esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Photocatalytic Activity Experiments  
Photocatalytic decomposition capabilities of the synthesized and investigated samples were evaluated under UV-light illumination by taking Tartrazine (C\({}_{16}\)H\({}_{9}\)N\({}_{4}\)Na\({}_{3}\)O\({}_{9}\)S\({}_{2}\), \(\lambda_{\rm max}\) = 427 nm, \(\geq\) 85%; ACROS organics (Waltham, MA, USA)) as the model dye pollutant (Acid Yellow-23). The pollutant concentration after irradiation and the optical absorption spectra were measured using the Evolution 300 UV-VIS spectrophotometer (Thermo Scientific, 50-60 Hz, 150 VA, Madison, WI, USA) with a wavelength range from 300 to 600 nm. The initial concentration of the organic pollutant was 7.5 ppm. For the photocatalytic reaction, a glass cylindrical vessel with a volume of 200 mL was used, which was equipped with a electromagnetic stirrer and a UV lamp that was placed above it. The duration of the dark period was 15 min. The degradation of the pollutant during the photocatalytic process was investigated using UV-Vis absorption spectroscopy, with aliquot samples (2 mL) taken at specific time intervals. The aliquot samples of the photocatalysis with the powders were passed through a microfilter to remove the suspension before measurement with the spectrophotometer. Over the irradiation time, a decrease in the intensity of the dye's absorption spectra in the solution was observed, and the degree of degradation was calculated using the following equation: \(D\%\) = ((\(C_{0}-C)/C_{0}\)) \(\times\) 100, where \(C\) represents the sample concentration, and \(C_{0}\) is the initial concentration. This allows us to determine the extent of Tartrazine degradation.  
The experiments were conducted in two series--powders annealed at 500 and 700 \({}^{\circ}\)C. All the photocatalytic tests were performed with distilled water at a constant stirring speed (200 rpm) and room temperature (23 \(\pm\) 2 \({}^{\circ}\)C).  
Additionally, experiments conducted without the presence (photolysis) of a photocatalyst revealed no evidence of direct dye degradation.
## 3 Supervised Learning of Inter-Floor Noises
### Position Classification Results  
The inter-floor noises in the dataset were labeled into the following \((C=9)\) position categories: 1F0m, 1F6m, 1F12m, 2F0m, 2F6m, 2F12m, 3F0m, 3F6m, and 3F12m. The weights of the three CNNs with estimated optimal parameters \(\mathcal{M}_{\hat{\lambda}_{\text{position}}}\) were optimized to minimize \(L\) using GD for 50 epochs. This process is sufficient to minimize \(\psi^{\text{(valid)}}(\hat{\lambda}_{\text{position}})\).  
The accuracy of position classification with the three CNNs was evaluated using 5-fold cross validation. The accuracies are arranged in Table 3. The first column of the table shows the name of the three CNNs, and the first row of the table shows the 9 categories.  
In position classification, VGG16 outperforms the other models, except for categories 2F and 3F0m. All models show comparatively poor performance for positions 1F0m and 1F6m. The models seem to confuse these two positions. The confusion in the position classification can be seen in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **MB** & **HD** & **HH** & **CD** & **VC** & **Average** \\ \hline AlexNet & 98.7 & **99.1** & **99.8** & 99.6 & **100** & 99.3 \\ VGG16 & **99.3** & **99.1** & **99.8** & **99.8** & **100** & **99.5** \\ ResNet V1 50 & 95.8 & 95.8 & 97.6 & 96.4 & 96.7 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Type classification accuracy (%) with the three CNNs.  
Figure 5: Confusion matrices of the position classification results (%). (**a**) AlexNet, (**b**) VGG16, and (**c**) ResNet V1 50.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **1F0m** & **1F6m** & **1F12m** & **2F0m** & **2F6m** & **2F12m** & **3F0m** & **3F6m** & **3F12m** & **Average** \\ \hline AlexNet & 85.5 & 79.5 & 95.0 & **100** & **98.0** & **99.2** & **96.0** & 92.0 & 95.5 & 93.8 \\ VGG16 & **86.0** & **89.5** & **96.5** & **100** & 96.0 & 98.4 & 95.5 & **95.0** & **99.0** & **95.3** \\ ResNet V1 50 & 80.0 & 73.0 & 86.5 & 92.4 & 86.4 & 92.8 & 87.5 & 82.0 & 87.0 & 85.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Position classification accuracy (%) with the three CNNs.  
If confusion between positions on the same floor are ignored, the classification accuracy with the three CNNs increases. Table 4 shows the floor classification accuracy with the three CNNs. VGG16 shows an accuracy of 99.5% for floor classification.  
