esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Lower Limb Joint Moment at Landing  
During both jumps, the ankle joint of the braking leg showed no differences between the moments before and after the RA fatigue. Likewise, during the CMJ, the ankle joint of the jumping leg showed no differences in the joint moments before and after the RA fatigue. However, during the SPJ, after fatigue, the moment of the ankle joint of the jumping leg decreased (\(p<0.05\)). For both jumps, the knee and hip joints of the braking and jumping legs showed no differences in the moments before and after RA fatigue (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Pre-Fatigue**} & \multicolumn{2}{c}{**Post-Fatigue**} & \multicolumn{2}{c}{**Mean Difference**} & \multicolumn{1}{c}{**95\%CI**} \\  & & **B** & **J** & **B** & **J** & & & \\ \hline \multirow{8}{*}{Take-off} & ankle & CMJ & \(34.73\pm 5.69\) & \(33.50\pm 3.89\) & \(34.61\pm 5.10\) & \(33.39\pm 3.82\) & B:–0.11 & B:0.73 \\  & dorsiflexion & SPJ & \(35.11\pm 4.74\) & \(14.51\pm 3.28\) & \(35.92\pm 4.91\)* & \(15.29\pm 2.21\) & B:0.71 & B:0.68 \\  & & & & & & & & \\  & knee & CMJ & \(107.65\pm 10.95\) & \(107.77\pm 11.18\) & \(103.13\pm 7.58\)* & \(103.22\pm 8.07\)* & B: 3.38 & B:3.42 \\  & flexion & SPJ & \(85.95\pm 4.80\) & \(61.07\pm 5.46\) & \(85.32\pm 4.35\) & \(60.14\pm 5.99\) & B:0.63 & B:1.64 \\  & & & & & & & & \\ \hline \multirow{8}{*}{Touchdown} & hip & CMJ & \(94.93\pm 8.51\) & \(95.74\pm 8.94\) & \(94.30\pm 9.17\) & \(95.47\pm 10.13\) & B:-0.64 & B:2.79 \\  & flexion & SPJ & \(72.33\pm 7.05\) & \(76.45\pm 8.26\) & \(70.17\pm 8.82\)* & \(76.14\pm 9.46\) & B:-0.27 & J:3.52 \\  & ankle & CMJ & \(21.44\pm 11.72\) & \(18.42\pm 6.98\) & \(22.38\pm 8.84\) & \(19.38\pm 5.96\) & B:-0.96 & J:2.05 \\  & dorsiflexion & SPJ & \(20.58\pm 8.80\) & \(21.86\pm 6.60\) & \(20.11\pm 10.40\) & \(21.42\pm 6.17\) & B:0.91 & B:2.06 \\  & & & & & & & & \\  & knee & CMJ & \(20.17\pm 5.09\) & \(18.94\pm 5.14\) & \(18.11\pm 4.49\) & \(17.45\pm 5.44\) & B:2.06 & B:2.35 \\  & flexion & SPJ & \(20.481\pm 3.52\) & \(21.19\pm 3.45\) & \(21.303\pm 5.06\) & \(21.07\pm 4.54\) & B:-0.82 & B:1.78 \\  & & & & & & & & \\  & hip flexion & CMJ & \(16.89\pm 8.36\) & \(22.21\pm 12.48\) & \(16.29\pm 6.38\) & \(22.47\pm 10.76\) & B:0.94 & B:5.46 \\  & flexion & SPJ & \(20.82\pm 6.23\) & \(25.26\pm 8.18\) & \(19.92\pm 5.76\) & \(22.89\pm 10.60\) & B:–0.47 & B:5.04 \\ \hline \multicolumn{8}{c}{Asterisks (*) indicate a significant difference (\(p<0.05\)); CMJ: counter-movement jump; SPJ: spike jump; B: braking leg: J: jumping leg; CI: confidence interval.} \\ \hline \hline \end{tabular}
\end{table}
Table 2: The angles of joints at take-off and touchdown (°).
