esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
###### Abstract
Additionally, another challenge is maintaining the significance of sequential data in our recommendations, considering the complexity of user interactions over time. Addressing the relevance of auxiliary contextual information to sequential interaction records, the hidden state in the gated recurrent unit (GRU) is suggested to be used as the model's contextual information [1]. This method ensures the generated contextual information, based on past interaction records, is relevant. It also mitigates the issue of the RNN framework forgetting older information in long-sequence data. Therefore, our paper proposes using the prediction representation of the GRU as the latent-contextual information, derived from learning based on past sequential-interaction records. To address the rigidity of defining long-term and short-term user preferences based on session duration, we propose a flexible window-based approach. This method utilizes a designated percentage of a user's interaction history to categorize preferences. For example, as illustrated in Figure 1, by setting the window (W) at 45%, we consider 45% of the interaction records closest to the most recent interaction as indicative of short-term preferences. The remaining interactions are categorized as long-term preferences. This approach more accurately reflects the user's actual preferences by dynamically adjusting the range between long-term and short-term interactions. While SBRS effectively captures user preferences within individual sessions, its capacity for personalized recommendations is somewhat limited by its focus on single-session data. This approach may result in less-tailored recommendations due to the absence of long-term preference analysis. Acknowledging this, our research extends to SARS, which encompasses both short-term and long-term user preferences, thus offering a more holistic view of user intent. Our method integrates latent-contextual information, closely linked to interaction records, which is often overlooked in traditional SARS. By considering both long-term and short-term preferences and employing a window-based approach, we aim to enhance the recommendation performance significantly. In summary, the contributions of this study include as follows:  
1. Innovative Combination: We combine GRU networks with attention mechanisms to enhance session-aware recommender systems, focusing on both short-term and long-term user preferences. 2. Latent-Context GRU Model: Introducing the latent-context GRU model, a novel approach for capturing latent interaction information, demonstrating improved performance in session-aware recommendation tasks. 3. Comprehensive Evaluation: Rigorous evaluation against current state-of-the-art methods provides a detailed analysis of its effectiveness and potential for further development.
# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
## 4 Experimental Validation
### Analysis of Sliding Window Size Results  
For long time series, the size of the sliding window constitutes an important parameter directly associated with the ultimate accuracy of a deep learning model. In this study, a sliding time window was introduced to reconstruct the 14-dimensional sensor data along with the remaining useful life (RUL) labeled data. To assess the impact of different window sizes on the accuracy of the prediction model, under an identical network structure, the window was set to sizes of 30, 40, 50, 60, 70, and 80, and the experimental results are presented in Table 2.  
Figure 11 illustrates the impact of the sliding time window size within the aero-engine remaining life prediction model. As the window length expanded, the prediction effect gradually enhanced. However, when the window length surpassed 30 for the FD001 dataset and 40 for the FD003 dataset, the prediction performance exhibited a downward trend. Based on this finding, we opted to set the time window lengths of FD001 and FD003 to 30 and 40, respectively, thereby ensuring that the model attains high accuracy in predicting the remaining life of the aero-engine.
# Key Bit-Dependent Side-Channel Attacks on Protected Binary Scalar Multiplication +
## 2 Conventional SCAs on Scalar Multiplication  
### Simple-Power Analysis  
Simple-power analysis (SPA) is a method of directly analyzing a secret scalar using only one trace or a few traces collected during cryptographic operations [9]. Because cryptographic algorithms have different power-consumption patterns according to the instructions of the processor, the secret scalar or instantaneous command could be analyzed from these patterns. For instance, in the case of a binary scalar multiplication algorithm that performs a point-doubling operation at all times, and performs a point addition operation only when the secret key bit value is 1, the secret key can be found if the point-doubling and point-addition operations have different power-consumption patterns. That is, as per Figure 0(a), this irregular sequence of instructions according to the secret scalar bit (i.e., the data-dependent conditional branch) leads to a serious security problem.
