esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Timely crop yield forecasts at a national level are substantial to support food policies, to assess agricultural production, and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) Vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, and (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for the 2000-2019 period, the relative RMSE for vovoidships (NUTS-2) are: 8% for winter wheat, and 13% for winter rapesed and maize. Respectively, for municipalities (LAU) it equals 14% for winter wheat, 19% for winter rapesed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments such as Data and Information Access Services (DIAS) or Amazon AWS, where data sets from the Copernicus programme are directly accessible.  
crop monitoring; crop yield; data calibration; extreme gradient boosting; growing degree days; machine learning; satellite data; thermal time +
Footnote †: journal: remote sensing
## 7 Experimental Confirmation and Discussion  
An experimental study was carried out in order to provide practical validation of the findings of the analytical work in the previous sections. The experimental setup using an ultrasonic pulse-echo technique is illustrated in Figure 5. The piezoelectric (PZT) transducer (Hagisonic, Daejeon, Republic of Korea) placed on the critical angle was first used to transmit a surface-wave pulse with a central frequency of \(f=0.5\,\mathrm{MHz}\) toward the cavity. It was then used as a receiver to record the echoes of the scattered surface waves. The distance from the transmitter to the defect was chosen as \(d=150\) mm, which was about twenty-five wavelengths. At this distance, the surface waves completely dominated the scattered wave field since the body waves had almost vanished.  
A 25 mm thickness steel plate, which had a shear modulus of \(\mu=7.9872\times 10^{10}\) N/m\({}^{2}\), a Lame's constant of \(\lambda=11.03\times 10^{10}\) N/m\({}^{2}\), and a density of \(\rho=7800\) kg/m\({}^{2}\), was used for the experimental program. The thickness, which was more than four wavelengths, guaranteed that no ultrasonic signal was reflected from the bottom of the plate. The spherical, circular cylindrical, and square cylindrical cavities were made on the surface of the steel material for the experiments. Each of three shapes included ten cavities of same width and different depth. This meant that for each shape the width of cavity \(2R_{0}\) was fixed while its depth \(D\) varied such that dimensionless quantity \(kD\), where \(k\) is the wavenumber, varied.  
By recording the echoes from the defects, the displacement amplitudes of the scattered wave fields were determined. These results were then divided by the referenced amplitude of the incident surface wave, which was measured using the pitch catch technique, to obtain the normalized amplitude ratios. For each cavity configuration, this experimental process was repeated five times to calculate the means  
Figure 5: Experiment set-up.
## 3 Proposed Framework
### Evaluation Metrics  
A confusion matrix is an N X N matrix used to evaluate model performance. The machine-learning models are compared with the actual target values [38; 39].  
The accuracy of the classification is measured by the percentage of instances that are classified correctly, per Equation (7)  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{7}\]  
In order to calculate precision, divide the true-positive results with the sum of true positives and false positives, per Equation (8).  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{8}\]Similarly, Equation (9) is used to calculate the recall by dividing the true positives with true positives and false negatives.  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{9}\]  
The F1-score combines the precision and recall into a single metric that captures both properties, as shown in Equation (10).  
\[\text{F1-score}=(2\times\text{Precision}\times\text{Recall})/(\text{Precision}+ \text{Recall}) \tag{10}\]
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 3 Results
### Data Classification
#### 3.2.1 Non-Normalized Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Predicted Class**} \\ \hline  & \multicolumn{2}{c}{**Walking Downstairs**} & \multicolumn{2}{c}{**Walking Upstairs**} & \multicolumn{2}{c}{**Running**} & \multicolumn{2}{c}{**Standing**} & \multicolumn{2}{c}{**Walking**} \\ \hline  & Walking Downstairs & 0 & 0 & 2000 & 0 & 0 \\ \cline{2-6}  & Walking Upstairs & 0 & 0 & 2000 & 0 & 0 \\ \cline{2-6} Actual Class & Running & 0 & 0 & 2000 & 0 & 0 \\ \cline{2-6}  & Standing & 0 & 0 & 2000 & 0 & 0 \\ \cline{2-6}  & Walking & 0 & 0 & 2000 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Confusion matrix related to non-normalized and imputed data from the accelerometer sensor. Considering the accelerometer, magnetometer, and gyroscope sensors' data, the results obtained with imputed and non-normalized data are reported in the confusion matrix presented in Table 11. The implemented method reported an accuracy of 20.19%, a precision of 60.02%, a recall value of 20.19%, and an F1 score of 30.22%.
