esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of \(\beta\)-Cry on IL-6, IL-8, and ROS Levels in CDDP-Treated hOMK and hOMF  
IL-6, IL-8, MMP-2, and MMP-9 levels were significantly higher in CDDP-treated hOMK (IL-6: 104.51 \(\pm\) 8.0 pg/mL, IL-8: 80.66 \(\pm\) 9.11 pg/mL, MMP-2: 26.74 \(\pm\) 3.73 ng/mL, MMP-9: 0.74 \(\pm\) 0.09 ng/mL) than in control cells (IL-6: 19.09 \(\pm\) 2.02 pg/mL, IL-8: 26.72 \(\pm\) 4.06 pg/mL, MMP-2: 6.22 \(\pm\) 0.46 ng/mL, MMP-9: 0.53 ng/mL). However, no significant changes were observed in \(\beta\)-cry-treated hOMK (Figure 3A). IL-8 levels were significantly lower in CDDP+\(\beta\)-cry-treated hOMK (IL-8: 62.60 \(\pm\) 4.53 pg/mL) than in CDDP-treated hOMK (Figure 3A).  
Figure 2: Effects of CDDP and \(\beta\)-cry on inflammatory cytokine and MMP mRNA expression levels in hOMK and hOMF. IL-6, IL-8, and MMP-9 mRNA expression levels were significantly higher in CDDP-treated hOMK (**A**) and hOMF (**B**) than in control cells. IL-6, IL-8, and MMP-9 mRNA expression levels were significantly lower in CDDP+\(\beta\)-cry-treated hOMK than in CDDP-treated hOMK (**A**). IL-6 and MMP-9 mRNA expression levels were significantly lower in CDDP+\(\beta\)-cry-treated hOMF than in CDDP-treated hOMF (**B**). Values are means \(\pm\) S.D. \(n\) = 4 \({}^{\ast}\)\(p\  
IL-6, IL-8, and MMP-2 levels were significantly higher in CDDP-treated hOMF (IL-6: 7416.01 \(\pm\) 2087.53 pg/mL, IL-8: 4095.12 \(\pm\) 638.94 pg/mL, MMP-2: 227.94 \(\pm\) 55.58 ng/mL) than in control cells (IL-6: 938.21 \(\pm\) 376.34 pg/mL, IL-8: 1405.01 \(\pm\) 145.01 pg/mL, MMP-2: 45.0 \(\pm\) 5.46 ng/mL). However, no significant changes were observed in \(\beta\)-cry-treated hOMF (Figure 3B). IL-6 levels were significantly lower in CDDP+\(\beta\)-cry-treated hOMF (IL-6: 3780.41 \(\pm\) 826.81 pg/mL) than in CDDP-treated hOMF (Figure 3B). MMP-9 levels were below the detection limit in all groups.  
Figure 3: Effects of CDDP and \(\beta\)-cry on inflammatory cytokine and MMP levels in hOMK and hOMF. IL-6, IL-8, MMP-2, and MMP-9 levels were significantly higher in CDDP-treated hOMK and in CDDP+\(\beta\)-cry-treated hOMK than in control cells (**A**). IL-6, IL-8, and MMP-2 levels were significantly higher in CDDP-treated hOMF and CDDP+\(\beta\)-cry-treated hOMF than in control cells (**B**). IL-6 levels were significantly lower in CDDP+\(\beta\)-cry-treated hOMF than in hOMK and CDDP-treated hOMF (**A**,**B**). Values are means \(\pm\) S.D. * \(p\) < 0.05, **_p_ < 0.01 vs. control, **_p_ < 0.01 between each group.  
