roughly and the LR method proposed in the previous section is used to train the mapping function.  
In summary, in the offline phase, because the coordinates of the collected RSS data are already known, the LR algorithm can be used to eliminate the device diversity problem directly. As a result, a uniform radio map can be built in the offline phase. In the online phase, the RSS values collected by the unknown device could be localized roughly by the Pearson correlation coefficient at the beginning. Then the RSS values can be mapped into the signal space of radio map using the LR algorithm. Finally, we can get a more accurate positioning result.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 4 Examining Noise in Quantum Computing
### Varieties of Errors in Quantum Computing  
We offer a detailed exploration and modeling of errors in quantum computing, noting that errors only manifest when their probability is not zero.  
1. Depolarizing error: Arises from a qubit's interaction with the environment or thermal shifts. It randomizes the qubit state based on coherence time and quantum gate exposure duration. In our tests, we emulate this by applying the depolarizing probability during the start of a stabilizer measurement. A single-qubit depolarizing error picks and applies a random Pauli error (X, Y, Z). Maximum mixing happens at 75% probability.
2. Gate error: Emanates from quantum gate applications due to factors like miscalibration or environmental noise. We model gate errors in two segments: single- and two-qubit Clifford operations. Each Clifford operation's execution considers the error probability. For two-qubit gate errors, a random pair of Pauli errors is applied, peaking the mixing at 93.75% probability.
3. Readout error: Occurs during qubit state measurements due to factors like detector inefficiencies. We model these errors as pre-measurement probabilistic operations, capturing the chance of X or Z flips.
4. Reset error: Happens if a qubit, usually initialized to the \(|0\rangle\) state, fails due to control issues or noise. We model these as post-reset probabilistic operations, applying potential X or Z flips based on the given probability.
5. Leakage error: In systems with multiple levels, these errors happen when a qubit shifts out of its computational space, often due to faulty gate operations.  
Crosstalk error: Occur when a qubit operation unintentionally affects another qubit, typically from close physical proximity or unwanted interactions.
## 1 Introduction
_Chlamydomonas reinhardtii_, a single-cell model microalga with complete genome sequencing and various types of mutant studies, is an ideal operational object for the genetic engineering of microalgae [12]. In May 2022, the National Health Commission of the People's Republic of China approved _Chlamydomonas reinhardtii_ (heterotrophic) as a new food material, indicating that commercialized _Chlamydomonas reinhardtii_ has entered a new hot flush. The protein content of _Chlamydomonas reinhardtii_ powder obtained via heterotrophic culture in fermenters was greater than 30% [13]. The carotenoid synthesis pathway of _Chlamydomonas reinhardtii_ is a non-mevalonate (MEP) pathway. Isopenoids, the main structural isopenroids, are synthesized from isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (Dmapp). IPP in turn condenses with Dmapp to generate geranylgeranyl diphosphate (GGPP) as a synthetic carotenoid substrate via geranyl diphosphate (GPP) and farnesyl diphosphate (FPP). Geranylgeranyl diphosphate GGPP) is sequentially converted into lycopene by phytoene synthase (PSY), phytoene dehydrogenase (PDS) and \(\zeta\)-carotene desaturase (\(ZDS\)). Lycopene is then synthesized separately into \(\alpha\)-carotene (lycopene \(\beta\) Cyclase and lycopene \(\varepsilon\) Cyclase) and \(\beta\)-carotene (lycopene \(\varepsilon\) Cyclase). The main products downstream of caroten are zeaxanthin and astaxanthin. Astaxanthin in _Chlamydomonas reinhardtii_ mainly passes through the \(\beta\)-carotenoid hydroxyles and \(\beta\)-carotenoid ketogenases for stepwise synthesis [14]. In _Chlamydomonas reinhardtii_, there have been numerous studies in which the directed engineering of carotenoids is performed for high-yield pigment producing algal strains. [PERSON] et al. [15], who accumulated zeaxanthin via the method of CRISPR RNP and added it to the feed of chickens, showed that zeaxanthin in eggs also increased markedly. [PERSON] et al. [16] overexpressed DnaJ such as the chaperone in _Chlamydomonas reinhardtii_, and the colonies produced a 1.9-fold increase in lutein and a 1.7-fold increase over that of wild-type \(\beta\)-Carotene, whereas other genes in the _Chlamydomonas reinhardtii_ carotenoid pathway, such as _ZDS_, lycopene epsilon cyclase (_LCYE_) and _BKT_ were all featured in overexpression studies indicating increased carotenoid production from colonies. Among these, \(\beta\)-carotenoid ketogenases are key enzymes in the astaxanthin synthetic pathway of _Chlamydomonas reinhardtii_. It has been shown that overexpression in _Haematococcus pluvialis_ can enhance astaxanthin production [17; 18]. However, different \(\beta\)-carotenoid ketogenase genes and the cultivation mode can cause the obtained strains to differ. Therefore, we overexpressed the endogenous _Chlamydomonas reinhardtii BKT_ gene and the exogenous _Pluffia rhodozyma BKT_ gene, and the colonies were heterotrophic, to explore the effects of overexpression and the metabolic pathway changes of the colonies.
