esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Downlink Mechanism for Industrial Internet of Things Using LoRaWAN Networks
## 4 Results
### Effect of DROB in Energy Usage  
Changing the DR used during communication is one of the parameters that impacts energy consumption the most in a LoRaWAN network, because it translates directly to the time that node's radio interface must be enabled (either for transmission or for reception). For this analysis only the downlink stage was considered, and the focus was on nodes, as the GW was assumed to be mains-powered. When nodes receive packets, the overhead power of a class B operation is a function of:  
* The time-on-air of the beacon, which is region-specific (SF9/125 kHz)
* The periodicity of the ping slots (one ping slot per beacon period in this case)
* The ping slot DR, which allows to the calculation of the time-on-air of the packet.
* Whether during the assigned pingslot there is a packet being sent or not.  
Leaving aside the beacons, which are constant for all nodes, for each device in the network during reception in class B, the energy consumed \(E_{RX}\) can be calculated with Equation (1), where:  
* \(I_{RX}\) is the current consumption, and \(V\) is the voltage.
* \(t_{airtimeDRi}\) is the time-on-air of the packet for the DR used.
* \(N_{DRi}\) is the number of packets received with that DR.
* \(t_{premble}\) is the time the radio is on listening to detect whether there is an incoming packet or not.
* \(N_{emptyslots}\) is the number of ping slots when the node did not detect incoming packets.  
\[E_{RX}=\sum_{DRi=3}^{DRi=5}I_{RX}*V*\left(t_{airtime_{DRi}}*N_{DRi}+t_{premble }*N_{empty_{slots}}\right) \tag{1}\]  
As expected, Figure 15 shows how DR5, due to the reduced airtime required, achieved the best results even after sending more messages, with the DROB being balanced between configurations. As explained before, this also means that if the channel or scenario conditions got to the point that some nodes in DR5 could not receive, the DROB would still work in reducing the DR but would maintain it as high as possible, therefore consuming less power than fixed DR3 with same levels of robustness.  
Figure 14: Downlink related events in GW output buffer.
## 3 Results and Discussion  
### Antioxidant Activities and Total Phenolic Content in Different Tissues of Aridchoke Genotypes  
The antioxidant activities of methanolic extracts from four artichoke tissues (leaf, stem, primary, and secondary flower heads) were evaluated using four different in vitro assays: FRAP, ABTS, DPPH, and Folin-Ciocalteu (TPC). Although the latter assay is predominantly employed to quantify total phenolic content, it also responds to all oxidizable groups interacting with the reagent. Therefore, it serves as an indicator of the overall reduction capacity, which directly correlates with phenolic content and antioxidant capacity [17]. Six representative genotypes of the \"Carciofo Ortano\" landrace were included in the analyses: three from the Orte 1 population (F4 P10, F3 P8, and F17 P3) and three from the Orte 2 population (F7 P2, F7 P5, and F15 P3). Additionally, four landraces/clones from the varietal platform of the PGI \"Carciofo Romanesco del Lazio\" (Campagnano, C3, Grato 1, and Castellammare) were used as reference genotypes. The aim was to select the genotypes and tissues within each of the three groups (Orte 1 and Orte 2 populations, and reference genotypes) that exhibited higher antioxidant activity for further characterization. ANOVA was conducted individually for each genotype group across the four assays, unveiling notable variations in antioxidant activities among the tissues and genotypes within each specific group (????????). However, a general trend was observed across the four tissues, regardless of the genotype group. Specifically, the highest antioxidant activities and phenol content, as measured by the four assays, were generally found in the stem and secondary flower head (SFH), closely followed by the primary flower head (PFH), while significantly lower values were observed in the leaves (???????? ).
