esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WiGuard Overview
### System Overview  
In order to defeat the CSI-based attack, a protection system, WiGuard, is designed in this paper. The key aspect of WiGuard is destroying the necessary requirements of a successful CSI-based attack by using channel interference. In the following sections, we will introduce the system design. First, the ICMP-based attacker AP acquirement detects whether there are abnormal ICMP Ping packets caused by the CSI measurements collected by an attacker. If there is no abnormal ICMP Ping packet, the user can input the sensitive information; if an attacker AP is detected, the user should detect the working channel of the target public AP and then switch the channel of a safe wireless transmitter to a proper channel to interfere with the target public AP. In order not to interfere with other normal public APs, the system will locate all the public APs and seek a proper position for the safe wireless transmitter, as shown in Figure 3.
## 3 Results
### Spatial Connection of CCD  
From a holistic perspective, cities in the BTHUA demonstrated significant network characteristics in terms of the CCD of SocR, EcoR, and TecR, with no isolated cities. The spatial network of the CCD has become increasingly dense and complex, as evidenced by the growing number of intercity connections. Specifically, in 2010, 2014, 2018, and 2022 (Figure 8), the total number of linkages from city A to city B was 20, 29, 36, and 39, respectively, while that from city B to city A was 23, 26, 34, and 40, respectively. In addition, the spatial interactions between cities intensified over time, with the number of strong connections (above level IV) from city A to city B increasing from one in 2010 to eight in 2022.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Year** & **CenterX** & **CenterY** & **XStdDist** & **YStdDist** & **Rotation (\({}^{\circ}\))** & **Flatness (\%)** \\ \hline
2010 & 2,527,964.198 & 4,597,275.394 & 126.340 & 255.172 & 22.243 & 50.488 \\
2014 & 2,525,369.788 & 4,590,548.178 & 128.740 & 259.588 & 21.791 & 50.406 \\
2018 & 2,526,436.333 & 4,596,557.055 & 129.802 & 259.881 & 20.856 & 50.053 \\
2022 & 2,525,910.888 & 4,596,941.887 & 130.568 & 259.767 & 20.864 & 49.737 \\ \hline \end{tabular}
\end{table}
Table 3: The results of the standard deviation ellipse of the CCD in 2010, 2014, 2018, and 2022.  
Figure 7: The standard deviation ellipse and average center of the CCD in 2010, 2014, 2018, and 2022.  
At the sub-regional level, with the advancement of integrated development from 2010 to 2022, BTHUA cities gradually formed several CCD connection clusters, including the Beijing-Tianjin-Langfang cluster, Shijiazhuang-Hengshui cluster, and Xingtai-Handan cluster. Beijing consistently maintained strong linkages with neighboring cities, exerting far greater influence on surrounding areas than it received, which demonstrates Beijing's leading role in the construction of the regional resilience network. Langfang, geographically adjacent to Beijing and Tianjin, was significantly influenced by these two cities, yet its own impact was relatively limited. As Langfang's CCD increased over time, its influence on Beijing and Tianjin grew, while connections with adjacent cities like Baoding and Cangzhou also strengthened. With the advancement of regional integration, cities within Hebei Province became increasingly interconnected. Shijiazhuang, as the capital of Hebei Province, significantly influenced surrounding cities, especially Hengshui. Moreover, Xingtai and Handan in the southern BTHUA developed relatively close intercity linkages, forming a cohesive sub-regional cluster.
## 4 Role of sRNA in Antibiotic Resistance
### Inhibiting Drug Uptake Systems
#### 4.1.2. _MicF_  
The micF RNA is a 93-nt sRNA that plays a significant role in the regulation of antibiotic resistance in _E. coli_. It primarily functions by binding to the mRNA of the outer membrane porin protein OmpF, preventing its translation [51; 181]. OmpF forms trimeric channels that allow the passive diffusion of small hydrophilic molecules, including nutrients and antibiotics, across the outer membrane [182]. By repressing OmpF production, MicF reduces the permeability of the bacterial cell membrane to antibiotics, thereby decreasing the intracellular concentration of these drugs and enhancing bacterial survival. The expression of MicF is tightly regulated by several global stress response regulators, such as MarA, Rob, and SoxS [181] (Figure 3a). These regulators are activated under various stress conditions, including the presence of antibiotics, and oxidative stress conditions. When activated, they induce the expression of MicF, leading to a rapid decrease in OmpF levels and an increase in antibiotic resistance.  
