esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Instrument description
### Instrument characteristics
#### Instrument main modes  
* Calibration modes  
Two calibration modes are available on the instrument. First, the Calibration 1 mode provides the altimeter point target response (complex spectrum). The transmission channel is looped back to the  
\begin{table}
\begin{tabular}{|l|l|} \hline
**Parameter** & **Value** \\ \hline Altimeter band & 35.75 GHz \(\pm\) 250 MHz \\ \hline Pulse bandwidth & 500 MHz \\ \hline Pulse duration & 110 \(\upmu\)s \\ \hline Altimeter Pulse repetition frequency & 4 kHz \\ \hline Echo averaging (altimeter) & 25 ms \\ \hline Spectrum analyser (altimeter) & 128 points \\ \hline Altimeter Link budget & 11 dB (sigma naught = 6.5 dB) \\ \hline Antenna diameter & 1000 mm \\ \hline Focal length & 700 mm \\ \hline Offset & 100 mm \\ \hline Radiometer band & 23.8 GHz \(\pm\) 200 MHz \\  & 36.8 GHz \(\pm\) 200 MHz \\ \hline Radiometric resolution & \textless{}0.5 K \\ \hline Radiometric accuracy & \textless{}3 K \\ \hline Radiometer averaging & 200 ms \\ \hline Mass (altimeter+radiometer) & \textless{}33 kg \\ \hline Power consumption & \textless{}76 W \\ (altimeter+radiometer) & \\ \hline \end{tabular}
\end{table}
Table 1: Altimeter / radiometer parameters and budgets.  
corresponding receiver input through the calibration attenuator. In order to obtain a high resolution for this response, the central frequency of the spectrum analyzer can be scanned by a step thinner than the frequency resolution. The second calibration mode, the calibration 2 mode, is designed to measure the receiver transfer function after deramp (in the frequency domain) by averaging the natural thermal noise in the reception channel over a long period. Altimeter analysis window has to be positioned to a range that guarantees the absence of returned echoes.  
These calibrations are available by ground telecommands. The results will be used in the ground processing in order to take into account the signature of the instrument.  
* The nominal acquisition and tracking modes Several options are available to run these modes, as a new experimental processing feature will be implemented AltiKa (as well as on the Jason-1 POSEIDON-3 altimeter): the coupling of the altimeter with the so-called DORIS/DIODE navigator (see [31] for instance, for a description and performances of the DIODE navigator onboard Jason-1). Indeed satellite position and velocity bulletins from this DORIS/DIODE navigator are available on board every 10 seconds. The idea is to use this information in the altimeter processing, either in acquisition or in tracking mode. The aim of this new mode is to improve the behavior of the instrument in coastal regions and above in-land waters. It shall be noted that the GLAS (Geoscience Laser Altimeter System) instrument onboard the ICESat platform already uses GPS (Global Positioning System) measurements and an onboard Digital Elevation Model (DEM) to control capture of the digitized laser echo return waveform on every pulse [32].
# Prediction of Temperature Distribution for Previous Cement Concrete Pavement with Asphalt Overlay
## 3 Pavement Temperature Field Solved Using Laplace Transform
### Laplace Numerical Inversion Transforms  
Taking the inverse Laplace transform is necessary to return the solution \(\overline{\theta}_{j}(z,s)\) to the time domain [20]:  
\[\theta_{j}(z,t)=\frac{1}{2\pi i}\int_{\
u-i\infty}^{\
u+i\infty}\overline{ \theta}_{j}(z,s)e^{st}ds,\,H_{j-1}<z<H_{j}. \tag{24}\]  
where \(i\) is the pure imaginary number with \(i^{2}=-1\); while \(\
u\) is the real number such that \(\overline{\theta}_{j}(z,s)\) converges absolutely along the line \(\mathrm{Re}(s)=\
u\).  
