esa-sceva/satcom-chunk-collection huggingface dataset:
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
## 2 Materials and Methods
### Infestation Detection  
Depending on the specific objective, the analyses considered both groups of trees (i.e., treating areas as single entities) and individual plants. Area-level analysis proved more effective in comparing healthy and infested zones, as observing the diachronic trend at the single-tree level presented challenges. This complexity arises due to pixel variability, which is partially \"smoothed\" by the presence of multiple trees within an area [46]. In contrast, single-tree analysis was particularly useful for identifying discriminant values between healthy and infested plants. It focused on the index value within a specific, limited timeframe [47], during which the values of the two conditions were best differentiated. In the area-level analysis, the monthly RDVI average for both infested and healthy areas was calculated based on all trees within each area over the entire study period (2019-2023).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Spectral Band** & **Wavelength** \\ \hline Blue & 464–517 nm \\ Green & 547–585 nm \\ Red & 650–682 nm \\ NIR & 846–888 nm \\ \hline \hline \end{tabular}
\end{table}
Table 2: PlanetScope spectral bands.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quality Band** & **Information** \\ \hline
1 & Clear mask \\
2 & Snow mask \\
3 & Shadow mask \\
4 & Light haze mask \\
5 & Heavy haze mask \\
6 & Cloud mask \\
7 & Confidence \\
8 & Unusable data mask \\ \hline \hline \end{tabular}
\end{table}
Table 3: PlanetScope quality bands.  
In the plant-level analysis, the summer average RDVI (RDVI\({}_{\text{Sua}}\)) values were calculated for the years 2020, 2021, and 2022, referring to the months of July and August, as, during these months, the difference between the spectral behavior of healthy and infested plants is higher. The year 2023 was excluded due to the absence of data for the considered period, and 2019 was also excluded, as the differences between healthy and affected plants were less significant. Then, the normal distribution (Equation (2)) of the RDVI\({}_{\text{Sua}}\), was analyzed using a spreadsheet, to provide a statistical representation of the variability of this parameter.  
\[f(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{x-\mu}{\sigma} \right)^{2}} \tag{2}\]  
where \(\sigma\) = standard deviation of RDVI\({}_{\text{Sua}}\), and \(\mu\) = mean of RDVI\({}_{\text{Sua}}\).
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 2 Optical Packaging  
As an indirect-gap semiconductor, Si offers very low direct-gap recombination for diode-laser emission. Therefore, the light signal needed to drive Si-photonics must come from an external laser source, either indirectly (i.e., the fiber-coupling of light from a discrete laser-device), or directly (i.e., the hybrid/heterogeneous integration of a III-V device/material on the Si-PIC). Many applications exist for both the indirect and direct coupling of laser light to the PIC, and each approach can be further broken down by the employed coupling scheme, i.e., grating-coupling, edge-coupling, evanescent-coupling, etc. Generally, Fiber-to-PIC coupling (indirect) is used for telecom and datacom applications, because it allows for the transfer of information over fiber networks, while integrated sources (direct) are used for security and sensing applications.
# Machine Learning Method for Prediction of Hearing Improvement After Stapedotomy
## 3 Results  
### Regression Evaluation: Hold-Out Method  
In Table 4, the accuracy scores (MAE \(\pm\) SD) of the five different models trained with the hold-out method are presented along with the RF without feature selection and best naive predictions column.  
Considering only the naive predictions, in the first seven rows (including the 3000 Hz frequency), the naive approach y_average, which includes the average of all postoperative samples at the chosen hearing intensity, performed best. In all other rows, the naive method x, which takes the preoperative hearing level at the same frequency as its prediction, performed best. Their MAE values are given in the rightmost column of Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Frequency** & **LR\_all** & **Lasso** & **Ridge** & **KNNs** & **RF** & **RF\_all** & **Naive** \\ \hline AC & & & & & & & \\ \hline
125 & 10.65 \(\pm\) 12.14 & **10.06 \(\pm\) 11.94** & 10.08 \(\pm\) 11.95  
LR_all achieved the lowest MAE scores in a total of six cases. For AC frequencies 500, 1000, and 3000 Hz, MAEs of 8.23, 5.60, and 6.94 were recorded. For BC frequencies 500, 1000, and 2000 Hz, MAEs of 3.89, 3.85, and 5.48 were achieved. RF was the best performing model for predicting the postoperative AC hearing intensity for three targets: at 250 Hz with MAE of 9.78, at 2000 Hz with MAE 5.07 and at 4000 Hz with MAE 9.15. Ridge was the best performing model for predicting the postoperative hearing intensity in two cases: at 1500 Hz AC with an MAE of 5.00 and at 3000 Hz BC with MAE 5.44. Lasso attained the lowest MAE of 10.06 for AC hearing at 125 Hz. RF_all was most accurate in one BC frequency with 4.59 MAE at 1500 Hz. Naive prediction approaches also performed best for three high frequency targets. MAEs of 10.60 and 11.80 were observed for AC frequencies 6000 and 8000 Hz. An MAE of 4.20 was achieved for the BC frequency at 4000 Hz. For the KNNs model, consistently higher MAE values were measured. Consequently, it was not the best performing model for any of the targets.  
