esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 2 Wireless Communication Technologies for Agriculture
### WiFi Wireless Protocol  
WiFi is currently the most extensively utilized wireless technology available in portable devices, including tablets, smartphones, laptops, and desktops. WiFi has a suitable communication distance of about 20 m and 100 m in indoor and outdoor environments, respectively. In PA applications, WiFi extends diverse architectures by connecting several types of devices via an ad hoc network. WiFi and 3G wireless technologies were employed in [36] to estimate the agricultural applications of mobile phones. Remote access and short message services have also been used for controlling and monitoring protected crops. In [37], agricultural data, such as soil temperature, soil moisture, weather temperature and humidity, sunlight intensity, and CO\({}_{2}\), were stored in a gateway before they were transmitted to the server computer over a WiFi network. A Wi-Fi (IEEE 802.11g)-based smart WSN is proposed for agricultural monitoring in [38]. The proposed system consists of three nodes: sensor, router, and server. The climate conditions of the greenhouse or agricultural field, such as humidity, temperature, air pressure, light, water level, and soil moisture, are monitored. The said work attempts to lower cost, minimize wiring connections, and enhance the mobility and flexibility of the sensing points in WSN. However, the energy consumption of the proposed system is extremely high at 42.17 J/h.  
Given that WiFi requires much power [5], long communication time, and huge data payload [26], WiFi technology is not preferable for agricultural applications in spite of the fact that a Wi-Fi server prevents data losses by adopting data redundancy techniques. In addition, WiFi cannot be employed for multi-hope applications and influenced by number of users and the signal intensity; thereby, it is inappropriate for agriculture WSNs. Moreover, the WiFi nodes listen all the time, so power consumption will increase [39].
## 2 Materials and Methods
### Land Cover Classification and Change  
#### 2.4.1 Land Cover Classes  
Other research carried out in Belize, including the southern coastal plain, identify the dominant land covers of PCNP as open savanna, dense tree savanna, forest and wetland [28; 33]. The open savanna classification is characterized by the domination of grasses and sedges with scattered trees and shrubs. Similarly, the species composition of the dense tree savanna is essentially the same as the open savanna, but with semi-closed oak and pine canopies. The forest land cover includes lowland broad-leaved wet forests, lowland broad-leaved moist scrub forests, and mangrove and littoral forests.  
Figure 3: Maps of data used as variables in the Support Vector Machine (SVM) classification: (**a**) Normalized Difference Vegetation Index (NDVI) map of Landsat-5 Thematic Mapper (TM) image from March 2011; (**b**) Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) showing elevation in meters of PCNP, from United States Geological Survey (USGS) Earth Explorer; (**c**) soil type map showing areas in PCNP with differing major soil types, from Selva Maya consortium.  
Wetland land cover in PCNP are comprised of tall herb lowland swamp and Eleocharis marshes [33; 34]. The focus of this study, however, is to characterize conversion to and from woody and non-woody vegetation over time, so the land cover classes used in this classification are more general: forest, grass, wetland, and water.
# Novel Fluidic Oscillator Evaluation Considering Dimensional Modifications
## 5 Geometrical Modifications Considered  
A parametric analysis based on 2D-CFD simulations was performed to evaluate the main characteristics of the fluidic oscillator at Reynolds number \(Re=\) 54,595 and when considering different geometrical modifications. Two different modifications were evaluated, the mixing chamber angle, (which mostly involves the MC entrance width variation), and the feedback channel width modification. Figure 3 schematically represents the dimensional changes studied in the present research. Although performed on both sides, the modifications are just shown on one side of the mixing chamber. In Figure 3a, the mixing chamber angular modifications considered are presented; note that the standard angle \(\delta_{STD}=\delta_{Baseline}\) was increased and decreased by \(15^{\circ}\); another case, where the \(\delta_{STD}\) angle was decreased by \(10^{\circ}\), was also studied. The details of the different angles studied as well as the new MC entrance widths obtained for each case are presented in Table 3. Figure 3b, summarizes the feedback channel modifications studied. Four different feedback channel widths were considered, the baseline case width \(W_{STD}=(R_{ext}-R_{int})\) was reduced by \(W_{1}=(1/3)W_{STD}\) and \(W_{2}=(2/3)W_{STD}\). A third case, where the FC width was enlarged by \(W_{4}=(4/3)W_{STD}\), was also considered. The MC entrance width was kept constant for all the FC widths studied. Table 4 summarizes the FC dimensional modifications considered; the four different FC widths are reported. The mesh and boundary conditions used for all the geometrical modifications considered are identical to those employed for the 2D baseline (STD) case. The mesh was simply squeezed or enlarged in the feedback channels and mixing chamber, but the total number of cells was kept constant. For the different dimensional modifications considered, the minimum, maximum, and averaged y+ showed minor variations versus the STD case; these were, respectively, 0.274, 8.047, and 0.927; see [42].
