esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 7 Conclusions  
In this paper, we proposed a dynamic spectrum access strategy for Internet of Things (IoT) service operating in the licensed bands of mobile cellular networks as well as the unlicensed bands of the cognitive radio-enabled LPWANs (CR-LPWANs). Our aim is to maximize the spectrum capacity for the unlicensed CR-LPWAN while ensuring that it never interferes with the licensed mobile cellular network. The simulation and the numerical analysis for dynamic spectrum access strategy by using a matrix geometric approach are performed. Finally, we obtain the blocking probability of the licensed users, the mean dwell time of the unlicensed user, and the total carried traffic and the combined service quality for the licensed and unlicensed users. The results show that the proposed strategy can maximize the spectrum capacity for the unlicensed users on IoT devices. The total service quality is also good enough to support the applications for the unlicensed users on sensor (and actuator) based IoT devices. Moreover, the service quality of the licensed user is independent of the unlicensed users using a lot of different IoT applications.  
**Acknowledgments: This work was supported by the Dongguk University Research Fund of 2010. Conflicts of Interest: The authors declare no conflict of interest.**  
Figure 9: Combined service quality factor for both PU and CU calls.
## 3 Materials and Methodology
### Experimental Setup  
As we ran many SBERT models and it required a more powerful graphical processor unit (GPU), we used Google Colab's T4 GPU with a high RAM (around 52 GB). Our study was based on the Hugging Face model based on Transformers for PyTorch 1.11.0 and TensorFlow 2.0. We also employed the OpenAI API, leveraging prompt-engineering techniques with GPT-3.5 and GPT-4, to paraphrase sentences for the data augmentation process.  
Initially, the general scenario that was formed was to compare the implementation of each model by fine-tuning the various hyperparameters mentioned previously. We tried with epoch values of 8, 10, 12, or 16, then batch sizes of 8, 16, or 32, and learning rate values of \(5\times 10^{-5}\) or \(5\times 10^{-6}\). We experimented with various scenarios that were combinations of these hyperparameters with the original dataset and the models mentioned. Based on these initial experiments, the best result was obtained from using epoch = 12, batch size = 16, and learning rate = \(5\times 10^{-5}\). So, the next experiment would use these three fixed parameters and the other parameters combined. These included whether or not to apply removing stopwords, whether to apply gradient checkpoints or not, and dataset-splitting sizes.  
The next scenario was related to the data augmentation process. Initially, we used the GPT-3.5 model with 2 different temperature values. \"Temperature\" refers to a parameter that influences the randomness degree of the generated text. A low temperature (close to 0) leads to more deterministic outputs, where the model tends to choose words with higher probabilities, resulting in a more conservative and repetitive text. A high temperature (with a maximum value = 2) increases randomness in the generated text, causing the model to sample from less predictable words, resulting in a more diverse but potentially less coherent text. The results for these new generated sentences were evaluated using METEOR and cosine similarity, giving better results for temperature = 0.7. Next, with the parameter value temperature = 0.7, we also tried generating a new sentence with GPT-4. So, we will compare the results of generated new sentences between GPT-3.5 and GPT-4.  
In the next scenario, we will compare the performance of the grading system using the dataset before augmentation and after the augmentation process. In addition, we also have a scenario for conducting experiments using larger SentenceTransformers models. Then the results will be compared between our recommended model and the larger model. The evaluation metrics used are as mentioned in the previous section. We will also check the running time of each model.
# Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery
## 2 Compressive Sensing and IST Algorithm  
The CS recovery method recovers a signal \(x_{0}\in\mathbb{C}^{n}\) from its randomized linear measurements \(y=\mathbf{A}x_{0}\), \(y\in\mathbb{C}^{m}\), where \(\mathbf{A}\in\mathbb{C}^{m\times n}\) is the measurement matrix. Since \(m<n\), it is an underdetermined problem. More than one solution can yield the same measurements, but if \(x\) is sparse, we can recover the original signal by minimizing the following L1 problem:  
\[\hat{x}=\operatorname*{argmin}_{x}\frac{1}{2}\|y-\mathbf{A}x\|_{2}^{2}+ \lambda\|x\|_{1} \tag{1}\]  
where \(\lambda\) is a regularization parameter.  
