esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation Details and Evaluation Metrics  
### Implementation Details and CNN Training  
First of all, we will describe image preprocessing and the preparation of training data, followed by an explanation about the training procedure.  
Initially, each image was zero-padded along its shorter axis to make it square shaped. Afterwards, it was resampled to a size of \(512\times 512\) pixels. A variability in the training set was increased by an augmentation. The training images were augmented \"on the fly\" by random rotations (\(\pm 5^{\circ}\)), uniform scaling with a scaling factor selected randomly between 0.6 and 1.2, and intensity changes with a random factor from an interval \([0.75,1.3]\).  
The ground truth heat maps were generated as instructed in [10]. Gaussian kernels were placed at known landmark positions. The Gaussian kernel values were multiplied by a constant \(\gamma=100\) to reduce training instabilities. The standard deviations of the kernels were the training parameters, where they were regularized by using L2-regularization with a weight of 20.  
All our own implemented neural networks were trained by using the Adam optimization algorithm [13], with an initial learning rate of \(1\times 10^{-4}\). The learning rate was reduced by a factor of 0.5 every 50 epochs without loss improvement on the validation set. The training was limited to a maximum of 150 epochs.  
Our software was implemented by using the Python programming language. The constructed and implemented deep neural networks were trained by using the TensorFlow software library. Originally, version 1.15 was employed, but later the code was ported to 2.x libraries (at the end, the models were trained in the 2.4 version library).  
All experiments were conducted on a computer system with an AMD Ryzen Threadripper 2920X 12-Core processor, an NVidia Quadro GV100 graphical card with 32 GB of VRAM and 64 GB of physical RAM, and Samsung EVO 970 NVMe 1 TB storage.
# A Channel Allocation Mechanism for Cellular Networks
## 3 Channel Allocation Mechanism in Cellular Networks
### The Number of Handover Out (\(O_{i}\))  
For estimating the number of Handover Out (\(O_{i}\)), two cases including (1) Call Arrival and (2) Handover In are discussed and analyzed. For instance, when a communicating MS in the car leaves Cell\({}_{i}\), the procedure of Handover Out can be performed in Figures 7 and 8. For the case of Call Arrival, a call is performed by a MS in Cell\({}_{i}\) at \(t_{2}\) and the communicating MS leaves Cell\({}_{i}\) at \(t_{3}\) (in Figure 8). Therefore, the number of Handover Out (\(O_{i}^{A}\)) can be estimated by Formula (5) in accordance with the probability of (\(\tau>l_{i}/V_{i}\)) (in Figure 8). For the case of Handover In, a handover process is performed by a communicating MS in Cell\({}_{i}\) at \(t_{1}\) when the MS enters Cell\({}_{i}\), and the communicating MS leaves Cell\({}_{i}\) at \(t_{2}\) (in Figure 7). Therefore, the number of Handover Out (\(O_{i}^{I}\)) can be estimated by Formula (6) in accordance with the probability of (\(\tau>x+l_{i}/V_{i}\)) (in Figure 7). Therefore, the number of Handover Out (\(O_{i}\)) is expressed as Formula (7):  
\[O_{i}^{A}=F_{i}\times\int_{x=0}^{\infty}\Pr\biggl{(}\tau>\frac{l_{i}}{V_{i}}-x \biggr{)}dx=F_{i}\times\int_{x=0}^{\frac{l_{i}}{V_{i}}}\int_{\tau=\frac{l}{V_ {i}}-x}^{\infty}\mu e^{-\mu\tau}d\tau dx=F_{i}\times\frac{1-e^{-\mu\frac{l_{i }}{V_{i}}}}{\mu}, \tag{5}\]  
\[O_{i}^{I}=F_{i}\times\int_{x=0}^{\infty}\Pr\biggl{(}\tau>x+\frac{l_{i}}{V_{i}} \biggr{)}dx=F_{i}\times\int_{x=0}^{\infty}\int_{\tau=x+\frac{l_{i}}{V_{i}}}^{ \infty}\mu e^{-\mu\tau}d\tau dx=F_{i}\times\frac{e^{-\mu\frac{l_{i}}{V_{i}}}}{ \mu}, \tag{6}\]  
\[O_{i}=O_{i}^{A}+O_{i}^{I}=F_{i}\times\frac{1}{\mu}=l_{i}. \tag{7}\]
## 4 Results and Discussion
### Makespan Valuation  
In this unit, the performance of the presented algorithm in terms of the time taken for the execution of the task was assessed. To evaluate performance, 100 to 400 tasks were considered, and these tasks were executed with onset rates ranging between 10 and 40. Here the onset rate represents the time duration (in microseconds) after which each task is given to the algorithm, that is, the algorithm will become overwhelmed with handling a large number of tasks when assigned immediately. Thus, to prevent this from happening and maintain a low bandwidth in the communication bus, the onset rate was used with different values. The results of the proposed method were compared among those of the HWACO, QANA, MTF-BPSO, ACO, MIN-MIN, and FCFS. Figures 1 and 2 show the results of the simulation.