## 2 Related Works  
### Event A3 Handover Mechanism  
The handover method of evolved Node B (eNB) can be divided into X2-Handover and S1-Handover [2]. X2 handover occurs when there is an X2 interface connection between eNBs, and each eNB is controlled by the same mobility management entity (MME). S1 handover is suitable for a non-X2 interface connection between eNBs or when eNBs are controlled by different MMEs. In this paper, we focus on the X2 handover. The X2 handover process is shown in Figure 1[3]. According to the roaming and measurement control messages sent by the source eNB, the UE generates the required measurement report and transmits the report to the serving eNB. The serving eNB decides whether to hand over based on the measurement report and radio resource management (RRM). The serving eNB sends a request and necessary related information to the target eNB when the handover occurs. When the target eNB agrees to handover, it sends an ACK back to the serving eNB. The serving eNB uses this information through a radio resource control (RRC) connection reconfiguration to deliver to the UE. Once the UE successfully accesses the target eNB, the UE sends an RRC connection reconfiguration complete message to the target eNB, indicating that the UE has successfully completed the handover to the target eNB. After the target eNB receives the message returned by the UE, it sends a path switch request message to the MME to notify the MME to switch the path of the resource transmission. After receiving the notification, the MME sends a request to the S-GW to update the user plane. when the path switch and user plane are updated, the target eNB returns an ACK message to the serving eNB. After receiving the message, the serving eNB can release the resources previously occupied by the UE.  
The common activities for handover measurements in LTE include events A1 to A5. Event A3 [4] is usually used as a measurement for the required signal strength of the handover. Among the different events in LTE, event A3 uses an offset to reduce the possibility of a ping-pong effect and does not require any adjustment of the signal threshold for different environments. The general process of event A3 is shown in Figure 2. When the signal strength of the neighboring eNB is continuously greater than the offset of the signal strength from the serving eNB during the measurement time, event A3 is triggered and the UE switches to the neighboring eNB. In this research, we refer to the previous research [5] and set the offset value as 3 dB.  
Figure 1: The X2 handover process.  
Figure 2: The event A3 handover.
## 12 Results and Experiments
### Gene Expression Feature Importance Validation  
To find the contribution of each class in the classification model, we employed Explainable Artificial Intelligence (AI) techniques, specifically the Shapley Additive Explanations (SHAP) method [28], to elucidate the significance of gene expression features in distinguishing between premenopause and postmenopausal breast cancer samples using the XGBoost regressor [29]. This approach allowed us to interpret the complex decision-making process of the XGBoost model and to understand the impact of individual gene expressions on the classification outcome. SHAP values provide a comprehensive understanding of the contribution of each gene expression feature to the model's predictions, thereby aiding in the identification of key factors influencing the classification of menopausal status in breast cancer samples. We aimed to extract meaningful insights into the biological mechanisms underlying the menopausal distinctions in breast cancer.  
