esa-sceva/satcom-chunk-collection huggingface dataset:
# _Saccharomyces cerevisiae_'s Response to Dysprosium Exposure
## 3 Results
### Dy Absorption in S. cerevisiae Cells Cultured in Dy-Containing Medium  
BY4741 cells were cultured for 12, 18, 24, or 48 h in a medium containing 0.1 mM Dy(CH\({}_{3}\)COO)\({}_{3}\). The Dy content in the intact cells, intracellular region (inside the cell), and cell wall solubilized fraction (on the cell surface) was measured (Table 1). The Dy content in the intact yeast cells gradually increased up to 24 h, after which it was stable at approximately 20 nmol per 1 mg of dry cell weight (DCW). These results suggest that an increase in Dy absorption was observed during the exponential growth of _S. cerevisiae_ (up to 24 h), but no significant change was observed during the stationary phase from 24 to 48 h. The Dy content in the protoplasts and cell wall solutions was assayed for the Dy-containing culture because our previous study [1] showed that Dy was located in both the protoplasts and cell wall fraction in _S. cerevisiae_ cells suspended in Dy(CH\({}_{3}\)COO)\({}_{3}\) solution. As shown in Table 1, Dy absorption was observed in both the protoplasts and cell wall solutions at all the time points tested, and similar increasing patterns were observed in both the protoplasts and cell wall solution to those in the intact cells. In addition, the Dy absorption in both the protoplasts and cell wall solution was approximately half of that in the intact cells. These results show the possibility that approximately half of the absorbed Dy is transported into the cells and the other half is located on the cell surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Culture** & \multicolumn{3}{c}{**Dy Content (nmol/mg DCW *)**} \\
**Time (h)** & **Intact Cells** & **Protoplasts** & **Cell Wall Solution** \\ \hline
12 & 7.9 \(\pm\) 3.5 & 3.7 \(\pm\) 1.3 & 2.6 \(\pm\) 0.8 \\
18 & 11.8 \(\pm\) 3.2 & 5.5 \(\pm\) 1.5 & 6.0 \(\pm\) 1.2 \\
24 & 18.5 \(\pm\) 0.5 & 8.6 \(\pm\) 0.6 & 7.2 \(\pm\) 2.3 \\
48 & 19.0 \(\pm\) 0.3 & 9.7 \(\pm\) 1.1 & 7.0 \(\pm\) 0.5 \\ \hline \hline \end{tabular}  
* DCW: dry cell weight.  
\end{table}
Table 1: Dy content in yeast cells.  
Figure 1: ROS content in yeast cells. Yeast was cultured for 24 h in GYP with or without 0.1 mM Dy(CH\({}_{3}\)COO)\({}_{3}\) (white and gray columns, respectively). Asterisk represents a significant statistical difference (\(p<0.05\)) between samples with and without Dy\({}^{3+}\)-treatment.
# Monitoring Citrus Soil Moisture and Nutrients Using an IoT Based System
## 3 Design Scheme  
### System Hardware Design  
#### 3.1.1 Sensor Control Node  
The main task of the sensor control node is to collect the citrus orchard soil temperature and humidity, air temperature and humidity and other parameters, and transfer data to the master node. We select HA2002 (Handan Dingrui Electronics Co., Ltd, Handan, China) as the soil temperature sensor, HA2001 (Handan Dingrui Electronics Co., Ltd, Handan, China) as the soil moisture sensor, and the type of air temperature and humidity sensor is FM-KWS (Hebei fly dream Electronic Technology Co., Ltd, Handan, China). These sensors have the same characteristics: fast response time, high accuracy, wide range, good stability. JN5139 module is a series of surface mount modules that enable users to realize IEEE802.15.4 or ZigBee compatible system in the shortest time and at the lowest cost. The field wireless sensor can send the parameters to the JN5139 control node through the ZigBee. The sensor node structure is shown in Figure 2.  
Figure 3: Part of the circuit diagram of the sensor node.  