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
## 4 Result and Discussion
### Classification Performance  
The effectiveness of increasing the number of training data through a pseudo-labeling is also apparent in the classification task. Table 6 shows accuracy, recall, specificity and f1-score for the augmented and the raw data. The overall accuracy, recall, specificity and f1-score increase by 11.3%, 9%, 1.6% and 7.6% at best when more data is used to train a deep learning model implying that the proposed method would most likely be correct on camera lens module classification and is more reliable in terms of prediction. The better f1-score provides more reliability to the conclusion because the test sets are class imbalanced and f1-score is a more suitable measure for such a case. The rates are calculated based on the summed confusion matrices for five test sets shown in Figure 7. Looking at the ROC curves, AUCs generally get higher when the augmented data is used for training. This implies that the constructed model is more capable of distinguishing between the two classes. Therefore, it is now better at predicting zeros as zeros and ones as ones.  
Figure 6: (a) Mean RMSE (b) mean MAE (c) mean \(R^{2}\) of validation sets.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Metric** & **SVM** & **Random Forest** & **Gradient Boosting** & **XGBoost** & **Proposed Method** \\ \hline RMSE & 9.83 & 6.19 & 6.19 & 5.98 & **5.89** \\ MAE & 6.84 & 4.53 & 4.59 & **4.34** & 4.78 \\ \(R^{2}\) & 0.25 & 0.58 & 0.58 & 0.61 & **0.65** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Regression results comparing with the proposed method. Better results are highlighted in bold.
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 3 Method
### Reward Function Design  
One of the goals of visual SLAM is to estimate camera motion and restore pose based on video frames, while traditional visual SLAM calculates camera pose based on information from adjacent frames. We leverage the feature of visual SLAM to construct a reinforcement learning reward module based on the absolute trajectory error between the predicted pose and the true pose of video frames and continuously provide feedback to the agent to enable the selected parameters (actions) to adapt to the current environment and obtain more accurate predicted poses, continuously reducing pose errors to maximize the expected reward.  
The estimated pose of a certain frame in visual SLAM is \(P_{estimate}\), and the true pose in the world coordinate system is \(P_{true}\). We use the Umeyama algorithm [34] to calculate the root mean square error (RMSE) of the translational pose of \(P_{estimate}\) and \(P_{true}\) as the uncertainty G of the system. Our goal is to reduce the uncertainty of the system and improve the accuracy of pose estimation. The formula for calculating uncertainty is shown in Equation (14):  
\[G=RMSE=\sqrt{\frac{\sum_{i=1}^{n}{(P_{estimate}^{i}-P_{true}^{i})}^{2}}{n}} \tag{14}\]  
where \(n\) represents the frame rate and \(G=RMSE\). Assuming the reward of the system is \(r\), the calculation method of the reward function is shown in Equation (15):  
\[r=-\log G \tag{15}\]  
Figure 4: State processing flow.