## 4 Computational and Mathematical Aspects
### Structural Modeling Techniques for Masonry Structures
#### 4.2.3 Modeling Masonry as a One-Phase Material (Macro-Modeling Approach)  
In this approach, there is no distinction between the masonry constituents (i.e., units and mortar) and in fact they are smeared out in a homogeneous, isotropic or anisotropic continuum (Figure 3d). For the cases in which blocky behavior is the dominant deformation mode such as small masonry walls or masonry structures with large masonry units, considering a masonry wall as a homogeneous media may lead to the difficulty of simulating the failure modes. However, this approach is preferred for the analysis of large-scale real masonry structures, because of the reduced time and memory requirements. On the basis of this approach, some computational models based on continuum plasticity ([PERSON], 1996 [100] and [PERSON] et al., 1998 [127]) and continuum damage ([PERSON] et al., 2000 [128]) have been developed and implemented successfully for in-plane analysis of masonry shear walls. [PERSON] et al. (1998 [127]) developed a homogeneous anisotropic continuum model for the analysis of masonry as a composite. In this model, the behavior of the composite media is stated, based on average stresses and strains, assuming different elastic and inelastic properties along the material axes.  
[PERSON] and [PERSON] [129]) proposed a general anisotropic (orthotropic) failure criterion for masonry under biaxial stress state, using a cubic tensor polynomial. This failure criterion was then used by [PERSON] and [PERSON] [130] for the non-linear macro-modeling and analysis of unreinforced masonry shear walls under biaxial stress state using the finite element method. The proposed failure criterion takes into account all possible combinations of plane stress state and makes it easier to be included into existing software for the analysis of masonry walls. The detailed description of this methodology is given in Sections 3.3 and 3.4. [PERSON] [131] also investigated the influence of the masonry infill panel opening in the reduction of the infilled frames stiffness by means of this technique.  
[PERSON] et al. [132] developed a macro-model for in-plane analysis of concrete masonry walls with and without reinforcements. In this multi-laminate model, a masonry wall is simulated by an equivalent homogeneous media consisting two sets of planes of weakness along the head and bed joints. To determine the global behavior of the model, the influence of these planes of weakness is smeared. This modeling technique allows prediction of the different possible failure modes, whether the planes of failure follow the mortar joints or not ([PERSON] et al., 2006, [132]). The advantage of this model is that it can predict the initiation and progress of different failure modes (i.e., head joint, bed joint or homogeneous media failure) in a separate or a combined manner.  
Figure 4: Ultimate loads for different variations of stone height (hst) and of stone unit length (lst) ([PERSON] and [PERSON], 2004 [102]).
## 8 Concluding Remarks  
This article analyzes highly nonlinear fault monitoring and recognition using the KPCA-KDE methodology. The Kernel Principal Components Analysis (KPCA)-based Generalized Likelihood Ratio (GLR) index is used for variational defect identification using a sequential sampling plan. The malfunction tracking issue was addressed so that the data are first patterned using the KPCA strategy, and afterward, the flaws are analyzed utilizing SSGLR statistics. The KPCA mechanism was thus evaluated as a forecasting algorithm for error tracking tasks. Throughout this study, the limits used to create the control charts were generated explicitly from its PDFs of the tracking metrics rather than applying limits depending on the Gaussian distribution. The fault tracking efficiency of the KPCA-KDE-based SSGLR was evaluated and correlated to that of the standard KPCA methodology centered on Gaussian assumptions by two instances: the synthetic data and the Tennessee Eastman method. Additionally, the hot strip rolling procedure was used to validate the performance of the proposed KPCA-KDE-SSGLR technique over the KLPP method.  