ROS levels were significantly higher in CDDP-treated hOMK (117.11% \(\pm\) 2.41) and CDDP+\(\beta\)-cry-treated hOMK (113.66% \(\pm\) 1.47) than in control cells. ROS levels were also significantly higher in CDDP-treated and CDDP+\(\beta\)-cry-treated hOMF (118.33% \(\pm\) 1.6 and 113.22% \(\pm\) 0.76, respectively) than in control cells (Figure 4)
# An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
## 2 Kernelized Correlation Filter Algorithm
The kernel matrix is:  
\[K^{z}=C\left(\kappa^{\chi z}\right) \tag{15}\]  
According to Equation (10), the response output value of all input samples can be calculated:  
\[f\left(z\right)=\sum_{i=1}^{n}\alpha_{iK}\left(z_{i},\chi_{i}\right)=\left(K^{ z}\right)^{T}\alpha \tag{16}\]  
For more efficient calculations, \(K^{z}\) can be diagonalized by the Discrete Fourier Transform:  
\[\hat{w}=\frac{\hat{\chi}^{*}\odot\hat{y}}{\hat{\chi}^{*}\odot\hat{\chi}+\lambda} \tag{8}\]0  
Target tracking with the KCF is considered an efficient algorithm, since it obtains the fastest and highest performance among the recent top-performing algorithms. However, the KFC cannot effectively track targets with varying scales or targets that disappear and appear again. To overcome this drawback, an IKCF is proposed to track the underwater target in forward-looking sonar image sequences in this paper.
## 5 Discussion  
The analysis of simultaneous GIC and magnetic field observations at Kola Peninsula has proved that a regression model can be a reliable tool for monitoring of the geomagnetic risks to electric power transmission lines. The regression method applied is somewhat similar to the transfer function approach [23]. In the case of a change in the configuration of the power transmission line, the coefficients of this relationship require recalculation. With the advent of real-time magnetometer data access, such an approach can be applied to the nowcasting and short-term prediction of GIC in a system under control. The relative simplicity of the algorithm makes these methods of the space weather risk evaluation competitive with the current widely developing global modeling of the magnetosphere with supercomputers.  
However, the application of expression (6) without recalculation of the regression coefficients to time intervals beyond 2015 may show a slightly worse quality of the GIC diagnostics (Figure 5) as compared to the results presented in Table 4. Thus, updating the regression coefficients would be necessary from time to time.  
Another promising application of the proposed regression method is the monitoring of geomagnetic risk to pipelines in the oil/gas-producing high-latitude regions (e.g., Yamal, the Arctic shelf) [24]. The development of GIC in pipelines results in an increased corrosion at grounding points or at points with insulation damage. GMD induce fluctuations in the pipe-to-soil potential, which take the pipeline voltage beyond the range of electrocorrosion protection. Thus, the influence of geomagnetic variations should be monitored regularly when maintaining the operation of the power transmission line and cathodic protection of pipelines, since the effect of GIC can manifest itself not only directly during the extreme GMD as failure of the equipment, but also has a long-term cumulative effect as pipeline electrocorrosion or the aging of industrial transformers [25].
## 3 Consolidation Properties
### Effects of Time-Cement Stabilization on the Compression Index of the Clay Soil  
The compression index, quantifying soil compressibility under stress where lower values indicate reduced settlement potential, decreased optimally from 0.48 at 0% additives to 0.20 at 6% cement and 15% lime through complex microstructural transformations involving pozzolanic reactions that generate calcium silicate hydrate and calcium aluminate hydrate gels (Figure 9). This 58% reduction results from these cementitious compounds creating robust inter-particle bonding networks that effectively constrain soil particle rearrangement by developing a more rigid, interconnected microstructure with enhanced deformation resistance, making the soil significantly more suitable for foundation design and construction applications requiring minimal settlement. The optimal combination at 15% lime demonstrates the critical balance for maximum compressibility reduction, as further lime increases to 30% at 8% cement cause the compression index to rise slightly to 0.23, indicating a threshold where excess binding agents disrupt the optimal gel structure and diminish stabilization effectiveness. This transformation fundamentally alters the soil's consolidation behavior by replacing weak van der Waals forces between clay particles with strong chemical bonds, effectively creating a quasi-cemented matrix that maintains structural integrity under loading while demonstrating the nuanced equilibrium required between chemical interaction and mechanical performance in geotechnical stabilization processes.  
Figure 8: Principal component analysis of pre-consolidation stress in lime–cement stabilized soil.  
Figure 9: Compression index variation with cement content (0–8%) for different lime percentages (0–30%).  
