esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Study Area Description  
Mu Us Sandy Land (37\({}^{\circ}\)27\({}^{\prime}\)-39\({}^{\circ}\)22\({}^{\prime}\)N, 107\({}^{\circ}\)20\({}^{\prime}\)-111\({}^{\circ}\)30\({}^{\prime}\)E) is a famous desertified area in China. It covers an area of approximately 40,000 km\({}^{2}\) in the southern part of Ordos City in the Inner Mongolia Autonomous Region, the northern part of Yulin City in Shaanxi Province, and northeast part of Yanchi County in the Ningxia Hui Autonomous Region [21]. Our study area is in the southern edge of the Mu Us Sandy Land, Yulin City in the northwest of Shaanxi Province, with an elevation of 1206\(\sim\)1215 m [5]. Wind erosion, water erosion, and desertification are all serious problems in this region. The annual average temperature is 8.1 \({}^{\circ}\)C, annual average precipitation is 250-440 mm, annual average evaporation is 1904 mm, and the average wind speed is 3.5 m/s in the region. It is in the typical semi-arid temperate semi-arid continental monsoon climate zone and is an ecologically fragile agriculture-pasture ecotone. The sand ridges in the area are mainly composed of red or gray Cretaceous feldspathic sandstone. Feldspathic sandstone and sand alternately distribute in Mu Us Sandy Land [22]. Aeolian sandy soil is the main soil type, accounting for 93.4% of the total area of Mu Us Sandy Land.
## 5 Conclusions  
In this article, a novel hybrid network is proposed to predict Vs. The proposed hybrid network can tackle the limitations of conventional logging methods, particularly in complex geological environments like the dense sandstone reservoirs of the Junggar Basin. The network replaces the traditional single CNN layer with the Inception module, enhancing feature extraction by capturing multi-scale information. The attention mechanism is also incorporated to dynamically focus on different time steps in the sequence, increasing the network's sensitivity to critical temporal features. BiLSTM captures spatio-temporal dependencies in both directions and focuses more comprehensively on logging parameter sequences. A sliding window approach, which considers features from four steps before and after the current depth, captures temporal variations and trends more comprehensively.  
The proposed network structure significantly outperforms previous models through multiple comparative tests, demonstrating superior prediction accuracy and generalization. The results indicate that in the formation of sandstone in Y3 and Y1 wells, The R\({}^{2}\) of the proposed network is higher than 99% while the MAE value is low, which is notably better than that of standalone Inception and BiLSTM models. This performance confirms the proposed network's ability to extract multi-scale features effectively while considering spatio-temporal dependencies in logging data, leading to enhanced sensitivity and improved Vs prediction accuracy.  
Conceptualization, P.W. and Y.L.; methodology, P.W. and Y.L.; validation, Y.L. and J.L.; formal analysis, Y.L. and J.L.; investigation, J.L.; resources, Z.G.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L.; supervision, Z.G.; project administration, Z.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the State Key Program of the National Natural Science Foundation of China (Grant Number: 42030805) and in part by the Scientific Research Start-up Funds of Guangdong Ocean University (Grant Number: 360302042301).  
The data presented in this study are available on request from the corresponding author due to author is not authorized to share the experimental data.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Generative Language Model for Character Utterances
#### 3.2.1 Character Clustering Using Utterances  
We adopted the Project Dialogism Novel Corpus (PDNC) dataset [33], which consists of 28 novels, to construct a language model. In the PDNC dataset, speakers (i.e., characters) and utterances are provided in a structured form, making it easy to use for our purpose. We have converted the PDNC dataset into a set of _character documents_. Assume that the original PDNC dataset can be represented as a set \(D_{o}=\{(c_{1},u_{1}),(c_{2},u_{2}),\ldots\}\), where \(c_{i}\) and \(u_{i}\) represent the \(i\)-th character name and an utterance, respectively. Let \(C\) be a set of unique characters; note that \(|D_{o}|\) is not necessarily equal to \(|C|\). Given the original PDNC set, we created a set of character documents \(D_{c}=\{d_{1},\,d_{2},\,\ldots\}\), where \(d_{j}\) denotes a set of all utterances of the \(j\)-th character in the character set \(C\). Since this creates a document for each unique character, \(|D_{c}|\) will be equal to \(|C|\).  
