esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
## 4 Results and Discussion
### Case Studies: Carbon Tax Price Projection for Canada and Sweden
#### 4.6.3 Comparative Analysis  
Comparing the two case studies reveals several insights: 1. Adaptability to policy changes: The TCN model showed strong performance in both cases, adapting well to Canada's introduction of a new national system and Sweden's long-term policy adjustments. 2. Accuracy across different scales: The model maintained good accuracy despite the different scales of carbon prices in Canada (tens of dollars) and Sweden (hundreds of kronor). 3. Long-term vs. short-term performance: While the Canadian case demonstrated the model's short-term adaptability, the Swedish case showcased its ability to capture long-term trends and adapt to gradual policy changes. 4. Robustness across different carbon pricing histories: The model's strong performance for both a relatively new system (Canada) and a well-established one (Sweden) suggests its robustness across different carbon pricing contexts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Actual Price (\$/tCO2e)** & **Predicted Price (\$/tCO2e)** & **Absolute Error** \\ \hline
2018 & 15.00 & 14.82 & 0.18 \\
2019 & 20.00 & 19.65 & 0.35 \\
2020 & 30.00 & 29.78 & 0.22 \\
2021 & 40.00 & 41.23 & 1.23 \\
2022 & 50.00 & 49.89 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Actual vs. predicted carbon tax prices for Canada (2018–2022).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Actual Price (\$/tCO2e)** & **Predicted Price (\$/tCO2e)** & **Absolute Error** \\ \hline
2000 & 370 & 368 & 2 \\
2005 & 910 & 895 & 15 \\
2010 & 1050 & 1080 & 30 \\
2015 & 1120 & 1135 & 15 \\
2020 & 1190 & 1175 & 15 \\
2022 & 1200 & 1190 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Actual vs. predicted carbon tax prices for Sweden (2000–2022).  
These case studies highlight the TCN model's versatility and effectiveness in projecting carbon tax prices across diverse policy environments, strengthening the case for its broader applicability in carbon price forecasting.
# Influence of Metro Track Irregularities on Pantograph Vibration and Its Interaction with Catenary
## 4 Result and Analysis
### PC Contact Forces  
The PC normal force and lateral friction force at speeds of 42 km/h and 53 km/h were extracted and transferred to the frequency domain. The results are shown in Figure 10.  
It can be observed from Figure 10 that there are two peaks on the amplitude-frequency curves of PC contact forces. When the operating speed increases, the amplitude of contact forces increases significantly in the entire frequency domain, and the peak frequencies also increase, but the corresponding vibration wavelengths remain unchanged. Among them, the main peaks of the normal force and lateral friction force are located in the wavelength range of 10-20 m. The secondary peak of the normal force is relatively wide, and the  
Figure 10: PC contact force under two speeds: (**a**) normal force; (**b**) friction force.  
Figure 9: Vibration responses of the vehicle system at 53 km/h: (**a**) vertical responses; (**b**) lateral responses.  
corresponding vibration wavelength range is 3-8 m, while that of the lateral friction force corresponds to a narrow vibration wavelength of 3-4 m.  
For the normal force, the two peaks correspond exactly to the three main peaks in the measured track irregularity spectrum. For the lateral friction force, the two peaks correspond to the first and the third peaks in the measured track irregularity spectrum. The results show that the multiple broadband spectral peaks existing in the track irregularity of Metro Line 1 in Z City not only aggravate the vibration of the vehicle but also increase the vibration and the friction of the pantograph.
