esa-sceva/satcom-chunk-collection huggingface dataset:
# A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion
## 2 Geometry Dictionary Construction and Fusion Scheme
### Geometry Dictionary Construction  
According to geometric patterns used in SISR [39], this paper classifies the image into three patch types: smooth patches, stochastic patches, and dominant orientation patches. The smooth patches, stochastic patches and dominant orientation patches mainly describe the structure information, texture information, and edge information respectively. Based on a priori knowledge, image patches can be classified into three groups for sub-dictionaries learning. Additionally, to obtain more compact sub-dictionary for each image patch group, this paper uses PCA method to extract the important information from each group. PCA bases of each group are used as the bases of corresponding sub-dictionary. The obtained dictionary can be more compact and informative by combining the PCA-based sub-dictionaries [38].  
This paper learns three different sub-dictionaries rather other one dictionary. Three different sub-dictionaries contain more detailed information and structure information, and less redundant information. It means that although the obtained sub-dictionaries are compact, they can still contain more useful information. Three types of geometric patches extract different image information from source images respectively.  
* **Smooth** patches describe the structure information of source images, such as the background.
* **Dominant orientation** patches describe the edge information to provide the direction information in source images.
* **Stochastic** patches show the texture information to make up the missing detailed information that is not represented in dominant orientation patches.  
Three learned dictionaries specialize in smooth, dominant orientation, and stochastic patches respectively. The structure information, edge information, and texture information can be accurately and completely shown in corresponding sub-dictionary. Comparing with one learned dictionary, three learned sub-dictionaries can not only supply different information of source images respectively, but also make up the deficiencies mutually to enhance the quality of merged dictionary. So each sub-dictionary is important to compose a compact and informative dictionary in the proposed approach.  
The proposed geometric approach is shown in Figure 1 that has two main steps. In the first step, the input source images \(I_{i}\) to \(I_{k}\) are split to several small image blocks \(p_{in},i\in(1,2...k),n\in(1,2...w)\), where \(i\) is the source image number, and \(n\) is the patch number. The total block number of each input image is \(w\). Then according to the similarity of geometric patterns, the obtained image blocks are classified into three groups, smooth patch group, stochastic patch group, and dominant orientation patch group. In the second step, it does PCA analysis on each sub-class for extracting corresponding PCA bases as sub-dictionary. Then these obtained sub-dictionaries are composed to construct a complete dictionary for image sparse representation.
## 3 Results and Discussion
### Effect of Adsorption Conditions
#### 3.3.3 Adsorption Kinetics  
Two typical adsorption kinetics models, a pseudo-first-order model and a pseudo-second-order model, were used to investigate the adsorption kinetics (Figure S1, Table S1- Supplementary Material). For the adsorption of MB by CPP200, the linear pseudo-second-order model provided an extremely high coefficient of determination (\(R^{2}\), 0.9994) and an accurate calculated \(q_{e}\) value (\(q_{e,cal}\) = 58.10 mg/g) that was very close to the experimental data (\(q_{e,exp}\) = 59.72 mg/g). In contrast, the pseudo-first-order model yielded inferior results; the difference between the experimental data (\(q_{e,exp}\) = 59.72 mg/g) and the calculated results (\(q_{e,cal}\) = 40.27 mg/g) was obvious. The experimental data of CPP200 for MB were better fitted using the linear pseudo-second-order model, assuming that chemisorption through the sharing or exchange of electrons between adsorbent and adsorbate was the rate-controlling step, and the adsorption rate simultaneously depended on the dosage of adsorbent and the concentration of adsorbate [43].
