esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Control Strategies in Greenhouses
### Sensors Used in Greenhouses  
In greenhouses, different types of sensors are used to monitor and control environmental conditions, ensuring healthy plant growth. These sensors are essential for the automation and optimization of processes within the greenhouse. Below are the main sensors used in greenhouses:  
1. Temperature Sensors: Monitor air and soil temperatures inside the greenhouse, allowing automatic adjustments to maintain optimal conditions for plant growth [232].
2. Humidity Sensors: Measure relative air humidity and soil moisture, helping to ensure that plants receive the appropriate amount of water [233].
3. Light Sensors: Monitor the intensity and duration of light received by plants, helping to control artificial lighting to optimize photosynthesis [234].
4. \(CO_{2}\) sensors: Measure carbon dioxide levels in the environment, which is crucial for photosynthesis and plant growth [235].
5. pH and EC (Electrical Conductivity) sensors: Monitor the pH and electrical conductivity of the nutrient solution, ensuring that plants receive nutrients appropriately [236].
6. Wind and Precipitation Sensors: Used mainly in greenhouses with natural ventilation, they help adjust ventilation openings based on external conditions [237].
7. Pest Presence Sensors: Detect the presence of pests and diseases, allowing quick and effective intervention [238].
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 3 Preliminaries
### Problem Statement  
The coverage of the wireless sensor network can be divided into area coverage, point coverage and barrier coverage. Point coverage requires specific points covered, are coverage needs entire region covered and barrier coverage aims to detect the intruders who try to cross the network [21]. In this paper we mainly discuss the areas coverage under circumstance of nodes randomly deployed. At first, assuming a 100 m \(\times\) 100 m scene and deploying a certain number of static sensors randomly in this area. Obviously, because of some places are covered densely while others sparsely, the current coverage is not the optimal. In this article, we studied the problem of how to detect and heal the coverage holes, and we need to solve the following three subproblems:  
* Estimate the coverage holes in the sensing areas more simply and accurately.
* Figure out the bound of assisted sensors which are used to heal the coverage holes in every triangle.
* Work out the optimal positions of assisted sensors.  
To simplify the analysis of the problem later, we assume that all sensors' sensing range is a circle with the radius of \(R_{s}\), \(R_{c}\) is defined as communication range. In order to facilitate reading, we introduce some parameters in Table 1:
# SIPNet & SAHI: Multiscale Sunspot Extraction for High-Resolution Full Solar Images
## 3 Method
### Training and Testing  
The main environment for deploying SIPNet & SAHI consists of CUDA 11.1, Ubuntu 16.04, PyTorch 1.10.1, and Python 3.7.13. A GTX2080 GPU was used. We implemented SIPNet using an object detection toolbox, MMDetection 2.0 [52], which contains a rich set of object detection, instance segmentation, and panoptic segmentation methods, as well as related components and modules based on PyTorch. The data labelling tool uses Labelme 5.0.1. Data augmentation was first performed by SAHI with a slice size of \(320\times 320\) pixels\({}^{2}\) and overlap rate of 0.2, and then by random image flipping. Multiscale training was used, where the input images were randomly resized to different scales to improve robustness. A linear warm-up policy [52] was used, with the initial learning rate set to 0.00125 and the warm-up ratio set to 0.1. The specific settings for other parameters were as follows: momentum set to 0.9, weight decay set to 0.0001, optimizer set to SGD, and batch size set to 4. After repeated experiments, we adopted a 24-epoch training schedule [52], which took about 13 h.  