\[s(i)=\frac{b(i)-a(i)}{max(a(i),b(i))} \tag{1}\]  
Before clustering the character documents, the number of clusters is determined using Silhouette scores and the Elbow method. The character documents are represented in a document-word matrix, and the silhouette score \(s(\cdot)\) is calculated using the Equation (1), where \(i\) is a data index, \(a(\cdot)\) is a mean distance to the other data instances within the same cluster, \(b(\cdot)\) is a mean distance to the other clusters. We got the optimal number of clusters is 2 using the silhouette score. We also used the elbow method, which finds the number of clusters using the sum of square errors (SSE), and found that the optimal number of clusters is 2 as shown in Figure 4. The K-means clustering algorithm with Euclidean distance is used to cluster the character documents into two groups. We denote the two clusters as \(M_{1}\) and \(M_{2}\), and per-cluster character documents \(D_{M_{i}}=\{d_{j}\mid d_{j}\in M_{i}\}\).  
Figure 4: The optimal number of clusters using the Elbow method, where the x-axis is the number of clusters, and the y-axis is the Within-Cluster Sum of Squares (WCSS).  
For each character cluster, the characters at the center have the strongest personality traits of the cluster, while the characters at the outer edges have the weaker personality traits. Using only the strongest characters at the center is not preferable because it suffers from the \"unconscious author style\" problem. However, using all characters within a cluster is also not a good choice because the outer characters are weak in representing the related personality. Therefore, we selected the top-k center characters for each cluster using silhouette scores, and merged or concatenated the top-k character documents into a single document. We define \(d_{M_{i}}^{k}\) as the resulting document from the top-k character documents in the cluster \(M_{i}\).
# Transportation Mode Selection Using Reinforcement Learning in Simulation of Urban Mobility
## 3 Materials and Methods
### Reinforcement Learning
\(a=\text{action selection}(s,Q,\epsilon)\); 2. Take action \(a\), and observe \(r\) and \(s^{\prime}\); 3. \(Q(s,a)\gets Q(s,a)+\alpha[r+\gamma\max_{a^{\prime}}Q(s^{\prime},a^{\prime}) -Q(s,a)]\) 4. \(s\gets s^{\prime}\). 3. until \(s\) is terminal. Unlike many other techniques of machine learning, RL requires the system to figure out which actions yield the highest reward through experimentation. Actions can affect not only the current reward but also the next state and all subsequent rewards in the most fascinating and difficult circumstances. The three primary characteristics of RL are its closed-loop nature, lack of explicit instructions on action selection, and the gradual emergence of action consequences, such as reward signals. The fundamental idea behind RL is to distill the salient features of the actual challenge that an agent faces when interacting with its environment in order to accomplish a goal. Evidently, such an agent should be able to sense the state of the environment to some extent and take actions that affect it. The goal of RL is to capture more complex structures and use more adaptive algorithms than classical machine learning, and RL algorithms are more dynamic in their behavior compared to classical machine learning algorithms. RL algorithms are based on the MDP. MDP is a specialized stochastic time control process for decision-making. The main actor of the RL algorithm is the following: an agent, which is a structural component designed to perceive and understand its surrounding environment, enabling it to make informed decisions and take appropriate actions within that context. SUMO, also known as Simulation of Urban MObility, is a potent simulator with a route-planning and car-following model that can manage a huge load network and defined traffic demand. Additionally, it offers a wealth of relevant data, such as vehicle speed, model, and location. The Traffic Control Interface (TraCI), a Python API that considers the SUMO simulation as a server and enables users to view or alter traffic simulations, is one of the main components of SUMO. The integration of external systems (or libraries) with SUMO traffic simulation is made possible by TraCI. Urban traffic management is a prime illustration of how reinforcement learning is used in the real world. As the number of cars in our urban transportation system rises, there is a growing need for a smart traffic management system that can provide intelligent vehicle routing. Nonetheless, the intricacy of the urban transportation network poses several challenges in traffic management because of the rapid alterations in traffic patterns and the extensive dispersion of automobiles on the road. Given that reinforcement learning has already demonstrated its ability to tackle difficult optimization problems, it would be beneficial to apply this method to address these challenges [18].