# A Differentiated Anonymity Algorithm for Social Network Privacy Preservation
## 3 The Differentiated Attribute-Preserving Model
### Graph Utility Measurement  
As pointed out in previous sections, the number of edge or node additions (deletions) is the most common utility loss measurement, though this metric is not effective as it neglects the different impacts of various graph operations on the social network structure. To settle this problem, in this paper, we try design a proper utility loss metric function named UL(G,G') shown in Equation (2). This metric measures both the structure interference and information loss of different anonymizing operations. Then we may optimize the 3 anonymizing operations according to the metric. Thus, the anonymity algorithm proposed in Section 3.2 may result in higher data utility based on the data utility metric UL.  
\[\begin{array}{c}\mathrm{UL}(\mathrm{G},\mathrm{G}^{\prime})=a\cdot(TC)+b \cdot(IL)\\ s.t.\left\{\begin{array}{c}TC=\alpha\cdot\frac{APL^{\prime}-APL}{APL}+\beta \cdot\frac{CC^{\prime}-CC}{CC}\\ IL=\frac{[SV\times RV]}{|V|}\\ a+b=1\\ \alpha+\beta=1\end{array}\right.\end{array} \tag{2}\]  
where G is the original social network graph and G' is the published anonymous graph. The formula TC captures the variable quantity of the social network structure and the formula IL measures the attributes' information loss. The constants a, b are used to trade off the information loss and structure interference, and the social network publisher may modulate a, b based on the purpose of publishing the data. For example, if the published data is targeted at analyzing characteristics of the network structure, then the publisher may make a larger than b.  
The APL and CC represent, respectively, the average path length and clustering coefficient of graph G that have been shown to be essential for evaluating the structure of the social network graph [3; 6; 11], while APL' and CC' are two parameters measuring the topology features of G'; The constant \(\alpha\), \(\beta\) may be different values, respectively, according to different social networks.  
The set SV is the set of sensitive vertices whose S is H or M, and we must protect these nodes' information before publishing social network data; the set KV is the set of key nodes in the social network which are important for the social network graph structure.
## 3 Dynamic Network Layout Algorithm
### Initial Positioning for Newly Added Nodes  
The position of a node is generally determined by the collective decisions of all its neighboring nodes. When the positions of a node's neighboring nodes are already established, ideally, the node should be placed at the centroid or weighted centroid of all its neighboring nodes, as shown in Figure 2. However, this approach has some drawbacks. First, when adding not just an isolated node but a set of nodes which forms an connected  
Figure 1: The flow diagram of the SIPA algorithm.  
structure, simultaneously adding these nodes result in unreasonable initial positions for the newly structure. Moreover, when the added nodes share the same neighboring nodes, these nodes will have the same initial positions, causing node overlap.  
To address these issues, we employ the Sorted Sequential Barycenter Merging (SSBM) method [42] to assign positions to the newly added nodes. The positions of the nodes to be added are determined sequentially based on the number of connections with existing nodes, thereby reducing the probability of random placement of nodes, as shown in Figure 3.  
The calculation of positions for the newly added nodes is as Formula (1).  
\[x_{i}=\begin{cases}\frac{1}{|C_{i}|}\sum\limits_{j\in C_{i}}x_{j}+e_{1},&|C_{i} |\geq 2\\ x_{j}+e_{2},&|C_{i}|=1\text{ and }j\in C_{i}\\ e_{3}&|C_{i}|=0\end{cases} \tag{1}\]  
where \(C_{i}\) represents the set of nodes that are already assigned positions and are connected to the new node \(n_{i}\). \(x_{i}\) represents the position coordinates of node \(n_{i}\), and \(x_{j}\) represents the position coordinates of node \(n_{j}\). \(e_{1}\), \(e_{2}\) and \(e_{3}\) are small random vectors that are used to ensure initial positions are not the same for nodes sharing identical neighboring nodes.
# Coronary Centerline Extraction from CCTA Using 3D-UNet
## 3 Proposed Method  
### Neural Network Architecture  
The idea for creating a 3D U-Net network came after observing the results of U-Net convolutional networks for biomedical image segmentation [21], which allowed a fully convolutional neural network to provide a good segmentation even when trained with a small training dataset. Additionally, the belief was further consolidated when observing the results of the U-Net applied for liver segmentation and vessel exclusion [22], and for retina vessel segmentation, a similar task of centerline segmentation was conducted. The work on retina vessel segmentation was refined many times, in several papers [23; 24; 25].  
