esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Chlorophyllcyaeae age, specifically _Chlorella_ spp., have been extensively researched for biodiesel production. This study focused on the alga _Chlorella variabilis_ RSM09, which was isolated from a brackish-water environment at Raksamae Bridge in Klaeng District, Rayong Province, Thailand. The effects of the carbon dioxide gas (CO2) concentration (0.03%, 10%, 20%, 30%, 40%, and 50% \(\upsilon\)/\(\upsilon\)), light intensity (3000, 5000, and 7000 Lux), and photoperiod (12:12, 18:6, and 24:0 h L/D) on algal growth and lipid production were investigated. The results indicated that C. _ururabilis_ RSM09 achieved optimal growth under 20% \(\upsilon\)/\(\upsilon\) CO2 aeration, with an optical density of approximately 2.91 \(\pm\) 0.27, a biomass concentration of 1.32 \(\pm\) 0.14 g/L, and a lipid content of 21.96 \(\pm\) 0.29% (wt.). Among the three different light intensities, higher optical density (4.20 \(\pm\) 0.14), biomass (1.79 \(\pm\) 0.25 g/L), and lipid content (20.75 \(\pm\) 2.0% wt.) were at the 5000 Lux of light intensity. Additionally, the photoperiod of 24:0 h (L/D) produced the highest biomass at 1.86 \(\pm\) 0.21 g/L, followed by the 18:6 h light/dark photoperiod with a biomass of 1.65 \(\pm\) 0.17 g/L, and the 12:12 h light/dark photoperiod with 1.35 \(\pm\) 0.43 g/L. In contrast, the 18:6 h L/D photoperiod yielded a higher lipid concentration of 25.22 \(\pm\) 2.06% (wt.) compared to the others. All cultured microalgae showed significant effects on fatty acid composition. Palmitic (16:0), linoleic (C18:2), and linolenic (C18:3) acids were predominant in C. _variabilis_ RSM09 under all photoperiods. This study exhibited that the microalga C. _variabilis_ RSM09 has great potential as a feedstock for biodiesel production.  
Chlorella variabilis; microalgae; carbon dioxide; light intensity; photoperiod; fatty acid; biodiesel +
Footnote †: journal: applied sciences
# New Flame Retardant Systems Based on Expanded Graphite for Rigid Polyurethane Foams
## 3 Results and Discussion
### Thermal Conductivity of PU Foams  
Low thermal conductivity (\(\lambda\)) is a key parameter indicating the use of rigid polyurethane foams as insulation materials in construction. The value of thermal conductivity is the result of several factors, namely: apparent density, number of closed cells and cell size [24; 25]. To investigate the effect of used modifiers on the insulating properties of PU foams, the value of thermal conductivity was measured at 10 and 20 \({}^{\circ}\)C.  
The results in Table 4, shows that as a result of the introduction of modifiers, the heat transfer coefficient changes. This was the result of a change in the foam structure due to the addition of additives, which was manifested in a change in foam density and porosity. It could be seen that as the amount of modifiers increased, the density and therefore the thermal conductivity coefficient increased (Figures 4 and 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{2}{c}{**Thermal Conductivity \(\lambda\) (mW/(m*K)**} \\ \cline{2-3}  & **10**\({}^{\circ}\)**C** & **20**\({}^{\circ}\)**C** \\ \hline PU–0 & 23.8 & 24.5 \\ PU–EG & 23.4 & 24.8 \\ PU–EG+SiO\({}_{2}\) & 23.6 & 24.9 \\ PU–EG+SiO\({}_{2}\)+IL & 24.0 & 25.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermal conductivity coefficient at a temperature difference of 10 \({}^{\circ}\)C.  
This is because the value of the thermal conductivity coefficient depends on the share of individual phases and their thermal conductivity. The skeleton has a much higher thermal conductivity than the gas component, so the greater the proportion of the skeleton, the greater the thermal conductivity of the porous composite. The increase in thermal conductivity caused by the introduction of a large amount of solid fillers of both organic and inorganic nature is widely described in the literature. [PERSON] et al. (2016) showed that despite the high content of closed cells, the thermal conductivity coefficient increased for samples modified with basalt waste in the amount of 3-40% by weight. In turn, [PERSON] et al. (2017) showed the effect of expanded flake aggregates as carriers for heat transport through the solid material, which resulted in increased thermal conductivity. Nevertheless, the value of thermal conductivity (about 0.025 (mW/(m*K)) is in the range that is acceptable from an application point of view ([PERSON] et al., 2016). A similar relationship was also observed in the case of our study.
