esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Microscopic Characterization of CaCO\({}_{3}\) NFs  
#### 4.1.1 FTIR Analysis  
Figure 2 presents the infrared spectrum of the original CaCO\({}_{3}\) NPs, CaCO\({}_{3}\) NPs grafted DC5700, and CaCO\({}_{3}\) NFs. In curve A, the absorption peaks at 1456 cm\({}^{-1}\), 875 cm\({}^{-1}\), and 713 cm\({}^{-1}\) correspond to the stretching and bending vibration of Ca-O bond, which is a characteristic group of calcite-type CaCO\({}_{3}\). Curve B had strong absorption peaks at 2924 cm\({}^{-1}\) and 2854 cm\({}^{-1}\) with respect to curve A, corresponding to the stretching vibration peak of C-H in methyl and methylene, respectively. Strong multiple absorption peaks appeared in the range from 1130 cm\({}^{-1}\) to 1028 cm\({}^{-1}\), corresponding to the absorption peaks of Ca-O-Si bonds, indicating that the DC5700 was successfully grafted onto the CaCO\({}_{3}\) NPs' surface. Compared to curve B, new absorption peaks appeared at 1511 cm\({}^{-1}\), 1580 cm\({}^{-1}\), and 1610 cm\({}^{-1}\) in curve C, corresponding to the skeleton vibration of the benzene ring. The stretching vibration absorption peak of RSO4' (NPES) appeared at 1249 cm\({}^{-1}\), showing that the anion exchanges between NPES and DC5700 were successful. In addition, the curves were gradually broadened from A to C at 1456 cm\({}^{-1}\), indicating that there were chemical interactions at each reaction stage rather than simple physical blending. In summary, the CaCO\({}_{3}\) NPs' surface was grafted with long-chain organic compounds DC5700 and NPES.
## 3 Problem Definition and Formulations
### Mathematical Formulation
#### 3.3.4 Linearization  
It can be seen that constraint (25) was a nonlinear constraint. To linearize the model, constraints (41)-(45) are introduced by adding two binary variables, \(U_{pij}\) and \(U_{qij}\). \(U_{pij}\) is aligned with the connection between arc \(\{(p,i)|\forall i\in N_{d},\forall p\in N_{PEC}\}\) and \(\{(i,j)|\forall i\in N_{d},\forall j\in N_{DEC}\}\), while \(U_{qij}\) reflects the connection between arc \(\{(i,q)|\forall i\in N_{d}\), \(\forall q\in N_{DEC}\}\) and \(\{(i,j)|\forall i\in N_{d},\forall j\in N_{DEC}\}\).  
\[\gamma_{pi}-y_{ij}\leq M\big{(}1-U_{pij}\big{)}\quad\forall i\in N_{d},\forall j \in N_{DEC},\forall p\in N_{PEC} \tag{41}\]\[\gamma_{pi}-y_{ij}\geq-M\U_{rij}\ +\epsilon\quad\forall i\in N_{d}, \forall j\in N_{DEC},\forall p\in N_{PEC} \tag{42}\] \[y_{iq}-y_{ij}\leq M\big{(}1-\U_{qij}\big{)}\quad\forall i\in N_{d},\forall j\in N_{DEC},\forall q\in N_{DEC}\] (43) \[y_{iq}-y_{ij}\geq-M\U_{qij}\ +\epsilon\quad\forall i\in N_{d},\forall j\in N_{DEC},\forall q\in N_{DEC}\] (44) \[\sum_{p\in N_{PEC}}\sum_{k\in K}x_{pik}\U_{pij}-\sum_{q\in N_{DEC} }\sum_{k\in K}x_{i\mu k}\U_{qij}+m_{i}\geq 0\quad\forall i\in N_{d},\forall j \in N_{DEC} \tag{45}\]  
Herein, \(\epsilon\) represents a sufficiently small positive constant. If \(\gamma_{pi}>y_{ij}\) or \(y_{iq}>y_{ji}\), meaning that the time at which arc \((p,i)\) or \((i,q)\) reaches or departs from the depot is later than the time at which arc \((i,j)\) departs from the depot, then \(U_{pij}=0\) or \(U_{qij}=0\). On the contrary, if \(\gamma_{pi}\leq y_{ij}\) or \(y_{iq}\leq y_{ij}\), meaning that the time at which arc \((p,i)\) or \((i,q)\) reaches or departs from the depot is earlier than or equal to the time at which arc \((i,j)\) departs from the depot, then \(U_{pij}=1\) or \(U_{qij}=1\). In conclusion, constraint (25) is synonymous with the linear constraints outlined above.