Summarizing the results, our approach to type/position classification based on the three adapted CNNs with knowledge transfer were compared. The CNNs showed feasibility on type/position classification of inter-floor noises in the building. VGG16 showed the best performance on both type/position classification.
## 2 Materials and Methods  
### Protocol and Eligibility Criteria  
This systematic review conforms to the guidelines set forth in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, as specified in reference [16]. Our systematic review was executed with meticulous attention to the PRISMA Statement's research methodologies. We collected exhaustive data through rigorous analysis and transparent reporting, resulting in a comprehensive and objective evaluation of the subject under investigation. However, it should be noted that the protocol for this systematic review was not registered in a public registry.  
Question: is there a difference in the treatment of multiple root coverage between collagen matrix and connective tissue?  
Participants: patients who received multiple root coverage.  
Interventions: collagen matrix.  
Comparisons: subepithelial connective tissue obtained from the palate.  
Outcomes: root coverage, change in probing depth, and change in keratinized tissue width after 3 and 6 months, and 1, 3, 5, and 9 years.  
Study design: randomized controlled trials (RCTs) limited to studies focusing on adults.  
The criteria for inclusion and exclusion are detailed below.
* [noitemsep,topsep=0 pt]
* Studies involving collagen matrix and subepithelial connective tissue;
* Cases with multiple recessions;
* Studies focused on natural teeth;
* Randomized controlled trials (RCTs).
* Exclusion Criteria:
* Patients who have undergone implant procedures;
* Patients with dentures;
* Studies on root coverage that do not involve the use of either collagen matrix or subepithelial connective tissue;
* Cases involving a single gingival recession.
# Effect of pH Change on the Microalgae-Based Biogas Upgrading Process
###### Abstract  
An alternative way to remove CO\({}_{2}\) from biogas is the use of photosynthetic microorganisms, such as microalgae. This can be achieved by the operation of an open photobioreactor, connected with a mass transfer column, such as a counterflow column. This technology provides up-graded biogas with high quality. The microalgal uptake of CO\({}_{2}\) from the biogas in counterflow columns generates pH changes in microalgae culture. To clarify the potential effect of these dynamic pH conditions in the culture, the effect of pH change on the photosynthetic activity and PSII quantum yield was studied for microalgae _Chlorella sonkiniana_. Thus, assays were carried out, where the pH drop reported in the counterflow columns was replicated in batch microalgae culture through HCl addition and CO\({}_{2}\) injection, moving the culture pH from 7.0 to 5.0 and from 7.0 to 5.8, respectively. Moreover, the effect of light/darkness on photosynthetic activity was tested when the pH decreased. The results obtained in this research showed that the photosynthetic activity decreased for the light conditions when the pH was shifted by HCl addition and CO\({}_{2}\) injection. Despite this, the value of the PSII quantum yield remained at 0.6-0.7, which means that the microalgae culture did not suffer a negative effect on the photosynthetic system of cells because a high value of PSII efficiency remained. In the same way, the results indicated that when the pH change was corrected, the photosynthetic activity recovered. Moreover, the apparent affinity constant for dissolved inorganic carbon (KDIC) was 0.9 uM at pH 5 and 112.0 uM at pH 7, which suggests that the preferred carbon source for _C.sorbraina_ is CO\({}_{2}\). Finally, all the results obtained indicated that the pH drop in the counter-flow column for biogas upgrading did not cause permanent damage to the photosynthetic system, and the decrease in the photosynthetic activity as a result of the pH drop can be recovered when the pH is corrected.  
biogas upgrading; microalgae; carbon sequestration; pH +
Footnote †: journal: _Sciences_
## 2 Data Quality Characteristic Analysis
### Data Description  
We collected datasets at a 1 Hz sampling rate in both static and dynamic scenarios, each of which featured two distinct environments. In the static scenario, we collected observations for approximately 2 h on 13 June 2023, encompassing both open-sky and complex environments. In the dynamic scenario, we gathered observations in both typical and complex urban environments on 14 June 2023, with a data collection time of roughly 30 min. All of these datasets were obtained at the China University of Mining and Technology in Xuzhou, China. It is important to emphasize that HP40 lacks the ability to output _ReceivedSvTimeNanos_ data for B1C and B2a, which is essential for pseudo-range calculation. Conversely, Mi11 lacks the ability to output _AccumulatedDeltaRangeMeters_ data for all frequencies, which is crucial for carrier-phase calculation. Consequently, pseudo-range data for B1C and B2a in the HP40 could not be obtained.  