The compression index of clay soils stabilized with mineral and pozzolanic additives, such as fly ash (Class C/F), rice husk ash (RHA), and ground granulated blast furnace slag (GGBS), can approach or match lime-cement mixtures when optimized for dosage and curing conditions, though lime-cement remains more predictable due to its immediate cementation and long-term pozzolanic effects. Fly ash (e.g., 5% fly ash + 8% cement [43]) and GGBS [33], which leverage calcium-based reactions and pozzolanic activity, reduce compressibility comparably to lime-cement, while RHA serves as a sustainable lime alternative with slightly lower efficacy unless combined with other stabilizers [44]. Zeolites [45] and dolomite [46] primarily address swelling potential and pH balance, respectively, with minimal impact on compression index, whereas organic additives (wood ash, bone meal) lack the cementitious properties to significantly alter mechanical behavior [47].
## 5 Results and Discussion
### Strengths and Limitations  
The ISO 3382-1 standard is applicable in performance spaces. The deconscerated church of San Luis de los Franceses is currently being used to stage theatrical performances, concerts and cultural events and therefore the application of this standard would be relevant. However, it must be emphasized that this standard was designed for concert halls and auditoriums whose acoustic behaviour differs greatly from that of churches, where absorption distribution is not homogeneous and the presence of chapels, domes and aisles results in a distribution of energy far from the diffuse field. In this case, depending on the position of the source and the receiver, as well as the cupola or choir which act as coupled spaces, the decrease in energy can cease to be linear, giving rise to two or more slopes. Caution must therefore be exercised when considering reverberation time. For the same reason, early energy is highly variable and intrinsic to the location of the source and receiver, so that EDT loses the homogeneity of values obtained in concert halls and auditoriums. The main limitation of the application of the standard consists of the JND associated with each acoustic parameter established in theatres. The Martellota [31] criteria for C\({}_{80}\) and Ts in this type of space have been taken into account for the analysis carried out. Finally, the typical values collected in the standard for each acoustic parameter do not have to coincide with those obtained in churches.  
Moreover, acoustic simulations are also subject to limitations. The main limitation is connected with the wave nature of the sound \"rays\" not being taken into account. In this case, the Schroeder frequency of each evolutionary period is around 45 Hz and therefore statistical acoustics would be valid from 250 Hz. Although all the simulations estimated the effect of diffraction and scattering, they were not able to capture the real wave phenomena. The level of detail used was estimated following the recommendations of [PERSON] [30]. However, the treatment of the dome as a set of flat surfaces hinders the adequate reproduction of its focal behaviour. In all hypotheses, the final number of rays assumed was considered so that the results obtained in different simulations of the same hypothesis do not differ by more than 2%. All the absorption and scattering coefficients were taken from the specialized literature. Only retouching below 1% was assumed in plaster to adjust the measured and simulated results.
# A Novel Suspended-Sediment Sampling Method: Depth-Integrated Grab (DIG)
## 2 Materials and Methods
### Equal-Width Increment or Equal-Discharge Increment (EWDI) Sampling Method  
Based on previous research, the EWDI-SSC was considered the representative measure of SSC and was used as a comparison to other sampling and laboratory methods [5; 6; 7; 8; 9]. Suspended-sediment samples were collected with isokinetic and depth-integrating samplers at EWIs or EDIs [12; 13]. For the collection of samples, the stream was either divided into 10 EWIs (Figure 1a) or 5 EDIs. Each depth-integrated, isokinetic sample was collected at the centroid of each increment [12]. The EWI method had equal widths while the EDI method's width increments varied depending on the distribution of discharge during the  
Figure 2: Study area with 9 suspended-sediment sampling sites in Minnesota.  
time of sample collection. The EWI method was the primary sampling method used in this study.  
Before an EWI sample could be collected, the sampler transit rate had to be determined. Different sampler transit rates (generally, 0.4 times the average velocity in the sampled vertical [12]) were tested by sampling the deepest and fastest part of the river, to ensure the sample container did not overfill during the final sample. The same transit rate was used for all the EWI sample verticals. Depending on the river depth and velocity, a US DH-48 bottle sampler (with a 0.5-L glass bottle), US D-74 bottle sampler (with a 0.5-L glass bottle or 1-L glass bottle) (Figure 3b), or US D-96 bag sampler (with a 3-L bag) [13]. The US D-74 sampler and 0.635 cm sampler nozzle were mostly used in this study.  
