esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 3 Results  
### Algorithm Validation  
Initially, we calculated the land cover classification for the year 2023 (Figure 4), employing RF and CART algorithms along with Landsat 9 imagery. The precision results revealed that RF exhibited superior performance in terms of OA (0.95) and Kappa coefficient (0.93), whereas CART yielded lower values (OA of 0.93 and Kappa coefficient of 0.89). Consequently, we opted to proceed with subsequent analyses exclusively utilising the RF algorithm.  
Afterwards, the result obtained with the RF algorithm was validated with in situ data. In Figure 5, the confusion matrix obtained is represented. The validation results demonstrate an OA of 0.88 and a Kappa coefficient of 0.81. Noteworthy are the producer and consumer accuracies, denoting the fidelity of predictions to actual instances, which stand at 0.89 and 0.88, respectively.  
Figure 4: Land cover classification for the validation obtained with the Random Forest algorithm, using Landsat 9 images collected during September and October 2023.  
Figure 5: Confusion matrix of the 2023 land cover map calculated with Random Forest algorithm and in situ data, gathered on the 30 and 31 of October, 2023.
## 4 Experiment
### Evaluation Metrics  
To comprehensively assess our framework's performance, particularly in addressing class imbalances in the D0 and IP102 datasets, we adopt the same evaluation metrics used in existing research [29; 32; 33; 34]. Specifically, we utilize five reliable evaluation metrics: accuracy (Acc), macro-average precision (MPre), macro-average recall (MRec), macro-average F1-score (MF1), and geometric mean (GM). The Acc provides an overall performance measure, while the macro-average metrics offer insights into class-wise performance, and the GM balances sensitivity and specificity. These metrics ensure a robust and comparable evaluation of our framework's effectiveness.
## 4 Experiments and Analysis of Results  
To realize this work, the experimental system is shown in Figure 8. Firstly, based on the SIC interference detection algorithm described in the previous section, the relevant device performance was tested. Secondly, the BER performance of the NOMA signal synthesized from two 20 Mbps OOK signals under different powers was analyzed. The BER performance of the NOMA-VLC system with different power allocation ratios, bias currents, and signal amplitudes are optimized.  
The block diagram of the experimental setup is shown in Figure 8a. First, two pseudo-random bit sequences of length \(2^{32}\)-1 were generated offline at the PC, followed by OOK modulation and superposition coding of the two sequences in the power domain to generate the NOMA OOK signal. It was further up-sampled and processed via a raised root cosine filter (RRC) before loading into an arbitrary waveform generator (AWG). The output signal passed through an amplifier (AMP) and a variable electrical attenuator (VEA), and then it passed through a T-shaped bias (Bias-T) to drive a blue LD. The optical signal emitted from the LD passed through the wireless channel. At the receiving end, the optical signal was detected by a 125-MHz PIN photodetector at different locations, where the PIN photodetector worked as different users' receiver. The signal was captured by a mixed-signal oscilloscope (MSO), which subsequently sent the signal to the PC for off-line processing, including resampling, RRC, timer synchronization, recursive least squares (RLS) equalization, and NOMA OOK demodulation. Finally, the system BER was calculated.  
Figure 8b represents the entire system based on blue laser diodes with a 1.5 m horizontal free-space transmission distance. Two NOMA signals were generated using an AWG and transmitted by two calibrated blue LDs of 450 nm. LD1 and LD2 were separated by 0.5 m. At the receiving end, we used QD thin film to capture two optical beams and then pass it through a single PIN to realize signal reception at DSO. The modulated blue laser beams were incident on the QD concentrator, fully overlapping each other. We precisely adjusted the position of the QD film to achieve concentrator-to-detector coupling over a short distance. After passing the blue light from the QD film, the generated green light from QD was collected by a 125 MHz PIN photodetector receiver. Next, we used offline MATLAB processing to successfully decode both signals. Furthermore, we divided the experiment  
Figure 8: Blocks diagrams of the experimental setup: (**a**) block diagram of downlink NOMA-VLC using LSTM SIC receivers; (**b**) block diagram of uplink NOMA-VLC using quantum dots.  
process into two parts: one was the performance test for the uplink NOMA-VLC system, and the other was the performance test for the downlink NOMA-VLC system.  