## 5 Future Perspectives
### Expansion of Application Fields
#### 5.2.3 Aerospace Field  
With the growing demand for lightweight, high-strength materials, the application of SiC materials in the aerospace field is becoming increasingly important. SiC materials, due to their exceptional strength, high temperature resistance, and outstanding mechanical properties, have become the ideal material for manufacturing key components such as aircraft engine parts and turbine blades [205; 206]. Using 3D printing technology, SiC parts with complex geometries can be precisely fabricated, meeting the aerospace industry's stringent requirements for lightweight, high-performance structural components. Furthermore, the high thermal stability and oxidation resistance of SiC make it highly promising for applications in extreme environments, such as space exploration and the manufacturing of high-temperature aircraft components.  
As SiC powder 3D printing technology continues to advance, its potential applications in emerging fields such as biomedical science, renewable and clean energy, and aerospace are becoming increasingly promising. This technology offers more flexible and efficient solutions for manufacturing high-precision, complex structural components, driving the application and development of SiC materials in a broader range of fields. Moreover, the expansion of these applications will create new opportunities for future technological innovation and further promote deep integration of 3D printing technology into these sectors.
## 2 System and Channel Models
### Instantaneous SNR Characterization  
The instantaneous SNR at the relay and eavesdropper can be written from (3) as \(\gamma_{i}=\mu_{i}h_{i}^{2}\), where \(i\in\{R,E\}\) and \(\mu_{i}=\big{(}\eta_{i}\mathcal{R}_{i}A_{i}\sqrt{\mathcal{P}_{S}}\zeta\big{)} ^{2}\mathbb{E}^{2}[h_{i}]/\sigma_{i}^{2}\) is the average SNR. It is assumed that the FSO links' channel gains, \(h_{i}\), follow the Malaga distribution with pointing errors, and the PDF and CDF expressions of \(\gamma_{i}\) can be calculated from (5) as [36]  
Figure 2: An illustration of the SLIPT-based time-splitting technique.  
\[f_{\gamma_{i}}(x)=\sum_{k=1}^{\beta_{i}}\frac{\phi_{1i}}{x}G_{1,3}^{3,0}\Bigg{(} \frac{\psi_{1i}x^{\frac{1}{2}}}{\mu_{i}^{\frac{1}{2}}}\Bigg{|}\frac{\zeta_{i}^{ 2}+1}{\kappa_{i}^{2}}\Bigg{|}\frac{\zeta_{i}^{2}}{\kappa_{i}^{2},\alpha_{i},k} \Bigg{)},\quad i\in\{R,E\}; \tag{11}\]  
\[F_{\gamma_{i}}(x)=\sum_{k=1}^{\beta_{i}}\phi_{2i}G_{3,7}^{6,1}\Bigg{(}\frac{ \psi_{1i}^{2}x}{2^{4}\mu_{R}}\Bigg{|}\frac{1,\kappa_{1i}}{\kappa_{2i},0} \Bigg{)},\quad i\in\{R,E\}, \tag{12}\]  
where \(\kappa_{1i}=\Delta(2\): \(1+\xi_{i}^{2})\), \(\kappa_{2i}=\Delta(2\): \(\xi_{i}^{2})\), \(\Delta(2\): \(\alpha_{i})\), \(\Delta(2\): \(k)\), \(\phi_{1i}=\frac{\zeta_{i}^{2}\mathcal{A}_{i}\mathfrak{h}_{2}}{4}\), \(\psi_{1i}=\frac{\zeta_{i}^{2}\alpha_{i}\beta_{i}\left(\beta_{i}+\Omega_{i}^{ \prime}\right)}{\left(\beta_{i}\beta_{i}+\Omega_{i}^{\prime}\right)\left(1+ \xi_{i}^{2}\right)}\), and \(\phi_{2i}=\frac{\zeta_{i}^{2}\mathcal{A}_{i}\mathfrak{h}_{2}\mathfrak{a}^{i+ 4}}{\pi}\). \(A_{i}\) and \(b_{k}\) are given in (6). Here, \(\Delta(p:q)=\frac{q}{p},\ldots,\frac{q+p-1}{p}\) is a notation for a vector comprising \(p\) terms.  
