esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
## 1 Introduction
### IoT Sensors and Actuators in Hydroonic Systems
* RQ2: Which infrastructure is superior in a comparison based on performance indicators such as response time, operational reliability, implementation costs, operational costs, scalability, data accessibility, and security? * RQ3: What implications do the performance differences have for the practical implementation of automatic hydroponic systems? The evaluation of these criteria provides insights into the trade-offs between cloud-based and local hydroponic infrastructures. The research will guide researchers, engineers, and agricultural practitioners in selecting the most effective solution.
# Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
## 7 Benchmark Datasets
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ref \#** & **Datasets** & **Image Resolution** & **Image Acquisition** & **Availability** & **No. of Images** & **Use** \\ \hline
[177] & Messidor-2 & \begin{tabular}{c} 1440 \(\times\) 960, \\ 2240 \(\times\) 1488, \\ 2304 \(\times\) 1536 \\ \end{tabular} & \
\begin{tabular}{c} Topon Digital Camera with \\ 45-Degree Field of View \\ \end{tabular} & Online/Free & 1748 & MA, HM, and
\begin{tabular}{c} Retinal \\ Vessel Detection \\ \end{tabular} \\ \hline
[178] & E\_ophtha Ex and
\begin{tabular}{c} E\_ophtha MAs \\ \end{tabular} & 2048 \(\times\) 1360 & Captured by OPHDIAT & Online/Free & 463 & MA and Ex Detection \\ \hline
[179] & STARE & 605 \(\times\) 700 & \
\begin{tabular}{c} Topon TRV 50 35 \\ Field of View \\ \end{tabular} & Online/Free & 400 &
\begin{tabular}{c} Irregular Blood Vessel, HM, \\ Ex, and MA Detection \\ \end{tabular} \\ \hline
[181] & Kaggle & \
\begin{tabular}{c} 433 \(\times\) 289 to \\ 5184 \(\times\) 3456 \\ \end{tabular} & Different Digital Cameras & Online/Free & 88,702 &
\begin{tabular}{c} Exudate, MA, HMs, and \\ Blood Vessel Detection \\ \end{tabular} \\ \hline
[182] & DIARETDB0 & 1500 \(\times\) 1152 & Digital Camera with 50\({}^{\circ}\) FoV & Online/Free & 130 &
\begin{tabular}{c} HE, SE, MA, HM, and \\ Neovascularization Detection \\ \end{tabular} \\ \hline
[183] & DIARETDB1 & 1500 \(\times\) 1152 & Digital Camera with 45\({}^{\circ}\) FoV & Online/Free & 89 &
\begin{tabular}{c} Irregular Blood Vessel, MA, \\ HM, and Ex Detection \\ \end{tabular} \\ \hline
[10] & IDRiD & 4288 \(\times\) 2848 & Digital Camera with 45\({}^{\circ}\) FoV & Online/Free & 516 &
\begin{tabular}{c} Exudate, MA, HM, and \\ Vessel Detection \\ \end{tabular} \\ \hline
[184] & DR1 and DR2 & 857 \(\times\) 569 & Digital Camera with 50\({}^{\circ}\) FoV & Online/Free & \
\begin{tabular}{c} 234 DR1 and \\ 520 DR2 \\ \end{tabular} &
\begin{tabular}{c} HE, SE, MA, HM, and \\ Neovascularization Detection \\ \end{tabular} \\ \hline
[185] & CHASE DB1 & 1280 \(\times\) 960 & Digital Camera with 30\({}^{\circ}\) FoV & Online/Free & 28 &
\begin{tabular}{c} Segmentation of Retinal \\ Blood Vessels \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 7: Description of DR datasets.
## 3 Results and Discussion  
### E-Nose  
To train the neural network, different experiments were carried out, exposing the electronic nose to three different substances for 30 s and recording the values obtained by the sensors during that time. In these experiments, a modification of the heating of the sensors was programmed. It started at 100% and, after 10 s, it dropped to 50%. In this way, changes in the resistance of each sensor to each target substance were observed. Figures 7 and 8 show the sensors' responses to ethanol and hydrogen peroxide, respectively. Figure 9 presents the data obtained when using a clean air sample. The data in figures are presented according to the value received by the analog-digital converter of the 12-bit microcontroller (value proportional to the voltage between 0 and 4096).  
