esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods
## 1 Introduction  
Ultraviolet and infrared sensors at high quantum efficiency on-board a small satellite (UVSQ-SAT) is a scientific and technological demonstrator dedicated to the observation of essential climate variables [1]. UVSQ-SAT was launched into a Sun-synchronous orbit by the LATMOS with the Falcon 9 rocket on 24 January 2021. After a commissioning phase, the routine phase started on 13 March 2021. Since then, the CubeSat has been fully functional, and first results have been published [2]. The methods used by [1] to obtain these results (maps of the solar radiation reflected by the Earth and of the outgoing longwave radiation at the top of the atmosphere) do not take into account the attitude of the UVSQ-SAT CubeSat. To improve the accuracy of the results, it is highly recommended to have an excellent knowledge of the attitude of the UVSQ-SAT CubeSat. This would allow researchers to obtain the Earth's incident flux on each UVSQ-SAT face, given that the CubeSat has Earth radiative sensors and photodiodes on all its faces [1]. To determine the attitude of the UVSQ-SAT CubeSat, two methods are used: the Tri-Axial Attitude Determination (TRIAD) method and the Multiplicative Extended Kalman Filter (MEKF) method. TRIAD is a basic method, which is implemented by considering that the instruments' measurements can be easily related to the information from modelsin an inertial frame of reference at the satellite's location. Then, it is necessary to find the perfect rotation matrix to move from one reference frame to another. The MEKF method improves the results obtained with the TRIAD method. It aims to calibrate and correct the data from noise and inaccuracies. The most commonly used methods in the literature are TRIAD and MEKF. Indeed, there are very few examples of results associated with CubeSat attitude determination in orbit. Table 1 presents a detailed background of the recent studies related to the problem of satellite attitude determination (AD). Simulations show that the restitution of the attitude can be better than 1\({}^{\circ}\). Moreover, the Radio Aurora Explorer satellites 3U CubeSat [3] demonstrated that it was possible to obtain knowledge of a satellite's attitude with an accuracy better than 1\({}^{\circ}\) (sunlight) in orbit. Despite the small size of the CubeSats, it seems possible to accurately determine their attitude in orbit. This is mainly valid when the satellite is aimed at the Sun because several sensors (solar photodiodes, magnetometers, and gyroscopes) are used to perform this task successfully. During eclipses, this determination is more complex. CubeSat attitude determination is still a challenge as they are small, and they still do not have active attitude determination and control systems (ADCS). Furthermore, new miniaturized space-based payloads are becoming increasingly complex and require accurate knowledge of the satellite attitude. One of the objectives of the UVSQ-SAT mission is to obtain knowledge of the CubeSat attitude with an accuracy better than a few degrees in sunlight.
## 4 Experimental Results
### Evaluation Metrics  
In this study, we selected commonly used metrics such as Precision, \(F_{1}\) Score, and Intersection Over Union (IOU) to assess the model's performance. These metrics are defined as follows.  
Precision is the ratio of the number of correctly predicted positive observations to the total predicted positive observations:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{8}\]  
where \(TP\) represents the true positives and \(FP\) represents the false positives.  
The \(F_{1}\) Score, which measures the accuracy of positive predictions, is the harmonic mean of Precision and Recall. It can be simplified and expressed mathematically as  
\[F_{1}=\frac{2 TP}{2 TP+FP+FN} \tag{9}\]  
where \(FN\) represents the false negatives.  
Intersection Over Union (IOU), which evaluates the pixelwise accuracy of the segmentation, can be mathematically illustrated as  
\[\text{IOU}=\frac{TP}{TP+FP+FN} \tag{10}\]  
Alternatively, IOU can be understood geometrically as the overlap between the predicted segmentation and the ground truth segmentation.