# A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
## 4 Computational Results and Discussion
### Oceanic Particle Simulation Using ADDA, IITM, and PGOM  
Section 4.1 describes an example of dinoflagellates while using the ADDA to compute the 16 Mueller matrix elements that were given by [PERSON] and [PERSON] [43], where the chromosome of the dinoflagellate has complex helical structure and is simulated mostly in fixed orientations. Generally, a simple nonspherical shape in random orientation is used to simulate the optical properties of oceanic particles. A hexahedron particle here is used as an example of an oceanic particle to show how Mueller matrix elements can be calculated by three typical methods: the ADDA, the IITM, and the PGOM. The relative refractive index of the particle is set to be 1.12 + i0.0005 and the incident wavelength is 0.658 \(\upmu\)m. Only the Mueller matrix of the particle under the random orientation condition is given.  
Figure 3 shows the comparisons of the non-zero Mueller matrix elements calculated by the IITM and the ADDA. The volume equivalent radius is 1 \(\upmu\)m. The element \(P_{11}\) is normalized to give the normalized phase function while other elements are normalized by the element \(P_{11}\). The simulation results calculated by the IITM and the ADDA are perfectly matched since they both are the exact solutions of [PERSON]'s equations. However, the computation using the IITM is much more efficient than the computation using the ADDA since the random orientation process is realized by ADDA through considering a large number of orientations. On the other hand, the ADDA for a fixed orientation in this case is more efficient than the IITM since the IITM has to compute the T-matrix of the particle, regardless of whether it is in a fixed orientation or under the random orientation condition.  
Figure 3: Comparisons of Mueller matrix elements of a hexahedron particle calculated by the invariant-imbedding T-matrix method (IITM) and the ADDA. The volume equivalent sphere radius is 1\(\upmu\)m and the incident wavelength is 0.658 \(\upmu\)m. The relative refractive index is 1.12 + i0.0005.  
Figure 4 shows the comparisons of the Mueller matrix elements calculated by the IITM and the PGOM. The volume equivalent sphere radius is 8 \(\upmu\)m. The PGOM results agree quite well with the IITM results, especially for the forward and backward scattering directions. Even though the PGOM is an approximate solution of Maxwell's equation, the process of including the interference between the diffracted and transmitted rays and mapping the near field to the far field significantly enhances its accuracy. The advantage of the PGOM is that it is computationally much more efficient than the IITM.
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 5 Design of La-CTP  
In the previous section we have deeply analyzed the major drawbacks of CTP in handling loops and the corresponding causes. We will detail the design of La-CTP in this section. Essentially, the wrong determination (or update) of parent node makes a loop; so we propose a new metric for parent updating  
Figure 5: Snapshots and beaconing of the CTP’s operation in experiment T-III, during which node \(X\) is shut down at the 260 th second and node \(Y\) is turned on at the 400 th second. The dynamics of link \(l_{YS}\) in T-III is the same as that of link \(l_{YS}\) in T-II. In the beaconing plot, for the clarity, we only show randomly-sampled 20% of the points between the 260 th and the 400 th seconds for two kinds of beaconing events, “A.broadcast” and “A.receive”. (**a**) critical snapshots; (**b**) route beaconing.  
in order to suppress the formation of loops as much as possible. In addition, then we design a proactive and adaptive beaconing scheme which can effectively unlock loops with the cost lower than CTP. Noticeably, La-CTP retains the framework of CTP, the interfaces provided by CTP, and even the parameters used by CTP. Thus La-CTP is totally feasible for any scenarios that support CTP.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 5 Transfer Learning Strategies
### Prompt-Tuning
#### 5.5.1 Prompt Engineering  
Prompt engineering involves the process of carefully designing optimal prompts to obtain optimal results. Prompts need to be constructed to best elicit knowledge and maximize the prediction performance of the language model. The prompt-based approach is a promising alternative to finetuning since, as the scale of LLMs grows, learning via prompts becomes efficient and cost-effective. Additionally, unlike finetuning, where a separate model is required for each downstream task, a single model serves multiple downstream tasks in prompt-tuning. They also help the model generalize better to held-out tasks and cross-tasks by using multitask prompts.  