Comparing the previous figures, we verified that each substance generated a different sensor response. In addition, as shown in the first two graphs, there is a period (the first 2 s) that must be discarded because it is the time it takes for the sensors to react. In the results obtained, three important aspects could be observed about the behavior of the electronic nose in the face of different substances:  
1. MICS-4515(RED) and MICS-5914 sensors were very sensitive to hydrogen peroxide. The difference in resistance increased above 1000, which is very positive for the classification of this substance.
2. MICS-4515(RED) and MICS-5914 sensors saturated in the presence of ethanol. They barely modified the resistance, unlike when there was no ethanol in the environment.
3. MICS-4515(OX) and MICS-5526 sensors similarly responded to all substances.  
Figure 8: E-nose response to a sample of hydrogen peroxide.  
Figure 9: E-nose response to a clean air sample.
## 4 Experiments and Results  
Three experiments, namely, prior decoder training, Deformer pre-training, and Deformer fine-tuning, were performed to thoroughly evaluate the effectiveness of the proposed Deformer-based method in terms of audio music genre classification. First, in the prior decoder training experiment, an autoencoder was trained to convert low-dimensional hidden states into patches of normalized Mel spectrograms. In the Deformer pre-training experiment, Deformer was pre-trained to understand musical deep representations by restoring the original Mel spectrograms from noisy Mel spectrograms. Finally, in the Deformer fine-tuning experiment, the pre-trained [PERSON] classified the audio music genre through supervised learning. To evaluate the performance and effectiveness of the proposed method, two baseline models are introduced for comparison. The first model [13] utilizes a residual neural network-bidirectional gated recurrent unit (ResNet-BiGRU), while the second relies on S3T [16]. These models serve as benchmarks, helping to underscore the advantages of the proposed technique for music genre classification.
## 3 Results and Discussion
### Point Source Simulation
Figures 5 and 6, respectively, show the A-weighting SPL MAE (mean absolute error) variation curves with different array apertures and array element spacings at the analyzed frequencies 1-1000 Hz (step length is 1 Hz) between the separation technique and theoretical values of point sources at three test points. Figure 4: A-weighting SPL comparison results (source intensity ratio and phase taken as the set values): (**a**) point source 1, (**b**) point source 2. Figure 3: A-weighting SPL comparison results (source intensity ratio and phase obtained by phase conjugation method): (**a**) point source 1, (**b**) point source 2. As can be seen from Figure 5, the MAE first decreases and then increases with the array aperture increases for all point sources and test points, the MAE increases rapidly when the array aperture is smaller than the source distances, and the smallest error appears where the array aperture is 6 m (i.e., 1.5 times the source distances). As can be seen from Figure 6, the MAE increases, with the array element spacing increasing for all point sources and test points, and the MAE increases rapidly when the element spacing is greater than half-wavelength at a minimum analyzed frequency of \(\lambda_{\text{min}}/2\). In practice, we can rarely obtain the best array form (aperture is 1.5 times the sources' distance and the element spacing is smaller than \(\lambda_{\text{min}}/2\)), which is restricted by noise source distribution and measuring conditions. We assume that the error tolerance for transformer noise separation in a substation is 2 dBA based on engineering application experience. It can be seen from Figures 5 and 6 that the MAE satisfies the error tolerance when the array aperture is greater than the source distances (i.e., 4 m for this example) and the array element spacing is less than \(4\lambda_{\text{min}}/7\), which is a more practical result. Figure 5: Influence of array aperture on MAE of separation technique: **(a)** point source 1, **(b)** point source 2. Figure 6: Influence of array element spacing on MAE of separation technique: **(a)** point source 1, **(b)** point source 2.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 5 Numerical Results  
Several simulation experiments have been conducted using MATLAB R2016b (The MathWorks, Inc., Natick, MA, USA), under Windows 7, on a regular PC 64-bit operating system, with an Intel(r) Core(tm)i5-4200U CPU @ 2.30 GHz, to verify the feasibility of the GNSS signal compression scheme described above. The simulations are executed in three parts: the GPS C/A signal compression with the static receiver, the BOC1 signal compression with the static receiver, and the GPS C/A signal compression for receiving under avionic conditions. All experiments used real data recordings, and cover both urban and suburban areas.
