esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions, Future Perspectives, and Challenges  
The large-scale use of hospital, household, and industrial wastewaters has led to widespread contamination of water by different hazardous drugs, increasing concern for human health and the environment. Various analyses have been tried to reduce the presence of drugs in water to save the environment and the human body. For the removal of pharmaceutical products from water, many methods were taken into consideration, such as adsorption and advanced oxidation processes. In this review, we demonstrated that the application of one technology to treat pharmaceutical wastewaters seems insufficient for the total removal of PPCPs from WWTPs before discharge. In fact, a trace of these PPCPs has also been found in rivers and lakes. An advanced oxidation process has been developed and constructed to show promising potential as a future generation for the treatment of water. The combination of adsorption and AOP processes can be considered an excellent way to completely remove pharmaceutical products such as antibiotics, NSAIDs, and analgesics, even at trace concentrations. A variety of techniques have been taken into account in this review to reduce the toxicity of PPCPs in water on humans via fresh drinkable water or food as well as on the environment (lakes and rivers).  
This review also highlights the combination of the different treatment methods to achieve a high percentage of PPCPs removal that reaches up to 100% in a short operation time. In the case of coupling AOPs to adsorption, a number of low-cost and local materials such as clay, activated carbon, and olive waste can be used as adsorbents. Moreover, the operation costs and sustainability of this kind of process have to be optimized, notably by using renewable energy, i.e., solar light instead of artificial illumination.  
Conceptualization, M.K. and N.R.; methodology, M.K.; writing--original draft preparation F.M.; writing--reviewing K.C.; supervision, M.K. and N.R.; project administration, M.K.; funding acquisition, N.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All authors ensure that data shared are in accordance with consent provided by participants on the use of confidential data.  
The authors gratefully acknowledge the support of the University of Sfax in Tunisia and Aix-Marseille University in France.
# Changes in Oil Quality and Peroxidase Activity during Germination of Rape Seeds and Mustard Seeds
## 3 Results and Discussion
### Comparison of Enzymatic Activity in Seeds and Sprouts of Rape and Mustard  
Absorbance measurements of samples prepared by the Lowry method allowed the amount of protein in the seeds to be calculated; the peroxidase activity was also determined using the spectrophotometric method. The results of these determinations are shown in Figures 7 and 8. Protein decreases during germination in both types of seed from 21.68 to 9.36 protein content in seeds [%] for rapeseed oil and from 24.65 to 8.40 protein content in  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Peroxide Value [meq O\({}_{2}\)/kg]**} & \multicolumn{2}{c}{**Acid Value [mg KOH/g]**} \\ \hline  & **Rapeseed Oil** & **Mustard Oil** & **Rapeseed Oil** & **Mustard Oil** \\ \hline Day 0 & 11.53 & 7.47 & 0.67 & 5.61 \\ \hline Day 1 & 1.56 & 4.54 & 0.99 & 3.80 \\ \hline Day 2 & 1.58 & 2.18 & 2.24 & 4.13 \\ \hline Day 3 & 1.58 & 2.14 & 10.31 & 5.61 \\ \hline \end{tabular}
\end{table}
Table 2: Acid [mg KOH/g] and peroxide values [meq O\({}_{2}\)/kg] of rapeseed and mustard oils.  
seeds [%] for mustard oil. The greatest decrease in protein was observed between days 0 and 1 of germination. Changes in protein after this were not significant. Two opposing processes take place during germination: as soon as the germinal root breaks through the seed coat, storage proteins begin to be broken down by proteolytic enzymes. Proteins then begin to be synthesized in the embryo [47]. In our experiment, we saw a decrease in the amount of protein associated with root puncture, but the process of synthesizing new proteins in the sprouts had not yet begun. During 7 days of peanut kernel germination, the percentage of proteins also decreased from 37.6 to 27.1% [48]. The enzyme peroxidase was found to be present. During the following days of sprouting of rape and mustard seeds, there was an increase in the activity of the enzyme peroxidase, which is synonymous with an increase in the vigor of germinating seeds. Peroxidase activity then decreases in mature seeds [49]. An increase in the level of peroxidase was also found in the research of [PERSON] and [PERSON] [50]. In rice seeds exposed to light during germination, a significant increase in peroxidase was found on the fourth day of germination. The increase in peroxidase was faster for seeds that grew in the dark: on day 3, there was already a significant increase in the amount of peroxidase. The increase in peroxidase during germination was also confirmed by the studies of [PERSON] et al. [51] and [PERSON] et al. [52] in which _Sorghum bicolor_ and _Pimus banksiana_ seeds were tested. The increase in the activity of the peroxidase enzyme and the use of O\({}_{2}\) for germination affected the quality of the oil obtained from the germinating seeds. In subsequent stages of germination, the peroxide value of the sprout oil decreased, which means that the fat present in the seeds was not oxidized due to the peroxidase activity.
