esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 From Single-Point to Multi-Point Equalization
### Common Acoustical Poles Compensation  
At low frequencies strong resonances can appear in the room response. These resonances are often independent of the position and are associated with long slowly-decaying modes. Different techniques have been proposed to compensate the low-frequency response. Many of these techniques exploit multi-point measurements to determine the spectral properties of the resonances.  
A model for a RTF using common acoustical poles corresponding to the resonances of a room is proposed in [63]. The common acoustical poles are estimated as the common pole values of many low-frequency RTFs estimated for different source and receiver positions. The poles are computed from an LPC model of the room response, estimated by two possible methods: (i) using a least-squares method, assuming all measured RTFs have the same LPC coefficients and (ii) averaging the LPC coefficients estimated from each measured RTF. The estimated poles correspond to the major resonance frequencies of the room. Then, using the estimated common poles, the method of [63] models the RTF with different moving average coefficients. The model is called by the authors the common acoustical pole and zero model, since it is a zero-pole model formed by the common acoustical poles and the zeros provided by the moving average coefficients. The approach was later expanded in [64; 65]. In [64], a multi-point equalization filter using the common acoustical poles is proposed. The common acoustical poles are again estimated as common LPC coefficients from multiple measurement of the RTFs. The equalization is then achieved with an FIR filter having the inverse characteristics of the common acoustical pole function. As for the other all-pole models, the equalization filter is a minimum-phase equalizer that cannot compensate for the notches of the frequency response. Nevertheless, the filter can suppress the common peaks due to resonances in the multiple positions, and has low sensitivity to changes in the receiver position. In [65] a pre-conditioning stage is added to the common acoustical poles equalizer. The pre-conditioning stage suppresses low-Q resonances in the entire spectrum, while a second stage based on the common acoustical poles suppresses or minimizes the low-frequency resonances. In [155], an empirical technique to select an appropriate order for the common acoustical pole model is proposed. The technique selects the first order for which a further growth does not lead to an improvement in the modeling accuracy for at least one of the measured RIRs. The model order depends on the chosen maximum frequency of the modeled poles. The iterative algorithm of [156] is also based on the common-acoustical-pole and zero model. It designs biquadratic filters suitable for multi-point RRE.  
The common acoustical poles compensation could also benefit from the filterbank technique based on second-order sections of [112; 113; 114; 115], exploiting in particular the logarithmic frequency resolution and the ability to customize the pole positions.
## 4 Results
### Dynamic Setup
#### 4.2.4 TTFF Results by Device Positioning
Figure 14: TTFF results of all device orientations placed over the front seat of the vehicle when moving following the path in Figure 4. For TTFF, the lower the value, the better. Figure 15: TTFF results of all of the device orientations placed under the front seat of the vehicle when moving following the path of Figure 4. For TTFF, the lower the value, the better. * TTFF Warm Star Summary by position Figure 16 shows the results obtained from the TTFFs performed depending on the position of the devices inside the vehicle. The data collected in each location where the device was installed in each position of the vehicle were averaged. For this section, the four scores obtained from time-to-fix were better in the linear polarization technology. Obtaining the lowest possible values is crucial in an IoT environment where battery longevity is critical. An IoT device does not obtain the geolocation continuously but receives the position every certain period to optimize the battery as much as possible. Every time a device tries to obtain its position, it must perform a fix, so the less time it takes, the less time the device is active; therefore, it consumes less battery. Therefore, since the linear polarization technology obtains the fix faster, we can say that it can make the device more energy efficient.
## 5 Experiments and Discussion
### Comparative Evaluation and Analysis of Performance
#### 5.3.3 Influence of Hyperparameters
Though the model is better able to learn the information of feature interactions with different orders, too many of them increases the model's complexity and produces overfitting. In addition, according to the experimental results, better results are obtained when the number of CIN hidden layers is 4. The main reason for this is that the CIN in the model design is a better complement to the DNN for high-order feature interactions, and does not need very many hidden layer units to achieve better training results. Another noteworthy observation is that the neural network-based model does not need a deeper network structure to obtain the best results; the best results can be obtained by setting the number of layers of both the CIN and DNN to 2 during the training process on this dataset.