The numeric technique can be used to achieve the complex integration for evaluating the inverse Laplace transform. Making a new variable \(p=st\), the following equation represents the complex integral shown in Equation (24):  
\[\theta_{j}(z,t)=\frac{1}{2\pi i}\int_{\
u-i\infty}^{\
u+i\infty}\frac{e^{p}}{p }F_{j}(p)dp, \tag{25}\]  
where \(F_{j}(p)=\overline{\theta}_{j}(z,\frac{p}{t})\frac{p}{t}\).  
A Gaussian quadrature formula [21] of the 10-point led to the following equation:  
\[\frac{1}{2\pi i}\int_{\
u-i\infty}^{\
u+i\infty}\frac{e^{p}}{p}F_{j}(p)dp \approx\sum_{j=1}^{10}w_{j}F(P_{j}), \tag{26}\]  
where \(w_{j}\) (\(j=1,2,\,\ldots\,,10\)) represent the weight coefficients of the Gaussian integration points and \(p_{j}\) (\(j=1,2,\,\ldots\,,10\)) represent the Gaussian integration points.  
The \(p_{j}\) and \(w_{j}\), which were applied in the 10-point Gaussian quadrature equation are given in Table 1[22]. Furthermore, \(p_{j}\) and \(w_{j}\) for \(j=2,4,6,8,10\) are the conjugate complex of \(p_{j}\) and \(w_{j}\) for \(j=1,3,5,7,9\), respectively [22].  
Substituting Equation (26) into Equation (25) leads to  
\[\theta_{j}(z,t)\approx\sum_{j=1}^{10}w_{j}F\Big{(}p_{j}\Big{)}. \tag{27}\]  
By substituting Equations (10) and (27) into Equation (11), the pavement temperature field can be determined as follows:  
\[T_{j}(z,t)=\theta_{j}(z,t)+c. \tag{28}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(j\) & **The Gaussian Integration Points**\(p_{j}\) & **Weights**\(w_{j}\) \\ \hline
1 & 1.283767707781087 \(\times\) 10 + i1.666062584162301 & \(-\)8.684606112670226 \(\times\) 10\({}^{2}\) + i1.545742053305275 \(\times\) 10\({}^{4}\) \\
3 & 1.22613148416215 \(\times\) 10 + i5.012719263676864 & 1.551634444257753 \(\times\) 10\({}^{3}\) - i8.439832902983925 \(\times\) 10\({}^{3}\) \\
5 & 1.09343034060001 \(\times\) 10 + i8.409672996003092 & \(-\)8.586520055271992 \(\times\) 10\({}^{2}\) + i2.322065401339348 \(\times\) 10\({}^{3}\) \\
7 & 8.77643640082609 + i1.192185389830121\(\times\) 10 & 1.863271916070924 \(\times\) 10\({}^{2}\) - i2.533323820180114 \(\times\) 10\({}^{2}\) \\
9 & 5.225453367344361 + i1.572952904563926\(\times\)10 & \(-\)1.03490190706237 \(\times\) 10 + i4.110935881231860 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Gaussian integration points (\(p_{j}\)) and the weight coefficients of the Gaussian integration points (\(w_{j}\)) values.
## 3 Data Processing
### Data Pre-Processing
#### 3.1.4 Saccades Smoothing: Moving Median Window  
As described by [PERSON] et al. [42], to calculate the gaze-target distance, saccade information is usually kept. To smooth the saccade's data and look for trends that could otherwise be overlooked due to a high number of saccades that occur during the initial phases of scotoma habituation [43], a moving median window was used. Different sliding windows of 5, 10, 20, and 40 s were compared until saccades smoothing was achieved, and a more clear trend with less volatility in gaze-target distance was observed.  
1.5 Colour Change Recognition Sub-Task and Scotoma Radius as Cutoff for the Maximum Positive and Negative Predictive Values  
[PERSON] theorem was applied to test the probability of colour recognition due to scotoma coverage. The distance between gaze and the edge of the target and whether it was above the scotoma's radii were used to indicate seen or not seen, and the colour change detection to determine correct or wrong.  