Figure 1a illustrates the locations of the static data. On the left, the smartphones were located in a challenging environment surrounded by trees, while on the right, they were situated in an open-sky environment atop a building. Figure 1b shows the trajectories of the dynamic data. In the figure, the red track indicates a typical urban environment with a wide road and trees on both sides, although the obstruction is not extensive. On the other hand, the green track represents a complex urban environment with narrow roads, tightly sheltered trees, and buildings nearby. Throughout the experiments, reference positions were provided by the geodetic receiver, namely the CHC i70.
# An Improved Real-Time Contrasts Control Chart Using Novelty Detection and Variable Importance
## 2 Real-Time Contrast Control Chart
### Real-Time Contrast (RTC)  
The characteristics of RTC control chart differ from those of a traditional control chart, specifically, in the application of the sequential hypothesis test. An RTC control chart is a type of sequential classification. This method defines reference data and real-time contrasts to classify each other in real time. The RTC control chart generates monitoring statistics by comparing reference data \(S_{0}\) and real-time contrasts \(S_{\rm w}\). \(S_{0}\) consists of the normal state of Phase I. The size of \(S_{0}\) is \(N_{0}\), while \(S_{\rm w}(t)\) is the contrasts containing the most recently observed data. \(S_{\rm w}(t)\) is constructed by applying the concept of a moving window. This involves setting the window size \(N_{\rm w}\) and constructing a contrast by moving the window to include the most recent observations. In the RTC control chart, \(S_{0}\) is set to class 0 while \(S_{\rm w}(t)\) is set to class 1 to learn the classifier. The classifier learns whenever the new \(S_{\rm w}(t)\) is created. If \(S_{\rm w}(t)\) consists of the normal state observations, it is difficult to classify each class. However, when \(S_{\rm w}(t)\) is composed of abnormal state observations, it utilizes the feature whereby the classification of decision boundary is clear and easy to classify. The probability that an arbitrary observation \(x_{i}\) until time \(t\) is classified as class \(k\) (\(k\) = 0, 1) is expressed as \(\hat{p}_{k}(x_{i})\).  
\[p(S_{0},t)=\frac{\sum_{x_{i}\in S_{0}}\hat{p}_{0}(x_{i}|t)}{N_{0}},\;\text{for }x_ {i}\in S_{0} \tag{1}\]  
\[p(S_{w},t)=\frac{\sum_{x_{i}\in S_{w}(t)}\hat{p}_{1}(x_{i}|t)}{N_{w}},\;\text{ for }x_{i}\in S_{w}(t) \tag{2}\]  
It can be seen that Equations (1) and (2) are affected by the size of the reference data and the size of the sliding moving window, respectively. Furthermore, \(N_{0}\) should be set such that it is much larger than \(N_{\rm w}\). Consequently, \(p(S_{0},t)\) is sensitive and its stable detection performance is better than \(p(S_{w},t)\). For this reason, in this study, we used only \(p(S_{0},t)\). Figure 1 illustrates the process of the RTC control chart.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 7 TN Domain
### Transport Technologies
Figure 16 shows the current protocol structure for the O-RAN fronthaul interface. As seen, the stack allows DU and RU to exchange (i) signalling information; (ii) user plane information, based on frequency-domain IQ samples; (iii) timing and synchronization information; and (iv) management plane information. However, no slicing related data is yet supported. The challenge that industry shall solve is how to encapsulate S-NSSAI (3 GPP signalling information) in the O-RAN fronthaul interface, considering that this interface is out of the 3 GPP specifications. O-RAN Working Group 4 is working towards this direction, though normative solutions (and therefore commercial solutions) are still far away.