SD values were higher than MAE in all cases. There was no significant difference in SD between the models. The model with the best MAE normally also achieved the lowest SD. However, exceptions occurred for AC and BC frequencies at 3000 and 2000 Hz, where lower SD values were observed for Lasso (7.05 \(\pm\) 8.70) and RF (5.53 \(\pm\) 6.63) than for the model with the lowest MAE--LR_all (6.94 \(\pm\) 8.88, 5.48 \(\pm\) 6.67), respectively.  
Figure 4 visualizes the audiogram measurements on a test sample together with the postoperative predictions. The predictions for each target are illustrated as AC and BC vectors. The poor prediction accuracy at high AC frequencies is evident from the graph, an observation consistent with the overall accuracy at these frequencies.  
Figure 4: Air and bone conduction hearing intensities—before and after procedure. This graph contains the same sample as Figure 1, only the predictions of our models are added (orange).
## 2 Data Acquisition and Processing
### In-Situ Data Collection  
#### 2.3.1 Soil Fertility Trials  
Cropping systems in the Koutiala district are based on cotton, maize, sorghum, and millet rotations. The five main crop types that were covered by the field campaign also included peanuts. Under rainfed conditions, the growing season ranges from late May to November. A set of 48 farmer fields was selected to monitor plant growth and development during the 2014 growing season. To cover the wide range of within-season variation that may be encountered in these heterogeneous environments, suitable fields (farming management units) were identified through a participatory selection process along the catena to adequately sample gradients in soil physical and chemical properties. Within each farmer field monitored, fertility treatment plots were included to ensure a full range of variability that may be present in the local landscape. A standardized protocol [19] with recommended fertilizer type and quantity for each crop type was used (Table 2). Urea was applied at earning, and other fertilizers at sowing. The fertility experiment layout is schematized in Figure 2. Within each field, five (cotton, maize, and peanut) or six (millet and sorghum) plots of 15 m \(\times\) 15 m were picketed (Figure 2: solid outline boxes). One plot, and the remainder of each field was managed as per farmer practice (Figure 2: dashed outline box). Plant growth, development, biomass and grain yields were measured in all plots. Each participating farm could contribute only one field. Small field sizes did not allow for replicates within a single field, but instead treatments were replicated (10 cotton, 10 maize, 10 millet, 9 peanut and 9 sorghum fields) across the landscape.
# Long-Term Occurrence and Fate of Microlastics in WWTPs: A Case Study in Southwest Europe
## 3 Results and Discussion
### Characterization of MPs by Size, Shape and Colour
Previous studies reported that fragments are the vast majority MPs [17, 34, 72, 73], in concordance with the results obtained in this case study. Following the evolution of MPs through the wastewater treatment process (Figure 5), in influent samples the concentration of fragments and fibres ranged between 44.8-77.6% (with an average value of 64.9 \(\pm\) 9.5%) and 20.0-55.2% (with an average value of 34.2 \(\pm\) 10.2%), respectively. These percentages remained constant after the secondary treatment. However, in the final effluent samples, the concentration of fragments and fibres ranged between 46.1-81.4% and 18.6-61.0%, respectively, which shows a certain decrease in the abundance of fragments (with an average value of 57.3 \(\pm\) 10.9%) and an increase in the percentage of fibres (with an average value of 40.3 \(\pm\) 10.8%). This means that the tertiary treatment allowed a better removal of fragments (38%) than fibres (24%), as can be seen in Table S2. It has been reported that the high length-width ratio allows fibres to remain in water masses for more time than particles with other morphologies [2]. In addition, the overall removal efficiency shows a better elimination of fragments in comparison with fibres (67% vs. 56%). Finally, it is noteworthy that films, pellets and foams only account for 1-2%. Respect to the MPs colour, white and black microparticles were the most common MPs at every sampling point, which means 81% of total MPs. The remaining percentage corresponds to red, blue, green, yellow and purple. This is agreement with previous studies that analyse MPs in WWTPs where higher abundances of white and black MPs were also detected [40, 48, 74]. Figure 4: Examples of some microplastic particles found in this work and classified by shape and colour. (**1**): Green and yellow fibres, (**2,3**): Yellow and transparent fragments, (**4**): Grey film, (**5**): Black pellet, (**6**): Transparent foam.
