esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
## 4 Numerical Experiments
### Impact of the Correlation Structure of the Arrival Process  
Next, we conducted some experiments to clarify whether the presence of correlations (SRD or LRD) in the arrival pattern induces any changes in the performance metrics. We used as input traffic the occupancy process of the a M/G/\(\infty\) queuing system, as described in Section 3.2. Recall that the marginal distribution is Poissonian.
## 1 Introduction
We redesigned the original duffing system by adjusting the frequency of its internal periodic driving force and put forward the adjusting zero value (AZV) method to estimate the carrier frequency of the weak PRBC-PAM signal, which significantly improves the estimation accuracy and stability at very low SNRs. Figure 1: The measurement and estimation scenario of PRBC–PAM signal. 2. To obtain a high-accuracy estimation of the pseudorandom sequence of the weak PRBC-PAM signal, the chaotic state ratio (CSR) method is proposed by analyzing the influence of the pulse signal duty ratio on the chaotic states of three-valued signals. The experiment results show a low bit error rate at an SNR of \(-30\) dB, which verifies the estimation accuracy of the chaotic state ratio (CSR) method. 3. The experiments were conducted under different SNRs, which mainly consists of the prototype of the PRBC-PAM sensor and the signal acquisition system. The estimation results of different methods show that the proposed method is more stable at a higher level under different SNRs. The NRMSE still reaches 0.01 at \(-30\) dB SNR, which is superior to the other methods. The overall structure of this paper is as follows. Section 2 demonstrates the Duffing oscillator system for weak signal detection, estimated using both the standard sinusoidal signal and the PRBC-PAM signal. Section 3 presents the AZV and CSR methods for estimating the carrier frequency and the pseudorandom sequence of the PRBC-PAM signal. In Section 4, the parameter estimation method described in this paper is simulated and verified under various SNRs. Finally, Section 5 concludes the study and summarizes the key findings.
###### Abstract  
The removal of both cation and anion contaminants in solution typically requires separate processes or multiple materials, resulting in added complexity and higher operational costs. A cost effective and environmental friendly hybrid adsorbent material has been developed for the removal of Cu(II) and phosphate from the solution. Ferric hydroxide (FHO) was prepared by precipitation methods and then dissolved alginate, a biopolymer, was coated on the surface of the FHO particles to generated FHO-A. In the preparation of FHO-A, the alginate concentration is a critical factor in keeping the FHO-A particles suspended in solution where high concentrations of alginate act as a stabilizer and the FHO-A particles are not settled by gravity. The Cu(II) removal efficiency was wholly dependent on the concentration of alginate via the interaction with the polymer carboxyl groups and was not influenced by the concentration of FHO. Conversely, alginate was not involved in the removal of phosphate and instead, FHO was found to be critical for phosphate removal through electrostatic forces. According to FTIR, the new peaks at 1394 and 1593 cm\({}^{-1}\) after coating indicated that the alginate was appended to the surface of the FHO. The shift of peak from 1593 to 1588 cm\({}^{-1}\) after Cu(II) adsorption confirms the presence of a Cu-carboxylate interaction. The adsorption of Cu(II) was completed within 5 min, which is very similar to nanoparticle mediated sorption processes. In comparison to Cu(II), the presence of alginate retards the phosphate removal rate. Further, pH dependence was observed in the process, where increasing pH results in increased Cu(II) and decreased phosphate removal rates due to alginate deprotonation and the surface charge effects, respectively. The slowed phosphate sorption rate and shifted peaks in the FTIR spectrum confirmed that the layer of alginate was coated on the FHO particle.  