The findings illustrate the viability of the KPCA-KDE-based SSGLR methodology over the KLPP and the KPCA through its two T\({}^{2}\) and Q charts for nonlinear fault detection. In particular, the research substantiates the assertion that the use of KDE-based critical bounds and the SSGLR process provides more substantial tracking outcomes in nonlinear procedures than Gaussian-based critical bounds and the KLPP process.  
By using KDE control limits and integration of KPCA with the SSGLR statistic for monitoring faults in industries, nonlinear fault detections can be improved dramatically. The importance of manufacturing zero-defect coils in the face of increasing global competition in the steel processing industries cannot be underestimated.  
As future study, the bootstrap re-sampling method [42] can be employed to estimate the control limit of the proposed method. Development of the mixed kernel function can also be a good alternative to exchange the conventional kernel used in this study. Still, somehow there are certain unanswered concerns that remain to be addressed in further research, such as how to pick the ideal values of variables, an essential topic in this study for the aim of model validation. Additionally, because it is of considerable practical importance, the study into how to categorize the discovered irregularity as a specific root-cause breakdown offers a major problem for process engineers.  
Conceptualization, methodology, software, resources, writing--original draft preparation, writing--review and editing, F.S.; Investigation and supervision, Z.H.; visualization, W.H.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare that they have no conflict of interest to report regarding the present study.
## 6 Simulation Result and Analysis
### Transmission Success Rate and Power Efficiency
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{\(\mathbf{\lambda=1.5}\)} & \multicolumn{3}{c}{\(\mathbf{\lambda=9.75}\)} \\ \cline{2-7}  & **Success Rate** & **Energy Efficiency** & **Computational Time (s)** & **Success Rate** & **Energy Efficiency** & **Computational Time (s)** \\ \hline random & 0.94 & 0.16 & - & 0.34 & 0.12 & - \\ GA & 0.99 & 0.41 & \(3.24\times 10^{3}\) & 0.53 & 0.22 & \(1.67\times 10^{4}\) \\ \hline DRL-RA & 1 & 0.27 & 64.77 & 0.89 & 0.23 & 64.77 \\ DRL-EERA & 1 & 0.69 & 71.52 & 0.48 & 0.38 & 71.52 \\ \hline DRL-QoS-RA & 1 & 0.66 & 78.31 & 0.71 & 0.33 & 78.31 \\ DRL-QoS-RA-transferred & 1 & 0.64 & 11.53 & 0.72 & 0.31 & 11.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of comparative methods with different request arrival rates. Figure 6: Methods’ performance with requested traffic increase. (**a**) The trend of transmission success rate with traffic increase. (**b**) The trend of energy efficiency with traffic increase.
# A 12 dBm B1 dB N-Path Notch Filter for Transmitter Leakage Suppression in Wideband Receiver
###### Abstract  
This paper proposes an N-path notch filter while offering high blocker power handling for co-address transmitter (TX) leakage suppression in a wideband receiver. The filter includes an impedance flip-flop over an adjustable transmission line in a block band, achieving a 12.3 to 13.6 dBm blocker 1 dB compression point (B1 dB) and a 12.8 to 14 dBm 1 dB compression point (P1 dB) in a 130 nm CMOS SOI process. This design effectively suppresses broadband interference in the receiving system and improves the dynamic range and linearity of the receiver (RX) channel. The filter consumes 186 to 242 mW in the 0.3 GHz to 0.6 GHz band and has an active chip area of 0.21 mm\({}^{2}\), providing maximum rejection >25 dB, with a passband third-order input intercept point (IIP3) of 22 to 25.2 dBm. The design of the adjustable transmission line structure is analyzed to reduce the insertion loss in terms of impedance and to achieve a 1.7 to 2 dB insertion loss shortfall over the RF tuning range.  