Seen and correctly recognised was defined as true-positive, while non-seen and detected was false-positive. Similarly, if the colour change was not detected but the target was visible, it was considered a false-negative. If it was not visible and not detected, it counted as a true-negative. The probability of the positive and the negative predictive value were then calculated for the group and individually, and can indicate that the central vision might not be correctly occluded due to eye movements such as saccades and blinks [44; 45]. Hence, the subject could have seen the target outside the intended radius of occlusion partially or entirely when he/she was not supposed to, influencing the correct and incorrect colour recognition task ratio. To test for possible errors in scotoma occlusion, five different scotoma radii extensions were considered, from 6\({}^{\circ}\) to 4\({}^{\circ}\), in steps of 0.5\({}^{\circ}\). Suppose an actual error due to partial occlusion of the central visual field was present. In that case, all subjects should present a low positive predictive value and a high negative predictive value for the 6\({}^{\circ}\) scotoma radii.  
The lower the margin for the scotoma radii, the higher number of positive predictive values are expected. This increase will occur until the positive predictive value would reach a plateau. The opposite would be observed for the negative predictive value. Furthermore, this test allows us to identify the subjects who performed the task correctly from those who did not. Each subject was looked at individually to observe the trend. If the positive and negative predictive values did not show the same trend as the majority did, they were identified as having a bad performance, not in line with the experiment and therefore excluded.
# Personalized Federated Learning with Progressive Local Training Strategy and Lightweight Classifier
## 2 Related Works
### Federated Learning Under Data Heterogeneity
After the client obtains the global feature extractor, it first executes a Progressive Local Training Strategy. Once the global feature extractor is localized, \(\mathcal{L}_{local}\) is then used for local training. to improve the convergence speed and overall performance of the global model, making it more robust to the variations in data distributions across clients. Various methods have been proposed to address this challenge. For instance, techniques introducing regularization terms such as FedDyn [30], FedDC [31], SCAFFOLD [12], FedDecorr [32], and [33] aim to reduce the gap between local and global models or correct the direction of local model training. These regularization terms help align the local models with the global model, thereby improving the overall consistency and performance of the federated learning system. In addition to regularization-based methods, several approaches have focused on improving federated model aggregation, such as pFedSim [34] and FedDisco [35]. They consider factors like the distribution similarity of local data and the model similarity to generate global models that are more suitable for each client. By taking into account these similarities, these approaches help create global models that better reflect the diverse data distributions across clients, thereby mitigating the impact of data heterogeneity to some extent. In addition, the work based on data enhancement methods, such as FedAug [16], FedM-UNE [19], and FedDA [36], has also alleviated the problem of Non-IID data to a certain extent. These methods aim to balance the classes for clients, reduce the degree of data heterogeneity, and enhance the diversity of the training data, which helps improve the generalizability of the models. Although the aforementioned approaches can learn useful features from heterogeneous data to some extent, they often fail to achieve satisfactory performance when the degree of heterogeneity is high. This is because the variations in data distributions can be too significant for these methods to handle effectively. Therefore, in this paper, we propose to address this issue using a personalized federated learning paradigm. Our approach focuses on tailoring the learning process to the specific characteristics of each client's data, thereby improving the overall performance and robustness of the federated learning system.
## 2 System Model
### Additive Quantization Noise Model (AQNM)  
Signal detection is required to recover the transmitted signal from the quantized received signal (6). The quantization introduces nonlinear distortion to the received signal, which complicates the design of optimal signal detectors. It is of interest to design suboptimal detectors which are able to achieve good tradeoff between performance and complexity. A widely used approach is to employ the AQNM [10; 40] which models the quantized received signal as  
\[\mathbf{y}_{q}=\alpha\mathbf{H}\sqrt{\mathbf{P}}\mathbf{x}+\alpha\mathbf{n}+ \mathbf{n}_{q} \tag{8}\]  
where \(\mathbf{n}_{q}\) is an additive Gaussian quantization noise vector uncorrelated with \(\mathbf{x}\) and \(\mathbf{n}\). For a fixed channel realization \(\mathbf{H}\) and given AWGN power, the covariance matrix of the quantization noise \(\mathbf{n}_{q}\) is  
\[\mathbf{R}_{\mathbf{n}_{q}}=\alpha\beta\text{diag}\left(\mathbf{H}\mathbf{P} \mathbf{H}^{H}+\sigma^{2}\mathbf{I}_{N}\right), \tag{9}\]  
where \(\beta\) is the inverse of the signal to the quantization noise ratio (SQNR) and \(\alpha=1-\beta\). Following [10; 15], the values of \(\beta\) are listed in Table 2 and determined approximately by \(\beta=\frac{\pi\sqrt{3}}{2}\cdot 2^{-2b}\) for \(b\leq 5\).