## 2 Materials and Methods
### Mathematical Model Development and Implementation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Description** & **Value** & **Unit** & **Reference** \\ \hline \(A\) & Soil column cross sectional area & 1 & m\({}^{2}\) & \\ \(C_{\text{CH4,in}}\) & Input methane concentration & 0 & mg/L & \\ \(C_{\text{Fe}}\) (II), in & Input iron(II) concentration & 0 & mg/L & \\ \(C_{\text{NO3,in}}\) & Input nitrate concentration & 0 & mg/L & \\ \(C_{\text{NO3,in}}\) & Initial soluble nitrate concentration & 10 & mg/L & [33] \\ \(C_{\text{O2,in}}\) & Input concentration of dissolved oxygen & 10 & mg/L & \\ \(C_{\text{O2,in}}\) & Initial concentration of dissolved oxygen & 10 & mg/L & \\ \(C_{\text{THH,in}}\) & Input concentration of hydrocarbon & 0 & mg/L & \\ \(C_{\text{SO4,in}}\) & Input sulfate concentration & 0 & mg/L & \\ \(C_{\text{SO4,in}}\) & Initial soluble sulfate concentration & 10 & mg/L & [33] \\ \(Q\) & Precipitation water flowing through the soil column & \({}^{\star}\) & L/d & \\ \(S_{\text{THH,in}}\) & Initial TPH concentration & 0.6 & g/kg & \\ \(S_{\text{Fe}}\) (III), in & Initial amorphous iron(III) concentration & 66.7 & g/kg & [34] \\ \(S_{\text{NO3,in}}\) & Initial nitrate concentration in the solid phase & 0.3 & g/kg & [35] \\ \(S_{\text{SO4,in}}\) & Initial sulfate concentration in the solid phase & 0.1 & g/kg & \\ \(T\) & & 20 & \({}^{\circ}\)C & \\ \(X_{\text{ser,in}}\) & Initial concentration of aerobic microorganisms & 0.08 & g/kg & \\ \(X_{\text{Hub,in}}\) & Initial concentration of denitriyzing microorganisms & 0.04 & g/kg & \\ \(X_{\text{Hub,in}}\) & Initial concentration of iron(III) reducing microorganisms & 0.04 & g/kg & \\ \(X_{\text{srb,in}}\) & Initial concentration of sulfate reducing microorganisms & 0.04 & g/kg & \\ \(X_{\text{met,in}}\) & Initial concentration of methanogenic microorganisms & 0.02 & g/kg & \\ \hline \hline \end{tabular} \({}^{\star}\) Values estimated based on the daily average precipitation for the city of Braga during one hydrological year.  
\end{table} 
Table 3: Initial conditions and input variables. in Equation (1)). All input and initial variables are presented in Table 3. Stoichiometric and composition matrix are shown in supplementary material (Table S2).
## 4 Simulation Results
### Convergence Performance  
As the time step \(m\) was fixed to 40 and the kernel size \(\mathcal{K}\) was three, the training cost of the proposed scheme is demonstrated in Figure 3, where the training signal-to-noise ratio (SNR) \(\lambda\) varies from 30 to 60 dB. As the results show, the loss curves with different \(\lambda\) tend to be stable gradually as the training epoch increases. The average final loss of the proposed scheme for \(\lambda=40\), 50 and 60 dB is around \(-26.5\) dB. In addition, it can be seen from the figure that the proposed scheme trained under the samples with a large \(\lambda\) converges faster than the one for a small \(\lambda\), e.g., the case with \(\lambda=40\) dB costs about 2500 epochs to achieve convergence, whereas it would use 1800 epochs for the one of \(\lambda=50\) dB. In general, the quality of training samples indeed affects the learning ability of a customized DL model to a certain extent; however, it is not dominant in the training process.  
As the time step \(m\) was fixed to 30 and the kernel size \(\mathcal{K}\) varied from 2 to 5, the corresponding NMSE loss is presented in Figure 4. All of the curves tend to be stable gradually with the training epoch increased and eventually achieve an acceptable training performance, e.g., the final loss for \(\mathcal{K}=5\) remains around \(-27.5\) dB, and that for \(\mathcal{K}=4\) is \(-28.2\) dB, which indicates successful network training since they are favorable for a QAM symbol recovery. We can also observe that the kernel size indeed had a significant effect on the convergence performance. In fact, as \(\mathcal{K}\) increased, the larger the receptive field obtained during the training, resulting in the extracted features being more global and discriminative. However, this will bring about a sharp increase in the amount of parameter calculation. Nevertheless, the small value of \(\mathcal{K}\) will lead to insufficient feature learning,which is unfavorable for network convergence. However, the model trained under \(\mathcal{K}=4\) converged faster and only cost about 1700 epochs to achieve an NMSE of less than 27 dB.  
