esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Land Cover and Relationship to CO\({}_{2}\)
While the land cover in Boston and Harvard forest was more uniformly urban and rural, respectively, we also parsed the CO\({}_{2}\) data by easterly and westerly wind sectors (Supplementary Material). In contrast to the Worcester results, the Boston and Harvard sector analyses in did not yield statistically significant differences, suggesting that the Worcester results were not driven by synoptic scale pollution patterns. Despite these strong associations between land cover, CO\({}_{2}\) flux, and observed mixing ratios at the sector scale (east _versus_ west), a more detailed spatial analysis, where we divided the source area into 45 degree wind sectors, yielded inconclusive results. There were no statistically significant correlations between ISA, EVI, or any of 8 land use classes and CO\({}_{2}\) mixing ratios for these more spatially resolved sectors. These results highlight several of the challenges associated with assessing the influence of heterogeneous land cover on CO\({}_{2}\) mixing ratios. Mean EVI was not necessarily lower in areas with higher human population and emissions: for example, the residential areas found in East Worcester (Figure 7) had both a high mean EVI and population density. We attribute these high EVI values in residential areas in part to grass, which increases mean EVI, but does not sequester as much carbon in biomass as forests [49]. This likely contributed to the inconclusive results in our analysis, further highlighting the challenges in using remotely-sensed measures of greenness as a proxy for biogenic fluxes in areas dominated by a mix of trees and grasses. Impervious surface area percentage is also a problematic proxy for CO\({}_{2}\) emissions. ISA can underestimate carbon emissions from point sources, which are small in area, and from major transportation arterials, which are relatively narrow and are often surrounded by vegetation to provide a sound buffer in urban areas. Furthermore, attribution of emissions to local energy usage is very challenging since energy demand is often spatially separated from power generation. While we generated mixed results using this directional analysis around the Worcester site, there appeared to be an association between land cover and CO\({}_{2}\) mixing ratios across Boston's urbanization gradient, as demonstrated by Figure 2. As ISA and anthropogenic emissions increased from Harvard Forest to Boston, so did CO\({}_{2}\) mixing ratios.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 5 Experiments
### Experimental Setup  
We initially train sentiment classification models for each of the three datasets. Then, for each instance \(X\) in the training and evaluation sets of each dataset, we generate candidate sequences using Algorithm 1. Since it is not possible to predict the proportion of tokens in \(X\) that are sentiment cues, denoted as \(p\), we test different values of \(p\) from the set {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7}.  
For both the sentiment classification model and the sentiment cue extraction model, we employed the bert-base-uncased ([[https://huggingface.co/bert-base-uncased](https://huggingface.co/bert-base-uncased)]([https://huggingface.co/bert-base-uncased](https://huggingface.co/bert-base-uncased)) (accessed on 19 March 2024)) architecture as our encoder and employed softmax [42] as the decoder. The learning rate is set to 0.00001, and we use the Adam optimizer with the applied cross-entropy loss function.  
In the training phase of the SCE model, we primarily use cross-entropy loss as the main evaluation metric. We systematically selected the model parameters that achieved the minimum loss in the validation set as the final parameters of the model.  
All computations are performed on a Tesla V100-SXM2-16 GB GPU manufactured by NVIDIA Corporation, headquartered in Santa Clara, CA, USA. Due to variations in the maximum length of samples in the three datasets and limitations in GPU memory, the number of candidate sequences generated per run differed. Specifically, we generated 100 mask sequences for SST-2 and Yelp in a single run, while for IMDb and ChnSentiCorp, we could only generate 10 mask sequences per run.  
In training the classification and SCE models, we adjust the batch size based on the dataset to optimize resource utilization and training efficiency. For SST-2 and Yelp, the batch size is set to 32, accommodating a larger number of instances per training step due to their relatively shorter text lengths. In contrast, for IMDb and ChnSentiCorp, which consist of longer text instances, the batch size is set to 8.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 5 Energy-Latency-Reliability Trade-Off
### Packet Success Probability
#### 5.2.1 Forward Error Correction (FEC)  
FEC is being widely used to cope with the random erroneous bits in a data packet. As an example, a convolutional encoder \((n,\ k,\ K)\) is implemented by adding \(n-k\) redundant bits to the actual \(k\) bits of data. The number of bits upon which the encoder's output depend, \(K\), is called the constraint length or depth of the code. The ratio \(r=\frac{k}{n}\) is called the code rate. The performance of a convolutional code is also characterised by its free distance, \(d_{m}\), which is the minimal hamming distance [61] between different encoded sequences. This means that changing one bit in the message sequence will change at least \(d_{m}\) bits in the coded output sequence [62].  