## 4 Experimental Results and Performance Evaluation
### Throughput  
Throughput defined the average successfully data packets received at the destination through communication link over a period of second. The throughput is measure after a change in network events such as the increase in flow arrival or failure. Figure 3 demonstrates a performance comparison between the proposed solution RPSO and counterpart RRT and FRT. RPSO achieves 29.54% and 55.73% improved throughput performance as compared to FSL and RPF respectively. The merit is attributed to the adopted methodology (link quality, minimal critical switches) of the RPSO scheme makes a more informed decision regarding path selection. [PERSON] et al. [37], stress the need for reliable link quality for  
Figure 2: Average Path setup latency.  
data transmission. Poor link quality affects data transmission and declines in throughput. Therefore, RPSO showed better link quality estimation and thereby ensuring a high number of critical data packets reach the intended destination. This achievement is consistent even when the traffic flows arrival increased. However, the counterpart shown declined in the throughput, when traffic flow arrival increases with respect to time, a change of behavior was observed which in turn reduced the throughput performance on FSL and RPF.
## 5 Case Studies
### Analysis of Day-Ahead Scheduling Results  
Table 2 presents the probabilities of the five day-ahead scenarios. Based on these scenarios, a stochastic optimization method is used to solve the day-ahead scheduling  
Figure 4: Day-ahead and Intra-day Scenario Generation and Reduction for PV and Load.  
Figure 5: PV and Load Output in 2023.  
model, considering the uncertainties of PV and load. The scheduling results are shown in Figures 6 and 7. Figure 6 illustrates the results for the scenario with the highest probability, Scenario 3. During the early morning period (0:00-6:00), when the load is low and electricity prices are low, PV output is 0. The system primarily relies on power purchases from the upper-level grid to meet the load demand, and surplus power is used to charge the ESS. As time approaches the morning peak, gas turbine output gradually increases, and PV begins to contribute to the power supply. In the morning period (6:00-10:00), PV output gradually increases, reducing reliance on other power sources. During this period, external power purchases are minimized, and as the load rises, the DES clusters and ESS discharge to reduce system operating costs. During the midday period (10:00-14:00), PV maintains high output, and no external power purchases are needed. Surplus PV power is used to charge the ESS and DES clusters, improving the accommodation rate of renewable energy. In the evening peak period (17:00-21:00), electricity prices rise as demand increases, PV output decreases, and GT output ramps up. This is supplemented by discharges from the ESS, DES clusters, and external power purchases to meet the demand. Flexible scheduling ensures both economic efficiency and a reliable power supply. Figure 7 shows the scheduling results for the other four possible scenarios. Since day-ahead scheduling fixes the power purchase quantities, on/off states of GTs, and outputs of DES clusters to be used in the intra-day and real-time models, these parameters remain identical across all scenarios. In Scenarios 1 (20:00-21:00) and 4 (19:00-20:00), high load periods occur when GT output reaches its maximum limit, resulting in a small amount of load shedding. To meet the load demand, adjustments to power purchase quantities are needed in the intra-day stage. The scheduling results for Scenarios 2 and 5 are similar to Scenario 3 and are not further detailed here.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenarios** & **Probabilities** \\ \hline
1 & 0.01 \\
2 & 0.22 \\
3 & 0.455 \\
4 & 0.125 \\
5 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Probabilities of Day-Ahead PV Output and Load for Each Scenario.  
Figure 6: Day-ahead and Intra-day Scenario Generation and Reduction for PV and Load Scenario 3 Day\(-\)ahead Scheduling Results.
# Correction of Young's Modulus Calculation in the Flexural Mode of Resonant Column Test
## 1 Introduction  
Soil dynamic properties (including shear modulus \(G\) and Young's modulus \(E\)) and damping ratio are indispensable parameters in the dynamic analysis of ground responses or foundation structures subjected to earthquake motions. In situ tests, such as the spectral analysis of surface waves, down-hole method, or cross-hole method, are typically used to measure the shear and compression wave velocities of the soil layer, which are then converted to the dynamic properties of soil (e.g., \(G\) and \(E\)) under small strain. However, the dynamic characteristics of soil under different strains (i.e., shear modulus reduction curve and damping curve) are primarily measured by laboratory tests, such as dynamic triaxial, cyclic simple shear, resonant column, shaking table, and centrifuge modeling.  