After training the network, the test dataset was fed into the trained model. The performance was evaluated using precision (P), recall (R), and average precision (AP). The definitions of these metrics are as follows:  
\[\mathrm{P}=\frac{\mathrm{TP}}{\mathrm{TP+FP}} \tag{3}\]  
\[\mathrm{R}=\frac{\mathrm{TP}}{\mathrm{TP+FN}} \tag{4}\]  
\[\mathrm{AP}=\int_{0}^{1}\mathrm{P(r)dr} \tag{5}\]  
Here, TP represents true positives, which is the number of positive class predictions correctly made within the positive samples. FP refers to false positives, which is the number of negative class samples incorrectly labeled as positive. FN, on the other hand, represents false negatives, i.e., the number of positive class samples that were incorrectly predicted as negative. In this work, only predicted targets with an IoU difference greater than 0.5 with the ground truth values are considered correct predictions. AP is the average of all recall values and ranges from 0 to 1. P(r) is the precision-recall curve. SIPNet obtained P, R, and AP values of 76.8%, 71.4%, and 77.0%, respectively.  
During inference, the SAHI was integrated into the trained SIPNet. The slice size was set to 400 \(\times\) 400 pixels\({}^{2}\) and the overlap rate between slices was set to 0.2. A total of 1922 high-resolution solar images in 2013 with a size of \(4096\times 4096\) pixels\({}^{2}\) were inferred by SIPNet & SAHI. The average time for an image was about 4 min (about 2 min without SAHI). After integrating SAHI into the trained SIPNet, the P, R, and AP values are improved to 95.7%, 90.2%, and 96.1%, respectively.
## 2 LPD Signal Design and Detection Method
### Principle of Under-Sampling Method for Intended Receiver  
As previously mentioned, the proposed LPD signal has a low power spectrum and is sparse in the frequency domain. Therefore, detecting the LPD signal at the intended receiver becomes a problem. In this part, the under-sampling method based on active aliasing is presented. The sampling rate is determined by the subcarrier spacing, and the sampling and combination for the proposed LPD signal can be finished simultaneously. For simplicity, the principle of the sampling process is explained in frequency domain. The complex-valued signal at the receiver can be given by  
\[r(t)=\sum_{k=L}^{L+N-1}h_{k}s(t)\cdot\alpha_{k}\exp(k\cdot j\omega_{c}t)+w(t) \tag{2}\]  
where \(h_{k}\) is the channel coefficient over the \(kth\) sub-channel determined by the channel environment [24]. For the additive white Gaussian noise (AWGN) channel, channel coefficient \(h_{k}\) is considered to be 1 for all \(k\). For the fading channel, the channel coefficients can be given by \(h_{k}=|h_{k}|\exp(j\rho_{k})\), which means the signal transmitted over the \(kth\) sub-channel is scaled by the attenuation factor \(|h_{k}|\) and phase-shifted by \(\varphi_{k}\). In this work, \(|h_{k}|\) is subjected to Rayleigh distribution and \(\varphi_{k}\) is subjected to uniform distribution. \(w(t)\) is the independent complex additive noise with power spectrum density \(N_{0}\). The sampling process can be modeled as a pulse modulation process, and the sampling pulse is a periodic ideal pulse sequence given by \(p(t)=\sum_{i=-\infty}^{+\infty}\delta(t-nT_{s})\), where \(\delta(t)\) is the unit impulse function. The sampling frequency can be calculated by \(f_{s}=1/T_{s}\).  
The frequency representation of the proposed sampling process is illustrated in Figure 2. As shown in Figure 2a, signal \(X(j\omega)\) consists of \(N\) sub-band signals \(S_{k}(j\omega)\) with sub-band spacing \(\omega_{c}\), and the total bandwidth of \(X(j\omega)\) is N\(\omega_{c}\). The frequency domain representation of the sampling function is illustrated in Figure 2b. The spectrum of the sampled signal can be represented as a convolution of \(X(j\omega)\) and \(P(j\omega)\). For each sub-band signal \(S_{k}(j\omega)\), a replica of \(S_{k}(j\omega)\) remains at each integer multiple of \(\omega_{s}\). If the sampling rate is chosen as \(f_{s}=f_{c}\), replicas of sub-band signals \(S_{k}(j\omega)\) may be aligned and added coherently, as shown in Figure 2c. The mean power of the sampled signal increases by \(N\) times. Consequently, \(S(j\omega)\) can be recovered from the sampled signal with an ideal low-pass filter. Otherwise, these sub-band signals would not be aligned as shown in Figure 2d if \(f_{s}\
eq f_{c}\), aliasing between adjacent sub-band signals can hardly be eliminated.