## 7 Experimental Results
### Performance Assessment  
Data were collected within the city center of Helsinki and its suburban areas, as shown in Figure 9. In particular, the dataset related to the urban environment (shown in Figure 9a) has a duration of approximately 4200 s collected at 1 Hz, and a path length of about 20 km. The dataset related to the suburban environment (shown in Figure 9b) has a duration of about 1000 s collected at 1 Hz rate, and a path length of about 21 km.  
Figure 8: Analysis of the collected GNSS raw samples in time domain (**top**) and frequency domain (**bottom**). PSD: power spectral density.  
The statistical characterization of the HPE related to the live and replayed trajectories is plotted in Figure 10a,b, respectively, by the continuous and dashed lines. Moreover, the blue and orange lines are related respectively to the suburban and urban environments. The CDFs of the HPE are plotted in Figure 10a.  
Figure 10: Statistical characterization of the HPE. (**a**) Cumulative distribution functions (CDFs) and (**b**) additional metrics.  
Figure 9: Datasets shown in Google Maps. (**a**) Urban and (**b**) suburban environments.  
The 50 th, 75 th, 95 th percentiles, and the mean value of the HPEs are plotted in Figure 10b. As expected, the error on the final accuracy was larger in the case of harsh scenarios, as can be seen in the plot by comparing the continuous blue and orange lines. Such environments present several challenges to GNSS signal reception, such as blockage and reflection of the signals by buildings or trees. On the other hand, the comparison between continuous and dashed curves, which states the fidelity of the reproduced environment with respect to the real one, led to different results in suburban and urban environments. Considering for example the 95 th percentile as a metric, in suburban environments they exhibited a difference of about 40 cm. This can be acceptable since it might be due to some additional noise introduced during the replay operation. On the other hand, in the urban environment they exhibited a difference of about 10 m. One of the causes of such a fidelity loss could be the signal phase noise induced by the sampling and down/up converting reference oscillator, which impacts twice in the record and replay chain, and has a larger impact in the case of the harsh urban environment with respect to the suburban one. This issue is discussed further in Section 7.4.
## 3 Current State of Object Detection Algorithms
### Region-Based CNN
#### 3.1.5 Sparse R-CNN  
Sparse R-CNN is an object detection model that is based on a predefined number of proposal boxes, which allows for more efficient processing of the input image. The model was introduced by [PERSON] et al. [38] in 2021 and is designed to address the limitations of existing object detection models that rely on dense candidate boxes or region proposal networks. Instead of using dense candidate boxes, as used in single-stage architectures, or dense-to-sparse candidate boxes obtained using a RPN in two-stage architectures, the Sparse R-CNN utilizes learnable proposal boxes that are updated iteratively during training. This approach reduces the computational complexity of the model, allowing for faster processing times. Sparse R-CNN uses an FPN based on the ResNet backbone, along with a dynamic instance interactive head. The authors showed that the Sparse R-CNN model performs comparably to well-established detector baselines, achieving 45.0 AP on the COCO dataset with a 3\(\times\) training schedule and ResNet-50 FPN, and an inference time of 22 FPS.  
As seen in Table 2, all five models (Fast, Faster, Mask, Cascade, and Sparse R-CNNs) offer unique improvements and advancements in object detection. However, each model also faces its own set of challenges and limitations. The Fast R-CNN improved accuracy by using a selective search for object proposals, but it suffered from high computational complexity. The Faster R-CNN improved speed and efficiency by incorporating the RPN, but it had limited scalability and precision. The Mask R-CNN increased accuracy with its binary mask output, but it also had a more complex architecture and limited scalability. The Cascade R-CNN improved accuracy with its series of detection heads, but it had limited scalability and could miss some objects. The Sparse R-CNN improved efficiency with its learnable proposal boxes, but it had limitations in accuracy for smaller objects [38].