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 4 A New Analysis Method of Structural Time-Variant Reliability with Random and Interval Variables
### Model Equivalence Proof
In the above mentioned equation, there is a sub-optimization problem \(\underset{\mathbf{Y}\in\mathbf{Y}^{{}^{\prime}}}{\text{min}}(\mathbf{X}, \mathbf{Y},t_{i})\), represented by an interval variable \(\mathbf{Y}\) in the constraint, and the Karush-Kuhn-Tucker necessary condition is mathematically expressed as follows:  
\[\left\{\begin{array}{l}\
abla_{\mathbf{Y}_{i}}g(\mathbf{X},\mathbf{Y},t_{i} )+\lambda_{3i}-\lambda_{2i}=0,\,i=1,2,\ldots,n\\ \lambda_{2i}\left(\mathbf{Y}_{i}^{L}-\mathbf{Y}_{i}\right)=0,\,i=1,2,\ldots,n \\ \lambda_{3i}\left(\mathbf{Y}_{i}-\mathbf{Y}_{i}^{R}\right)=0,\,i=1,2,\ldots,n \\ Y_{j}^{L}\leq Y_{j}\leq Y_\[\left\{ \begin{array}{l}\max_{\mathbf{X}}f_{\mathbf{X}}(\mathbf{X})\\ \text{s.t.}\ g(\mathbf{X},\mathbf{Y},t_{i})=0\\ \
abla_{\mathbf{Y}_{i}}g(\mathbf{X},\mathbf{Y},t_{i})+\lambda_{3i}-\lambda_{2i }=0,\,i=1,2,\ldots,n\\ \lambda_{2i}\Big{(}\mathbf{Y}_{i}^{L}-\mathbf{Y}_{i}\Big{)}=0,\,i=1,2,\ldots,n \\ \lambda_{3i}\Big{(}\mathbf{Y}_{i}-\mathbf{Y}_{i}^{R}\Big{)}=0,\,i=1,2,\ldots,n \\ Y_{j}^{L}\leq Y_{j}\leq Y_{j}^{R},j=1,2,\ldots,n\end{array} \right. \tag{37}\]  
Then, the Karush-Kuhn-Tucker necessary condition of Equation (37) is obtained, which is expressed the same as Equation (33). Therefore, it is mathematically proved that the original problem and the equivalent problem have the same solution when calculating reliability in this form. It should be indicated that the interval variable is treated with the uniform distribution, which is regarded as a random variable. in this situation, the limit state function of the time-variant reliability model with mixed uncertainties only contains random variable. Complex nested optimization problems for time variance can be successfully avoided.
## 3 Results of Analysis of Ground Subsidence Risk Levels Using Machine Learning
### Random Forest  
The Random Forest (RF) algorithm is a tree-based ensemble model that is developed to solve regression and classification problems in machine learning [16]. An ensemble model derives better results than a model that trains a single model once, as it trains multiple algorithms iteratively. It includes techniques such as voting and bagging.  
RF presents the best result among the results derived from the trees after creating multiple tree-based algorithms as the representative result. RF is based on a tree algorithm, has a low overfitting risk, and can be easily applied to various data. It is widely used in problem-solving through machine learning to derive a good result [17; 18; 19; 20].  
RF predicts the outcome as a binary value of 0 or 1, as presented in (1), after extracting an arbitrary number of input data from a number of single-algorithm predictors and performing a final decision by majority vote on the results derived from each predictor, where \(y_{i}=f_{i}(\text{X})\), and \(w_{i}\) refers to the weight. If the calculated value is larger than the threshold value, the predicted value is 1, otherwise it is 0 [21].  
\[\text{F}(\text{X})=\sum w_{i}y_{i} \tag{1}\]
# An Attention-Based CNN-LSTM Method for Effluent Wastewater Quality Prediction
## 4 Experiments
### Experimental Results
#### 4.3.2 Ablation Study  
The proposed method combines a CNN, an LSTM and an attention mechanism. We conducted ablation experiments on the proposed method. The ablation experiment was divided into two parts. In the first part, we removed the CNN, the LSTM and the attention mechanism modules, respectively, as shown in Table 4. The first row presents the results of the standard model, the second row represents the model excluding only the LSTM module, and the third row represents the model excluding only the CNN module. The last row represents the replacement of the attention mechanism in the model with a Dense layer, which is used for data dimensionality reduction and output prediction.  