The instantaneous SNR at the destination node can be calculated from (4) as \(\gamma_{D}=\mu_{D}h_{D}^{2}\), where \(\mu_{D}=\big{(}\eta_{D}\mathcal{R}_{D}A_{D}\sqrt{\mathcal{P}_{R}}\xi_{D}\big{)} ^{2}\mathbb{E}^{2}[h_{D}]/\sigma_{D}^{2}\). Additionally, as a DF relaying scheme is assumed at the relay node, the e2e instantaneous SNR for the communication link can be described as  
\[\gamma_{e2e}=\min(\gamma_{R},\gamma_{D}). \tag{13}\]
## 3 Robust Low-Complexity CFO Estimator  
In this section, we develop a robust CFO estimator whose computational complexity is greatly improved. Especially, the proposed CFO estimator is able to promise a reliable performance without knowledge of the channel order \(L\). To this end, we apply the CP length, \(N_{g}\), to compute the CRE instead of \(L\). In spite of avoiding dealing with the estimation of \(L\), the proposed CFO estimator cannot work properly in a channel with a long delay spread. This is because that large channel order results in the degree-of-freedom deficiency problem while computing the CRE. Fortunately, here, we present the reconstruction of a full-dimensional CFR of size \(N\) by imposing the interpolation technique on the CFR of the pilot symbols.
## 3 Results
### Fossil Pigments Analysis  
A total of ten fossil pigments were identified from the sediment core for LGSP (Figure 4a) and nine for LCSP (Figure 4b). Pigments concentrations were minimal in the sediment core's deepest part and increased toward the uppers layers, mainly in the modern sediments. A relatively high abundance of chlorophyll-\(a\) and phaeophytin-\(a\) for LGSP were observed. The abundance of canthaxanthin, echinenone and alloxanthin remained relatively low over time. Notable values were measured at 54, 36 and 9 cm for zeaxanthin and diatoxanthin, while lutein showed an increase between 9 and 4 cm. Chlorophyll-\(b\) was identified from 24 cm onwards, and phaeophorbide-\(a\) showed an increased trend upwards of the core from 24 cm. The total flux of chlorophylls and carotenoids remained low from the bottom, but relatively high flux was found at the end of the core (Figure 4b).  
LCSP is characterised by a dominance of chl-\(a\), phaeophytin-\(a\) and phaeophorbide-\(a\). The chl-\(b\) was detected at 61 cm. Zeaxanthin, canthaxanthin and lutein displayed a relatively low abundance. Alloxanthin and diatoxanthin decreased gradually from 69 cm to the top of the core. The chlorophylls and carotenoid fluxes were relatively low at the bottom and increased in the upper sediments.  
Figure 4: Fossil pigments stratigraphy of Laguna Grande (**a**) and Laguna Chica (**b**) de San Pedro sediment cores.
# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 1 Introduction
Reference [14] utilizes a subdivision approach to further segment target detection, enhancing the performance of the clustering algorithm. Reference [16] achieves sea-land segmentation by distinguishing land clutter from sea clutter through an iterative covariance matrix. Reference [18] integrates three modules--the edge enhancement module, maximum fusion difference convolution, and multiscale spatial attention  
Figure 1: Map and corresponding radar scan results. Radar scanning can only distinguish between the presence or absence of targets, but it cannot determine their attributes. The map is obtained from OpenStreetMap. The copyright and license information can be found at [[https://www.openstreetmap.org/copyright](https://www.openstreetmap.org/copyright)]([https://www.openstreetmap.org/copyright](https://www.openstreetmap.org/copyright)) (accessed on 29 July 2025) [10]. module--to extract richer features. References [15; 16; 17; 18; 19] were able to determine the boundary between the ocean and land through clustering or classification. Building upon these methods, this paper introduces a neural network-based approach for attribute analysis of radar targets, enabling precise discrimination between land-based targets (including islands in the ocean) and sea-based vessel targets. Our method takes into account both the characteristics of pulse compression radar echoes and the task of target attribute recognition. The core idea of this method is to fuse the output features across the layers of the network, employing dynamic augmentation and weighted combinations of convolutional outputs to enhance the feature extraction capabilities of the network. Specifically, this approach considers the interrelations between various levels while incorporating a dynamic adjustment mechanism, allowing the network to flexibly optimize the feature extraction process based on the characteristics of the input data. Furthermore, the method employs multiscale feature fusion techniques to improve sensitivity to targets of varying sizes and shapes, further enhancing the accuracy of target recognition. The experimental results demonstrate that the proposed attribute recognition method outperforms current mainstream deep network architectures in terms of accuracy and efficiency, validating its effectiveness and practicality in radar target detection and providing new insights and solutions for future related research and applications. The remainder of this paper is organized as follows: Section 2 introduces the proposed method, detailing the structure and principles of the DynseNet model. Section 3 presents the experimental results and discussion. Finally, Section 4 concludes the paper.
# Focusing 3D Small Objects with Object Matching Set Abstraction
## 2 Related Work  
### Three-Dimensional Object Detection  
Contemporary approaches to 3D object detection primarily adopt two distinct paradigms: grid-based and point-based methodologies [1, 3]. The first paradigm involves the conversion of unstructured point cloud data into structured features, commonly through BEV projections or volumetric voxel grids. These regularized representations subsequently facilitate the application of a 2D or 3D conventional CNN. VoxelNet [4] is the first grid-based work that transformed point clouds into voxels and utilized a 3D CNN to detect objects. In [5], SECOND employs a sparse-convolution-based backbone, significantly increasing the speed of training and inference. VP-Net treats voxels as discrete points and leverages the intrinsic structural relationships within objects to enhance feature extraction capabilities [6]. BEV images, a kind of pseudo-image feature map, are obtained in PointPillars, accelerating the quantization process and reducing the complexity of the detection model [7]. [PERSON] et al. find object centers for the flattened grid-based features first and boost the local representations [8]. BEV-SAN introduces a Slice Attention module designed to leverage semantic information for enhancing discriminative feature representation across distinct height levels [9]. CluB strengthens the representation at both feature and query levels by adding an auxiliary cluster-based branch [10]. [PERSON] et al. use conditional random field to improve instance-level features [11]. Nonetheless, this type of method has an inherent drawback. It inevitably introduced errors during the quantization process.  
The second paradigm learns the representations from the unprocessed point cloud data [12]. These methods compute the point-wise representations for the small regions via PointNet families and predict the 3D objects with the representations. PointNet++ utilizes a multi-level feature learning architecture, which incorporates farthest point sampling (FPS) for key point selection, ball query for building local regions, and PointNet for extracting features within these regions [13]. Regarding most point-based methods, downsampling is embedded in the backbone to lower the computational complexity. However, these semantic-agnostic methods discard many points in foreground objects. As a pioneering framework in 3D object detection, 3 DSSD introduces a novel integration mechanism that combines Distance FPS (D-FPS) and Feature FPS (F-FPS), thereby improving the feature representation through semantic information incorporation, which significantly boosts detection accuracy [14]. FSD groups the points into instances with the guidance of semantic information to boost feature learning [15].