# Coupled Dual-Loop Optoelectronic Oscillator Based on Stimulated Brillouin Scattering
###### Abstract  
A tunable optoelectronic oscillator (TOEO) with coupled dual-loop (CDL) based on stimulated Brillouin scattering (SBS) is proposed. In our scheme, the CDL is constructed by cascading a dual-output Mach-Zehnder intensity modulator (DOMZM) and a single output Mach-Zehnder intensity modulator (MZM). One optical output of the DOMZM is directly injected to the MZM, and the other optical output of the DOMZM is transformed into microwave signals to modulate the MZM. The narrow gain spectrum of SBS is used to select the oscillation frequency and realize frequency tunability. By joining the CDL, we can not only increase side mode suppression ratio (SMSR) and decrease phase noise, but also improve stability of the TOEO. Experimental results show that microwave signals from 2 GHz to 18 GHz with the SMSR as high as 60 dB and the phase noise as low as \(-\)95 dBc/Hz at 10 kHz frequency offset are achieved. Furthermore, the frequency drift is less than 0.3 ppm and the power drift is lower than 0.2 dB at 10 GHz within 30 min in lab condition.  
Keywords: optoelectronic oscillator; coupled dual-loop; stimulated Brillouin scattering; side-mode; phase noise; stability +
Footnote †: journal: Applied
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 4 The Anticipated Cloud Storage Structure
### Implementation Steps of Proposed Framework  
To ensure the security and authenticity of user data, organizations must follow a series of crucial steps in creating and deploying a comprehensive cloud storage and privacy framework. The following outlines the process:  
_Step 1: Requirement Assessment and Architecture Planning_ (Identify the nature of stored data, privacy requisites, and compliance needs; formulate the cloud storage architecture accordingly). _Step 2: Infrastructure Setup and Data Encryption_ (Establish the cloud infrastructure and implement encryption protocols for data at rest, in transit, and during processing). _Step 3: Access Control and Privacy Policy_ (Implement stringent access controls, employ identity management, and articulate explicit privacy policies governing data access). _Step 4: Data Segregation and Redundancy_ (Categorize data based on sensitivity, introduce redundancy measures for data availability, and ensure systematic backup procedures). _Step 5: Monitoring, Audit, and Incident Response_ (Deploy monitoring systems, conduct routine audits, and create an incident response plan to address security events effectively).  
_Step 6: Security Assessments and User Training_ (Execute regular security audits encompassing vulnerability assessments and offer continuous user training on evolving security practices).  
_Step 7: Updates and Patch Management_ (Keep all software components current with the latest security patches for the operating system, applications, and services).  
_Step 8: Continuous Improvement and Emerging Technologies_ (Continually enhance the security framework based on evolving threats and integrate emerging technologies to fortify defenses).  
_Step 9: Regular Audits and Compliance Checks_ (Conduct regular audits to ensure conformity with data protection regulations and perform checks to uphold security standards).  
_Step 10: Trust and Customer Assurance_ (Foster trust through transparent communication, adherence to privacy commitments, and assuring customers about the robustness of data protection measures).  
By adhering to these sequential implementation processes (_Step 1_ through _Step 10_), organizations can build a resilient framework for cloud storage and privacy. This framework ensures the security of user data and fosters trust among customers. Regular assessments, updates, and ongoing monitoring contribute to the continuous improvement of the framework, enabling it to adapt to emerging technologies and evolving threats.
## 5 Simulation and Result of MANET-ESO in IoT Techniques
### Results and Analysis in Terms of Residual Energy vs. Number of Rounds  
This section discusses the residual energy of the node after 'n' rounds. The results are contrasted with the existing methodology. Figure 3 shows the results of residual energy vs. the number of rounds; the results of the proposed methods are compared to those of the EECHS-ISSA DE and Butterfly and Ant method. Both the cluster energy method and MANET trusted route algorithm compute and identify the best route to improve the residual energy of the route. The overall energy utilization is based on the number of adjacent nodes in the route. The performance of MANET-ESO in IoT is compared to other existing work and observed that EECHS-ISSA DE and Butterfly-Ant methods are wasting the energy level. The EECHS-ISSA DE reduces the energy level over the course of 1000 rounds. The energy balancing approach is being used in Butterfly and Ant method, which has maintained an energy level for up to 8200 rounds. Finally, when compared with the proposed method, the trusted route energy optimization method is choosing the best route based on the total energy utilization of the node, minimum distance and minimum hop count. So, the selected route is using less energy to complete the task and improved the performance of the network compared to other methods. The residual energy of nodes is increased by an average of 75% in this proposed algorithm compared to EECHS-ISSA DE and a 7.1% increase compared to Butterfly and Ant method.  