## 3 Performance Analysis of Modified Algorithm  
To verify the effectiveness and robustness of the modified algorithm, we experiment with and analyze its performance in two different scenes of the Chengmenshan copper mine in Jiangxi. The original image data of the cutting zone are obtained by quad-rotor UAV oblique photogrammetry (Figure 11), and the corresponding C-MAPs of two scenes are constructed by the method proposed in this paper (Figure 12). In these two scenes, we set three different start positions and a fixed position of truck for path planning and verification. Path length, accumulative path cost, and path total score are used for the path evaluation.  
Figure 15: Verification of path planning in two scenes. (**a**–**c**) are the path planning experiments in scene 1; (**d**–**f**) are the path planning experiments in scene 2.The black line represents the path planned in OCM, and the red line represents the path planned in C-MAP. The dotted line is the path without CG optimization, and the solid line represents the path after CG optimization.  
In the first part of the test, it is evident that after adding the virtual rough area in the obstacle cost map, the path avoids the rough area. It is proved that the modified Hybrid A* algorithm can detect and avoid the large roughness.  
In the second part of the test, it is intuitive to see in the comparison diagram that the red line deviates from the rough area better than the black line. In order to quantitatively evaluate the different paths in two scenes, we measure the path length and the accumulative path cost of the tires on both sides.  
Since it is challenging to integrate the path length and accumulative cost into actual vehicle operating cost, we use the entropy method [39] to calculate the weight and get the total score of each path to approximate the operating cost with the Equations (13) and (14).  
\[E_{j}=-ln(n)^{-1}\sum_{i}^{n}p_{ij}ln\Big{(}p_{ij}\Big{)}, \tag{13}\]  
\[\omega_{i}=\frac{1-E_{j}}{k-\sum E_{j}}(i=1,2,\ldots,k), \tag{14}\]  
where \(p_{ij}=Y_{ij}/\sum_{i=1}^{n}Y_{ij}\), \(Y_{ij}=\frac{X_{ij}-min(X_{i})}{max(X_{i})-min(X_{i})}\), \(X_{i}=\{length,cost\}\). \(X_{i}\) is a matrix of path length and accumulative cost information for each path.  
In Table 4, the optimization rate refers to the optimization degree of the total score of C-MAP compared with the total score of OCM. The results of path evaluation Table 4 show that the planning path with consideration of roughness is better than that without consideration. In these three scenes, the optimization rate is more significant than zero, which shows that the modified algorithm can reduce the potential cost of the planning path.
## 3 Engineering Application
### On-Site Data Cleaning  
The acquired data include the \((H,\ P,V)\) records from 20 January 2019, to 11 October 2019, and the sampling frequency is 30 min per sample, including 12,638 samples. The raw data are shown in Figure 8. There is a long-term fluctuation trend in the water head data because of the seasonal fluctuation of upstream and downstream water levels. The active power is specified by the dispatching center according to the real-time power network load demand. Hence, it has high-frequency short-term fluctuation characteristics. The vibration amplitude is affected by the variation in these operating parameters, so its variation is complicated. Therefore, the operating condition parameters should be considered in data cleaning and subsequent evaluation. In addition, the overall missing rate of the raw data is 0.243, and the data anomaly in the vibration data is obvious.  
Figure 7: The position of the monitoring sensor.  
The raw data \((H,\ P,V)\) were combined into a three-dimensional point set \(\Psi\), as shown in Figure 9. Due to the characteristics of the FTU, the oblique blank area is the restricted operating region. Usually, the FTU would avoid working in this restricted operating region because of the high vibration and low efficiency. The valid data are concentrated in the operating condition area on both sides. In addition, the anomaly data include singulars whose amplitude is different from the standard values and the outliers whose values are within the normal range, but with a distribution inconsistent with the standard values.  