The US DH-48 bottle sampler and US D-74 operate identically with the only difference being that the US D-74 can hold either a 0.5- or 1-L glass bottle while the US DH-48 can only hold a 0.5-L bottle. The US DH-48 and US D-74 have vents which are located on the side of the sampler and point downstream during sample collection. The vent allows air to escape as it is displaced by the sample being collected in the bottle. The US D-96 bag sampler can sample at deeper depths and higher velocities than the US DH-48 and US D-74. The US D-96 does not have a vent but, prior to sample collection, the person sampling collapses the bag with their hands which pushes the air out from the bag and nozzle prior to sample collection.  
Contamination can occur when a sampler's nozzle digs into the streambed. A depth-sounding was taken before each sample was collected to determine the water depth. Each vertical was sampled approximately 0.15 to 0.3 m above the streambed to prevent bed contamination. The distance above the streambed varied depending on the estimated height of the river's sand dunes to avoid inadvertently sampling the sand dune. All samples collected from the centroids of the stream transect were composited into one sample and sent to the laboratory for analysis.
## 5 Conclusions  
The modeling and analysis of gamma and neutron dose rate distributions and variations with time for cast iron and metal-concrete containers used for RBMK-1500 spent fuel storage revealed the following:  
* Approximately 97-99% of the gamma dose rate on the side wall of a cast iron or metal-concrete container is caused by photons that are emitted from spent fuel and have energies within 7-12 gamma energy groups. The influence of the remaining 13 gamma energy groups on the gamma dose rate is insignificant. For longer storage periods (50, 300 years) the major contributors to the gamma dose rates on the side wall of both containers are Group No. 7 (0.6-0.8 MeV) photons.
* About 99% of the neutron dose on the side wall of the analyzed containers is determined by neutrons that have energies within 21-27 neutron energy groups. The contributions of the remaining 20 neutron energy groups on the neutron dose rate are negligible. Unlike the contributions of gamma energy groups, the neutron dose rate for all storage periods is determined by the same dominant neutron energy groups.
* Steel lids at the top of the containers provide effective shielding against gamma photons. Therefore, neutron dose rates are dominant at the top of both containers. The CASTOR\({}^{\circledR}\) RBMK-1500 container side and bottom walls are made of ductile cast iron that provides better shielding against gamma photons, whereas the metal-concrete structures of CONSTOR\({}^{\circledR}\) RBMK-1500 are better shielding materials against neutrons. For extended storage periods, the total dose rates are mainly determined by neutrons for both containers.
* The total dose rates on the surface of the metal-concrete container are less than those of a cast iron container. The smallest difference between dose rate values occurs at the top of the containers, and this difference is constant during the whole storage period. More significant differences in the dose rate values occur at the side and bottom walls of the containers. The differences vary with storage time, i.e., the total dose rates  
Figure 8: Variation of the total dose rates with storage time.  
range from 2-7 times for storage periods up to 50 years, whereas, for extended storage periods, the difference grows to 60 times.  
Conceptualization, A.S. and G.P.; methodology, A.S. and E.N.; software, A.S. and E.N.; formal analysis, A.S., E.N. and G.P.; writing--original draft preparation, A.S. and E.N.; writing--review and editing, A.S., G.P. and P.P.; supervision, P.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 4 Experimental Validation
### Practical Deployment Considerations  
In real-world deployment, several practical challenges is addressed. First, environmental constraints such as beam coverage limitations and Earth curvature effects over long-range links necessitate careful placement of CF-mMIMO base stations. In our field validation in Figure 4, we successfully conducted UAV offloading tests at a distance of 28.44 km from the edge communication station, demonstrating the feasibility of long-range connectivity. Second, the proposed CF-mMIMO system exhibits high scalability, benefiting from large bandwidth and distributed processing. As shown in Figure 4, our hardware testbed successfully supported real-time flight of all nine drones, confirming that the system architecture meets the requirements of large-scale cluster access. Third, in terms of fault tolerance and interference resilience, the cell-free architecture ensures operational continuity even if one or more EDUs are disabled, as each EDU handles local detection and precoding independently. For PDSCH/PDCCH transmission, the system utilizes ACK/NACK feedback to estimate sub-band-level error rates, enabling dynamic avoidance of interfered sub-bands, adaptive modulation and coding scheme (MCS) adjustments, and interleaving. Synchronization robustness is maintained through real-time SSB monitoring; upon detecting loss of sync, the system dynamically switches the synchronization frequency to ensure link continuity.  