### Literature Review
The objective functions to be minimised comprise energy loss, voltage deviation of the power system network, and minimisation of resources to incorporate large-scale EV integration. In [28], the optimal placement of EV charging stations is determined in a radial distribution network by using DE and Harris Hawks optimisation methods, while, in [29], the reptile search method is employed to investigate the impacts of adding significant EV loads to active distribution networks. Techno-environmental variables are also included in a distribution system to identify appropriate DG and EV sites using a future search algorithm [30]. The effect of EV charging stations on the 33-bus and 85-bus test systems by minimising power loss and maintaining voltage level in the presence of EVs is discussed in [31]. There have also been a lot of studies regarding managing active and reactive power [32; 33] in a smart distribution network with EVs, which has shown that EVs may be utilised to minimise congestion on the power grid [34] or provide auxiliary services, such as frequency management [35].
# Sensorless Air Flow Control in an HVAC System through Deep Learning
## 4 Experimental Evaluation
### Static Pressure Prediction for Untrained Seasons  
We further explored the power of the LSTM-based predictor for forecasting values in different seasons for which it was not trained. In general, HVAC systems operate for heating in winter and cooling in summer, while they simply blow air in spring and autumn. In this experiment, we tested whether the prediction model trained by the spring (May) data could predict the static pressure during the cooling operation in summer (July and August) for the 4 th floor. The importance of this experiment is that we are testing the prediction performance when the cooling valve is open using the trained model without cooling or heating. Since the winter operation is different from the summer operation only in terms of the set-point temperature, we do not need to test our model against the winter data. For this experiment, we used 44,567 samples from May for training. Then, for the out-of-season test, we used 44,635 data samples from July and 39,805 data samples from August.  
The LSTM-based prediction model trained by the May data produces results for July with MAE = 0.084 mmAq, MAPE = 1.115%, and RMSE = 0.184. Figure 17 shows that the LSTM prediction closely matches the ground truth given by the July data, except for some spikes at the start of the operation every day. Even though the air-cooling operation was performed in July, these two months  
Figure 16: The LSTM-based predictor even catches the momentary spikes at the start of fan operation.  
both use a constant set-point for the static pressure. On the other hand, the results for August are again relatively less accurate as a result of the test operation, which pushed the system to the designed operating range limits. In August, the set-point was 19, which is much larger than the set-point of 9 used in the other months.  
The error values for August are an MAE of 0.617 mmAq, MAPE of 3.876%, and RMSE of 1.448. Considering the large variability in August, however, even this prediction result is not completely off the mark. In Figure 17a, when the HVAC system operation is in the normal state, the static pressure in the duct remains at its set value. However, as shown in Figure 17b, the system operation is at the extremes of the designed range, and the static pressure in the duct is highly unstable. Even in this case, however, the LSTM-based predictor outputs stable values within the fluctuating range (15-18 mmAq). Recall that this result is obtained despite the lack of such highly fluctuating data in August. If these data were provided, as we observe in Section 4.1, these wild fluctuations can be predicted with even more precision, with MAE = 0.317 mmAq, MAPE = 2.941%, and RMSE = 0.968.
## 4 Self-Tuning Exploratory Data Analytics
### Probabilistic Approach
#### 4.2.3 ToPIC-Similarity  
To find the appropriate number of topics into which to divide documents, ESCAPE proposes an automatic methodology, called ToPIC-Similarity, whose steps are described by pseudo-code in Algorithm 2. After setting a minimum threshold (\(K_{min}\)) and a maximum threshold (\(K_{max}\)), a new LDA model is generated for each K within the range defined by the thresholds. Each of these models is then evaluated through two main steps:  
