esa-sceva/satcom-chunk-collection huggingface dataset:
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 2 Materials and Methods
### Material Characterization  
Scanning electronic microscope (SEM) images were obtained on a field emission scanning electron microscope (FeSEM, ZEISS SUPRA 40 VP, Oberkochen, Germany) at an acceleration voltage of 3 kV. The FTIR spectrum was obtained by using Thermo Scientific Nicolet iD5 spectrometer (Madison, WI, USA). The zeta potential value over pH range was obtained from Malvern Panalytical Zetasizer Pro/Ultra and MPT-3 Autotirator. The UV-Vis absorbance spectra were measured with a UV-Vis spectrophotometer Shimadzu (Rydalmere, NSW, Australia). All MS and LC-MS analyses were performed by using Agilent 1290 Infinity II UHPLC with a 6545 Q-TOF MS (Agilent Headquarters, Santa Clara, CA, USA).  
Direct injection method was used for the sample analysis to quantify the removal efficiency and loading capacity of PFAS into resin. Data acquisition and analysis were derived via Mass Hunter B.08.00 software. MS detection was carried out in negative ionization mode with the help of dual ESI and AJS (Agilent Jet Stream Technology) source (Agilent Headquarters, Santa Clara, CA, USA) ion source. The reference ion \(m/z\) values used for the detection of PFOA, PFOS, PFBA, PFBS, PFHxA, and PFHxS were 413.97, 499.93, 213.98, 299.95, 399.94, and 421.92, respectively. Other specifications include 0.3 mL/min flow rate, 4000 V capillary voltage, 350 \({}^{\circ}\)C sheath gas temperature, and 10.0 L/min and 20 psi nebulizer gas (nitrogen) flow rate and pressure, respectively. Sample injection volume was 5 \(\mu\)L. Solvent composition was 95% H\({}_{2}\)O containing 0.1% formic acid and 5% acetonitrile. Samples were analyzed via LCMS to quantify the regeneration efficiency of MIEX-Gold resin where the separation was performed by using Agilent ZORBAX Eclipse Plus C18 column (USA) at 50 \({}^{\circ}\)C. Other specifications include 0.4 mL/min flow rate, 3500 V capillary voltage, 375 \({}^{\circ}\)C sheath gas temperature, and 8.0 L/min and 25 psi nebulizer gas (nitrogen) flow rate and pressure, respectively. A 95% H\({}_{2}\)O containing 0.1% formic acid and 5% acetonitrile was selected as the mobile phase.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Baseline Results  
We first evaluated the performances of our speech and singing voice alignment models. The Absolute Alignment Error (AAE) obtained on each words of the reference corpus has been computed, and the distribution is shown in Figure 12.  
In Figure 12a, the AAE distribution for speech is shown. The mean of AAE (MAAE) is of 46 ms, close to the median (51 ms). Even the most severe outliers (quantiles 95% and 99%) remain below 150 ms. The Percentage of Correct Onsets (PCO) is 100%, as there is not a single AAE value greater than 300 ms.  
The AAE distribution for the singing voice is shown in Figure 12b. The mean of the AAE (MAAE) is 120 ms and is therefore three times higher than for speech. This is clearly due to the more challenging context (i.e., singing word recognition, longer pauses, and musical accompaniment) that leads to extreme outliers, with some AAE even greater than 1.2 s, which naturally impact the mean value. However, the median (around 40 ms) remains in the same order of magnitude as the one reported for speech. The Percentage of Correct Onsets (PCO) is 95%.  
Figure 12: Absolute alignment error distribution. (**a**) Speech (Chapter 10); (**b**) Singing (Playlist 50).  
The MAAE obtained for the speech and singing voice will serve as a comparison baseline in the following experiments.
# The Progress in Treatment of \(\beta\)-Thalassemia and Research on Animal Models of the Disease
## 1 Introduction  
Thalassemia, also referred to as globin synthesis disorder anemia or Mediterranean anemia, is the most prevalent single-gene recessive genetic disorder globally and is among the genetic diseases with the highest incidence and greatest impact in the provinces south of the Yangtze River in China [1, 2]. Approximately 5% of the global population carries variants in the \(\alpha\)-globin gene, while about 1.5% harbor mutations in the \(\beta\)-globin gene [3]. Clinically, thalassemia is classified into \(\alpha\)-thalassemia and \(\beta\)-thalassemia based on pathogenic genetic mutations and the globin chains affected in the hemoglobin tetramers [4]. In \(\beta\)-thalassemia, impaired synthesis of \(\beta\)-globin chains results in the production of ineffective red blood cells, thereby resulting in anemia, iron overload, and various complications [4, 5]. Animal disease models are primarily employed for studying pathogenic mechanisms and therapies, including new drug screening. Utilizing animal models facilitates a more effective understanding of the progression of human diseases, alongside the investigation of preventive and therapeutic strategies. This article provides a review of the current advancements in \(\beta\)-thalassemia and its associated animal models, aiming to provide researchers with resources for developing new \(\beta\)-thalassemia models.