## 6 Limitations and Future Directions  
All ten studies included in this review indicate that EMS may help improve muscle strength, support post-exercise recovery, and aid injury rehabilitation in soccer players. However, several limitations should be noted. One major issue is the inconsistency in study designs and EMS protocols. Most studies did not use high-quality randomized controlled trials, and few were single-blind, which affects the reliability and generalizability of their findings. In addition, there was considerable variation in the EMS parameters such as intensity, frequency, pulse width, and timing of application. These inconsistencies, along with the lack of standardized outcome measures, make it difficult to compare results and establish clear guidelines for practice.  
Because of this variation in protocols, outcome measures, participant characteristics, and overall study quality, a meta-analysis was not feasible. Many studies also lacked key statistical data like means, standard deviations, or effect sizes, preventing a quantitative synthesis. For these reasons, we chose a descriptive approach to summarize the findings and highlight common trends and methodological concerns.  
Another limitation is that most interventions were short in duration and lacked follow-up. As a result, it remains uncertain whether the benefits of EMS can be sustained over time or lead to long-term functional improvements. While some studies showed gains in measures such as strength or jump height, few examined whether these improvements had any real impact on match performance, limiting the practical value of the findings. Individual factors such as sex and athletic level may also influence how athletes respond to EMS. Differences in muscle fiber types, hormonal profiles, and neuromuscular patterns between males and females, as well as between elite and amateur athletes, could affect training outcomes [51, 52]. Future studies should take these variables into account to better tailor EMS protocols.  
To strengthen future research, studies should aim for higher methodological quality, including well-designed randomized controlled trials with longer follow-up periods. It is also important to include female athletes, use validated tools to measure outcomes, and assess performance in settings that reflect actual competition. These improvements would help make the findings more reliable and relevant for practical use.
## 3 Material and Methods
### Differential Expression Analysis  
Expression levels of uni-genes were calculated based on RPKM (reads per kilobase transcriptome per million mapped reads) and separate sequence read datasets were used as inputs into the DESeq R package ([[http://bioconductor.org/packages/release/bioc/html/DESeq.html](http://bioconductor.org/packages/release/bioc/html/DESeq.html)]([http://bioconductor.org/packages/release/bioc/html/DESeq.html](http://bioconductor.org/packages/release/bioc/html/DESeq.html)))) to analyze the uni-genes expression. Differential expression analysis (fold changes) and related statistical computations of the two conditions were conducted using the EBSeq R package ([[https://bioconductor.org/packages/release/bioc/html/EBSeq.html](https://bioconductor.org/packages/release/bioc/html/EBSeq.html)]([https://bioconductor.org/packages/release/bioc/html/EBSeq.html](https://bioconductor.org/packages/release/bioc/html/EBSeq.html)))). The resulting \(p\)-values were adjusted using Benjamini-Hochberg approach for controlling the false discovery rate (FDR). Genes with an adjusted FDR \(<\) 0.05 and \(|\) log2 (RPKM\({}_{\text{Sample}}\)/PRKM\({}_{\text{Control}}\)) \(|\) \(>\) 1 were classified as differentially expressed. Gene set enrichment analysis was carried out using DEGs by fisher's exact text and adjusted using Benjamini approach to identify functional GO terms and KEGG pathways.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 5 Simulations and Results
### Network Modeling and Simulation Environment  
The following assumptions are made about the network:  