###### Abstract  
The femtocell has evolved as a great solution for improving coverage and traffic offloading from the current LTE cellular networks, and it accomplishes the dreams of the high data rate for indoor mobile users. However, the exponentially expanding LTE femtocells cause interference in the network, as they share the same licensed spectrum with a macrocell. To tackle this issue, numerous interference mitigation techniques have been proposed in the literature. In this paper, we proposed an Active Power Control (APC) technique, which not only reduces Inter-Cell Interference (ICI) in a Macro User Equipment (MUE), generated from the downlink transmission power of an inadequately deployed femtocell, but also reduces unnecessary power consumption to achieve a green femtocell network. The simulation results show that the proposed APC technique effectively reduces ICI and optimizes the throughput performance of the MUE. Compared with the existing power control techniques, the APC technique provides a balanced trade-off in attaining necessary Quality-of-Service (QoS) of the Femto User Equipment (FUE) and reducing ICI to the victim MUE existing in the close proximity of the femtocell.  
active power control; Signal-to-Interference-plus-Noise-Ratio; inter-cell interference; femto user equipment; macro user equipment; path loss; green Impact and CO\({}_{2}\) emission +
Footnote †: journal: Sensors
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 4 Methods
### Atmospheric Correction in Planetary Albedo
#### 4.1.1 V01 Method  
The V01 method uses an atmospheric transmission (\(\tau_{SW}\)) equation in which the meteorological values are of great importance [37]. In this case, the partial pressure of water vapor is calculated taking into account the minimum air temperature, as shown in Equation (7) [53]:  
\[\tau_{SW}=0.35+0.627\cdot exp\left[-\frac{0.00146\cdot P_{O}}{K_{t}\cdot cos \theta}-0.075\left(\frac{W}{cos\phi}\right)^{0.4}\right], \tag{7}\]  
being  
\(P_{O}\): local atmospheric pressure (KPa).  
\(K_{t}\): air turbidity coefficient or clearness index. The value \(1\) is used for clear air, and 0.5 for extremely turbid or polluted air.  
\(W\): precipitable water (mm), computed with Equation (8):  
\[W=0.14\cdot e_{a}\cdot P_{O}+2.1\, \tag{8}\]  
\(e_{a}\): partial pressure of atmospheric water vapor (KPa), calculated as in Equation (9):  
\[e_{a}=0.6108\cdot exp\left(\frac{17.27\cdot T_{min}}{T_{min}+237.3}\right)\, \tag{9}\]\(T_{min}\): daily minimum air temperature from the most relevant weather station (\({}^{\circ}\)C).
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks
### Communication Model  
In this system, there are two channels to consider: the IoT-HAP drone channel and the HAP drone-LEO channel.
## Theories and Methods
### Planeness Error Evaluation Model  
It is possible to calculate the flatness of a metal surface if the multi-point spacing to a certain ideal plane is obtained. The planeness error refers to the variation of the measured actual surface relative to the ideal plane. The key to error evaluation is to find the ideal plane with the minimum condition [26; 27]. Let the ideal plane equation be as follows:  
\[z=ax+by+c \tag{1}\]  
The least square method is used in this work to evaluate the planeness of the measured surface, and the error function is constructed from the set ideal plane equation:  
\[\mathrm{S}=\sum(ax_{i}+by_{i}+c-z)^{2} \tag{2}\]  
In order to minimize the value of \(S\), which is the distance from each point to the plane, it should be satisfied that \(\frac{\partial\mathrm{S}}{\partial a}\), \(\frac{\partial\mathrm{S}}{\partial b}\), and \(\frac{\partial\mathrm{S}}{\partial c}\) are all 0. Then, we can obtain the following:  
\[\left\{\begin{array}{l}\sum 2(ax_{i}+by_{i}+c-z_{i})x_{i}=0\\ \sum 2(ax_{i}+by_{i}+c-z_{i})y_{i}=0\\ \sum 2(ax_{i}+by_{i}+c-z_{i})=0\end{array}\right. \tag{3}\]  
In the equations, \(i\) means the serial number of the measuring point. Arrange these equations, then obtain the following:  
\[\left\{\begin{array}{l}a\sum xi^{2}+b\sum x_{i}y_{i}+c\sum x_{i}=\sum z_{i}x _{i}\\ a\sum x_{i}y_{i}+b\sum y_{i}^{2}+c\sum y_{i}=\sum z_{i}y_{i}\\ a\sum x_{i}+b\sum y_{i}+cn=\sum z_{i}\end{array}\right. \tag{4}\]  
Figure 4: Schematic diagram of clearance measurement.  