# A Survey of Advances in Landscape Analysis for Optimisation
## 2 Beyond Fitness Landscapes  
The notion of a fitness landscape was introduced by [PERSON] [2] at a congress on genetics in 1932. He proposed an abstract two-dimensional contour plot of fitness values as an intuitive picture of evolutionary processes taking place in high dimensional space. Because fitness landscapes have been used in contexts beyond biological and computational evolution, the fitness metaphor is no longer generally applicable and many studies have opted for more general terms such as _search space analysis_, _exploratory landscape analysis_ or just _landscape analysis_.  
A fitness landscape was originally defined as consisting of three elements [3]: (1) A set \(X\) of potential solutions to the problem; (2) a notion of neighbourhood, nearness, distance or accessibility on \(X\), and (3) a fitness function \(f:X\to\mathbb{R}\). Replacing \(f\) with a more general objective function, these three basic elements can be used to describe landscapes in a wide range of contexts such as combinatorial optimisation, continuous optimisation, search spaces of programs (as in genetic programming) and so on. There are some contexts, however, where these elements differ further from the original definition and these are discussed in this section.
# Turbo Decoder Design based on an LUT-Normalized Log-MAP Algorithm
## 5 Complexity Analysis of the Proposed Algorithm
The synthesis results of the complexity for different Log-MAP approximation algorithms are shown in Table 5.  
We can see from Table 5 that the Max-Log-MAP algorithm only needs two additions and one comparison in the max\({}^{\star}\) function, but its decoding performance is the worst. Compared with the LUT-Log-MAP algorithm, the Nor-Log-MAP algorithm reduces a subtraction and four comparisons in the max\({}^{\star}\) function, but increases three shifts. According to Table 3, it can be seen that Nor approximation reduces consumption of logic resources compared with LUT approximation. By adding a mode comparison, the LUT-Nor-Log-MAP algorithm can be switched between LUT and Nor approximation in function max\({}^{\star}\) processing. The simulation results of Table 3 show that the LUT-Nor-Log-MAP algorithm consumes more logic resources than the Nor-Log-MAP algorithm, which is in line with expectations. However, compared to the LUT-Log-MAP algorithm, the proposed algorithm still reduces logic resource consumption considerably.
## 3 Results and Discussion
### Impact of Drilling Fluid  
#### 3.2.1 Drill String Vibration Response  
The drilling fluid inside the drill string significantly influences its vibration characteristics, while the supporting effect of the wellbore alters the boundary conditions of the drill string. The upper end of the drill string at the wellhead is set as a fixed constraint, and radial degrees of freedom are restricted at the bottom hole, as shown in Figure 20a. Under the combined effects of gravity and rotation, the drill string contacts the lower-right region of the wellbore. The wellbore support is simplified as a spring support, with a support angle of 20\({}^{\circ}\) and a spring stiffness of 0.08 N/m\({}^{3}\) based on prior studies, as illustrated in Figure 20b.  
The influence of drilling fluid flow fields on drill string vibration frequencies is illustrated in Figure 21. The drill string primarily exhibits lateral vibrations, with the first eight vibration modes dominated by coupled horizontal and vertical lateral vibrations. The 9 th mode manifests as torsional vibration, while the 10 th mode represents further intensified coupled lateral vibrations. Due to the lower-order frequencies being more susceptible to excitation from bit vibrations and drill string-wall collisions, horizontal and vertical coupled lateral vibrations are more prone to occur in horizontal drilling. For the first ten modes, the natural frequencies of the drill string generally increase. When accounting for drilling fluid effects, all natural frequencies decrease by 20-25%, indicating that internal fluid and annular fluid significantly reduce the drill string's natural frequencies.  