N-path filter; TX leakage; blocker; linearity; notch; impedance +
Footnote †: journal: Electronics Letters  
Article
## 4 Proposed QLHH-II Algorithm
### General Pseudo-Code  
Algorithm 2 describes the whole process of the proposed QLHH-II. Firstly, \(\Theta\) is randomly initialised (line 3). Then an epoch consists of tackling every instance of \(\mathcal{I}\). For example, every UAV has the same process asynchronously. As long as there are still unvisited vertices according to their knowledge (line 9), they choose a new destination  
Figure 6: Representation of predicted and targeted values and the corresponding error for items of a mini-batch.  
according to the evaluation \(Q_{u}\) function (line 11, see Section 4.2.1). With a certain probability depending on the exploration rate \(\epsilon\in[0,1]\), the choice of the new destination is made uniformly at random among every vertex instead. The UAVs then use the shortest path to go to their chosen vertex (line 12). After the movement is completed, the reward can be computed (line 13, see Section 4.2.2). If at least \(\tau\) movements have been performed, the memory and \(\Theta\) are updated (lines 15-16, see Section 4.2.3). Lines 21-24 simply correspond to the action of a UAV to fly back to its initial position. It should be noted that for that last action, the final solution is not added to the memory tuple, since it corresponds to a terminal state and is therefore not used in the computation of the targeted evaluation. Finally, \(\Theta\) returns after the last epoch, which represents the returned low-level heuristic.  
```
input :Distribution \(\mathcal{I}\) of instances output :Updated \(\Theta\)
1 Randomly initialise \(\Theta\)
2\(\mathcal{M}\leftarrow\emptyset\)forepochdo
3 forepisode
4 Generate an instance \(I\hookrightarrow\mathcal{I}\)
5\(t\gets 0\)while\(S\
eq\emptyset\)do
6\(S_{t}\leftarrow\operatorname*{save}(S)\)\(v_{t}\leftarrow\operatorname*{arg\,max}_{v\in V}Q_{u}(S_{t},v;\Theta)\)\(P_{u}\gets P_{u}+\operatorname*{shortest\_path}(pos(u),v_{t})\)\(r(S_{t},v_{t})\gets O(S_{t})-O(S)\)if\(t\geq\tau\)then
7\(\mathcal{M}\leftarrow\mathcal{M}\cup\{(u,S_{t-\tau},v_{t-\tau},r_{t-\tau},S)\}\) Update \(\Theta\) with a SGD step for \(\mathcal{B}\subset\mathcal{M}\)
8 end if
9\(t\gets t+1\)
10 end for
11\(S_{t}\leftarrow\operatorname*{save}(S)\)\(P_{u}\gets P_{u}+\operatorname*{shortest\_path}(pos(u),P_{u}[1])\)\(r(S_{t},P_{u}[1])\leftarrow O(S_{t})-O(S)\)\(\mathcal{M}\leftarrow\mathcal{M}\cup\{(u,S_{t-\tau},v_{t-\tau},r_{t-\tau})\}\) Update \(\Theta\) with a SGD step for \(\mathcal{B}\subset\mathcal{M}\)
12 end for
13
14 end for return\(\Theta\)
```  
**Algorithm 2**Pseudo-code of QLHH-II
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 4 Results
### Test 4  
Figure 16 and Table 12 show the results obtained during Test 4 for the Galileo + GPS combination using smartphone A in pillar 9 (Figure 1), for four hours.  
Test 4 results show values at the level of the other tests, very similar to Test 2. The CEUT station shows RMS values below 20 cm, while the other three stations show values below 5 cm.