1. Links between nodes are symmetric: For each link \(i\Rightarrow j\), there is a reverse link \(j\Rightarrow i\).
2. The gateway is located at the center of the topology.
3. Sensor nodes are equal in terms of processing power, radio technology, battery, and memory.
4. The gateway node has unlimited energy and higher storage and processing capacity.
5. The CSMA/CA protocol is assumed for medium access control.  
Assumptions 1 and 4 are usual in WSN simulations, and aim to simplify the interpretation of the results. Assumption 2 does not affect the comparison of protocols. We place the gateway at the center of the topology because the distribution of data messages in the network becomes more uniform as the number of nodes close to the gateway increases. Assumption 3 is also usual for WSN simulations, because the gateway commonly has essentially unlimited energy and a higher storage and processing capacity in real scenarios. Assumption 5 refers to the use of IEEE 802.15.4 for medium access control.  
We consider grid network topologies with a random disturbance in the position of nodes. To perform the simulations, we use the software Mathematica [53], along with the SensorSim library [54]. The SensorSim library uses the transmission parameters of the IEEE 802.15.4 [38]. Table 3 shows the Trickle parameters chosen to control the forwarding rate of interest messages, as well as the parameters \(T_{u}\) and \(n\) used to control the forwarding rate of update messages. The value assigned to \(I_{\max}\) describes the number of doublings of the minimum interval size (\(I_{\min}\)).  
Figure 8: The ICENET protocol stack.
## 4 Discussion  
### Resisted Sprint Performance and Mechanical Variables  
The results showed that sprint times and peak speed were not significantly different between RST2M and RST40S when group and trial were included as two independent variables in a two-way ANOVA. This study's analysis went beyond simply examining differences between groups. Despite conducting sophisticated analyses on a trial-by-trial basis, no significant differences were observed. Furthermore, there are no studies that have analyzed horizontal pull-resisted sprints within a repeated sprint protocol, and it is also difficult to find prior research examining changes in resisted sprint performance based on rest intervals. Consequently, this limits the direct comparison of the results of this study with previous research. A recent literature review [18] reveals that the typical repeated sprint training set figuration consists of 6 repetitions of 30 m straight-line sprints with 20 s rests between repetitions. Shorter rest intervals (\(\leq\)20 s) and longer sprint distances (\(\geq\)30 m) substantially increase physiological demands and lead to greater fatigue between sets. In contrast, longer rest intervals (\(\geq\)30 s) and shorter sprint distances (\(\leq\)20 m) enhance immediate sprint performance and reduce physiological strain. Many recent studies on resisted sprints utilize protocols involving 6-8 repetitions of 20 m resisted sprints with rest periods of 2 min or more, as sufficient recovery is essential for the muscular power output required during resisted sprints. Prior research indicates that resisted sprint training with complete rest typically results in higher blood lactate levels, indicative of anaerobic energy exercise intensity, compared to non-resisted sprint training [47; 70; 71]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Fatigue** \\ **Variables** \\ \end{tabular} } & \multirow{2}{*}{**Group**} & \multirow{2}{*}{**M**} & \multirow{2}{*}{**SE**} & \multicolumn{3}{c}{**95\% CI**} & \multicolumn{3}{c}{**Paired \(T\)-Test**} \\ \cline{5-10}  & & & & **LL** & **UL** & **t** & \(p\)** & **ES** \\ \hline \multirow{3}{*}{FI (\%)} & RST2M & 4.61 \(\pm\) 2.20 & 0.568 & & & & & \\ \cline{2-10}  & RST40S & 3.92 \(\pm\) 2.54 & 0.656 & & & & & \\ \hline \multirow{3}{*}{Sdec (\%)} & RST2M & 2.17 \(\pm\) 1.02 & 0.26 & & & & & \\ \cline{2-10}  & RST40S & 1.17 \(\pm\) 1.05 & 0.27 & & & & & \\ \hline \multirow{3}{*}{M = mean; SE = standard error; CI = confidence interval; LL = 95\% confidence interval lower limit; UL = 95\% confidence interval upper limit; H (\%) = fatigue index; Sdec (\%) = percentage of sprint time decline.} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 6: Group differences in FI (%) and Sdec (%).