Figure 2: Sensor nodes structure.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 3 Adaptive Multipath Traffic Loading Based on RPL, AMTL-RPL
### End-to-End Delay Optimization
#### 3.3.4 Adaptive Traffic Assignment Algorithm  
The above section considers the energy balance and the end-to-end delay in a multi-path situation, separately. The focus may be different in some practical applications. For instance, we should think more about the end-to-end delay when we facing the real-time circumstances, while for normal surveillance data, we pay more attention to maximizing the network lifetime. Thus, this paper proposes adaptive multipath traffic loading based on RPL. This algorithm chooses different objective functions according to the different requirements. We set out the details in Algorithm 4. We use Algorithm 2 (Line 2) if we consider the energy balance. If the energy balance is achieved at a certain threshold, then we adjust the ratio of data distribution based on bottleneck node's waiting time to shorten the end-to-end delay (Line 4). If we consider the end-to-end delay first, we use Algorithm 3 (Line 6); when the delay is smaller than the determined threshold, then we change to Algorithm 2.
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 2 Literature Review
The XOR ciphering technique is computationally less intensive while having a similar symbol error rate to the phase cipher. Instead of a traditional first encrypt then encode technique, [PERSON] et al. evaluated the concept of cryptcoding schemes [44], whereby encryption and channel coding are integrated into a single block. The evaluated combined systems were more efficient and less complex. In an underwater acoustic channel, frequency dependent pathloss, doppler spreading and multipath fading make it very difficult to achieve a good bit rate. Similarly, securing the transmitted data while maintaining computational simplicity is a challenge. Thus, the idea of a reliable channel coding technique, combined with a moderate level of security, that can depress the high PAPR of a UWA OFDM system is intriguing.
## 2 Provisioning MB Optical Connections  
Lightpath provisioning is one of the most important problems that needs to be addressed to automate network operation. Lightpath provisioning includes the selection of a route and a channel assignment to ensure that the required QoT, e.g., in terms of the pre-FEC BER, is met. In case the required QoT cannot be assured, the lightpath request is rejected, i.e., blocked.  
In this section, we first present the MB scenario considered in this paper and provide observations that will be used afterwards for improving lightpath provisioning. Next, the OCATA time domain digital twin is introduced, as it includes precise models trained for different channels. To facilitate its application in MB optical transmission, a specific methodology is proposed. Finally, OCATA-MB is proposed as a method to mitigate the impact of the ISRS effect. Because OCATA can generate the expected optical constellations for an optical connection and a channel assignment, such an expected signal can be used to instruct the DSP on the Rx side, which can improve the QoT of the lightpath.
## 3 Results and Discussion
### Improving the Conversion Loss of InGaAs Diodes  
As discussed, a main limitation of the InGaAs mixer used in this study is the high conversion loss compared to the GaAs mixer. The InGaAs diodes used here were not optimized for mixing purposes, and the epilayer doping level was moderately low. However, if the doping level is increased, the conversion loss can be improved, reducing the performance gap between InGaAs and GaAs diodes. This was verified through simulations by considering the same mixer circuit and waveguide block while testing different diode configurations. The comparison of the conversion loss is depicted in Figure 12. It is observed that, by increasing doping, the conversion loss was reduced by 2 dB, resulting in a similar CL to those achieved with the GaAs mixer.  
Figure 11: Results with PLL-based carrier phase recovery DSP: (**a**) BER and (**b**) 40 Gbps constellations of narrow-band InGaAs mixer with electrical pumping (**left**) and optical pumping (**right**).  
The simulations accurately predict the experimental results shown in Figure 12. As previously observed with high- and low-doped GaAs and InGaAs diodes, we are confident that the fabrication of highly doped InGaAs diodes will yield performance consistent with the simulations, experimentally reducing the performance gap between semiconductor technologies by 2 dB.