# Identification of Wiener Box-Jenkins Model for Anesthesia Using Particle Swarm Optimization
## 3 System Identification
### Prediction Error Method (PEM)  
The parameters for the Wiener Box-Jenkins model can be collected in the following vector  
\[\theta = \big{[}b_{0}\ b_{1}\cdots b_{nb}\ f_{1}\cdots f_{nf}\ c_{1}\cdots c _{nc}\ d_{1}\cdots d_{nd} \tag{9}\] \[\beta_{0}\ \cdots\ \beta_{M}\big{]}\]  
In PEM setting, the parameter vector \(\theta\) is optimized in such a way to minimize the error, i.e., the difference between the measured and predicted output. The one step ahead predictor (OSAP) is commonly used, where the current output at time instant \(t\) is predicted given input and output values up to time instant \(t-1\). The OSAP of the proposed Wiener Box-Jenkins model can be expressed as follows:  
\[\hat{g}(t|t-1,\theta)=y_{d}(t,\theta)+\hat{o}(t|t-1,\theta) \tag{10}\]  
where the OSAP for the noise part is found from  
\[\hat{o}(t|t-1,\theta) = \frac{H(z^{-1})-1}{H(z^{-1})}v(t,\theta) \tag{11}\] \[= \big{(}1-H^{-1}(z^{-1})\big{)}v(t,\theta)\]  
and the deterministic part is computed using Equation (5).  
By substituting the formulas of \(\hat{o}(t|t-1,\theta)\) and \(y_{d}(t,\theta)\), the one-step ahead predicted output is given by [30]:\[\hat{g}(t|t-1,\theta) = \big{(}1-H^{-1}(z^{-1})\big{)}y(t) \tag{12}\] \[+ H^{-1}(z^{-1})\left[\sum_{l=0}^{M}\beta_{(i)}\big{(}G(z^{-1})u(t) \big{)}^{i}\right]\] \[= \frac{C(z^{-1})-D(z^{-1})}{C(z^{-1})}y(t)\] \[+ \frac{D(z^{-1})}{C(z^{-1})}\left[\sum_{i=0}^{M}\beta_{(i)}\left( \frac{B(z^{-1})}{A(z^{-1})}u(t)\right)^{i}\right]\]  
The prediction error of the model \(\epsilon(t)\) is, therefore, given by  
\[\epsilon(t) = y(t)-\hat{g}(t|t-1,\theta) \tag{13}\] \[= H^{-1}(z^{-1})\big{(}y(t)-y_{d}(t,\theta)\big{)}.\]  
Usually, the mean square error (MSE) cost function, defined as  
\[V_{N}(\theta)=\frac{1}{2N}\sum_{t=1}^{N}\epsilon^{2}(t,\theta), \tag{14}\]  
is used to assess the performance of the model. The objective of PEM method is to search for the parameter vector \(\theta\) that minimizes \(V_{N}(\theta)\), that is  
\[\hat{\theta}=\arg\;\min_{\theta}\;V_{N}(\theta) \tag{15}\]  
The challenge is that some parameters such as the elements of \(C(z^{-1})\) and \(A(z^{-1})\) appears _nonlinearly_ in the the predictor \(\hat{g}(t|t-1,\theta)\) defined by Equation (12). Therefore, an iterative optimization technique is needed. In this paper, PSO meta-heuristic optimization technique is employed for the estimation of the parameter vector (9) such it minimizes the cost function (14). The details of the PSO algorithm are elaborated next.
# Directional Paging for 5G Communications Based on Partitioned User ID
## 2 Related Work  
DRX for power saving can be configured in both the Radio Resource Control (RRC) states for a UE: (1) RRC-connected state and (2) RRC-idle state. In an RRC-connected state, the UE remains connected with evolved Node B (eNB). However, DRX configurations navigate the UE to the RRC-idle state, if there is no data activity for long. The network releases the RRC connections and facilitates the UE to enjoy extended sleep. In the RRC-idle state, the UE is free to roam among a group of eNBs in a specific area (i.e., Tracking Area (TA)) such that the handover procedures are prevented. The paging procedure helps in determining the exact cell in which the idle mode UE is located such that the incoming call is redirected properly [11]. The network broadcasts the paging messages in the cells of a TA, to convey the intimation about the incoming call to the UE in IDRX mode [9]. Since paging messages are monitored by the UE and broadcasted by the network, in this section, we highlight the paging complications from both the UE's and network's point of view.
