esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.3.1 Observation and Determination of Water Quality Parameters  
Groundwater samples were taken from observation wells selected from 20 different locations to determine NO3- concentration in Harran Plain. These observation wells were chosen primarily because they are situated in the shallow aquifer and as a result are susceptible. They are also near to fields of crops. In the current study, nitrate analyses were carried out in March and October of 2023. Because of the irrigation system in the Harran Plain, the months of October (post-irrigation) and mid-March (pre-irrigation) were chosen for the sample periods. Agriculture is the main industry in this area from mid-March until October. Mid-March is when the sowing procedures for corn and cotton are applied. One-liter samples were gathered in high-density polyethylene bottles. Before use, the sample container was distilled using water and washed two to three times using HNO3 acid. The standard protocol was followed during sample collection, including pumping the boreholes for five to ten minutes to prevent the effect of accumulated water in the pipeline. The samples gathered were moved to the lab and kept at 4 \({}^{\circ}\)C until examined [24]. A total of 40 samples were collected in the pre- and post-irrigation period. The same sample was analyzed three times. The samples were homogenous and filtered (0.45 \(\upmu\)m) before analysis. All analyses were performed on the same instrument and under the same conditions. Apart from NO3- analyses, other water quality indicators such as electrical conductivity, TDS, pH, temperature, Na+, K+, Mg2+, Ca2+, F-, Cl-, and SO42- were also investigated. The mean depth of the wells is roughly 10 m. The pH, conductivity, and temperature values of the observation wells were measured using a HACH LANGE HQ40D (HACH COMPANY, Dusseldorf, Germany) portable instrument, while other data were analyzed in the laboratory. The anion, cation, and NO3- analyses were performed by ion chromatography in accordance with the requirements outlined in the Standard Methods for the Examination of Water and Wastewater [25]. All the analyses were carried out as a service procurement at Harran University Research Center for Science and Technology (HUBATAM).
## 3 Results
### Impact of Radiation Changes on GPP  
#### 3.4.1 Simulated Interannual Variability of Total GPP in China  
The total amounts of GPP for China during the period 1981-2012 from three simulations are shown in Figure 8. In S1, the mean annual total GPP for the period 1981-2012 ranged from 7.43 to 8.47 Pg C y\({}^{-1}\). The total amount of GPP was lowest in 2000, likely because of the large-scale severe drought in China at that time. In S2, the multi-year mean annual GPP for the period 1981-2012 was 7.73 Pg C y\({}^{-1}\), which was 0.09 Pg C y\({}^{-1}\) higher than the average value for S1. The difference in GPP between S1 and S2 reflects the effects of the 1.3% reduction in \(R_{\rm g}\), which caused a reduction of 1.7%  
Figure 7: Spatial distribution of the annual (**a**) GPP sequestrated by diffuse radiation (unit: g C m\({}^{-2}\) y\({}^{-1}\)) and its standard deviation (**b**), and the ratio of annual mean (**c**) GPP sequestrated by diffuse radiation and global radiation and its standard deviation (**d**) during the period 1981–2012 with a spatial resolution of 0.0727\({}^{\circ}\).  
in GPP. In S3, the multi-year mean national total GPP was 7.58 Pg C y\({}^{-1}\) for the period 1981-2012, which was higher than the average value for S1. The difference in GPP between S1 and S3 reflects the increase in the diffuse radiation fraction over the period 1981-2012, which positively affected GPP in China and partially compensated for the loss in GPP because of the reduction in \(R_{\rm g}\).
## 2 Related Work
### Exam Question Evaluation Scale  
Effective evaluation frameworks are essential for assessing and improving the quality of automatically generated questions. This section examines existing evaluation approaches and explains how they inform the development of our Multi-Examiner evaluation rubric.  
The development of evaluation frameworks for automated question generation has evolved significantly, reflecting advancements in educational assessment methodologies [10; 39]. Early frameworks focused on linguistic accuracy, but modern frameworks integrate both technical and pedagogical metrics to better assess question quality [51].  
Modern evaluation frameworks are founded on three principles: content validity, cognitive alignment, and pedagogical effectiveness, with studies showing significant improvement in assessing question quality when incorporating these dimensions [48]. Machine learning systems have enhanced the evaluation of question difficulty and discrimination power [52]. Advanced NLP techniques further improve the evaluation of linguistic clarity and semantic coherence [53].  