# Robust Background Subtraction via the Local Similarity Statistical Descriptor
###### Abstract  
Background subtraction based on change detection is the first step in many computer vision systems. Many background subtraction methods have been proposed to detect foreground objects through background modeling. However, most of these methods are pixel-based, which only use pixel-by-pixel comparisons, and a few others are spatial-based, which take the neighborhood of each analyzed pixel into consideration. In this paper, inspired by a illumination- invariant feature based on locality-sensitive histograms proposed for object tracking, we first develop a novel texture descriptor named the Local Similarity Statistical Descriptor (LSSD), which calculates the similarity between the current pixel and its neighbors. The LSSD descriptor shows good performance in illumination variation and dynamic background scenes. Then, we model each background pixel representation with a combination of color features and LSSD features. These features are then embedded in a low-cost and highly efficient background modeling framework. The color and texture features have their own merits and demerits; they can compensate each other, resulting in better performance. Both quantitative and qualitative evaluations carried out on the change detection dataset are provided to demonstrate the effectiveness of our method.  
Keywords:background subtraction; locality-sensitive histograms; local similarity statistical descriptor; video surveillance +
Footnote †: journal: Applied Sciences
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 2 Algorithm  
The algorithm proposed in this paper contains four main parts: preprocessing, signal denoising by combining RLS filtering and empirical wavelet transform, adaptive spectral peak tracking based on FSM, and reprocessing. The specific algorithm flowchart is shown in Figure 1:
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results
### Characteristics of Spectral Changes in Different Forest Stand Ages
#### 3.1.2 Spectral Change Patterns of Different Forest Stand Ages  
For this study, the spectral change patterns of different forest stand ages were analyzed, as shown in Figure 4. Figure 4a shows the typical spectral information of different forest stand ages, and the color of the canopy spectral curve represents the forest stand age. The forest canopy spectral curve of 1-60a was mainly selected for analysis. Figure 4b shows the trend of the change in the near-infrared (\(B_{8}\)) reflectance value with forest stand age. The results show that with the increase in forest stand age, the near-infrared reflectance of vegetation canopy shows a significant decline trend. Figure 4c shows the trend of the change in the red edge (\(B_{5}\)) reflectance value with forest stand age. The results show that the red edge reflectance of the vegetation canopy presents a \"U-shaped\" change trend with the increase in forest stand age. Figure 4d shows the trend of the variation in the red edge (\(B_{8A}\)) reflectance value with forest stand age. It is found that with the increase in forest stand age, the red edge reflectance of the vegetation canopy shows a significant decreasing trend. Therefore, these results indicate that forest canopy spectra of different ages have significant change patterns, and they further confirm that canopy spectra can reflect forest stand age.
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 2 Materials and Methods
### Design of the Decoder Module
#### 2.4.1 Type I STDecoder-CD  
The Type I STDecoder-CD architecture shown in Figure 2a is UNet-like [1], but there are five differences in its function:  
1. The pre-change (t1) and post-change (t2) RS images are input into two branches of the Siamese network simultaneously, and the Siamese network with shared parameters separately extracts bitemporal representations.
2. Unlike UNet [6], there are only four blocks attached to each Siamese branch. Before the UNet-like encoder-decoder operation, there is a \"Conv Block\" layer, and after the encoder-decoder operation, there is an \"FCN\" layer. The \"Conv Block\" layer splits the input image into tokens of non-overlapping patches with a resolution of 16 \(\times\) 16, while the classifier \"FCN\" interpolates the decoded feature maps by four times and identifies the change of interest from them.
3. The encoder's network architecture (X) is completely different from that of the decoder (Y); the former adopts the Siamese TransCNN structure, and the latter retains the \"Conv + BN + ReLU\" structure used in UNet.
