esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The variability of the reported data concerning the incidence of PTMC is large. A recent study on a large cohort of 1793 patients operated on for benign diseases discovered a 4.62% incidence of incidental PTMC [16]. As the percentage of incidental PTMC in our cohort was higher, our results agree with two other studies that reported that PTMC in histological specimens after thyroid surgery for benign diseases was found in 22% and 27.4% of cases, respectively [15; 17]. Age has long been debated as a prognostic factor for differentiated thyroid carcinoma. The data are still controversial, as there are studies that support age as a factor correlated to an increased risk for lymph node metastases and lymphatic recurrence [18] and other studies that argue that age is not a prognostic factor for lymph node metastases and local recurrence [19]. Almost half of our patients (45.1%) were less than 55 years old, the raised age cutoff considered by the 8 th edition of The American Joint Committee on Cancer AJCC/TNM staging [20]. Even if the previous cutoff age (45 years), used until 2016, were to be used, more than a quarter of our patients (25.9%) would have been under the threshold, young age being a risk factor for disease progression [21]. However, our data cannot support \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Odds Ratio**} & \multicolumn{2}{c}{**95\% Confidence Interval**} & \multirow{2}{*}{\(p\)} \\ \cline{3-3} \cline{5-5}  & & **Lower** & & **Upper** \\ \hline
**Large tumors (\(\geq\) 5 mm)** & 28.25 & 3.33 & 239.52 & 0.000 \\ \hline
**Lymphatic emboli** & 55.28 & 9.39 & 325.18 & 0.000 \\ \hline
**Extracapsular invasion** & 0.09 & 0.05 & 0.16 & 0.000 \\ \hline
**Perineural invasion** & 73.88 & 11.82 & 461.77 & 0.000 \\ \hline
**Vascular invasion** & 0.99 & 0.97 & 1.00 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multivariate analysis—risk factors for positive lymph nodes in the central compartment.
# The Efficiency of Large Hole Boring (MSP) Method in the Reduction of Blast-Induced Vibration
## 4 Real-Scale Test Blasting
### Results of PPV Histories  
Based on the measured blast-induced vibration data from each of the five test blast patterns, the PPVs were extracted for each detonation time. Figure 9a-e illustrates the relationship between the PPV values and the delay time of detonators (DTD) for each of the five test blast patterns. The base case, pattern B, was measured with the maximum PPV of 0.894 and 1.260 cm/s in the cut area and stoping area, respectively. Combined CB with PS and LD was applied from CB-1 to CB-4 patterns, and although low-vibration explosives were used, a relatively high-amplitude vibration in the contour line was generated in the CB-1 to CB-3 patterns. In particular, higher vibrations were measured in the CB-2 pattern despite the use of the single-MSP method, and the vibration of 1.340 cm/s occurred in the PS holes, followed by the maximum vibration of 1.480 cm/s in the stoping holes. The CB-3 pattern greatly reduced the vibration due to the use of single-MSP and reduced charge weight by decreasing the advance. The CB-4 pattern had a significantly lower PPV compared with other methods due to the use of multi-MSP and low-vibration explosives.  
Figure 9: Histories of blast-induced vibration according to detonation delay time for: (**a**) B, (**b**) CB-1, (**c**) CB-2, (**d**) CB-3, and (**e**) CB-4.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 5 Conclusions  
The presented paper describes a novel strategy for the application of neural networks to optimize spectral efficiency in land mobile satellite communications. The simulated channels are described by the Rice-shadowed model, and the DVB-S2X satellite protocol is considered. For channel simulation, a range of expected SNR from 0 dB to 15 dB was observed, with five different values of the \(f_{Dm}\) alongside three levels of shadowing, resulting in a total of 90 different channels. The proposed machine learning algorithms have shown a consistent improvement in the SNR prediction compared to the baseline OI algorithm. For spectral efficiency improvement, the best results were obtained by using NN2, a neural network that is penalized when overestimating predicted values, in order to provide a sufficiently low error rate while still using the best available MODCOD. The improvements are obtained for all single-channel scenarios but are most prominent for \(f_{Dm}\)s of 50 and 75 Hz for single-channel evaluation, regardless of the shadowing level. For dual-channel evaluation, the improvements were most prominent for average shadowing but were also present for all scenarios. The obtained results show promise for future applications of neural networks with specialized loss functions for the optimization of LEO satellite communications, including optimization for user-centric handover procedures.  