Figure 2: Live nodes vs. number of rounds.
# Traffic-Aware Optimization of Task Offloading and Content
## 3 System Model and Problem Formulation
### Edge Data Caching Model  
Task caching refers to the storage of completed tasks and their associated data on the edge cloud. This paper formulates the content caching problem using a binary cache decision variable \(s_{m}^{i,n}\in\{0,1\}\). If \(s_{m}^{i,n}=1\), it signifies that the task generated by the \(n^{th}\) vehicle in region \(i\) is cached to edge server \(m\). Otherwise, it is set to 0. Therefore, the task caching policy is expressed as \(S=\left\{s_{1}^{1,1},s_{1}^{1,2},\ldots,s_{m}^{i,n}\right\}\).  
Considering the joint processing of task offloading and content caching to vehicle local, edge, and cloud servers, the total execution latency of task \(q^{i,n}\) generated by the \(n^{th}\) vehicle in region \(i\) is  
\[T_{q}^{i,n}(t)=s_{m}^{i,n}\times\frac{q_{q}^{i,n}}{v_{m}}+(1-s_{m}^{i,n})\times TT _{q}^{i,n}(t) \tag{9}\]  
Furthermore, the total consumption of energy is:  
\[E_{q}^{i,n}(t)=s_{m}^{i,n}\times EM_{q}^{i,n}(t)+(1-s_{m}^{i,n})\times ET_{q}^ {i,n}(t) \tag{10}\]  
Therefore, the average execution time of vehicles in the region \(i\) is  
\[\overline{T}^{i}(t)=\frac{1}{TS_{i}(t)}\times\sum_{i=1}^{TS_{i}(t)}T_{q}^{i,n} (t) \tag{11}\]  
The energy consumption of vehicles in the region \(i\) is  
\[E^{i}(t)=\sum_{i=1}^{TS_{i}(t)}E_{q}^{i,n}(t) \tag{12}\]  
The combined size of the data contained in the regional edge servers is:  
\[CS_{i}=s_{m}^{i,n}\times\sum_{i=1}^{TS_{i}(t)}o_{q}^{i,n}+(1-s_{m}^{i,n})\times \sum_{i=1}^{TS_{i}(t)}\alpha_{i,n}^{i,n}d_{q}^{i,n} \tag{13}\]
## 3 Numerical Study
### Material Model and Input Parameters
#### 3.2.2 Rock Constitutive Model and Parameters  
The HB model is a common empirical model used to predict the deformation and damage of rock masses, and it has been validated by many engineering cases. In Plaxis3D, four models can be used to simulate rock masses, among which the linear elastic model is a crude first-order approximation, the Mohr-Coulomb model is a reasonable model, and the jointed rock model and HB model are the best standard models. The jointed rock model is used to simulate rock masses that are stratified or clearly anisotropy, and the HB model is applicable to general rock masses. The moderately weathered muddy sandstone involved in this study has no obvious joints, so the HB model is used. There are seven main parameters in the HB model, including heaviness \(\gamma\), Poisson's ratio \(\
u\), Young's modulus \(E\), uniaxial compressive strength \(\sigma_{\rm ci}\), intact rock material constant \(m_{\rm i}\), geological strength index \(GSI\), and disturbance factor \(D\). Among them, \(\sigma_{\rm ci}\) is measured by tests. \(\
u\)\(m_{\rm i}\), \(GSI\), and \(D\) are taken from the literature references according to the rock type and degree of weathering [27]. \(E\) is determined based on Equation (9) [28].  
\[E=MR\sigma_{\rm ci}(0.02+\frac{1-D/2}{1+e^{[(60+15D+GSI)/11]}}) \tag{9}\]  
where \(MR\) is taken as 275 on account of rock type, joint development, and degree of weathering. Table 5 lists the rock parameters used for the HB model.