In this experiment, the P-I and V-I curves of the LD are illustrated in Figure 9. In the operating range, with the increase of current, the output power and output voltage increased, and there was a linear region from 0.2 to 0.8 A.
# Research on Nonlinear Vibration Characteristics of Internal Beveloid Gear Transmission System
## 3 Numerical Solution of Internal Beveloid Gear with Parallel Axes
### Influence of Excitation Parameters on Nonlinear Vibration Characteristics of Transmission System
#### 3.2.2 Influence of Torque Fluctuation on Vibration  
As shown in Figure 3b, the mean value of its axial vibration varies with time according to a simple harmonic function. This phenomenon is connected to the variation in the input torque, as determined by its fluctuation frequency and axial vibration equation.  
Ignore the bearing backlash, use the torque fluctuation \(T_{f}=0,0.05\) and \(0.2\)\(T_{at}\)N \(\cdot\) m, and generate the vibration time domain curve comparison diagram as depicted in Figures 4 and 5 below.  
Figures 4 and 5 show that the radial vibration amplitude is \(8.053\)\(\upmu\)m, \(8.111\)\(\upmu\)m, and \(8.475\)\(\upmu\)m when the torque fluctuation is \(T_{f}=0,0.05,0.2\)\(T_{at}\)N \(\cdot\) m, whereas the axial  
Figure 4: Time domain diagram of external gear radial vibration under different torque fluctuations.  
Figure 5: Time domain diagram of external gear axial vibration under different torque fluctuations.  
vibration amplitude is 0.960 \(\upmu\)m, 1.064 \(\upmu\)m, and 1.419 \(\upmu\)m. The presence of torque fluctuation causes the change in the gear's axial vibration equilibrium point to appear as a near\(-\)harmonic vibration with the torque fluctuation, hence increasing its axial vibration amplitude. Under these conditions, the effect of torque fluctuation on the radial vibration of the gear is not readily apparent. With a rise in torque fluctuation, the vibration peak rises little, whereas the vibration trend rarely alters. In light of the fact that the input torque selected at this moment is minimal, the change in its radial and axial vibration amplitude is not readily apparent. When the input torque rises, the torque fluctuation's degree of effect will grow.  
In order to better assess the influence of parameter changes on the vibration of gears at all levels, further study disregards the influence of torque fluctuation for the time being and uses \(T_{f}=0\) N \(\cdot\) m.
# Adjacent Vehicle Number-Triggered Adaptive Transmission for V2V Communications
## 3 Adaptive Transmission Scheme for V2V Based on Physical Topology
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Number of Connectable Vehicles, N** & **0** & **1** & **2** & **3** & **4** \\ \cline{2-6}
**Rician Fading K-Factor** & **Scenario I** & **10 dB** & **10 dB** & **10 dB** & **10 dB** \\ \cline{2-6}  & **Scenario II** & **10 dB** & **10 dB** & **5 dB** & \(-5\) dB** & \(-10\) dB \\ \hline Channel Coding Scheme & \multicolumn{4}{c}{Turbo code with \(1/2\) coding rate} \\ \hline Frame Size & \multicolumn{4}{c}{1024 bits/frame} \\ \hline Data Generation Speed & Scenario I & \multicolumn{4}{c}{240 frames/s} \\  & Scenario II & \multicolumn{4}{c}{120 frames/s} \\ \hline Data Transmission Speed & \multicolumn{4}{c}{QPSK} \\  & 16 QAM & \multicolumn{4}{c}{240 frames/s} \\  & 64 QAM & \multicolumn{4}{c}{360 frames/s} \\ \hline Simulation Time & \multicolumn{4}{c}{Scenario I} \\ \cline{2-6}  & Scenario II & \multicolumn{4}{c}{2000 s} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters for scenarios I and II.
## 5 Results and Discussion
### 250 kHz Results  
The results from the healthy panel and the panel with the disbonded region are shown in Figure 7. As with the 100 kHz results, there is a significant reduction in wave amplitude as the A\({}_{0}\) mode interacts with stiffener. The presence of the stiffener is clearly seen in Figure 7 at 60 \(\upmu\)s. As with the results from the 100 kHz excitation, the S\({}_{0}\) mode interacts with the disbonded region illustrated at 30 \(\upmu\)s in Figure 7 by the increase in wave amplitude. The S\({}_{0}\) mode wave energy transmitted through the disbonded region is also shown by a fringe of increased amplitude to the right of the disbonded region.  