As per [79], finetuning on downstream tasks for trillion-scale models results in poor transferability. Also, these models need to be larger to memorize the samples in finetuning quickly. To overcome these issues, the prompt-tuning or P-tuning approach [80] is used, which is a parameter-efficient tuning technique. For example, GPT3 [29] (which was not designed for finetuning), heavily relied on handcraft prompts to steer the model for downstream applications. Prompt-tuning came into play to scale this (manual) prompt engineering technique. Prompt-tuning can be categorized into discrete and continuous approaches.  
Unlike finetuning, where a separate model is required for each downstream task, in prompt-tuning, a single model serves multiple different downstream tasks. In discrete prompt-tuning, as human efforts are involved in crafting the prompts, the process becomes time-consuming and fallible as human efforts are involved in crafting the prompts. It sometimes can be non-intuitive for many tasks (e.g., textual entailment). Additionally, improper construction of contexts leads to low model performance. To overcome these issues, a continuous or tunable prompt-tuning technique was proposed.
## 3 Results and Discussion  
In the present study, the experimental models with a soil slope of 700 were prepared with four nails lengths, 0.5 H, 0.75 H, H and 1.2 H, in multiples of slope height (H), three nail orientations, i.e., 0\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\), and two horizontal and vertical spacings, i.e., 0.2 H and 0.4 H. The horizontal nails were used with equal horizontal and vertical spacing, i.e., 0.4 H, for the parametric study besides the inclined nail slope behavior. The results of the models without the nails were also obtained. The displacement of the slope, force in the nail, settlement of the footing and the earth pressure in the backfill soil due to the nailing parameters (nail length, inclination and spacing) were measured and the obtained results are discussed below. The lateral displacement of the nailed slope facing were recorded with 3 dial gauges at three measurements points, A, B and C, at distances of Z = 70, 350, and 630 mm from the top surface. These distances are expressed as 0.1 H, 0.5 H, and 0.9 H. Pressure cells were mounted flush with the back of the slope face in special arrangement to  
Figure 3: Construction of the inclined nail.  
Figure 2: Construction stages of the slope-nailed system.  
measure the horizontal pressure as shown in Figure 4 at points A, B, and C. The readings were measured after each increment of footing pressure from 5.0, 10.0, 20.0, to 30.0 kPa.
# Efficient Power Control Framework for Small-Cell Heterogeneous Networks
## 3 Proposed Power Control Solution
### Problem Transformation  
From (12), one can observe that it is nonconvex quadratic function. Due to this reason, the problem in (13) can be categorized as a nonconvex combinatorial problem. However, it is worth noting that due to the on-off switching of small-cell BSs, the solution for the formulated problem has group sparsity [30]. This means that if any \(l\)-th small-cell BS is switched off, the corresponding beamforming coefficients \(v_{l}\) would also be set to 0. Therefore, we introduce the weighted mixed norm to introduce group sparsity and relax the combinatorial composite function. Thus, for any weight of beamforming coefficient group \(\rho_{l}>0\), we have  
\[J(v)=\sum_{l=1}^{L}\rho_{l}|v^{{}^{\prime}}|_{2}. \tag{14}\]  
Besides, due to the constraint of quality of service, it is possible that the formulated problem becomes infeasible for a large number of UEs. In such a situation, the design problem would be to maximize the number of users that can be supported via admission control. Thus, we can add an auxiliary variable \(x_{k}\) such that maximizing the number of users in the network is equivalent to minimizing the number of nonzero \(x_{k}\). Thus, the problem can be reformulated as  
\[\min_{v,x}|x|_{0} \tag{15}\]  
\[\text{s.t.}\ \ \mathcal{Z}_{k,m}(v)\leq x_{k}\]  
Note that when \(x_{k}=0\), it means that \(k\)-th UE can be addition and vice versa. Thus, the solution of (15) has individual sparsity and sparsity increases for a larger number of admitted UEs.  