Figure 16 shows the impact of the free distance on the performance of the code. The asymptotic coding gain that can be achieved can be expressed as follows [62]:  
\[G_{FEC}=10\ \log(d_{m}\,r). \tag{18}\]  
Therefore, the used code (\(d_{m}=6\), \(r=\frac{1}{2}\)) has a theoretic asymptotic coding gain of 4.77 dB. However, the real achievable gain is considerably less and it is generally between 2 and 3 dB [63]. In this case study, a convolutional code with a rate of \(r=\frac{1}{2}\), a constraint length of \(K=4\), and free distance at \(d_{m}=6\) is considered [62]. Moreover, the error correcting ability of a code depends on the decoding method. The probability that a bit will be received in error when using convolutional FEC and Viterbi decoding, without considering the influence of an interleaver to cope with burst errors, can be approximated using [41]  
\[P_{e}^{FEC}\approx\frac{\beta_{free}}{k}\Big{(}2\sqrt{P_{e}(1-P_{e})}\Big{)}^{ d_{m}}\approx\frac{\beta_{free}}{k}2^{d_{m}}P_{e}^{\frac{d_{m}}{2}}, \tag{19}\]  
where \(\beta_{free}\) is the total number of non-zero information bits of all paths with a weight of \(d_{m}\). It should be noted that \(\beta_{free}\) depends on the data pattern and is totally random. Figure 17 shows the impact of \(\beta_{free}\) on the performance of the error correcting code. For the sake of simplicity, in this study, \(\beta_{free}\) was fixed at 200, which is logical when using payloads of 160 bits. In this case, the probability that a packet from A arrives error-free at B is given by  
\[P_{FEC}=\Big{(}1-P_{e}^{FEC}\Big{)}^{N}. \tag{20}\]  
When FEC is used, the required time to send a packet is given by  
\[t_{TRX}^{FEC}=\frac{|FEC}{D_{R}}, \tag{21}\]where \(l^{FEC}\) is the packet length when using FEC. Therefore, the energy consumption of the transceiver can be expressed as follows:  
\[E_{TRX}^{FEC}=V\ t_{TRX}^{FEC}\ (I_{TX}+I_{RX}). \tag{22}\]
## 2 The IoV Scenario and Use Cases
### Use Cases
#### 2.2.3 Extended Sensors  
Extended sensors area encompasses the collection and exchange of data (e.g., video, sensors, etc.) captured by any node in the scenario, including the network, vehicles, or pedestrians, to improve the perception composited by each vehicle. Requirements in terms of reliability and maximum latency are similar to the ones defined for advanced driving.
