esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The following conclusions can be drawn from the study:  
1. The results of the microscopic experiment showed that the APD and PSC of Luo'he formation sandstone were larger than those of Zhi'luo formation sandstone, but the APD and PSC of Zhi'luo and Yan'an formation sandstone do not differ considerably. The pore connectivity of Luo'he formation sandstone was much better than that of Zhi'luo and Yan'an formation sandstone. The Luo'he formation is the main aquifer in this region.
2. The results of the macroscopic experiment revealed that the average porosity of Luo'he formation sandstone was 7.39% higher than that of Zhi'luo formation sandstone and 13.41% higher than that of Yan'an formation sandstone. The Luo'he and Zhi'luo formations exhibit weak compaction, develop pores and have strong water-bearing capacity. The porosity of the Yan'an formation is low, and its water-bearing capacity is weak. In addition, the NWA difference between sandstone in the Zhi'luo and Yan'an formations was 0.62%, and the difference between sandstone in the Luo'he and Zhi'luo formations was 2.31%. The water-carrying capacity of the sandstone in Luo'he and Zhi'luo formations was considerably higher than that of the sandstone in the Yan'an formation.
3. An objective weighting model based on the similarity between indexes was proposed. This model was used to analyze pore structure parameters of various formations in the Guojiahe coal mine. The calculation model of water-bearing capacity of various formations was established. According to this model, the water-carrying capacity of 3\({}^{\#}\) coal roof in the study area was analyzed. The results of previous mining studies indicated that there is a large-scale area with strong water inrush risk in the east of the study area, which is prone to water inrush if normal mining continues in the future. However, according to the model analysis in this paper, it was found that the actual water capacity in this area is medium, and there is no need to perform large-scale pumping, drilling or geophysical exploration before mining. The research results can save money for coal mining, which is beneficial for protecting the ecological environment and groundwater resources.  
Conceptualization, L.S. and T.L.; methodology, L.S.; validation, L.S., X.Z. and D.X.; formal analysis, T.L.; data curation, L.S.; writing--original draft preparation, L.S.; writing--review and editing, W.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation Program under Grant No. 42002282, the National Natural Science Foundation of China under Grant No. 41807283, the National Natural Science Foundation of China under Grant No. 51804184, the Natural Science Foundation of Shandong Province under Grant No. ZR2020K6023, Open Fund of State Key Laboratory of Coal Resources and Safe Mining under Grant No. SKLCRSM19 KFA10, and Science and Technology Research Directive Plans of China National Coal Association under Grant No. MTKJ2018-263.
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 4 Experimental Results
### Segmentation Results of Real Images  
In this subsection, we continue testing our algorithms, this time using real images. The SPFLIF-IS method is compared with the same five methods (C-V, LBF, SBGFRLS, LIF, and LSACM). The original real images and the segmentation results of the six models are shown in Figure 6, where the first and second images in Figure 6a can be found in the literature [27; 31], and the third and fourth images shown in Figure 6a are selected from Berkeley segmentation data set 500 (BSDS500) (The code is available at [50]). The first image in the third and fourth columns shows that the LBF and SBGFRLS models fail to segment the image; the results are shown in Figure 6c,d. The first image in the fifth and sixth columns shows that most of the boundaries are obtained by the LIF and LSACM models. However, the internal details are not recognized; the results are illustrated in Figure 6e,f. The LBF, LIF, and LSACM models fail to segment the second and third images, as shown in Figure 6c,e,f. The object boundaries of the fourth images are not accurately extracted by the C-V, LBF, SBGFRLS, LIF, and LSACM models, as shown in the fourth rows of Figure 6. As shown in Figure 6g, the object boundaries are accurately extracted by our proposed model. Table 5 objectively offers the segmentation results of Figure 6. Table 5 indicates that the SPFLIF-IS achieves the best results; the C-V exhibits slightly better results than those obtained by the LBF, SBGFRLS, LIF, and LSACM; and the LBF performs as poorly as the LSACM. The segmentation results demonstrate that our SPFLIF-IS model can efficiently analyze real images and yield great segmentation results.