Integrated frameworks now simultaneously assess content accuracy, cognitive engagement, and pedagogical alignment, showing considerable improvement in identifying relevant quality issues [50]. Research on cognitive complexity reveals improvements in evaluating higher-order thinking skills [49]. However, current systems struggle with accuracy consistency across domains and predicting student performance [54]. Future research should focus on refining metrics for complex cognitive assessments, particularly higher-order skills, and on hybrid frameworks combining educational metrics with AI to enhance adaptability across diverse learning contexts [55]. The integration of real-time feedback and adaptive criteria based on specific student and contextual needs offers potential for responsive and effective evaluation systems.
## 3 Results and Discussion
### Determination of Point of Zero Charge
Different surface charges of the selected MPs depending on the pH value and chemical composition of the water matrix can lead to different behaviors in the environment [43]. The obtained results shown in Figure S3 imply that at pH 7.23 \(\pm\) 0.06 in the synthetic matrix, the surfaces of the pure PEp, PE_PCPs_1 and PE_PCPs_2 were positively charged(pH! pH_PZC). On the other hand, the pH_PZC values for PP, PET, PLA and PEg (pH_PZC = 4.04, 4.53, 5.28 and 5.90, respectively) indicates that at the pH of the synthetic water matrix, their surfaces were negatively charged.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 3 JRC-CWG and DR-JRC-CWG Design  
### JRC-CWG Design with Low Sidelobe Level  
In the ITS, when the relative radial velocity between the JRC system and the target is small or can be obtained, the effect of Doppler shift on the matched filter output can be ignored. For this case, the JRC-CWG design method is proposed without considering the Doppler shift in this section.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 4 Simulation and Experiments Analysis
### Validation and Analysis Based on Experimental Signals  
#### 4.2.1 Test Overview  
The test area is located in Moganshan Lake, and the speed of sound under the lake is about 1500 m per second, which is typical in complex lake data. As shown in Figure 6, the ULA is composed of 27 sensors, and the array element spacing is 5.48 mm, each array element of ULA sampling frequency is 1000 KHz, and the sampling time is 0.05 s. This experimental system uses linear frequency modulation (LFM) signal as the transmission signal of active sonar, and the active sonar emits an LFM signal in the range of 100-200 KHz, and the Unmanned underwater vehicle (UUV) model is the target of this experiment, the combination of active sonar and ULA is used to try to capture the trajectory of the target and to achieve the tracking and monitoring of the target.  
The top view of the layout of the experimental scene is shown in Figure 7, which also clearly shows the approximate layout of the UUV model and the actual array. Since the array is smaller than the UUV model in the experimental environment, we denote it with a black square in Figure 7, and we added an interference target to test the final reconstruction effect. Through the environment of the experimental arrangement, we can roughly estimate the approximate bearing of the strong target bright spots in the UUV model--the head, middle compartment, and stern part in the UUV model, such as the position pointed by the dotted line, have a stronger capture ability than other parts.  
Based on the layout of the above experimental scene, we calculate the estimation of the three major reflex highlights of the UUV model. As shown in Figure 7, of course, the calculation estimate will have a certain error compared with the actual environment.
## 2 Materials and Methods
### Creation and Statistical Analysis of the PTML-MLP Model  
We employed only the \(D[GBI]ej\) descriptors as inputs in all the steps described here. First, we analyzed the most potential discriminatory power of the \(D[GBI]ej\) descriptors. In doing so, we used the software IMMAN v1.0 (in its default parameter configuration) [64] to calculate the information index named gain ratio (GR) [65]; the highest GR values corresponded to the \(D[GBI]ej\) descriptors with the greatest discriminatory power. After sorting the \(D[GBI]ej\) descriptors in decreasing order of their GR values, the degree of redundant information among them was analyzed by computing Pearson's correlation coefficient (\(PCC\)). Those \(D[GBI]ej\) descriptors out of interval \(-0.7<PCC<0.7\) were deleted. The software used to study the redundancy among \(D[GBI]ej\) descriptors was STATISTICA v13.5.0.17 [66]. The artificial neural network (ANN) menu belonging to this same software was employed to search for the most appropriate PTML-MLP model. In this context, except for the maximum number of hidden neurons (in this work, it was 80), we used the same predefined values for the setting of parameters as reported in a recent work [46]. We would like to emphasize that the reason for choosing the MLP architecture to generate our PTML-MLP model is justified by our previous experience and the good performance achieved when creating PTML-MLP models [35; 46; 63]. When searching for the best MLP network (PTML-MLP model), we took into consideration the following equation, which prevents an ANN from overfitting the training data:  
\[\rho=\frac{T}{[(I+1)H+(H+1)O]} \tag{3}\]  
Notice that Equation (3) characterizes the topology of an artificial neural network. Here, \(T\), \(I\), \(H\), and \(O\) refer to the number of training cases, the number of input nodes (equal to the number of \(D[GBI]ej\) descriptors), the number of hidden neurons, and the number of output nodes, respectively. To prevent an ANN from overfitting the training data, a value of \(\rho>3\) should be reached [67].  