[PERSON] et al. recently elucidated the role of MicF in the activation of the outer membrane porin OppA [183]. OppA plays a key in the survival of uropathogenic _E. coli_ by importing peptides, allowing the bacteria to thrive in the otherwise nutrient-limited and hostile urinary tract [137]. Additionally, OppA has also been implicated in antibiotic sensitivity in UPEC, particularly in response to polymyxin B [184]. The uptake of certain toxic compounds, including the synthetic tripeptide tri-L-ornithineine [107] and the novel antibiotic GE8112 [108], is also dependent on OppA. Under standard growth conditions, GcvB and Hfq inhibit ribosomal binding to the SD sequence, resulting in low _oppA_ expression. However, under membrane stress, MicF is activated and binds to the 5' UTR of the OppA transcript, enhancing translation and leading to increased _oppA_ expression.
## 3 System Descriptions and Assumptions
### DESCO Task Processing Model
#### 3.4.2 Pure Offloading Mode  
Communication cost is determined by the transmitted data volume, channel coherence parameters, and the user's transmission power. Notably, when both the local cache pool of server and the Cache Chord lack the application data \(\omega_{k}\) for task \(k\), i.e., \(\text{ServerIP}^{t}_{k}\lor p^{d,t}_{k}=0\), the user should upload both the application data \(\omega_{k}\) and the real-time computational data \(\theta_{k}\) to the edge. Consequently, it can be inferred that the current energy consumption of the \(n\) is  
\[\psi_{n}\frac{\omega_{k}+\theta_{k}}{l_{n}^{d,t}}, \tag{8}\]  
where \(\psi_{n}\) represents the transmission power of a user. Furthermore, we can infer that the latency of pure offloading is  
\[T^{p}_{n,k,d,t}=\frac{\phi_{k}}{\eta_{d}}+\frac{\omega_{k}+\theta_{k}}{l_{n}^{ d,t}}, \tag{9}\]  
where the rightmost term represents the transmission delay for user \(n\) when sending data of task \(k\), and the middle term represents the computation delay for server \(d\).
## 2 Conventional MIMU/GNSS UTC Integration Architecture
### Integration Kalman Filter  
The integration Kalman filter estimates INS navigation errors and MIMU errors. The following 17-dimensional error state vector is adopted in this study:  
\[\mathbf{X}=\left[\begin{array}{cccc}\mathbf{\phi}^{T}&(\delta\mathbf{v}^{n})^{T}&(\delta \mathbf{p})^{T}&\left(\mathbf{\varepsilon}_{r}^{b}\right)^{T}&\left(\
abla_{r}^{b} \right)^{T}&\delta t_{u}&\delta f_{u}\end{array}\right]^{T} \tag{11}\]  
where \(\mathbf{\phi}\) represents the attitude misalignment angles, \(\delta\mathbf{v}^{n}\) represents the velocity errors, \(\delta\mathbf{p}\) is the position (latitude, longitude and altitude) errors, \(\mathbf{\varepsilon}_{r}^{b}\) and \(\
abla_{r}^{b}\) are the first-order Markov process errors of gyroscopes and accelerometers, respectively, \(\delta t_{u}\) is the receiver clock error, and \(\delta f_{u}\) is the clock drift error.  
The system equation of the integration Kalman filter usually comes from the error propagation equation of the INS. The complete form is a complex set of differential equations, which can be found in [1,23].  
Equation (12) converts the state estimations of the prefilter into the PR residual and the PRR residual. The residuals of all available channels constitute the measurement vector of the integration filter.  
\[\left\{\begin{array}{l}\Delta\rho=\frac{\hat{\tau}_{p}\times c}{f_{o}}\\ \Delta\hat{\rho}=\frac{f_{c}\times c}{f_{a}}\end{array}\right. \tag{12}\]  
where \(\Delta\rho\) is the PR residual, \(\Delta\hat{\rho}\) is the PRR residual, \(\hat{\tau}_{p}\) is the estimated code phase error, \(\hat{f}_{e}\) is the estimated carrier frequency error, and \(c\) is the speed of light.  
The measurement equation of the UTC Kalman filter is the same as the measurement equation of the standard tight integration system, and for the specific form, one can refer to [24]. In the UTC navigation system, the corrected inertial navigation solutions are applied to generate the carrier and code NCO control commands. A detailed treatment of NCO commands may be found in [24,25].
