esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 6 Evaluation Results
### Comparison with State-of-the-Art  
Several prior studies investigated player salary prediction using traditional ML technologies, often relying on basic features like goals, assists, and appearances. As shown in Table 12, these models generally lack advanced performance metrics or contextual feature engineering, which limits their predictive power.  
Figure 3: Comparison of salary discrepancies by demographic groups. (**a**) Analysis by age groups; (**b**) analysis by position groups.  
For example, [PERSON] et al. [47] reported a moderate \(R^{2}\) of 0.71 using standard performance indicators, though their error metrics such as RMSE, were not provided. [PERSON] and [PERSON] [10] included an error metric, reporting an \(R^{2}\) of 0.90, of approximately \(3.22\times 10^{6}\), but did not incorporate predicted performance metrics such as expected goals. In contrast, the proposed model integrates refined predicted metrics like \(xGg\) and \(aGg\), along with league and position adjustments, which enhances its predictive capability. It achieves a substantially higher \(R^{2}\) of 0.91, representing a significant improvement in explained variance over previous works, and reduces prediction errors with an MAE of approximately \(9.28\times 10^{5}\). Additionally, our dataset and models differ from others, outperforming all existing metrics. This comparison highlights the benefits of integrating domain-specific features with advanced ML techniques, leading to more precise and dependable salary predictions in soccer.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 5 Simulation Results
### Positioning Accuracy  
For performance comparison, TDOA-only, AoA-only and joint TDOA + AoA are tested. As depicted in Figure 9, the performance of the joint positioning method outperforms that of TDOA only and AoA only under lower SNR (smaller than 22 dB) and the performance gap is around 17%, while the performance gap diminishes gradually with the increase in SNR (larger than 22 dB). It is reasonable that sequence detection is better in case of good radio link environment. In this figure, it can be found that TDOA only and AoA only have similar positioning accuracy. However, it should be noted that the measurement load is larger under TDOA only, since at least three TRPs are required to localize a target device exactly. Table 4 shows the running time required by TDOA only, AoA only and Joint TDOA + AoA to complete a UE positioning.  
Figure 8: Illustration of timing estimation error with SRS-Pos configuration \(\left\{K_{\text{TC}}^{\text{SRS}},N_{\text{symb}}^{\text{SRS}},N_{\text{PRB}}^ {\text{SRS}}\right\}\).  
Figure 9: Illustration of positioning accuracy vs. SNR.  
Moreover, the performance impact of TRP number on the positioning accuracy is illustrated in Figure 10 with a working SNR as 22 dB. There is no doubt that better positioning accuracy can be obtained under the joint positioning method, and the accuracy can be improved by around 13%. With the increase in TRP numbers, the performance gap decreases. While the TRP number is larger than seven, there is basically no difference between the three methods.  
One thing to declare is the accuracy is obtained by a 1% error estimation of delay and angle, i.e., working SNR, is 22 dB, as shown in Table 3. It can be found that the accuracy is limited to around 60 cm. The main reason is the system setting of 120 KHz and frequency range restricts the positioning accuracy, as shown in (8). Until now, the testing or simulation for 5G positioning has been quite different, and this paper compares the performance with results in [15] wherein the localization is based on time estimation measurement. It could be found that the joint positioning method can improve the localization accuracy to submeter around 27 GHz. However, it should be also noted that the performance enhancement is at the cost of increased computation complexity. According to (15) with \(N_{\theta}=360\) and \(N_{\theta}=180\) corresponding to a \(1^{\circ}\) angle searching granularity, and system parameters in Table 3, the processing complexity under the joint positioning is around 3.2\(\times\) and 1.4\(\times\) of that under the TDOA only and AoA only. According to the positioning accuracy specification in [1,10], the requirement is down to 10-20 cm for indoor positioning. Therefore, we think the complexity is worth the increased accuracy.