The above problem can be efficiently solved by the IST algorithm [11]. Specifically, the general step of IST is summarized in Algorithm 1. IST recovers the original signal with forward-backward iterations alternating between a gradient descent step (step (a)) and a proximal denoising correction (step (b)). In step (a), the data-fidelity term is \(f(x)=\|y-\mathbf{A}x\|_{2}^{2}/2\) and \(\
abla f(x^{k})=\mathbf{A}^{*}(\mathbf{A}x^{k}-y)\) is its gradient at the point \(x^{k}\); \(\mathbf{A}^{*}\) is the conjugate transpose of \(\mathbf{A}\); and \(c\) is a step size. \(\mathbf{A}=\mathbf{S}\mathbf{F}\) is the undersampled Fourier matrix used in the paper, where \(\mathbf{F}\) is the 2D Fourier transform and \(\mathbf{S}\) is a selection matrix containing \(m\) rows of the identity matrix. Step (b) is a proximity operator, which has a close form solution:  
\[x^{k}=\operatorname{soft}(x_{g},\lambda c)=\operatorname{sgn}(x_{g})\cdot \max(\operatorname{abs}(x_{g})-\lambda c,0) \tag{2}\]  
```
Input:\(y\), \(\mathbf{A}\), \(\lambda\), \(c\), \(x^{0}=0\) for\(k=1\)to\(K\)do (a) \(x_{g}=x^{k}-c\
abla f(x^{k})\) (b) \(x^{k}=\operatorname*{argmin}_{x}\{\lambda\|x\|_{1}+\|x-x_{g}\|_{2}^{2}/(2c)\}\) endfor
```  
**Algorithm 1** IST [11]
## 2 Distribution Network Reliability Evaluation Method
### Collaborative Evaluation Method for Reliability of Multi-Voltage Levels Distribution System  
The multi-voltage levels distribution system studied in this paper refers to the network structure between the outlet end of a 10 kV medium-voltage distribution line and low-voltage users, including the 10 kV medium-voltage distribution line, distribution transformer, and low-voltage distribution network. A distribution transformer is the key component connecting medium and low-voltage distribution networks. The secondary side outlet of the distribution transformer is not only the end of the power supply reliability evaluation of medium-voltage distribution networks but also the beginning of the power supply reliability evaluation of low-voltage distribution networks. Therefore, this paper divides the multi-voltage levels distribution system into the following two layers: the medium-voltage distribution system and the low-voltage distribution system [17].  
The specific method of collaborative evaluation analyzes the impact of the load point caused by the failure of each component and sense-control terminal in medium-voltage distribution, substitutes the reliability parameters of the fault component, quantitatively calculates the influence results, and then counts the results to obtain a reliability index.  
Then, in a low-voltage distribution system it takes the reliability index of the load point as one of the input parameters, analyzes the influence of the user power supply caused by the fault of each component, calculates the reliability index of the user power supply under each distribution transformer, makes statistics and a weighted average of the results, and then obtains the average power supply reliability index of the multi-voltage levels distribution systems.  
The failure mode and effect analysis (FMEA) method is used to analyze the influence of low-voltage distribution system failure, and the failure mode and effect analysis table (FMEA table) is established to analyze the influence of component failure at different positions on the power outage time caused by load points at different positions. When the network structure is radial, the process of establishing the FMEA table for a single component fault is not complicated. At the same time, the fault mode impact analysis table can be quickly formed by combining the fault diffusion method, which makes the rapid calculation of the reliability index easier.  
The reliability collaborative evaluation framework for medium and low-voltage distribution networks is shown in Figure 6. Please note that the main contribution of this paper is to propose a reliability evaluation method for multi-voltage levels distribution systems in which the influence of sense-control terminal faults is considered. The input parameters of the reliability evaluation are not the main focus of this article, and they are only boundary conditions.  
Figure 6: Reliability and collaborative evaluation processes of multi-voltage levels distribution networks.