The SHAP values \(\phi_{i}(f)\) for feature \(i\) are calculated using the following formula:  
\[\phi_{i}(f)=\sum_{S\subseteq N\setminus\{i\}}\frac{(|S|-1)!\cdot(|N|-|S|)!}{ |N|!}[f(S\cup\{i\})-f(S)] \tag{12}\]  
Therein, we have the following:  
* \(N\) represents the set of all features.
* \(S\) is a subset of features excluding feature \(i\).
* \(|S|\) denotes the cardinality of set \(S\).
* \(f(S)\) denotes the model's prediction with the features in subset \(S\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model/** & **Precision** & **Recall** & **F Measure** & **AUCROC** & **Accuracy** & **Confusion Matrix** \\ \hline Naive & 0.7614 & 0.788 & 0.775 & 0.816 & 72.98\% & \begin{tabular}{c} TP = 201, \\ FN = 54, \\ TN = 115, \\ FP = 63 \\ \end{tabular} & \begin{tabular}{c} TP = 201, \\ FN = 54, \\ TN = 115, \\ FP = 21 \\ \end{tabular} \\ \hline Random & 0.914 & 0.886 & 0.90 & 0.962 & 88.45\% & \
\begin{tabular}{c} TP = 226, \\ FN = 29, \\ TN = 157, \\ FP = 21 \\ \end{tabular} \\ \hline SVM-RBF & 0.919 & 0.894 & 0.907 & 0.886 & 89.14\% &
\begin{tabular}{c} TP = 228, \\ FN = 27, \\ TN = 158, \\ FP = 20 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance measurements of the three classification models on the breast cancer multiomics dataset.  
These SHAP values provide a quantitative measure of the impact of each gene expression feature on the output. The XGBost prediction for a sample \(i\) is given by \(\hat{y}_{i}=\psi(\mathbf{x}_{i})\), where \(\psi\) is the sum of predictions from individual decision trees \(\psi_{k}\) in the ensemble. Mathematically, this is represented as  
\[\hat{y}_{i}=\sum_{k=1}^{K}\psi_{k}(\mathbf{x}_{i}),\quad\psi_{k}\in\mathcal{Y} \tag{13}\]  
Therein, we have the following:  
* \(K\) is the total number of trees.
* \(\psi_{k}\) represents an individual decision tree from the set of all possible decision trees denoted by \(\mathcal{Y}\).  
The final prediction combines predictions from all trees, thereby providing a powerful and flexible model for classification tasks.
## Appendix A
\begin{split}\rho^{(\omega)}(\xi\cdot\tau)=-\frac{4 at\eta_{0} \gamma}{\mathrm{d}}\times\sum_{\mathrm{k}=1}^{\infty}\left(\frac{8 at\eta_{0} \Lambda\eta_{0}\gamma}{\pi^{2}\epsilon_{0}\epsilon_{0}\epsilon_{0}\epsilon_{0}} \right)^{k-1}\cdot\sum_{\mathrm{n}=1}^{\infty}\left(\frac{\sin^{2}\left(\frac{m_{ \mathrm{1}}}{2}\right)}{\frac{1}{\tau_{\mathrm{n}}}+\mathrm{i}\frac{\omega}{w^{( 0)}}}\right)\times\\ \times\cos\ \left(\frac{7\eta\eta}{\mathrm{d}}\xi\right)\exp\left( \frac{\mathrm{i}\omega\tau}{W^{(0)}}\right),\end{split} \] (A11a) \[ \begin{split}\rho^{(2\omega)}(\xi\cdot\tau)=\frac{16\pi^{2}n_{0} \gamma^{2}}{\mathrm{d}^{2}}\times\sum_{\mathrm{k}=2}^{\infty}\left(\frac{8 at\eta_{0} \Lambda\eta_{0}\gamma}{\pi^{2}\epsilon_{0}\epsilon_{0}\epsilon_{0}\epsilon_{0} \epsilon_{0}}\right)^{k-2}(\mathrm{k}-1)\cdot\sum_{\mathrm{n}=1}^{\infty} \sum_{\mathrm{s}=1}^{\infty}\left(\frac{n^{2}\cdot\cos^{2}\left(\frac{m_{\mathrm{ 1}}}{2}\right)\cdot\sin^{2}\left(\frac{m_{\mathrm{s}}}{2}\right)}{(n^{2}- \
