esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 4 State of Art
The electrical-to-optical bandwidth is up to 655 MHz at an optical output power of 2.3 mW. And a data transmission rate of 7.36 Gb/s is achieved in an adaptive bit and energy loading OFDM-based VLC system using a single pixel of the proposed violet micro-LED. In 2017, [PERSON] et al. realized a 17.6 Gb/s transmission in a GaN LD based VLC system with impedance-matched transmission-line circuit board and a UFMC additive 16-QAM OFDM [30]. And this is the highest data rate ever obtained in high-speed VLC systems till now. Besides the common transmitters, VLC systems based on other transmitters, such as the combinations of LD and phosphors, \(\upmu\)-LED and polymer color converter as well as RC-LED and \(\upmu\)-LEDs also can achieve Gb/s class transmission data rates with proper system setups. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Transmitter** & **Receiver** & **Modulation** & **Data Rate** & **Distance** & **Comments** & **Ref.** \\ \hline
2017 & Blue LD & Ultrafast & UFMC additive & 17.6 Gb/s & 16 m & / & [30] \\  & & photodiode(UPD) & 16-QAM OFDM & & & & \\ \hline
2017 & RYGB LDs &
\begin{tabular}{c} Imaging Diversity \\ Receiver \\ \end{tabular} & COK & 10 Gb/s & / & / & [59] \\ \hline
2017 & NUV LD + RGB & APD & OOK-NME & 1.25 Gb/s & 15 cm & / & [60] \\ \hline
2017 & \(\upmu\)-LED & PIN & OFDM & 7.91 Gb/s & / & / & [28] \\ \hline
2017 & \(\upmu\)-LED & APD & PAM(spatial) & 7 Gb/s & 0.5 m &
\begin{tabular}{c} 9 \(\times\) 9 MIMO system \\ Post-equalization \\ \end{tabular} & [61] \\ \hline
2017 & RGB LED & PIN & \
\begin{tabular}{c} OFDM(adaptive bit \\ loading) \\ \end{tabular} & 6.36 Gb/s & 1 m &
\begin{tabular}{c} 2 \(\times\) 2 MIMO system \\ Pre-and Post-equalization \\ \end{tabular} & [58] \\ \hline
2017 & Red LED & silicon PN & OOK & 600 Mb/s & 6 m &
\begin{tabular}{c} 2 \(\times\) 2 imaging MIMO system \\ Pre-and Post-equalization \\ \end{tabular} & [62] \\ \hline
2016 &
\begin{tabular}{c} Red RC-LED and blue \\ and green \(\upmu\)-LEDs \\ \end{tabular} & PIN & OFDM(WDM) & 10 Gb/s & 1.5 m & / & [64] \\ \hline
2016 & \(\upmu\)-LED & / & DCO-OFDM & 5 Gb/s & & / & [29] \\ \hline
2016 & \(\upmu\)-LED & / & PAM & 3.5 Gb/s & & Post-equalization & [29] \\ \hline
2016 & \(\upmu\)-LED & APD & PAM & 2 Gb/s & 0.6 m & Pre-equalization & [65] \\ \hline
2016 & RGB-LED & APD & OAM-OFDM & 1 Gb/s & 100 m & / & [66] \\ \hline
2016 & ps-LED & PIN &
\begin{tabular}{c} OFDM(adaptive bit \\ and power loading) \\ \end{tabular} & 2.08 Gb/s & 1 m & Pre-equalization & [48] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Survey of practical demonstrations of high-speed VLC systems.
## 6 Model Analysis
### CTMC with Absorbing State: MTTSF Analysis  
Mean time to failure (MTTF) provides the mean time it takes for the system to reach one of the designated failure states, given that the system starts in a good state. We use the mean time to security failure (MTTSF) as a measure for quantifying the security of the WSN. MTTF or MTTSF can be evaluated by making the compromised state of the CTMC an absorbing state, as shown in Figure 3. Once the system reaches the absorbing state (the compromised state \(C\)), the probability of moving out of this state is zero, i.e., there are no outgoing arcs from such states.  