The resonant column test, including torsional and flexural modes (Figure 1), is commonly used to measure the resonant frequencies of specimens to obtain soil dynamic properties. High-frequency torsional or flexural vibrations are applied to the specimen by electromagnetic drive discs (EDDs), and the torsional/flexural resonant frequencies are detected at the corresponding strain. On the basis of the measured resonant frequency, the \(G\) or \(E\) of the soil is determined using the calculation formula. In general, torsional mode is commonly used because \(G\) and its reduction curve are indispensable properties in the site response analysis for propagating horizontal ground motions [1; 2]. With the demand for the assessment of vertical ground movement under earthquake, \(E\) or constrained modulus and its reduction curve have become increasingly important [3; 4; 5].  
The formulas proposed by [PERSON] et al. [6] and documented in ASTM D4015 [7] can be used to calculate the \(G\) and \(E\) of the soil with torsional and flexural resonant frequencies, respectively. The torsional inertia of the EDD is considered in the calculation formula for torsional vibration mode to ensure the accurate evaluation of \(G\). Later studies further examined the counter electromotive effect [8; 9] and equipment effects [10; 11] on the measurement of stiffness and damping in soils. However, these studies focused on the effect in torsional vibration mode and the improvements to thecalibration in flexural vibration mode are limited. The calculation formula of flexural vibration in [6] only considers the mass inertia of the EDD but does not include the rotational inertia, thus possibly leading to inaccurate assessment for \(E\). Therefore, this study considers the rotational inertia effect of the EDD and corrects the formula of [PERSON] et al. [6] for the \(E\) calculation. The accuracy of the modified formula is verified through the testing of an aluminum calibration rod with known \(E\), \(G\), and Poisson's ratio (\(\
u\)).
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 3 Corrosion Prediction Model
### Probabilistic Model  
The probabilistic model is utilized for corrosion detection in scenarios where there is inadequate historical data and real-time data are available. Corrosion is a time-dependent and space-dependent process, thus, the probabilistic model must be able to ascertain the uncertain nature of materials. This model requires expertise and an in-depth knowledge of physics and mathematics to develop the sophisticated mathematical model. Consequently, this model is quite difficult to develop. However, the probabilistic model can be replaced by the fuzzy logic model if the available data are ambiguous and vague. The fuzzy logic model also requires professional experience and expert judgments in order to determine the rule set for corrosion detection.  
Gamma distribution [23] was used to investigate the cumulative deterioration of materials. The reliability of the corrosion progression was analyzed using a decreasing monotonic pattern. The number of counts was used to reconstruct a 2D image of the materials. The corrosion pattern can be analyzed using the reconstructed 2D image. Moreover, the gamma process and copulas [24] of the spatio-temporal aspects of the image were used to predict and detect the corrosion of the buried pipelines. The correlation length model calibrated the temporal and spatial correlations of the probabilistic model. The failure reliability can be estimated, and thus, corrosion progression can be detected. The probabilistic model and finite element [25] were used to study the effect of fly ash, the rebar diameter, and current density on the spatial variability of a concrete beam based on X-ray images. Gumbel distribution parameters were obtained from the spatial probabilistic corrosion model. The results showed that the uniform corrovisions in images can be detected with a high current density. Moreover, a low current density can detect non-uniform corrosion patterns such as large cracks and pits. The probabilistic prediction model managed to derive the accurate Gumbel distribution parameters from the steel weight loss. The Monte Carlo finite element [26] was used with the probabilistic framework to estimate the corrosion-induced cracks in images. The corrosion was predicted and assessed with an efficient computational approach and two machine learning models. The deterioration prediction of the structural capacity was demonstrated with this method. The accurate corrosion classification of the probabilistic model was further improved with linear population size reduction and image processing [27]. The automated pitting corrosion was detected with multilevel thresholding for the metal surface extraction of corrosion images. This method also managed to extract the corrosion features of multilevel color spaces, such as grayscale and color channels.