When checking the performance of the PTML-MLP model, we analyzed the statistical indices named sensitivity (\(Sn\)), specificity (\(Sp\)), accuracy (\(Acc\)), and [PERSON]'s correlation coefficient (\(MCC\)). However, we chose the best PTML-MLP models by relying on the local counterparts of the \(Sn\) and \(Sp\)[35; 46; 63], i.e., \([Sn(\%)]ct\), \([Sp(\%)]ct\), \([Sn(\%)]mb\), and \([Sp(\%)]mb\). These local statistical indices depend on specific elements of the experimental condition (_ct_ or _mb_). Details on the chemical and bioactivity data explained in this study are available in Supporting Information S1.
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 4 The Proposed Model (WFSE)
### Wefse  
WFSE uses a convolutional function to compose word representation vectors from the constituent syllable embeddings. As illustrated in Figure 1, the model has a highway network for processing the interaction among the syllables. With a slight modification of the work by [PERSON] et al. [21], a word \(k\) that comes in as input is decomposed into a sequence of syllables by looking up the syllables from a finite syllabet. That is, with the definite syllable alphabet \(S\), the input word \(k\) is split into a sequence of syllables \(s_{1}...s_{l}\) where \(l\) is the length of \(k\):  
\[k=[s_{1},...,s_{l}]. \tag{2}\]  
Given \(S\) as a finite vocabulary of syllables and \(s_{i}\in S\), the index of \(s_{i}\) is defined as a one-hot vector \(\mathbf{I}_{s_{i}}\in\mathbb{R}^{|S|\times 1}\), \(|S|\) being the size of the syllable vocabulary. We define a projection layer \(\mathbf{P}_{s}\in\mathbb{R}^{d\times|S|}\), where \(d\) is the dimension of parameters for each syllable. The embedding for each syllable is therefore obtained as:  
\[\mathbf{E}_{s_{i}}=\mathbf{P}_{s}.\mathbf{I}_{s_{i}}\in\mathbb{R}^{d\times 1} \tag{3}\]  
The composed word embeddings \(\mathbf{E}_{w}\) from the syllable embeddings \(\mathbf{E}_{s_{i}}\) are given by:  
\[\mathbf{E}_{w}=[\mathbf{E}_{s_{1}},...,\mathbf{E}_{s_{l}}]. \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subword** & **Model** & **Study** \\ \hline \multirow{3}{*}{Morphemes} & – & [PERSON] et al. [14] \\  & Morphological RNNs & [PERSON] et al., 2013 [27] \\  & Morph2 Vec & [PERSON] et al., 2018 [29] \\ \hline \multirow{2}{*}{Characters} & [PERSON] & [PERSON] et al., 2015 [8] \\  & LSTM-char & [PERSON] et al., 2016 [11] \\ \hline \multirow{2}{*}{Word + Morpheme} & Morph-LBL & [PERSON] et al., 2015 [55] \\  & MorphemeCBOW & [PERSON] et al., 2014 [28] \\ \hline \multirow{3}{*}{Syllable-like} & – & [PERSON] et al., 2017 [30] \\  & LSTM-syl & [PERSON] et al., 2017 [21] \\  & Subword RNN & [PERSON] et al., 2012 [9] \\ \hline \multirow{3}{*}{n-gram} & [PERSON] & [PERSON] et al., 2016 [26] \\  & Fasttext & [PERSON] et al., 2017 [13] \\  & – & [PERSON] et al., 2018 [56] \\ \hline \multirow{2}{*}{Word + Characters} & CWE & [PERSON] et al., 2015 [12] \\  & Bi-LSTM/CNN & [PERSON] et al., 2017 [57] \\ \hline Characters + Morphemes & Char2 Vec & [PERSON], 2016 [15] \\ \hline Word, characters, Trigrams & Bi-LSTM & Vania [PERSON], 2017 [25] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The various compositional models for word representation.