## 2 Results and Discussion
### Profile of Volatile Compounds  
Application of the SPME/GC/MS method enabled determining and identification of volatile compounds in two types of kefir which belonged to alcohols, acids, aldehydes, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component, g/100 mL** & **Lactose-Free Kefir** & **Traditional Kefir** \\ \hline Proteins & 3.20 \(\pm\) 0.05 & 3.16 \(\pm\) 0.03 \\ Lipids & 1.52 \(\pm\) 0.02 & 1.55 \(\pm\) 0.03 \\ Lactose & 0.01 \(\pm\) 0.01 & 3.12 \(\pm\) 0.10 * \\ Glucose & 1.72 \(\pm\) 0.10 & 0.35 \(\pm\) 0.12 * \\ Galactose & 2.47 \(\pm\) 0.21 & 0.62 \(\pm\) 0.09 * \\ \hline Total acidity, \% of LA & 0.62 \(\pm\) 0.08 & 0.86 \(\pm\) 0.01 * \\ pH & 4.82 \(\pm\) 0.10 & 4.45 \(\pm\) 0.03 * \\ Ash, \% & 0.62 \(\pm\) 0.04 & 0.75 \(\pm\) 0.02 * \\ \hline Microbial enumeration, log & & \\ CFU/mL & & \\ _Lactobacillus_ spp. & 7.83 \(\pm\) 0.09 & 8.06 \(\pm\) 0.13 \\ _Lactococcus_ spp. & 8.35 \(\pm\) 0.10 & 7.96 \(\pm\) 0.24 * \\ _Leuconstoc_ spp. & 5.64 \(\pm\) 0.16 & 5.82 \(\pm\) 0.08 \\ LAB & 8.21 \(\pm\) 0.13 & 8.65 \(\pm\) 0.20 * \\ Yeast & 3.70 \(\pm\) 0.08 & 4.34 \(\pm\) 0.12 * \\ \hline \hline \end{tabular}  
* Significantly (\(p<0.05\)) different from the lactose-free kefir. LA—Lactic acid; CFU—colony-forming unit; LAB—lactic acid bacteria.  
\end{table} 
Table 1: Proximate composition and microbial count of kefir samples (n = 9) stored for 7 days. ketones, terpenes, and others groups. The lactose-free kefir contained a slightly lower number of volatile compounds than the traditional kefir (28 and 31, respectively) (Table 2). A similar number of volatile compounds in the traditional kefir was reported elsewhere [11]. Semi-quantitative determination revealed also some interesting differences in the relative abundance of volatile compounds of two types of kefir (Table 2). Acid-based volatiles are important for fermented milk products, such as kefir and yoghurt [14]. Both types of kefir significantly (\(p<0.05\)) differed in the acetic acid content
## 8 Cramer-Rao Bound on Covariance Matrix of Localization Errors
### Discussion on RMSE of Direct Localization
#### 9.1.2 The Second Set of Experiments  
In the second set of experiments, the source location is estimated in 3-D space and we assume that the array error is caused by sensor gain and phase uncertainties, which corresponds to case 2 in Section 4 in [44]. The second-order moments of \(\widetilde{\boldsymbol{\varphi}}_{n}\) can then be expressed as  
\[\left\{\begin{array}{l}\mathrm{E}[\widetilde{\boldsymbol{\varphi}}_{n} \widetilde{\boldsymbol{\varphi}}_{n}^{\mathrm{H}}]=\boldsymbol{\Phi}_{n}^{(1) }=(\sigma_{\widetilde{\varphi}_{1}}^{2}-\sigma_{\widetilde{\varphi}_{2}}^{2}) \odot\mathrm{diag}[\boldsymbol{a}_{n}(\boldsymbol{p})\boldsymbol{a}_{n}( \boldsymbol{p})]\,,\\ \mathrm{E}[\widetilde{\boldsymbol{\varphi}}_{n}\widetilde{\boldsymbol{\varphi} }_{n}^{\mathrm{H}}]=\boldsymbol{\Phi}_{n}^{(2)}=(\sigma_{\widetilde{\varphi}_ {1}}^{2}+\sigma_{\widetilde{\varphi}_{2}}^{2})\odot\mathrm{diag}[ \boldsymbol{a}_{n}(\boldsymbol{p})\boldsymbol{a}_{n}^{*}(\boldsymbol{p})]\,, \end{array}\right.\quad(1\leq n\leq N)\,, \tag{104}\]  
where \(\sigma_{\widetilde{\varphi}_{1}}\) and \(\sigma_{\widetilde{\varphi}_{2}}\) are the sensor gain and phase perturbation standard deviation, respectively. Moreover, we assume \(\sigma_{\widetilde{\varphi}_{1}}=2\sigma_{\widetilde{\varphi}_{2}}\) hereafter, and thus if \(\sigma_{\widetilde{\varphi}_{1}}\) is changed, \(\sigma_{\widetilde{\varphi}_{2}}\) alters accordingly.  