Figure 22 shows the effect of pump pressure on drill string vibration frequencies. Compared to the standalone drill string structure, the drilling fluid flow field effectively reduces the natural frequencies, with all modal frequencies decreasing as pump pressure increases. The drilling fluid primarily affects the natural frequencies through the pressure field and added mass effects induced by inertial forces. A higher pump pressure intensifies the pre-stress imposed by the fluid domain on the drill string, thereby amplifying its influence on the overall natural frequencies. Computational results indicate that When the pump pressure escalates from 0.5 MPa to 8 MPa, the 2 nd-order mode exhibits the most signifi  
Figure 19: Annular velocity field under different feed pressures: (**a**) 10 kN; (**b**) 13 kN; (**c**) 15 kN.  
Figure 20: Boundary conditions of the drill string: (**a**) constraint; (**b**) spring support.  
cant frequency reduction of 46.5%, whereas the 9 th- and 10 th-order modes demonstrate minimal reductions of approximately 0.2%. This highlights that pump pressure exerts the most significant influence on lower-order frequencies. During drilling operations, priority should be given to monitoring low-order vibrations when adjusting pump pressure to avoid resonance risks.
## 2 Methodology
### Optimization of Design and Control Parameters  
A suitable selection of PCM melting temperature depending on the climatic conditions, building constructive system, and heating demand is mandatory for the correct performance of the system. Moreover, the activation of the system and an appropriate schedule of positioning PCM facing outdoors or indoors plays a crucial role.  
Design (phase change PCM temperature) and control (POS[t]) variables have been optimized using a generic optimization program (GenOpt v3.11) [30]. A particle swarm optimization algorithm with constriction coefficient (PSOCC) [31] was used in this study. The algorithm was linked to the finite volume numerical model and was set with the following settings: (i) 20 generations, (ii) 20 particles per generation, and (iii) 2 seeds.  
The PSOCC varies these three variables to minimize the heating load (\(Q_{load}\) in J/m\({}^{2}\)-day):  
1. PCM peak melting temperature (\(T_{PCM}\)) exploring integer values between 8 to 29 \({}^{\circ}\)C.
2. Hour to start exposure of PCM to solar energy, POS [t] is set to 1 (\(t_{1}\)). Continuous exploration between 0 to 24 h.
3. Hour to finish exposure of PCM to solar energy, POS [t] is set to 0 (\(t_{0}\)). Continuous exploration between 0 to 24 h.  
It has to be noticed that control was optimized based on the average values of a specific period, meaning that t\({}_{1}\) and t\({}_{0}\) were set to the same value for all days during the analyzed period. Finally, the daily heating load is computed as shown in Equations (1) and (2). Where \(Q_{heating}\) is the rate of heating loads per unit area (W/m\({}^{2}\)) at each time step, \(h_{in}\) is the heat transfer coefficient of the internal surface,  
Figure 2: Possible mechanical design of the rotatable system.  
\(T_{indavor}\) is the temperature of the indoor environment, \(T_{si}\) is the temperature of the interior surface, and \(Q_{load}\) is the heating load for a specific period between t\({}_{0}\) and t\({}_{end}\),  
\[\dot{Q}_{heating}h_{in}(T_{indavor}-T_{si}) \tag{1}\]  
\[Q_{load}=\int\limits_{t_{0}}^{t_{end}}\dot{Q}_{heating}\cdot dt \tag{2}\]
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 3 Materials and Methods
### Direct Spread Spectrum Technology in Telecommunications
Several information bits can be simultaneously transmitted to the communication channel, i.e., the receiver obtains an additive mixture:  
\[Mix=N+\sum_{j=1}^{k}M_{j} \tag{4}\]  
where the symbol \(N\) denotes the sequence of random elements caused by noise (for example, additive white Gaussian noise). The receiver has synchronized copies of the chip codes \(s_{1},s_{2},\ldots,s_{k}\in s\). In order to restore the information bits, it is necessary to calculate the correlation between the received signal and the corresponding chip codes. For example, to restore the value \(m_{i}\) in (3), the receiver computes \(\rho(s_{i},Mix)\). Equation (2) is linear, i.e., write down  
\[\rho(s_{i},Mix)=\rho(s_{i},N)+\rho(s_{i},\sum_{j=1}^{k}M_{j}) \tag{5}\]  
For random noise, \(N\), we have:  
\[\rho(s_{i},N)\approx 0 \tag{6}\]  
Moreover, \(\forall i\
eq j:\rho(s_{i},s_{j})\approx 0\); therefore,  
\[\rho(s_{i},Mix)\approx\sum_{j}\rho(s_{i},m_{i}s_{j})\approx\rho(s_{i},m_{i}s_ {i}) \tag{7}\]  
In this way, we can write the information bit recovery rule  
\[m_{i}=\rho(s_{i},Mix)=\left\{ \begin{array}{l}-1,\;\rho(s_{i},Mix)<0;\\ +1,\;\rho(s_{i},Mix)>0.\end{array} \right. \tag{8}\]  
Note that in the above reasoning, individual bits \(m_{i},i=1,2,\ldots,k\) may belong to different senders. In this case, code division multiple access is implemented [18]. Let us note the following advantages of using direct spread spectrum technology:  
* Resistance to unintended or intended jamming;
* Sharing a single channel among multiple users;
* Reduced signal/background-noise level hampers interception;
* Determination of relative timing between transmitter and receiver and much more. We are considering the use of this technology in steganography, i.e., to hide informational messages in multimedia files.