## 4 Discussion
Overall, bioceramic materials, particularly calcium silicate-based formulations like EndoSequence(r), have gained widespread popularity in endodontics due to their ability to create strong seals, promote healing, and prevent microleakage. Their bioactivity, ease of handling, and ability to penetrate dental tubules make them preferred materials for endodontic treatments, particularly when durable and biocompatible support for healing and tissue regeneration is required [105]. Studies report high success rates for bioceramics in retrograde fillings, suggesting that they may outperform traditional materials in some aspects [5,106]. Nevertheless, while initial clinical results are promising, more extensive long-term studies are needed to evaluate their durability and performance compared to established materials such as MTA and IRM(r) [107]. Gutta-percha is the most used root canal obturation material due to its ease of manipulation, plasticity, and biocompatibility. It consists primarily of trans-1,4-polyisoprene (natural rubber), zinc oxide, heavy metal sulfates, and waxes. Although it provides a good seal when used with an appropriate sealer, gutta-percha lacks bioactivity and does not actively promote healing or stimulate regeneration of surrounding tissues. This makes it inferior to newer materials like MTA and bioceramics, which encourage hard tissue formation and hydroxyapatite development [108]. This systematic review has several limitations. First, the number of available clinical studies evaluating retrograde filling materials remains limited, which may affect the generalizability of the findings. Second, significant heterogeneity was observed among the included studies in terms of follow-up duration, surgical techniques, and outcome measures, which precluded quantitative meta-analysis. Additionally, some included studies lacked rigorous methodological design or complete reporting, which may have introduced bias in the interpretation of results. While this systematic review highlights the effectiveness of the materials discussed, it is important to acknowledge that other materials, such as newer bioceramic formulations, were not included in this analysis. This gap indicates a need for future research to conduct meta-analyses to comprehensively evaluate the comparative effectiveness of these materials in root-end filling applications. Additional studies focusing on long-term follow-up could provide valuable data regarding the longevity and success of these materials in clinical practice. Furthermore, investigating the potential synergistic effects of combining different materials may yield insights that could lead to improved clinical outcomes [62,63].
## 3 Staff Detection and Tracking System  
The staff tracking algorithm is made up of several subsystems aimed at preparing the input image for performing the actual tracking and adjust the generated path to a smooth curve. The parameters are established taking into account their relative value with respect to the score image size and the specific task at hand. Thus, the parameters are chosen specifically for the book under consideration (see [[http://salzinnes.simssa.ca/](http://salzinnes.simssa.ca/)]([http://salzinnes.simssa.ca/](http://salzinnes.simssa.ca/))). Concretely, the size of score pages is approximately 61.5 cm \(\times\) 39.5 cm and they were scanned with a resolution of \(N\times M\) = 6993 \(\times\) 4414 pixels; the scores contain four-staff-line music notation with a separation between consecutive staff lines around 40-60 pixels.  
When the staff image (Figure 1b) obtained by the Pixelwise scheme is fed into the tracking scheme, a number of stages will be performed that can be grouped into the following main tasks:  
* Pre-processing.
* Search for staves.
* Track staff lines.
* Fit a smooth model to staff lines.  
These main stages contain several sub-tasks that will be described next. Note that the search-for-staves stage is an iterative process performed until no more staves are found. The detection and complete tracking of a certain staff implies its removal from the image to allow the next staff search process. This procedure, together with the main scheme stages, is shown in the diagram depicted in Figure 2.  
The system stages, their sub-tasks and their parameters will be described in the next sections. Note that the system parameters are mainly based on the relation between the scanning resolution and the size of the music objects and, specifically, on the observed area of the smallest object detected previously, the expected range of the staff line separation and its estimation by the scheme itself.  