**Author Contributions:** Conceptualization, I.V. and S.B.; methodology, I.V., S.B., P.I. and D.D.; software, I.V., P.I. and S.B.; validation, I.V., S.B., P.I. and D.D.; formal analysis, I.V.; investigation, I.V., S.B., P.I., and D.D.; resources, I.V. and S.B.; data curation, I.V.; writing--original draft preparation, I.V. and S.B.; writing--review and editing, P.I. and D.D.; visualization, I.V.; supervision, D.D.; project administration, P.I. and D.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Authors acknowledge the support of the Science Fund of the Republic of Serbia, grant No 7750284 (Hybrid Integrated Satellite and Terrestrial Access Network--hi-STAR). This work was also supported by the Ministry of Science, Technological Development, and Innovation of the Republic of Serbia under contract numbers: 451-03-65/2024-03/200103 and 451-03-66/2024-03/200223.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Tannera Technologies LLC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Material and Methods
### Implementation Details  
Before the model training stage, a channel selection is carried out to reduce the training computational cost. For the MI dataset, channels are selected based on the evidence that body movement triggers neural activity in the opposite brain hemisphere within the sensorimotor area. Regarding the DEAP dataset, channels related to brain regions more likely to participate in affective states are considered. Figure 2 depicts the subset of selected channels for both EEG datasets. Regarding the DMOSM-GP free parameter, the rank of decomposition is chosen from a grid search within the range \(Q\in\{1,\ldots,10\}\) to minimize the mean absolute error (MAE) of the model prediction against the original EEG data. The GPflow framework is employed for the model definition [29], and the kernel function is optimized via the minimization of NLL cost function using the autograd library. Finally, for the statistical significance assessment of the classification performance, an 10-fold cross-validation scheme was applied.  
Figure 1: Proposed framework for discriminative multispectral mixture kernel (MOSM).  
Figure 2: Positioning scheme of electrodes for acquisition in both datasets. Green boxes highlight the selected channels for testing the proposed methodology.
# A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method
## 4 Complex Prediction Method with Backward Adaptation
### Alcm+
#### 4.1.1 ALCM Rapid Adaptation Method  
The adaptive prediction method Activity Level Classification Model (ALCM) [27] was developed in the 1990s for image coding purposes. It is characterised by a lower, though similar, computational complexity compared to the classic LMS solution. Originally, the method operated on linear prediction models of the fifth or sixth order, with the set of all coefficients in each step being adapted (by the constant \(\mu=1/256\) at 8-bit samples) only two (up to six in the ALCM+ version proposed in this paper) properly selected.  
Despite its simplicity, this method gives relatively good results also with regard to audio coding. The method is applied with respect to the interchannel stereo mode, and therefore two prediction orders \([r_{L},r_{R}]\) correspond to the data from the currently coded and opposite channels, and the total prediction order of the ALCM+ method equals \(r=r_{L}+r_{R}\). Generalising some formulas, we can introduce the designation \(Ch\) (channel) in place of \(L\) (left) and \(R\) (right).  
