esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dexterous Grasping
#### 2.3.4 Gaussian Process  
The Gaussian Process (GP) models the relationship between inputs (X) and outputs (Y) as a distribution over functions as in Equation (6):  
\[\text{f}(\text{x})\ \sim\ \text{GP}\big{(}\text{m}(\text{x}),\text{k}\big{(} \text{x},\text{x}^{\prime}\big{)}\big{)} \tag{6}\]  
where \(\text{m}(\text{x})\) is the mean function and \(\text{k}(\text{x},\text{x}^{\prime})\) is the kernel or covariance function. Assuming a zero mean \(\text{m}(\text{x})=0\), the Radial Basis Function (RBF) kernel was employed due to its ability to capture both linear and non-linear relationships, as shown in Equation (7):  
Figure 8: Training results for LSTM, GP and Transformer models: (**a**) training and validation plot for LSTM, (**b**) training and validation plot for GP, (**c**) actual vs prediction for GP, (**d**) training and validation plot for Transformer model.  
\[\mathrm{k}\big{(}\mathrm{x},\mathrm{x}^{\prime}\big{)}=\sigma_{\mathrm{f}}^{2}\mathrm{ exp}\Bigg{(}-\frac{\left\|\mathrm{x}-\mathrm{x}^{\prime}\right\|^{2}}{2\mathrm{l}^{2}} \Bigg{)} \tag{7}\]  
where \(\mathrm{l}\) is the length scale controlling the smoothness and \(\sigma_{\mathrm{f}}^{2}\) is the signal variance.  
Given training data \(\mathrm{X}\) and \(\mathrm{y}\), Equation (8) shows the GP posterior distribution at a new input \(\mathrm{x}_{*}\):  
\[\mathrm{p}(\mathrm{f}_{*}|\mathrm{X},\mathrm{y},\mathrm{x}_{*})=\mathcal{N} \big{(}\mathrm{\mu}_{*},\sigma_{*}^{2}\big{)} \tag{8}\]  
where:  
\[\mathrm{\mu}_{*}=\mathrm{k}_{*}^{\mathrm{T}}\Big{(}\mathrm{K}+\sigma_{\mathrm{ n}}^{2}\mathrm{I}\Big{)}^{-1}\mathrm{y} \tag{9}\]  
\[\sigma_{*}^{2}=\mathrm{k}(\mathrm{x}_{*},\mathrm{x}_{*})-\mathrm{k}_{*}^{ \mathrm{T}}\Big{(}\mathrm{K}+\sigma_{\mathrm{n}}^{2}\mathrm{I}\Big{)}^{-1} \mathrm{k}_{*} \tag{10}\]  
Here, \(\mathrm{K}\) is the covariance matrix of the training data, \(\mathrm{k}_{*}\) is the covariance vector between \(\mathrm{x}_{*}\) and the training points, and \(\sigma_{\mathrm{n}}^{2}\) is the noise variance. The posterior variance \(\sigma_{*}^{2}\) provides a measure of uncertainty in the predictions.  
The hyperparameters \(\mathrm{l},\sigma_{\mathrm{f}},\sigma_{\mathrm{n}}\) were optimized by maximizing the log marginal likelihood:  
\[\mathrm{logp}(\mathrm{y}|\mathrm{X})=-\frac{1}{2}\mathrm{y}^{\mathrm{T}}\Big{(} \mathrm{K}+\sigma_{\mathrm{n}}^{2}\mathrm{I}\Big{)}^{-1}\mathrm{y}-\frac{1}{2 }\mathrm{log}\big{|}\mathrm{K}+\sigma_{\mathrm{n}}^{2}\mathrm{I}\big{|}- \frac{\mathrm{N}}{2}\mathrm{log}\,2\pi \tag{11}\]  
The model achieved an average Mean Squared Error (MSE) of 0.00833. Figure 8c,d illustrates the training data and prediction results.