# An Adaptive Model Predictive Voltage Control for LC-Filtered Voltage Source Inverters
## 4 Adaptive MPC
### Augmented State-Space Model  
The value of parameters \(L_{f}\) and \(C_{f}\) are not precisely known in practice, and they could vary from the nominal amounts. Therefore, system state-space Equation (3) by considering parameters mismatch \(\left\{\begin{array}{l}A_{d}=A_{dt}+\Delta A_{d}\\ B_{d}=B_{dt}+\Delta B_{d}\\ D_{d}=D_{dt}+\Delta D_{d}\end{array}\right.\) and load current as a disturbance input can be written as:  
\[\begin{split}& x(k+1)=A_{dn}x(k)+B_{dn}v_{inv}(k)+G_{dn}w(k)\\ & w=\left[\begin{array}{c}w_{1}\\ w_{2}\end{array}\right]=G_{dn}{}^{-1}(\Delta A_{d}x(k)+\Delta B_{d}u(k)+D_{d}i _{o}(k)+n)\\ & G_{dn}=\left[\begin{array}{cc}D_{dn}(1,1)&0\\ 0&D_{dn}(2,1)\end{array}\right],n=\left[\begin{array}{c}n_{1}\\ n_{2}\end{array}\right]\end{split} \tag{6}\]  
Also, \(\Delta\) denotes the deviation from the nominal values, and \(n_{1}\) and \(n_{2}\) represent unmodeled dynamics and system nonlinearities as unstructured uncertainties. It is worth noting that Equation (6) is known as the system's augmented state-space model, which includes all system uncertainties and disturbances. Moreover, as seen in (6), all system structured (parametric) and unstructured uncertainties are lumped in disturbance input \(w\).
## 2 Fundamentals
### Operation of IM-OFDM Receiver  
At the IM-OFDM receiver, the received signal is processed to achieve both target sensing and communication decoding. We consider one frame of OFDM data; the received signal in the frequency domain after passing through the channel can be expressed as  
\[\mathbf{y}(j)=\mathbf{x}(j)\mathbf{h}_{\text{F}}(j)+\mathbf{w}_{\text{F}}(j),\quad j=1,\ldots, N_{s}, \tag{5}\]  
where \(\mathbf{y}=[y_{1},\ldots,y_{M}]^{T}\), \(\mathbf{h}_{\text{F}}=[h_{F,1},\ldots,h_{F,M}]^{T}\), and \(\mathbf{w}_{\text{F}}=[w_{F,1},\ldots,w_{F,M}]^{T}\) are the received signal, channel fading coefficient, and noise sample in the frequency domain, respectively. \(\mathbf{w}_{\text{F}}\) is additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\). Regarding target sensing, we can handle \(\mathbf{x}_{c}\) and \(\mathbf{x}_{s}\) separately thanks to the statistical orthogonality between them. Considering time delay \(\tau\) and Doppler shift \(f_{D}\), the channel function can be expressed as \(\mathbf{h}_{\text{F}}=H_{p}e^{-\text{i}2\pi m\Delta f\tau}e^{\text{i}2\pi f_{D}/T_{ s}}\), where \(H_{p}\) is the attenuation due to the pass loss, scattering and radar cross-section. By correlating \(\mathbf{y}\) with \(\mathbf{x}_{s}\) at the estimated value, the estimate of \(\tau\) is identified as the peak of range power spectrum.  
For bit decoding, we analyze received signal by subtracting the sensing component. The indices of activated subcarriers and their corresponding information symbols can be detected. In this process, we employ detection algorithms for IM-OFDM, specifically using the Log-Likelihood Ratio (LLR) detector. The posterior probability ratio of frequency-domain symbols being non-zero versus zero provides information about the activation status of index \(m\), which is given by  
\[\lambda(m)=\ln\frac{\sum_{i=1}^{M}P(x_{m}=c(i)|y_{m})}{P(x_{m}=0|y_{m})}, \tag{6}\]where \(c(i)\in\mathcal{S}\). A higher \(\lambda(m)\) value indicates a greater likelihood of index \(m\) being activated by the transmitter's index selector. Based on [PERSON] theorem, we get  
\[\lambda(m)=\ln\biggl{(}\frac{k}{n-k}\biggr{)}+\frac{|y_{m}|^{2}}{N_{0}}+\ln \biggl{(}\sum_{i=1}^{M}\exp\biggl{(}-\frac{|y_{m}-h_{F,m}c(i)|^{2}}{N_{0}} \biggr{)}\biggr{)}, \tag{7}\]  
where \(N_{0}\) is the noise variance in the frequency domain. With higher-order modulations, we can define  
\[\ln\biggl{(}\sum_{i=1}^{M}e^{a_{i}}\biggr{)}=f_{\max}(\cdots f_{\max}(f_{\max}( a_{1},a_{2}),a_{3}),\ldots,a_{M}), \tag{8}\]  
where \(f_{\max}(a,b)=\ln(e^{a}+e^{b})=\max(a,b)+\ln(1+e^{-|a-b|})\).  