## 3 Supervised ML Models
### Stand-Alone Algorithms
#### 3.2.2 Logistic Regression (LR)  
Logistic regression normally arises from a conditional probability modelling that suggests that the outcome or predicted variable say \(Y=1\), given a set of input or predictor variables say \(X\). Mathematically, the conditional probability (or the hypothesis function) is modelled by LR as:  
\[P_{\omega}(y=\pm 1|x)=\frac{1}{1+exp^{-y\omega^{T}x}} \tag{3}\]  
where \(x\) represents the dataset, \(y\) represents class label and \(\omega\)\(\epsilon\)\(\Re^{n}\) is the weight vector. For a binary classification problem with two-class training dataset (\(x_{i}\),\(y_{i}\))\({}_{i=1}\), \(x_{i}\)\(\in\)\(\Re^{n}\), \(y_{i}\)\(\in\)\((1,-1)\), then LR tends to maximise the following regularised negative likelihood:  
\[p\ (\omega)=C\sum_{i=1}^{l}\log\Bigl{(}1+exp^{-y\omega^{T}x_{i}}\Bigr{)}+\frac{ 1}{2}\omega^{T}\omega \tag{4}\]  
where \(C\) > 0 is regarded as a penalty parameter. It is important to mention that several optimisation techniques have been applied on a LR problem and some of which are documented in the literature [35; 36].  
For a multinomial classification problem, the conditional probability could be modelled using a maximum entropy as:  
\[P_{\omega}(y|x)=\frac{\exp(\omega^{T}f(x,y)}{\sum_{y^{\prime}}\exp(\omega^{T} f(x,y^{\prime}))} \tag{5}\]  
Figure 6: Typical structure of stacking ensemble.  
Here, the function vector is expressed \(f(x,y)\in\Re^{n}\).
## 2 Dataset and Network
### Prediction Network Construction  
The test results of 330 thrusters selected randomly with their enhanced data were regarded as the training set, and the rest were regarded as the test set. As for the RNN, the sequence length of the feature data is not suggested to be long, so the sequence length must be controlled under 5. This bidirectional network was then combined with an influence-based approach. Only the features with a higher weight could be extracted as the sequence data. The factors with a higher weight were put behind in the sequence, so their influence on the final output will increase accordingly. However, the MMH and NTO flow rate with the adjusted orifice must be the last two features to adjust the mixture ratio. Therefore, in the sequence, these two features need to be put behind close to the output, in order to realize a correction for a better prediction target. The number of hidden layers in the RNN was 2, and the number of each hidden layer neurons was 200, which was also determined after many network tests. All weights were randomly initialized with a gaussian distribution. The hidden layer was activated by the Tanh function and LeakyReLU function, which are two different non-linear activation functions, the loss was computed by the CrossEntropyLoss function, which is useful when training a classification problem, and the output model was based on the Softmax layer, which is a generalization of logistic regression that can be used for multi-class classification. The optimization method is the combination of the Adam optimizer in the RNN layer and Adagrad optimizer in the Softmax layer [29], which was shown to be the most efficient in time and accuracy.
## 3 Results and Discussion
### Seasonal Variation of Estimated Rain Heights Using KDE
#### 3.3.2 Rain Height Distribution in Autumn  
Rain height in Polokwane has a wider range compared to the summer values. The peak rain height is 5100 m, with a probability density close to 0.001. The peak is slightly lower compared to the summer season, where the peak was closer to 5200 m. The rain height distribution also appears to be more spread-out during autumn, indicating a wider range of rain events at varying altitudes, as shown in Figure 5a. Similar range distributions  
Figure 4: Probability density functions of rain height during summer for (**a**) Polokwane, (**b**) Nelpruit, (**c**) Pretoria, (**d**) Mahikeng, (**e**) Upington, (**f**) Bloemfontein, (**g**) Durban, (**h**) Port Elizabeth, and (i) Cape Town.  
are observed in Nelspruit, as shown in Figure 5b, but the peak rain height is approximately uniform with a higher probability, which suggests more frequent rain events. Pretoria experiences a slightly lower peak rain height of about 5000 m, with a probability density of 0.001 in autumn, as depicted in Figure 5b. Figure 5c,d indicate that the peak rain heights in Mahikeng and Upington dropped slightly to 5000 m, with a probability of 0.0016. The rise in the probability density suggests frequent rain events, while the sharp peak indicates well-defined altitudes.  