# Synthetic Text as Data: On Usefulness and Limitations
## 3 Data Explanation  
### Kaggle Data  
MBTI (Myers-Briggs Type Indicator) is a personality assessment based on [PERSON]'s theory of psychological types. It classifies individuals into 16 personality types by combining four dichotomous dimensions: extraversion-introversion, sensing-intuition, thinking-feeling, and judging-perceiving. The dataset used in this study was obtained from a publicly available MBTI dataset on Kaggle [14], which compiles forum posts from users of PersonalityCafe, an online community. Each sample consists of the user's MBTI label and their recent posts. The text is informal and conversational in nature, often including abbreviations, slang, and internet-specific language, presenting unique challenges for natural language processing (NLP) tasks. This dataset includes 8675 user samples, each consisting of an MBTI label--one of the 16 types--and the user's 50 most recent forum posts on PersonalityCafe. A summary of the dataset is provided in Table 1.  
This dataset is particularly suitable for studying text data augmentation for several reasons. First, it presents a severe class imbalance: some types such as INFP and INTJ comprise over 1000 samples each, whereas others like ENFJ and ESFP appear fewer than 100 times. Figure 1 illustrates the distribution of sample counts across the 16 MBTI personality types in the dataset.  
This imbalance reflects a common real-world challenge in natural language datasets and offers a realistic setting to test whether synthetic data can improve minority class representation. For example, while the INFP class contains 1832 entries, ESTJ appears only 39 times--making this a practical scenario for evaluating the impact of augmentation. First, a manual inspection was conducted as a minimal verification step to ensure that the generated texts were appropriate for their corresponding personality types. Second, the dataset naturally gives rise to a few-shot learning situation for the underrepresented types.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Posts** \\ \hline ENTP & I’m finding the lack of me in these posts... \\ \hline INTJ & Dear INTP, I enjoyed our conversation the... \\ \hline INTJ & I tend to build up a collection of things... \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample text posts classified by MBTI types in [14].  
In conventional supervised learning, classes with only a handful of examples typically suffer from poor generalization. Augmenting those classes with GPT-generated text provides a direct way to assess how language models can alleviate data scarcity. Third, the dataset is composed of informal, user-generated text spanning personal anecdotes, reflections, and opinions. This variability in tone, vocabulary, and topic makes it a challenging yet realistic test case for generating stylistically consistent synthetic samples using LLMs.  
In summary, the MBTI dataset serves as an ideal case study for this work, as it simultaneously exhibits class imbalance, few-shot conditions, and stylistic diversity--three key factors that motivate the need for high-quality text augmentation.
# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems  
Power systems have become an integral part of the modern civilization and it consist of electronic components. It is important for stable and uninterrupted supply of power to identify the faults in the power systems whenever there is an accident. Over the yearsmany methods [1; 2; 3; 4; 5; 7; 8; 9; 10] have been introduced by many researchers for identification of faults in the power systems efficiently and quickly. However, the use of membrane computing models, i.e., spiking neural P systems for fault identification in power systems is a recent phenomena. In this section, we discuss these models. More specifically, we divide this section into the following subsections based on type of power systems and faults, i.e., (1) fault diagnosis for transformers; (2) fault diagnosis for power transmission networks; (3) fault diagnosis for traction power supply systems; (4) fault diagnosis in metro traction power systems; (5) fault section estimation of power systems; (6) fault location identification of distribution network; (7) fault lines detection; (8) fault diagnosis in electric locomotive systems.
## 2 Related Work
### Comparison with Existing Works  
While many existing ABE schemes support revocation at either the user or attribute level, few provide a revocation for both attributes and keywords that simultaneously addresses dynamic ciphertext and index updates in applications such as video anomaly detection. Compared with schemes such as [24, 31, 35, 37], which rely on time synchronization or re-encryption, our approach enables timely revocation and searchability withoutintroducing heavy computational or synchronization overhead. Compared to revocation schemes using negation clauses [25; 26; 27; 28], our approach achieves more efficient and flexible user-level revocation without modifying access policies. Compared with schemes that support user-level attribute revocation [15; 23; 30], our approach further introduces user-level keyword revocation. Under the post-search decryption model, our approach improves revocation efficiency and enhances security. In addition, few existing studies address revocable keyword searching, especially in combination with ABE techniques. Our approach introduces a novel use for the user management tree to revoke keyword index updates, while integrating an inverted index structure for efficient searching. This makes the proposed approach well-suited for accessing video surveillance data and revoking access to anomalies. The integration present in our methodology enables efficient user-level revocation and fine-grained keyword access control in dynamic security environments.  