After computing \(M\) LLR values, the receiver selects \(k\) indices with maximum LLR values per subblock. The detected indices are then passed to an index demapper to estimate \(N_{1}\) bits, followed by standard constellation demodulation.
# Performance Analysis of Hybrid Optical-Acoustic AUV Swarms for Marine Monitoring
## 6 Conclusions  
In this study, we compared the performance of the acoustical and optical modems of the Venus AUV robot in different network simulated scenarios, using parameters coming from the real implementation of the vessel. We analyzed the power consumption and the delay in the case of linear topologies and evaluated how the optical communication performance varies according to the quality (turbidity) of the water expressed by the diffuse attenuation coefficient \(K\). The use of an optical communication link presents a sensible, lower power consumption, allowing communication with \(0.3\times 10^{6}\) bit/J with respect to \(\approx 5000\) bit/J of the acoustic case. A simulation campaign shows that this  
Figure 10: Broadcast energy consumption in the case of optical communication.  
Figure 9: Energy consumption in the case of communication among randomly selected peers on a linear topology—Acoustic.  
holds also under different network simulated topologies and considering multi-hop communication where the optical communication range (4 to 60 m with a SNR of 12 dB) is far less than the acoustic communication range (typically 100 m).  
Networking and simulations, P.L. and L.B.; Dual channel modem, C.L. and S.B.;  
**Acknowledgments:** The Authors wish to thank [PERSON] for his helpful suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 3 Materials and Methods
### Control Algorithm
It is worth noting that all input parameters, excluding sensor measurements, were initialized based on certain assumptions, as in common scenarios where covariance data are unavailable. The EKF algorithm can be conceptually divided into two primary steps: prediction and measurement update. During the prediction step, the algorithm forecasts the state using the past state (\(x_{k-1}\)) and the linearized function \(F\) (the Jacobian matrix of \(f(x_{k-1},k)\)) to find \(F_{k}^{i}\):  
\[x_{k+1}=F_{k}^{i}x_{k-1} \tag{20}\]  
And, simultaneously, the state covariance matrix \(P\) is predicted based on \(F\). \[P_{k+1}=F_{k}^{i}P_{k-1}\Big{(}F_{k}^{i}\Big{)}^{T}+Q_{k} \tag{21}\]  
Subsequently, in the measurement update step, the differential equation of the measurement model is linearized by computing the Jacobian matrix \(h(x_{k+1},k)\), to find \(H_{k}^{i}\). This linearization is crucial for calculating the measurement prediction and determining the Kalman gain (\(K\)), given as:  
\[K_{k}^{i}=\frac{P_{k}^{i}{H_{k}^{i}}^{T}}{H_{k}^{i}P_{k+1}^{i}{H_{k}^{i}}^{T}+ R_{k}^{i}} \tag{22}\]  
The Kalman gain, along with the sensor measurements and the expected measurement based on the predicted state (\(y\)) is then employed to update the predicted state vector:  
\[y_{k}^{i}=H_{k}^{i}x_{k+1} \tag{23}\]Furthermore, the state covariance matrix undergoes an update using the gain (\(K\)) and the linearized observation matrix (\(H\)). The optimal estimate, denoted as x_k, represents the output of the filter and serves as the refined estimation of the system's current state. This iterative process continues until the maximum number of iterations is reached, as determined by either the specified error threshold or the designer's predetermined limit. The inclusion of the prediction and measurement update steps, along with the iterative nature of the algorithm, enables the EKF to continually refine its estimates, providing an optimal and accurate representation of the system's evolving state over time.