Figure 7: 250 kHz three-component displacement magnitude showing Lamb wave interaction with the healthy disbonded stiffened panel.  
The A\({}_{0}\) mode is transmitted through the disbonded region with a lower reduction in amplitude than the bonded regions of the stiffener. This has led to a fringe of high amplitude to the right of the stiffener.
## 5 Performance Evaluation
### Dataset Description
#### 5.1.2 Dataset Individualization  
In order to provide prediction with 30 min validity, we recreated the dataset in a way that it contains availability observations at approximately 30-min intervals.  
In addition, we separated the obtained dataset into 420 datasets where each new dataset contains availability observations at approximately 30-min intervals for only one individual parking segment. There are two following reasons behind this decision. First, based on the use case under study (see Section 3), we need to predict parking place availability for a requested segment by drivers individually. In addition, for the prediction of parking availability in one special segment, we found that utilizing the whole dataset (i.e., including information of all segments together which are usually unrelated) can make a negative effect on the trained models, and therefore the results of the prediction of parking availability for that special segment. As a result, this idea could help us to reduce complexity, processing time and improve performance.
# The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey
## 4 Results
### RQ2: What Is the Level of Adoption of Agri 4.0 Solutions?
Moreover, from a visual perspective (Figure 8), the boxplot graph helps in seeing the main message of this analysis: the utilization values increase as the number of hectares increases, but it is interesting to note the strong increase from 50 hectares onwards. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{1}{c}{**Agri 4.0 Solution Adoption Level**} & \multicolumn{1}{c}{**At Least One Solution Adopted**} & \multicolumn{1}{c}{**No Solutions**} \\ \hline _Hectares_ & A. \textless{}10 & 57 & 53 \\ _Cluster_ & B. 10–20 & 26 & 56 \\  & C. 20–50 & 46 & 66 \\  & D. \textgreater{}50 & 94 & 54 \\ \hline \hline \end{tabular} Pearson’s \(\chi^{2}\) test \(p\)-value = \(4.618\times 10^{-15}\) (significant).  
\end{table} 
Table 10: Pearson’s \(\chi^{2}\) test for adoption level and land size cluster. Figure 7: Boxplot graph of the adoption rate depending on company size (revenue). Subsequently, the focus of the analysis shifted to another important control variable in the questionnaire: the respondent's educational qualification (whether agricultural or not). In Table 11, it is possible to see the strong relationships between the degree of utilization and type of education received by the business owner. The Pearson's chi-square test \(p\)-value results in a very small value, ensuring the statistical significance of the analysis. The graphical relationship of the effect that the control variable has on the degree of adoption is depicted in Figure 9. The subgroup of respondents with an educational background in agriculture presents a greater degree of adoption of Agri 4.0 solutions than the subgroup without this type of background. This can be seen in all aspects of the boxplot, from the minimum to the maximum, as well as for the interquartile range (0-21% vs. 7-29%), the mean (13% vs. 18%), and the median (7% vs. 14%). \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Agri 4.0 Solutions Adoption Level** & \
\begin{tabular}{l} **At Least One So-** \\ **lution Adopted** \\ \end{tabular} &
\begin{tabular}{l} **No Solutions** \\ **Adopted** \\ \end{tabular} \\ \hline _Agricultural_ & A. No & 205 & 194 \\ _education_ & B. Yes & 191 & 80 \\ \hline Pearson’s \(\chi^{2}\) test: \(p\)-value = 7.98 \(\times\) 10\({}^{-7}\) (significant). & & \\ \end{tabular}
\end{table} 
Table 11: Pearson’s \(\chi^{2}\) test for adoption level and type of education. Figure 8: Boxplot graph of the utilization rate depending on company size (land size). Figure 9: Boxplot graph of adoption level and type of education.