icefrac{{\epsilon}}{{\tau_{\mathrm{s}}}}+\mathrm{i}\frac{\omega}{w^{(0)}} )\left(\frac{1}{\tau_{\mathrm{n}}}+2\frac{\mathrm{i}\omega}{w^{(0)}} \right)}\right)\times\\ \times\cos\ \left(\frac{\gamma\eta\eta}{\mathrm{d}}\xi\right)\times \exp\left(2\frac{\mathrm{i}\omega}{W^{(0)}}\right),\end{split} \] (A11b)  
Here, we finally calculated the density of the volumetric charge in the function of spatial variable \(\xi\) and time \(\tau\) with respect to the infinite approximation of the perturbation theory \((\mathrm{k}\ =\infty)\); this was carried out at frequencies of the alternating electric field \(\omega\), \(2\omega\). \[\rho^{(\omega\cdot 2\omega)}(\xi\cdot\tau)=\rho^{(\omega)}(\xi\cdot\tau)+\rho^{(2 \omega)}(\xi\cdot\tau).\] (A12)  
The functions introduced in (A12) are as follows:  
\[\rho^{(\omega)}(\xi\cdot\tau)=-\frac{4 at\eta_{0}\gamma}{\mathrm{d}\left(1- \frac{8 at\eta_{0}\Lambda\eta_{0}\gamma}{\pi^{2}\epsilon_{0}\epsilon_{0} \epsilon_{0}\epsilon_{0}\epsilon_{0}}\right)}\times\sum_{\mathrm{n}=1}^{+\infty} \left[\frac{\sin^{2}\left(\frac{m_{\mathrm{1}}}{2}\right)}{\frac{1}{\tau_{ \mathrm{n}}}+\mathrm{i}\frac{\omega}{w^{(0)}}}\right]\\[ \begin{split}\rho^{(2\omega)}(\xi_{\
u}\tau)=\frac{16a^{2}n_{0} \gamma^{2}}{\mathrm{d}^{2}\Big{(}1-\frac{8 aqn_{0}\lambda_{0}\gamma}{\pi^{2}\ell_{ 0}\epsilon_{\
u}\kappa_{0}}\Big{)}}\times\sum_{n=1}^{\infty}\sum_{s=1}^{ \infty}\Bigg{(}\frac{n^{2}\cdot\cos^{2}\big{(}\frac{\varpi n}{2}\big{)}\cdot \sin^{2}\big{(}\frac{\varpi n}{2}\big{)}}{(n^{2}-s^{2})\big{(}\frac{1}{\tau_{s }}+\mathrm{i}\frac{\omega}{W(0)}\big{)}\big{(}\frac{1}{\tau_{s}}+2\frac{ \mathrm{i}\omega}{W(0)}\big{)}}\Bigg{)}\times\\ \times\cos\ \big{(}\frac{\varpi n\alpha}{\mathrm{d}}\xi\big{)} \times\exp\Big{(}2\frac{\mathrm{i}\omega}{W(0)}\tau\Big{)},\end{split}  \tag{12b}\]  
The expressions (12a), (12b) were investigated as form of (73), (88a) and (75), (88c) in the main sections of this study.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 3 Results  
### Alder Root Tip Enzyme Activities  
Alder root tip acid phosphatase activity and phosphodiesterase activity showed strong responses to substrate concentration, with rates being approximately double at the higher substrate concentration for both acid phosphatase (6.06 \(\pm\) 0.35 versus 2.59 \(\pm\) 0.18 pmoles min\({}^{-1}\) mm\({}^{-2}\), respectively, \(p<\) 0.01) and phosphodiesterase (0.75 \(\pm\) 0.04 versus 0.44 \(\pm\) 0.02 pmoles min\({}^{-1}\) mm\({}^{-2}\), respectively, \(p<\) 0.01) when averaged across all stages. Activities of acid phosphatase (100 and 250 \(\upmu\)M) and phosphodiesterase (100 and 500 \(\upmu\)M) were positively intercorrelated (all four comparisons, \(p<\) 0.0001), while phytase activity was positively correlated only with acid phosphatase measured at 250 \(\upmu\)M (\(p<\) 0.05) and phosphodiesterase measured at 100 \(\upmu\)M (\(p<\) 0.01).  