Figure 1: Example of application of the Pixelwise scheme on folio 7r of [PERSON] book. (**a**) Scanned image. (**b**) Extracted staves. (**c**) Image background. (**d**) Musical symbols extracted.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 4 Experiments and Results
### Results and Analysis
#### 4.2.1 Scenario 1: Baseline Performance  
In the simplest environment (\(cellsize=1\)), both _XSQ-Learning_ and conventional _Q-Learning_ achieved successful policy convergence, though with notable differences in learning efficiency. _XSQ-Learning_ achieved its first stable maximum reward of 93 in an average of 6650 steps, whereas _Q-Learning_ required approximately 22.56% more steps on average (8150 steps) to reach the same outcome. In contrast, the similarity-based variants (_QL-SmoothActions_, _QL-SmoothObservations_, and _QS-Learning_) frequently failed to surpass the minimum reward threshold of \(-200\). Specifically, _QL-SmoothActions_ failed in 10 out of 20 trials, while _QL-SmoothObservations_ and _QS-Learning_ failed in all trials. These results suggest that, in small state spaces, the computational overhead introduced by similarity mechanisms may hinder performance unless carefully controlled, as demonstrated by _XSQ-Learning_'s adaptive \(\delta\) threshold and _sim_clip_value_ parameters.
## 4 Experiment Setup and Results
### Experiment Results and Discussions
The RMSE is a quality estimator derived from the square of Euclidean distance, where it is always a positive value, with the error reducing as the error reaches zero. The RMSEs for all the considered propagation models are shown between the measured data and the predicted path loss, which in turn affects the rest of the link budget parameters. Clearly, the modified Hata-Davidson model performed the best compared to other models, since it had the lowest RMSE, of 3 dB. Figure 10: GUI channels of latitude (**left**), and longitude (**right**) from GPS sensor with satellite map view. Figure 9: Vertical view using thermal camera to detect person at different altitudes: (**a**) 5 m, (**b**) 10 m, (**c**) 15 m.
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks
### Model Assumptions
#### 3.1.3 Traffic Assumptions  
* All users generate traffic with identical characteristics, following an ON/OFF model.
* There are no handover mechanisms considered in this model.
* A fixed number of mobile stations (\(N\)) share the available bandwidth.
* Each mobile generates elastic ON/OFF traffic, where ON periods correspond to downloading data, and OFF periods correspond to idle times (e.g., reading content). ON and OFF durations are exponentially distributed.
* Since only the Best Effort (BE) service class in WiMax is considered, each of the \(N\) mobile stations is assumed to generate an infinite ON/OFF sequence of elastic traffic. An ON period corresponds to downloading content (such as a webpage with embedded objects). The duration of the ON period depends on the system load and the quality of the radio link, so ON periods are characterized by their data volume. An OFF period represents the time spent reading or viewing the last downloaded item. The duration of the OFF period is independent of the system load and is characterized solely by its length.
* The sizes of the ON periods and the durations of the OFF periods are exponentially distributed. The variable \(x_{on}\) represents the average data volume during ON periods (in bits), while \(t_{off}\) represents the average duration of OFF periods (in seconds).
## 5 Final Remarks
#### Rq1.  
Does the approach for hyperparameter optimization with a GA outperform traditional techniques?  
The proposed GA must be evaluated to emphasize that testing different combinations of hyperparameters within the same algorithm is a complicated and time-consuming task that may require a large amount of processing time. However, the accuracy of prediction models is directly linked to the quality of hyperparameter optimization. Thus, the more adjusted they are, the more accurate the rates of the models tend to be. The alternatives to applying exhaustive search methods, such as grid-search, are computationally expensive when searching in large spaces [53]. Thus, the refinement obtained by GA with its mutation and crossover stages produces better results for model generation, surpassing the traditional techniques and grid-search. Compared to standard algorithms, the performance of the proposed method is clearly superior.
# Target Selection Strategies for Demucs-Based Speech Enhancement
## 6 Results
### Overall Output SIR  
The overall output SIR is shown in Figure 6. The number of interferences is at 0 for this evaluation, with the objective to establish a baseline of only the speech enhancement side of both target selection strategies.  
