esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection for IOT Systems Using Active Learning
## 4 Proposed Methodology
### Model Architecture Design  
The method under consideration employs a random forest classifier sourced from the Scikit-Learn toolkit as its primary constituent. The classifier comprises a collection of 50 decision trees, each with a depth of 15. The random forest classifier utilizes ensemble learning to perform classification and regression tasks. The algorithm constructs a set of decision trees and afterwards calculates the class or average prediction by combining the outputs of these trees. The ultimate forecast of the random forest model is derived from a majority vote among the individual trees that have been trained on a subset of randomly chosen features. Furthermore, the input features undergo pre-processing and normalization through the utilization of the StandardScaler approach. As shown in Figure 11.  
Figure 10: The architectural framework of an IoT-based smart city.  
The modAL package is utilized for the implementation of active learning. This enables the model to independently determine the data samples that require labelling in each iteration, in contrast to labelling all the data samples simultaneously, as is customary in traditional techniques. The ActiveLearner class within the MODAL framework is employed for this specific objective. During the initialization phase, the necessary components for the experiment are configured, including the training data, the random forest classifier as the chosen estimator, and the uncertainty_sampling query technique. The query approach known as uncertainty sampling is responsible for selecting examples that the model exhibits the least certainty in classifying. The implementation of this technique utilizes the uncertainty_sampling function provided by the modAL library. The uncertainty sampling approach is employed to choose data points by considering the model's highest level of uncertainty in its predictions. This level of uncertainty is determined by subtracting one from the likelihood of the label that is most likely to be correct. During each iteration of the active learning loop, the model selects the instances with the highest level of ambiguity from the test set, assigns labels to these instances, and subsequently integrates them into the training set. The procedure is iterated for a predetermined number of inquiries, specifically 50. Following each query, the model is subjected to retraining using the adjusted training data, followed by the computation of accuracy using the test data. The correctness of every iteration is documented, and the cumulative accuracy is stored as the variable \"acc\". The algorithm incorporates an active learning framework to efficiently identify anomalies in the Internet of Things (IoT) domain. This is achieved by harnessing the capabilities of random forest classification, uncertainty sampling, and an iterative approach for selecting and labelling cases that exhibit ambiguity. The provided description offers a thorough understanding of the operational mechanism of the algorithm under consideration.
## 2 Materials and Methods
### Electrical Architectures, System Efficiency and Thresholds  
For a passenger car, three possible electrical architectures can transfer the PV energy into the vehicle's battery. These realistic architectures allow either charging the solar energy directly in the high-voltage (HV) battery or charging first a low-voltage (LV) battery and then intermittently transmitting the energy from the LV battery into the HV battery.  
The PV system consumes additional energy, and the system's efficiency depends on the chosen architecture for charge. For a direct charge in the HV battery, this additional consumption is 12 W during driving and when plugging into the grid and 26 W during parking. For charging via a low-voltage auxiliary battery (12 V or 48 V), this consumption is constant at 12 W. In this case, there is a periodic wake-up (each 100 Wh) of the vehicle's calculators and the HV battery management system in order to transmit the electricity from the LV battery to the HV battery. The consumption is 8.5 Wh per wake-up.  
The additional electricity consumption divided by the system's efficiency is equal to the thresholds applied to decide to trigger or not the PV use, which gives 13 W and 28 W for 12 W and 26 W extra consumption, respectively. The system transmits the PV energy to the battery only if the available power during the time step is higher than these thresholds. The model subtracted this additional electricity consumption from the PV's produced energy during all computing time steps. More details on the thresholds are in Section 3.2.1.  
Figure 2: PV monthly production for 1 kWp for Paris and Malaga.  
Table 1 gives the efficiency at each conversion and the total system efficiency for all architectures of a passenger car.
## 3 Methodology
### PSO Deep-ConvLSTM Network
#### 3.2.3 Steps of the PSO Deep-ConvLSTM  
The flowchart illustrating the process is depicted in Figure 5. The specific procedure for optimizing the parameters of the ConvLSTM network using PSO can be methodically outlined in six steps as follows:  
Step 1: Preprocessing. The data preprocessing comprises several stages: conversion of the data into a format compatible with CONVLSTM, data normalization, sorting, and further normalization processing. Subsequently, we proportionally divide the data into the training and test sets.  