Mixed effects model comparisons indicated that modeling enzyme activities as a function of fixed effects (STAGE, HORIZON) and a random intercept effect of PLANT provided the lowest AIC score for all enzymes except for phytase, where the full model (also including the STAGE*HORIZON interaction) was the best fit. Although both STAGE and HORIZON appeared in the best model for all enzymes, most of the explained variance was contributed by the random intercept effect of PLANT in all cases, indicating that variability among plants within stages was more important than either successional stage or horizon in predicting enzyme activities (Figure 1). Acid phosphatase activities of alder root tips measured at both 100 and 250 \(\upmu\)M tended to be highest in late successional stages and in organic horizons, but the only statistical differences among stages were found at 250 \(\upmu\)M, where mid-successful balsam poplar stands had lower rates than either early or late successional stands (both where the full model (also including the STAGE*HORIZON interaction) was the best fit. \(p<\) 0.05), which did not differ (Figure 1). In contrast, phosphodiesterase activities were highest in early successional stands when measured at both concentrations. Phytase activity tended to increase across the successional gradient, but differences were not significant. No significant differences were detected for any alder root-tip enzymes harvested from organic versus mineral horizons.
# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 5 Discussion of Results
### Graphical Techniques for Data Imputation Verification
For safety critical components, it is important to characterise the presence of any surface defects including cracks, manufacturing flaws, service-induced cracking, or suspected degradation, as these defects can initiate and grow during service and may cause \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & \begin{tabular}{c} **Data Size** \\ **(Columns)** \\ \end{tabular} & \begin{tabular}{c} **\% Aug \({}^{*}\)** \\ **Increase** \\ \end{tabular} & \begin{tabular}{c} **(NN) Sum Squared** \\ **Error (SSE)** \\ \end{tabular} & \
\begin{tabular}{c} **(NN) R\({}^{2}\)** \\ \end{tabular} &
\begin{tabular}{c} **(CART) R\({}^{2}\)** \\ \end{tabular} \\ \hline mp (Tx) & 98 (2) & 0 & \(7.31\times 10^{16}\) & \(1\) & \(1\) \\ \hline mp (TXLx) & 98 (3) & 0 & \(6.65\times 10^{15}\) & \(0.9998\) & \(1\) \\ \hline mp (TxLx) \& Param & 98 (6) & 0 & \(4.24\times 10^{14}\) & \(0.8338\) & \(1\) \\ \hline mp (TxLx) \& Param \& Augmented & 108 (6) & \(10\) & \(6.09\times 10^{11}\) & \(0.9939\) & \(1\) \\ \hline mp (TxLx) \& Param \& Augmented & 118 (6) & 20 & \(1.00\times 10^{10}\) & \(0.9929\) & \(1\) \\ \hline mp (TxLx) \& Param \& Augmented & 158 (6) & 60 & \(1.10\times 10^{9}\) & \(-0.0168\) & \(0.0882\) \\ \hline mp (TxLx) \& Param \& Augmented & 205 (6) & 100 & \(6.03\times 10^{9}\) & \(0.3156\) & \(-1.23\) \\ \hline mp (TxLx) \& Param \& Augmented & 158 (6) & 60 ** & \(2.80\times 10^{12}\) & \(0.3220\) & \(0.509\) \\ \hline \hline \end{tabular}  
* Key: \({}^{*}\) Aug: Augmentation; \({}^{**}\) Other half of data; Tx: Transversal measurement; Lx: Longitudinal measurement.  