## 2 Metallic Nanoparticles in Conjugation with Chlorin Derivatives
### Metal-Organic Frameworks  
The metal-organic frameworks with high flexibility and porosity can be synthesized by combining metal-containing subunits (inorganic clusters) and organic linkers (carboxylates, imidazolates, and phosphonates). Such systems constitute promising drug delivery carriers, as well as theranostic agents. The MOF nanoparticles bearing Ce6 and campothecin (Cam) for photodynamic therapy and chemotherapy as multimodal agents were presented by [PERSON] et al. (Figure 9) [65]. Moreover, the obtained nanoplatform was attached to folic acid (FA), thus facilitating targeted delivery. The authors fabricated the nanoagent with the Ce6 derivative triggered by cathepsin B, an enzyme present mainly in cancerous cells. Ce6 linked with the peptide--a substrate for cathepsin B--did not reveal any photodynamic activities, but after cutting the peptide moiety off and releasing Ce6, the photodynamic properties were turned on. In the in vitro experiments, [PERSON] et al. showed that the nanoagent was efficiently delivered into HeLa cells compared to that with HaCaT (immortalized human keratinocytes) cells. It is known that HeLa and many other cancer cells possess folic acid receptors, unlike non-cancerous HaCaT cells. The authors presented the targeted delivery of Ce6 into the cells, which was indicated by photosensitizer fluorescence turned on by intracellular cathepsin B. The intracellular singlet oxygen production was indicated with Singlet Oxygen Sensor Green. The cytotoxic assessment experiments showed that combined chemo- and photodynamic therapy influenced the apoptosis of 99.27% HeLa cells upon light excitation (60 J/cm\({}^{2}\) at 660 nm), while in the dark condition, only 35.17% of cells were eliminated. Interestingly, the same level of cancer cell death was achieved only for Ce6 MOF, loaded after 30 min of irradiation. Simultaneously, the experiments using cells without folic acid receptors were performed, and no activity was noted. In the in vivo trials, the usability of the nanoagent was tested with the HeLa tumor mouse model. The developed MOF preferentially accumulated in the tumor within 24 h after intravenous injection. In the tumor, Ce6 was triggered by cathepsin B and emitted strong fluorescence. A significantly higher decrease in the tumor volume was noted when treated with Cam-Ce6-pept-FA-MOF nanoparticles in comparison to that with Cam-FA-MOF or Ce6-FA-MOF [65]. In another study, [PERSON] et al., synthesized a novel MOF based on _meso_-5,10,15,20-tetrakis(4-carboxyphenyl)chlorin combined with Hf\({}^{4+}\)[66]. MOF was then loaded with an indoleamine 2,3-dioxygenase inhibitor. The material was tested on mice bearing CT26 and MC38 colorectal cancer tumors and was found to be effective in PDT and the immunotherapeutic treatment of metastatic tumors [66].
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 7 Recent Advances in Deep Learning-Based SLAM
### Datasets for Underwater SLAM  
DL models require substantial data for training, validation, and testing. Recent research has focused on providing comprehensive datasets covering various underwater scenarios while also providing ground-truth poses to train and evaluate DL models. For instance, Ferrera et al. [150] introduce a comprehensive dataset called Aqualoc to enhance SLAM methods for underwater vehicles operating near the seabed. This dataset, recorded in diverse environments, such as harbors and archaeological sites at depths of 270 m and 380 m, includes synchronized data from a monocular camera, an IMU, and a pressure sensor. It is available as robot operating system (ROS) bags and raw data, providing offline computed trajectories enabling benchmarking real-time localization methods. This dataset promotes significant advancements in underwater vision-based localization.  