## 5 Materials and Methods
### LSTM Model Training and Performance Evaluation  
#### 5.4.1 Feature Selection and Input Data  
The LSTM model was trained on a preprocessed dataset containing relevant features essential for accurate travel time prediction:  
* Geospatial Data: Latitude and longitude of pickup and drop-off locations.
* Temporal Features: Hour of the day (0-23), day of the week (0-6), weekend flag.
* Trip Distance: Direct distance between pickup and drop-off locations.
* Historical Traffic Trends: Implicitly captured through time-based patterns in the dataset.  
To ensure training stability and improve model convergence, the input features were normalized using Min-Max Scaling, standardizing values within the range [0, 1].
# A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
## 3 Fine-Grained Deep Learning Methodologies
### Feature Engineering  
Researchers aim to achieve high-precision retrieval by efficient feature extraction, overcoming complex environments and the network topology. Referring to extensive studies, we classified the feature extraction into global and local features based on the granularity, as shown in Figure 3. Subsequent studies leverage global features, such as VSE++ [61], ACMR [62], and DSPE [63]. In contrast, local features were employed in works such as DAN [64], SCAN [56], SCO [65], and PVSE [66].  
We further categorized feature extraction into two types, visual embeddings and textual embeddings, which are crucial components of many cross-modal retrieval systems. Visual embedding greatly influences retrieval efficiency, and current studies are extensive and in-depth. The BERT-like structure is commonly utilized to extract features in textual embedding methodologies. Unlike textual embedding, visual embedding employs different degrees of extraction, including region, gird, and patch levels. The Faster-RCNN, a second-order object detector, is widely used for extracting regional characteristics based on target detection. For example, ViLBERT and LXMBERT employ co-attention to combine multi-modal information. VisualBERT, VL-Bert, and UNITER use merged attention for multi-modal information fusion, whereas OSCAR and VinVL need extra image tags. Despite this, there are significant drawbacks to the approach. Training may freeze object detection.  
It limits visual concept recognition and loses context information. Moreover, it cannot describe the connection between many objects. All of the limits described above are based on region extraction characteristics. CNN-based techniques are another popular method of extracting visual features. Grid characteristics are obtained using typical CNN networks in pixel-Bert and CLIP-ViL, whereas text is obtained by using a transformer. SOHO utilizes a learnable visual vocabulary to discretize grid features, which are subsequently fed into multi-modal modules. It performs worse than the OD-based method compared to inconsistent optimizers, i.e., CNN using SGD and transformer using AdamW. The Patch projection enables image slices to extract features. A common approach, such as ALBEF, utilizes ViT directly.
# Simulation-Oriented Analysis and Modeling of
## 5 Model Calibration  
### Model Calibration Results  
The genetic algorithm (GA) was used to calibrate a set of parameters for each type of distracted car-following model. GA is a heuristic optimization algorithm, which is often used in the calibration of parameters of car-following models [39]. According to previous studies [24; 38], minimizing the calibration error regarding the car-following distance can result in a better model, so the objective function of GA in the calibration process is set as the Root Mean Square Normalized Error (RMSNE) of the car-following distance (i.e., spacing):  
\[\text{RMSNE of spacing}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\frac{s_{i}^{sim}-s_ {i}^{obs}}{s_{i}^{obs}}\right)^{2}} \tag{8}\]  
The calibration was carried out in MATLAB (r) v. R2022a (Natick, MA, USA). The parameters of GA are shown in Table 3. In addition, since GA is a random search algorithm, the output of each run may be different; therefore, the algorithm was repeated five times, and the one with the smallest error was taken as the calibration result. The parameters and their specific meanings, boundary constraints, and initial searching values are shown in Table 4. The calibration results are shown in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Population size & 300 \\ Number of generations & 1000 \\ Number of stall generations & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Genetic algorithm parameter setting.  
The results show that drivers who are excessively distracted exhibit the highest desired speeds and greater accelerations/decelerations; this indicates that these drivers might fail to reduce their speeds in time, and they have many intensive changes in their speeds. For moderately and mildly distracted driving patterns, more variations can be observed in different road environments, which demonstrates the complexity of distracted driving behavior.