Change the equation into the matrix form:  
\[\left|\begin{array}{ccc}\sum x_{i}^{2}&\sum x_{i}y_{i}&\sum x_{i}\\ \sum x_{i}y_{i}&\sum y_{i}^{2}&\sum y_{i}\\ \sum x_{i}&\sum y_{i}&n\end{array}\right|*\left|\begin{array}{c}a\\ b\\ c\end{array}\right|=\left|\begin{array}{c}\sum z_{i}x_{i}\\ \sum z_{i}y_{i}\\ \sum z_{i}\end{array}\right| \tag{5}\]  
Bring the \(x\), \(y\), and \(z\) values of each measuring point into the matrix equation and then the equation coefficients, \(a\), \(b\), and \(c\), could be obtained as well as the plane equation. The distance from each measuring point to the fitting plane, i.e., \(d_{i}\), is given by  
\[d_{i}=\frac{z-ax_{i}-by_{i}-c}{\sqrt{a^{2}+b^{2}+1}} \tag{6}\]  
Set the point \(P_{max}(x_{M},y_{M},z_{M})\) as the maximum deviation from each measuring point to the least square plane, and the \(P_{min}(x_{L},y_{L},z_{L})\) minimum deviation from each measuring point to the least square plane. Then, the planeness could be calculated by  
\[f=(d_{i})_{max}-(d_{i})_{min}=\frac{(z_{M}-z_{L})-a(x_{M}-x_{L})-b(y_{M}-y_{L}) }{\sqrt{a^{2}+b^{2}+1}} \tag{7}\]  
The above equations calculating the surface planeness are commonly used in many previous works, like in reference [27].
## 6 Conclusions  
1. Based on the analysis method of elastic mechanics, the correlation between the normal stress of the coal seam and the principal stress of the coal under the influence of the dip angle is presented. By integrating with the stress state model at the TLEZ boundary under macro-scale conditions, the quantitative characterization of the TLEZ boundary in SDCS is achieved.
2. The boundary spatial morphology of the TLEZ in a fully mechanized top-coal caving face with SDCS takes on an \"asymmetric arc-shaped ribbon-like curved surface\". The middle-upper part of the boundary is the farthest from the longwall face, then comes the upper part and the middle-lower part, and the lower part is the closest to the working face. The boundary spatial morphology of the TLEZ along the inclined direction of the working face exhibits a distribution in the form of an \"asymmetric circular-arc arch\". The distance between the boundary spatial morphology of the TLEZ and the working face, along the strike direction of the working face, demonstrates a pattern of gradually increasing from top to bottom.
3. Through the comparison between numerical simulation and theoretical calculation, it can be seen that the overall pattern of the boundary spatial morphology of the TLEZ in SDCS is consistent. Moreover, this boundary spatial morphology is further validated by the on-site monitoring results. The findings indicate that the analytical characterization of the TLEZ boundary in the fully mechanized top-coal caving face of SDCS is reasonable and feasible.
4. According to the boundary spatial morphology of the TLEZ in the fully mechanized top-coal caving face with SDCS, the top coal exhibits maximum fragmentation intensity in the middle-upper section along the working face inclination, necessitating targeted protective measures. By putting forward a reasonable caving principle, the stability of the \"support-surrounding rock\" system is ensured.  
**Author Contributions:** D.L.: writing--review and editing, supervision, project administration, and funding acquisition. X.W.: writing--original draft, conceptualization, methodology, software, validation, data curation, and formal analysis. Y.W.: conceptualization, supervision, and project administration. P.X.: investigation, data curation, and supervision. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Program of National Natural Science Foundation of China (Grant number: 52274137) and the Program of National Natural Science Foundation of China (Grant number: 52474145).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 3 Results
### Validation of Up-Scaled GPP with Field Data
#### 3.1.2 Time Series of the Up-Scaled GPP Using Machine Learning Models  
Temporal dynamic patterns of the up-scaled GPP using machine learning methods in nine eddy covariance systems are shown in Figure 5. In general, up-scaled GPP using five machine learning methods could reflect the time series of GPP at flux tower sites. The time series of up-scaled GPP agreed well with GPP in Daman (cropland), Shidi (wetland) and Arou (grassland). Up-scaled GPP using RF agree well with GPP in Sidaoqiao (shrub land), Huyanglin (DBF), Arou (grassland) and Dashalong (grassland). In Daman (cropland, mainly maize), RMSE was the least using SVM. In Huazhaizi (bare land) and Bajitan (bare land), RMSE were the highest using ANN. On the whole, up-scaled GPP using Cubist and DBN could most accurately reflect the temporal dynamic patterns of GPP at flux tower sites.  