## 6 Numerical Experiment
### Methods
#### 6.1.3 Performance by the Missing Value Rate of the User Trajectory  
As in Section 3.3, the missing value rate \(\theta_{k}\) of trajectory \(T_{k}\) indicates its sparsity. Hence, the classification performance increases if only trajectories with low sparsity are learned by providing the threshold \(\theta_{\mathrm{th}}\) for the missing value range \(\theta_{k},\forall k\in K\). Let \(T_{k}^{\theta_{\mathrm{th}}}\) be a trajectory generated from element \(L_{k}^{\theta_{\mathrm{th}}}\) of  
\[L^{\theta_{\mathrm{th}}}=\{L_{k}\mid\Theta(\mathcal{T},L_{k})<\theta_{\mathrm{ th}},\forall k\in K\}, \tag{5}\]  
obtained using any \(\theta_{\mathrm{th}}\). Then, we use the function \(\Theta\) defined in Equation (1) into Equation (5). The dataset for month \(m\) consists of the pair \((f_{k}^{\theta_{\mathrm{th}}},c)\) of feature \(f_{k}^{\theta_{\mathrm{th}}}\) generated by \(T_{k}^{\theta_{\mathrm{th}}}\), and the corresponding class \(c\) is denoted by \(\mathcal{D}_{m}^{\theta_{\mathrm{th}}}\). If we conduct a sensitivity analysis of the threshold \(\theta_{\mathrm{th}}\) for \(\mathcal{D}_{m}^{\theta_{\mathrm{th}}}\), we can verify the effect of trajectory sparsity on the classification accuracy.  
In this experiment, a sensitivity analysis is conducted in the range \(\theta_{\mathrm{th}}\in\{0.95,0.96,\ldots,1.00\}\). Then, \(\mathcal{D}_{m}^{\theta_{\mathrm{th}}=1.00}\) indicates that the entire trajectory \(T_{k}\forall k\in K\) is to be used for training and testing. When sampling data using any random seeds, we ensure that the datasets become  
\[\mathcal{D}_{m}^{\theta_{\mathrm{th}}=0.95}\subseteq\mathcal{D}_{m}^{\theta_ {\mathrm{th}}=0.96}\subseteq\cdots\subseteq\mathcal{D}_{m}^{\theta_{\mathrm{ th}}=1.00},\forall m\in M.\]  
The sizes of the training and test data in the datasets \(\mathcal{D}_{m}^{\theta_{\mathrm{th}}},\forall m\in M\) for each \(\theta_{\mathrm{th}}\) value are listed in Table 1. Additionally, for the models for each month, we adopted the GMD components and the IDNN hyperparameters with the maximum average accuracy in Sections 6.1.1 and 6.1.2.
## 4 Performance Evaluation
### Experimental Results and Discussion  
The results and discussion were presented in this section to validate the effectiveness of the SensorAnt comparing with EEBAR results; the results obtained from the QualNet simulator that offers high fidelity simulations for wireless communication. The result was studied based on network size, mobility and under different periods of times to show the accuracy of the proposed protocol.
# Internet of Things (IoT) and the Energy Sector
## 4 IoT in the Energy Sector
### Smart Cities
consumption in different hours of the day can be gathered. If it is found that a section, e.g., residential areas, consumes the most energy in the afternoon, then automatically energy devoted to other sections, e.g., factories, can be minimized to balance the whole system at a minimum cost and risk of congestion or blackout. In a smart city, different processes, i.e., information transmission and communication, intelligent identification, location determination, tracing, monitoring, pollution control, and identity management can be managed perfectly by the aid of IoT technology [120]. IoT technologies can help to monitor every object in a city. Buildings, urban infrastructure, transport, energy networks, and utilities could be connected to sensors. These connections can ensure an energy-efficient smart city by constant monitoring of data gathered from sensors. For example, by monitoring vehicles with IoT, street lights can be controlled for optimal use of energy. In addition, the authorities can have access to the gathered information and can make more informed decisions on transportation choices and their energy demand.