# Deep Reinforcement Learning for Energy-Efficient 6G V2X Networks
## 4 Simulations and Results
### Results Evaluation
This implies that optimal and DRL associations of vehicles with serving RSUs led to efficient resource utilization, minimization of energy consumption, and energy savings. Figure 6 shows the energy consumed per each active RSU for a different number of vehicles at various values of the maximum tolerable latency ranging from 50 to 20 milliseconds. The energy consumed per active RSU is an observed output, and it is calculated from the optimal solution of our objective function in (12). We noticed that the optimal solution showed a high increase in energy consumption per RSU when the latency level decreased. In contrast, the DRL solution is characterized by an equally balanced energy consumption per RSU regardless of the choice of latency level. We also observed that the average energy consumed per each active RSU according to optimal and DRL solutions was 1.06 and 1.09 millijoules, respectively. The maximum allowed latency decreased 2.5 times, while the energy consumption increased 1.2 times. This implies that the resource allocation of optimal and DRL solutions led to minimized energy consumption. Figure 4: Number of vehicles vs. number of active RSUs. Figure 5: Number of vehicles vs. energy consumption. Figure 7 shows the percentage of the number of active RSUs for various numbers of vehicles with different values for the maximum allowed latency. We observed that, to serve 32 vehicles, the percentage of activated RSUs was 50 and 60 for optimal and DRL associations, respectively. We also noticed that, to serve the number of vehicles ranging from 48 to 159, the percentage of activated RSUs was 35 and 50 for optimal and DRL associations, respectively. The number of vehicles increased 1.5 to 5 times, while the percentage of activated RSUs decreased 1.2 to 1.4 times, implying that the optimal and DRL assignments of the vehicles to the RSUs led to a reduction in energy consumption.
# Antioxidant and Antimicrobial Properties of Selected Phytogenics for Sustainable Poultry Production
## 4 Selected Plant Sources of Antimicrobial and Antioxidant Compounds
### Mentha piperita  
Peppermint (_Mentha piperita_) is a perennial herb that belongs to the _Lamiaceae_ family [105]. _Mentha piperita_ is one of the most commonly used medicinal plants in the world [106]. This is mainly because of the presence of phytochemicals that have anti-inflammatory, anti-aging, antimicrobial, antioxidant, emmenagogue, antinociceptive, and rubefacient properties [107]. Bio-compounds, such as proteins, carbohydrates, lipids, minerals, vitamins, alkaloids, saponins, glycosides, steroids, and tannins, have been extracted from the plant [105,108]. Peppermint products have high concentrations of minerals, such as P, K, Na, Ca, Mg, and Zn, and vitamins A, C, and E [105]. The leaves contain flavonoids and phenolic acids, along with acetaldehyde, amyl alcohol, cadinene, caffe acid, cardiac glycosides, dimethyl sulfide, limonene, menthol, menthone, menthol, menthy esters, phellandrene,pinene, and pugelone [2,109], which have antioxidant and antimicrobial activities.  
Furthermore, the plant contains mint oil, which is composed primarily of linoleic, palmitic, and linolenic palmitic acid [110]. About 0.5 to 4% of the essential oils found in peppermint leaves are composed of menthol (25-78%), menthone (14-36%), isomenthone (1.5-10%), menthyl acetate (2.8-10%), and cineol (3.5-14%) [111]. Due to its bioactive substances, peppermint is frequently used in the poultry industry to boost the immune system, in addition to its potent antibacterial and antioxidant capabilities [112]. According to [PERSON] et al. [113], peppermint leaves had positive impacts on broiler ammonia generation, abdominal fat deposition, and antioxidant activity. Early in a broiler's life, peppermint leaves are effective at promoting growth [114]. When added to broiler diets in various concentrations (0, 5, 10, or 15 g/kg), peppermint leaves significantly boosted their body weight and daily body weight gain compared to a control diet [115].
# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 2 Related Works
These deep learning-based fatigue detection methods have enhanced model resistance to light interference, feature extraction capability, and feature localization accuracy, collectively improving detection performance, but they also have limitations. Although non-intrusive methods avoid interfering with the subject's normal work during real-time detection, existing non-intrusive approaches exhibit limitations in fatigue detection for ATCOs. The ATCO's continuous verbal interactions during work significantly impact fatigue judgment methods based on mouth opening/closing degree. Factors such as spectacle occlusion and illumination variations in the working environment also reduce detection accuracy. Through the analysis and summary of the aforementioned studies, it is evident that subjective assessment methods can comprehensively reflect the overall impact of task load on individuals. However, subjective assessment methods rely on retrospective self-reports, which suffer from limitations such as time delays, recall biases, and differences in subjective perception. Objective detection methods can be categorized into intrusive and non-intrusive types. While intrusive methods can obtain high-precision physiological data, they may cause discomfort, leading to decreased work efficiency or even psychological resistance in ATCOs. Non-intrusive methods can achieve real-time detection without interfering with ATCOs' work, but current non-intrusive methods often experience reduced detection accuracy due to factors such as the need for continuous verbal communication, eyewear occlusion, and lighting interference in the work environment. Therefore, this study proposes a deep learning-based non-intrusive fatigue detection method called FF-YOLO. This method is based on an improved YOLO11 model and enhances fatigue detection accuracy in ATCO work scenarios by identifying fatigue features in facial regions, such as facial muscle relaxation and lower eyelid swelling, through a deep learning model.