Figure 8: On-site raw data: (**a**) water head \(H\); (**b**) active power \(P\); and (**c**) vibration amplitude of the top cover \(V\).  
The dataset \(\Psi\) was inputted into the DBSCAN model, and the radius \(\varepsilon\) and the minimum number of samples within cluster \(Z\) were determined by the silhouette score \(s\), defined as:  
\[s=\frac{1}{n}\sum_{i=1}^{n}\frac{b_{i}-a_{i}}{max(a_{i},b_{i})} \tag{27}\]  
where \(a_{i}\) is the average distance between the \(i\)th sample and other samples in the same cluster, and \(b_{i}\) is the average distance between the \(i\)th sample and all other samples in the nearest cluster. \(s\in[-1,1]\) A larger \(s\) indicates that the samples within clusters are condensed, and the samples between clusters are dispersed.  
After several experiments, \(s\) reached its maximum of 0.63 when \(\varepsilon=7.5,~{}Z=196\). The clustering result is shown in Figure 10. The DBSCAN model effectively identified two valid data agglomerations and marked both singulars and outliers as noise points. The noise point was dropped out, and the valid data were retained, defined as the valid data set \(\Psi^{\prime}\), for subsequent analysis.  
Figure 10: Clustering result of DBSCAN.  
Figure 9: The three-dimensional sample set.
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 2 System Overview
### PB-GB-SAR Concept
Apart from these, we also need a data sampling component (a digital oscilloscope is used in this study), a positioner used to move the antenna to generate a synthetic aperture, and a controller to remotely control the data acquisition and antenna moving. Based on the above-described hardware components, we have designed a PB-GB-SAR system, as shown in Figure 5, where the reference antenna is a 45 cm parabolic antenna with a gain of \(\sim\)34 dB and a working frequency from 11.71 to 12.75 GHz. As to the surveillance channel, only the feed-horn of the parabolic antenna is used in order to achieve a wider beam-width for synthetic aperture processing. Two LNBs with different local oscillators are synchronized by an oscillator controller, which will be presented in detail in Section 2.3. Two boosters with a working frequency from 10 to 2600 MHz and a gain from 26 to 34 dB adjustable were used to amplify the received signal. The surveillance antenna is mounted on a programmable positioner to generate a synthetic aperture with a fixed step in the horizontal and/or vertical directions. The signal flow is sampled by a four-port digital storage oscilloscope (Agilent 54855A Infiniium Oscilloscope) with a sample rate of 10 GSamples/s: since the LNB will downconvert the frequency of the satellite digital TV signal to 1 GHz to 2 GHz, a 10 GSamples/s sampling rate is needed to avoid the distortion of the signal sampled by the oscilloscope. Finally, the dataset is transferred to a local PC for off-line signal processing, i.e., SAR imaging and displacement estimation. Figure 4: The imaging geometry of PB-GB-SAR: (**left**) the general geometry without the requirement of the antenna moving direction and (**right**) the simplified geometry with plane wave approximation. Figure 5: The designed PB-GB-SAR system, where the low noise block (LNB) oscillator controller is used to synchronize the phase and frequency of two LNBs, which will be detailed in Section 2.3.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 3 Methods
### Ensemble Learning
#### 3.2.4 Soft Voting Ensemble  
The model employs a soft voting method to integrate the above three base learners. By averaging their predictions to make the final decision, this approach effectively integrates the predictive capabilities of multiple models. The soft voting ensemble strategy is defined as follows: assuming there are \(M\) base learners, each model provides a probability distribution \(p_{m}(i)\) as the predicted output for sample \(i\), representing the probabilities of the sample belonging to each class. The prediction \(p(i)\) of the ensemble model is calculated as the arithmetic average of predictions from all individual models:  
\[p(i)=\frac{1}{M}\sum_{m=1}^{M}p_{m}(i),\]  
where \(p_{k}(i)\) represents the probability distribution predicted by model m for sample \(i\), and \(M\) is the total number of models (\(M\) = 3).  