Step 2: Parameters Initialization. Initial settings for particle swarm parameters, such as particle swarm size, the number of algorithm iterations, and the range of iteration steps, are established. Afterward, we randomly initialize the position and velocity of each particle.  
Step 3: Fitness Value Calculation. MSE (Mean Square Error) serves as the fitness function in our method. By training the CONVLSTM neural network model, we can assess the fitness of each particle. Consequently, the personal historical best value (pbest) and the global best value (gbest) are deduced based on the current fitness values.  
Step 4: Particle Speed and Position Update. Particles' velocities are dynamically adjusted according to the individual historical best position and the global best position of the population. The particle's position is influenced by both its previous position and the current particle velocity. In particular, the update of each particle's velocity and position can be executed as per Equations (4) and (5).  
Step 5: Iteration Loop Evaluation. If the iteration is completed, the best value is outputted; otherwise, the process returns to Step 3.  
Step 6: Prediction. The optimal hyperparameters are revealed and the model is trained based on these hyperparameters. Following this, the model's predictive capabilities are utilized to forecast future data.  
The PSO Deep-ConvLSTM model we finally built is based on the algorithm-optimized parameters, which determine the structure of the model and give a final decision-making ability to the model. The process of algorithmic optimization search demonstrates the origin of our parameter settings and model building while maximizing the elimination of human factors and enhancing the interpretability of the model structure and parameters.
## 2 Generator Dynamic State Estimation Model
### Equation of Measurement and State Estimation  
In the electromechanical transient process of power system, the system topology and bus voltage cannot be acquired in real time. Thus, the rotor power angle and angular velocity, which do not abruptly change and are constrained by the rotor differential equation, are selected as the state variables of the generator DSE.  
The second-order differential equations of synchronous generators can be expressed as follows  
\[\left\{\begin{array}{l}\frac{\mathrm{d}\delta}{\mathrm{d}t}=(\omega-1) \omega_{0}\\ \frac{\mathrm{d}\omega}{\mathrm{d}t}=\frac{1}{T_{J}}(T_{m}-T_{e}-D\omega)= \frac{1}{T_{J}}(\frac{P_{m}}{\omega}-\frac{P_{e}}{\omega}-D\omega)\end{array}\right. \tag{3}\]  
where \(\delta\) indicates the power angle, \(\omega\) represents the electric angular velocity; \(T_{m}\) denotes the mechanical torque, \(T_{e}\) is the electromagnetic torque; \(P_{m}\) is the mechanical power, \(P_{e}\) is the electromagnetic power, \(T_{J}\) represents the generator's rotor's inertia time constant, and \(D\) is the damping coefficient.  
The differential equation of generator model in (3) can be rewritten as  
\[\left[\begin{array}{c}\dot{\delta}\\ \dot{\omega}\end{array}\right]=\left[\begin{array}{ll}0&\frac{180}{\pi} \omega_{0}\\ 0&-\frac{D}{T_{J}}\end{array}\right]\left[\begin{array}{c}\delta\\ \omega\end{array}\right]+\left[\begin{array}{ll}1&0\\ 0&\frac{1}{T_{J}}\end{array}\right]\left[\begin{array}{c}-\frac{180}{\pi} \omega_{0}\\ (P_{m}-P_{e})/\omega\end{array}\right] \tag{4}\]  
where the unit of \(\delta\) is degree. For convenience, one has  
\[\left\{\begin{array}{l}X=\left[\begin{array}{c}\delta\\ \omega\end{array}\right]\\ A=\left[\begin{array}{ll}0&\frac{180}{\pi}\omega_{0}\\ 0&-D/T_{J}\end{array}\right]\\ B=\left[\begin{array}{ll}1&0\\ 0&1/T_{J}\end{array}\right]\\ U=\left[\begin{array}{ll}-\frac{180}{\pi}\omega_{0}\\ (P_{m}-P_{e})/\omega\end{array}\right]\end{array}\right. \tag{5}\]  
In general, the first three terms of [PERSON] expansion of general state transition matrix can meet the precision requirements  
\[\Phi(T)\approx I+AT+\frac{A^{2}T^{2}}{2} \tag{6}\]  
\[\Gamma(T)=\left[\int_{0}^{T}\Phi(\alpha)d(\alpha)\right]B \tag{7}\]  
where \(T\) represents the sampling period.  