## 2 Materials and Methods
### Design of ADRC and ST-SMC Hybrid Control
#### 2.3.2 ST-SMC Theory  
The inherent chattering phenomenon in sliding mode control remains a persistent challenge that critically constrains its real-world deployment, driving sustained academic investigation to address this fundamental limitation. [PERSON] introduced the concept of the sliding order, which is defined as the degree to which the sliding mode variable ss has continuous derivatives of zero on the sliding surface. In first-order sliding mode control, the first derivative of the sliding mode variable exhibits discontinuity, resulting in a sliding order of 1. To reduce chattering, higher-order sliding mode control hides the discontinuous terms within the higher-order derivatives of the sliding mode variable, causing the higher-order derivatives to progressively approach zero on the sliding surface. This is coupled with integral filtering to smooth the switching control, fundamentally eliminating chattering [28].  
While higher-order sliding mode control effectively reduces chattering, excessively high orders may increase computational complexity and hinder theoretical analysis. Therefore, second-order sliding mode control retains the benefits of higher-order sliding mode while simplifying the controller design, making it more suitable for engineering applications. Among these, the super-twisting algorithm is a typical second-order sliding mode algorithm. It only requires information about the sliding mode variable, is computationallysimple, and achieves remarkable control performance, which is why it is widely used in engineering practice.  
The basic form of the super-twisting algorithm is shown below:  
\[\begin{cases}u_{ass}=-k_{1}|s\,|\,\frac{1}{2}sgn(s)+u_{1}\\ \dot{u}_{1}=-k_{2}sgn(s)\end{cases} \tag{21}\]  
where \(s\) is the sliding mode variable, \(u_{1}\) is the intermediate variable, and \(k_{1}\) and \(k_{2}\) are the parameters to be designed. The sufficient conditions for finite-time stability are as follows [25]:  
\[\begin{cases}k_{2}>\overline{d}\\ k_{1}^{2}>2\Big{(}k_{2}+\overline{d}\Big{)}\end{cases} \tag{22}\]  
From Equation (21), it can be observed that although the super-twisting algorithm involves two discontinuous switching terms, this does not affect the overall continuity of the control system. When the system state approaches the sliding surface, i.e., \(\mathrm{s}\approx 0\), the term \(k_{1}s^{1/2}sgn(s)\) in Equation (21) ensures that the term \(s^{1/2}\) tends to zero. Additionally, the nonlinear switching function of the intermediate variable \(u_{1}\) acts on its first derivative and is processed by integration, thus mitigating the impact of the switching function. Therefore, near the sliding surface, the sliding variable and its first derivative remain continuous, ensuring smooth operation of the system. The corresponding phase trajectory is shown in Figure 3, characterized by the sliding variable spiraling toward the equilibrium point.
## 2 Literature Review
### Overview of Previous Studies
He developed mathematical models that extend the traditional school bus routing problem. The study introduced unified and area-based solution methods, demonstrating significant cost reductions and improved efficiency in shuttle operations. In another study, [PERSON] et al. [57] proposed demand-responsive transport (DRT) systems to adopt passenger demands by offering flexible routing and scheduling, which is particularly beneficial when dealing with variable employee schedules and diverse fleet compositions. The study employed metaheuristic algorithms to solve the model, highlighting the effectiveness of demand-responsive services in enhancing operational efficiency. In a similar study, [PERSON] and [PERSON] [58] developed a model to assist carriers in simultaneously determining optimal fleet routes and schedules. Their approach formulated the problem as an integer multiple-commodity network flow and applied a Lagrangian relaxation-based algorithm to achieve efficient solutions. This methodology is particularly relevant for managing inhomogeneous fleets in shuttle services. The 2004 report of FHWA [59] aimed at maximizing traveler choices and managing demand to improve transportation system efficiency, providing travelers with effective choices to enhance travel reliability. Implementing travel demand management strategies, such as flexible work hours and ride-sharing incentives, can complement shuttle services by reducing peak demand pressures and optimizing fleet utilization. Planning for a zero-emission mixed-fleet public bus system requires the consideration of various operational constraints and environmental goals, which are also important for bus schedule planning and operations. In the study of [PERSON] and [PERSON] [60], they proposed integer linear programming models that utilize comprehensive input databases, allowing for customized solutions tailored to specific operational contexts. These models facilitate effective scheduling and routing decisions in mixed-vehicle fleets, aligning with sustainability objectives. In summary, effectively managing employee shuttle services under inhomogeneous fleet constraints necessitates the integration of advanced routing algorithms, demand-responsive strategies and comprehensive scheduling models. By leveraging these approaches, organizations can enhance the efficiency and sustainability of their transportation services. The application of all these methods in the optimization of transport systems allows for the effective modeling and solution of problems related to route planning, vehicle allocation and cost reduction.