FHO; alginate; adsorption; Cu(II); phosphate +
Footnote †: journal: Applied Physics Letters
## 2 Modeling the Problem under Study
### Micro-Network Performance  
For a distribution system consisting of several microgrids with distributed generation sources, it is necessary to recalculate the index of unsupplied energy. A microgrid was utilized to assess the reliability indicators. With such a design, in the event of an error in the microgrid, the exact part is turned off and separated from the rest of the system. This error causes the downstream microgrid to shut down unless there is enough scattered generation in other microgrids to supply loads. In the design phase, to minimize blackouts, the microgrid's unsupplied energy, which is equal to the base value plus the unsupplied amount of energy, results from a mismatch between load and output. In other words, in addition to the amount of unsecured power of the microgrid, a positive number is added as a penalty coefficient. Therefore, the unsupplied energy and the amount and hour of unsupplied power for the microgrid can be calculated as follows [4]:  
\[ENS_{p\_MG} =ENS_{P\_MG}\bigg{|}_{self}+(1-p_{MG})\times\sum_{i=1}^{us}ENS_{Pi} \tag{21}\] \[ENS_{Q\_MG} =ENS_{Q\_MG}\bigg{|}_{self}+(1-p_{MG})\times\sum_{i=1}^{us}ENS_{Qi}\] (22) \[EVS_{P\_MG} =EVS_{P\_MG}\bigg{|}_{self}+(1-p_{MG})\times\sum_{i=1}^{us}EVS_{Pi}\] (23) \[EVS_{Q\_MG} =EVS_{Q\_MG}\bigg{|}_{self}+(1-p_{MG})\times\sum_{i=1}^{us}EVS_{Qi} \tag{24}\]  
The subtitle represents the microgrid index, \(p_{mg}\) probability number is the part of the year in which the output of the microgrid is more than the load, and it is calculated by using the probability states of the output-load. The general process of assessing reliability includes the following steps:  
Step 1: Enter network information and its components, such as reliability data \(ENS_{p}\) and \(VNS_{p}\) network parameters.  
Step 2: Calculate the amount of local compensated reactive power and the new value of feeder failure rate.  
Step 3: If \(Pi\) is bigger than \(Pdi\), the algorithm goes to the next step; otherwise, the difference between the output and loads is calculated, updated, and completed.  
Step 4: If \(Qi\) is greater than \(Qdi\), the algorithm goes to the next step; otherwise, the difference between the output and the proportional active and reactive load in all buses is calculated, updated, and completed.  
Step 5: Calculate system reliability indicators.  
Step 6: The planning problem compensates for the lack of active and reactive power by choosing the location and type of scattered products until the indicators reach their minimum value.  
Step 7: If all the conditions are considered, the algorithm transfer to another step; otherwise, it transfers to 3 for the next state.  
Step 8: Print the results.
## 7 Results and Discussion  
Through the review of previous studies and research articles, we could conclude that there is significant potential for adapting this concept outside of its native economic sectors. Most researchers believe that digital technology is in a period of rapid and increasing  
Figure 1: Digital twins for construction projects (DTCP) model, Stage 3.  
development and that digital twin systems can support many management strategies for companies [7].  
Digital twins have many advantages, as indicated by [5], and can be used in various domains, such as the automotive industry, health care, natural resources exploitation, smart cities, etc. However, a significant number of challenges remain for fully digitalized industries, even in fields with more mature results than the construction sector, which range from the visualization of the entire system and its relations to checking the results of the physical model and verifying the correctness of the processes, including operations, safety and maintenance, and the fully developed artificial intelligence that commands the digital model [33; 34; 35; 36; 37; 38; 39]. In the same context, digital twins in the construction industry have a lot to learn from similar solutions for other fields in which work cannot stop, such as aviation and aircraft maintenance [33; 36; 37; 38]. Today, companies implement projects by relying on digital charts and graphs and using data from measuring devices to support project leadership [37], but the visualization options of digital twins that use digital native data are significantly increased, allowing for full immersion and virtually validated decisions. Companies are preparing to introduce digital technology into many industries that are related to construction, such as work site administration, building operation and real estate. Whether by exploiting large databases [33; 34; 38] or using augmented reality to make sense of collected data, digital twins are considered to be among the tools that can be used in complex decision-making processes, from planning to implementation processes that can be carried out in a timelier manner in construction projects.  