Figure 5 shows the training performance for different time steps. The average final loss of the last 1000 epochs was \(-24.18\), \(-26.18\), \(-28.58\), and \(-30.58\) dB for \(m=10\), \(20\), \(30\), and \(40\), respectively. The figure indicates that the larger the \(m\) used, the smaller the training NMSE achieved is. However, as \(m\) is set too large, the size of the input spatial pattern will increase, which will not only increase the complexity in the convolution operation, but also increase the difficulty in LSTM prediction. Moreover, the network structure will become intricate. Under the consideration of convergence speed and training quality, \(m=30,\mathcal{K}=4\) was employed in the following model, although the kernel size was an even value.  
Figure 4: The cost performance comparison under different kernel sizes.  
Figure 3: The cost performance comparison under different training SNRs.
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### General Operation Flow
To inform the IdP's AAA that this EAP authentication is for a bootstrapping purpose, the Controller includes the _Service-Type_ attribute with a new value: _Bootstrapping_. This is required so that the IdP's AAA can apply authorization decisions adapted to the bootstrapping service. Based on this information, the IdP's AAA server selects the proper EAP method for authentication. In our example, this will be the EAP-PSK method, which implies 4 messages (steps 7-14). EAP requests of EAP-PSK are transported to the Controller from the IdP's AAA server by using _Access-Challenge_ messages and responses are received from the Controller by means of _Access-Request_. At the end of the message exchanges, if everything has gone as expected, the Controller receives cryptographic material (_i.e._, MSK) from the IdP's AAA server along with the _EAP Success_ message in a _Access-Accept_ message. In turn, the Smart Object will be able to generate the same keying material as defined by the EAP KMF specification. In addition to this information, the Controller may receive authorization information from the AAA infrastructure, for example, the session lifetime (_Session-Timeout_ attribute in RADIUS) related with this bootstrapping process. Part of this information may be passed to the Smart Object through, for example, a generic CoAP option named _Authorization Option_ (step 16). All this information is stored in the so-called _bootstrapping state_ (see Section 3.5). At this point, the Smart Object and the Controller share cryptographic material (MSK) to establish a security association enabling the signaling to be protected between both entities for further service requests. This corresponds to _phase 2a_ in the EAP KMF [14]. In this paper, we show two examples of usage of this keying material, though other alternatives may be considered in the future: first, the integrity protection at application level of CoAP messages by means of a new AUTH option; second, the establishment of a DTLS security association. In the following sections, we present some details about these alternatives, describe how to manage the bootstrapping state and provide additional considerations. Figure 4: CoAP-EAP bootstrapping service flow (using EAP-pre-shared key (PSK) as example of EAP method).
## 3 Numerical Experiments Setup
### Benchmark Problems
#### 3.2.2 Rosenbrock Function  
The Rosenbrock function is a well-known D-dimensional optimization benchmark problem in \([-2,2]^{D}\) described by the following equation:  
\[f_{1}(\mathbf{x})=\sum_{i=1}^{D-1}100\Big{(}x_{i+1}-x_{i}^{2}\Big{)}^{2}+(1-x_{ i})^{2} \tag{15}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Function**} & \multirow{2}{*}{**Dimension, D**} & \multirow{2}{*}{**Comp. Budget**} & \multicolumn{3}{c}{**Cost per Fidelity Level**} \\ \cline{3-6}  & & & **1** & **2** & **3** & **4** \\ \hline Forrester & 1 & 100 & 1 & 0.5 & 0.1 & 0.05 \\ \hline Rosenbrock & 2 & 200 & 1 & 0.5 & 0.1 & - \\  & 5 & 500 & 1 & 0.5 & 0.1 & - \\  & 10 & 1000 & 1 & 0.5 & 0.1 & - \\ \hline Rastrigin & 2 & 200 & 1 & 0.0625 & 0.00390625 & - \\  & 5 & 500 & 1 & 0.0625 & 0.00390625 & - \\  & 10 & 1000 & 1 & 0.0625 & 0.00390625 & - \\ \hline ALOS & 1 & 100 & 1 & 0.2 & - & - \\  & 2 & 200 & 1 & 0.2 & - & - \\  & 3 & 300 & 1 & 0.2 & - & - \\ \hline Spring-Mass & 2 (springs) & 200 & 1 & 1/60 & - & - \\  & 4 (springs + masses) & 400 & 1 & 1/60 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of employed benchmark problems.  
Figure 2: All four fidelity levels of Forrester function.  
The global minimum is inside a long, narrow, parabolic shaped flat valley. It is relatively straightforward to capture the overall quartic shape, but it is difficult to capture the local flat valleys.  