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation Communication Technologies
#### 2.2.1 Global Navigation Satellite System  
The Global Navigation Satellite System (GNSS) is preferred in the field of ITS to access location information in travel planning, routing applications, vehicle tracking systems, and similar applications [22].
## 3 Proposed Methodology
### Hybrid HHO-LP Optimization Algorithm
#### 3.5.2 Linear Programming  
To convert the NLP into a linear one, the value of \(\mathrm{I}_{\mathrm{p}}\) is fixed as extracted from HHO. The linear programming subproblem is called repeatedly by HHO to compute the value of TMS and the fitness of each hawk corresponding to the \(\mathrm{I}_{\mathrm{p}}\). A penalty relative to the severity of violation is added to the fitness value of each hawk if it violates the inequality coordination constraint. The complete algorithm of the proposed methodology for forecasting wind coupled with HHO-LP optimization algorithm is shown in Figure 6. In this study, short-term wind forecasting is used. The predicted wind speed, wind direction, and metrological variables are compared with the actual ones, and the predicted data are modified if the error exceeds the limits. On the basis of the predicted wind dynamics, the fault current is calculated in advance, and HHO-LP optimizes the ORC problem. The algorithm continuously checks if there is any change in wind data during a specified time interval; then, the fault current is calculated accordingly, and the relay settings are updated. The total time from forecasting to the upgrading of relay settings is given as  
\[\Delta\mathrm{T}=\Delta\mathrm{T}_{\mathrm{F}}+\Delta\mathrm{T}_{\mathrm{HHO -LP}}+\Delta\mathrm{T}_{\mathrm{t}}, \tag{40}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Strategies** & **Escape Energy (E)** & **Escape Probability (r)** \\ \hline
1 & Soft siege (SS) & E \(\geq\) 0.5 & r \(\geq\) 0.5 \\
2 & Soft siege with progressive rapid dives (SSPRD) & E \(\geq\) 0.5 & r \(<\) 0.5 \\
3 & Hard siege (HS) & E \(<\) 0.5 & r \(\geq\) 0.5 \\
4 & Hard siege with progressive rapid dives (HSPRD) & E \(<\) 0.5 & r \(<\) 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hunting strategies of Harris hawks during exploitation phase.  
where \(\Delta\mathrm{T_{F}}\) is the time taken by ANFIS-SARIMA to calculate the predicted wind power and fault current, \(\Delta\mathrm{T_{HHO-LP}}\) is the time consumed by HHO-LP to compute the optimum values of relay variables, and \(\Delta\mathrm{T_{t}}\) is the time required to transfer the values of \(\mathrm{I_{p}}\) and TMS to the relay.
# Research on Variable Universe Fuzzy Multi-Parameter Self-Tuning PID Control of Bridge Crane
## 3 Variable Domain Fuzzy Multi-Parameter Self-Tuning PID Controller
### Bridge Crane Control Scheme  
During crane movement, the trolley's displacement and velocity, swing angle, and angular velocity constantly fluctuate. The fuzzy controller can adjust the PID control parameters by monitoring these parameters' changes to improve control performance. To achieve precise control during anti-sway operations, a PD controller is employed due to its ability to eliminate steady-state errors. On the other hand, trolley positioning control utilizes a PID controller for improved accuracy. The displacement s and swing angle \(\theta\) are used as inputs for the two controllers, respectively. The controllers' outputs are linearly combined to produce the overall controller output, as illustrated in Figure 2.  