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 4 Method
### Pixel Label Extraction for Binary Documents  
Text line extraction defines text lines precisely via pixel labels. An energy function [28] is used with connected components to extract the text lines from binary document images. Minimizing the energy function assigns the components the label of the closest blob lines, while trying to assign close together components the same label. This framework is free of an orientation assumption and can be used for text lines with disjoint strokes and close interline proximity.  
Let us denote the set of blob lines by \(\mathcal{L}\) and the set of connected components in the document image by \(\mathcal{C}\). Minimizing the energy function, find the labelling function \(f:\mathcal{C}\rightarrow\mathcal{L}\), assigning each component \(c\in\mathcal{C}\) to label \(\ell_{c}\in\mathcal{L}\). The energy function \(E(f)\) is expressed as follows:  
\[E(f)=\sum_{c\in\mathcal{C}}D(c,\ell_{c})+\sum_{\{c,c^{\prime}\}\in\mathcal{N}}d (c,c^{\prime})\cdot\mathbb{1}(\ell_{c}\
eq\ell_{c^{\prime}}) \tag{2}\]  
Figure 4: Illustration of thresholding on a synthetic component. (**a**) A Component is composed of three grey-levels with ascending values \(t_{1},t_{2},t_{3}\). (**b–d**) Child components from thresholding the parent component with \(T\geq t_{1},t_{2},t_{3}\), respectively, and their approximate splines. (**e**) Illustrates the breadth-first traversal of the component tree.  
where \(D\) denotes the data cost, which is the cost of assigning the label \(l_{c}\) to the component \(c\); and \(d\) denotes the smoothness cost. We define \(D(c,\ell_{c})\) as the Euclidean distance between the centroid of the component \(c\) to the nearest blob line \(\ell_{c}\). The smoothness cost \(d(c,c^{\prime})\) is the cost of assigning neighbouring components \(c\) and \(c^{\prime}\) different labels. Let \(\mathcal{N}\) be the set of the nearest component pairs. The smoothness cost is set as follows: For all neighbouring components \(\{c,c^{\prime}\}\in\mathcal{N}\)  
\[dist(c,c^{\prime})=e^{-\alpha\cdot dist_{c}(c,c^{\prime})} \tag{3}\]  
where \(dist_{c}(c,c^{\prime})\) denotes the Euclidean distance between the components \(c\) and \(c^{\prime}\) from their centroids. \(\alpha\) is defined as follows:  
\[\alpha=\frac{1}{2\times\langle dist_{c}(c,c^{\prime})\rangle} \tag{4}\]  
where \(\langle\cdot\rangle\) denotes expectation over all pairs of neighbouring components [29] in a document image.
# Development and Application of Intelligent Temperature Control System for Large Aqueduct
## 3 Engineering Application of Intelligent Temperature Control System for Large Aqueduct
### Simulation Calculation Module  
One span of the Dongjia Village Aqueduct was selected as the calculation model. The aqueduct and water pipe model was established by the heat flow coupling method, as shown in Figure 3. The material parameters are shown in Table 3.  
The formula of the outside air temperature \(T_{q}\) during the construction period of the aqueduct is shown in Equation (1), the cooling water temperature is taken as the average temperature \(T_{j}\) in the month of construction and the pouring temperature of concrete is set to 25 \({}^{\circ}\)C. The simulated calculation time is 20 days, and the calculation step is 3 h. The  
Figure 3: Layout simulation diagram of cooling water pipes poured by stages in an aqueduct. (**a**) Overall model of aqueduct (**b**) The first stage of concrete pouring water pipe arrangement (**c**) The second stage of concrete pouring water pipe arrangement (**d**) water pipe simulation element.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & **Specific Heat Capacity** & **Thermal Conductivity** \\ \cline{2-4}  & **(kg/m\({}^{3}\))** & **(kJ/kg \({}^{\circ}\)C)** & **(kJ/mh \({}^{\circ}\)C)** \\ \hline Concrete & 2462 & 0.958 & 9.32 \\ Cooling water pipe & 1350 & 1.4 & 1.66 \\ Water & 1000 & 4.2 & 2.12 \\ Plastic foam & — & — & 0.1256 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters.  