**Organization:** Section 3 covers the necessary preliminaries. Section 4 outlines the framework and security models. Section 5 provides a detailed construction of the proposed scheme. Section 6 presents the correctness and security analyses. Section 7 discusses the performance evaluation. Finally, Section 8 concludes the paper.
## 3 Scheme Model
### Scheme Model  
As depicted in Figure 1, the considered network model has the following entities.  
* _Blockchain Network._ The private Hyperledger Fabric blockchain hosted on IBM's cloud infrastructure. The Hyperledger Fabric requires the use of a minimum of three peers: an endorser peer, which utilizes the smart contract to write new transactions; an orderer, which verifies the new transaction and writes it to a new block on the blockchain; and the committer, which checks the validity of all transactions on the blockchain and writes valid transactions to the ledger. Our blockchain network utilizes a voter peer, which utilizes our developed smart contract to write our transactions (any action taken within the election), and an orderer, which verifies and writes each transaction. Each of these peers act also as the committers for our network. In a production environment, additional peers can be added to act solely as committers, funds permitting. In accordance with [34], our organization and orderer have separate Certificate Authorities (CA). Hyperledger Fabric utilizes certificate authorities to identify each peer and define each functionality. If additional peers are added solely as committers, a third CA is required; however, all additional committer-only peers can share the third CA.
* _Voters._ The voter is an entity that can cast votes to the ballot. Note that voters do not maintain a whole copy of the ledger; but they can run the scheme using lightweight nodes, which lets them communicate with the network, read from the blockchain and submit transactions [35]. In our scheme, we implemented a preferential voting scheme; so, when casting a vote, the voters rank three candidates to receive vote payouts. The candidate selected first receives three votes, the second receives two votes, and the third one vote.
* _Organizer._ This is the organization hosting the election, such as small businesses, states and governments, etc. Specifically, the organizer is represented by a person in the IT department who has the technical skills to set up a blockchain environment on the IBM Cloud Platform. The organizer's main duty is to initiate the election, e.g., defining the candidates' names and configuring the blockchain on the IBM Cloud by  
Figure 1: Illustration of our considered scheme model. (Author’s own processing).  
defining the nodes necessary to be used to carry out the election, creating the channels between the nodes, and uploading/initializing the packaged smart contract.
# Challenges Associated with Implementing 5G in Manufacturing
## 1 Introduction
### Massive Machine-Type Communications (mMTC)  
5G offers massive machine-type communication (mMTC), which aims to support tens of billions of network-enabled devices to be wirelessly connected [1]. Today's communication systems already serve many MTC applications. However, the characteristic properties of mMTC, i.e., the massive number of devices and the tiny payload sizes, require novel approaches and concepts. 5G allows a density of one million devices per square kilometer [3; 6]. 5G will be able to carry a lot more data and transfer it much faster than 4G LTE, however, faster is not always better or even necessary in the Internet of Things (IoT) world, especially when it typically requires more power on the end device. So the 5G NR standard will introduce new device types, like Cat-M1 (operates at 1.4 MHz bandwidth) and narrow band (NB-IoT). Both NB-IoT and Cat-M1 devices can sleep for extended periods of time with extended discontinuous reception (eDRX) and power-saving mode (PSM) functionalities, which greatly reduce device power consumption This will enable lower-power, and battery-driven devices to be utilized. 5G in industrial automation will facilitate real-time wireless sensor networks with location and asset tracking. 5G networks will eventually be able to supersede wired connections in even the most demanding of applications, such as automation control and high throughput vision systems [7; 8].  