## 3 Results
### Sensory Evaluation
#### 3.4.2 QDA  
TDS effectively characterized the differences in retronasal aromas on different grades of FJ. In addition to the retronasal aroma, the orthonasal aroma is also an important aspect of Baijiu aroma quality. However, TDS cannot be used for the static sensory analysis and the characterization of the aroma attribute intensity. Therefore, QDA was selected to compare the orthonasal aroma differences of the samples. The retronasal aroma of roasted and earthy could hardly be perceived in QDA. In addition, \"sweet\" was a new attribute that referred to the aroma of sweet honey rather than sweetness in the taste. The radar chart showed (Figure 3A) that the four different grades of FJ had significant differences in all aroma attributes. The aroma profiles of F20 and F30 were relatively similar. PCA score plot (Figure 3B) demonstrated that there were significant differences between high-grade and low-grade samples, which were located on two sides of the coordinate axis. F00 and F10 mainly presented the aroma of grain and acidic, while high-grade F20 and F30 mainly exhibited the pleasant aroma of floral, fruity, sweet and grassy. The differences in sensory perception may be caused by the differences in aroma characteristics of base liquor used for blending. Some studies have proven that the aroma profile of Baijiu changes significantly during aging [11; 12].  
Figure 2: (**A**) TDS curves of four different grades of FJ. Notations: Chance level is defined as the dominance rate that an attribute can be obtained by chance, and its value is equal to the reciprocal of the attribute number. The significance level represents the minimum value the dominance rate should equal to be considered significantly higher than the chance level. It is calculated using the confidence interval of a binomial proportion based on a normal approximation. (**B**) PCA score plot based on the parameters that summarize the TDS curves of four samples. M = maximum dominance rate, T = time of the maximum dominance, D = Time interval when the dominance rate is \(\geq\)90% of the maximum dominance rate.
## 3 Results and Discussion
### Antibacterial Activity Is Also Dependent on the Length of Fermentation
#### 3.4.2 pH  
From Figure 3, we can observe that there are no major differences between the pH of the different treatments, with a tendency for a slight increase along the 10-day period. The reference, i.e., lettuce washed with distilled water, is the sample where the increase of pH is significantly (\(p<0.05\) i.e., \(p=8.8\times 10^{-6}\)) higher, from 6.16 to 7.00, but still less than a unit of variation for a storage period of ten days. [PERSON] et al. [8] reported a similar increase in pH in a study with lettuce and carrots and stated that this was a good index for the potential quality maintenance of the products.  
According to [PERSON] [47], vegetable products retain adequate quality in a pH range of 5-6.5. Our results show that samples treated with whey exceeded this value (6.57) only on day 9, and the samples treated with chlorine exceeded it on day 7 (6.75).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Days of Storage**} & \multicolumn{3}{c}{**O\({}_{2}\) (\%)**} & \multicolumn{3}{c}{**CO\({}_{2}\) (\%)**} \\ \cline{2-7}  & **Water** & **Chlorine** & **Whey** & **Water** & **Chlorine** & **Whey** \\ \hline Day 0 & \(19.79\pm 0.41\) & \(19.9\pm 0.61\) & \(19.4\pm 0.50\) & \(0.01\pm 0.04\) & \(0.00\pm 0.00\) & \(0.02\pm 0.07\) \\ Day 1 & \(19.31\pm 0.34\) & \(18.5\pm 0.86\) & \(18.0\pm 0.71\) & \(0.06\pm 0.09\) & \(0.43\pm 0.25\) & \(0.60\pm 0.32\) \\ Day 3 & \(19.44\pm 0.55\) & \(16.2\pm 2.31\) & \(17.5\pm 1.69\) & \(0.23\pm 0.32\) & \(0.95\pm 0.78\) & \(0.46\pm 0.49\) \\ Day 5 & \(19.22\pm 0.71\) & \(17.1\pm 1.68\) & \(17.7\pm 1.90\) & \(0.57\pm 0.46\) & \(0.96\pm 0.80\) & \(0.56\pm 0.61\) \\ Day 7 & \(19.02\pm 0.81\) & \(17.5\pm 1.80\) & \(17.8\pm 1.42\) & \(0.56\pm 0.47\) & \(0.93\pm 0.83\) & \(0.63\pm 0.58\) \\ Day 9 & \(18.97\pm 0.91\) & \(16.5\pm 1.86\) & \(17.8\pm 2.00\) & \(0.64\pm 0.54\) & \(1.33\pm 0.70\) & \(0.66\pm 1.01\) \\ Day 10 & \(19.20\pm 0.94\) & \(16.7\pm 1.18\) & \(17.3\pm 2.17\) & \(0.61\pm 0.57\) & \(1.33\pm 0.52\) & \(0.78\pm 0.78\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Oxygen and carbon dioxide measurements for packed sanitized shredded lettuce with different sanitizer treatments, over 10 days in cold storage.  
Figure 3: The pH measurements of the lettuce after sanitizer treatments, over 10 days in cold storage. Results represent the average of at least three replicate experiments (n = 3) \(\pm\) SD.