To solve the previously mentioned problems, we use \(l_{p}\) minimization to find the approximation of both the objective functions. We adopt \(||x||_{p}^{p}\) and induce a quadratic form to have a more sparser solution. Specifically, we adopt following smoothed version of \(||x||_{p}^{p}\) :  
\[f_{p}(x;\varepsilon)\in\sum_{i=1}^{m}{(x_{i}^{2}+\varepsilon^{2})^{\frac{p}{2 }}}. \tag{16}\]  
Thus, the power control problem can now be written as  
\[\min_{v}\sum_{l=1}^{L}\rho_{l}(|v^{{}^{\prime}}|_{2}^{2}+\varepsilon^{2})^{ \frac{p}{2}} \tag{17}\]  
\[s.t.\mathcal{Z}_{k,m}(v)\leq 0\]  
It can be seen that the induced sparse beamformers would help select the appropriate small-cell BS. Moreover, the resulting problem is a quadratic optimization problem which can be solved in a feasible manner.  
For admission control, we take advantage of smoothed \(l_{p}\)-norm approach to approximate the objective function. Thus, we have  
\[\min_{v,x}\sum_{k=1}^{K}{(x_{k}^{2}+\varepsilon^{2})^{\frac{p}{2}}} \tag{18}\]  
\[\text{s.t.}\ \ \mathcal{Z}_{k,m}(v)\leq x_{k} \tag{19}\]  
Note that the above formulation would help induce an individual sparsity, thereby, helping in UE admission control.
## 4 Discussion  
### Reasons for the Variations in Precipitation Frequency and GPP Values  
#### 4.1.1 Variation in Precipitation Frequency  
Current global climate change causes precipitation to vary in intensity and frequency. Changes in precipitation frequency can directly affect vegetation and surface soil moisture. Overall, precipitation frequency in the Northern Hemisphere decreased while precipitation intensity increased from 1982 to 2015. Wet air contributes to precipitation in areas covered with vegetation, and more precipitation can support more evaporation (for example, in forests [53]), while it rarely rains in dry areas or barren land [53]. This study showed that the frequency of trace precipitation was highest in the tundra, which is distributed mostly in the Arctic and northern latitudes. Lower temperatures contribute to low saturated vapor pressure, which causes droplets to be easily released from clouds and form precipitation, although this phenomenon is also controlled by wind direction [54]. However, numerous studies have reported the \"dry soil advantage\", which means that the atmosphere produces more local precipitation over dry soils than over wet soils, because the former may prime the atmosphere for intense convection. This explains the increase in precipitation frequency detected in shrubland [55; 56; 57].  
Undoubtedly, the frequency of precipitation with an intensity between 5 and 10 mm/day obviously decreased in all land cover types, except in barren land. Vegetation coverage in barren land is normally less than 10% of the total area, and no incidences of precipitation intensity >10 mm/day have been reported. Different changes in precipitation frequency were detected among seasons in different land cover types. Interestingly, in cropland, grassland, and barren land, all precipitation frequencies decreased in all seasons. Seasonal precipitation is critical for springtime plant activity, and a direct connection may exist between precipitation frequency and intensity and vegetation productivity in dryland ecosystems [58]. At the same time, soil moisture conditions in different land cover types determine the climatic conditions of local precipitation [59; 60].
# Optical Design and Stray Light Analysis of Underwater Spectral Radiometer
## 6 Conclusions  
The text describes the design of an in situ underwater spectral radiometer for remote sensing of water color. The system uses a concave holographic grating as a dispersive element, enabling spectral observations within the range of 400-950 nm with a spectral resolution better than 3 nm. The mechanical structure of the underwater spectral radiometer has been completed, employing a method that bonds the grating to a watertight casing, reducing unnecessary mechanical components and ensuring a compact and lightweight design. Furthermore, utilizing Zemax non-sequential mode, stray light analysis has been conducted for the underwater solar spectral radiometer to provide means for stray light suppression. Various methods have been employed, including coating the detector surface with a layer, blackening critical mechanical surfaces, adding light-absorbing threads on the inner wall, and increasing the shading hood. These measures effectively suppress stray radiation to the order of \(10^{-4}\), significantly reducing the system's stray light signal and improving the system's signal-to-noise ratio. The prototype was developed, and wavelength calibration experiments were carried out with a tunable laser. The experimental results show that the spectral resolution of the instrument meets the design requirements. The spectral irradiance calibration experiment is carried out to obtain the spectral response characteristic curve of the detector, which provides data support for underwater experiments.  