Figure 4: Validation of up-scaled GPP using **(a)** Cubist, **(b)** RF, **(c)** ANN, **(d)** SVM, **(e)** DBN against GPP at flux tower sites. These figures demonstrate the relationship between 8-day average GPP at flux tower sites and 8-day average up-scaled GPP from May to September in 2014 using five machine learning methods. The solid line is the fit line between GPP at flux tower sites and up-scaled GPP, and the dashed line is the 1:1 line.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 5 Discussion of Results  
The ultrasonic microimaging methodology demonstrates non-destructive monitoring capability for solder joint degradation, overcoming the inherent limitations of conventional destructive cross-sectioning methods. Through systematic implementation of denoising, normalization, and intensity compensation algorithms, the image processing pipeline effectively enhanced signal integrity, enabling precise tracking of defect evolution from initiation to complete failure.  
The extracted mean intensity of solder joint central regions exhibited a strong correlation with thermal cycling progression, serving as a quantifiable degradation indicator. This metric successfully captured the three-stage failure process: initial stability, gradual defect accumulation, and final abrupt failure. Position-dependent lifespan variations were clearly observed, with corner joints showing significantly shorter service life compared to central joints, consistent with the anticipated thermal stress distribution patterns in electronic packaging.  
Figure 15: Comparison of lifespan prediction effectiveness for solder joints. (**a**) Simulation results of solder joint lifespan; (**b**) solder joint lifespan according to the method proposed in this paper.  
The two-parameter Weibull distribution model provides a statistically robust framework for failure probability estimation, as evidenced by the low residual errors between experimental data and model predictions. This mathematical approach effectively differentiates failure characteristics across solder joint locations while maintaining computational efficiency.  
While the method demonstrates strong experiment-model consistency, its current implementation focuses on steady-state thermal cycling conditions. Further validation is required for transient thermal shock scenarios and multi-axial mechanical loading environments. The image resolution threshold (exact value specified in original data) may also limit detection of sub-micron scale early-stage defects.
# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
## 3 Experimental Setup
### Camera Setup  
A camera is installed on the monitored road. The camera data capture script runs synchronously with the \(\phi\)-OTDR capture script. Therefore, for every collected \(\phi\)-OTDR trace, a 5 min camera video is collected and stored. The two recording systems have a ping delay in the order of milliseconds, which is negligible considering the 5 min recording time and the vehicle transit times. The codes run concomitantly and save \(\phi\)-OTDR data and camera videos with the same label. Due to power limitations at the laser and the photoreceiver maximum input power to avoid saturation, only heavy vehicles can be detected by using the collected images (vans, buses, and trucks). To avoid biases and allow subsequent experiments with preprocessing techniques, no prior preprocessing is carried out over the \(\phi\)-OTDR raw data and over camera recordings. Raw data arrays are transformed into images and an image dataset is built. To assess image features and establish a ground truth, we apply the YOLOv8 [34; 37] algorithm with tracking to detect buses and trucks in 5 min videos synchronized with the \(\phi\)-OTDR traces. YOLOv8 is a computer vision model that classifies  
Figure 7: Characterization of vehicle speeds around the campus avenue, showing four speeds: 10, 20, 30, and 40 km/h. Below, an example of waterfall diagram in a busy 5 min traffic frame.  
objects along video frames. Depending on video quality, YOLOv8 accurately detects people, vehicles, trucks, and buses. The deployed YOLOv8 architecture is used for bus and truck tracking, processing at 15 FPS and assigning stable identifiers (IDs) across frames. This method accurately counts and determines the speed of heavy vehicles by correlating unique IDs with the actual distance covered by object boxes in video frames. Diagonal linear features are correlated with unique YOLOv8-tracked IDs, working as a ground truth. Two labels are generated with YOLOv8: vehicle car count in a 5 min time frame and average speed. Vehicle counts are extracted by counting tracking IDs from YOLOv8. Average speed is obtained by calculating it from the displacement of object boxes detected by the YOLOv8.