The process of domain adjustment involves calculating scaling factors \(\alpha(e)\) and \(\alpha(ec)\) for the input variables \(e\) and \(ec\), respectively, as well as scaling factor \(\beta_{(PID)}\) for the output variables \(\Delta K_{P}\), \(\Delta K_{I}\) and \(\Delta K_{D}\). These factors are determined based on the deviation \(e\) and deviation change rate \(ec\) to adjust the size of the input and output domains. Once the scaling factors are determined, the fuzzy controllers perform fuzzification, fuzzy inference, and defuzzification to obtain the output variables \(\Delta K_{P}\), \(\Delta K_{I}\) and \(\Delta K_{D}\). The resulting output parameter values are then added to the initial values of \(K_{P}\), \(K_{I}\) and \(K_{D}\) for the PID controller. This process leads to achieving precise control of the controlled object using the self-tuned \(K_{P}\), \(K_{I}\) and \(K_{D}\) values.
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 2 Methodology
### Semantic Segmentation Architecture
#### 2.2.4 Data Augmentation  
To tackle the problem of having limited training images, data augmentation techniques are frequently used in training deep learning networks [65]. In this work, random reflection and translation techniques were applied to increase the training dataset. The augmentation procedure can provide variation in the training data but at the same time keep the same size of the training sample, which can save the total training time. The augmentation techniques help in avoiding overfitting of the network, which results in better segmentation accuracies even with a small number of the training sample.
# The Role of Ultrasonography in the Diagnostic Evaluation of Patients with Polyneuropathy
## 3 Results
### Electradigasostic (EDx) Evaluation  
EDx studies were abnormal in 198 (97%) of our 201 patients with EDx reports available at the time of the US study. The most common EDx diagnoses were axonal polyneuropathy (87 patients, 43%), followed by demyelinating (68 patients, 34%) and mixed polyneuropathy (23 patients, 11%; Figure 1). The most common final etiological diagnosis was diabetic polyneuropathy, followed by hereditary polyneuropathy (Table 5).  
Figure 1: The most common and clinically relevant pathways of patients with polyneuropathy through our diagnostic evaluation. Numbers describe patient numbers. Several US findings may be demonstrated in individual patients. ALS—amyotrophic lateral sclerosis; EDx—electrodiagnostics; MMN—multifocal motor neuropathy; US—ultrasonography.  
\begin{table}
\begin{tabular}{l r r r r} \hline \hline
**Diagnosis** & \begin{tabular}{c} **Number** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Mean Age (y)** \\ **(SD)** \\ \end{tabular} & \
\begin{tabular}{c} **Males** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **US Sensitivity** \\ **(\%)** \\ \end{tabular} \\ \hline Final cohort & 201 (100\%) & 62 (15) & 66 & 64 \\ Diabetic polyneuropathy & 38 (19\%) & 66 (12) & 82 & 60 \\ Hereditary polyneuropathy & 35 (17\%) & 52 (17) & 49 & 63 \\ Idiopathic polyneuropathy & 28 (14\%) & 67 (14) & 75 & 28 \\ Typical CIDP & 22 (11\%) & 62 (19) & 41 & 82 \\ DADS & 12 (6\%) & 74 (6) & 92 & 58 \\ Multifocal CIDP & 11 (5.5\%) & 56 (12) & 91 & 91 \\ MMN & 8 (4\%) & 62 (14) & 50 & 63 \\ Multiple monoeuropathy & 7 (3.5\%) & 60 (20) & 29 & 57 \\ Parproteinemic polyneuropathy & 5 (2.5\%) & 70 (6) & 80 & 60 \\ TTR amyloid polyneuropathy & 4 (2\%) & 60 (7) & 50 & 75 \\ \hline \hline \end{tabular}  
* CIDP—chronic inflammatory demyelinating polyneuropathy; DADS—distal acquired demyelinating symmetric polyneuropathy; MMN—multifocal motor neuropathy; TTR—transhytretin.  
\end{table}
Table 5: Demographics and sensitivity of peripheral nerve ultrasonography (US) in a series of 201 consecutive patients with various polyneuropathies.