After removing the LSTM, the prediction performance of the model decreases significantly. Combined with the PCAF analysis of effluent wastewater quality prediction in Section 3.4, the wastewater quality at adjacent sampling times is highly correlated, and the LSTM can learn the dependence relationship in the time series, making it suitable for the prediction of wastewater quality. The function of the attention mechanism is to integrate and extract wastewater quality information at adjacent sampling  
Figure 11: PCMs showing (**a**) the target and predicted values of COD before and after using SWP; (**b**) the target and predicted values of NH3-N before and after using SWP; (**c**) the target and predicted values of TP before and after using SWP; (**d**) the target and predicted values of TN before and after using SWP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MSE** & **MAPE (\%)** & **LER (\%)** \\ \hline CLATT & 1.17 & 0.13 & 0.18 \\ SWP-CLATT & 0.92 & 0.08 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Predicted results before and after the sliding window is used.  
of the attention mechanism, the prediction performance of the model decreases. After the removal of the CNN, the prediction performance decreases to a certain extent.  
In another part, we evaluate the impact of several major modules of the CNN: the residual block and the BN layer. The results are shown in Table 5. The first row presents the results of the standard model, while the rest of the rows present the results of the variants of the standard model. In the second row, we remove the residual block in the CNN module. In the third row, the BN layer is removed.  
The massive drop in evaluation indicators when the residual block is removed proves it is a pivotal step in our method. This structure ensures that original information can be transmitted further into the network and gradient explosion is avoided in training. The result also demonstrates that the BN layer is also needed to balance the contributions of each indicator in wastewater quality prediction.
# RFID Based Embedded System for Sustainable Food Management in an IoT Network Paradigm
## 5 Demonstration  
This section evaluates our recipe recommendation algorithm's approaches. We performed the recipe recommendation algorithms by using the passive RFID tags as labeled-products, and the RFID system, composed of an Arduino board and a RFID module, as the reading system installed in the kitchen furniture. Figure 7 shows the elements mentioned above, while Figure 7a represents the passive RFID tags, which store product information, Figure 7b shows the RFID system ready to read and process the information contained in the passive RFID tags. The evaluation of the recipe recommendation algorithms was done by adding, step by step, ingredients to the RFM App, and reading the passive RFID tags associated to the different products. Initially, we assumed the kitchen furniture was empty. Later, we checked the recommended recipes according to the products introduced in each step. Table 4 tabulates the recipe recommendations returned when ingredients were introduced, with both Ingredient-Percentage and Ingredient-Weight based algorithms. The column \"Inserted products\" shows the ingredients in each \"Step\" returning the new recipes recommended by the the system in the recommended recipes columns. At Step 1, no ingredients are introduced in the embedded system so that no recipe is shown with neither the Ingredient-Percentage nor with the Ingredient-Weight based Algorithm. In Step 2, the Ingredient-Percentage based Algorithm returns a mashed carrot recipe, despite the fact that no carrots where introduced. Since milk represents 50% of the mashed carrots recipe ingredients and is available, the recipe is recommended. The Ingredient-Weight based algorithm performs a better recommendation as it recommends a mashed potatoes recipe, in which potatoes are the main ingredient. In step 5, the Ingredient-Percentage algorithm recommends stuffed eggplant recipe, despite neither meat nor eggplants being present. However, the Ingredient-Weight based algorithm recommends the stuffed eggplants when the eggplant is introduced in step 7. Note that, from these recommended recipes shown at each step, we also have to consider also previous ones. For instance, at Step 3 with the Ingredient-Weight based algorithm, no new recipes are recommended, however, the mashed potatoes recipe will be displayed. From Table 4, we observed how both Ingredient-Percentage and Ingredient-Weight based algorithms return recipes when ingredients are introduced, and more importantly, the recommendations adapt in progression with the new ingredients. Overall, the Ingredient-Weight  
Figure 6: Example of a recipe recommendation using our proposed algorithms. based Algorithm returns better matches, according to the ingredients introduced, and the recommended recipes, as it considers the main ingredient to be key for recipe preparation.