4. A deep supervision strategy was developed to enrich the model capacity with extra regularization. As shown in Figure 2a, during the training process, the loss of each deep supervision is computed independently and is directly backpropagated to intermediate layers. To be specific, outputs of the FCN layer and each decoder block are fed into a plain 3 \(\times\) 3 convolution layer followed by a bilinear upsampling layer, and then their losses can be computed (in total, there are four losses in the network). In this way, the intermediate layers are effectively trained, and their weights can be suitably updated [12], thus alleviating the presence of the vanishing gradient.
5. As shown in Figure 2a, to fuse the hierarchical feature representations \(\mathrm{X^{n}}_{\mathrm{A}}\) and \(\mathrm{X^{n}}_{\mathrm{B}}\) (n = 1, 2, 3, 4), we apply the concatenation method to the top layer of the TransCNN layer and use the subtraction method for the remaining layers. This combination is termed Difference Detector I in this study. Actually, for a Siamese network, the concatenation weight sharing mode lacks focus on change information between the former and later phases, while the subtraction weight sharing mode lacks focus on the former phase information [30], so the best strategy is to combine the two weight sharing modes within one network architecture. However, [30] also warns that if we first apply the subtraction method and subsequently use the concatenation method, there will be a significant drop in accuracy.  
Figure 2: Flowchart of the Type ISTDecoder-CD architecture. The legends in this figure are consistent with those in Figure 1 if not specified.
## 4 Results and Discussion
### Average Network Throughput  
Average network throughput, which may be defined as the number of packets received per second, is shown in Figure 13.  
\[S=\frac{NP_{PktRx}\times L_{Pkt}}{T_{S}} \tag{11}\]  
where \(T_{S}\) denotes the simulation duration in seconds and \(L_{Pkt}\) is the size of the packet in bytes.  
Figure 12: Latency between all packets on an average end-to-end basis.  
Figure 13: Average network throughput (bps).  
Figure 13 provides the average network throughput. The results show that throughput increases linearly when the number of sensor nodes increases. As can be observed, EQPD-MAC and PMME-MAC provide the maximum throughput among all protocols. EQPD-MAC achieves the highest throughput for any number of sensor nodes deployed in the network, with gains of up to 23.3% and 12%, when compared to RMP-MAC and MPQ-MAC, respectively. Due to EQPD-MAC's ability to maintain its operation, the sensor nodes can function longer when compared to other protocols. On the other hand, RMP-MAC and MPQ-MAC cannot route a large number of data packets and the network throughput decreases rapidly. Although RMP-MAC protocol achieves good throughput when the number of sensor nodes is small, as the number of sensor nodes rises, the protocol tends to drop packets, resulting in low network throughput.
# Empowering Large Language Models to Leverage Domain-Specific Knowledge in E-Learning
## Appendix A Appendix A
### Case 3 The Research Paper (More Specific Domain Knowledge)  
* Question: What is the from-text-to-action grid.
* Ground Answer: From text to strategy: Developing the FTTA grid. While the metrics gleaned from text analysis can be interesting barometers and trackers of whatever is measured, the metrics alone often do not provide the depth needed to inform firm strategy. This has meant that despite the ease at which textual data can be collected and analyzed, insights from such efforts are often relatively thin. In this section we describe how data on what topics are mentioned in a text (aspect), coupled with data on how frequently those topics are mentioned (attention) and the sentiment they receive (opinion), can provide strategic insight.  
We next develop a new framework, the From-Text-To-Action (FTTA) grid, to help marketers better interpret the data that can be gleaned from textual data. The FTTA grid is based on importance-performance analysis, a widely accepted method that is well known for its simplicity and ease of application ([PERSON] and [PERSON], 1977). Importance-performance analysis is based on customer satisfaction being a function of both expectations regarding a specific attribute and judgment of the attribute's performance. Key is the gap between a customer's expectations and their judgment of the performance received during service. Such analysis enables identification of attributes that have the most impact on customer satisfaction and repetitive purchase behavior.  