To increase the efficiency of the method, the initial DPCM block is used, the DPCM block returns an \(e_{Ch}^{\text{DPCM}}(n)\) error at the time \(n\), which is buffered as a set of errors from previous \(i\)-th moments marked as \(g(i)\). Therefore the ALCM+ input stream consists of elements that are the difference of two consecutively coded samples \(g(i)=x(n-i)-x(n-i-1)\). Assuming that the left channel sample are encoded before right channel sample, the ALCM+ encoder input vectors at time \(n\) have the following form; for the left channel, \(\mathbf{g}_{i}(n)=[g_{L}(1),g_{L}(2),\ldots,g_{L}(r)]^{\text{T}}=[x_{L}(n-1)- x_{L}(n-2),x_{L}(n-2)-x_{L}(n-3),\)\(\ldots,\)\(x_{L}(\underline{n}-r_{L})-x_{L}(n-r_{L}-1),\)\(x_{R}(n-1)-x_{R}(n-2)-x_{R}(n-3),\)\(\ldots,\)\(x_{R}(n-r_{R})-x_{R}(n-1)\)\(]^{\text{T}}\), while for the right channel \(\mathbf{g}_{R}(n)=[g_{R}(1),g_{R}(2),\ldots,g_{R}(r)]^{\text{T}}=[x_{R}(n-1)-x_{R}(n-2),\)\(x_{R}(n-2)-x_{R}(n-3),\)\(\ldots,\)\(x_{R}(n-r_{L})-x_{R}(n-1),\)\(x_{L}(n)-x_{L}(n-1),\)\(x_{L}(n-1)-x_{L}(n-2),\ldots,\)\(x_{L}(n-r_{R}+1)-x_{L}(n-r_{R})\)\(]^{\text{T}}\).  
Due to the specificity of the vector \(\mathbf{g}_{Ch}(n)\), the predicted values are determined as  
\[\hat{x}_{Ch}(n)=x_{Ch}(n-1)+\sum_{i=1}^{r}w_{Ch}(i)\cdot g_{Ch}(i). \tag{6}\]
## 2 Materials and Methods
### Methods  
For the purpose of the study, an appropriate training cycle was constructed, which included testing on 2 days, planned in a specific order. The research started with the evaluation of body mass, body height, and body composition using bioelectric impedance. The measurements of body height were performed with a stationmeter (Seca 284, Seca Instruments Ltd., Hamburg, Germany) with a precision of 0.1 cm. Body composition was evaluated using the electrical impedance technique (Inbody 720, Biospace Co., Tokyo, Japan). Each testing day was preceded by a typical pre-training standardized 20-25 min warm-up, which consisted of jogging, dynamic stretching, skipping, backward and lateral movements, as well as several accelerations over 10 m. Depending on the test, a short specific warm-up was conducted, activating the muscles used in a particular movement. The linear sprint tests at 5 m and 20 m were performed first after a day of rest, followed by the MAT test. The following day, all strength and power evaluations were performed, including the CMJ, DJ, the leg press exercise, and the abductor and adductor lower limb strength measurements. The 5 and 20 m sprint tests and the MAT test were conducted on a wooden basketball court, while all other evaluations were performed in the Strength and Power Laboratory of the Institute of Sport Sciences, The Academy of Physical Education in Katowice, Poland. The study protocol was approved by the Bioethics Committee for Scientific Research, at the Academy of Physical Education in Katowice, Poland (3/2021), according to the ethical standards of the Declaration of Helsinki, 1983.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
###### Abstract  
For the Taiji program or other LISA-like space-based gravitational wave (GW) detection missions, establishing laser links is a prerequisite for entering the normal science mode. There has been a lack of in-depth research on inter-satellite link acquisition under weak light and low-speed conditions. In this paper, we comprehensively analyze the impact of key parameters, including scan speed, track width, acquisition camera integration time, and jitter, on the acquisition process. By introducing laser spot location error under weak light conditions, we derive an analytical expression for the acquisition failure probability. Focusing on variations in scan speed and track width and carefully selecting the appropriate acquisition camera integration time, we then simulate the actual acquisition process to closely replicate real conditions. Analytical results of the acquisition failure probability align closely with the simulation results. Under the Taiji program's parameter settings, the scan speed is set to 3.31 \(\upmu\)rad/s, the track width to 0.87 \(\upmu\)rad, and the integration time to 800 ms. These parameters are optimized to minimize the mean acquisition time over multiple scans, resulting in a single link acquisition time of 223.77 s. The analytical model can be used for the parameter design of inter-satellite laser link acquisition under weak light conditions.  