# Upgrading _In Vitro_ Digestion Protocols with Absorption Models
## 2 Technological Approaches of Intestinal Epithelium Models
### Cell Cultures Used in Epithelium Models
#### 2.3.1 Transwell(r) Systems  
The commercially available Transwell(r) systems can imitate the epithelial barrier to some extent. These are plastic supports with a permeable, porous membrane that can be placed in the wells of a microtiter plate. The epithelial cells can be cultured on the membrane, which is coated with extracellular matrix [28]. The support separates two compartments: the upper one represents the intestinal lumen, while the lower one the blood vessels [29].  
On the membrane of Transwell systems, a polarized epithelium monolayer can be grown due to the tight junctions formed between the cells [30]. The Caco-2 cells that are cultured on the Transwell membranes also express brush border enzymes [22]. This is an important advantage since in the standardized international INFOGEST method [3] brush border enzymes are not yet included. Thus, when the absorption of the digesta is investigated with this model, the effect of brush border enzymes can also be evaluated, although their activities are lower than in the case of enterocytes [7]. Cells from enzymatically fragmented organoids can also be seeded on Transwell supports, which facilitate access to the apical side of the enterocytes [17].  
The models that apply Transwell(r) systems are simple and can be standardized easily but they oversimplify the physiological processes [29]. The intestinal epithelium cells that are cultured on Transwell systems do not form villi-like structures, and do not differentiate into the epithelial cell-types that are characteristic of the normal intestine [56].  
The permeability of the static Caco-2 cell layer is much lower than _in vivo_, because the shear stress that is caused by the flow of digesta and the peristaltic motility is not reproduced in the Transwell system, so the water layer above the cells is not stirred [30]. _In vivo_, the fluid flow also ensures nutrient supply and removal of the components of the digesta that are not absorbed [57].
## 4 Our Approach: General Architecture  
The work done in this paper falls under the scope of a USAID sponsored research project called MiGrid. The main goal of this project is to develop a holistic approach that couples the integration and the storage of renewable energy sources into smart buildings providing a smart microgrid. The deployment of this project is taking place on a university campus. The general architecture of the project is shown in Figure 4 below [21].  
From the architecture depicted above, we can notice six main components for this project:  
1. Wireless sensor network
2. Wireless actuator network
3. Big data analytics platform
4. Renewable energy source
5. NI CompactRIO controller
6. Storage device  
In this work, we are introducing a new component to the previously described architecture and that is an edge platform. The aim behind is to test and deploy a cost effective and energy efficient approach to deliver edge computing in an attempt to reduce the response time and improve the overall performance.  
The edge platform in question is based on SBCs, namely NVIDIA Jetson Nano Developer kit. It consists of three units networked together using ethernet cables. The edge platform sits between the WSN and the big data analytics platform, this will allow the data to be closer to the processing unit for time sensitive applications. The communication between the WSN, the edge platform, and the big data analytics platform is done wirelessly using Zigbee protocol through XBee communication module. The communication consists of sending and receiving data along with the processing results. Data refer to the values sensed by the WSN (temperature, humidity, and motion), in addition to data related to the consumption of the different appliances and the production of the PV panels.  
The edge platform will take care of running time sensitive jobs mainly related to control tasks, while the big data analytics platform will be responsible for batch processing jobs, permanent storage of the data while making data visible to the user in readable and understandable formats.  
The literature review conducted shows the existing gap in the deep and comprehensive analysis of the performance of SBC related to big data analytics jobs. Furthermore, while a large number of research articles are tackling edge computing as a new paradigm intending to reduce the latency and provide a better user experience, they do not emphasize on the platform delivering edge computing. Thus, in our work, we are not only studying the impact of edge integration on our overall architecture, but also looking into the possibility of building an edge platform on SBC. The idea behind this study is to implement an edge platform while respecting the energy efficiency and cost effectiveness pillars of smart grids.
## 3 Security and Privacy-Preserving Solutions for IoE
### Edge AI and Secure Computing in IoE  
In traditional IoE systems, data is typically processed and analyzed in the cloud environment [99]. However, this approach involves disadvantages such as high latency, network bandwidth requirements, and privacy risks [100; 101; 102; 103]. Edge AI solutions overcome these issues by processing data closer to the devices, providing significant improvements in security, privacy, and real-time decision-making processes [104; 105].