Figure 5: _Cont._  
Figure 5c-i show that the range and peak of the rain height decrease gradually and attain minimal values at the coastal regions. Hence, Durban, Port Elizabeth, and Cape Town possess rain height ranges of 2500-5500 m and a peak rain height of 4500 m, with a probability density of 0.0008. Generally, the peak rain height in autumn is lower than that in summer. The measured PDFs in autumn are more jagged and variable, suggesting that rain events are less consistent and more variable in terms of height during this season. This could be attributed to the changing weather conditions as South Africa transitions out of the wet summer season into the drier autumn. The lower probability observed in the three coastal stations implies a lower occurrence of rain events, as reported by [PERSON] et al. [39]. These observations suggest seasonal changes in the dynamics of rain formation, likely influenced by the transition into cooler and drier weather.
## 4 Theoretical Analysis of DE-MPFSC Algorithm  
[PERSON] [45] pointed out through experiments and analysis that, when solving the nonlinear optimization problem with constraints, if the optimal solution is at the feasible domain boundary, using a single objective function as the test function is not conducive to searching the optimal point, which easily falls into local optimization, resulting in an error solution. To effectively avoid the phenomenon, this paper uses a selection variable with adaptive accuracy to modify the equality constraints, expands the feasible domain searching range, establishes the DE-MPFSC algorithm. First, we theoretically analyze the validity of the DE-MPFSC algorithm (judging whether the Markovmodel conditions of the DE-MPFSC algorithm is true) and the convergence analysis (judging whether the DE-MPFSC algorithm possesses local or global convergence).
## 3 Uncertainty-Controlled Remaining Useful Life Prediction with a Data Augmentation Strategy  
Figure 1 shows the proposed method's structure, including two training and testing processes. In the training process, RMS of the vibration data is calculated firstly as the health indicator. Then, the Fault Occurrence Time (FOT) of each run-to-failure data is determined by the \(3\sigma\) rule. RMS data of the degradation stage is prepared for the data augmentation. The data augmentation based on the degradation modeling and Sobol sampling are introduced in detail in Section 3.1. The run-to-failure data generated in the next step is used to train the Wiener-LSTM model. Then the PSO algorithm is applied to optimize the hyperparameters in the Wiener-LSTM model. The detailed descriptions of the Wiener-LSTM model are introduced in Section 3.2. In the testing process, the procedure is similar to the training process. The RMS of the testing datasets is calculated, and the FOT is determined by the \(3\sigma\) rule. Then the testing datasets are sent to the trained Wiener-LSTM model to verify the effectiveness of the proposed method.
## 3 Results
### Permeability Study  
After validating some key BBB characteristics, permeability measurements can be performed. We used lucifer yellow (LY), a small molecule known to exhibit a restricted paracellular transport at the BBB, to demonstrate the feasibility of this type of experiment in our device. LY was incubated in the upper compartment for 30 min to estimate the endothelial paracellular permeability. Results are reported in Table 2.  
Figure 4: hCMEC/D3 cells cultured for seven days (**a**) in static condition or (**b**) including four days of shear stress exposure at 10 dyn\(\cdot\)cm\({}^{-2}\) (flow direction is horizontal). Immunostaining was performed and observed directly in our home-made barrier device with a spinning disk confocal microscope. From the left to the right: nuclei in blue, F-actin in green, \(\beta\)-catenin adherent junction in yellow, and ZO-1 tight junction in red (providing from distinct experiment). Each image is representative of at least two independent experiments. Scale bars represent 50 \(\upmu\)m.  
The recovery values up to 85% suggest that adsorption on the channel surface remains low in our barrier device. hCMEC/D3 apparent permeabilities (_Papp_) are consistent with data previously reported [21]. The _Papp_ in the dynamic condition (10.2 \(\pm\) 1.11 \(\times\) 10\({}^{-6}\) cm\(\cdot\)s\({}^{-1}\)) was significantly lower than the one in the static condition (14.2 \(\pm\) 0.66 \(\times\) 10\({}^{-6}\) cm\(\cdot\)s\({}^{-1}\), _p_-value \(<\) 0.05), which correlates with a tighter barrier, as noticed above. This experiment attests to the ability to perform transport assays in our home-made fluidic device.