inter-satellite laser link acquisition; space-based gravitational wave detection; acquisition failure probability; simulation 2020
# Design and Implementation of a Tether-Powered Hexacopter for Long Endurance Missions
## 3 Experimental Results  
Two types of tests, namely a functional test and an endurance test, have been executed. Parameters of each test are listed in Table 4. In the functional test, the hexacopter will reach the altitude of 15 m and fly for 30 min. The objective of the functional test is to confirm the whole functionality of the system, rationale of test procedures and gather flight data to verified design parameters. The goal of the endurance test is to prove that the system is capable of \"near infinite\" air time. The hexacopter will reach the altitude of 50 m and fly for 240 min (4 h) or more to prove such a feature. All experiments were performed outdoors on the campus lawn.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Parameter** & **Functional Test** & **Endurance Test** \\ \hline Target endurance & 30 min & 240 min \\ Target altitude & 15 m & 50 m \\ Tether length & 26 m & 61 m \\ Ground supply voltage & fixed 90 V & 90 V or higher \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test parameters.  
Figure 5: Theoretical maximum altitude and the corresponding power needed and received by the hexacopter for different tether sizes. With the 1723 W power supply, selecting 12 AWG achieves 103 m altitude; with the 438 W power supply, selecting 20 AWG achieves 69 m altitude.
# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 3 Green Underwater Sensor Network Framework using Adapted Whale and Wolf Optimization
### Underwater Relay Node Optimization Model
#### 3.1.1 Adapted Whale Optimization for Underwater Networks  
The whale optimization algorithm (WOA) is influenced by the natural characteristics of the underwater movement of whales [30]. The technical optimization steps involved in WOA are given in the following description, which can be divided into two major phases, namely exploitation and exploration phases. In the exploitation phase, the encircling prey and spiral position updating are performed, and searching for prey is implemented in the exploration phase. The mathematical modeling of these operations is carried out as follows:  
* Encircling prey  
Traditionally, whales encircle the prey once they discover its location or position. Similarly, the position of the optimal next-hop sensor nodes is identified in the underwater network environments. In the proposed algorithm, the present leading candidate solutions, i.e., the positions of the direct neighbor nodes, are the target prey near the optimal solution. Subsequently, the other search agents attempt to change their position towards the best search agents. The encircling prey can be mathematically represented as given in Equation (4):  
\[\overset{\rightarrow}{W}=|\overset{\rightarrow}{G}\cdot\overset{\rightarrow }{M}(k)-\overset{\rightarrow}{M}(k)| \tag{4}\]  
\[\overset{\rightarrow}{M}(k+1)=\overset{\rightarrow}{M}^{*}(k)-\overset{ \rightarrow}{X}\cdot\overset{\rightarrow}{W} \tag{5}\]  
where '\(K\)' represents a current iteration, \(\overset{\rightarrow}{X}\), \(\overset{\rightarrow}{G}\) represent coefficient vectors, \(\overset{\rightarrow}{M}^{*}(k)\) demonstrate the previous best solution or position of the previous best node, \(\overset{\rightarrow}{M}(k+1)\) depicts the current best state or the position of the current best node for next-hop forwarding in the neighborhood. Furthermore, the coefficient vectors \(\overset{\rightarrow}{X}\), \(\overset{\rightarrow}{G}\) can be calculated as expressed in Equations (6) and (7):  
\[\overset{\rightarrow}{X}=2\overset{\rightarrow}{x}\cdot\overset{ \rightarrow}{J}-\overset{\rightarrow}{x} \tag{6}\]  
\[\overset{\rightarrow}{G}=2\cdot\overset{\rightarrow}{J} \tag{7}\]  
where \(\overset{\rightarrow}{x}\) reduces from 2 to 0, \(\overset{\rightarrow}{J}\in[0,1]\) also results in the reduced range of \(\overset{\rightarrow}{X}\). The new position of the current best forwarding node can be determined anywhere between the previous best position of the node and the encircling preposition.  