A similar extension of the network architecture to 3D was also presented in [26], for Xenopus kidney segmentation, in which the ground truth segmentation was completed after training using sparse annotation. This is, in a way, similar to the annotation for the dataset used in this paper, where not every vessel is annotated, and the network needs to learn to extrapolate the provided cases. Another variation of the 3D U-Net was introduced in [27] for multiple organ segmentation tasks.  
The design of the U-Net follows two important steps, similar to an autoencoder network. The first one is contraction, in which successive rounds of two convolutions and a max pooling to reduce the output size by half are applied on the input, as presented in Figure 1. For each round, the number of filters for the convolution is doubled. The bottom layer will have the most feature maps but will also be the smallest in size. Its purpose is to learn an encoded representation of what it needs to be segmented. The convolution with 3 \(\times\) 3 \(\times\) 3 kernels means that one pixel from all the borders will be lost. To alleviate this problem, padding was employed.  
The second one is expansion. Starting from the bottom layer, successive rounds of upsampling, concatenate, and two convolutions (also with padding) are applied. The upsampling resizes the feature vector. Along with the information concatenated from the same-size input of the contraction, the two convolutions can reconstruct the image in its original size. Transposed convolution can be used as an operation instead of upsampling. However, this leads to an increased number of parameters to train.  
After the last expansion, another convolution is applied with the number of kernels equal to the number of features to extract. Since centerline extraction aims for binary segmentation, only one last kernel with a sigmoid activation function was required.  
The loss function is chosen such that the segmentation behaves similarly to a pixel-wise classification function.
# An Analysis of the Use of Feed-Forward Sub-Modules for Transformer-Based Image Captioning Tasks
## 2 Background  
### Algorithms in Deep Learning  
There are several deep learning algorithms in use today in research and industry by the computer vision and natural language processing communities. The recent popularity of deep learning algorithms can be attributed to their success in the image recognition field where AlexNet [28], which uses deep convolutional neural networks, achieved a top-5 error rate of 15.3%; 10.8 percentage points higher than the second ranked model, in the ImageNet Large Scale Visual Recognition Challenge [17]. This error rate has been further cut down to 3.5% [29] compared to the human error rate of 5%. CNNs excel at computer vision tasks where they are able to process the spatial and temporal image data dependencies by applying convolutions using filters to create feature maps of different regions of the input image. They are usually applied to tasks such as image classification, image recognition, and image retrieval using powerful architectures such as ResNet [14], and VGGNet [15]. RNNs are used to deal with sequential data. This kind of data requires a network to use previously generated data to make future predictions. They achieve this by incorporating a sort of memory referred to as a hidden state, to save contextual information, that is propagated through the network at each step of a sequence. They are mostly applied as LSTMs or GRUs rather than Vanilla RNNs in tasks such as machine translation [18; 30], and automatic summarization [31; 32]. MLPs also referred to Artificial Neural Networks (ANNs) and feed-forward networks are the simplest and most versatile of the bunch of deep neural network algorithms. They are comprised of a bunch of neurons in each layer which process the inputs in the forward direction. These neurons can learn a mapping of almost any input data to a required output form with varying degrees of accuracy. They are commonly used to process text and tabular data in applications such as classification and regression prediction. As the subject of this paper, they are discussed in more detail in later sections of this paper. Due to the fact that image captioning involves both images and the text, the two most common deep learning algorithms involved are the CNN to encode the image features into a fixed vector representation, and an RNN to learn to generate the text captions. This is further discussed in the subsequent sections.  
Figure 2: The general framework diagram showing the fully linear encoder using deep fully-connected layers (FC) and a decoder that also uses linear layers to encode caption embeddings. Intra-attention is still maintained in the cross-attention section. The normalization and residual sections are not shown for the sake of brevity.