## 5 Conclusions  
A dual-band dual-port MIMO RDRA fed by two symmetric coupling apertures is proposed and investigated. Two orthogonal radiating modes are excited in the DRA simultaneously at two overlapping frequency ranges around 1.8 and 2.6 GHz. The measured isolation is better than 17 dB over the operating frequency ranges. Given the same frequencies and same dielectric material,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Modulation** & **Measured** & **Measured** & **Maximum** & **Maximum** \\
**Scheme (CQI)** & **Throughput SISO** & **Throughput MIMO** & **Throughput SISO** & **Throughput MIMO** \\ \hline
**QPSK(Mbps) (6)** & 14.10 & 28.21 & 14.112 & 28.23 \\
16 QAM (Mbps) (9) & 30.33 & 56.65 & 30.352 & 56.68 \\
64 QAM (Mbps) (12) & 50.190 & 93.152 & 50.197 & 93.161 \\ \hline \end{tabular}
\end{table}
Table 2: Measured throughput with three modulation schemes at 1.8 GHz.  
Figure 18: Channel capacity measurement setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Modulation** & **Measured** & **Measured** & **Maximum** & **Maximum** \\
**Scheme (CQI)** & **Throughput SISO** & **Throughput MIMO** & **Throughput SISO** & **Throughput MIMO** \\ \hline
**QPSK(Mbps) (6)** & 14.11 & 28.22 & 14.112 & 28.23 \\
16 QAM (Mbps) (9) & 30.350 & 56.66 & 30.352 & 56.68 \\
64 QAM (Mbps) (12) & 50.191 & 93.158 & 50.197 & 93.161 \\ \hline \end{tabular}
\end{table}
Table 3: Measured throughput with three modulation schemes at 2.6 GHz.  
the proposed antenna is the first with a rectangular shape to be operated at given frequencies with such a high isolation values at both the bands for MIMO applications. ECC calculated from S-parameters and 3-D radiation pattern is well below 0.5 which is the required acceptable level. Measured DG is almost 10 dB and MEG ratios are maintained close to zero at both bands. The throughput measurement at both the frequency bands indicates superior performance to an SISO system in terms of more bits transferred per second. The presented results prove that the proposed antenna can provide considerable performance for LTE MIMO applications.  
The authors would like to thank the Ministry of Higher Education (MOHE) under FRGS (vote 4F283 and 4F733) and under Research University Grant (votes 11H59, 03G33, 05H62, and 04H36) and the Higher Center of Excellence Grant (vote 4J220 and 4J211). Also thanks to Universit\(\acute{\text{e}}\) Teknologi Malaysia under vote number 4F818 and 12H35, the Ministry of Science and Innovation (MOSTI), under vote 48076 for supporting this research work.  
[PERSON] and [PERSON] designed the experimental setup and performed the experiments; [PERSON] and [PERSON] conceived the idea and helped in finalizing the manuscript; [PERSON] and [PERSON] contributed in analyzing and writing the results. The authors declare no conflict of interest.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 3 System Model and Problem Statement
### Problem Statement  
To jointly achieve multiple objectives, we aim to find an optimal parent node according to changes in network conditions. For this, we consider multiple decision criteria; however, they are in a trade-off relationship. Hence, we aim to provide a reasonable balance between conflicting goals (i.e., high reliability, low latency, and energy efficiency) rather than jointly achieving them. In our previous approach [19], each node calculates the weighted sum of multiple criteria for each neighbor, and then chooses the node with the highest weighted value. It shows reasonable trade-offs between the performance metrics; however, the decision maker must calculate the weight factors based on its preference or background knowledge of the network. By contrast, in this work, RL is used to enable each node to utilize empirical data about the network so that the parent node can be adaptively selected according to the network state.
# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
## 5 General Discussion
### Limitations and Implications  
This study has several limitations. First, all participants were sampled from a university; therefore, the results of this study must be interpreted with caution. In addition, this study built artificial smart spaces (a living space and workspace). Although the participants did not doubt the authenticity of the IoT-CAs and smart spaces and were satisfied with the layouts of these spaces, further studies among a broader user sample in real smart spaces are needed to further validate our results. Despite these limitations, this study is the first--to the best of our knowledge--to measure users' willingness to disclose different types of information that is based on the actual experience of interacting with IoT-CAs and considers the effects of spaces and user context. The key implications are as follows.  