## 3 System Model and Problem Formulation
### Offloading Cost
#### 3.3.1 Offloaded to SatEC Server  
In this subsection, we formulate the cost of real tasks when tasks are offloaded to the SatEC server for execution. In this case, the cost mainly consists of the STs transmission cost and SatEC server's computing cost. We denote \(\alpha_{n}\) as the proportion of the bandwidth allocated for the \(n\)-th ST, then the \(n\)-th ST 1 st hop transmission rate is given by \(C_{1,n}=\alpha_{n}B\log_{2}(1+p_{n}h_{n}/N_{0})\), where \(B\) denotes the total bandwidth of the access satellite, \(p_{n}\) denotes the transmission power of the \(n\)-th ST, \(h_{n}\) denotes the channel gain between the \(n\)-th ST and its access satellite, and \(N_{0}\) denotes the noise power at the receiver.  
Based on the 1 st-hop transmission rate \(C_{1,n}\), the transmission latency is given by \(T_{1,n}=L_{r}/C_{1,n}\), where \(L_{r}\) denotes the real task size (in bits). Then, the energy consumed by the \(n\)-th ST for transmission is given by \(E_{1,n}=p_{n}T_{1,n}\).  
We simply ignore the queuing delay. The computing latency at the SatEC server is given by \(T_{1,n}^{c}=k_{r}L_{r}/f_{1}\), where \(k_{r}\) denotes the computational intensity (in cycles/bit) of real task, and \(f_{1}\) denotes the CPU frequency (in cycles/s) of SatEC server. The energy consumed by the SatEC server for computing is given by \(E_{1,n}^{c}=p_{c}T_{1,n}^{c}\), where \(p_{c}\) denotes the computing power consumption (in watts) of the SatEC server.  
Therefore, the total latency that the \(n\)-th ST perceives and energy consumed for the \(n\)-th ST are respectively given by  
\[T_{n}^{SAT}=T_{1,n}+T_{1,n}^{c} \tag{1}\]  
\[E_{n}^{SAT}=E_{1,n}+E_{1,n}^{c} \tag{2}\]
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 5 Transfer Learning Strategies
### Prompt-Tuning
#### 5.5.2 Continuous Prompt-Tuning  
In continuous prompt-tuning, additional \(k\) tunable tokens are used per downstream task, which are prepended to the input text. These prompts are learned through backpropagation and are tunable or adjustable to incorporate signals from any number of labeled examples. Unlike finetuning, only the parameters of these inserted prompt tokens get updated in prompt-tuning. Hence, they are also called soft prompts. Ref. [80] demonstrated how their approach outperformed GPT-3's few-shot learning based on discrete text prompts by a large margin. They also demonstrated that prompt-tuning becomes more competitive with scale, where it matches the performance of finetuned models. For example, prompt-tuning of T5 matched the model's finetuning quality as the size increased while enabling the reuse of a single frozen model for all the tasks.  
P-tuning uses a small trainable model that encodes the text prompt and generates task-specific tokens. These tokens are then appended to the prompt and passed to the LLM during finetuning. When the tuning process is complete, these tokens are stored in a lookup table and used during inference, replacing the smaller model. In this approach, the time required to tune a smaller model is much less. Ref. [79] utilized a P-tuning technique to automatically search prompts in the continuous space, which enabled the GPT-style model to perform better on NLU tasks. Unlike the discrete-prompt approach, in continuous prompt, as there are trainable embedding tensors, the prompt encoder can be optimized in a differentiable way. P-tuning helped to augment the pretrained model's NLU ability by automatically searching for better prompts in the continuous space. As demonstrated in [79], the P-tuning method improves GPTs and BERs in both few-shot and fully supervised settings.  