water is opened immediately after pouring, and the water opening time is 8 days. The adiabatic boundary was set to the bottom surface, the equivalent exothermic coefficient \(\beta_{s}\) of the slot body and the air is calculated by Formulas (2) and (3) [12].  
\[T_{q}=T_{j}+5\times\cos\left(\frac{\pi\times i}{4}\right) \tag{1}\]  
\[\beta=18.46+17.36\
u_{a}^{0.883} \tag{2}\]  
\[\beta_{s}=\frac{1}{(1/\beta)+(h/\lambda)} \tag{3}\]  
where \(i\) is the number of calculation step, \(\beta\) is the exothermic coefficient of solid surface in air, \(\
u_{a}\) is the average wind speed at the construction site, \(h\) is the thickness of insulation material and \(\lambda\) is the thermal conductivity of insulation material.
# RUL Prediction of Rolling Bearings Based on a DCAE and CNN
## 5 Conclusions  
This paper primarily studied the prediction method of the RUL of a rolling bearing based on deep learning. First, as a modeling method based on the vibration signal equipment performance degradation condition, a DCAE was utilized to analyze the data feature extraction along with an SOM network for high-dimensional feature dimension reduction. The method accurately modeled the degradation state of the roller bearings. The entire modeling process was conducted in an unsupervised manner, which reduced manual dependence. Subsequently, a 1D-CNN-based RUL prediction model was proposed. The original data were directly input into the model training, and the output was the predicted value of the RUL, which accurately predicted the RUL of the bearing and realized end-to-end prediction. The best evaluation index RMSE reached 0.054. The average score of the seven bearings was 0.677.  
In this paper, two main parts of the RUL prediction scheme were improved by one-dimensional convolution operation. Experimental verification was carried out through the PHM2012 dataset. The results show that one-dimensional convolution can effectively extract the features of time series vibration data, and the two models proposed in this paper have good robustness and generalization abilities.  
The method of adding labels put forward in this paper greatly enhances the prediction effect of the model, no longer relies on artificial labeling and reduces the labeling error rate. Using the HI value as the label of the data greatly improves the quality of the training data. The overall scheme proposed in this paper reduces the dependence on manual and complex signal processing algorithms, and the method has good stability and versatility.  
Formal analysis, [PERSON]; funding acquisition, [PERSON]; investigation, [PERSON]; methodology, [PERSON]; project administration, [PERSON]; resources, C.W.; software, [PERSON] and X.Y.; validation, [PERSON] and X.Y.; writing--original draft, [PERSON]; writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant No.51875498, 51475405), and the Key Project of Natural Science Foundation of Hebei Province, China (Grant No. E2018203339, F2020203058).  
Not applicable.  
Figure 16: Life prediction effect of four bearings under working condition 2: (**a**) Bearing2_1, (**b**) Bearing2_2, (**c**) Bearing2_3 and (**d**) Bearing2_4.  
Data Availability Statement: The data used to support the findings of this study are available from the corresponding author upon reasonable request.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 51875498 and 51475405) and a key project of the Natural Science Foundation of Hebei Province, China (Grant Nos. E2018203339 and F2020203058). The support is gratefully acknowledged. The authors would also like to thank the reviewers for their valuable suggestions and comments.  
The authors declare no conflict of interest.
# Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing
## 6 Conclusions and Discussion  
In this paper, we identify a new emerging communication tower placement problem and formulate the continuous communication tower placement problem as a continuous function optimization problem. We also propose a hybrid SA algorithm for the new communication tower placement problem. The hybrid SA algorithm employs the powerful exploration capability of SA to explore the continuous search space and enhances its local exploitation capability by incorporating an effective local optimization procedure. In addition, in this paper, we demonstrate through experiments the effectiveness of the hybridization, the performance, and the scalability of the hybrid SA.  