The power angle and angular velocity of generator can be measured directly by PMU, which are usually selected as the measurements  
\[\left\{\begin{array}{l}Z=\left[\begin{array}{c}\delta\\ \omega\end{array}\right]\\ C=\left[\begin{array}{cc}1&0\\ 0&1\end{array}\right]\end{array}\right. \tag{8}\]  
According to (4) to (7), the discrete form of generator DSE model can be obtained  
\[\left\{\begin{array}{l}X_{k+1}=\Phi X_{k}+\Gamma U_{k}=f(X_{k},U_{k})\\ Z_{k}=CX_{k}=h(X_{k},U_{k})\end{array}\right. \tag{9}\]
## 2 Structure Design  
As shown in Figure 1, the two-axis four-gimbal coarse-fine composite structure can be simplified to a cantilever beam. Because the integrated shafting structure requires high precision, and there are deflection errors in actual processing and manufacturing, it is necessary to check the mechanical parameters of the uniaxial structure to observe whether it meets the performance requirements of the cantilever beam.
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 4 Rate Adaptation
### Rate Calculation
#### 4.2.1 No Occupancy Stage  
During the startup stage, the multicast source knows nothing about the network. In order to prevent congestion of the network caused by sending a large number of data packets, at this beginning stage, we adopt a conservative method that starts sending at a slow rate. At this stage, the \(R(t+1)_{min}\) or \(R(t+1)_{max}\) calculation method for each round is as follows:  
\[R(t+1)=2.0\times R(t) \tag{6}\]
## 3 Materials and Methods
### Validity of Using Character Utterances
Since the degree to which a character's personality is expressed in their utterances may be limited and such utterances may also be influenced by the author's stylistic tendencies, we cluster utterances from similar characters appearing in different works by different authors, in order to extract more representative personality traits. For the word-based clusters, we removed all nouns and digits in the utterances and performed stemming using a Porter stemmer. The characters with utterances shorter than 100 letters are filtered out. The word-based clusters are created by a bag-of-words approach with TF-IDF on 8000 vocabulary (i.e., unique words), using the k-means algorithm, and the number of clusters is 2. We also get average personality trait scores and normalize the scores to a range between 0 and 1, and create the personality-based clusters using the k-means algorithm. If the personalities of the characters are well reflected in their utterances (i.e., words), those with similar personalities should show close linguistic patterns to each other. Figure 3 shows the t-SNE results of the two different clusters, where the dots are characters. Characters with similar linguistic patterns are shown as nearby dots in the figure, and the color of each dot indicates its arbitrary personality cluster; we do not specify the personalities of the orange and blue clusters here, because we intend to check if the personality-based clusters are consistent with the word-based clusters. The figure shows that the personality clusters (i.e., the orange and blue clusters) have their own distinct centroids even though they overlap somewhat, which means that the characters with similar linguistic patterns tend to have the same personality. Based on this finding, we designed our method for tuning a language model for utterance translation. In particular, we expected that using utterances from similar characters (i.e., characters in close proximity in Figure 3) would reduce the impact of the \"unconscious author style\" problem. Details of the method are given in the next subsection. \begin{table}
\begin{tabular}{l l c c c c c} \hline
**Title** & **Character Name** & **O** & **C** & **E** & **A** & **N** \\ \hline _Northanger Abbey_ & [PERSON] & 1 & 2 & \(-1\) & 1 & 1 \\ _The Sport of the Göds_ & [PERSON] & 2 & \(-1\) & 2 & \(-1\) & 2 \\ _Mansfield Park_ & [PERSON] & \(-2\) & \(-2\) & 3 & 2 & \(-1\) \\ _Sense and Sensibility_ & [PERSON] & 1 & \(-1\) & 2 & 1 & 1 \\ _A Passage to India_ & [PERSON] & \(-1\) & 1 & \(-1\) & \(-1\) & \(-1\) \\ \hline \end{tabular}
\end{table} 
Table 2: Samples of OCEAN scores on novel characters. Figure 3: Graphical representation of two different clusters, where the dots are characters, the colors indicate personality-based clusters, and the position of the dots represent linguistic patterns of the characters.