# Missing Data Imputation in the Internet of Things Sensor Networks
## 4 Experiments
### Methodology
#### 4.2.3 MissForest  
MissForest is another iterative technique based on the Random Forest (RF) algorithm. Previous research has shown the efficiency of this algorithm in handling multivariate missing values in high dimensional datasets in a computationally efficient manner [17]. To impute missing values, the algorithm first trained the RF on the observed data using an iterative imputation scheme, after which the missing values were imputed iteratively. Section 4.3.2 provides a detailed description of the missForest technique, which is the foundation of our proposed approach.
## 3 Results and Discussion
### Determination of Total iodine  
The content of iodine in the potato tubers biofortified with the control, KIO\({}_{3}\), 8-Hydroxy-7-iodo-5-quinolinesulfonic acid and 5-Chloro-7-iodo-8-quinolinol treatments is presented in Figure 1A. The iodine content of the potato tubers biofortified with KIO\({}_{3}\), 8-OH-7-I-SQSA and 5-Cl-7-I-8-Q acid was significantly higher (compared to the control 24.96 ug\(\cdot\)kg\({}^{-1}\) d.w.). The highest iodine content was in the tuber enriched with KIO\({}_{3}\), with a value of 1400.15 ug\(\cdot\)kg\({}^{-1}\) d.w. In the case of the organic forms of iodine, the higher iodine accumulation in potato tubers was shown by 8-OH-7-I-SQSA acid at 693.65 ug\(\cdot\)kg\({}^{-1}\) d.w., when compared to the application of 5-Cl-7-I-8-Q acid (502.79 ug\(\cdot\)kg\({}^{-1}\) d.w.). Thus, it can be concluded that biofortification is effective in increasing the iodine content of the potato tuber _Solanum tuberosum_ L. Several techniques for the biofortification of plants with iodine have been developed in studies carried out worldwide by various research centres. Iodine accumulation through the use of biofortification has been demonstrated mainly in studies on vegetables, but also on fruit. So far, it has been possible to enrich strawberries, cucumbers, kohlrabi, lettuce, potatoes, carrots, spinach, beans, pak choi, cabbage, Chinese cabbage, tomatoes, rice, celery and radish [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65]. The high level of research sophistication and the large number of results that demonstrate the positive effects of iodine biofortification provide a good basis and opportunity for this agrotechnology to be used in the future.  
\begin{table}
\begin{tabular}{c c c} \hline
**Treatment** & **Tuber Yield/Plant (g)** & **Total Number of Tubers/Plant (Pcs.)** \\ \hline Control & 239.17 a \(\pm\) 18.60 & 8.17 a \(\pm\) 0.92 \\ KIO\({}_{3}\) & 244.38 a \(\pm\) 18.46 & 8.23 a \(\pm\) 0.44 \\
8-OH-7-I-SQSA & 250.63 a \(\pm\) 32.30 & 8.50 a \(\pm\) 0.57 \\
5-Cl-7-I-8-Q & 243.54 a \(\pm\) 13.17 & 8.60 a \(\pm\) 1.07 \\ \hline \end{tabular} Results are shown as mean \(\pm\) standard deviation (SD); \(n\) = 4; means followed by the same letter are not significantly different (\(p\) < 0.05).  
\end{table}
Table 2: Average weight of tubers per plant and average number of tubers per plant.  
Figure 1: Total iodine content of biofortified potato tubers (**A**). Quantitative iodoquinoline content of 8-hydroxy-7-iodo-5-quinolinesulfonic acid in potato tubers (**B**). Quantitative content of 5-chloro-7-iodo-8-quinolinol iodoquinoline in potato tubers (**C**). The designations A,B,C are those representing the results of three different analyses.
# Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism
## 3 Problem and Definition  
Knowledge tracing formalizes the learning domain through a set of students \(\mathbf{S}=\{s_{1},s_{2},\cdots,s_{i},\cdots,s_{l}\}\), a set of exercises \(\mathbf{E}=\{e_{1},e_{2},\cdots,e_{j},\cdots,e_{l}\}\), and a set of knowledge concepts \(\mathbf{C}=\{c_{1},c_{2},\cdots,c_{m},\cdots,c_{M}\}\), where each exercise is related to specific knowledge concepts. Exercise-concept associations are encoded in a binary mapping matrix \(\mathbf{Q}\in[0,1]^{l\times M}\), which elements satisfy \(Q_{jm}=1\) represents that the knowledge concept \(c_{m}\) is required for exercise \(e_{j}\), otherwise, \(Q_{jm}=0\). The Q-matrix constitutes the foundational relational schema between assessment items and latent competencies.  
Given a student's temporal interaction sequence \(H_{t}=\{(e_{1},a_{1}),(e_{2},a_{2}),...,(e_{t},a_{t})\}\), the KT problem constitutes two interdependent objectives: (1) knowledge state diagnosis--estimate a latent knowledge state vector \(\mathbf{h}_{t}\in[0,1]\) that characterizes the student's mastery--and (2) performance prediction--modeling the conditional response probability \(P(a_{t+1}=1\mid e_{t+1},H_{t})\) for any subsequent exercise \(e_{t+1}\) at \(t+1\).
## 3 System Model and Problem Formulation
### UAV-Aided Wireless Power and Information Transfer Model
Compared with the charging time, this process is considered to be instantaneous, and hence the time of wireless information transfer is ignored. In Equation (8), \(t_{k}^{i}\) denotes the charging time of the \(i\)-th UAV for the \(k\)-th SD. Once the trajectory of the UAV is determined, the time delay of information transfer in SD \(I_{j}\) can be expressed as  
\[\Delta\tau_{j}=\sum_{k=1}^{j-1}t_{k}^{i}+\frac{d_{j-1,j}}{V_{eco}} \tag{8}\]  
Moreover, the average time delay can be given as  
\[\overline{\Delta\tau}=\frac{1}{N}\sum_{j=1}^{N}\Delta\tau_{j} \tag{9}\]  
The objective of this paper is to maximize the UAVs' energy utilization rate while minimizing the average time delay of information transfer. The numerator in \(\phi\) is related to hovering time, and the denominator involves both flight time and hovering time. Shorter flight time and longer hovering time can increase \(\phi\); however, the average time delay is positively correlated with hovering time. Therefore, maximizing the energy utilization rate and minimizing the average time delay are in conflict with each other, which is a multi-objective optimization problem.
## 3 Results and Discussions
### Flow Simulation Model
#### 3.2.2 Transient Model  
The purpose of this part was to use the results of this simulation to predict future groundwater vulnerability in the Tebessa-Morsott alluvial aquifer located in a semi-arid region. The results of [50] show that the climatic conditions and the net recharge would not highly contribute to a change in groundwater vulnerability in the future, especially in semi-arid and arid regions. Accordingly, two scenarios were simulated maintaining the same recharge rate but modifying the exploitation rate over a period of 20 years.  
(a) First scenario: With the existing water points and their current flows, a period of 20 years was simulated. The piezometric map obtained with this scenario (Figure 7a) shows that the aquifer essentially keeps the same flow directions as in the case of the reference piezometry. Moreover, the aquifer budget is quite balanced (Figure 7b).  
(b) Second scenario: In this case, the operating rate of the most productive boreholes was doubled in four different sectors of the study area, namely, El Hammamet, Ein Chabro, Tebessa and Bekkaria. The piezometric map obtained for this second scenario (Figure 8) shows a considerable drawdown of up to 40 m in the El Hammamet region. This situation could lead to aquifer pollution since this region is known for its agricultural activities. Moreover, the aquifer budget is unbalanced, marking a worrying situation.