In this paper, we focus on the core of the new communication tower placement. The hybrid SA algorithm can be extended to handle pre-deployed communication towers by simply removing users that have already been covered by those pre-deployed communica  
Figure 8: The computation time with respect to the number of wireless communication towers.  
Figure 7: The computation time with respect to the number of users.  
tion towers before applying the hybrid SA algorithm to solve the wireless communication tower placement problem. It can also be extended to consider the situation in which there exist sites that are not suitable for placing a communication tower, such as ponds, by not accepting any new neighbor (new solution) in which there exists a communication tower that is located at a place where a communication tower cannot be deployed.  
Finally, the local optimization algorithm is not specific to this hybrid SA. It can be used in other continuous space communication tower placement algorithms to further improve the quality of their solutions.  
Individual contributions: conceptualization, M.T. and W.L.; design of experiments, M.T. and W.L.; implementation of experiments, M.T.; analysis of experimental results, M.T.; original draft preparation, M.T.; writing--review and editing, M.T. and W.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
## 4 Neutrophils and Blood Transcriptome Signature in Soriasis
The study, involving two separate human psoriasis cohorts, showed increased levels of PCSK9 in psoriatic patients compared to age-, sex-, and cholesterol-matched controls [82]. The PCSK9 levels correlated with impaired endothelial vascular health and coronary artery calcium score, suggesting an association between circulating PCSK9 and early, as well as advanced, stages of atherosclerosis in psoriasis. [PERSON] et al. demonstrated significantly elevated levels of serum concentrations of PCSK9 in patients with mild to moderate psoriasis, suggesting impaired lipid metabolism in psoriasis [83]. Serum PCSK9 levels had a strong correlation to BMI and triglyceride levels,indicating that PCSK9 could be a novel marker for psoriasis and associated cardiovascular risks in psoriatic patients. The authors also showed that three months of monotherapy with methotrexate greatly reduced the levels of PCSK9 in the blood, indicating that methotrexate could be a treatment choice in such patients. Differentially expressed circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and mRNAs were reported in a whole blood analysis from patients with blood heat psoriasis [84]. Using a high-through microarray, it was revealed that a total of 205 circRNAs, 393 lncRNAs, and 157 mRNAs were differentially expressed, and these were associated with lipid metabolism, autoimmune pathways, and signal transductions. In an Affymetrix HG-U95A microarray study that compared nine sets of previously reported microarray data, 20 skin- and two blood-associated hot spots were identified, with 34.5% of genes overlapping from these multiple studies [85]. Most of the blood-related DEGs were associated with lipid and fatty acid metabolism, immune function, and proteolysis [85]. Very recently, the role of gut microbiome and host microbe associations in psoriasis was investigated by [PERSON] et al. in a cohort of 26 psoriasis patients and 16 healthy controls. Diversity in gut microbiome was reported in psoriasis patients, and this gut flora was previously reported to be associated with several other autoimmune diseases [86]. Additionally, blood transcriptome analysis revealed higher numbers of activated CD4+ effector T cells and CD8+ T cells in psoriasis blood compared to healthy controls [86]. However, it should be noted that blood transcriptomic profiling has its inherent limitations. While systemic inflammation and interferon responses can be measured in whole blood, an immune response might not be possible, because such changes are mostly observed in affected skin.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation  
This section addresses general design aspects and gives details of the software implemented in the system, including the blocks of the GNU Radio flowgraphs.
## 4 Conclusions and Future Work  
Accurate detection and segmentation of a single citrus crown during its seedling, flourishing, and fruiting stages in a complex environment are crucial for an orchard spraying robot to achieve variable spraying. Therefore, in this paper, a citrus tree crown RGB-D segmentation method and an improved Mask R-CNN were proposed. The MSEU R-CNN is a Mask R-CNN with fused SE blocks and ResNet-15 that replaces the original mask branch with the UNet++. The main conclusions are as follows:  
1. The detection accuracy of the MSEU R-CNN RGB-D tree-crown image was higher than that of RGB, indicating that the depth image can effectively reduce the interference of complex backgrounds and non-targeted tree crowns.