Given a CTMC with one absorbing state, we may enter this chain at some state \(i\) with probability \(\alpha_{i}\). For each state that is visited, the time before going to the next state follows an exponential distribution. Thus, the time required to reach the absorbing state from an initial state \(i\) is a sum of samples from exponential distributions. The mean time to absorption can be solved easily by introducing the phase-type distribution (PH distribution). For a given CTMC, a PH distribution is defined as the distribution of the time to absorption that can be observed along the paths in a CTMC with one absorbing state [32]. The first moment of a PH distribution exactly expresses the mean time to absorption in an absorbing CTMC:  
\[E[X]=-\underline{\alpha}\mathbf{T}^{-1}\underline{1}\, \tag{12}\]  
where \(\underline{\alpha}=\{\alpha_{i},i\in X_{s}\}\) is the row vector of initial probabilities of the states and \(\underline{1}\) is the column vector of ones. Substituted into our model parameters, we get:  
\[MTTSF=\frac{(\lambda_{1}+\lambda_{4})(\lambda_{1}+\lambda_{2}+\lambda_{3}+ \lambda_{6})}{\lambda_{2}\lambda_{3}\lambda_{4}}. \tag{13}\]  
When a software lifetime is given as \(T_{l}\), we assume the supremum to be denoted by \(sup(T_{l})\). In order to ensure system security, the MTTSF should be larger than the \(sup(T_{l})\). That is:  
\[\frac{(\lambda_{1}+\lambda_{4})(\lambda_{1}+\lambda_{2}+\lambda_{3}+\lambda_{ 6})}{\lambda_{2}\lambda_{3}\lambda_{4}}>sup(T_{l}). \tag{14}\]  
Given the system parameters, the required rekeying rate \(\lambda_{1}\) can be obtained by solving the inequality Equation (14). It has two roots, and we only take the positive root as:  
\[\lambda_{1}>\frac{1}{2}\left(-\lambda_{2}-\lambda_{3}-\lambda_{4}-\lambda_{6} +\sqrt{\varrho^{2}}\right)\, \tag{15}\]  
Figure 3: CTMC model with an absorbing state.  
in which:  
\[\sigma^{2}= \lambda_{2}^{2}+\left(\lambda_{3}-\lambda_{4}+\lambda_{6}\right)^{2}\] \[+2\lambda_{2}\left(\lambda_{3}-\lambda_{4}+\lambda_{6}+2\lambda_{3 }\lambda_{4}sup(T_{l})\right). \tag{16}\]  
Then, the system rekeying rate can be determined for a certain software lifetime. After determining the security and performance measures for the model, we conduct the analysis of the tradeoff between security and performance of WSNs in the following section.
## 2 Wireless Multimedia Sensor Networks
### Battery-Powered
#### 2.1.3 Dynamic Power Management  
Dynamic power management (DPM) refers to the exploitation of idleness of different system parts of a sensor node in order to perform selective shutdown of idle components [35]. By keeping track of energy consumption at different components and taking into account the network dynamics, a DPM framework can derive policies to decide when a component can be shut down or when a sensor node can go to sleep and how long the component or sensor node stays in the hibernation state. For WMSNs, a major challenge in designing DPM frameworks is the necessity to consider QoS guarantees. The work by [PERSON] and [PERSON] [36] is a good example of QoS-aware DPM framework. It uses a Markov decision process to build the DPM framework by taking into consideration the operation states of the camera and transceiver, the network dynamics including queuing status and channel condition, and the video traffic arrival process. And a dynamic programming approach is used to derive the optimum policy of the DPM framework. Nonetheless, one drawback in DPM is the risk of missing events when shutting down the camera. To tackle this problem, [PERSON] and [PERSON] [37] proposed an approach--disallow sleep of camera if the task is critical and allow for sleep of camera in non-critical tasks when the sleep threshold is satisfied.
## 3 Modeling Methodology
### Data Collection and Preparation  
#### 3.1.1 Generation of an Inventory Map of Historical Landslides  
A map of the landslides that have occurred in the recent past is an important component for modeling of the landslide susceptibility using machine learning methods [52]. To generate such a map for the Nam Dan commune, the records for historical landslides were obtained from the SRV-10/0026 project that had been conducted in the study area. These records were verified and updated via visualization interpretation of remotely sensed data and field surveys (October 2018 and August 2019), also adding the recently occurred landslide locations at the talus provincial road No 178, in August 2019, to the initial records. In total, 76 locations of landslide events were identified across the Nam Dan commune. Most of the slides were of the shallow landslide type. Dividing the detected landslides into separate sets, the training and validation datasets that respectively comprised 53 (-70%) and 23 (-30%) landslides, were generated.