Figure 7 illustrates the geometry for the source location in the second set of experiments. Obviously, it depicts a 3-D localization scenario. The source is positioned at [1000, 500, 1500] m, and the coordinates of three base stations are set to [0, 2000, 0] m, [0, 0, 0] m, and [0, \(-\)2000, 0] m. Each base station is equipped with a uniform circular array. The envelope of the transmitted signal and array model errors are generated in exactly the same manner as previously. Additionally, unless stated otherwise, we adopt the settings (1) \(K=64\) samples; (2) SNR of 5 dB; (3) \(M=5\) sensors; (4) \(\sigma_{\widetilde{\varphi}_{1}}=0.1\); and (5) an array radius equal to the wavelength. Figures 8-12 show the RMSEs of the DPD method by varying the SNR of the emitter signal, standard deviation of sensor gain perturbation \(\sigma_{\widetilde{\varphi}_{1}}\), the number of array elements \(M\), the ratio of array radius to wavelength, and the number of snapshots \(K\).  
Figure 11: RMSE of DPD as a function of ratio of array radius to wavelength.  
Figure 10: RMSE of DPD as a function of number of array elements.  
Figure 9: RMSE of DPD as a function of standard deviation of sensor gain perturbation.  
The results presented in Figures 8-12 coincide with the results presented Figures 2-6 although the dimensionality of the localization scenario and the model of the array error differ from each other. Owing to limited space, we do not present the results again. We simply highlight that the good agreement between the empirical and theoretical RMSE once again demonstrates the effectiveness of the theoretical development in Section 6.
## 2 Materials and Methods  
FEM was used to determine the remaining strength of middle-to-high strength pipelines, namely API 5L X52, API 5L X65, and API 5L X80 grade pipelines, subjected to combined loadings of internal pressure and longitudinal compressive stress; the results of these evaluations were then used to train an ANN model. The datasets for X52 and X80 were obtained from previous studies [20; 21], while the dataset for X65 was generated in this study. The FEM results of the X65 grade pipeline's remaining strength was validated with results from burst tests. The FEA datasets of the X52, X65, and X80 grade pipelines were then used to train an ANN model. The weights and biases of the ANN model were used to as the basis for the artificial neural network-based equation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Corrosion Type** & **Loads** & **Material** & **ANN Algorithm** \\ \hline [PERSON] et al. (2007) [16] & \begin{tabular}{c} Interacting defects (longitudinally and \\ circumferentially aligned) \\ \end{tabular} & Internal pressure & X52 & \begin{tabular}{c} Feedforward neural \\ network \\ \end{tabular} \\ \hline [PERSON] et al. (2017) [17] & \begin{tabular}{c} Single and interacting defects \\ (longitudinally and \\ circumferentially aligned) \\ \end{tabular} & Internal pressure & X80 & \
\begin{tabular}{c} Feedforward neural \\ network \\ \end{tabular} \\ \hline [PERSON] and [PERSON] (2021) [18] & Single defects &
\begin{tabular}{c} Internal pressure and axial \\ compressive load \\ \end{tabular} & X65, X70, X80 & Not specified \\ \hline \hline \end{tabular}
\end{table}
Table 1: ANN based on FEA dataset to predict the failure pressure of corroded pipelines.