## 5 Security Analysis
### BPR Model Analysis
These layers make guessing \(P_{ID_{O}}\) from leaked server-side data computationally infeasible. If \(\mathcal{A}\) queries \"Test\" on a fresh session where it does not know \(P_{ID_{O}}\), the game simulator directly provides a truly random string, and the adversary cannot distinguish this from the real key. The only way \(\mathcal{A}\) can distinguish this game from \(G_{3}\) is by successfully guessing \(P_{ID_{O}}\) (without corruption) or by breaking the KDF (which is covered in \(G_{1}\)). The probability of guessing \(P_{ID_{O}}\) from available information (without corruption) is negligible, denoted as \(P_{guess}\). Therefore, \(|Adv_{G_{3}}(\mathcal{A})-Adv_{G_{4}}(\mathcal{A})|\leq P_{guess}\). * **Game**\(G_{5}\) (final game): In this final game, the output of the 'Test' query for any fresh instance is always a truly random string. Since the adversary cannot distinguish this from the real session key (as established in previous games), its advantage in this game is \(\Pr[\mathcal{A}\text{ wins in }G_{5}]=1/2\), which means \(Adv_{G_{5}}(\mathcal{A})=0\). Combining these, \(Adv_{\text{Bio-2 FA-10D}}^{AKE}(\mathcal{A})\leq\epsilon_{FE}+\epsilon_{SymEnc} +P_{guess}+q_{H}^{2}/2^{|H|_{output}}+q_{KDF}^{2}/2^{|KDF|_{range}}\). If \(P_{ID_{O}}\) is chosen from a sufficiently large space or effectively protected by \(K_{\beta\O}\) (which itself has high entropy), then \(P_{guess}\) is negligible. Thus, if the underlying cryptographic primitives and the fuzzy extractor are secure, the session key \(KS\) derived by Bio-2 FA-10D is AKE-secure.
## 3 Results
### Load-Displacement Characteristics
As is apparent from Figure 7a-c, the load-displacement curves for tensile anchor rods display a two-stage distribution. If the applied load is below the anchor rod's ultimate pullout capacity, displacement increases gradually, while the load-carrying capacity rises rapidly. As the failure load is approached, a noticeable trend of a slower increase in load becomes apparent, and the displacement exhibits a sharp and distinctive steep rise. This similarity aligns with the findings of [PERSON] et al. [37] and [PERSON] et al. [38] for tensile-type anchors, but differs from the recent results of GFRP anchors. According to [PERSON] et al. [8], this phenomenon is attributed to the elastic modulus of the anchor material. On the other hand, the load-displacement curve of the composite anchor exhibits small slope changes before and after failure, with slow displacement increase post-failure, indicating that it still maintains a certain bearing capacity. Consequently, as an engineering anchor, the composite anchor demonstrates superior deformation adaptability, and its failure exhibits a certain level of predictability.
## 2 Data and Methods
### Data Processing  
C-responses can be estimated by the bounded influence of the remote reference processing (BIRRP) method [33]. The method has the capability to eliminate the interference contained in \(H_{r}\) and \(H_{\theta}\) and the correlated noise from both. The remote reference method is used in BIRRP, but the results critically rely on the quality of observed data collected in the remote reference observatory [34; 35]. The self-reference method is a wonderful alternative, as its estimation has proven to be closely related to that of the remote reference method for the C-responses used in the present work [21]. Therefore, in this paper, geomagnetic fields are processed using the BIRRP method with self-reference. The hourly mean values time series of three components of the geomagnetic field can be obtained from the World Data Center. Due to the \(L_{1}\)-norm inversion used in this paper, we can use as many stations as possible in the following inversion. Therefore, after selection according to the duration and noise, as we presented before [3], we determined 50 geomagnetic observatories in North Africa and Southern Europe for further consideration (Figure 1, Table 1). In order to ensure the data quality, only those recorded after 1950 are used. The length of the selected time series at stations can reach 72 years (1950-2021). C-responses and their errors within 16 periods logarithmically ranging from 3.5 to 113 days at the selected stations are estimated and displayed in Figure 2. The variation in responses at different stations indicates that the electrical structure beneath the area is heterogeneous.
# Analysis and Optimization of Output Low-Pass Filter
## 4 Design Trade-Offs
### Effect of fc Variation  
The THD of the output current is shown in Figure19a at different levels of irradiance. The cutoff frequency \(f_{c}\) was set to 5, 10, and 20 times the fundamental frequency. As shown, a cutoff frequency of 250 Hz causes the inductance \(L_{n}\) to increase, which in turn causes the characteristic impedance \(Z_{0}\) to rise. This is similar to what happened when \(C_{n}\) = 0.05 pu in Figure 18a. The results shown in Figure 19a suggest that the performance of the low-pass filter decreases when the \(f_{c}\) gets closer to the switching frequency \(f_{sw}\). In these cases, the filter allows more high-frequency components with larger amplitudes to pass through, leading to an increase in the total harmonic distortion of the output current at the rated power. On the other hand, as the filter introduces less phase delay at higher cutoff frequencies, the harmonic content decreases. This observation is crucial since there is an inverse relationship between phase delay and the filter's cutoff frequency. Figure 19b also shows that using smaller \(f_{c}\) values might not meet grid needs because they cause more phase delay, even when phase advance \(\varphi\) is added. Therefore, adjusting the cutoff frequency not only controls the harmonic distortion but also influences the timing characteristics of the filter, impacting the overall performance and efficiency of the power system.  