* Exploitation phase  
There are two mechanisms presented to carry out the exploitation phase in the whale-based search space discovery:
## 4 Discussion
The commercial software used for our study, Geomagic control X, processes the best-fit alignment based on the ICP algorithm. A standard best-fit alignment minimizes the mesh distance errors for all vertices of a dataset [34]. However, if there is a large defect or difference (i.e., on the occlusal surface of a treated tooth in the case of our study) between the datasets, this can cause an erroneous result during the metrological alignment process [34]. To avoid this, a reference best-fit alignment has been proposed in recent research [61]. In a reference best-fit alignment, the alignment process of the dataset is restricted to operator-identified areas of the dataset, where the least change is expected [62]. In our study, the occlusal surface of the treated tooth was excluded from the field of interest for an arrangement based on a reference best-fit alignment. Furthermore, a recent study suggested that volumetric analysis based on custom ICP algorithms has high analysis reliability in addition to linear analysis using commercial software [63]. When calculating the deviation from the reference data, the accuracy of the IOS and control of the milling process and sintering shrinkage can influence the accuracy of the deviation. The tolerance range of 100 \(\upmu\)m was determined in consideration of these factors [35, 36, 37, 38, 39]. In previous studies, the precision of the quadrant scan using Trios was about 50 \(\upmu\)m [37, 38, 39]. The accuracy of the manufacturing and post-processing of a milled single zirconia crown was about 50 \(\upmu\)m [35, 36]. Because the STL files from CAD were used in the PSM and NA groups, negative deviation occurred in the occlusal groove, but the effect of this systematic error might have been minimized in the rest of the area. However, the relatively broad range of tolerance levels was a limitation of this study, in addition to the heterogeneity of the occlusal surface data between groups. This study was limited to the comparison of occlusal error in a single posterior crown restoration. Future clinical research should investigate the positive outcome of PSM using different positions and spans of restoration, such as single anterior restoration or a long-span fixed partial denture. In addition, a randomized controlled clinical trial is further required to reduce the dataset heterogeneity between the experimental groups and to enhance the evidence level.
## 3 Materials and Methods
### Combining Adaptive Ensemble Attention with GAN
The model integrates these mechanisms adaptively, allowing it to dynamically assess the relative significance of each attention type according to the input image and scene complexity. Unlike prior attention-based GANs such as [PERSON] et al. [10], which focus on binary road segmentation, the proposed method targets more complex multi-class segmentation in highly variable street environments. Furthermore, compared to static attention modules in [31] and classification-oriented ensemble attention in [30]. The proposed adaptive ensemble attention module dynamically recalibrates spatial and channel-wise features during both generation and discrimination stages, enabling context-aware feature refinement across different scene types. Figure 1: Architecture of the proposed AEA-GAN model. The proposed generator in the AEA-GAN is designed as a hybrid architecture that combines MobileNetV3 and EfficientNetB7 to balance computational efficiency and feature richness. MobileNetV3, known for its lightweight and fast inference capabilities, is used to extract low-level and intermediate features, making it suitable for real-time applications such as autonomous driving. In contrast, EfficientNetB7, with its deeper and more expressive layers, contributes high-resolution and semantically rich features. To effectively merge the strengths of both backbones, the architecture employs an AEA module that integrates self-attention, spatial attention, and channel attention mechanisms. Self-attention captures global contextual dependencies, spatial attention highlights critical areas such as pedestrians and vehicles, and channel attention prioritizes the most informative feature maps.