# An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
## 2 System Model and Existing Schemes
### System Model
Let \(\mathcal{T}=\{t_{1},t_{2},\cdots,t_{l}\}\) be a subset of \(\mathcal{N}(j)\) such that all nodes in \(\mathcal{T}\) have already decoded \(X\) in the previous transmission. With the cooperative beamforming model, the signal received at \(j\) can be expressed as  
\[y_{j}=\sum_{t\in\mathcal{T}}h_{t,j}w_{t}X+z_{j}, \tag{5}\]  
where \(\omega_{t}\) is beamforming coefficient satisfying \(|w_{t}|^{2}=P_{i}\leq P_{\text{max}}\). The received SNR is given by  
\[\gamma_{j}=\frac{\left|\sum\limits_{t\in\mathcal{T}}h_{t,j}w_{t}\right|^{2}}{ \sigma^{2}}. \tag{6}\]  
Similar to [10; 19], we assume that each time there is only one active communication session which is the one from \(V_{s}\) to \(V_{d}\) as shown in Figure 1. In case there are multiple active flows, the cooperative beamforming should address the interference that may occur at some relay nodes, for example with multi-channel operation [27; 28], interference aware beamforming [27; 29], non-orthogonal multiple access (NOMA), local scheduling, full-duplex operation [30; 31], etc. We also assume that the network is quasi-stationary such that the topology of network and the channel coefficients do not change or change slowly so that the cost for topology discovery and CSI acquirement is negligible. In addition, we assume that the data volume of traffic data is much larger than the signalling message, hence the energy of nodes is mainly consumed in data delivery phase rather than the routing setup phase. We also assume that all nodes have the same initial energy \(E_{\text{ini}}\) and can adjust its transmit power \(P_{i}\) continuously up to the maximum limit \(P_{\text{max}}\).
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 4 Results
### Model Fitting and Validation  
This study involved a contrast of the accuracy of commonly used machine learning methods in GPP estimation, including CatBoost, Random Forest, XGBoost, PyTorch Neural Net model, ExtraTrees, and LightGBM. As shown in Table 3, LightGBM outperforms the other models without considering the spatial factors provided by the ESF method. After incorporating spatial factors, all models showed different levels of enhancement in performance. It is evident that the SA-LGBM model outperforms the others. With the inclusion of EV, we observed an 8.5% increase in \(R^{2}\) and a 20.8% decrease in _RMSE_. This indicates that incorporating spatial vectors can enhance the model's precision and the influence of spatial autocorrelation is mitigated in the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Importance** & **Stddev** & _p_**-Value** \\ \hline Band2 & 1.52 & 0.04 & 7.45 \(\times\) 10\({}^{-9}\) \\ \hline Band1 & 1.31 & 0.02 & 2.81 \(\times\) 10\({}^{-8}\) \\ \hline DSR & 1.18 & 0.05 & 3.70 \(\times\) 10\({}^{-7}\) \\ \hline EVI & 0.96 & 0.02 & 1.21 \(\times\) 10\({}^{-8}\) \\ \hline Band7 & 0.79 & 0.01 & 4.28 \(\times\) 10\({}^{-8}\) \\ \hline Band5 & 0.59 & 0.02 & 5.82 \(\times\) 10\({}^{-7}\) \\ \hline Band6 & 0.31 & 0.01 & 4.75 \(\times\) 10\({}^{-7}\) \\ \hline Band31 & 0.18 & 0.01 & 5.47 \(\times\) 10\({}^{-6}\) \\ \hline Band11 & 0.01 & 0.00 & 0.000283 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature importance of variables and _p_-values.  
Figure 5: The Pearson correlation coefficients between each variable and GPP are computed. A white-colored pixel signifies a lack of significant correlation (_p_ > 0.05) between the two variables. The transparency of the numbers’ colors represents the degree of correlation between variables.