As can be seen, Demucs-Embed is not able to perform its speech enhancement objective as well as the original Demucs. This may be because the sequence modeling stage of the Demucs-Denoiser model now needs to carry out a double duty (target selection and speech enhancement), resulting in refocusing resources that would have been spent carrying out speech enhancement to now carry out target selection. Thus, as can be observed in Section 6.2, the speech enhancement performance is diluted to provide a better target  
Figure 6: Overall output SIR.  
selection. The reader may be interested to know that additional experiments were carried out with an increased number of LSTM layers, so as to provide more resources to the sequence modeling stage. However, such an increase did not improve upon the results shown in Figure 6. Additionally, it is noteworthy that the performance reduction is not considerable as the difference of the median values is around 2 dB; thus, Demucs-Embed is still providing (for the most part) a good enhancement performance.  
With regard to Demucs-PhaseBeamform, it can be seen that it not only provides a similar average performance to the original Demucs, but improves upon it by reducing the result variability.
# Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area
## 2 Material and Methods
### Satellite Observations
#### 2.3.3 Gross Primary Production  
[PERSON], [PERSON] et al. [30] used machine learning algorithms to convert meteorological parameters to variations of Terrestrial Ecosystem Respiration (TER) and GPP. This dataset is called FLUXNET-MTE (FLUXNET network-Multi Tree Ensemble) and is available monthly at the global scale at 0.5\({}^{\circ}\) resolution from 1982-2011. These machine learning algorithms were first trained using FLUXNET [51] in situ TER and GPP fluxes estimated using two flux partitioning methods [52; 53]. Figure 1 shows the average GPP product for the whole period 2007-2015. Pixels too close to the coasts and pixels where the vegetation cover was too low (less than 10% of the pixel covered by any kind of vegetation according to ECOCLIMAP) were filtered out.
## 2 Chemical Composition of _Chlorella vulgaris_
### Proteins  
Proteins are among the most abundant components of _C. vulgaris_, with studies reporting levels between 43% and 61%, depending on cultivation conditions [9; 10; 30]. This makes _C. vulgaris_ a notable protein source among microalgae. The proteins in _C. vulgaris_ are considered high quality as they contain all essential amino acids, making them a complete protein source comparable to that found in animal products. The essential amino acids present include leucine, isoleucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine [31], which are often limiting in plant-based protein sources [3]. _C. vulgaris_ also contains significant quantities of non-essential amino acids such as alanine, arginine, and glycine, further contributing to its nutritional profile [4]. This composition positions _C. vulgaris_ as a competitive alternative to traditional protein sources like soybean meal, eggs, and meat.  
Microalgae, including _C. vulgaris_, have been reported to have a protein digestibility-corrected amino acid score (PDCAAS) ranging from 0.63 to 0.77 [32]. This score can vary depending on cultivation conditions and processing methods [33]. For comparison, soybean meal typically scores around 0.91, eggs score a perfect 1.0, and meat ranges between 0.75 and 1.0, depending on the type [3]. Despite falling slightly below animal-based proteins, _C. vulgaris_ is still regarded as a valuable source of plant-based protein due to its favorable PDCAAS.  
Protein digestibility is also an important factor for _C. vulgaris_. Studies indicate that its digestibility is comparable to other plant proteins, with rates reported between 80% and 90% [30]. This digestibility is crucial for ensuring that amino acids are available for absorption in the human body. In contrast, animal proteins, such as those found in eggs and milk, often have higher digestibility rates, typically exceeding 90% [3]. When comparing _C. vulgaris_ to other protein sources, it is essential to consider not only the protein content but also the overall nutritional profile. _C. vulgaris_ is rich in vitamins, minerals, and omega-3 fatty acids, which enhances its value as a dietary supplement [3]. In contrast, while animal proteins provide high-quality protein, they may also come with higher levels of saturated fats and cholesterol, which are linked to an increased risk of cardiovascular diseases and other health concerns. [3].  
While _C. vulgaris_ provides valuable protein, its rigid cellulose cell wall can limit protein digestibility and bioavailability. To overcome this, pre-treatment processes are often necessary. Mechanical methods such as bead milling, sonication, and high-pressure homogenization have proven effective in disrupting the cell wall, thereby improving the accessibility of nutrients and enhancing the nutritional value of _C. vulgaris_ proteins in various applications [27; 34; 35].