# _Pseudomonas aeruginosa_ Response to Acidic Stress and Imipenem Resistance
## 3 Results
### Biofilm Formation  
Biofilm formation was noticed after 48 h of incubation in a flask with pH 4.0 and above. Interestingly, population analysis of biofilm consortia also indicated the presence of two colony phenotypes. However, a difference was noticed in the biofilm of NCP and SGCP. SGCP showed biofilm formation at pH 4.0 and above, whereas NCP showed biofilm formation at pH 5.0 and above (Figure 2a,b). The electron micrographs showed the presence of two types of cells in biofilm consortia: small size rods and large size rods (Figure 2c). This size variation was attributed to colony morphotypes. NCP represented the larger ones nearer to the normal size of wild-type _P. aeruginosa_, and SGCP were the resistant smaller variants.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**pH of Medium before** & \begin{tabular}{c} ** CFU/mL** \\ **at 24 h** \\ \end{tabular} & \begin{tabular}{c} **pH of the** \\ **Medium after** \\ **24 h Incubation** \\ \end{tabular} & \
\begin{tabular}{c} **Biofilm** \\ **OD at 48 h** \\ \end{tabular} &
\begin{tabular}{c} **CFU/mL** \\ **at 48 h** \\ \end{tabular} \\ \cline{2-6}
**Inoculation** & **NCP (SD)** & **SGCP (SD)** & **24 h Incubation** &
\begin{tabular}{c} **NCP (SD)** \\ \end{tabular} & **SGCP (SD)** \\ \hline
4.0 & \(2.7\times 10^{2}\) (16.8) & 09 (1.8) & 4.2 & 0.81 & \(3.1\times 10^{3}\) (250) & 14 (4) \\ \hline
5.0 & \(1.8\times 10^{4}\) (8.8 \(\times 10^{2}\)) & \(1.6\times 10^{2}\) (17.7) & 5.1 & 1.09 & \(7.7\times 10^{4}\) (1.7 \(\times 10^{3}\)) & \(2.6\times 10^{2}\) (21) \\ \hline
6.0 & \(9.4\times 10^{4}\) (9.6 \(\times 10^{Figure 1: Imipenem sensitivity assay showing the presence of two different phenotypes of _P. aeruginosa_. The majority of the cells (NCP) were susceptible and a minor population (SGCP) was resistant, as shown growing inside the zone of inhibition.  
Figure 2: Scanning electron micrographs of biofilm consortia. (**a**) SGCP vibrant biofilm formation at pH 4.0. (**b**) NCP dense biofilm formation at pH 5.0. (**c**) The presence of two types of cell in the biofilm consortia of subject isolates: small size rod shape (SGCP) and large size rods (NCP). This size variation was attributed to colony morphotypes.  
Biofilm was also noticed at pH 7.0 and 8.0 in both phenotypes; however, the density of biofilms was much higher at lower pH (Table 1). This was confirmed using crystal violet assay (Figure 3). Established biofilms of both phenotypes were equally resistant to impenem (16 ug/mL) and no signs of cell lysis or biofilm dispersion were observed. Interestingly, the percentage of SGCP increased with impenem exposure to the established biofilms (Figure 4). The data is a Gaussian distribution and the difference was statistically significant (\(p<0.0001\)).
###### Abstract  
Passive Dynamic Ankle-Foot Orthoses (PD-AFOs) are medical devices prescribed to individuals with foot drop, a condition characterized by weakness of the ankle dorsiflexor muscles. PD-AFOs can store and release energy during the stance phase of the gait cycle, while supporting the foot in the swing phase. This study aimed at estimating the energetics of a novel fiberglass-reinforced polyamide custom PD-AFO in a population of mild foot drop patients. Eight PD-AFOs were designed and 3D-printed via selective laser sintering for eight participants with a unilateral foot drop condition. Lower limb kinematics and AFO flexion/extension were recorded during comfortable walking speed via skin marker-based stereophotogrammetry. The stiffness of each AFO was measured via an ad hoc experimental setup. The elastic work performed by the PD-AFO during gait was calculated as the dot product of the calf-shell resisting moment and the rotation angle. The average maximum energy stored by the calf-shell across all PD-AFOs was 0.013 \(\pm\) 0.005 J/kg. According to this study, 3D-printed custom PD-AFOs made with fiberglass-reinforced polyamide can store some elastic energy, which is released to the ankle during push-off. Further studies should be conducted to assess the effect of this energy return mechanism in improving the gait of individuals with deficits of the ankle plantarflexor muscles.  
ankle foot orthosis; custom; 3D-printed; selective laser sintering; fiberglass-reinforced polyamide; energetics; gait; foot drop; ankle power
## 3 Results and Discussion
### Abrasion Resistance  
Since the surface layer of the concrete is made up almost entirely of cement mortar, only when the grinding is carried out to a certain extent, the aggregate inside will be ground. The purpose of this study is to explore the effect of aggregate grades and replacement rate on abrasion resistance. Therefore, abrasion resistance is divided into two parts, the surface layer and the internal layer, for analysis. First, IRPC was ground for 30 cycles to explore the abrasion resistance of the surface layer and then IRPC was continued to be ground for 40 cycles to explore the abrasion resistance of the internal layer.