2. The MSEU R-CNN's segmentation results at different stages showed that the detection and segmentation accuracies of tree crowns at the flourishing stage were the highest, whereas those at the seedling and fruit-bearing stages were lower. However, the average bbox and seg AP50 measures were more than 95%, indicating that the overall performance was excellent with strong generalizability.
3. Compared with the original Mask R-CNN model, the proposed model effectively improves the recognition and segmentation accuracies of a single citrus crown under the condition of a small average running time change, and the segmentation quality of the crown mask is more precise, which helps accurately evaluate crown parameters. Compared with other models, the experimental results show that the segmentation performance of the proposed model is obviously better than that of BoxInst and CondInst models.  
In this paper, the citrus tree crown was segmented effectively, but the variety of fruit trees was narrow. Additionally, the real-time performance of the network model should be further improved. We plan to collect images of different fruit tree varieties and expand the canopy dataset to multiple conditions to further simplify the network structure and improve real-time performance.  
Conceptualization, P.C. and J.Z.; methodology, P.C. and J.Z.; Writing--review and editing, P.C.; writing--original draft preparation, P.C. and J.Z.; data curation, S.L. and K.L.; validation, K.L. and H.F.; formal analysis, S.L. and H.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Central Leading Local Science and Technology Development Special Fund Project, grant number ZY19183003; Guangxi Key Research and Development Project, grant number AB20058001.  
Not applicable.  
We thank all of the founders and all of the reviewers.  
The authors declare no conflict of interest.
# A Perfect Decomposition Model for Analyzing Transportation Energy Consumption in China
## 3 Effective Verification and Case Study  
### The Effective Verification of the Perfect Decomposition Model  
To examine the effectiveness of the proposed decomposition model, transportation energy consumption data based on the transportation sectors in China are decomposed from 1985 to 2012. To illustrate the remaining items and to omit for index, we use period wise decomposition. The change is only analyzed by the validation between the two base years, i.e., setting two different time intervals, 10 years (1985-1995) and 27 years (1985-2012). The results are presented in Table 1.  
It can be seen from Table 1 that the total contribution of the perfect model and the complete decomposition model is identical (\(\Delta E=\Delta E_{D}+\Delta E_{S}+\Delta E_{I}\)), because the residual items are completely decomposed in both models. However, the Laspeyres model neglects the residual, therefore the results \(\Delta E\) is different from the sum of influence factors change \(\Delta E_{D}+\Delta E_{S}+\Delta E_{I}\). Compared with Laspeyres model and the complete decomposition model, the results of the proposed perfect decomposition model are more accurate and the method is more appropriate.  
For the influence of each factor, compared with Laspeyres, the errors of three influence factors (transportation turnover volume, transportation structure and transportation energy consumption intensity) are 10.69%, 33.08% and 94.91%, respectively, from 1985 to 1995. When the analysis period is longer (from 1985 to 2012), the errors are larger, i.e., \(-34.29\%\), 259.77% and 517.42%, respectively. It can be seen that the errors caused by the negative factors are very obvious, and the perfect decomposition model takes into account the effects of the different factors, when dealing with the remaining items. The weight is more consistent with the actual situation, and the perfect decomposition model is necessary.  
Compared to the complete decomposition model, the errors of three influence factors are 16.91%, 0.79% and 36.74% during the period from 1985 to 1995. When the analysis period is enlarged, the error will be greater, which are 27.60%, \(-17.66\%\) and 126.09%, respectively, during the period from 1985 to 2012. The longer the analysis time is, the larger the error percentage is, because the residual processing, are even greater than the total change of a single factor. Therefore, the Lapsers index and its model of residual term ignore will produce a larger error. The changing directions of the factor are considered in the perfect decomposition model when dealing with the remaining items; therefore, decomposition results are more reasonable.