## 4 Results
### Accuracy Assessment  
The overall classification accuracies of the Landsat OLI data, ALOS-2 data, and the post-fusion data were mainly measured using the overall classification accuracy (overall's accuracy) and Kappa coefficient (Table 4). The classification accuracy of several types of data is depicted in Figure 15. The Landsat OLI data (cloudiness 26.28%) have the lowest overall classification accuracy, with the total accuracy of 58.09% and a Kappa coefficient of 0.4913. The overall classification accuracy of the Landsat OLI data (cloudiness 8.43%) is comparable to that of the ALOS-2 data, with the total accuracy of approximately 83% The Kappa coefficient is approximately 0.8; the overall classification accuracy of the Landsat OLI and ALOS-2 data is the highest, with the total accuracy of 86.97% and the Kappa coefficient of 0.8447. The fused data contains both the spectral information of the optical data and the polarization decomposition characteristics of the fully polarized SAR data. It has strong information complementarity. Compared with the Landsat OLI image or ALOS-2 image alone, the overall accuracy is improved by about 4%. The specific classification accuracy of each locality type (forest, shrubland, gardenland, farmland, water, artificial surfaces and others) is shown in Figures 15-17. In terms of three types of data, the feature fusion data has higher extraction accuracy for forest, shrubland, gardenland, farmland, water and artificial surfaces, especially in extracting forest and farmland. Landsat OLI data (cloudiness 8.43%) only has higher extraction accuracy for shrubland, ALOS-2 data has higher extraction accuracy for artificial surfaces and forest. The experimental data has lower accuracy in producer accuracy of shrubland and gardenland, indicating that these two types are most likely to be misclassified, forest and farmland are less likely to be misclassified. From the overall classification accuracy, it can be seen that, for the same Landsat OLI data, different cloud covers have a great impact on the classification accuracy of land cover. When the cloud cover is 8.43%, the OA is about 82.75%, and the Kappa coefficient is 0.791, which basically meets the general feature extraction requirements. When the cloud cover is 26.28%, the OA is 58.10%, and the Kappa coefficient is only 0.4913, under this situation the accuracy is low and cannot be used for basic feature type extraction, and it is difficult to identify the LULC types with more mixed pixels or objects. Figure 16, Figure 17, and as expected--the improved SVM method--is found as best suited for forest and shrubland features classification, accuracy of both UA and PA got increased. Accuracy of forest areas was improved, and this was reflected by the intensity values present in the microwave span image which contributed to the fused features image. Figure 14: Comparison of classification results. (Remark: the accuracies in subfigures represent the PA of different datasets).
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
## 2 Materials and Methods
### Acoustic Feature Extraction  
In this section, we describe the four features that have been commonly reported to be used for audio-based precision beekeeping, namely RMS, spectrogram, MFCCs and DWT. Moreover, in Section 4, we describe the modulation spectrogram that we used. Before feature extraction, audio recordings were resampled from 48 kHz to 2048 Hz, as the literature suggests that the majority of the bee sounds are produced below 1000 Hz [24].
###### Abstract  
The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in EPF and ESP over a mountain river basin in China. This evaluation was carried out based on both deterministic and probabilistic metrics at a daily temporal scale. The effectiveness of two postprocessing methods, the Generator-based Postprocessing (GPP) method, and the Bayesian Model Averaging (BMA) method were also investigated for EPF and ESP. Results showed that (1) The ECMWF shows better performances than NCEP in both EPF and ESP in terms of evaluation indexes and representation of the hydrograph. (2) The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances. (3) Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, there are desirable EPF performances for both ECMWF and NCEP at all 7 lead days, as well as highly skillful ECMWF ESP for 1-5 lead days and average moderate skillful NCEP ESP for all 7 lead days. The results of this study can provide a reference for the applications of TIGGE over mountain river basins.  