For the 250 Hz cutoff frequency, the power dissipation \(P_{d}\) is relatively high, around 3%, especially when compared to the higher cutoff frequencies shown in Figure 19c. This increased \(P_{d}\) is a result of the elevated \(Z_{0}\), which is a similar situation with \(C_{n}\) = 0.05 pu in Figure 18c. However, in this particular case, the power dissipation is noted to be doubled. This emphasizes the need for careful consideration of the cutoff frequency and the associated parameters to optimize filter performance in practical applications.  
Figure 18: The output power of the CSI varies with (**a**) output current THD, (**b**) power factor, and (**c**) damping resistance power loss (\(P_{d}\)) as the normalized capacitance is changed for different irradiances. Here, \(Q=4\) and \(f_{c}=500\) Hz.
## 5 Deep Learning Algorithm Model Employment
Therefore, DNN algorithms are commonly used for accident- and risk-related prediction and classification [56; 57]. Hence, while considering the characteristics of the input and output variables, this paper proposes a model framework for forecasting financial losses stemming from weather-related railway accidents using the DNN algorithm. The DNN algorithm is composed of several layers to extract various features from complex datasets, and it learns nonlinear relationships through a multi-layer structure to more effectively model complex interactions between weather factors and railroad accidents. It is also possible to exercise the optimal function of DNN algorithm selection for the collected data set. DNN algorithms can process large numbers of data, and they can also process large numbers of weather data and railroad accident records to enable more accurate predictions. Moreover, since DNN learns non-linear relationships, it can improve prediction performance by comprehensively understanding various interactions and patterns between weather factors and railroad accidents. The developed model was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values. MAE and RMSE are symbolic measures of the evaluation of artificial neural network models, and they are used to capture a model's prediction accuracy by calculating the deviation between the predicted and actual result values [46]. For example, MAE represents the absolute value obtained by averaging the difference between the projected and actual values of the model; the lower the MAE value, the lesser the prediction error. Meanwhile, RMSE measures the difference between the predicted and actual values of a model on a scale; the lower the RMSE value, the more accurate the prediction. For data preprocessing, the accuracy and completeness of the data was maintained through a data integrity check to ensure that the model's training and prediction are reliable. Missing values or outliers were identified and corrected, duplicate data identified, and data types and data labels checked. After the data integrity check, the input data were normalized through the z-score normalization method. The collected data were also split into three categories of training data, validation data, and test data: 70% of the data was used as training data, with 30% of that being designated as validation data. The remaining 30% of the data was used as test data.
## 5 Optimal Dispatch Model for DN
### Constraints of the Optimal Dispatch Model
#### 5.2.4 Second-Order Cone Relaxation and Linearization of Constraints  
Equation (40) contains the product term of voltage and current. In order to improve the efficiency of the solution, (40) is first relaxed to the following form [32]:  
\[\left(P_{i,t}^{\text{L}}\right)^{2}+\left(Q_{i,t}^{\text{L}}\right)^{2}\leq U_ {i,t}^{2}I_{i,t}^{2},\;\forall i\in N^{\text{D}},\;\forall ij\in L^{\text{D}} \tag{47}\]  
Further, (47) is transformed into a second order cone constraint as shown in (48):  
\[\|[2P_{ij,t}^{\text{L}},\quad 2Q_{ij,t}^{\text{L}},\quad I_{i,j,t}^{2}-U_{i,t}^{ 2}]^{\top}\|\leq I_{i,t}^{2}+U_{i,t}^{2},\;\forall i\in N^{\text{D}},\;\forall ij \in L^{\text{D}} \tag{48}\]  
In addition, (41) contains the squared terms of active and reactive power; in order to improve the solution efficiency, (41) is linearized based on the quadratic constraint linearization method, and two square constraints (49) are employed to substitute for (41) [32].  
\[\left\{\begin{array}{l}-S_{ij}\leq P_{ij,t}^{\text{L}}\leq S_{ij}\\ -S_{ij}\leq Q_{ij,t}^{\text{L}}\leq S_{ij}\\ -\sqrt{2}S_{ij}\leq P_{ij,t}^{\text{L}}+Q_{ij,t}^{\text{L}}\leq\sqrt{2}S_{ij}\\ -\sqrt{2}S_{ij}\leq P_{ij,t}^{\text{L}}-Q_{ij,t}^{\text{L}}\leq\sqrt{2}S_{ij} \end{array}\right.,\;\forall ij\in L^{\text{D}} \tag{49}\]