## 3 Preliminaries
#### System Model  
The channel fading affects the overall network performance, so to model this scenario for wireless communication, the Nakagami distribution [33] is used to represent it.  
\[p(x;m,\Omega) =1-\frac{\Gamma_{mx^{2}/\Omega}(m)}{\Gamma(m)} \tag{1}\] \[=e^{-\frac{m}{\Omega}x^{2}}\frac{2m^{m}}{\Gamma(m)\Omega^{m}}x^{2m-1} \tag{2}\]  
The cumulative distributive function for signal reception is represented by \(\frac{\Gamma_{m2}{}^{2}/\Omega}{\Gamma(m)}\), where \(x^{2}\) stands for the reception threshold of signal propagation and \(\Gamma\) stands for gamma function. The average power level for the received signal is given as \(\Omega\). The fading parameter \(m\) is determined on the basis of the distance between the vehicles \(i\) and \(j\):  
\[m=\left\{\begin{array}{ll}3.0&:d<50\;\mathrm{m}\\ 1.5&:50\;\mathrm{m}\leq d<150\;\mathrm{m}\\ 1.0&:\mathrm{otherwise}\end{array}\right. \tag{3}\]  
The average communication transmission scope [34] of the transmitter can be communicated as  
\[\bar{r}=\left(\frac{p_{t}K}{mY_{0}W}\right)^{\frac{1}{\beta}}\left(\frac{1}{ \beta}\right)\sum_{k=0}^{m-1}\frac{\Gamma\left(1+\frac{1}{\beta}\right)}{k!} \tag{4}\]  
where the transmission power is denoted by \(p_{t}\), path loss coefficient by \(\beta\), and the fading coefficient of the Nakagami channel is represented by \(m\). The carrier-to-noise ratio threshold is represented by \(Y_{0}\) and the total input noise power of an authentic receiver is indicated by W, whereas K represents the constant for the path loss model and is defined as  
\[K=\frac{A_{t}A_{r}C^{2}}{4\pi f_{c}} \tag{5}\]  
The gain of the transmitting antenna is represented by \(A_{t}\), whereas the gain of the receiving antenna is represented by \(A_{r}\). The speed of light is denoted by C, and the carrier frequency is expressed as \(f_{c}\).  
The vehicles communicate with each other as well as with neighboring RSUs. Therefore, the time the messages take to propagate from the sending entity to the receiving entity can be calculated as follows [35]:  
\[t_{p}=t_{r}-t_{s} \tag{6}\]  
The propagation time of the message is represented by \(t_{p}\); \(t_{r}\) represents the timestamp of the message reception, whereas \(t_{s}\) represents the timestamp of the message of when it was sent.  
Using Equation (6) and the average velocity of a vehicle \(v\), the distance between the two entities can be derived as  
\[d=t_{p}\times v \tag{7}\]  
If \(d>\bar{r}\), then the RSU can forward the relevant important information to the neighboring RSU depending on the direction of the flagged or authentic vehicle.
# Kalman Filter-Based Differential Privacy Federated Learning Method
## 3 Proposed Method
### Method Overview  
There are two types of nodes in federated learning, the Request Node (RN) and the Training Node (TN). The nodes that need others to assist in training the model are called the RN, which deliver task requests to other nodes and initialize or update the global model. The nodes that assist the RN in training the model are called the TN, which train the model using local data and provide the training results, which is also called the gradient, to the RN. There are \(C\) TNs participating in training each round. The workflow of KDP-FL is shown in Figure 1. Federated learning contains \(T\) rounds of global training. Global training includes task definition, model update, and local training. The RN defines the task and initializes the global model. Then, it loops the following steps to finish the global training:  
1. The RN updates the global model parameters and sends them to the TN.
2. The TN trains the model locally and obtains the local training result for the \(t\)th round of training after \(e\) iterations. This step is also called local training.
3. The TN adds Gaussian noise to the training result and sends them to the RN.
4. The RN uses Kalman filtering on all the training result to reduce the noise.
5. The RN aggregates all the training results to obtain the global model parameters for the next round.
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 3 MEC as a Learning System  
At the SDN controller's side, the end-users are represented and considered as stochastic learning automata that sense the environment and make future decisions based on their past experience. At each time slot \(t\), the end-user can select to be served by a MEC server \(s\), thus, the set of end-users' actions at time slot \(t\) is \(a(t)=\{a_{1},...,a_{s},...,a_{s}\}\). The SDN controller has the information of the end-users' offloaded data \(\mathbf{b^{(t)}}\) and the prices \(\mathbf{p^{(t)}}\) that the MEC servers announce regarding offering their computing services. The SDN controller can determine the reputation score \(R_{s}^{(t)}\) for each MEC server, which can be normalized towards defining the reward probability as follows.  