## 2 Background Studies and Plate Load Test Technique Principles
Rock mass deformations are measured in boreholes behind each loaded area and across the test gallery as illustrated in Figure 1. Two types of plate load tests exist: \"flexible plate loading method\" which is associated with uniform stress boundary conditions and \"rigid plate loading method\" which implies uniform displacement boundary conditions. When testing rock, then the plate may not have adequate stiffness leading to complex stress distributions, thus, the plate stiffness should be at least 100% more than the stiffness of the rock mass [14]. The loading and unloading deformation moduli are calculated from the data determined from the cyclic loading of the rock mass. Based on elasticity theory, deformation measurements for various load cycles are utilized to compute the deformation moduli. Depending upon the type of the test, i.e., rigid or flexible loading, the following equations are used respectively [10]. These equations are commonly used in practice to determine rock mass modulus from in-situ testing data:  
\[w=\frac{aq(1+v)}{2E}\left[2(1-v)\cot^{-1}\left(\frac{z}{a}\right)+\frac{az}{z^ {2}+a^{2}}\right] \tag{1}\]  
\[w=\frac{zq(1+v)}{E}\left[1-z\left(a^{2}+z^{2}\right)^{-1/2}\right]+\frac{2q \left(1-v^{2}\right)}{E}\left[\left(a^{2}+z^{2}\right)^{1/2}-z\right] \tag{2}\]  
\[E_{m}=\frac{2q_{av}\left(1+v^{2}\right)}{w_{z}}\left[\left(a^{2}+z^{2}\right) ^{1/2}-z\right]-\frac{q_{av}z(1+v)}{w_{z}}\left[z\left(a^{2}+z^{2}\right)^{-1/ 2}-1\right] \tag{3}\]  
where \(E_{m}:Deformation\) modulus, \(v\) = Poisson ratio of the rock mass, \(W_{Z}\) = Measured displacement at depth \(Z\) under a loaded surface, \(a\) = radius of the loaded rock mass surface, \(q_{av}\) = average applied stress and \(Z\) = depth of the displacement measurements. \[E_{m}=\frac{q_{av}a}{2w_{z}}\left[2\left(1-v^{2}\right)cot^{-1}(z)+(1+v)\frac {z}{z^{2}+1}\right] \tag{4}\]  
\[E_{m}=q_{av}\left(\frac{K_{z1}-K_{z2}}{W_{z1}-W_{zz}}\right) \tag{5}\]  
Figure 1: Plate load test set up by ISRM suggested method [5]. where \(w\) is the vertical displacement, \(a\) is the radius of the loading plate, \(q\) is the applied stress, \(E\) is the modulus of elasticity, \(v\) is the Poisson's ratio and \(z\) is the depth of deformation measurement along the loading plate axis.
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 2 Materials and Methods  
### Data Collection and Expansion  
In this research experiment, the resources from the \"PlantVillage dataset\" were utilized, which contains samples of images of disease on potato leaves, including select images of potato leaf diseases, as shown in Figure 1. Owing to the uneven distribution of data labels, three types of potato leaf disease samples were chosen: late blight, early blight, and anthracose. A total of 486 relevant dataset images were selected.  
Figure 1: Partial Potato Leaf Disease Dataset.  
Considering the relatively limited dataset available, a series of image preprocessing techniques were employed to enhance the useful information within the images. This encompassed operations such as Gaussian blur, brightness adjustments, contrast enhancement, image rotation, and flipping, with the effects of some of these operations shown in Figure 2. The dataset for potato diseases is comparatively smaller than larger datasets, making it susceptible to overfitting during training. To address this issue, data augmentation was applied to increase the dataset size and mitigate overfitting. Data augmentation not only enhances the characteristics of disease spots in the sample images, but also improves the model's ability to learn these spots, thereby enhancing model accuracy. Consequently, the original set of 486 images of late blight, early blight, and anthracose on potato leaves underwent augmentation, resulting in a total of 2000 augmented images, with 1600 designated for the training set and 400 for the validation set.  
Finally, the images were normalized to a resolution of 256 \(\times\) 256 pixels, and the disease spots on potato leaves in these images were annotated and analyzed. The \"Labelme\" tool was utilized for marking the disease spot regions on the potato leaves in the images. These annotation details were stored in a format known as JSON. Subsequently, the \"Labelme_to_dataset\" command was employed to convert these label data into binary PNG images for further processing. Figure 3 presents examples of annotated images, with the disease spot regions marked in red, while black represents the background.
### Motivation  
The random deployment of UWSNs nodes, the difficulty of charging these nodes, minimizing power consumption, propagation delay, and bandwidth issues must all be addressed. Data forwarding through multiple nodes from source to destination is better to solve the network reliability issues. Still, the latency will be high due to various nodes in the data processing. The motivation of this paper is from [7], where the authors focused on reducing energy consumption, increasing network lifetime, and enhancing the packet delivery ratio. Data is transferred on demand to avoid voids and trapped nodes in the data participating process. Due to the abovementioned issues, we proposed the cooperative energy-efficient routing protocol (CEER) to increase the network's lifetime and reliability.