Conceptualization, Y.Z., K.W. and X.Y.; methodology, Y.Z., K.W. and X.Y.; software, Y.Z.; validation, Y.Z., K.W. and W.Y.; formal analysis, Y.Z. and J.Y.; investigation, S.L.; resources, K.W. and X.Y.; data curation, Y.Z., J.Y. and S.L.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; visualization, Y.Z.; supervision, K.W. and X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National key research and development plan project of China, grant number No. 2022 YFB3903200 and No. 2022 YFB3903201.  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Modeling and Formulation for the Optimization Problem
### Constraints
#### BESS configuration and operation:  
To suppress large fluctuations of power flow at the interconnection point, the power flow constraint is supported by using the BESS at the substation. The power controller of BESS is shown in Figure 3b. The optimized control reference signal is generated based on the forecasted information. In this paper, the BESS is assumed to have NASbatteries; the charge/discharge efficiency of the BESS is 80%; whilst the self-discharging of the BESS is not considered. These constraints are described as follows:  
\[\sqrt{P_{LBinv}^{2}(t)+Q_{LBinv}^{2}(t)}\leq S_{LBinv} \tag{8}\]  
\[\zeta_{LB}(t+1)=\left\{\begin{array}{ll}\zeta_{LB}(t)-\frac{P_{LB}(t)/\eta} {C_{LB}}&(P_{LB}(t)\geq 0)\\ \zeta_{LB}(t)-\frac{P_{LB}(t)\cdot\eta}{C_{LB}}&(P_{LB}(t)<0)\end{array}\right. \tag{9}\]  
\[20\leq\zeta_{LB}(t)\leq 80\qquad\quad[\%] \tag{10}\]  
These equations are constraints regarding the large capacity BESS, considering battery SOC and the efficiency of charging and discharging.  
PV system:  
DGs using PV have a simple configuration and maintenance-free characteristics. Details of the PV cell and its inner structure are described in [43]. For utilization improvement of the PV system in an ever-changing climate, it is assumed that the PV system has the maximum power point tracking (MPPT) algorithm. The reactive power control scheme utilizes capacity margins of the installed PV inverter shown in Figure 3a. The inverter constraint is defined as Equation (11):  
\[\sqrt{P_{PVinv}(t)^{2}+Q_{PVinv}^{2}(t)}\leq S_{PVinv} \tag{11}\]  
Figure 3: Active and reactive power control system. (**a**) Interfaced inverter of the PV system and (**b**) inverter of BESS at the interconnection point.  
The feedable reactive power from the PV inverter is rewritten as follows:  
\[|Q_{PVinv}(t)|\leq\sqrt{S_{PVinv}^{2}-P_{PVinv}^{2}(t)} \tag{12}\]  
Here, the amount of \(Q_{PVinv}(t)\) is the main factor for cooperative operation, which is discussed in Section 4.  
OLTC tap constraints:  
The OLTC concludes the LRT and SVR, the tap positions' constraint of which is given by:  
\[T_{k}^{min}\leq T_{k}(t)\leq T_{k}^{max}. \tag{13}\]  
In LRT operation, short-term operation is undesirable. Thus, LRT can control at a rate of one tap-step ratio in one hour. The tap position variable is discrete. Therefore, the optimization problem is known as a mixed integer non-linear problem (MINLP).