# An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning
## 4 Experiments
### Results
The results indicate that PCA-based reduced feature set has shown compromised performance of classifiers compared with complete feature set. Overall, the results of our experiments demonstrate that SVM and ensemble techniques are more stable and accurate to identify extremism from the content of the Twitter dataset. From a feature sets like TF-IDF from uni-gram and bi-gram features and PCA based reduced feature set, the TF-IDF computed from bi-gram features was observed to be more effective with an average accuracy of 84%.
# Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
## 6 Performance Evaluations  
### Network Environment  
We conducted performance evaluations to identify the validity of the proposed algorithm. For the performance evaluations, we adopted the Bellhop channel model for generating the underwater acoustic channels, which was introduced in [63]. This channel model reflects the large-scale effects due to path-loss and multiple propagation paths as well as the small-scale effects such as scattering in UASNs. The ambient noise of UASNs in kHz was modeled as \(10\log\mathrm{N}\left(f\right)=\mathrm{N}_{1}-\tau\log\left(f\right)\), where \(\mathrm{N}_{1}\) and \(\tau\) were set to 50 dB re micro Pa per Hz and 18 dB/decade, respectively, which were determined with empirical experiments [64]. Table 1 presents the system parameters adopted for performance evaluations.
## 5 Conclusions  
The existing methods for structural damage identification using the TF and MSD are only applied for damage warning. In this paper, an improved method based on MSD of strain TF under unknown seismic excitation is proposed to identify the location of structural damage. The strain TF is employed to eliminate the influence of the unknown seismic excitation and is more sensitive to damage than the traditional TF based on acceleration and displacement data. The fusion of strain TF and MSD is used to extract damage features and identify damage location more effectively. The effectiveness of the method is verified by two numerical simulations of a simply supported beam and a cable-stayed bridge. The results obtained indicate that the proposed method can be used to accurately locate the structural damage with good noise resistance. In addition, in the case of multiple damages, the severity of damage can be estimated tentatively by comparing the degree by which MSD (test) exceeds its threshold value.  
It is noted that the selection of frequency points is based on the experience of selecting points near the resonance frequency. In addition, because bridge structures often exhibit nonlinear behaviors under strong earthquakes, it is necessary to study the detection of their nonlinear behaviors and carry out experimental verification in further research. Moreover, according to engineering experience, the reference sensor should be selected in the place  
Figure 8: (**a**) Single-damage of the 69 th element under Chi-Chi earthquake; (**b**) multi-damage of the 59 th and 64 th elements under Chi-Chi earthquake.  
with the smallest damage probability (away from the vulnerable area). In future research, the vulnerability analysis of the structure can be carried out first to determine the location that is not prone to damage. The reference sensor can be arranged at this position, and the influence of different reference sensor positions on the damage localization method can be further studied.  
Conceptualization, X.Z., L.L. and Y.L.; methodology, X.Z. and Y.L.; validation, X.Z. and Y.L.; investigation, X.Z.; data curation, X.Z.; writing--original draft preparation, X.Z.; writing--review and editing, L.L.; funding acquisition, L.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China through Grant No. 52178304 and the National Key R&D Program of China via Grant No. 2017 YFC1500603 and Fujian Natural Science Foundation via the Grant No. 2019J01008.  
The authors also thank [PERSON] of Tongji University for providing the benchmark model of Puqian bridge.  