## 3 Spatial Location Representation Map Construction
```
Input: Grid cell response value, place cell distance threshold \(r_{th}\) Output: Spatial Location Representation Map BEGIN: FOR Get grid cell response values Updating winning place cells through competitive Hebb learning network Calculate the Euclidean distance \(r_{b}\) between Current place cell and nearby place cell IF \(r_{b}\)<\(r_{th}\) The previous place cell can represent the current scene, continue run forward ELSE The previous palace cell is not enough to represent the current scene and construct a new place cell END IF IF IF the movement is not over Continue forward motion and update grid cell response value information ELSE Output spatial location representation map END IF ND FOR
```  
**Algorithm 1** Spatial location representation map construction algorithm  
Figure 7: Comparison of the localization errors under different distance thresholds. Step 4: When the mobile robot runs to the boundary cell excitation zone, the distance-aware information and angle-aware information of the mobile robot are relative to the environment boundary. They are mutually excited and inhibited in order to activate the boundary cells, which are used to correct the place cell response values and to eliminate the accumulated errors that are generated due to the long time running of the mobile robot. At the same time, the boundary cell response values are mapped to the input layer of LAHN, and the grid cell distribution law in the current scene is updated simultaneously. The place cell correction update rules are as follows:  
\[p_{bc}(t)=\left[p_{c}(t)+\left(\prod_{i=1}^{n}f_{i}(R,\theta,t)/\max_{x} \right)^{\frac{1}{n}}\right]_{+} \tag{15}\]  
where \(f_{i}(R,\theta,t)\) is the boundary cell firing response value, \(p_{bc}(t)\) is the place cell response value after the boundary cell response correction, and \(p_{c}(t)\) is the place cell response value before the boundary cell response correction. \(n\) represents the number of boundary cells.\(\left[\bullet\right]_{+}\) indicates that the output value is non-negative. Figure 8a depicts the pre-correction place cell response map, revealing the presence of an accumulated error resulting from prolonged operation of the mobile robot. This error is evident in the place cell response map, where a single place cell fails to generate a response to the current position. In Figure 8b, the post-correction position cell response map is presented. The comparison with Figure 8a demonstrates that the corrected place cell response map exhibits a solitary, distinct place cell response point, thereby enhancing the accuracy of current position estimation by eliminating interference caused by redundant place cell responses.
## 1 Introduction
Conventional optical systems, based on \"glass/air\" architecture, lead to an index contrast (\(\Delta n\)) equal to 0.5. However, most BSW devices have an index contrast in the range from 0.1 to 0.3. This causes weaker refractive power when conventional component designs are applied to the BSWs. We will mitigate this problem by modifying the circular geometry of the lens to the prolate ellipse, which can compensate the shortened optical path length due to lower \(\Delta n\). The remaining part of this paper is organized as follows. Section 2 presents the investigation of the smallest number of index steps for a conventional Luneburg lens design. In Section 3, to transit to the Bloch surface wave devices, we briefly introduce the multilayer and its properties, e.g., band-gap diagram, dispersion curves, and the effective refractive indices of the surface mode. The mitigation plan by elongation of the lens shape will be discussed in Section 4. Section 5 summarizes and concludes.
## 2 Materials and Methods
### DLSS Data Construction
#### 2.2.3 U-Net, SegNet, DeepLabv3+ Algorithms
DeepLabV3+ uses atrous separable convolution by combining atrous convolution with separable convolution in addition to the ResNet structure proposed in DeepLabV3 to solve the performance limitations of the existing fully convolutional network (FCN) [20]. In addition, low-level features were extracted using a deep convolutional neural network (DCNN) for the input image, and to obtain high-level features in the encoder, multi-scale targets were extracted after DCNN using atrous spatial pyramid pooling (ASPP). The ASPP technique has the advantage of efficiently utilizing various features of the image through atrous convolution [23]. In this study, the learning data were constructed through vectorizing to extract the boundary data for PWDT from the ground truth based on TSUI.