These practical enhancements demonstrate that the proposed CF-mMIMO-enabled offloading framework is not only technically effective but also resilient and scalable in real-world UAV swarm deployments.
# Development of Automatic Berthing Support Program for Autonomous Ships
## 2 Methods
### Thruster Output Calculation  
#### 2.2.1. Theoretical Calculation  
For automatic berthing, as shown in Figure 3, it is necessary to calculate the external forces and moments acting on the ship when berthing at a constant speed (\(U\)) and determine the corresponding thruster output values. At this stage, the external forces can be divided  
Figure 2: Lateral current force coefficient (loaded tanker) [25].  
into longitudinal (\(F_{X}\)) and lateral (\(F_{Y}\)) components, as shown in Equation (9), and the total turning moment (\(M\)) is given by Equation (10).  
\[\begin{split} F_{X}=F_{X}cos\theta+F_{F}cos\beta,\\ F_{Y}=F_{W}sin\theta+F_{C}+F_{U},\\ M=M_{W}+M_{C}+M_{U},\end{split} \tag{9}\]  
where \(F_{X}\) represents the longitudinal force, \(F_{Y}\) represents the lateral force, \(M\) represents the total turning moment, \(M_{W}\) represents the wind-induced moment, \(M_{C}\) represents the current-induced moment, and \(M_{U}\) represents the current moment due to ship speed.
## 5 Experimental Results
### Results without Pathologies
#### 5.2.1 Results without Feature Selection  
After testing the dataset without pathologies with the algorithms without feature selection, a reduction in the accuracy rate of the models was observed with respect to the results with pathologies, obtaining an average accuracy rate of 0.6825 and a standard deviation of 0.0408. RFC obtained the highest result, reaching 0.7331 accuracy, as can be seen in Table 3.  
Figure 7 shows the impact of the variables for the RFC model, using the dataset without pathologies. Age and sex maintain the highest positions with the same characteristics seen in previous cases. Among the variables referring to eating habits, vegetables and soda stand out. The values of vegetables seem to have an inversely proportional relationship with the possibility of being overweight or obese while in the case of soda this relationship is direct. In Figure 8, we can see the Pearson Correlation Matrix of the variables in Figure 7. As we can see, no significant correlation can be appreciated between variables with the exception of _smoke_ (smoker or non-smoker) and _nsmoke_ (number of cigarettes per day).  
Figure 7: Impact of variables with SHAP, Random Forest without pathologies, and no FS.
# Singing Voice Detection in Electronic Music with a Long-Term Recurrent Convolutional Network
## 3 Experiments
### Effects of Singing Voice Separation  
In this implementation, SVS was adopted as a preprocessing step for SVD through the U-Net network to separate vocals from musical accompaniment and generate two versions of audio signals (raw and audio vocals). The third experiment consisted of extracting the features of both versions in both datasets to train the LRCN and validate the effects of the preprocessing by comparing its F1-measure. The model was set with the block size of 20 frames set on the previous experiment, and a vector of 276 components to remove the feature of chroma (12). The results were compared with both the split vocal and raw audio. Performances are shown in Figure 10.  
From the comparison of the classification results from both datasets, the use of separated vocal signals exceeded the raw signals in terms of \(F1\)-measure, so the application of SVS as a preprocessing step does improved SVD performance. However, it is noticeable that the difference was way more pronounced in Jamendo (more than 20%), whereas in Electrobyte it was minimal (less than 2%). This contrast of results between datasets can be attributed to the unique characteristics of electronic music in terms of voice management. However, further research needs to be performed to pinpoint the exact causes of the result disparities.  
Figure 9: Performance of block sizes (5–29 frames) as input of the LRCN for the Electrobyte dataset.