Moreover, other domains with successful digital twin implementation, such traffic management and energy consumption monitoring [39; 40], demonstrate that a dynamic location, such as a construction site, can be successfully administered by a digital system. So far, many of these overarching objectives have not been implemented for the construction industry, hence the need to define the three-stage approach proposed in this paper.
## 3 Results
### Purification of Cellulase  
In DEAE Sepharose Fast Flow chromatography of the protein sample prepared by ammonium sulfate precipitation of the J3-9 culture broth, most of the enzyme activity was detected in the fraction eluted with 100 mM NaCl. The active fractions were concentrated from 15 mL to 1 mL and applied to a Superdex 75 column. The two fractions (fraction no. 11 and 12) that showed the highest enzyme activity and a single protein band on SDS-PAGE were combined (Figure 4A) and further analyzed. The purification yields for each step are summarized in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Purification Step** & **Volume (mL)** & \begin{tabular}{c} **Total Protein** \\ **(mg)** \\ \end{tabular} & \begin{tabular}{c} **Total Activity** \\ **(U)** \\ \end{tabular} & \
\begin{tabular}{c} **Specific Activity** \\ **(U/mg)** \\ \end{tabular} &
\begin{tabular}{c} **Purification** \\ **Fold** \\ \end{tabular} \\ \hline Culture supernatant & 880 & 2551.12 & 110,588.57 & 43.35 & 1.00 \\ Ammonium sulfate & 75 & 279.68 & 14,705.36 & 52.58 & 1.21 \\ precipitation & & & & \\ Diethylaminoethyl & & & & \\ sepharose Fast Flow & 15 & 14.85 & 5721.98 & 385.32 & 8.89 \\ chromatography & & & & & \\ Superdex 75 & 2 & 1.92 & 1838.03 & 957.31 & 22.98 \\ chromatography & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of purification for CelAj93.  
Figure 3: Cell growth and cellulase production by _Cellulophaga_ sp. J9-3 in Marine Broth containing carboxymethylcellulose at different cultivation times. Cell density (A\({}_{600}\)) in Marine Broth (MB, \(\Circinus\)) and MB + 0.2% carboxymethylcellulose (CMC, \(\bullet\)), cellulase activity (A\({}_{540}\)) in MB (\(\square\)) and MB + 0.2% CMC (\(\blacksquare\)).
## 2 Results and Discussion
### Multi-Services Application over Polymer Fiber
The Radio over Fiber (RoF) analyses done at 1,300 nm are reported in Figure 22. Three standards have been tested as presented in Figure 20 in order to demonstrate the capacities and robustness of the multi-services concept for several applications (radiocellular WCDMA, WLAN IEEE802.11g and WPAN Wimedia BG1). In each case, EVM values below the standard requirement have been achieved when the fiber length is limited to 100 m. As demonstrated during the digital analyses, the GigaPOF62-SR exhibits better results owing to the reduced core diameter mismatching, in spite of a higher attenuation (as compared to the Lucina-X). Figure 22 clearly illustrates the penalties induced by the WDM devices when the IEEE802.11g transmission is achieved through the two PFGI-POF being tested. A higher distance could be reached with the GigaPOF62-SR to extend the indoor coverage of wireless signals (200 m up to 300 m) using such a system topology as compared to the Lucina-X PFGI-POF. Nevertheless, the 10 GbE transmission seems to be limited to 100 m owing to the PFGI-POF attenuation in spite of a very promising bandwidth length product. The high attenuation of the fiber being tested clearly impacted the optical power budget of the 10 GbE link.
## 6 Results
### Comparison with Other Models
#### 6.3.3 Comparison with Methods Using DL and AMs on Different Types of Images  
Previously, we compared our method with other methods using DL or using different types of images. Since our aim was to investigate the use of AMs with DL on thermal images, we considered and compared the results achieved in other methods that used AMs with DL but on different types of images. We reviewed several methods using an AM with DL on the two types of image modalities, mammography and histopathology.  