# Comparative Study on Load Monitoring Approaches
## 3 Advantages and Disadvantages of Intrusive and Non-Intrusive Load Monitoring
### Advantages and Disadvantages of Non-Intrusive Load Monitoring
#### 3.2.2 Disadvantages  
Like ILM, non-intrusive load monitoring also observes limitations in its operation. Among the obstacles that contribute to its reduced performance are:  
Lower accuracy: NILM tends to provide less accurate data than ILM because it is based on mathematical models that may not take into account all relevant variables, unlike the individual sensors placed on each load in the case of ILM. This lower accuracy in load classification increases the rate of undetected errors.  
Limited detection: The ILM is unable to detect devices of a certain type. Continuously variable loads (HVAC), such as dimmers, electric drills, and split air conditioners, have electronic components that vary their power consumption over time, without a fixed number of states, which makes it difficult to allocate power consumption. In addition, it is difficult for the MILM to distinguish between units with identical electrical operations. However, to overcome this difficulty, it is possible to make use of harmonic current signatures to differentiate between devices that are confused.  
Technical limitations: NILM may be limited by the technical capabilities of the load detection algorithms, which may limit the reliability and accuracy of the monitoring.  
In summary, while the NILM approach has the advantage of being less expensive and less intrusive than ILM, it may be less accurate and have technical limitations.
## 3 Results
### Temporal and Spatial Resolution
In terms of spatial resolution, 28.4% of studies employed very high spatial resolution data with a pixel size smaller than 10 m. Data with a spatial resolution below 30 m is used in 53.7% of the reviewed articles. Medium spatial resolution data were applied in 17.3% of the articles. In total, more than 70% of the reviewed articles used a spatial resolution lower than 50 m to assess small-scale agriculture in West Africa.
## 1 Introduction
Reanalysis datasets have many advantages, such as global coverage, multivariable outputs, and spatial integrity [4]. These characteristics can provide great value which results in reanalyses being widely used to understand atmospheric processes. However, the degree of sophistication to which water vapor and its variability are represented depends on the reanalysis system, which observations it assimilates, which microphysical and chemical parameterizations it includes, and how those parameterizations affect water vapor distributions [25]. Additionally, reanalysis humidity records have inhomogeneity issues due to the assimilation of inhomogeneous radiosonde humidity data [26] and reanalysis water vapor information may be less reliable in areas where limited observations are available for the data assimilation, which often means humidity variables are less certain than other reanalysis outputs such as temperature or air pressure for which many more observations are available [27]. Therefore, it is important to compare water vapor observational data with independent reanalysis data in order to validate both types of datasets. COSMIC-2 (C2), a joint Taiwan/United States six-satellite mission, is a GNSS RO mission launched in June 2019 as a follow-on to the highly successful COSMIC research mission launched in 2006 [28]. Importantly, one of the main upgrades that C2 provides over its predecessor is a higher SNR than any other RO mission to date due to the advanced receiver and high-gain antenna onboard. This higher SNR reduces the contribution of thermal noise to bending angle errors and allows for the penetration of soundings lower into the troposphere [29]. Additionally, sampling totals and density have been improved relative to the COSMIC mission. Once fully operational, the C2 satellites are expected to produce over 5000 high vertical resolution profiles of bending angles and refractivity per day in the tropics and subtropics [29]. These advancements make C2 ideal for tropical moisture research. In this study, we validate this new C2 data by comparing C2 specific humidity (SH) profiles with profiles from two widely used atmospheric reanalysis datasets, ERA5 and MERRA-2. Our goals in this research are to assess the data quality of these datasets and determine their strengths and limitations, to quantify tropical and subtropical moisture and its variability, and to analyze when and why moisture differences can occur between the datasets. The structure of the paper is as follows: Section 2 highlights the three datasets and details the methodology used for this study; Section 3 presents the key results of the study, such as identifying the correlations between C2 and each reanalysis, quantifying the magnitude of any differences, and providing possible physical explanations as to why these differences can occur; Section 4 offers a brief discussion of the results; finally, the conclusions are provided in Section 5.