## 3 Test Results
### Wave Forces According to Wave Periods of Irregular Waves
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sea Levels**} & \multirow{2}{*}{**Wave**} & \multirow{2}{*}{**P\({}_{\text{w}}\)**} & **Monopile** & **GBS** & **Hybrid 0\({}^{\circ}\)** & **Hybrid 45\({}^{\circ}\)** \\ \cline{3-7}  & & **Sec** & **kN (Ratio)** & **kN (Ratio)** & **kN (Ratio)** & **kN (Ratio)** \\ \hline \multirow{3}{*}{HSWL} & IR-\#1 & 7.5 & 1792 (1.00) & 5883 (1.00) & 2186 (1.00) & 1912 (1.00) \\  & IR-\#2 & 9.5 & 1437 (0.80) & 4570 (0.78) & 1726 (0.79) & 1694 (0.89) \\  & IR-\#3 & 13.7 & 1332 (0.74) & 4664 (0.79) & 1916 (0.88) & 1918 (1.00) \\ \hline \multirow{3}{*}{MSL} & IR-\#1 & 7.5 & 1544 (1.00) & 6014 (1.00) & 2101 (1.00) & 2027 (1.00) \\  & IR-\#2 & 9.5 & 1444 (0.94) & 4251 (0.71) & 2031 (0.97) & 2132 (1.05) \\  & IR-\#3 & 11.5 & 953 (0.62) & 4095 (0.68) & 1631 (0.78) & 1628 (0.80) \\ \hline \multirow{3}{*}{LSWL} & IR-\#1 & 7.5 & 1459 (1.00) & 7248 (1.00) & 3185 (1.00) & 3127 (1.00) \\  & IR-\#2 & 9.5 & 1351 (0.93) & 6533 (0.90) & 2764 (0.87) & 2781 (0.89) \\ \cline{1-1}  & IR-\#3 & 13.7 & 1662 (1.14) & 6965 (0.96) & 3214 (1.01) & 3248 (1.04) \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of maximum wave forces for real-scale models: P\({}_{\text{w}}\) under irregular waves. Figure 19: _Cont._  
Figure 18: Measured wave forces for hybrid under irregular waves: (**a**) IR-#1: HSWL; (**b**) IR-#1: LSWL; (**c**) IR-#3: HSWL; (**d**) IR-#3: LSWL.
## 3 Algorithm Improvement and Scheme Evaluation
### Improvement Strategy for the NSGA-III Algorithm
#### 3.1.2 Dynamic Solution Retention Mechanism  
To enhance the convergence speed of the NSGA-III algorithm in the early iteration stages, this paper proposes a probabilistic mechanism to retain the solution closest to the ideal point, aiming for faster convergence in the initial phase.  
The implementation is as follows: During the first 25% of the evaluation count in population evolution, this retention strategy is triggered with a random probability of 50%. Each time it is triggered, the set of objective values for all individuals in the population that meet the constraint conditions is recorded as matrix \(M\). The Euclidean distance from each individual to the current ideal point \(Z\) is then calculated (Equation (10)). The ideal point \(Z\) is determined by calculating the minimum value of each objective function, resulting in \(Z(x_{min},y_{min},\ldots,z_{min})\). Based on this, the individual \(q\) in the population closest to the ideal point \(Z\) is identified using a minimum distance search method and marked as the retained solution. In the subsequent environmental selection phase, this solution \(q\) is prioritized for entry into the next generation population to strengthen the algorithm's early convergence capability.  
For two points \(A(x_{1},x_{2},\ldots,x_{n})\) and \(B(y_{1},y_{2},\ldots,y_{n})\) in an n-dimensional space, the Euclidean distance (\(d\)) is given by the following formula:  
\[d=\sqrt{\sum_{i=1}^{n}(y_{i}-x_{i})^{2}} \tag{10}\]  
In the formula, \(x_{i}\) and \(y_{i}\) represent the coordinate values of points \(A\) and \(B\) in the \(i\)-th dimension, respectively.