## 6 Continuous Transmission of Packet Traffic
### AOA based
#### Uw-Mac  
UW-MAC is a UWSN-specific distributed medium access (DMAC) protocol. The transmitter-based AOA system, known as UW-MAC, uses a ground-breaking closed-loop distributed method to determine the proper transmit power and code length to reduce the near-far impact [63]. On equipment with numerous resources, such as superficial positions, gateways, and vehicles, UW-MAC uses a multi-user detector.  
In order to lessen the near-far impact, a low-complexity yet optimal solution to the distributed power and code self-assignment problems is also proposed. The earlier works only looked at AOA from the perspective of the physical layer, but UW-MAC takes advantage of AOA features to enable multiple accesses to the constrained underwater bandwidth. The results of experiments demonstrate that UW-MAC outperforms other MAC protocols in every network design scenario and simulation situation.
## 3 Results
### RQ5. What Are the Emerging Trends in IoT for Pets and What Impact Might They Have in the Future?  
The IoT is revolutionizing pet care through a series of technological innovations. Emerging trends in the IoT for pets increasingly leverage artificial intelligence (AI) and machine learning to enhance pet care, automate tasks, and improve pet-owner interaction. AI has been integrated into behavioral analysis and anomaly detection, allowing the real-time monitoring of activity levels, emotional states, and potential health risks (e.g., S7, S17, and S33). Computer vision and deep learning enable AI-powered pet recognition for access control, smart feeders, and stray pet detection (e.g., S15, S17, and S81). Sound event classification using AI helps to assess stress or behavioral changes in pets, enhancing the early diagnosis of potential issues (S33). Additionally, AI-driven environmental monitoring adjusts the temperature, humidity, and air quality to maintain optimal pet well-being.  
As technology advances, there is a growing trend towards expanding and connecting devices in smart homes. The vision is to integrate a variety of appliances, from smart cages to interactive toys and wearable devices, into a cohesive system that responds to the demands of pet owners. This approach facilitates a more enriched ecosystem, improving the overall pet care experience and enabling the more personalized and effective management of pet well-being. In this line, multifunctional robots are designed to improve both the feeding and monitoring of pets, although they face technical challenges such as food obstruction and the transfer speed (e.g., S4, S42, S69, and S73). These issues are being addressed with new technologies, promising more precise food transfer and reduced operational errors, ensuring adequate and efficient nutrition for pets.  
Moreover, the implementation of advanced identification and tracking technologies is revolutionizing pet monitoring. Multi-camera tracking systems and improved real-time data models allow for more detailed pet control, even in environments like air transport, where IP-RFID tags must function seamlessly to guarantee continuous supervision (e.g., S23). These innovations facilitate more effective tracking and provide greater peace of mind for owners.  
Finally, the ability to interact with pets in real time from anywhere is constantly evolving. Future devices are designed to enable real-time communication, including the ability to transmit the owner's voice to the pet and remote feeding (e.g., S19 and S69). Furthermore, the scalability of these systems enables their implementation in industrial settings, where managing the care of multiple animals simultaneously becomes more efficient. Additionally, it is crucial for prototypes to adapt to the specific characteristics of each pet, including the species, size, weight, and level of physical activity, ensuring personalized and effective care. These trends reflect a future where the IoT will transform pet care, making the process more accessible, interactive, cheap, and efficient.
## 2 Materials and Methods--Porosity Classification and Fracture Identification
### Pixel-Wise k-IN Approach to Determine Rock Fractures and Pores
#### 2.4.1 Fracture Shape Geometry in a CT-Scan Image Sample  
A fracture is defined as any separation in a geological formation that divides the rock into two or more pieces [27], which in the subsequent grayscale image can be seen as a region with its length much larger than its width and containing a large number of contiguous dark pixels. From a reservoir point of view, fractures are planar discontinuities or deep fissures in rocks due to mechanical deformations or physical diagenesis [17; 28]. Fractures are often found enlarged by the solution activity or can be healed by secondary calcite or sparite. Based on the previous experience, in general, we found the lengths of the fractures are much larger than their widths, and hence, in the current study, we assume that, to be a fracture, \(\mathcal{H}_{i}\) should follow \(\frac{\text{major}\left(\mathcal{H}_{i}\right)}{\text{minor}\left(\mathcal{H} _{i}\right)}>\beta\) (=1.3 in the current study) and in general, their shapes are much deviated from a circle or ellipse of the same perimeter as \(\mathcal{H}_{i}\). In the current work, we measure the length of \(\mathcal{H}_{i}\) as major(\(\mathcal{H}_{i}\)), whereas the width of \(\mathcal{H}_{i}\) is measured as minor(\(\mathcal{H}_{i}\)) (defined later). The shape variations of a fracture from that of the circles and ellipses of same perimeter are found much larger than for pores. In the current work, we encourage the readers to decide the \(\beta\) value based on the porosity and other necessary geophysical properties of the sample rock used for their experiments.