# Fault Analysis of a Small PV/Wind Farm Hybrid System Connected to the Grid
## 5 Results and Simulation
### Without Fault  
Initial grid analysis of all parameters is conducted without a distribution network fault. Figure 13 shows that the PV array produces voltage, current, and power of approximately 250 V, 287 A, and 71.9 KW, respectively, at STC. Figure 14 displays the DC voltage that the boost converter delivers. The boost converter elevates the natural PV voltage around 250 V to 500 V DC voltage at STC. Figure 15 displays the output voltage of the inverter. It is clear that the three-phase VSC preserves power factor constant at 1 and transforms 500 V DC to 260 V AC. Figure 16 displays the inverter's filtered output voltage. To reduce the harmonics that the voltage source inverter produces, an \(L\) filter is used here. According to Figure 17, the PV farm's active power injection was about 141 KW, and since the reactive power was zero, the power factor was one. As seen in Figure 18, the SCIG consumed 2.85 MVAR of reactive power, whereas the active power harnessed by the wind farm was about 6 MW. Figure 19 displays a PCC bus with constant voltage and a peak voltage per phase of 20 kV (25 kV L-L). Regardless of changes in power from the combination system that was injected, the PCC-bus voltage remains constant. The three-phase injected the current of the PCC bus as mentioned in Figure 20. As is evident, the DC/AC converter controller controls current amplitude in relation to injected power. The power balance of a hybrid power system is visualized in Figure 21. It should be mentioned that the combined amount of power using the PV and wind farms is injected from the combination system to the PCC bus.  
Figure 14: DC link voltage.  
Figure 13: PV panel parameters.  
Figure 15: Voltage produced by the inverter.  
Figure 16: Voltage produced by the inverter after filtering.  
Figure 17: The power output of the small PV farm.  
Figure 18: The power output of the wind farm.  
Figure 19: Voltage of PCC bus.  
Figure 20: Current of PCC bus.  
Figure 21: The PCC bus’s receipt of active and reactive power.
# Thurstonian Scaling for Sensory Discrimination Methods
## 4 Discussion
### Advantages of Thurstonian discriminant distance (\(\delta\) or \(d^{\prime}\))  
Thurstonian scaling for sensory discrimination methods offers advantages in sensory research, particularly in assessing subtle differences between products. Some of the key benefits include the following:  
(1) Improved sensitivity, making the method more sensitive to small perceptual differences between products compared to traditional testing; (2) quantitative interpretation, offering a quantitative estimate of the perceived difference between products, enabling researchers to measure and compare the intensity of sensory attributes more precisely; (3) statistical robustness and reliability, accounting for individual variability and response bias, leading to more accurate representations of true sensory differences; (4) clearer decision-making across methods based on a probabilistic framework, facilitating more informed decisions; and (5) compatibility across methods using a common parameter \(d^{\prime}\), thus allowing selection of the most appropriate method for the nature of the product and study objectives.  
As mentioned earlier, Thurstonian scaling can be used with trained panels or consumer panels. Trained panels are used to detail sensory differences, make objective assessments of product formulations, ingredient changes, or quality control, and conduct product optimization to fine-tune product formulations or processes to achieve the desired sensory characteristics. Consumer panels are used to provide critical information on perceptual relevance, helping to quantify if consumers can detect differences between products and, if so, the probability of those differences being meaningful for consumer acceptability and whether sensory differences will impact consumers buying behavior, driving decisions regarding product launch, positioning, or marketing strategies and success in the marketplace. Thurstonian scaling provides quantitative and probabilistic insights, enabling better decision-making in product development and consumer research.