# A Novel Method for Cross-Modal Collaborative Analysis and Evaluation in the Intelligence Era
## 4 Results and Discussion
### Experimental Results and Analysis  
It can be seen from the ablation experiment results that the performance of text-based sentiment analysis in unimodality is the best. The sentiment analysis performance of all modal combinations in bimodality is generally better than that of the best unimodality analysis type. The trimodal sentiment analysis model performed the best among all models. Its accuracy was increased by 1.36% and 1.61% compared with the best models in the unimodal and bimodal, respectively. Its \(F1\) index is improved by 2.33% and 1.98% compared to the best model in unimodal and bimodal, respectively. Therefore, the effective combination of multiple modalities can well obtain sentiment classification prediction and improve the model's performance. Moreover, during the process of students' learning, it can accurately grasp the emotional status of students by capturing the information from multiple modalities, adopting multi-modal emotional analysis, and presenting evaluation results that could provide emotional guidance to students promptly or adjust the difficulty level and teaching mode of the course to help students acquire knowledge much better and achieve the learning aims much easier.  
From the experimental data in Table 2, the proposed method has achieved excellent results accuracy and \(F1\) index. Compared with early fusion, the performance improvement is the most obvious. Compared with recent more advanced deep learning and other methods, the accuracy and \(F1\) index are also 0.17% and 0.95% higher. Therefore, the model can extract meaningful information from multi-modalities for fusion and accurately classify and predict, which is helpful to apply to the scenarios where students learn emotional status analysis in the process of learning.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 6 Discussion  
In this article, the IQDN based on modified MobileNetV2 and the SISNet are proposed. Compared to current algorithms, the IQDN employs a lightweight CNN to capture the characteristic differences between images with RFI artifacts and without RFI artifacts. MobileNet has a strong feature extraction ability and few network parameters. Therefore, it performs well in both speed and accuracy for detecting whether there is interference in quick-look images, which is beneficial for building an automatic interference identification system that searches and screens RFI artifacts in massive SAR data.  
Methods proposed by previous researchers are either poor in robustness or require a large amount of non-interference comparison SAR data, which restricts their application. This paper introduces an interference segmentation algorithm called SISNet. Different from previous work, the SISNet performs well in interference segmentation and does not require interference-free comparison data, which reduces the difficulty of implementation. After performing image transformation on the segmentation mask image output from the network according to the latitude and longitude information and image size information of the SLC or GRD products, the output image can be connected to Google Earth Engine [57] to be combined with actual geographic information in order to realize SAR interference positioning. However, it is worth mentioning that the training data of SISNet still needs to be manually labeled, and the impact of the interference multi-scale problem has not been completely solved, which means further improvements are still needed. Future research will add more interference-affected data to facilitate the functionality of our framework.
## 2 Materials and Methods
### Hardware for Magnetic Susceptibility Measurements  
Magnetic susceptibility measurements, or the kappametry method (named after the symbol \"\(\kappa\)\"), can be implemented not only as a support for several geophysical methods [11] but also as a standalone geophysical method for various purposes, for example, for soil contamination studies [12; 13] and archaeological surveys [14]. Many magnetic susceptibility measurement devices (kappa), such as SatisGeo KM-7 [15], Bartington MS-2 [16], or PIMV [17], are portable and designed for both in situ measurements and for laboratorial work. These devices are based on an inductive measurement method, which implies the measurement of an inductance-related signal change in a coil when applied to a specimen, compared to the inductance-related signal of the air. This allows for evaluation of the magnetic susceptibility in the volume of a specimen.  