The authors declare no conflict of interest.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 2 Datasets
### CYGNSS Data  
Cyclone GNSS (CYGNSS) data are widely used GNSS-R data in recent years on account of their high performance in resolution and data quality. CYGNSS satellites were launched at the end of 2016, and the mission was firstly designed for monitoring tropical cyclones over low-latitude ocean (38\({}^{\circ}\)S-38\({}^{\circ}\)N) by GNSS-R. It is a constellation of eight satellites working in the low earth orbit (LEO) of 510 km, whose revisit period is merely about several hours [37]. Nowadays, applications of CYGNSS data have been expanded to land observation and have received satisfactory results. Apart from the high temporal resolution, the spatial resolution has also progressed a lot relative to previous GNSS-R missions, and its highest spatial resolution is about 0.5 km \(\times\) 3.5 km [38], which is sufficient for cyanobacteria bloom monitoring. In this research, 2913 data points of CYGNSS Level 1 Version 3.0 product from August 2018 to May 2022 are employed from 212 days, where the reference MODIS data with high quality could be obtained, and they are available at [[https://podaac-tools.jpl.nasa.gov](https://podaac-tools.jpl.nasa.gov)]([https://podaac-tools.jpl.nasa.gov](https://podaac-tools.jpl.nasa.gov)), accessed on 1 June 2022. Because of the inactive growth of cyanobacteria on cold days, data in winter months (December, January and February) are excluded. Delay-Doppler map (DDM) is the basic observable of GNSS-R; many other observables can be calculated from it to judge the surface condition of SPs. In the CYGNSS dataset, each DDM is saved in 11 Doppler shifts and 17 time delays, containing the reflected signals from SP and the surrounding area. Basic information about every CYGNSS SP is also included in the dataset, for example, its longitude and latitude coordinates, signal incidence angle, and antenna gains. Data points with an incidence angle \(>60\)\({}^{\circ}\) and receiver antenna gain \(<0\) are removed in this study due to their low quality. It needs to be pointed out that only CYGNSS data obtained from 7 a.m. to 7 p.m. on the days where (nearly) cloud-free MODIS images could be accessed are adopted in the research.
## 3 Results and Discussion
### Effects of Various Parameters on MB Removal  
3.2.1. pH Influence  
The determination of the point of zero charge (pH\({}_{\text{PZC}}\)) is crucial for assessing the adsorption capacity of a material. In the case of zeolite (ZGO10), a calculated pH\({}_{\text{PZC}}\) of 4.60 (Figure 5a) indicated that at a pH above this value, the material would acquire a net negative surface charge. This is attributed to the deprotonation of surface functional groups, such as carboxyl, epoxide, and hydroxyl groups, resulting in the generation of negatively charged sites. Consequently, the material exhibited enhanced adsorption capacity for methylene (MB), a cationic dye, due to strong electrostatic interactions. The pK of MB is 3.8, implying that at pH values greater than 3.8, the dye predominantly exists in its deprotonated, cationic form [45]. The electrostatic attraction between the negatively charged zeolite surface and the positively charged MB molecules will maximize the adsorption efficiency.  
Figure 5b illustrates the pH dependency of methylene blue (MB) adsorption onto ZGO. The adsorption capacity showed a significant increase within the pH range of 5 to 9, reaching a maximum efficiency of approximately 92%. This trend is primarily attributed to the electrostatic interactions between the negatively charged ZOG surface (pH\({}_{\text{PZC}}\) = 5.3) and the cationic MB species (pKa = 3.8).  
At pH values below 5, electrostatic repulsion and ionic competition reduced the adsorption efficiency, resulting in a lower removal rate of 73%. Conversely, at pH values above 10, the formation of complexes between hydroxide ions and MB molecules decreased the availability of the dye for adsorption. Based on these observations, a working pH of 6.0 was chosen to optimize MB adsorption.  
Figure 5: **(a)** Point of zero charge of ZGO10; **(b)** effect of pH on the equilibrium adsorption capacity of the composite ZGO10 towards MB.
## 3 Result and Discussion
### Assay Validation
#### 3.2.2 Calibration Curves and Linearity  
The developed analytical method exhibited a good linearity over the concentration range of 1.0-666 ng/mL with excellent fit (r\({}^{2}\geq\) 0.995). The mean calibration curve linear regression equation of y = 0.102579x + 0.0376 was obtained by using weighing factor 1/x\({}^{2}\). The lowest concentration of CS, 1.0 ng/mL was considered as LLOQ of the method which was quantified with acceptable limit of precision and accuracy (\(\pm\)20%) and having an acceptable signal-to-noise (S/N) ratio, revealing the good sensitivity of the UPLC-MS/MS assay (Figure 2B). The back calculated concentration of remaining calibration standard was also within the acceptable limits of precision and accuracy (\(\pm\)15%).