TIGGE; ensemble precipitation forecast (EPF); postprocess; ensemble streamflow prediction (ESP); mountainous river basin +
Footnote †: journal:  
0000-0001-6000-0001-6000][PERSON]. [PERSON]  
0000-0001-6000-0001-6000][PERSON]. [PERSON]  
0000-0001-6000-0001-6000][PERSON]  
0000-0001-6000-0001-6000][PERSON]. [PERSON]  
0000-0001-6000-0001-6000-0001-60000][PERSON]
# Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning
## 1 Introduction
Deep learning techniques, employed to standardize lighting and normalize colors, demonstrate greater promise in dermatological contexts. They apply to histopathological and other medical images [16]. The main challenges of these techniques include the variability of resulting color tones and the balance between normalization and the preservation of details [24]. The investigation of lighting correction techniques is still relevant for improving accuracy in the diagnosis of skin cancer. Studies show that lighting variations can distort colors in images. Maintaining color consistency has been shown to increase the accuracy of results, highlighting the importance of choosing and applying appropriate adjustments. While traditional methods, such as Gray World and Shades of Grays, are used in images of skin lesions, potentially resulting in losses of color consistency, deep learning techniques like IECET and DermoCC-GAN present promising solutions. These solutions may be a viable alternative for improving classification rates of malignant lesions. As deep learning is also widely used in classifying skin cancer images, it is important to investigate how color variations are handled in such techniques [6]. Lighting can affect the perception of colors in lesions, and subtle differences in hues may lead to incorrect diagnoses. Understanding how colors are handled in deep learning could allow adjustments in architectures to identify color nuances, improving early detection of lesions in malignant conditions.
## 1 Introduction
The main contributions of this study can be summarized as follows:  
* To measure how well our framework generalizes well despite differences for highlands and shooting dates, we operated UAVs equipped with a multispectral sensor and collected multispectral images under different spatiotemporal conditions. * Our proposed framework shows exceptional detection performance for test images collected from the areas included in the training data but on different dates. Moreover, our method generalizes well to unseen areas not used during training. * To analyze which wavelength in multispectral images has a positive effect on detection performance, we experimented with four different combinations of input wavelengths and compared their detection performances. Based on the results, we demonstrate that the semantic segmentation model trained with blue, green, red, and red edge wavelengths is the most suitable for automating the process of identifying cabbage cultivation fields. The remainder of this paper is organized as follows. In Section 2, we review the papers associated with semantic segmentation models based on CNN, land-cover classification, and applications of CNN to UAV imagery. In Section 3, we describe the proposed framework used to detect cabbage cultivation. In Section 4, we give a thorough analysis of the experimental results of different semantic segmentation models. In Section 5, we discuss our results in terms of semantic segmentation models and input wavelengths. In Section 6, we summarize our study with conclusions and future research directions.
## 4 Results of the Statistical Analysis of DARP
### Statistical Analysis of the Number of Control Message
To solve this question, additional comparisons can be made between groups of means factors, by using Multiple Ranges Test. The method used to discriminate between means is [PERSON]'s least significant difference (LSD) procedure, which is based on the construction of hypothesis tests for the difference of any pair of means. Table 4 presents the Multiple Range Test for Reconnection Timer, which is one of the most relevant factors having influence on the variable Number of control messages sent during the set-up time. From Table 4, it can be established that there are three homogenous groups without intersection (identified using columns of X's), which means that the three different levels of the Reconnection Timer have all significant and different influences on the dependent variable. The level T_REC3 (with values in the interval [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50]) has the best performance and therefore lowest mean value, and the level T_REC1 (with values in the interval [0.1-4]) has the worst performance with the highest mean value. This information can also be presented graphically (Figure 5). A similar analysis can be performed with the ACK Timer factor. In this case, three completely different and statistically significant levels for ACK Timer factor are presented in Table 5 and graphically in Figure 6.  