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work
### Machine Learning in Cyberbullying Detection
#### 2.2.6 Multinomial Naive Bayes  
Multinomial Naive Bayes (Multinomial NB) is widely used for document/text classification problems. However, in the cyberbullying detection field, NB was the most commonly used to implement cyberbullying prediction models, such as in [78] and [64; 105; 106].  
NB classifiers were developed by applying the theorem of Bayes among features. This model assumes that a parametric model produces the text and makes use of training data to determine Bayes-optimal parameter estimates of the model. With those approximations, it categorizes produced test data [107]. NB classifiers can accommodate an arbitrary number of separate continuous or categorical functions. Assuming the functions are distinct, a task for estimating high-dimensional density is reduced to estimating one-dimensional kernel density. The NB algorithm is a learning algorithm based on the Bayes theorem's use with strong (naive) assumptions of independence. Therefore, in [108], NB was discussed in detail.
## 2 System Model
### Scenario 1: Single Transmission through a Relay  
In the first scenario, as shown in Figure 3, the source (transmitter) passes data to the relay and the relay transmits to the destinations (receivers) in the group if the relay receives the data successfully. In the first scenario, we did not consider the direct link from the source to the destinations, and the destinations received data only for the second transmission phase. The reception at a destination is successful if both the source-to-relay and relay-to-destination transmissions are successful. We assumed that in the cell area there was one interferer per sub-channel, which may be a D2D or cellular transmitter. We also assumed that the locations of interferers were independent on the sub-channels or transmissions.
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 8 Packaging and Testing
Such passive alignment schemes can be implemented on both planar and vertical coupling and is faster, cost effective and provides for sub-micrometer alignment accuracies. Like packaging, wafers-scale assembly and testing also form an integral part of the product cycle. In electronics foundries, most chips on a wafer are tested on probe stations before being diced and packaged. Although grating couplers enable non-contact based optical probing, a precise control over fiber-device positioning is a prerequisite. _Luxtera_ offers a grating coupler enabled wafer scale test platform with active alignment [54]. The system consists of a probe with a single mode fiber array connected to a tunable laser source and detector. The total measurement time per passive device is 30 swhich includes a 4 s active alignment of fiber over device is performed via piezoelectric stage motors which enable a positioning accuracy of 1 \(\upmu\)m. Such wafer probing scans enable a statistical analysis of devices comprised of similar design in different regions of the wafer. This in turn helps in identifying the good and bad dies, which is critical for rationalizing cost per device.
## 4 Experiment
### Comparison Model  
In our study, we evaluated the effectiveness of RHRNet by comparing it to four other models: PredNet [47], ConvLSTM [47], Autoencoder [14; 47], and UNet [47; 48], as well as a baseline that calculates the performance gap between the input and ground truth images. PredNet [47] is made up of 30 layers, including 12 convolutional layers, five downsampling layers, five upsampling layers, four LSTM layers, and one flatten, reshape, input, and output layers. The convolutional, downsampling, and upsampling layers all use ReLU activation with a dropout of 0.1 and batch normalization, while the LSTM layer has a 0.2 dropout. ConvLSTM has six layers with a configuration of [48; 36; 24; 24; 12; and 4], using filter size (\(3\times 3\)), strides (\(1\times 1\)), and zero padding. Each layer, except the last one, has ReLU activation, 0.1 dropout, and a batch normalization layer, while the last layer uses softmax activation. For the Autoencoder, we followed the literature [14] and used the configuration [512; 384; 256, and 128], with ReLU activation for each layer. We changed the last layer's sigmoid activation to softmax activation and the loss function to categoricalcross-entropy for fair comparison. For the UNet [47; 48], we used the same architecture as the PredNet without the recurrent layer between the convolutional encoder and decoder, and we trained the model with the same hyper-parameters as PredNet.  