This paper differs from previous publications in this space in that it provides an overview of 5G and the technology required, how it will positively impact the future of the manufacturing sector, enabling it to move to industry 4.0 and the infrastructure requirement to enable an industry to adopt 5G. The paper is structured as follows: Section 2 discusses the potential and impact of 5G in manufacturing; Section 3 presents the use cases of 5G in manufacturing while Section 4 lists the infrastructure requirements for a 5G network; Section 5 discusses where 5G lies in the wireless spectrum and Section 6 outlines the concept and benefits of private 5G networks; Section 7 briefly discuss 5G security issues and Section 8 the 5G standard; Finally our conclusions regarding 5G and its role in manufacturing are presented.
# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 3 Proposed Method
### C2 Loss  
Edge detection is a common method for image segmentation based on grayscale variation. Its purpose is to extract the features of discontinuous parts of the image. The Canny operator is an edge detection technique introduced by [PERSON] in 1986 [PERSON] (1986). It involves five main steps: Gaussian filtering, pixel gradient calculation, non-maximum suppression, hysteresis thresholding, and isolated weak edge suppression.  
The Gaussian kernel used in Gaussian filtering is a two-dimensional Gaussian function with dimensions \(x\) and \(y\), which is typically used to remove the noise. The standard deviation in the two dimensions can be expressed in the form  
\[G(x,y)=\frac{1}{2\pi\sigma^{2}}\exp(-\frac{x^{2}+y^{2}}{2\sigma^{2}}) \tag{10}\]  
The Sobel operator calculates the gradient magnitude \(S\) and direction \(\theta\), and the formula is as follows, where \(d_{x},~{}d_{y}\) denotes the convolution kernel in the \(x\) and \(y\) directions.  
\[d_{x}=\begin{bmatrix}-1&0&1\\ -2&0&2\\ -1&0&1\end{bmatrix},~{}d_{y}=\begin{bmatrix}-1&-2&-1\\ 0&0&0\\ 1&2&1\end{bmatrix} \tag{11}\]  
\[S=\sqrt{d_{x}^{2}+d_{y}^{2}} \tag{12}\]  
\[\theta=\arctan\biggl{(}\frac{d_{y}}{d_{x}}\biggr{)} \tag{13}\]  
Non-maximum suppression retains the maximum gradient value at each pixel and filters out other values to make boundaries clear. The Canny operator sets an upper and lower bound: if the pixel value is greater than the upper threshold, it is considered as the boundary; if the pixel value is less than the lower threshold, it is considered not the boundary. If a weak boundary connected to a strong boundary at a pixel is considered as the boundary, the other weak boundaries are deleted.  
U2 Net utilizes the BCE loss function to calculate the pixel-level loss between the ground truth and predicted maps, like semantic segmentation tasks, where \(P_{G(r,c)},~{}P_{S(r,c)}\) denotes the pixel value at the location \((r,c)\) of the ground truth and predicted maps, respectively. But, in this way, U2 Net treats all pixels equally and has no focus, which may lead to the failure of edge detection.  
\[L_{1}=-\sum_{(r,c)}^{(H,W)}\Bigl{[}P_{G(r,c)}\log P_{S(r,c)}+(1-P_{G(r,c)}) \log(1-P_{S(r,c)})\Bigr{]} \tag{14}\]  
This paper utilizes the Canny detector to obtain the boundary of the objects in the ground truth and predicted maps and sums up the number of pixels where the true edge and predicted edge are not equal. The Canny loss formula is as follows:  
\[L_{2}=-\sum_{(r,c)}\Bigl{[}P_{G(r,c)}\
eq P_{S(r,c)}\Bigr{]} \tag{15}\]  
Figure 2: The structure of SCCA.  