## 2 Mathematical Formulation
### Characteristic Curve  
In this study, normal inverse characteristic curves are considered for all relays. This type of curve is indicated in Equation (2), where \(A\) and \(B\) are parameters and \(TMS_{r}\) is defined as the time multiplying setting of relay \(r\). Additionally, the ratio between the fault current \(I_{rf}\) experienced by the relay and the pickup current \(ipickup_{r}\), labeled as \(PSM_{rf}\), is defined by Equation (3).  
\[t_{rf}=\frac{A.TMS_{r}}{PSM_{rf}^{B}-1} \tag{2}\]\[PSM_{rf}=\frac{I_{fr}}{ipickup_{r}} \tag{3}\]
## 3 Approach  
### Hardware Setup and Data Acquisition  
In this study, we used three cameras, one camera capable of capturing visible-range data and two cameras for capturing SWIR-range images (Figure 4). The visible light camera depicted in Figure 4a uses a Bayer filter to capture red, green, and blue wavelengths on the sensor (Figure 5a). This type of filter selectively permits the passage of visible light wavelengths corresponding to the red, green, and blue spectra, which are subsequently captured by the sensor. Figure 4b presents the ANT SWIR multispectral snapshot camera (Somerville, MA, USA), which possesses the capability to acquire images across nine distinct wavelength bands concurrently, again using the Bayer-like filter to store each wavelength's information on the image (Figure 5b). Figure 4c shows the Crevis HG-A130 SW camera (Yongin, Republic of Korea), which can capture light from a range of 400 nm to 1700 nm within a single image. As depicted in Figure 5c, each pixel receives all light within a specific wavelength range. Since our focus is on the SWIR range, we utilized the Crevis HG-A130 SW camera with a long-pass filter (900 nm \(<\lambda<\) 1700 nm) to capture the desired wavelengths selectively.  
Each camera offers distinct advantages for data acquisition. The Crevis HG-A130 SW camera (Figure 4c) captures a wide range of wavelengths in a single image, allowing flexibility in acquiring data at specific wavelengths through targeted filters (see Figure 6a). On the other hand, the ANT SWIR multispectral snapshot camera (Figure 4b) is effective in constructing a paired dataset for each wavelength because it simultaneously acquires data in each wavelength region for the given nine bands. This has the disadvantage of lowering the resolution. Figure 6b illustrates the data acquired with this camera.  
For the fruit and vegetable dataset, we captured RGB and SWIR images synchronously. In contrast, for the human vs. others dataset, we utilized eight visible-range and SWIR-band images captured using an ANT SWIR camera. Since all objects in the scenes were stationary, no calibration or pixel-wise alignment techniques were required.  
Additionally, the data were collected under two distinct lighting conditions. Fruit and vegetable images were acquired under natural sunlight, whereas human and mannequin datasets were captured under lighting designed to simulate solar radiation. Although the  
Figure 4: The cameras used in this study (**a**) FLIR RGB camera, (**b**) ANT SWIR multispectral snapshot camera, and (**c**) Crevis HG-A130 SW camera.  
Figure 5: Principles of image acquisition for each camera: (**a**) RGB camera, (**b**) snapshot SWIR camera, and (**c**) standard SWIR camera.  
absolute intensity of the reflected light varied due to factors such as angle, distance from the light source, and acquisition time, the relative intensity differences remained consistent across the scenes.
## 4 Evaluation  
The evaluation of the key enablers has been carried out in three experimental application cases: electric bike, smart metering and car sharing. The autonomic manager, service capability layer and CoAP based communication with low power networks has been evaluated in the smart metering case. The XMPP based communication overlay and Bluetooth smart connectivity has been evaluated in the electric Bike system case. XML based information exchange and M2M Gateway concept has been evaluated in car sharing case.