```
1.Input: Traffic congestion map input: \(\{X_{i-p;i}\}|i=p+1,p+2,...,n-1\),  Sequence of past image: \(p\),  Congestion prediction horizon: \(k\)
2.Required: learning rate: \(\alpha\)
3.Output: RHRNet model \(\mathcal{F}\)
4.\(\mathbf{S}\leftarrow\emptyset\)
5.for all available frame instances \(l,(p+1\leq i\leq(n-1)-k)\)do
6.\(X_{i-p;i}=concat\{x_{i-p},x_{i-(p-1)},...,x_{i-1},x_{i-0}\}=X_{i}\)
7.\(Y_{i}^{k}=\{x_{i+k}\}=Y_{i}\)
8. put a training instance \((X_{i},Y_{i})\) into \(\mathbf{S}\)
9.end
10. initialized a model parameter \(\theta\)
11.while the stopping criteria is not satisfied do
12. sample random mini batch of training instance \(S_{b}\) from S
13. find \(\theta\) by minimizing the objective \(\mathcal{L}(\theta)\) with \(S_{b}\)
14. Output the learned RHRNet model \(\mathcal{F}\)
```  
**Algorithm 1**Training process of RHRNet
## 3 Material and Methods
### Derivative Variable Generation and Key Variable Selection  
Derivative variables were used to improve the reliability and accuracy of the classification model, and new variables were generated based on the existing variables to discover significant factors for the model. Generally, they are generated by combining individual variables at a commonsense level. For example, they were generated by applying the four fundamental arithmetic operations on continuous variables and logical values between variables (e.g., whether certain conditions are applied) for the categorical variables.  
As shown in Table 3, derivative variables were generated based on TFA, burnt area, number of casualties, and property damage, which are continuous variables. According to the Building Act of Korea, a fire-resistant structure is mandatory for a TFA of 5000 m\({}^{2}\) or larger. Thus, it was set as a standard.  
In particular, the fire damage rating was set as the dependent variable to increase the learning success rate. Following the classification of the property damage, the distribution of each rating was analyzed and adjusted to have a distribution rate of approximately 33 (Table 4).  
The variables derived through the aforementioned process are shown in Table 5. The proposed ML is developed in Section 4, and its performance was examined using only building register information. Further, the fire scenario information was added to the building register information to compare the learning model's performance.  
\begin{table}
\begin{tabular}{c c} \hline
**Fire Damage Rating** & **Range (KRW)** \\ \hline Small scale (low) & \(\leq\)3,600,000 \\ Middle scale (moderate) & \(>\)3,600,000 \\  & \(\leq\)25,000,000 \\ Large scale (severe) & \(>\)25,000,000 \\ \hline \end{tabular}
\end{table}
Table 4: Fire damage ratings and dependent variable setting.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Recategorization** & \multirow{2}{*}{**Target Variable**} & \multicolumn{2}{c}{**Number of Recategorized Levels**} \\ \cline{3-4}
**Sequence** & & **Before** & **After** \\ \hline
1 & Month of fire & 12 & 4 \\  & & (Month) & (Season) \\
2 & Time of fire & 24 & 4 \\  & & (Hours) & (6 h) \\
3 & Facility location information & 226 & 3 \\  & & (District) & (Region Area) \\ \hline \end{tabular}
\end{table}
Table 2: List of variables to be recategorized.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Order** &
\begin{tabular}{c} **Name of the Derivative** \\ **Variable** \\ \end{tabular} & **Variable Type** & **Number of Levels** \\ \hline
1 & Fire-resistant structure & Categorical & 2 \\
2 & Burnt area/TFA & Continuous & - \\
3 & Property damage rating & Categorical & 3 \\
4 & Human casualties & Categorical & 2 \\ \hline \end{tabular}
\end{table}
Table 3: List of generated derivative variables.  
This was undertaken to examine whether a prediction is possible with only the data provided by the national agency first because the building register information is the only national data that can be obtained for fire damage prediction, and researchers must set certain values in fire scenarios for prediction.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 4 Case Study
### Step 2: Develop and Down-Select Nanogrid Architectures
#### 4.2.3 Step 2.3: Develop Nanogrid Operational Concept View  
Next an operational concept view of the baseline (microgrid only) and proposed nanogrids for the fictionalized version of the Northern California-located Naval Support Activity Monterey base which is shown in Figure 6. The illustration highlights the infrastructure similarities and differences while also illustrating key generic elements of DOD installations (e.g., base perimeter which is often fenced, entry control points, utility grid connection (PCC), etc.