# A Framework to Determine the Utilization of Vacant Taxis on HOV Lanes with the Optimal Deployment
## 3 Formulations
### Model Constraints  
Based on [8], the movements of occupied taxis and vacant taxis follow some mathematical relationships. The relationship between the total demand and the demands of the modes is supposed to satisfy the following equation:  
\[d_{rs}=\sum_{m\in M}d^{m}_{rs},\ r\in R,s\in S \tag{8}\]  
The demand of occupied taxi mode is satisfied with:  
\[\sum_{s\in S}d^{o}_{rs}=O^{o}_{r},\ r\in R,s\in S \tag{9}\]  
\[\sum_{r\in R}d^{o}_{rs}=D^{o}_{s},\ r\in R,s\in S \tag{10}\]where \(O_{r}^{o}\) and \(D_{s}^{o}\) are the demands of occupied taxis from origin zone \(r\) and to the destination zone \(s\), respectively. The relationships between flow patterns and demands satisfy:  
\[\sum_{p\in P}f_{rs,p}^{m}=d_{rs}^{m},\ r\in R,s\in S,m\in M \tag{11}\]  
\[\sum_{p_{v}\in P_{v}}f_{sr,p_{v}}^{o}=d_{rs}^{o},\ r\in R,s\in S \tag{12}\]  
Meanwhile, the flow patterns must be non-negative:  
\[f_{rs,p}^{m}\geq 0,\ r\in R,s\in S,m\in M,p\in P \tag{13}\]  
\[f_{sr,p_{v}}^{o}\geq 0,\ r\in R,s\in S,p_{v}\in P_{v} \tag{14}\]  
In the taxi market the distribution of vacant taxis corresponds to that of occupied taxis. To be specific, it satisfies the following equations:  
\[\sum_{r\in R}d_{sr}^{v}=D_{s}^{o},\ s\in S \tag{15}\]  
\[\sum_{s\in S}d_{sr}^{v}=\sum_{s\in S}D_{s}^{o}\cdot\Pr_{r/s}=O_{r}^{o},\ r\in R \tag{16}\]  
where \(\Pr_{r/s}\) is the probability of vacant taxis traveling to origin \(r\) from all the destinations belonging to \(s\), which is calculated by:  
\[\Pr_{r/s}=\frac{\exp[-\sigma(\mu_{sr}^{v}+\lambda_{v}w_{r}^{v})]}{\sum\limits \limits_{r\in R}\exp[-\sigma(\mu_{sr}^{v}+\lambda_{v}w_{r}^{v})]},\ s\in S,r\in R \tag{17}\]  
where \(\sigma\) is the non-negative uncertain parameter. Equations (15)-(17) imply that after the occupied taxis travel from origins to destinations, they turn into vacant taxis. Then the vacant taxis, whose distribution yields to the description expressed by the equations, travel from the destinations to the origins to take passengers. Therefore, Equation (9) can be rewritten as:  
\[W_{r}^{o}=\frac{\eta Z_{r}}{O_{r}^{o}w_{r}^{v}},\ r\in R \tag{18}\]  
In the taxi market the occupied taxis and vacant taxis abide by following constraints [8]:  
\[\sum_{r\in R}\sum_{s\in S}d_{rs}^{o}h_{rs}+\sum_{s\in S}\sum_{r\in R}d_{sr}^{ o}(h_{sr}+w_{r}^{v})=N \tag{19}\]  
where \(N\) is the taxi fleet size, and \(h_{rs}\), represented as \(h_{rs}=\frac{\sum\limits_{p\in P}(f_{rs,p}^{o}\cdot\sum\limits_{s\in A}t_{s} \theta_{s\sigma}^{o})}{\sum\limits_{p\in P}f_{rs,p}}\), is the average travel time from \(r\) to \(s\) and \(h_{sr}\), and vice versa. Equation (19) demonstrates that the total service time of taxi demand, including occupied taxi demand and vacant taxi demand, is the total service time of the whole taxi market in a 1 h duration.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 5 Results: Findings from Simulations and Testbed Activities
### Testbed Activities
* The delay over the GEO link can be considered only three times larger than the delay over the NGSO link in the case of reliable and deterministic communication. This is due to the buffering that is needed to compensate for the large jitter. * Control plane considerations: As expected, there were no limitations in the testbed system due to the increased delay. However, the UE used was software with no retransmission timers. Adding retransmission timers would require their proper customization, considering the different available backhaul links as well as the proper handling of retransmitted messages on the network side, so as not to reach storms of messages for the same procedures. This is only parametrization. * Granularity matters: A more refined granularity of the behavior of the system would offer more conclusive results regarding the specific procedures.