* _topic characterization_, to find the \(n\) most important words for each of the K topics identified;
* _similarity computation_, to assess the similarity between the various topics found, expressed through an index. Finally, a third step, called \(K\)_Identification_, allows us to select the best configuration of the K parameter to use in the analyses. **Topic characterization. ** In this step, each topic identified is summarized with a list of its most-significant \(n\) words. In order to automatically find the value of \(n\), ESCAPE considers the number of words that appear most frequently, and then filters this number by dividing it by the average frequency of terms within the topic. In particular, the quantity of the most-significant words, named \(Q\), is defined as: \(Q:=\frac{|V|\cdot LTR}{AvgFreq}\), where \(|V|\) is the variety of the corpus dictionary and TTR is the type-token ratio (total number of unique words divided by the total number of words). Given \(Q\), the number \(n\) is then set as follows:  
\[n=\left\{ \begin{array}{ll}\frac{Q}{K}\,&\quad\text{if}\ Q\geq K\cdot AvgFreq \\ AvgFreq\,&\quad\text{if}\ Q<K\cdot AvgFreq\end{array} \right. \tag{1}\]  
When the average frequency of terms in the corpus is higher than the amount of words taken into account, the number \(n\) of words is set equal to the average frequency of terms in the corpus. Finally, for each word in each topic, the word is associated with the probability that the term has to be taken up in the topic (0 if it is not included in the list of \(n\) words). **Similarity computation. ** Here, all possible pairs of topics are considered, and for each of them, their similarity is calculated. Cosine similarity is used to determine the similarity between two topics. Considering two topics, \(t^{\prime}\) and \(t^{\prime\prime}\), belonging to the same partitioning \(K\), the similarity between the topics is computed as follows: \(similarity(t^{\prime},t^{\prime\prime})=\frac{\mathbf{N}_{t^{\prime}}\cdot \mathbf{N}_{t^{\prime\prime}}}{\|\mathbf{N}_{t^{\prime}}\|_{2}\|\mathbf{N}_{t^ {\prime\prime}}\|_{2}}\), where \(\mathbf{N}_{t^{\prime}}\) is the set of the representative words of topic \(t^{\prime}\) and \(\mathbf{N}_{t^{\prime\prime}}\) is the set of of the representative words of topic \(t^{\prime\prime}\).
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
## 5 Results and Analysis
### Path-Following Results: Experiment 1  
All participants finished all of the tasks successfully. One example trial is shown in Figure 7, trajectories of the participants and stroller, the linear and angular velocities are shown in (a) and (b) in Figure 7. The data source was directly obtained from visual odometry and the UWB sensor. The estimated distance error and estimated angle error are shown in (c); both errors converge to zero near the end of each recording. Figure 8 illustrated the path dissimilarity results of the hands-free interface and joystick for all participants. The dissimilarity of the human-guided system is \(0.0126\pm 0.0385\), which is higher than \(0.0008\pm 0.031\) of the joystick. Furthermore, there is no significant difference between the two interfaces from the results of the Wilcoxon Signed-Rank Test, indicating that the position control precision of the human-guided system is close to that of the joystick.  
Figure 7: One example result of Experiment 1 with the human-guided system. (**a**) Trajectory of the robot and human. The robot starts from (0,0). (**b**) Robot real linear velocity (top) and angular velocity (bottom). (**c**) Results of \(e_{d}\) and \(e_{e}\).
## 2 Data and Methods
### Performance Metrics  
To evaluate the models' performances, this study utilized the root-mean-square error (RMSE), mean absolute error (MAE), mean relative error (MRE), determination coefficient (\(R^{2}\)), and relative bias (Bias). These indicators can be calculated as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}{\left(y_{i}^{\prime}-y_{i}\right)^ {2}}}}, \tag{4}\]  
\[\text{MAE}=\frac{1}{N}{\sum_{i=1}^{N}{\left|y_{i}^{\prime}-y_{i}\right|}}, \tag{5}\]  
\[\text{MRE}=\frac{1}{N}{\sum_{i=1}^{N}{\left|\frac{y_{i}^{\prime}-y_{i}}{y_{i} }\right|}}, \tag{6}\]  
\[\text{Bias}=\frac{1}{N}{\sum_{i=1}^{N}{\left(y_{i}-y_{i}^{\prime}\right)}}, \tag{7}\]  
where \(y_{i}\) and \(y_{i}\)' are the measured and predictive values for the \(i\)-th sample, respectively, and \(N\) represents the total number of samples.