## 2 Materials and Methods
### Duty Cycle Measurement
#### 2.2.3 Low Radio Quality Conditions  
To evaluate the DC under poor wireless communication conditions, where poor radio conditions are defined as a signal-to-noise ratio (SNR) of less than 17.5 dB, while good radio conditions constitute SNR > 44 dB [14], the same UD (laptop) used in the single-user scenario measurements was moved to a separate room with significant physical obstructions to the AP. The pyramidal EMF absorber wall was placed in front of the UD, as shown in Figure 3, to simulate extreme signal degradation. While the position of the AP and the three-axis antenna was maintained and the channel of the AP was configured to channel 58 with the bandwidth fixed at 80 MHz; for the rest of the experimental setups, all of them, as well as the Internet activities, were consistent with the measurements of the single-user scenarios--the DC was measured for each of these internet activities.
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
###### Abstract  
In this paper, a novel concept for cooperative orbit determination (OD) using inter-spacecraft angle-only measurements is proposed. Different from the conventional cooperative OD that only estimates orbit states, the attitude of the observer spacecraft is considered by incorporating the attitude into the estimated vector. The observability of a two-spacecraft system is analyzed based on the observability matrix. Observability analysis reveals that inter-spacecraft angle-only measurements are inadequate to estimate both the attitude and the orbit states in two-body dynamics. The observability of the two-spacecraft system can be improved by considering high-order gravitational perturbation or executing an attitude maneuver on the observer spacecraft. This is the first time that we present the observability analysis and orbit estimation results for a two-spacecraft system considering attitude uncertainty for the observer. Finally, simulation results demonstrate the effectiveness of the proposed method. The results in this paper can be potentially useful for autonomous managements of a spacecraft constellation and formation.  
c pacs: **Keywords:** cooperative orbit determination; angle-only measurement; observability matrix; constellation; cubature Kalman filter
## 3 Results and Discussion
### Zinc  
The mean Zn concentration in the _A. tuberculosis_ samples was 56.38 \(\pm\) 1.75 mg/kg (range: 81.60-40.00 mg/kg). According to the Joint FAO/WHO Expert Committee on Food Additives (JECFA), the average daily intake of Zn should not exceed 20 mg/day for adults or a maximum tolerable daily intake of 0.3-1 mg/kg body weight [36].  
Compared with other metals, Zn is in higher amounts in the tissues of _A. tuberculosis_. These results are consistent with those obtained by a previous study that also observed elevated Zn concentrations in bivalves of the genera _Anadara_, _Perna_, _Crassostrea_, _Meretirix_ and _Amusium_[37].  
These elevated Zn concentrations could be due to several causes. First, Zn is a natural constituent of bivalve tissues and is in considerable concentrations [36]. Zn is a trace element necessary for the proper functioning of living organisms, but in quantities lower than those reported in Table 1. Therefore, its high concentrations in the tissues of this bivalve may be due to a greater facility for the uptake and bioaccumulation of Zn from the sediment or water. To confirm this hypothesis, the bioavailable zinc content in sediment and water should be evaluated and correlated with the zinc content in bivalve tissues in longitudinal studies.  