# SoK: Delegated Security in the Internet of Things
## 4 Formalisation of the Security Properties
### Authentication Delegation  
Assume that \(\exists\mathsf{id},\mathsf{i}:(\mathsf{id},\mathsf{i})\in M\), then, in this application domain, a party \(\mathcal{P}_{\mathsf{id}}\) included in an RC system requests from a delegate \(\mathcal{D}\) to authenticate against a third party \(\mathcal{TP}\) on behalf of \(\mathcal{P}_{\mathsf{id}}\), in order to \(\mathcal{P}_{\mathsf{id}}\) grants access to \(\mathcal{R}_{\mathsf{i}}\). In case of successful authentication, \(\mathcal{TP}\) sends to \(\mathcal{D}\) a token \(\mathsf{t}\) such that \(\mathcal{P}_{\mathsf{id}}\) is allowed to access desired \(\mathcal{R}_{\mathsf{i}}\).  
An adversary \(\mathcal{A}\) targets the system elements in order to violate CIA properties. To model this process, a security game described in Definition 4 is introduced.  
Definition 4: Let \(\Pi\) be an authentication delegation protocol, and \(\mathsf{AD}\) be the following experiment:  
1. Initialising \(M\), \(F\), \(H\), \(S\).
2. \(\mathcal{A}\) receives as input the access matrix \(M\) or \(F\).
3. \(\mathcal{A}\) is assigned identifier \(\mathsf{id}_{\mathcal{A}}\).
4. \(\mathcal{A}\) can query the following oracles: \[\{\mathsf{t},\bot\} \leftarrow\mathtt{Del}(\mathsf{id},\mathsf{i}),\] \[\{\mathsf{s},\bot\} \leftarrow\mathtt{Cor}(\mathsf{id})\text{, and}\] \[\{\mathsf{ts},\bot\} \leftarrow\mathtt{Obs}(\mathsf{id}),\] where \(\mathsf{id}\) identifies an RC system party \(\mathcal{P}_{\mathsf{id}}\) which wants to be authenticated to interact with resource \(\mathcal{R}_{\mathsf{i}}\) of the third party \(\mathcal{TP}\).
5. \(\mathcal{A}\) wins if any of the following cases hold: * Confidentiality: \(\mathcal{A}\) outputs a resource identifier \(\mathsf{i}\) and token \(\mathsf{t}\), such that 1. \(\mathcal{A}\) is not allowed to access \(\mathcal{R}_{\mathsf{i}}\) according to \(M\), but 2. \(\mathcal{TP}\) grants \(\mathcal{A}\) access to \(\mathcal{R}_{\mathsf{i}}\) when provided with \(\mathsf{t}\).
* Integrity: \(\mathcal{A}\) outputs a party identifier \(\mathsf{id}\), a resource identifier \(\mathsf{i}\), a token \(\mathsf{t}\), and a modified item \(\mathsf{h}_{\mathcal{A}}\) such that 1. \(\mathcal{P}_{\mathsf{id}}\) _is allowed to access_ \(\mathcal{R}_{i}\) _according to_ \(M\) _with provided token_ \(\mathsf{t}\)_, and_
2. \(\mathcal{A}\) _is not allowed to access_ \(\mathcal{R}_{i}\) _according to_ \(M\)_, but_
3. \(\exists\mathsf{h}_{\mathcal{A}}\) _which does not match corresponding item_ \(\mathsf{h}\in H\)_._
4. _Availability:_ \(\mathcal{A}\) _outputs a party identifier_ \(\mathsf{id}\) _and a resource identifier_ \(i\)_, such that_ 1. \(\mathcal{P}_{\mathsf{id}}\) _is allowed to access_ \(\mathcal{R}_{i}\) _according to_ \(M\)_, but_ 2. \(\mathcal{TP}\) _does not grants_ \(\mathcal{P}_{\mathsf{id}}\) _access to_ \(\mathcal{R}_{i}\) _when provided with_ \(\mathsf{t}\)_._