# The Summertime Diurnal Cycle of Precipitation Derived from IMERG
## 4 Discussion
Whilst systematic errors in the IMERG precipitation estimates could be taken into consideration, IMERG only slightly underestimates precipitation at \(2^{\circ}\times 2^{\circ}\) resolution (the version-3 IMERG product has a normalised mean error of \(\sim\)\(-\)0.06 [21]). A future IMERG validation study could assess the systematic bias in the IMERG product as a function of rainfall accumulation and intensity, however it is expected that such systematic errors are unlikely to drastically affect the amplitude and phase of the diurnal cycle estimated in this study. Future research should aim to better account for the correlation length of precipitation estimates when averaging spatially. In particular, deduction of correlation lengths for different precipitation types, latitudes and land types are recommended. Other future work could address the physical reasoning for why the harmonic function (Equation (4)) better fits to the diurnal cycle of accumulation as opposed to the diurnal cycles of occurrence and intensity. Finally, a note of caution is raised for the interpretation of the results in the 45\({}^{\circ}\)-60\({}^{\circ}\)S region where high mean precipitation accumulations, intensities and occurrences are found in the Pacific Ocean compared to the Indian and Atlantic Oceans. These features are not found in other multi-satellite products [14] with mean accumulations up to \(\sim\)80% larger in IMERG than for other precipitation products such as DPR, CORRA (DPR & GMI combined), GPROF (multi-satellite PMW product), etc. [15]. The cause of these artifacts is currently under investigation.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 4 OCTA Algorithms  
The previous section showed that OCTA signals depend on the type of dynamics (Doppler shift or decorrelation), the observed parameter (intensity, phase, or field), and the possible presence of static scattering in the coherence volume. With this discussion in mind, we now present the main classes of angiography algorithms.
## 4 Experimental Results
### Evaluation Metrics  
More than one method was used to test the models prepared throughout the study. RMSE, a metric that was used to evaluate the accuracy of statistical predictions, was enacted in the initial testing processes [27]. RMSE was calculated as the square root of the mean square of the differences between the predicted values and the actual values (Equation (2)).  
\[RMSE=\sqrt{\sum_{i=1}^{n}\frac{\left(\hat{a}_{i}-a_{i}\right)^{2}}{n}} \tag{2}\]  
When RMSE was used to test a developed model, RMSE was expected to be low. A high RMSE means that the predictions were very different from the actual values. This situation shows the low performance of the model. A lower RMSE value indicated that the predictions were closer to the actual values, and thus, the model performed better. Additionally, the MSE results have been also provided penalizing larger errors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Dataset & 45,699 \\ Test & 10\% \\ Validation & 10\% \\ Training & 80\% \\ Max Epoch & 1000 \\ Optimization & Levenberg–Marquardt \\ Input Data & Cycle index, temperature, voltage A, current A \\ Output Data & Discharge capacity Ah \\ Hiden Layer Size & 33 \\ Mu & 0.001 \\ Loss Function & Mean squared error \\ Number of Fully Connected Layers & 1 \\ Activation & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training information of the model.  
The other approach used for model testing in experimental studies is MAE [28]. Often used to measure the performance of regression models, MAE represents the mean absolute difference between the predicted values and actual values. In other words, it attempts to determine how \"wrong\" each prediction is. MAE was calculated as provided in Equation (3).  
\[MAE=(1/n)\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{3}\]  
For each prediction, the difference between the predicted value and the actual value was calculated, as shown in Equation (3). The absolute value of this difference was counted. Here, n represents the number of data points, while \(y_{i}\) refers to the true value (observation) and \(\hat{y}_{i}\) refers to the value predicted by the model. Essentially, MAE measures the magnitude of forecast errors and treats each error with the same weight. Additionally, R-squared was used as the evaluation metric within the frame of this study. R-squared measures the explanatory power of the model on the data and expresses how much of the variance of the dependent variable it explains. In this regard, it was employed in the whole study to measure the effect of independent variables on the dependent variable and to evaluate the explanatory ability of the model.