## 3 The Proposed Protocol
### System Model  
5G is the next mobile radio generation that supports ultra-high data speeds and low latency [25]. It was predicted that the number of 5G devices would be high, with high generated traffic [26]. Thus, there is a critical need for the improvement of cell deployment. The 5G new antenna techniques use the mmWave carrier frequencies. mmWave offers a short range of communication with a large amount of data [27]. There are several advantages of using mmWave frequencies in 5G networks, such as privacy and security, due to the short transmission range of mmWave, and also reusing the same frequency in a very short distance. Thus, many Base Stations should be placed at short distances to offer local management services with better frequency reuse [28]. The deployment of 5G infrastructure requires many Base Stations (BSs), due to its shorter communication range than 4G. For network management purposes, the 5G standard uses a notion of a zone, which is a group of a few BSs. Therefore, we assume that the 5G-V2X network is divided into \(N\) zones, where each zone consists of a few BSs, as shown in Figure 2. Our proposed system model includes the certificate authority (CA) that offers security services for the BSs and vehicles. CA has a map for the surrounding road and the positions of BSs on the road. CA initializes security parameters for the BSs in each zone, as shown in Figure 3. Each BS stores the security parameters that are configured by the CA. When entering a zone, each vehicle connects to the nearest BS and
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 2 Research Methodology
### Study Selection  
A comprehensive search of databases (presented in Figure 2) and other sources yielded a total of 730 search results. After removing duplicate entries, 356 studies remained for a title and abstract screening, and their eligibility was assessed based on predefined inclusion criteria. From this screening process, 41 papers were identified for further analysis, and their full texts were thoroughly examined. The included papers comprise 15 from conferences and 26 from journals. Among the journal papers, the sources are diversified across 19 different journals, with 13 classified as Q1, 4 as Q2, and 2 as Q3. The papers were divided into three categories based on the type of BCI (active, reactive, or passive).
# Formation Control of Nonlinear Multi-Agent Systems with Nested Input Saturation
## 2 Problem Formulation and Preliminaries
In order to impose adaptive performance characteristics we build on the adaptive performance control technique [30], to design online adaptation laws for the user-predefined specifications (i.e., speed of convergence and steady-state error) with respect to the input limitations that inevitably prevent the accurate tracking and may put the system at risk, i.e., lead to unstable behavior. To address the previously stated problem, we establish the following assumptions:  
**Assumption 1**. : _The communication graph \(\mathcal{G}\) is connected, and there exists at least one \(b_{i}\
eq 0\), \(i=1,2,\ldots,N\)._  
**Assumption 2**. : _The system (1) under consideration is input to state stable._  
**Remark 1**. : _Assumption 1 implies that \(L+B\) forms a strictly diagonally dominant \(\mathcal{M}\)-matrix [31]. An \(\mathcal{M}\)-matrix possesses non-positive off-diagonal entries and all principal minors non-negative, ensuring the positive definiteness of \(L+B\). Assumption 2 is reasonable for stabilizing input constrained systems [32]. Within the input-constrained MAS framework, characterized by strong inter-agent couplings, maintaining bounded signals for a general system is quite challenging, even if each agent initializes within a set contained within its own region of attraction._
###### Abstract  
The simulation of external heat flow has a pivotal role in the vacuum thermal test of spacecraft. The key to simulating the external heat flow of a spacecraft through an infrared heating cage lies in the calculation of radiative heat transfer, and existing Monte Carlo simulation methods for simulating the external heat flow of an infrared heating cage have the disadvantages of complicated modeling and slow calculation speed. In this paper, we consider the spacecraft and infrared cage spacing, partition height, partition arc length, curvature, circumferential relationship, radial relationship, and other variables. The particle swarm optimization-support vector regression (PSO-SVR) method is used to establish the angular coefficient relationship model between spacecraft and infrared cages with different shapes, which realizes the rapid prediction of heat flow in the infrared cage. The angular coefficients obtained by the rapid prognostic model are essentially the same as those obtained by Monte Carlo simulation, while the efficiency is improved by 29,750 times. Taking the vacuum thermal test of a small thermal control star as a case study, the prognostic error gradually decreases with the increase of heat flow, and the maximum error is 6.1%.  
infrared heating cage; radiant heat transfer; rapid prediction method; PSO-SVR model Article