\[rew_{s}^{(t)}=\frac{R_{s}^{(t)}}{\sum_{s\in S}R_{s}^{(t)}} \tag{8}\]  
The reward probability \(rew_{s}^{(t)},0\leq rew_{s}^{(t)}\leq 1\) represents the potential reward that an end-user may experience by choosing to offload its data to the MEC server \(s\). Following the theory of the stochastic learning automata, the action probability vector of an end-user \(u,u\in U\) is \(\mathbf{Pr_{u}^{(t)}}=[Pr_{u,1}^{(t)},...,Pr_{u,s}^{(t)},...,Pr_{u,S}^{(t)}]\), where \(Pr_{u,s}^{(t)}\) is defined as the probability of the end-user \(u\) to select the MEC server \(s\) to offload its data. Based on the theory of stochastic learning automata [22], the rule of updating the end-users' action probabilities at the SDN controller is defined as follows.  
\[Pr_{u,s}^{(t+1)}=Pr_{u,s}^{(t)}-b\cdot rew_{s}^{(t)}\cdot Pr_{u,s}^{(t)}, \quad s^{(t+1)}\
eq s^{(t)} \tag{9a}\] \[Pr_{u,s}^{(t+1)}=Pr_{u,s}^{(t)}+b\cdot rew_{s}^{(t)}\cdot(1-Pr_{ u,s}^{(t)}),\quad s^{(t+1)}=s^{(t)} \tag{9b}\]  
where \(0<b<1\) denotes the learning parameter expressing how fast the end-users explore the available options of the MEC servers towards offloading their data. Equation (9a) represents the probability of end-user \(u\) selecting a different MEC server to offload its data in the next time slot \(t+1\) compared to end-user's choice in the current time slot \(t\), while Equation (9b) expresses the probability of end-user \(u\) to keep being served by the same MEC server. It is noted that, initially, the end-users' action probabilities are initialized as \(Pr_{u,s}^{(t=0)}=\frac{1}{5}\). The MEC servers selection learning process executed at the SDN controller is presented in the Data Offloading and MEC Server Selection (DO-MECS) algorithm (see Section 5).
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 2 Classification of DLC Coatings
### Properties
#### 2.4.3 Refractive Index  
Generally, ta-C films exhibit a high refractive index, sometimes higher than diamond (2.419). In a-C films carrying a refractive index range of 1.6 to 2.6, it has been seen to be less than that of ta-c films. Even though the tests showing a refractive index above 2.0 demonstrated a moderately high extinction coefficient of over 0.1, the samples with refractive records less than 2.0 showed a moderately low extinction coefficient than 0.1 [65].
# Multi-Parametric Analysis of Reliability and Energy Consumption in IoT: A Deep Learning Approach
###### Abstract  
Small-to-medium scale smart buildings are an important part of the Internet of Things (IoT). Wireless Sensor Networks (WSNs) are the major enabler for smart control in such environments. Reliability is among the key performance requirements for many loss-sensitive IoT and WSN applications, while Energy Consumption (\(EC\)) remains a primary concern in WSN design. Error-prone links, traffic intense applications, and limited physical resources make it challenging to meet these service goals--not only that these performance metrics often conflict with one another, but also require solving optimization problems, which are intrinsically NP-hard. Correctly forecasting Packet Delivery Ratio (\(PDR\)) and \(EC\) can play a significant role in different loss-sensitive application environments. With the ever-increasing availability of performance data, data-driven techniques are becoming popular in such settings. It is observed that a number of communication parameters like transmission power, packet size, etc., influence metrics like \(PDR\) and \(EC\) in diverse ways. In this work, different regression models including linear, gradient boosting, random forest, and deep learning are used for the purpose of predicting both \(PDR\) and \(EC\) based on such communication parameters. To evaluate the performance, a public dataset of the IEEE 802.15.4 network, containing measurements against more than 48,000 combinations of parameter configurations, is used. Results are evaluated using root mean square error and it turns out that deep learning achieves up to 98% accuracy for both \(PDR\) and \(EC\) predictions. These prediction results can help configure communication parameters taking into account the performance goals.  
Keywords:IEEE 802.15.4; packet delivery ratio; energy consumption; prediction; deep learning; internet of things; wireless sensor networks +
Footnote †: journal: Sensors