Regarding the interactions factor, one of the six more representatives is the interaction between the main factor Reconnection Timer and ACK Timer. Figure 7 presents this evolution for the different levels of these factors, showing that a higher time configuration presents a lower use of control message. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reconnection Timer (T\_REC)** & **LS Mean** & **LS Sigma** & **Homogeneous Groups** \\ \hline T\_REC3 & 17.9 & 0.045 & X & \\ T\_REC2 & 19.8 & 0.046 & X & \\ T\_REC1 & 27.3 & 0.045 & & X \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the Multiple Range Tests for the factor Reconnection Timer (T_REC), using as dependent variable the Number of control messages sent during the set-up time. Figure 5: Mean values of the levels of the factor Reconnection Timer, analyzing the dependent variable Number of control messages sent during the set-up time.
## 5 Conclusions  
Here, we studied how soil tillage operations can be detected at the parcel level using remote sensing data in Portugal. We used radar and optical data from Sentinel-1 and -2, respectively. A rules-based approach and two machine learning approaches were implemented. In the machine learning case, two different approaches were used to deal with the imbalance in the dataset, (1) using the original dataset and (2) using the SMOTE approach. Our study shows the difficulty of detecting soil tillage operations using remote sensing data. Nevertheless, machine learning seems to be more promising than the rules-based approach. The best approach was the XGB-SMOTE with a recall of 67%, a precision of 8%, an F1 score of 14%, an F2 score of 27%, and an AUC-ROC of 74%. These results also show that further studies are necessary to have a fully automatized model to detect soil tillage operations in Portugal. Applying the same methods for other regions and tillage systems would require making adaptations to the algorithms produced here, which are based on the characteristics of the specific farms used for training. We conclude by inviting other researchers to complement our efforts by providing robust techniques that can be used to reliably identify tillage practices in Portugal and other countries worldwide.  
Conceptualization, T.G.M., J.F., M.C., A.M., I.N., H.L. and R.F.M.T.; methodology, T.G.M. and R.F.M.T.; software and formal analysis, T.G.M.; writing--original draft preparation,T.G.M.; writing--review and editing, T.G.M., R.F.M.T. and T.D.; visualization, T.G.M.; supervision, R.F.M.T. and T.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Fundacao para a Ciencia e Tecnologia through the project \"GrassData--Development of algorithms for identification, monitoring, compliance checks and quantification of carbon sequestration in pastures\" (DSAIPA/DS/0074/2019) and CEECIND/00365/2018/CP1572/CT0012 ([PERSON]). The work was also supported by the FCT/MCTES (PIDDAC) through projects UIDB/50009/2020, UIDP/50009/2020, and LA/P/0083/2020.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request. The Python script used in this work is available on Github ([[https://github.com/tgmorais/tilage_detection_PT](https://github.com/tgmorais/tilage_detection_PT)]([https://github.com/tgmorais/tilage_detection_PT](https://github.com/tgmorais/tilage_detection_PT)), accessed on 25 May 2024).  
**Conflicts of Interest:** [PERSON] was employed by VirtuaCrop, Lda. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### Mobility in Three Dimension (3D)
#### 3.3.1 Communication Coverage in 3D  
The data transmission coverage of a wireless network is referred to as \"communication coverage\". When the coverage area shrinks, so does the RSS. The RSS may be defined in 3D space using the altitude value. During the transition phase, the terminal will decide whether to remain on the current network or transfer to an adjacent one as the new base station. The conventional two-dimensional (2D) HO determination approach does not apply to UAVs due to their varied altitude values. To compute the coverage of a UBS, the following equation can be applied [49]:  
\[coverage=\pi d^{2}=\pi\Big{(}Radius^{2}-Altitude^{2}\Big{)} \tag{1}\]  
\[d=\sqrt{(R^{2}-A^{2})} \tag{2}\]  
Several strategies and algorithms are used to make HO decisions. The RSS determines the best coverage option, which can be calculated using the following equation [49].  
\[RSS_{cur}=RSS_{min}-10\beta\log(d)+\varepsilon \tag{3}\]  
The path loss exponent is \(RSS_{min}\), which is the lowest value necessary for a terminal with a one-meter distance between the sender and receiver, \(d\) is the distance between the receiver and transmitter, and \(\varepsilon\) is a zero-standard-deviation Gaussian random variable. Figure 6 presents the radius of the BS, where \(A\) indicates the height of the UAV and \(d\) represents the radius of the BS's coverage in 3D space. HO occurs when the link between the BS and the UAV has stretched beyond its coverage.