Section (C) of Table 4 illustrates that those studies achieved an accuracy above 90%. In [4], the CNN model with AMs achieved the highest accuracy of 99.3% based on histopathology images. As we discussed earlier, our proposed models based on CNNs with three AMs (SF, HD, and SL) achieved competitive performance results on thermal images. Our CNN-HD model outperformed the results achieved in [4] with an accuracy of 99.49%.  
The AMs used with the CNNs outperformed in outcomes of sensitivity and F1-score metrics for SL (99.52%, 99.32), CNN-HD (99.71%, 99.49), and CNN-SF (99.21%, 99.36). On the other hand, in the study conducted on histopathology images in [27], sensitivity was 98.70% and the F1-score was 98.28%. In addition, the outcomes of the AMs used with the CNNs showed higher results than [32] for the AUC ROC score, with an increase above 0.08.  
Finally, we can conclude that, based on the results discussed in this section, our proposed methods achieved a higher performance than previous DL-based studies, whether using different types of images or using AMs. This comparison also proved that incorporating AMs with DL, whether or not CNNs, on thermal images for breast cancer diagnosis, a methodology that had not been explored, offered promising results and is worth further investigation for additional enhancements.
## 2 Materials and Methods
### Improved Unsupervised Homography Estimation Model
#### 2.2.3 Data Augmentation Techniques Applied to Stamping Monitoring Images  
There are no publicly available datasets containing real-world images for supervised and unsupervised homography estimation tasks. Typically, the COCO dataset is used to generate synthetic data for training. Neural network training requires large datasets, and one major challenge in applying deep learning in industry is the dataset issue. Therefore, this paper uses a portion of the MSCOCO2014 dataset as the original image library and integrates stamping process images captured by industrial cameras for data fusion training. This approach ensures the dataset's scale while enhancing the model's generalization ability and robustness in the specific industrial application of stamping monitoring.  
[PERSON] et al. (2014) first proposed the S-COCO synthetic dataset for homography estimation, using PatchA and PatchB as inputs to the HomographyNet network model and 4 sets of \((\Delta x_{i},\Delta y_{i})\) as supervision labels. Although it performs poorly for homography estimation on real-world images, it pioneered the task of homography estimation based on deep learning. [PERSON] et al. (2015) improved robustness to lighting variations by randomly injecting color, brightness, and gamma shifts when training unsupervised deep models. [PERSON] et al. (2017) introduced the PDS-COCO dataset, which builds on the S-COCO dataset by using data augmentation techniques, such as randomly adjusting brightness, contrast, saturation, and noise in certain image regions, and randomly swapping image color channels with a 50% probability. This approach addresses the impact of lighting and noise on homography estimation.  
So far, datasets used for homography estimation tasks are based on three-channel color images. Considering the specific industrial application of stamping monitoring, where the images captured by industrial cameras are single-channel grayscale images, the COCO dataset used in this paper is first converted to grayscale. Additionally, this paper adopts the data generation algorithm and data augmentation techniques from [PERSON] et al. (2014). To expand the dataset for the specific industrial application scenario of stamping process monitoring, data augmentation is performed by randomly adding different perturbations, lighting variations, and degrees of blurring, simulating the uncertainties in actual stamping environments. This approach makes the model more suitable for the homography estimation of sheet metal displacement in stamping vibration environments, enhancing its robustness. The dataset generation process is shown in Figure 6.
## 3 Model Formulation
### Proportional Hazards Model  
The PHM is a statistical tool for estimating failure risk. This model consists of a compound function, integrating a baseline hazard function and one accounting for the life signals of an asset [2]. The PHM distribution selected for this research was the Weibull distribution, as depicted in Equation (1).  
\[h(t,Z(t))=\frac{\beta}{\eta}\left(\frac{t}{\eta}\right)^{\beta-1}\exp\left(\sum _{i=1}^{n}\gamma_{i}Z_{i}(t)\right). \tag{1}\]  
Here, \(h(t,Z(t))\) represents the conditional probability of failure at time \(t\), given the covariates \(Z_{i}(t)\). \(\beta\) denotes the asset's failure shape factor, \(\eta\) corresponds to the scale life factor, and \(\gamma_{i}\) describes the weights or impacts of life signals on the model.