## 2 Background and Literature Review
### Trust and Protocol-Based Mitigation Methods
Apart from that, large storage capacity is required by devices so that essential monitoring information is stored. Another disadvantage is that the majority of studies do not present complete implementations of their proposed mitigation methods. In conclusion, many studies focus on how RPL routing attacks affect network operations and solutions are developed to stop these attacks. Table 1 depicts scientific efforts that study and propose a mitigation method for RPL attacks. Most of the suggested solutions are either IDS-based or protocol-based utilising the features of RPL protocol. A new method should consider combining these two fields to achieve higher and better detection performance. In our previous work [12, 30, 31] we implemented two types of DoS attacks, namely DIS flooding and version number modification. Simulations showed that these attacks affect devices' power consumption. Moreover, we provided a high-level design of a threshold-based IDS for protecting IoT networks from these attacks. In our latest work [12], we implemented a first prototype of the proposed IDS in ContikiOS. IDS consisted of two special devices: IDS root and IDS detectors. The developed system had the detection module embedded into the BR, and sensor-like devices, called IDS detectors, executed lightweight algorithms for detecting and reporting malicious behaviour to the BR. Results showed that a high detection rate can be achieved if there are three or more IDS detectors. In this work, a collaboration between RPL-based devices and IDS is achieved. Our aim is to identify and avoid routing attackers by embedding a trust-based objective function (OF) in RPL protocol. This OF will allow nodes to securely select a parent. The trust-based method works along with an external IDS that provides the monitored devices with useful metrics. Our solution is evaluated against rank and blackhole attacks in a simulation environment, but it can be extended to detect other attacks. Compared to rule-based approaches such as SBIDS [21], our proposed solution supports the detection of multiple attacks and the isolation of attackers by embedding trust concept in the RPL protocol and using an external IDS. The combination of IDS and trust-based methods will help enhance detection performance.
## 4 Conclusions  
In this paper, a feasibility study of the 24 GHz ISM-band Doppler radar antenna for near-field sensing of human respiration in electromagnetic aspects is presented. Since the Doppler radar antenna is assumed to be used in wearable devices, it is designed and fabricated as the slot-type antenna that is less affected by human body effects. The designed slot-type Doppler radar antenna is satisfied with \(-10\) dB reflection coefficient at the target frequency band and lower than \(-30\) dB mutual coupling between the transmitter and the receiver. To study the performance of the designed Doppler radar antenna for near-field sensing human respiration, the Doppler radar antenna is placed in front of the Duke phantom's abdomen and it is simulated while the distance between the Duke phantom and the antenna is varied from 5 to 15 mm. It is observed that the phase of the returned signal is linearly proportional to the displacement, and the phase change of the returned signals is about 0.8 rad corresponding to 1 mm of displacement. Moreover, time-varying returned signals are analyzed using I/Q and arctangent demodulation. Numerical examples demonstrate that the designed Doppler radar can successfully sense human respiration in electromagnetic aspects. It should be noted that the Doppler radar for near-field sensing of human vital signs will be practically feasible when the electromagnetic performance as well as the performance of other parameters in RF systems, baseband systems, and signal processing (e.g., to mitigate DC offset or clutter interference) are fully considered.  
The presented work was carried out in collaboration by all authors. [PERSON] and [PERSON] performed the simulations; [PERSON], [PERSON] and K.[PERSON] participated to the conception and the design; [PERSON] wrote the paper which was edited by all co-authors. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Time-varying I/Q data and arctangent demodulated signals: (**a**) I/Q data for time-varying returned signals; (**b**) converted displacement by arctangent demodulation.  
**Funding:** This research was funded by the Electronics and Telecommunications Research Institute grant funded by the Korean government (19 ZH1500, Robust Contactless Wearable Radar Technology with Motion Artifact Removal for Easy-to-Wear Vital-sign Sensing Devices).  
**Conflicts of Interest:** The authors declare no conflict of interest.