Another commonly used dataset for underwater SLAM evaluation is the EuRoC dataset [151]. Although originally captured in a terrestrial environment using a drone, the EuRoC dataset remains relevant for underwater research due to its inclusion of challenging conditions that sample those encountered underwater. It features sequences with variable lighting, motion blur, and diverse noise that simulate underwater challenges such as low visibility due to turbidity, dynamic lighting from surface reflections, and sensor noise from water particulates. These similarities allow researchers to test the robustness of SLAM algorithms under scenarios where direct underwater data may be limited. Furthermore, the scarcity of large-scale, publicly available underwater datasets with ground-truth poses enhances the EuRoC dataset's utility as a proxy for benchmarking. While underwater-specific datasets like Aqualoc are ideal for direct relevance, the EuRoC dataset's established benchmarks and challenging conditions make it a valuable and often necessary resource for assessing the performance and generalization of SLAM methods in underwater contexts.  
Moreover, addressing the issue of underwater imaging affected by uneven lighting and scattered light, Shivaswamy et al. present a dataset of 1000 images with depth maps from a black smoker field at a depth of 1400 m. This study compares classical Markov random field-based segmentation and DL-based U-Net segmentation for detecting free space and enhancing clean mapping and navigation in complex underwater terrains [152]. Wang et al. [153] introduce a new dataset created with a controllable AUV equipped with high-precision sensors, including fiber-optic inertial sensors, DVL, and depth sensors. This rigorously tested dataset provides valuable data for evaluating navigation algorithms based on actual and calculated positions, focusing on the challenges of weak textures and image degradation in underwater environments.  
Figure 14: CaveSeg network architecture for semantic mapping in underwater environments. (Adapted from Burguera et al. [149]).
## 2 Materials and Methods
### The Scientific Analyses
#### 2.2.3 Spectroscopic Techniques  
* Micro X-ray fluorescence spectroscopy (XRF): Considered the specific object to investigate, with fine brush strokes, a micro-EDXRF Bruker ARTAX 200 spectrometer was used, combined with a \"fine focus\" Mo target X-ray tube, ADC with 4096 channels, able to analyze an area with a variable radius ranging between 0.65 and 1.5 mm; the tube voltage and the anode current of the tube were fixed, respectively, to 30 kV and 1300 mA, and the acquisition time was 60 s, while the acquisition window was 0-25 keV.
* Fiber optic reflectance spectrophotometry (FORS): A portable Vis-NIR spectrophotometer (HR2000 + ES, Ocean Optics Dunedin, FL, USA) was used for FORS analysis, 360-1100 nm, 2.5 nm spectral resolution with a 2 \(\times\) 45\({}^{\circ}\)/0\({}^{\circ}\) geometry of measurement using a reflectance probe holder (RPH-2, Ocean Optics). The spectrophotometer was connected to a tungsten halogen light source (D65, HL2000-FHSA, Ocean Optics): light is transmitted through a quartz fiber optics bundle 1.5-m-long (Ocean Optics, 400 \(\upmu\)m core). The spectrometer was connected to a laptop and calibrated using white reflectance standard (Spectralon(r) 99% and a dark-trap reference). Visible-NIR reflectance spectrum from 350 nm to 1000 nm was recorded for each sample.
## 3 Test Results
### Excess Support Pressure
#### 3.1.3 Maximum Heave  
Figure 8 presents the evolution of the maximum ground surface heave during the increase of support pressure at different buried depths. The location of the maximum uplift position monitored on the surface is basically near L3. It can be seen from Figure 8 that at buried depths of 0.5 D and 1 D, the surface heaves firstly increase slowly as the support pressure increases. After the pressure reaches a certain limit, the heaves begin to increase sharply. However, at the buried depth of 1.5 D, the surface uplift has not changed much, and there is no inflection point similar to the relationship curve at the buried depth of 0.5 D and 1.0 D, indicating that the induced ground deformation is difficult to extend to the surface.
## 3 Results
### AD-Related HCRU  
The most common diagnosing physician specialty was the 'dermatologist', reported in 71.7% (132/184) of patients, followed by the 'allergist' in 14.1% (26/184), 'pediatrician' in 13.0% (24/184), and 'general practitioner' in two patients (1.1%). Over the past year prior to expert center consultation, 95.7% (176/184) of patients had consulted a median of 2.0 (1.0-2.0) different HCP specialties (including pharmacists) and performed a median (IQR) of 5.0 (3.0-10.0) visits [4.0 (2.0-6.0) visits excluding pharmacists]. The most common physician specialties were dermatologists [median (IQR) visits: 2.0 (2.0-4.0) for the overall and m-AD and s-AD subpopulations] and allergists [median (IQR) visits: overall 2.0 (1.0-4.0); m-AD 2.0 (1.0-4.0); s-AD 2.5 (1.0-8.5)] (Figure S3).  