Figure 6 also shows significant differences between the means of Zn concentrations and those of the other metals. Zn concentrations were higher in La Libertad (\(59.73\pm 9.57\) mg/kg; range: 45.70-75.30 mg/kg) and lower in Puerto El Morro (\(53.78\pm 11.10\) mg/kg; range: 40.00-81.60 mg/kg). This suggests that the higher Zn levels in La Libertad are the result of some type of contamination and not only due to its natural content. To determine the causes, further analysis is needed considering the sources of contamination in the area, soil and water conditions, and industrial activity.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 2 Related Works
### Disparity Refinement Neural Networks Based on the RAFT  
The RAFT-Stereo [12] is a variant of RAFT [29] for stereo matching using only 2D convolutional neural networks (2D CNNs), where the sampled slices of the image correlation volume and the image contextual feature are fused to iteratively update the disparity field's hidden state via a multi-scale iterator constructed with ConvGRUs [30]. This iterative framework can be considered as a combination of implicit cost aggregation with a monocular depth estimation pipeline, which avoids the explicit modeling and aggregation of the matching cost volumes, thus achieving higher disparity refinement efficiency and generalization performance compared to 3D CNN-based methods [4; 20]. In addition, this RNN-like framework is theoretically capable of predicting arbitrary disparities within the co-visible regions of image pairs. Recently, some RAFT-based works [13; 14; 16; 31; 32] have been reported with further improvements. Incorporating the vision transformer techniques [28; 33], information interaction between feature map pairs is introduced [13; 16] before the iterative refinement process to augment the matching features, thus improving the accuracy of the initial disparity, optical flow or the handling of occluded regions. On the other hand, a lightweight cost aggregation using 3D CNNs is reintroduced by [32], where the cost volume with limited disparity range is used as an additional look-up table as well as to compute more accurate initial disparity, thus improving the geometric awareness of the model. Furthermore, [14; 31] achieve gains in generalization and accuracy by replacing the iterator with ConvLSTMs [14] and using additional disparity adjustments after upsampling. In contrast, the MUIR proposed in this paper is trained to evaluate the reliability of feature matching and estimated disparity, which enhances the detection and handling of ill-posed regions, thus improving accuracy and efficiency. Our model takes RAFT-Stereo [12] as the baseline to ensure that the improvements can be applied to most RAFT-based models.
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 3 Experiments
### Implementation Details  
#### 3.2.1 Target Models  
The U-Net model is employed as the target segmentation model [15]. The segmentation model is based on an encoder-decoder architecture enhanced with skip connections. According to the hyper-parameters defined in U-Net [15], four encoders paired with an equal number of decoders are utilized. The model undergoes 200 epochs of training with a batch size of 16, relying on the AdamW optimizer [19] and a learning rate set at 0.0001. As for the loss function, it aims to minimize the combined effect of cross entropy and dice loss, comparing true and predicted segmentations. Therefore, the loss function of the target model can be formulated as follows.  
\[\text{CrossEntropy Loss}=-\sum_{i}P(Y_{i})\log P(\hat{Y}_{i})\]  
\[\text{Dice Loss}=2\frac{|Y\cap\hat{Y}|}{|Y|+|\hat{Y}|}\]  
\[\text{Total Loss}=\alpha\cdot\text{CrossEntropy Loss}+\beta\cdot(1-\text{Dice Loss})\]  
where \(Y_{i}\) and \(\hat{Y}_{i}\) are true segmentation and predicted segmentation, respectively. Furthermore, \(\alpha\) and \(\beta\) represents the weight of each loss component. In this work, both \(\alpha\) and \(\beta\) are set to 0.5. Note that in this context, the terms dice loss and dice score are used interchangeably.  
In addition, the structure of the target model, including the detailed hyper-parameters, is summarized in Table 1. The model structure and hyper-parameters are adopted from the original U-Net paper [15]. Since the target model accommodates both grayscale and color images, the input channels can be set to either 1 or 3, depending on the color scale used. Moreover, given that segmentation tasks may involve a varying number of segments, the number of output channels can also be adjusted to match the specific task at hand. For instance, in a CT segmentation model that encompasses 14 organs along with the background, the number of output channels is set to 14. Conversely, in a polyp segmentation model, which only differentiates between the polyp mask and the background, the number of output channels is reduced to 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Operation** & **In Channels** & **Out Channels** \\ \hline Input & DoubleConv 1  & 1 or 3 & 64 \\ \hline Down 1 & MaxPool 2 + DoubleConv & 64 & 128 \\ \hline Down 2 & MaxPool + DoubleConv & 128 & 256 \\ \hline Down 3 & MaxPool + DoubleConv & 256 & 512 \\ \hline Down 4 & MaxPool + DoubleConv & 512 & 1024 \\ \hline Up 3 1 & Upsample + DoubleConv & 1024 & 512 \\ \hline Up 2 & Upsample + DoubleConv & 512 & 256 \\ \hline Up 3 & Upsample + DoubleConv & 256 & 128 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the target model architecture based on U-Net.