esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
PLS regression models based on FTIR-ATR spectra of freeze-dried samples were successfully developed for the assessment of total carotenoid content of _Cucurbita_ spp., with \(R^{2}\) values of 0.95 and 0.93 for calibration and prediction, respectively. A wide IR region between 900 cm\({}^{-1}\) and 3000 cm\({}^{-1}\) was appropriate for the PLS regression models, with absorption bands in the region 950-980 cm\({}^{-1}\) providing useful quantitative information, probably associated to _trans_ C=C groups from isoprenoids, such as \(\beta\)-carotene and lutein, which are known to be abundant in _Cucurbita_. Although extracts collected from the mixture of solvents had a greater isolation degree of the analyte, the regression models presented poor predictability, which can be attributed to the overlapping of carotenoid bands with the solvent bands. By the application of PLS regression models in spectra between 920-3000 cm\({}^{-1}\), TCC could be determined in pumpkin and buttemut squash through a fast methodology that does not entail the use of solvents nor extraction protocols and requires minimal training, thus representing an advantage in terms of simplicity, cost, and environmental impact.  
Conceptualization: C.A.F., C.Z.-D., C.D.-M., and S.G.; methodology: N.Q.-O., and M.G.-M.; software: N.Q.-O.; validation: N.Q.-O., and S.G.; formal analysis: N.Q.-O., and S.G.; investigation: N.Q.-O.; resources: C.A.F., C.D.-M., and S.O.-G.; data curation: N.Q.-O.; writing--original draft preparation: N.Q.-O. and C.A.F.; writing--review and editing: C.Z.-D., S.G., S.O.-G., and M.G.-M.; visualization: N.Q.-O. and C.A.F.; supervision: C.A.F.; project administration: C.A.F.; funding acquisition: C.A.F., C.D.-M., and S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Minicencias and Patrimonio Autonomo Fondo Nacional de Financiamiento para la Ciencia, la Tecnologia y la Innovacion Francisco Jose de Caldas (grant number FP44842-271-2018). The article processing charge was partially covered by the University of Milan.  
The authors thank Minicencias for the financial support; the Institute of Food Science and Technology, in particular [PERSON], for the acquisition of the FTIR instrument and his kind guidance on the use of the freeze-dryer instrument, and also [PERSON] and [PERSON], for their kind technical support in the data acquisition and sample processing.  
The authors declare no conflict of interest.  
Figure 3: Regression line of the PLS model for total carotenoid content (in \(\upmu\)g/g, dry basis) obtained from freeze-dried samples. In grey circles (\(\bullet\)), the training set; in red rhombuses (\(\blacklozenge\)), the prediction set.
## 1 Introduction
The PODEn4 DVar method has the superiority that: (1) the dynamic constraint can use complete simulation equations; (2) it is capable of simultaneously assimilating observations at multiple times; (3) it can reflect the spatial structure of the 4D variables and characteristics of time evolution by using POD-transformed base vectors; and (4) this method does not require an integral adjoint model during the computational process, and it is easy to realize. The PODEn4 DVar method can simplify the process of data assimilation and maintain most advantages of the traditional 4 DVar method, which has shown considerable satisfactory performance within land data assimilation [22; 23], Tan-Tracker joint data assimilation [24], and radar data assimilation [25]. Considering that EnKF and PODEn4 DVar each have unique features, each method was applied into LPJ-DGVM separately, and their respective performances were compared in this study. In this study, we developed a GPP assimilation system for coupling the remotely-sensed leaf area index (LAI) with the LPJ-DGVM dynamic vegetation model, which can be used to optimize the GPP simulation at a given time. The specific objectives of this study were to: (1) apply two assimilation algorithms into the process-based model combined with remotely-sensed LAI data; (2) test and evaluate the ability of different assimilation algorithms within the LPJ-DA system by using six eddy-covariance flux tower sites, and select the optimal model for spatial GPP estimation; and (3) obtain the spatial distribution and calculate the total amount of terrestrial GPP in China during the period of 2003-2012.
# The Biology of _Pichia membranifaciens_ Killer Toxins
## 3 Production of PMKT and PMKT2  
Having established that the secreted toxins increased in culture liquid as growth progressed and then stabilized when the yeast population reached the stationary phase (as considered for primary metabolites), the toxins of _P. membranifaciens_ were purified from samples taken at the early stationary phase, where the toxins concentration was observed to be higher [84]. The production of killer toxins in complex media (which contained peptone, malt extract and yeast extract) and minimal media (containing growth factors, mineral salts and trace elements) has been compared. Although production of PMKT in complex media is higher than in minimal media, the specific activity is higher (seven-fold higher) in the latter case, according to similar observations that were reported previously [85; 86] and probably due to the greater stability of the toxin in a media with high contain in peptides and proteins. However, for the purpose of PMKT2 production and purification, _P. membranifaciens_ CYC 1086 is developed in complex medium because PMKT2 is purified easier [20; 39].  