Additionally, as only the parameters of prompt tokens are stored, which are less than 0.01% of the total model parameters, the prompt-tuning approach saves a significant amount of storage space. For example, CPM-2 [34] used only 100 prompt tokens, where only 409.6 K trainable parameters were to be updated compared to the 11B parameters of finetuning. As demonstrated in CPM-2, except for the Sogou-Log task, CPM-2 with prompt-tuning achieved comparable performance to the finetuning approach. In prompt-tuning, as the number of parameters to be optimized is much smaller, the size required fortensors (gradient and optimizer state) significantly decreased. As a result, prompt-tuning can save at most 50% GPU memory as compared to finetuning.  
However, prompt engineering also has limitations, such as prompt-tuning taking many more steps to converge and hence more time. Additionally, only a small number of examples can be used, which limits the level of control. Also, as the examples are part of the prompt, it affects the token budget.
## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Biological Profile Estimation
#### 3.1.1 Sex Estimation from Skeletal Structures
In [77], the authors employ an ensemble of shallow multilayer perceptrons to perform sex estimation from six cranial measurements (cranial sagittal arc, cranial sagittal chord, apical sagittal arc, apical sagittal chord, occipital sagittal arc, and Occipital sagittal chord). They tested their approach on 267 whole-skull CT scans (153 females and 114 males) from the Uighur ethnic group in the north of China (females aged 18-88 and males aged 20-84). An accuracy >94% was reported in all cases. On the other hand, other authors such as [PERSON] and [PERSON] [75], have focused on the estimation of sex in children. They proposed a technique called GDCNN (Gender Determination with ConvNets), where left hand radiographs of the children between a wide range of ages (ranging from 1 month to 18 years old) are examined to determine the gender. To identify the area of attention (part of the hand) they used Class Activation Maps, discovering that the lower part of the hand around carpals (wrist) is more important than other factors for child sex estimation. They obtained an accuracy of 98%, identifying the gender of a child even with half of the lower part of the hand, which is impressive considering the incompletely grown skeleton of the children. In another contribution [78], the authors examined the possibilities offered by 3D descriptors on sex identification accuracy, and tested their multi-region based representation on 100 head PM CT scans (54 male and 46 female subjects between the ages of 5 to 85 years, from south east Asia). The authors yield comparable results to the commonly reported sex prediction range (70-90%) using morphometric or morphological assessment by forensic anthropologists. As conclusion, most works for sex estimation employ either morphological methods (that rely on the visual assessment of sexually dimorphic traits) or metric methods (based on the variability in male and female dimensions, mostly utilizing different statistical methods to derive models/equations). This field has not been particularly explored using AI approaches and, generally, when tackled, the different presented approaches have been focused on sex estimation in adult individuals.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 1 Introduction
### Natural Language Preprocessing Techniques in Short Text  
Preprocessing is a process in which machines transform raw sentences into meaningful organized sentences and weighted data prior to carrying out any task. Preprocessing involves multiple operations to organize the text into a meaningful structure. Here, we discuss a few of its operations, as depicted in Figure 2.
## 3 Results and Discussion
### Physicochemical Characterization of SHGCs
#### 3.1.2 Ash Content  
Ash content ranged from 3.9% to 4.4%, with significant differences observed among all goat cheese samples (\(p\) = 0.0495). In general, the ash content in hard goat's cheese without supplementation was higher than values reported by [PERSON] et al. [56] after 30 days of ripening (2.4 g/100 g) and by [PERSON] et al. [57] after 22 days (2.67%) and 45 days of ripening (2.93%).  