## 6 Experiments and Results
### Experiment Environments and Parameter Setting
#### 6.1.2 The MAC Protocol and Routing of the Simulated and Real Networks  
To the regular grid of the simulated network, the experiments are implemented on a scheduled medium access control (MAC). Specifically, IEEE 802.15.4 is adopted as the MAC protocol. The data transmission rate is set 250 kbps. To the nodes irregularly deployed in the real network, the experiments are constructed with 433 MHz mica2 dot nodes which adopt the default CSMA based B-MAC as the MAC layer protocol.  
To model the sensing data delivering procedure, we adopt a simple routing method to transport a sensing packet from a node to the sink node, in which a node selects the one-hop adjacent node with maximal remaining energy to transmit the sensing packet. In the simulated network, the one-hop adjacent nodes of a node are the directly linked nodes in the grid. In the real networks, we set a maximal distance for a node to transmit data package to others in one-hop. In our experiments, the upper bound is assigned to 10 m. Under this setting, we obtain the connection of the nodes in the real network. For example, the one-hop links from node 1 are shown as the arrowed lines in Figure 3.
# Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy
## 4 Conclusions  
This study is a preliminary investigation of using dispersive NIR and FT-NIR spectroscopy to predict the contents and types of dry soy protein products added to ground beef. The high \(R_{\text{cv}}\)2 (>0.91) and low RMSECV (<0.12) of PLS regression indicated that both NIR spectroscopy methods are  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Predicted vs. Actual**} & \multicolumn{10}{c}{**Calibration**} & \multicolumn{10}{c}{**Validation**} \\ \cline{2-13}  & **Ar** & **To** & **Pr** & **TV** & **Ar** & **To** & **Pr** & **TV** & **Ar** & **To** & **Pr** & **TV** & **Ar** & **To** & **Pr** & **TV** \\ \hline Ar & 0 & 0 & 4 & 1 & 5 & 0 & 0 & 0 & 5 & 0 & 0 & 0 & 6 & 0 & 2 & 0 \\ To & 0 & 1 & 3 & 1 & 0 & 5 & 0 & 0 & 0 & 5 & 0 & 0 & 2 & 4 & 2 & 0 \\ Pr & 0 & 0 & 4 & 1 & 0 & 0 & 5 & 0 & 0 & 0 & 5 & 0 & 0 & 0 & 7 & 1 \\ TV & 0 & 0 & 0 & 5 & 0 & 0 & 0 & 5 & 0 & 0 & 5 & 0 & 0 & 0 & 8 \\ Accuracy & & & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classified results of four soy categories using SVM with FT-NIRS models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Predicted vs. Actual**} & \multicolumn{10}{c}{**Calibration**} & \multicolumn{10}{c}{**Validation**} \\ \cline{2-13}  & **Ar** & **To** & **Pr** & **TV** & **Ar** & **To** & **Pr** & **TV** & **Ar** & **To** & **Pr** & **TV** & **Ar** & **To** & **Pr** & **TV** \\ \hline Ar & 5 & 0 & 0 & 0 & 5 & 0 & 0 & 0 & 5 & 0 & 0 & 0 & 7 & 0 & 1 & 0 \\ To & 1 & 2 & 2 & 0 & 0 & 5 & 0 & 0 & 0 & 5 & 0 & 0 & 1 & 5 & 2 & 0 \\ Pr & 0 & 0 & 5 & 0 & 0 able to quantify the soy content in ground beef. The SVM classification approach was developed to identify mixed soy categories and showed a total accuracy of 95% and 83.33% for models developed by dispersive NIR and FT-NIR spectra, respectively. However, when the soy content is less than 5%, neither method can precisely classify the products (<85%). The overall result indicates that it is feasible to use a spectrographic method to predict soy products in ground meat. However, it should be also emphasized that this work involved soy contents only beyond 1% and more development is needed for using NIR methods to predict less than 1% of soy contents in ground beef products.  
The authors would like to thank organizations or individuals who had contributed to this manuscript. This work was supported financially by the China National Science and Technology Support Program (2012 BAK08B04). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors collaborated in the interpretation of the results and on the preparation of the manuscript. The authors declare no conflict of interest.