In addition, with the aim of increasing toxin production, stabilizing agents such as polyols, detergents and protease inhibitors were added. Results indicated that the non-ionic detergents, in particular Brij-58, increased the killer activity present in the extracellular media after growth. Accordingly, the synthetic medium with Brij-58, was used for purification of PMKT [19]. Probably, non-ionic detergents stabilize the killer toxin produced or inhibit the adsorption by (1\(\rightarrow\)6)-\(\beta\)-D-glucans. (1\(\rightarrow\)6)-\(\beta\)-D-glucans in yeast cell walls are known to function as primary receptors of some killer toxins in sensitive cells [47; 49; 50; 51; 52; 59; 75; 87]. Killer cells have these (1\(\rightarrow\)6)-\(\beta\)-d-glucans, and it is easy to understand that some amount of the secreted toxins can be attached by (1\(\rightarrow\)6)-\(\beta\)-d-glucans and are not released to the culture medium. The same can be applied to _KRE1_ mutants, which show a 50% reduction in cell wall (1\(\rightarrow\)6)-\(\beta\)-d-glucans that are superkillers due to an increased secretion of the K1 toxin of _S. cerevisiae_[88].  
Production of toxins by killer yeasts with an increased degree of activity also depends on the culture conditions of pH and temperature. Briefly, the maximum killer toxin production for both toxins was achieved at acidic pH values of around 4.5 at 20 \({}^{\circ}\)C, accordingly to the activity and stability properties described later in this review for both toxins.
###### Abstract  
(1) Background: Online health communities (OHCs) serve as ecosystems connecting doctors, patients, and medical resources. Studying their deep network structure and impact mechanisms on medical service quality provides a comprehensive understanding of digital healthcare ecosystems and has guiding significance for platform service optimization. (2) Methods: Using the \"Good Doctor Online\" platform as the data source, we employed social network analysis methods to construct network models from the professional title and disease-type dimensions, and used multiple linear regression statistical analysis to identify the influencing factors of doctor recommendation rates. (3) Results: Our analysis found that depression doctors exhibit the highest network connectivity (average degree = 17.378), and chief physicians demonstrate significantly higher internal connectivity (average degree = 9.353) compared to resident physicians (average degree = 0.804). The doctor recommendation rate is significantly correlated with post-consultation evaluation (r = 0.602, \(p<0.001\)) and shows a 45% variance explanation (R\({}^{2}\) = 0.450) in our regression model. (4) Conclusions: Different disease types in OHCs demonstrate distinct organizational patterns, with depression networks showing significantly denser connections than diabetes networks. Professional titles strongly influence network position, with chief physicians forming highly connected hubs while resident physicians remain peripheral. Recommendation rates emerge through multi-dimensional trust processes primarily driven by post-consultation evaluation quality.  
online health communities; social network analysis; doctor recommendation rate; network structure; digital healthcare +
Footnote †: journal: Applied sciences
# A Performance-Based Airspace Model for Unmanned Aircraft Systems Traffic Management
###### Abstract
As a consequence, the airspace structure and sector volumes are driven by the dynamically varying performance of the separation services provided. In this paper, particular emphasis is given to navigation performance, as it also affects other systems. However, the presented framework is generally applicable to the entire set of Communication, Navigation and Surveillance (CNS) systems deployed and available in a given scenario. For verification purposes, the presented models are applied to realistic UTM/UAM scenarios, including an urban canyon given the significant challenges this poses to navigation and cooperative surveillance performance. In particular, a sensitivity analysis of airspace sector capacity is performed considering the computational complexity of trajectory planning/re-planning and communication overheads associated with sector handovers. This paper builds upon the revisited Dynamic Airspace Management (DAM) concept introduced in [14] and presents some significant evolutions from the initial concept introduced in [21], which are aimed to support an advanced management of low-altitude traffic exploiting high levels of autonomy, for which new human-machine interaction strategies are being developed [22]. The paper is structured as follows: Section 2 presents a review of prior work in airspace design and partitioning strategies and the Concept of Operations (CONOPS) for UAS/UAM operations. The proposed airspace model is presented in Section 3. This includes the strategy to discretise the airspace and generate sectors, along with the algorithmic implementation of the model. Section 4 applies the proposed model to two verification case studies. Section 5 provides the conclusions of this study and future research directions.
## 3 Result and Discussion  
Many methods have been proposed in the literature to estimate SOH, whereby accurate battery parameters are needed to build the empirical model, which could be inefficient and expensive.  