# Evaluation of Algae-Based Biodiesel Production Topologies via Inherent Safety Index (ISI)
## 2 Materials and Methods
### Process Description  
#### 2.1.1 Topology 1.  
Conventional Method (Lipid Extraction and Transesterification)  
The process diagram for biodiesel production from microalgae by the conventional method of lipid extraction and transesterification is depicted in Figure 2. Microalgae (1000 kg/h) are cultivated in the first stage along with a nutrient concentration of 90%. Next, the microalgae are harvested by centrifugation and subjected to drying to remove excess moisture [45]. Microalgae with 5% moisture are sent to the next stage where lipidsare extracted; the extraction is carried out using hexane as a solvent in a lipid:hexane ratio equal to 1:20 [43]. Hexane is an interesting alternative that provides high efficiency in lipid extraction [46]. Next, the mainstream is sent to the hexane recovery unit, while the carbohydrate- and protein-rich phase (microalgae cake) is discarded from the process. Approximately 97% of the hexane is recovered for further recycling through [47]. Then, the transesterification reaction takes place by adding methanol in methanol:lipid molar ratio 12:1 [48] and sulfuric acid as a catalyst in a catalyst:lipid molar ratio at 1:1 [49]. The catalyst was selected taking into account the content of free fatty acids in the lipid-rich phase (<1%) since soap formation could be favored when using a basic catalyst [50]. The mainstream rich in biodiesel (methyl esters) and glycerol is sent to the neutralization stage to remove the acid catalyst by calcium oxide addition.  
Figure 1: Schematic representation of the methodology.  
Figure 2: Process diagram for biodiesel production from microalgae via lipid extraction and transesterification.  
The neutralized stream undergoes a methanol recovery of up to 87% and the resulting stream is recirculated to the transesterification unit. In the washing stage, water is added at a temperature of 70 \({}^{\circ}\)C to isolate the biodiesel from the glycerol. Two streams are obtained from the washing step, a hydrophilic stream containing the glycerol and a hydrophobic stream containing the biodiesel and small traces of water. The hydrophilic stream is sent to a distillation process to purify the glycerol and obtain it as a by-product. The biodiesel-rich stream goes to a flash separator where all impurities are removed, and the biodiesel leaves the process at a rate of 208 kg/h.  
The transesterification reactions are shown in Equations (1)-(12) and the neutralization reaction is presented in Equation (13).  
\[\mathrm{CaO+H_{2}SO_{4}\to CaSO_{4}+H_{2}O} \tag{13}\]
# Stochastic Extreme Wind Speed Modeling and Bayes Estimation under the Inverse Rayleigh Distribution
###### Abstract  
Inverse Rayleigh probability distribution is shown in this paper to constitute a valid model for characterization and estimation of extreme values of wind speed, thus constituting a useful tool of wind power production evaluation and mechanical safety of installations. The first part of this paper illustrates such a model and its validity to interpret real wind speed field data. The inverse Rayleigh model is then adopted as the parent distribution for assessment of a dynamical \"risk index\" defined in terms of a stochastic Poisson process, based upon crossing a given value with part of the maximum value of wind speed on a certain time horizon. Then, a novel Bayes approach for the estimation of such an index under the above model is proposed. The method is based upon assessment of prior information in a novel way which should be easily feasible for a system engineer, being based upon a model quantile (e.g., the median value) or, alternatively, on the probability that the wind speed is greater than a given value. The results of a large set of numerical simulation--based upon typical values of wind-speed parameters--are reported to illustrate the efficiency and the precision of the proposed method, also with hints to its robustness. The validity of the approach is also verified with respect to the two different methods of assessing the prior information.  
Bayes estimation; beta distribution; gamma distribution; extreme values; inverse Rayleigh distribution; negative exponential beta distribution; negative log-gamma distribution; Poisson process; risk; wind power +
Footnote †: journal: Applied Physics Letters B