The final class selection is made based on the averaged probability distribution, whereby each sample is classified into the category with the highest average probability, denoted as follows:  
\[c(i)=\text{arg max}_{k}p_{k}(i),\]  
where \(p_{k}(i)\) denotes the average probability of sample \(i\) belonging to category \(k\).  
The ensemble integrates the unique capabilities of random forest (RF), gradient boosting trees (GBT), and FCN-Transformer models. The RF model contributes through its multi-tree structure, offering robust classification stability and effectively handling the complexity and high dimensionality of the data. The GBT progressively corrects errors from previous predictions and optimizes its accuracy over iterations, and it is particularly good at capturing non-linear relationships. Meanwhile, the FCN-Transformer excels in processing time-series data, capturing long-term dependencies and intricate patterns inherent in the dataset. This synergy allows the ensemble model to outperform any single model by leveraging their combined strengths to effectively manage the complex interactions and non-linear patterns present in the dataset.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 1 Introduction
The CoAP Simple Congestion Control/Advanced (CoCoA) [19; 20] is one of the well-known schemes to enhance congestion control for CoAP. We propose a scheme that enhances uIP TCP's performance by employing the notions introduced by CoCoA such as weak RTT and backoff with variability. Some might argue that in-network congestion control is better than per-flow end-to-end congestion control in IoT networks. In-network congestion control is effective because network devices can use the information which is not available to end systems. Therefore, to improve congestion control and load balancing at the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) layer, numerous RPL schemes, including ORPL (Opportunistic RPL), QU-RPL (Queue utilization based RPL), PC-RPL (Power-controlled RPL), and BRPL (Backpressure RPL), were proposed [21; 22; 23; 24]. However, to prevent the sources from overwhelming the network, we also need an end-to-end congestion control mechanism that makes the sources control the sending rates. Both in-network approaches and end-to-end approaches can complement each other. Our main contribution can be summarized as follows:  
* We investigate the effect of RTT and RTO estimation on uIP TCP performance in constrained networks. * We examine how to adopt, for uIP TCP, the notion of weak RTT and backoff with variability, which CoCoA uses for congestion control in CoAP. * We propose a new mechanism for managing the retransmission timer to enhance TCP performance in constrained networks with RDC enabled. The rest of the paper is organized as follows: Section 2 provides the related work; Section 3 provides a brief overview of uIP TCP and CoCoA; Section 4 discusses the preliminary test results;Section 5 presents our proposed scheme; Section 6 discusses performance evaluation. Lastly, in Section 7, we draw conclusions.
## 5 Hardware Implementation Analysis
### Overhead Analysis  
This part presents a discussion on the execution time required for Algorithm 1 and the identification of the optimal configuration (\(H_{opt}\)) to achieve the research objective. The reflecting units of the developed RIS prototype are controlled through positive-intrinsic-negative (PIN) diodes, which switch between two-phase states. The individual control of each unit element allows for operation in the near field and channel estimation. The configuration is generated using a Hadamard codebook in MATLAB, which is transferred over WiFi using a transmission control protocol/internet protocol (TCP/IP) link to a server program running on the Raspberry Pi-3 (Model B). The clock speed of the Raspberry was optimized at 7.8 MHz, with an operational power consumption of 12-15 watts and a beam switching speed of 8 ms. Based on the updating time, the overall running time for 4096 RIS configurations is calculated as \(4096\times 0.008=32.7\) s which is acceptable as a prototype RIS with limited performance capabilities. However, this time can be significantly reduced using a high-speed field programmable gate array (FPGA) that operates at a clock speed of up to 500 MHz. Specifically, this would entail updating the control circuits of the PIN diodes to ensure compatibility with the FPGA's clock speed. This strategy holds the potential to significantly shorten the required running time.  
The security robustness of the proposed secret key-extraction scheme depends on the infeasibility of solving the Diffie-Hellman problem through the use of the Chebyshev chaotic mapping operation presented in (1) [5]. This is facilitated by the straightforward multiplication and modular arithmetic operations involved in the calculation of \(T^{\prime}_{n_{i}}(\theta_{i})\). Hence, the proposed method exhibits significantly reduced computational complexity in comparison to that of the computationally intensive elliptic curve-based Diffie-Hellman key exchanging protocol.