Overall, combining BCE loss and Canny edge detection loss, this paper proposes a novel loss function, namely, C2 loss. While U2 Net effectively detects the salient objects, the edges of objects of predicted pictures are clearer. Figure 3 depicts the loss calculation process. The C2 loss can be expressed in the form as follows, where \(L\) is the total loss, and \(\alpha\) is the learning weight for edge detection:  
\[L=L_{1}+\alpha L_{2} \tag{16}\]
# A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
## 5 Experiments for Clustering in WSNs Problem
### Model Solution Representation  
A WSN is assumed to be a graph G with \(n\) nodes randomly distributed in the desired area. There are \(N\) nodes (\(N\) is set to 100, 200,... ) in a network spread out in a distributed two-dimensional space of \((0:M,0:M)\), where \(M\) equals 100, 200. The nodes of the WSN circulate in a two-dimensional area and are deployed as follows: (1) a model solution is constructed with spatial coordinates of nodes that can obtain distances to the BS and attribute links between the nodes, (2) clusters are figured out randomly with \(k\) clusters, and (3) the distance of the cluster member to the CH and the CHs to the BS are calculated for optimization of the network layout. Table 5 shows an example of the position of randomly distributed nodes in two-dimensional coordinates of a network zone. Each node can communicate with others by \(r\) transmission ranging. Node \(i\) can receive the signal of node \(j\) if node \(i\) is in the transmission range \(r\) of node \(j\). Table 6 indicates the attribution of the existing CHs in the sensor networks.  
In the following experiment, we consider two scenarios of equal clustering and unequal clustering in a WSN to create an optimization network. The equal clustering has cluster radii which are the same size. However, the group size of unequal clustering close to the BS is smaller than those that are farther  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Index** & **Node\({}_{i}\)** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **-** & **n** \\ \hline
**Cluster head** & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & - & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The attribution of existing cluster heads (CHs) if flag = 1 and node is set to cluster head (CH). Otherwise node is set to member node (SN).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Index** & **Node\({}_{i}\)** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **-** & **n** \\ \hline
**x** & **05** & 55 & 85 & 101 & 72 & 60 & 45 & 40 & 50 & 75 & 45 & 20 & - & 10 \\ \hline
**y** & **01** & 5 & 10 & 30 & 15 & 20 & 45 & 60 & 85 & 90 & 95 & 80 & - & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A sample of representative positions of the sensor nodes.  
from the BS. The uneven cluster sizes occur because the traffic relay load of the CHs near the base station is heavier than the CHs farther away from the BS.
# Optimization of Virtual Loudspeakers for Spatial Room Acoustics Reproduction with Headphones
## 1 Introduction
Since only one reproduction loudspeaker at a time is involved in reproducing the RIR sample, this approach is free from the effects of HRTF averaging, but at the cost of increased angular error. The aforementioned studies have used predetermined physical loudspeaker arrays for reproduction. Similar studies could still benefit from increased fidelity of spatial sound reproduction either by increasing the number of loudspeakers or by using the existing loudspeakers more efficiently. However, in many cases, adding loudspeakers is less feasible than optimizing the existing physical setup. When the reproduction of spatial audio is substituted with headphone listening, as in this study, the optimization becomes even more sensible. In theory, headphones enable the use of practically an unlimited number of virtual loudspeakers in any given direction. However, since the computational cost is directly proportional to the number of spatial channels rendered for headphones, using a smaller number of virtual loudspeakers is preferred. This requirement justifies smarter allocation of resources, which, in this case, means optimized virtual loudspeaker positions. This paper presents a method of determining a room-specific virtual loudspeaker array that reproduces spatial sound perceptually more efficiently than a predetermined conventional array. The proposed method aims to minimize the directional error of NLS as well as to enhance labor-to-quality ratio of the rendering. In other words, spatial sound can be reproduced either more accurately with the same number of virtual loudspeakers, or at the same quality with a reduced channel number. These so-called optimized loudspeaker setups are compared with uniformly distributed setups to measure how recognizable are the differences in reproduction. The paper is structured as follows. First, Section 2 outlines the structure of the position optimization system. Next, Section 3 describes the listening test, followed by the results in Section 4. Finally, more detailed analysis is presented with discussion in Section 5 and wrapped up in Section 6.
# An Efficient Framework for Securing the Smart City Communication Networks
## 2 Related Work
### Securing Smart City Communication Network  
Smart cities have piqued the interest of numerous disciplines, including scholars, enterprises, and governments, as a result of the rapid growth of ICT. Because a lack of proper cyber security can result in the theft of a user's sensitive data, utility fraud, and grid instability, cyber security is a major concern in the implementation and adoption of smart cities [5]. The United States' National Institute of Standards and Technology (NIST) established three critical needs for smart city security. These requirements are based on information availability, integrity, and confidentiality [6].