Over the past year prior to expert center consultation, 62.1% (113/182), 28.0% (51/182), and 6.6% (12/183) of evaluable patients had performed a median of 3.0 (IQR: 1.0-4.0) AD-related hospital/specialized center consultations (total of 358 consultations), 2.0 (IQR: 1.0-2.0) AD-related emergency room (ER) visits (total of 108 visits), and 1.0 (IQR: 1.0-1.0) AD-related hospitalization (total of 14 hospitalizations), respectively. The overall frequency of AD-related ER visits and hospitalizations in the past (ever performed) was 36.5% (66/181; total ER visits: 283; median: 3.0; IQR: 1.0-5.0) and 12.6% (23/182; total hospitalizations: 39; median: 1.0; IQR: 1.0-1.0), respectively.  
The rate of AD-related hospital/specialized center consultations over the past year was similar between m-AD (61.2%; 71/116; total consultations: 226) and s-AD patients (63.6%; 42/66; total consultations: 132). Conversely, a higher frequency of ER visits and hospitalizations due to AD was observed among s-AD patients than m-AD patients. Specifically, among m-AD and s-AD patients, 22.4% (26/116) and 37.9% (25/66) reported any AD-related ER visits in the last year (total of 53 and 55 visits, respectively), while five (4.3%) and seven (10.4%) patients reported any AD-related hospitalization in the last year (total of seven hospitalizations in both subpopulations), respectively. The overall frequency of AD-related ER visits and hospitalizations in the past (ever performed) among m-AD patients was 28.7% (33/115) and 11.2% (13/116), respectively, while the respective proportions for s-AD patients were 50.0% (33/66) and 15.2% (10/66).  
Of the overall population, 54.3% (100/184) were referred to the study site by a healthcare professional (HCP); most commonly by a dermatologist (77.0%), a general practitioner (8.0%), or an allergist (7.0%). The frequencies of required patch testing, biopsies and other AD-related diagnostic tests (pre- and/or post-visit) are presented in Figure 5. Of note, almost 4 out of 10 M2S-AD patients required biopsy at some point.
## 4 Discussion
### Oxygen Radical Absorbance Capacity Assay (ORAC)  
In addition to the previous methods, which are the most widely used to assess antioxidant capacity, the hydrolysates of porcine liver proteins were also evaluated using ORAC. This method is widely applied to evaluate the antioxidant activity of phenolic compounds, however few studies have used it to evaluate the antioxidant activity of peptides [42]. The enzyme which provided the best results for ORAC was alcalase. This is in agreement with the results found by other authors in bovine lung hydrolysates, where alcalase achieved higher ORAC activity than papain and pepsin [47]. Better results were also found for alcalase than those found by the mixture of pepsin and pancreatin in chicken skin enzymatic protein hydrolysates (35.0% vs. 33.0%, 95.0% vs. 80.0%, and 3800 vs. 3200 \(\upmu\)M Trolox equivalent/g for DPPH, metal chelating effect, and ORAC, respectively).  
Regarding hydrolysis time, its increase resulted in the increase of the ORAC antioxidant activity [53]. This trend was observed for all the enzymes evaluated, but was in contrast with the results found in other studies with porcine liver protein. These authors found that this behavior was only observed for alcalase, while the activity of papain extracts decreased [7].  
On the other hand, [PERSON] et al. [38] reported higher ORAC values in small peptides. This could be due to the greater ability to interact and donate electrons compared to larger peptides, which could have a reduced ability to interact with the free radical. However, the results obtained in the present study showed that hydrolysates filtered through intermediate and higher pore size membranes were those that showed the best capabilities (36.4 and 45.2 mg Trolox/g for bromelin 10 kDa, 40.1 and 53.2 mg Trolox/g for alcalase 30 kDa, and 37.4 and 44.3 mg Trolox/g for papain 30 kDa).