Nevertheless, the above developed techniques are capable of dealing with the complexity of the system modeling, insufficient data, and can still describe the system behavior.
## 4 Simulation Evaluation
### Data Throughputs  
Figure 7 shows the distribution of throughputs of the three protocols. In this simulation, 500 sensor nodes are randomly deployed in the sensor fields. Each curve shows the throughput Cumulative Distribution Function (CDF) of the geographic routes for the same 50 randomly-selected node pairs. The \(x\) value of any point indicates throughputs in packets per second. The size of a packet is 100 bytes. The \(y\) value indicates what fraction of pairs has fewer throughputs.  
The left two curves are the throughput CDFs achieved by using traditional beaconless routing protocols, BLR and BGR. The right two curves are the throughput CDFs achieved by BOSS and the proposed protocol, respectively. As shown in Figure 7, the proposed protocol provides almost three-times as much throughput as BLR and BGR for the median pair. This is because they have more routing failures than our protocol. In these protocols, only a few neighbor nodes could get a chance to be the next forwarding node. Unfortunately, this routing failure is related to thethroughput, because of unnecessary time loss. Every beaconless routing protocol has its own functional timer in order to be selected as an appropriate next hop forwarding node among neighbor nodes (for the contention process). This means that if routing fails in a hop, it has to start from the beginning, including the waiting time for the competition. For example, as shown in the previous Figure 1, the sender \(S\) can realize the routing failure only after the contention process is done. Namely, if \(T_{max}\) is set to 300 ms, \(S\) could rebroadcast after 300 ms. If the timer is long, it affects the data throughput more. For low-throughput pairs, BLR and BGR have similar graphs. Because their processes are almost alike, however, since BGR exploits an improved recovery strategy, unlike BLR, it shows better performance than BLR. BOSS shows a similar graph as the proposed protocol. This is because both BOSS and the proposed protocol could use the full greedy area on every intermediate node during the routing process. However, the proposed protocol shows better performance than BOSS in most cases, due to the static contention time of BOSS.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 4 Proposed Sub-Band BadNets Backdoor Attack in Remote Sensing Imagery
### Score-CAM Based Saliency Map Generation  
We utilize the Score-CAM method [52] to identify the important bands for the target class as shown in Figure 2b. Score-CAM extracts activation maps from the last convolutional layer of the DCNN model and then up-samples them to match the image size. First, normalized activation maps are projected onto the input images through dot multiplication, and then, these are passed to the DCNN model with softmax output. These outputs are used as confidence weights \(w^{k}\) for a class \(c\) in the activation maps \(A^{k}\) of the last convolutional layer. To identify only the positive impact on the target class, Score-CAM uses ReLu [53] to remove negative influence from the final activation map,  
\[S^{c}=\text{ReLU}\left(\sum_{k}w^{c}_{k}A^{k}\right) \tag{3}\]  
We feed the \(i\)th band \(x(i),i=1\dots n\), separately to the Score-CAM model to generate a saliency for the target class as shown in Figure 2b. This is achieved by keeping \(x(i)\) unchanged and zeroing out all other bands. We generate \(n\) saliency maps for the \(n\) bands. By comparing these \(n\) saliency maps, we identify the important bands for placing the backdoor trigger.  
There are other saliency models, such as Grad-CAM [54] and Grad-CAM++ [55], [PERSON] et al. compared Grad-CAM and Score-CAM to identify a preferable explainable AI method for remote sensing datasets. They found that the Score-CAM method performs better compared to the Grad-CAM method on the EuroSat [51] and UC-Mered [47] datasets. This is because Score-CAM selects the last layer to compute importance without relying on gradient information, which typically provides better semantic information for classification tasks. We use Score-CAM to generate a saliency map for each band, then select the important bands for the chosen target class to plant a backdoor trigger.
## 5 Experimental Results
### High-Data and Multi-Level Modulated Traffic
A smaller dynamic range is mainly due to the inadequate output power level when lower input power is applied. For the DMT traffic, we first evaluated the effect of the input power to the optical switch PIC chip on the achievable data-rate. As illustrated in Figure 15a, 0 dBm is found to be the optimum (absolute  
Figure 14: (**a**) BER curves and (**b**) the dynamic range with adjusted SOA current for 20 Gb/s PAM4 traffic. Figure 13: (**a**) BER curves and (**b**) the dynamic range with adjusted SOA current for 40 Gb/s traffic. data-rate is not optimized). An example of the optimal bit allocation after bit loading for the 10 GHz DMT with 256 sub-carriers is included in Figure 15b. Channel 1 with 0 dBm optical power is sent to the PIC and the maximum achievable data-rate with an average BER < 1 \(\times\) 10\({}^{-3}\) is reported in Figure 15c. A 1 dB penalty in the data-rate of 32 Gb/s is introduced by the WDM optical switch PIC compared with the B2B traffic.
# AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks
## 2 AI-Based QoT and Impairment Modeling
### AI-Based Impairment Modeling
#### 2.3.3 ASE Noise  
In a practical system, to accurately model the ASE noise generated by EDFAs, the noise figure (NF) of each EDFA at each wavelength should be precisely known. According to [36], the NF of an EDFA is related to the gain at each wavelength. Therefore, the ASE noise can be more accurately estimated with the aid of an accurate EDFA gain model. However, the spectral hole burning [54] (SHB) effect makes the spectral gain profile of an EDFA change dynamically under channel reconfigurations, thus leading to a power excursion. Since it is hard for the traditional model to efficiently model the gain spectrum of an EDFA with different power loadings in each channel, data-driven ML methods can be adopted. In [34], deep learning is adopted to estimate the gain of each channel individually. To simplify the  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Modeling Targets** & **Algorithms** & **Input Features** \\ \hline \multirow{4}{*}{Nonlinear Noise} & Artificial Neural Networks [5] & Nonlinear SNR from the GN model, span number, maximum span length, average span length, optical launch power, link length, net CD, average gamma of fiber spans, average attenuation of fiber spans, number of wavelength-division multiplexing (WDM) channels \\ \cline{2-3} Optical Filtering Effect & Artificial Neural Networks [9] & Number of ROADMs, OSNR, loaded noise distribution, bandwidth distribution \\ \hline Gain Spectrum of EDFA & Deep Neural Networks [34] & Power levels of all WDM channels \\ \hline \multirow{2}{*}{EDFA Gain Excursion} & Multilayer Perceptron [35] & Gain setting, total input power, input power of each channel \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the ML-based impairment modeling techniques discussed in Section 2.3.  
structure of ML algorithms, a multilayer perceptron neural network is introduced to estimate the gain of all channels at the same time [35].
# A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model
## 3 Experiment and Discussion
### Results and Analysis  
Comparing the GCM-YOLO model with the YOLOv8 baseline network, there is an improvement of 1.2% in the [EMAIL_ADDRESS] and 1.1% in the recall rate. At the same time, the parameter count and model size are reduced by 38.3% and 34.9%, respectively, which indicates that the enhanced model effectively reduces resource consumption while maintaining high accuracy.  
During the model training process, as shown in Figure 6(a), the [EMAIL_ADDRESS] of GCM-YOLO remains stable and consistently higher than that of the baseline YOLOv8n model after 100 epochs. This demonstrates superior stability and robustness during training. The comparison of loss function curves in Figure 6(b) reveals that the loss of GCM-YOLO decreases rapidly in the initial stages of training and stabilizes quickly. This indicates faster convergence and, in the later stages of training, the loss remains consistently low and stable, highlighting the stability of the model and efficient parameter updates throughout training.  
The GCM-YOLO model demonstrates robust detection performance across diverse environments such as indoor, road, and complex backgrounds, as illustrated in Figure 8. Across various scenarios, it shows an improved ability to recognize flames and smoke, underscoring its adaptability and effectiveness in different settings. Figure 7(a) highlights the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **[EMAIL_ADDRESS]\%** & **Recall\%** & **Parameters** & **FLOPS/G** &
\begin{tabular}{c} **Model** \\ **Size/MB** \\ \end{tabular} \\ \hline YOLOv5n & 81.5 & 74.7 & 2,503,334 & 7.1 & 5.3 \\ YOLOv5s & 82.1 & 76.7 & 9,112,310 & 23.8 & 18.5 \\ YOLOv8n & 81.7 & 75.8 & 3,006,038 & 8.1 & 6.3 \\ YOLOv8s & 82.2 & 76.7 & 11,126,358 & 28.4 & 22.5 \\ GCM-YOLO & 82.9 & 76.9 & 1,854,596 & 5.3 & 4.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of comparison experiment with different networks.  
Figure 7: Comparison curve between YOLOv8n and GCM-YOLO. (**a**) Comparison Curve of [EMAIL_ADDRESS]; (**b**) Comparison Curve of Training Loss.  
model's capability to enhance smoke detection accuracy in situations where smoke blends with road-like backgrounds, and it effectively detects smaller flame targets. In Figure 8b, the model's improvements are evident in reducing false detections, particularly in dimly lit conditions. Figure 8c further showcases GCM-YOLO's enhanced detection accuracy for flames and smoke amidst complex backgrounds. As shown in Figure 8d, when the image contains artificial light sources such as lamps, the YOLOv8n model mistakenly identifies the light as flames. However, the improved model significantly reduces the false detection rate under artificial light interference, enhancing the overall robustness of the detection.