In supplemented cheeses, a reduction in ash content was observed compared to SHGC-C, with SHGC-PP exhibiting lower values than SHGC-U. Previous studies have reported higher ash content in _Ulva lactuca_ (31.62 \(\pm\) 0.42% DW) [62] compared to _Palmaria palmata_ (13.95 \(\pm\) 0.33% DW) [27]. However, in the present study, the ash content of these seaweeds was similar (25.5% in _Ulva_ sp. vs. 25.7% in _P. palmata_). It is important to note that seaweed composition varies depending on several factors, particularly seasonality [63].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Physicochemical** & \multicolumn{3}{c}{**S seaweeds**} & **Control** & \multicolumn{2}{c}{**Supplemented Products**} \\ \cline{2-6}
**Parameters [a]** & _P. palmata_ & _Ulva_ **sp.** & **SHGC-C** & **SHGC-PP** & **SHGC-U** \\ \hline Total solids (\% WW) & 90.3 \(\pm\) 0.02 A  & 88.8 \(\pm\) 0.04 B  & 68.22 \(\pm\) 0.69 a  & 68.16 \(\pm\) 1.07 a  & 58.21 \(\pm\) 0.45 b  \\ Moisture (\% WW) & 9.7 \(\pm\) 0.02 A  & 11.2 \(\pm\) 0.04 B  & 32.74 \(\pm\) 0.87 a  & 33.04 \(\pm\) 0.95 a  & 42.89 \(\pm\) 0.86 b  \\ Ash (\% DW) & 25.7 \(\pm\) 0.12 A  & 25.5 \(\pm\) 0.09 B  & 4.36 \(\pm\) 0.02 a  & 3.87 \(\pm\) 0.04 b  & 4.00 \(\pm\) 0.07 c  \\ NaCl (g/100 g) & n.a. & n.a. & 2.08 \(\pm\) 0.17 ab  & 3.13 \(\pm\) 0.31 a  & 2.53 \(\pm\) 0.15 b  \\ pH & n.a. & n.a. & 5.13 \(\pm\) 0.06 a  & 5.10 \(\pm\) 0.00 a  & 5.17 \(\pm\) 0.06 a  \\ Carbohydrates (\% DW) & 34.0 \(\pm\) 2.23 A  & 31.0 \(\pm\) 1.37 B  & 3.27 \(\pm\) 0.25 a  & 4.13 \(\pm\) 0.40 b  & 2.85 \(\pm\) 0.03 c  \\ Lipids (\% DW) & 1.6 \(\pm\) 0.18 A  & 2.3 \(\pm\) 0.32 B  & 38.60 \(\pm\) 1.29 a  & 40.20 \(\pm\) 0.52 b  & 39.90 \(\pm\) 0.52 b  \\ Crude protein (\% DW) & 14.4 \(\pm\) 0.87 A  & 15.6 \(\pm\) 0.13 B  & 34.80 \(\pm\) 0.06 a  & 34.79 \(\pm\) 0.05 a  & 36.94 \(\pm\) 0.51 b  \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physicochemical characterization of the SHGCs * after 30 days of cheese ripening.
## 2 Results and Discussion
### Application to Real Matrices  
To evaluate the applicability of the proposed methodology, assays on three surface water matrices were performed. To account for intrinsic contamination and particular pronounced matrix effects, the standard addition method (SAM) was preferred instead of the conventional external calibration. In a first approach, the matrix was fortified with four working standards to produce the corresponding spiking levels (5.0-20.0 \(\mathrm{\SIUnitSymbolMicro g}\)/L) for five compounds under study. Blank assays (\"zero-point\") were also performed without spiking to assure maximum control of the analytical methodology. The results obtained from the assays performed by the SAM using the propose methodology, present an excellent selectivity and very good linearity with determination coefficient (\(r^{2}\)) ranging from 0.9907 to 0.9959. The concentrations of the five phenol compounds under study were always below the LODs attained for all surface water matrices studied. It must be emphasized, according to EEC Drinking Water Guideline 80/779/EEC, No. L229/11-29, that in surface water matrices the maximum admissible values is between 1 and 3 \(\mathrm{\SIUnitSymbolMicro g}\)/L. Therefore, the present methodology is suitable for the analysis of phenol compounds in surface waters, since the LODs achieved are below. Figure 3 exemplifies a chromatogram from a surface water sample spiked at the 20.0 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L, obtained by BA\(\mathrm{\SIUnitSymbolMicro\text{E}}\)(PS-DVB)-LD/HPLC-DAD, under optimized experimental conditions, where good selectivity and sensitivity are notice. It must be emphasized that the present methodology, besides easy to work-up and environmentally friendly, exhibits a remarkable range of applicability, since the methodology promotes very high selectivity for phenol compounds, avoiding potential interferences and simultaneously a great analytical sensitivity at the trace level. In spite of the present methodology has proven to be a suitable tool to analyze phenols in surface waters at trace level, the performance can be further enlarged by using HPLC coupled to mass spectrometry or tandem systems (HPLC-MS or HPLC-MS/MS), in order to achieve even better analytical selectivity and sensitivity, lowering the LODs and helping in the identification of the analytes in complex matrices.