## 1 Introduction  
Osteonecrosis of the femoral head (ONFH) is a common disorder characterized by bone necrosis of the hip induced by decreased blood flow to the femoral head [1]. It often develops into subchondral fracture and results in collapse of the femoral head, which causes disabling hip pain and limitation of hip motion, eventually requiring hip arthroplasty [2]. Magnetic resonance imaging (MRI) is the gold standard for the diagnosis of ONFH, with a sensitivity of almost 100%, even for early ONFH [3]. However, in clinical practice, radiography is usually the first choice for diagnosing ONFH in patients with hip pain owing to its low cost [2]. Hip or pelvic radiography is limited: its diagnostic sensitivity for ONFH is low [2, 4, 5]. In particular, the sensitivity of radiography for diagnosing pre-collapse ONFH is only 50-70% [2]. Therefore, ONFH in the pre-collapse stage is often overlooked or misdiagnosed.  
Deep learning is a technique in which the artificial intelligence algorithm learns rules and patterns from the given information using various trial-and-error approaches and extracts meaningful information [6, 7, 8, 9]. In particular, artificial neural networks have structures and functions similar to those of the human brain using a large number of hidden layers [6, 7, 8, 9]. The deep learning technique can outperform traditional data analysis techniques and can learn from unstructured and perceptual data, such as images and languages, and can extract key features [10]. A convolutional neural network (CNN) is a deep learning technique widely used in image recognition and classification [11, 12].  
The CNN model commonly consists of an input layer, convolution layers, a pooling layer, a fully connected layer, and an output layer. The learnable kernel of the convolutional layer plays a crucial role in reducing the computational complexity compared to performing image classification using a basic artificial neural network model [13]. The CNN represents substantial progress in automated image classification tasks without the need for image preprocessing that is essential for classical machine learning algorithms [14].  
CNN can find features in image data that humans cannot recognize or detect [6; 7; 11; 12]. Recently, several studies have demonstrated the usefulness of CNN for diagnosing several disorders based on various imaging data, and the clinical use of CNN is gradually increasing [15; 16; 17]. We believe that a CNN would also be useful for diagnosing pre-collapse ONFH.  
In the current study, we developed a CNN algorithm to diagnose pre-collapse ONFH using anteroposterior pelvic radiographs as input data and evaluated its diagnostic accuracy.
## 3 Results
### FTIR-ATR Analysis  
The NPs produced under optimized conditions were validated through FTIR analysis. As can be seen in Table 2, six characteristic chitosan peaks can be observed (3320, 1655, 1581, 1413, 1374 and 1061 cm\({}^{-1}\)), which correspond to O-H stretching, stretching C=O from amide I, N-H bending and C-N stretching from amide II, -CH2 bending, -CH3 symmetrical deformation and skeletal vibration of C-O stretching, respectively. For TPP, four characteristic peaks were identified (1210, 1130, 1090 and 888 cm\({}^{-1}\)) that correspond to the stretching vibration of P=O, the symmetric and anti-symmetric vibration of O-P=O, PO\({}_{3}\) symmetric and anti-symmetric vibration and the P-O-P bridge stretching vibration, respectively [27; 28].  
Regarding the chitosan NPs formed, it is possible to observe the peaks representing the NPs characteristic increased hydrogen bonding (3292-3356 cm\({}^{-1}\)), the pyranose ring's asymmetric and symmetric CH2 vibration (2910 cm\({}^{-1}\)), the C=O (1657 cm\({}^{-1}\)) and the C=O symmetric (1397 cm\({}^{-1}\)) stretching in the NPs and the amide I peak (1373 cm\({}^{-1}\)). The characteristic chitosan-TPP interactions peaks start with the P=O stretching (at 1100 cm\({}^{-1}\)), followed by the C-C-C skeletal linkage (1066 cm\({}^{-1}\)), the P-O for PO4 and COH stretching (1056 cm\({}^{-1}\)), the C-N vibrations (1028 cm\({}^{-1}\)) and the N-H deformations' vibrations (890 cm\({}^{-1}\)), with the latter representing the interaction between CS and TPP. Lastly, at 1586 cm\({}^{-1}\), the peak representing the chitosan-TPP crosslinking was observed, and that indicated the interaction of TPP with the chitosan amino groups [29; 30; 31].