\end{table} 
Table 4: Comparison metric results for NN and CART techniques using different levels of data augmentation. catastrophic failure by fracture. Hence, most of the structural integrity assessment methodologies tend to be highly conservative. By accurately predicting the surface quality levels, it is possible to obtain more reliable NDT measurements that can avoid being overly conservative with fracture assessments, and thus improve the possibility of life extension of existing power plants. Looking at both techniques and results given in Tables 2-4, the results are not 100%, however the predictions are within the required thresholds. The measurements of concern would be based on a range of historic measurements where damage would display a decrease and greater level of confidence. Nevertheless, whilst such NDE techniques mature, Destructive Testing would still be carried out until a level of confidence can be passed with that of NDE techniques.
## 2 Materials and Methods
### Test Procedure  
All scenes were categorized beforehand as scenes with or without \"motions\" based on whether the test subject physically moved during the scene (Table 1). As the impact of the breathing intensity or a vibrating mobile phone on motion detection was unclear and subject to the identification of potential confounders, these scenes were assigned to the category \"no motion\".  
Before starting the test session, all 22 scenes were explained to each test subject (Table 1). Each scene had a duration of 1 min and was recorded separately. Subjects were asked to lie on a prepared ICU bed with the motion detector attached out of reach and sight, underneath the bed's headboard at shoulder height (Figure 1). Test subjects were kept uninformed about where the motion detector was connected exactly. Recordings were stopped between each scene to allow for a short break and to re-explain the subsequent scene (complete list provided in Table 1).  
In brief, the test started with the recording of an empty bed. The subject was then asked to get into bed, and the noninvasive monitoring system was connected, including recording of ECG, oxygen saturation, and blood pressure measurement. Afterward, the subject was asked to remove all cables and to perform all other scenes described in Table 1. The scene \"lying motionless in bed, not breathing or keeping the breathing very shallow\" (defined as the \"emergency scene\") was performed 5 times per subject and was used as the primary endpoint. The test subject could improvise one scene (scene number 19, \"Joker\"), whereby all test subjects chose motions of different intensities (Table 2).  
Figure 1: Orbit IIoT MainBox (**left**) used at the ICU. The detector is attached underneath the bed with a bandage to allow the greatest possible transmission of vibrations (**right**). Abbreviations: cm—centimeter.  
Figure 2: Illustration of pitch, roll, and yaw.  
For the \"mobile phone scene,\" an iPhone XR 10 was placed below the pillow of each test subject with an activated vibrating function. One study team colleague called the number repeatedly to generate a constant vibration during the 1 min test period. In total, 330 (15 \(\times\) 22) scenes were anonymized, recorded, and stored live on the Orbit IIoT Cloud-based platform time series database.
## 3 Methodology
### Segmentation Model  
From the chosen dataset, 1404 available aerial images and their corresponding segmentation labels were used to train the segmentation model. The dataset provides six different classes so that the output layer of the segmentation model is made for six categories. In other words, for each input pixel, the output will be a list with a length of six for each pixel, which provides the probabilities as to which class that pixel belongs to. As an accuracy metric, the basic definition of intersection-over-union (\(IoU\)) is used. \(A\) is the prediction set and \(B\) is the label set.  
\[IoU=(A\cap B)/(A\cup B) \tag{1}\]  
Since there are six region types in the chosen dataset, the total number of classes \(C=6\). For each class \(c\), the \(IoU_{c}\) is defines as:  
\[IoU_{c}=\frac{TP_{c}}{TP_{c}+FP_{c}+FN_{c}} \tag{2}\]  
\(TP_{c}\) is the number of true positive pixels for a class \(c\in C\), and it is divided by the total number of pixels in the union in the prediction set and label set. From (2), the mean \(IoU\) is defined as:  
\[mIoU=\frac{1}{6}\sum_{c=1}^{6}IoU_{c} \tag{3}\]  
The Jaccard loss was utilized as a loss function for training. The following is its definition for prediction set \(A\) and label set \(B\).  
\[L(A,B)=1-(A\cap B)/(A\cup B) \tag{4}\]