Such information helps marketers both evaluate competitive position and enables prioritization of available strategies to enhance customer satisfaction.  
The FTTA grid, based on importance-performance analysis, provides a generalized method for using text-derived metrics to determine which attributes are important to customer service. Given a selected attribute or aspect, the following two components are classified: (1) the importance of the attribute and (2) judgments of its performance. Proxies for these components can be calculated from textual data analytics (see Section 2.3). The significance of an attribute can be calculated based on the level of interest (attention) a specific aspect receives. Similarly, the perceived performance of an attribute can be estimated using the expressed opinion (or sentiment) for that aspect. The standardized data for each aspect's interest and opinion can then be paired and used as coordinates to plot the aspect on a two-dimensional FTTA grid.
###### Abstract  
Slight variations induced by thermal effects may bring unexpected discrepancies in both the system's linear and non-linear responses. The present study investigates the temperature effects on the non-linear coupled motions of suspended cables subject to one-to-one internal resonances between the in-plane and out-of-plane modes. The classical non-linear flexible system is excited by a uniform distributed harmonic excitation with the primary resonance. Introducing a two-mode expansion and applying the multiple scale method, the polar and Cartesian forms of modulation equations are obtained. Several parametric investigations have highlighted the qualitative and quantitative discrepancies induced by temperature through the curves of force/frequency-response amplitude, time history diagrams, phase portraits, frequency spectrum, and Poincare sections. Based on the bifurcation and stability analyses, temperature effects on the multiple steady-state solutions, as well as static and dynamic bifurcations, it is observed that the periodic motions may be bifurcated into the chaotic motions in thermal environments. The saddle-node, pitch-fork, and Hopf bifurcations are sensitive to temperature changes. Finally, our perturbation solutions are confirmed by directly integrating the governing differential equations, which yield excellent agreement with our results and validate our approach.  
thermal conditions; suspended cable; non-planar motion; bifurcation analysis; chaotic motion
## 3 Results and Discussion
### The Effects of PSA Emulsions on the Fracture Properties of Modified Mortar
Increasing St content and decreasing BA content in the PSA emulsion first increases and then decreases the fracture energy of the modified mortar. In Figure 10a, each curve represents mortars with the same P/C ratio. As the damping performance of the PSA emulsion latex film increases, the fracture energy of the modified mortar first decreases and then increases. However, the adhesion performance shows a correlation different from the damping performance, and an effective relationship cannot be established here (Figure 10b). Figure 9: The variation in fracture energy as a function of P/C for five PSA emulsion-modified mortars: (**a**) 7 days standard curing; (**b**) 28 days standard curing; (**c**) 28 days mixed curing. As shown in Figure 11, the results of the fracture toughness are different from those of the fracture energy. The fracture toughness of the mortars modified with the series of PSA emulsions is lower than that of the blank mortar at different ages, under different curing conditions, and at different dosages. The fracture toughness of the modified mortars generally shows a gradually decreasing trend with increasing P/C ratios, with the lowest value being approximately 0.4 MPa\(\cdot\)m\({}^{1/2}\), which is nearly half that of the blank control group. This may be because the addition of the emulsion leads to a reduction in cementitious materials, and the chemical reaction between them significantly affects the generation of hydration products, resulting in a significant decrease in the maximum load of the modified mortar. Comparing fracture toughness differences between different PSA emulsion-modified mortars shows that the differences are not significant at 7 days of curing. There is no obvious correlation with the composition of the PSA emulsion itself and the performance of the latex film. However, at 28 days, the fracture toughness values seem to show a linear arrangement, with the following order: SA5M > SA4M > SA3M > SA2M > SA1M. The fracture toughness of the modified mortar increases to a certain extent with the increase in age, and there is little difference in the range of increase of the mortars modified with different emulsions. In addition, the fracture toughness of the modified mortar under mixed curing was higher than under standard curing, indicating that dry curing is favorable for polymerization film formation, thus improving the toughness of its modified mortar.