The first implication concerns the collection of user data and the services provided by IoT-CAs. The results of this study suggest that a company's collection of user information can be divided into three levels according to the user's willingness to self-disclose: high self-disclosure (tastes and interests), medium self-disclosure (work, attitudes and opinions, body, and personality), and low self-disclosure (money). Therefore, it is a good start to collect high self-disclosure information in the early stages of use--tastes and interests--and provide personalized taste- and interest-related services that will not cause privacy concerns. Then, the company could gradually collect medium self-disclosure information and provide related services; this would help deepen users' closeness to IoT-CAs and increase their confidence and patience with services [12]. After a period of time, high self-disclosure information could be collected for money-related services (e.g., shopping) according to users' needs. Companies should exercise caution when adding money-related services to an IoT-CA because they could give users a strong sense of privacy violation. Therefore, as a new product, it might not be a good idea to promote money-related services during the early marketing or use stages of IoT-CAs.  
The second implication is that users expect reciprocity in their interactions with IoT-CAs. Users were willing to disclose more information to an IoT-CA in the living space, but they also expected the IoT-CA to provide more diverse and personalized services in this space. IoT designers could design services for IoT-CAs in the living space and take advantage of users' disclosed information to provide personalized and appropriate services. This is a lucrative new market because rewarded self-disclosure is more likely to be continued compared with unrewarded self-disclosure [64]. If users receive satisfactory service after self-disclosure, they might more actively engage in more self-disclosure with an IoT-CA. This would form a benign use cycle in human-IoT-CA interaction.
# Cyclic Detectors in the Fraction-of-Time Probability Framework
## 2 Materials and Methods
### Monte Carlo \(P_{\text{md}}\) versus \(T\)
Energy detector (black): (\(\ast\)). Quadratic form detectors (blue) and single cycle detectors (green): (\(\bigtriangledown\)) cyclic pdf (kernel-based estimator); (\(\square\)) cyclic CDF (Fourier coefficient of the indicator); (\(\bigcirc\)) cyclic autocorrelation (cyclic correlogram); (\(\lozenge\)) cyclic CDF (kernel-based estimator); (\(\star\)) cyclic 4 th-order moment. Receiver Operating Characteristic (ROC): Monte Carlo \(P_{\text{d}}\) versus Monte Carlo \(P_{\text{fa}}\)  
In this experiment, the receiver operating characteristic (Monte Carlo \(P_{\text{d}}\) versus Monte Carlo \(P_{\text{fa}}\), in short MC-\(P_{\text{d}}\) vs. MC-\(P_{\text{fa}}\)) is computed for quadratic-form detectors. The results are reported in Figure 3.  
For SC detectors and for the energy detector, the nominal \(P_{\text{fa}}\) is practically coincident with the Monte Carlo \(P_{\text{fa}}\), since the threshold is obtained using Monte Carlo simulation under H\({}_{0}\). Therefore, the results were practically coincident with those of Section 3.5 and are not reported here. In contrast, for QF detectors, the nominal \(P_{\text{fa}}\) is practically coincident with the Monte Carlo \(P_{\text{fa}}\) only if the assumed distribution for the decision statistic \(\mathcal{T}_{0}\) under H\({}_{0}\) is correctly predicted. By comparing the curves in Figure 3 with the corresponding ones in Figure 2, it follows that, when the data-record length increases from \(T=2^{15}\) to \(T=2^{19}\), the ROCs MC-\(P_{\text{d}}\) vs. MC-\(P_{\text{fa}}\) are practically coincident with the ROCs MC-\(P_{\text{d}}\) vs. nominal-\(P_{\text{fa}}\) for the QF detectors based on estimates of second- and 4 th-order cyclic statistics or based on estimates of the cyclic CDF (Fourier coefficient of the indicator). In contrast, the ROC MC-\(P_{\text{d}}\) vs. MC-\(P_{\text{fa}}\) for the QF detectors based on estimates of the cyclic CDF (kernel-based estimator) presents irregularly spaced points and based on estimates of the cyclic pdf (kernel-based estimator) does not fit the corresponding ROC MC-\(P_{\text{d}}\) vs. nominal-\(P_{\text{fa}}\). Such a behavior for the ROCs of the detectors based on the cyclic CDF and pdf kernel estimators suggests that for these estimators the normalized estimation error is not Gaussian and, consequently, the threshold determined by (78) is not correct.