## 4 Results and Discussion
### Evaluation of Models Performance
In the matter of the predictive power (i.e., validation performance) (Figure 6b), the ROC-AUC method exhibited that the ABHP model with an AUC of 0.922 was the best model for predicting future landslide susceptibilities, followed by the RABHP, BHP, Dagging-HP, Decorate-HP, and HP that achieved AUC values of 0.919, 0.909, 0.897, 0.865, and 0.856, respectively. Although the models ranked differently in the training and validation phases, and the ROC-AUC method clearly demonstrated that the ensemble models successfully outperformed the single HP model. Integrating the single HP model with the ensemble learning techniques, the generalization and predictive power of the HP model improved by up to \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{6}{c}{**Model**} \\ \cline{2-7}  & **HP** & **ABHP** & **BHP** & **Dagging-HP** & **Decorate-HP** & **RABHP** \\ \hline PPV (\%) & 100.00 & 98.44 & 92.19 & 89.06 & 100.00 & 98.44 \\ NPV (\%) & 75.81 & 70.31 & 78.13 & 81.54 & 75.81 & 85.94 \\ SST (\%) & 70.33 & 76.83 & 80.82 & 82.61 & 70.33 & 87.50 \\ SPF (\%) & 100.00 & 97.83 & 90.91 & 88.33 & 100.00 & 98.21 \\ ACC (\%) & 78.91 & 84.38 & 85.16 & 85.27 & 78.91 & 92.19 \\ Kappa & 0.758 & 0.687 & 0.703 & 0.703 & 0.758 & 0.844 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model performance over the training phase. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{6}{c}{**Model**} \\ \cline{2-7}  & **HP** & **ABHP** & **BHP** & **Dagging-HP** & **Decorate-HP** & **RABHP** \\ \hline PPV (\%) & 92.86 & 89.29 & 82.14 & 82.14 & 92.86 & 85.71 \\ NPV (\%) & 75.53 & 75.00 & 85.71 & 82.14 & 75.53 & 78.57 \\ SST (\%) & 66.67 & 78.13 & 85.19 & 82.14 & 66.67 & 80.00 \\ SPF (\%) & 88.24 & 87.50 & 82.76 & 82.14 & 88.24 & 84.62 \\ ACC (\%) & 73.21 & 82.14 & 83.93 & 82.14 & 73.21 & 82.14 \\ Kappa & 0.464 & 0.643 & 0.679 & 0.643 & 0.464 & 0.643 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance over the validation phase. 8% and 7%, respectively. In agreement with our results, several previous research reported on the superiority of ensemble modeling over single modeling approaches [79; 98]. Figure 5: Training and validation errors of the modeling process.
### Related Works  
In contrast to [27; 28; 29; 30; 31], in this paper, ICT is used to reduce one transmission time slot for the DNC TWR networks. Unlike [33; 34; 35], our proposed scheme applies PRS to the secondary network. Moreover, this paper considers block fading channels, in which channel coefficients do not change during one data transmission cycle, but independently change after each cycle. Although the published works [27; 36; 37] were concerned with TWR in the underlay CRNs, these references did not study ICT. Moreover, Ref. [36] studied the two-phase TWR model exploiting a direct link between the sources, while Ref. [37] considered a single-relay model operating on the RF-EH environment. The authors in [38] proposed the underlay TWR scheme using RIS and full-duplex transmission. However, Ref. [38] did not study relay selection as well as ICT.  
To the best of our knowledge, the published works [39; 40] are the most relevant to the topic of this paper. Indeed, [39; 40] both considered the DNC TWR underlay CRNs using ICT. In [39], the secondary relay node was selected to maximize the channel capacity at both sources as well as to minimize the collection time of CSI. However, the main differences between this paper and Ref. [39] are given as follows: (i) different relay selection methods were proposed in this paper and [39]; (ii) in [39], block fading channel was not considered; (iii) the secondary transmitters in [39] used the instantaneous CSI of the cross-interference links to adjust their transmission power. Then, it is worth noting that this paper was developed from our previous work [40], and the main difference between this paper and [40] can be listed as follows: (i) this paper applies PRS for the secondary network; (ii) the block fading channel was not considered in [40]; (iii) the transmit power adjustment method for the secondary transmitters in this paper is different to that in [40]; and (iv) the mathematical derivations in this paper are more challenging because we study the PRS and block fading channel.