Another group of kappameters includes indoor devices designed for laboratories. These kappameters are based on various measurement techniques [18]. In our research, we used mainly a PIMV kappameter for field and indoor measurements, although some magnetic susceptibility determinations were performed using the AGICO MFK-1 FA laboratory kappameter at the Laboratory of the main geomagnetic field and petromagnetism (Schmidt Institute of Physics of the Earth of the Russian Academy of Sciences). The magnetic susceptibility sensitivity of this device is 2 \(\times\) 10\({}^{-8}\) SI.
# A Review on Reconfigurable Liquid Dielectric Antennas
## 4 Discussion
It was found that even though the switching speed of the liquid reconfigurable antennas is much slower (\(\sim\)sec) than that of the conventional PIN diode-based ones (\(\sim\)usec), liquid reconfiguration approach offers advantages in terms of the higher radiation efficiency (22% compared to 10% in the PIN diode case), longer electrical length, and zero DC interference [86]. It means that liquid dielectric reconfigurable antennas can be useful in application where the switching speed is of less importance than the antenna performance.
# Study of a Miniaturizable System for Optical Sensing Application to Human Cells
## Materials and Methods
### Cell Culture and Labeling  
HDF and LoVo cells were expanded in 75 cm\({}^{3}\) flasks in medium and maintained at 37 \({}^{\circ}\)C in a humidified atmosphere of 5% CO\({}_{2}\) and 95% air. Medium changes were performed after 3-4 days and, upon confluence (80%), cells were trypsinized and prepared for experiments. Vital cell count was performed in a Burker chamber with Trypan blue method. The solution of 13.5 \(\upmu\)M (corresponding to 10 mg/mL) Ru(bpy)\({}_{3}\)\({}^{2+}\) was prepared by dissolving the fluorophore in PBS 1\(\times\). Subsequently, the solution was sonicated for 20 min in order to avoid the formation of fluorophore molecules aggregates. 13.5 \(\upmu\)M Ru(bpy)\({}_{3}\)\({}^{2+}\) solution was, then, diluted to 0.14 \(\upmu\)M, 0.4 \(\upmu\)M, 0.7 \(\upmu\)M, 1 \(\upmu\)M, 1.4 \(\upmu\)M, corresponding to 0.1 mg/mL, 0.25 mg/mL, 0.5 mg/mL, 0.75 mg/mL, 1 mg/mL, respectively. Finally, cells were incubated in modified medium for 24, 48, or 96 h, to perform the fluorophore uptake for labeling.  
As control samples for all assays, we prepared both HDF and LoVo cell using the same medium without fluorophore, and keeping the same experimental conditions reported before.
## 3 Results and Discussion
### Method Validation
To ensure simplicity and high method performance in terms of precision and trueness, the unweighted linear regression model was chosen for both analytes. Since the absolute \(\beta\)-ETI fell within the absolute acceptance limits (Figure S4), the linearity of the proposed method was confirmed. Regarding the calculation of the LOD and the lower LOQ (LLOQ) of MEL and PIR the signal-to-noise ratio criteria (S/N) of 3 and 10 were used, respectively. The LODs were estimated to be 0.02 and 0.03 \(\mathrm{\SIUnitSymbolMicro m}\)\(\mathrm{L}^{-1}\) for PIR and MEL, respectively, and the LLOQ was estimated to be 0.1 \(\mathrm{\SIUnitSymbolMicro m}\)\(\mathrm{L}^{-1}\) for both drugs. The herein developed SI-HLIME protocol exhibited good method trueness since the relative biases for MEL ranged between \(-1.7\) and \(1.7\)% and for PIR between \(-5.7\) and \(3.4\)%. Moreover, sufficient method precision was achieved for the examined concentration levels. Specifically, the method precision (\(\mathrm{s_{r}}\), %) was better than 3.9% and the time-dependent intermediate precision (\(\mathrm{s_{R}}\), %) was better than 4.4% in all cases. Finally, the lower and upper \(\beta\)-ETIs were within the acceptance limits of \(\pm 15\)%, demonstrating good method accuracy for the selected drugs within the examined concentration range.