## 5 Imperfections and Side Channels in Practical CVQKD  
Bringing theoretical protocols to the realm of practical implementations unavoidably implies that some assumptions need to be made, such that real-life constraints can be satisfied. This may be innocuous in some cases; however, when it comes to cryptographic applications where rigorous security proofs are required, such assumptions may have dramatic consequences for the security obtained in practice. This is of course true for quantum key distribution implementations, as well, especially as this technology is reaching a certain maturity. Let us consider, for example, the CVQKD implementation of the GG02 protocol described above. While we saw that a phase reference, the local oscillator (LO), is necessary for the implementation, in fact, this signal does not appear in any way in the description of the protocol and in the security proof. The implicit assumption made then is that [PERSON] does not tamper with the LO; under this assumption, the security proof holds. In reality, though, there is nothing preventing [PERSON] from manipulating this strong classical signal in order to obtain information on the transmitted key. Indeed, this is possible, as we will see below. This is a simple example of a so-called side-channel attack, which illustrates that it is crucial to consider the practical security of CVQKD implementations.  
In order to address this issue of great practical relevance, one solution is to consider exhaustively all of the possible discrepancies between the underlying theoretical model and the actual implementation, to take into account the assumptions due to experimental requirements or imperfections and to refine the model accordingly. This approach has been pursued extensively for discrete-variable QKD, where powerful side-channel attacks have been demonstrated, in particular against commercial systems [70; 71; 72]. In CVQKD, this process involves developing better models for the state preparation, the local oscillator manipulation and the detection stages of the implementation. We summarize below a few concrete examples of security issues that have been studied in recent years.
###### Abstract
The change in microseismic energy is affected by nonlinear factors, and thus, artificial intelligence algorithms are at a disadvantage in nonlinear analysis of high-dimensional data sets [18]. The most commonly utilized methodologies for anticipating rock bursts are convolutional neural networks (CNNs), extreme gradient boosting models (XGBoost), random forests (RFs), long short-term memory (LSTM), and lightweight gradient boosters (LightGBM) [19]. Given that the predictive accuracy of a single algorithmic model is insufficient for the analysis of nonlinear data, the study and application of algorithmic combination models has become a widely researched and applied field [20]. The results demonstrate that [PERSON] et al. [21] proposed a microseismic signal recognition method based on LMD energy entropy and a probabilistic neural network (PNN) by analyzing the characteristics of microseismic signals. [PERSON] et al. [22] used principal component analysis to extract features, used particle swarm optimization to optimize the ELM model, and proposed a rock burst warning model based on the PCA-PSO-ELM combined algorithm. [PERSON] et al. [23] analyzed the variation characteristics of physical indicators before multiple large energy events and statistically analyzed the shortcomings of impact risk prediction indicators driven only by physical indicators and proposed a time series prediction method for rock bursts driven by the fusion of physical indicators and data features. [PERSON] et al. [24] established a database using shock ground pressure patterns with multiple impact features in microseismic monitoring events of the Jinping secondary hydropower project. They then used three methods of t-distributed stochastic neighbor embedding (t-SNE), K-means clustering, and extreme gradient boosting (XGBoost) to predict the short-term shock ground pressure risk. The preceding research findings have been utilized to develop a novel approach for the prediction of rock burst risk. This approach employs a long short-term memory (LSTM) neural network model, which is optimized using a Bayesian algorithm to establish a Bayesian long short-term memory (BO-LSTM) neural network model. This model is capable of processing time series data in accordance with the characteristics of microseismic energy change. The 13,200 working face of Gengcun Coal Mine was selected as the application object for the establishment of an early warning model for rock bursts. This involved the selection of field-measured microseismic monitoring data for the purpose of combining big data prediction with early warning of rock bursts. The prediction of rock bursts in the future is established by obtaining the potential characteristic information of microseismic monitoring data. This improves the prediction accuracy of rock burst risk and provides a new method for monitoring and early warning of rock bursts.