The extension to multi-fidelity is described by the following equations, where \(f_{2}\) can be considered as a medium-fidelity level and \(f_{3}\) as the lowest fidelity.  
\[f_{2}(\mathbf{x})=\sum_{i=1}^{D-1}50\Big{(}x_{i+1}-x_{i}^{2}\Big{)}^{2}+(-2-x_ {i})^{2}-\sum_{i=1}^{D}0.5x_{i} \tag{16}\]  
\[f_{3}(\mathbf{x})=\frac{f_{1}(\mathbf{x})-4-\sum_{i=1}^{D}0.5x_{i}}{10+\sum_{ i=1}^{D}0.25x_{i}} \tag{17}\]  
The three-fidelity levels are shown for two dimensions in Figure 3.
## 3 Physical Layer Design
### Voltage Controlled Oscillator  
The VCO is responsible for generating the 60 GHz RF carrier wave for the OOK modulator. The maximum single-ended voltage swing with minimum phase noise can be achieved by an NMOS cross-coupled VCO as it has comparatively less phase noise compared to the other oscillators such as ring or LC oscillator [66]. In the cross-coupled NMOS oscillator shown in Figure 6, the positive feedback is given via the M1, M2 transistors and the LC tank circuit. The startup time is a critical part of the oscillator design. To avoid the startup time from limiting the data rate we do not use the VCO as a modulator. Therefore, the output of the VCO, the Local Oscillator (LO) signal, is coupled to a separate OOK modulator input via a transformer, X1 with a 1:1 turns ratio to ensure symmetrical swing at the input of the modulator. We adopt transformer matching technique as it reduces overall power consumption by eliminating buffers or coupling capacitors.
# Power-Efficient Random Access Design for Machine Type Communication
## 4 Link Budget Analysis and Simulation Results  
Simulations have been carried out to examine the performance of LTE-nMTC random access design developed in the previous section, under the typical MTC environment (EPA 1 Hz [25]). The operating bandwidth of an MTC device is 180 kHz, with subcarrier spacing of 3 kHz and a CP length of 35 us for random access channels, and 25 us for traffic channels. The frequency offset is assumed to be uniformly-distributed in [\(-\)50, 50] Hz, and the time offset is assumed to be uniformly-distributed in [0, 35] us.  
Figure 8: Illustration of access request signal hopping for time offset estimation.
## 3 Results
### Mean Comparison Test on USD Guidelines  
Table 3 and Figure 3 show the results of mean comparison tests for the subjective scores between UD and USD according to six areas of the USD guidelines.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**UD Guideline**} & \multicolumn{2}{c}{**UD Scores**} & \multirow{2}{*}{**USD Scores (B)**} & \multicolumn{2}{c}{**Mean Test**} \\  & **(A)** & & & \(p\)**-Value * \\ \cline{2-6}  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline U1. Equitable use & 5.170 & 0.924 & 5.472 & 0.816 & 0.002 * & 105.8\% \\ U2. Flexible design & 4.992 & 0.775 & 5.175 & 0.742 & 0.029 * & 103.7\% \\ U3. Simplicity & 5.158 & 0.829 & 5.539 & 0.709 & 0.001 * & 107.4\% \\ U4. Perceptibility & 5.167 & 0.788 & 5.496 & 0.754 & 0.001 * & 106.4\% \\ US: Error-proof design & 5.623 & 0.769 & 5.795 & 0.772 & 0.043 * & 103.1\% \\ U6. Low physical effort & 5.407 & 0.846 & 5.489 & 0.831 & 0.376 & 101.5\% \\ U7. Appropriate size and space & 5.153 & 0.810 & 5.292 & 0.749 & 0.109 & 102.7\% \\ Total mean & 5.233 & 0.618 & 5.448 & 0.607 & 0.002 * & 104.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean comparison tests between UD and USD scores according to UD guidelines.  
Figure 2: Mean scores of UD and USD according to UD guidelines.  
In Table 3 and Figure 3, mean scores of USD are higher than UD scores in accessibility (S3), ensuring safety and health (S4), diversity and inclusion (S5), and sustainability (S6) at a significance level of 0.01. The increase in subjective score, (% = USD scores / UD scores), was highest in sustainability (S6), followed by accessibility (S3), and diversity and inclusion (S5). On the other hand, there was no difference in physical support (S1) and flexibility (S2) at a significance level of 0.01. Overall, average USD scores in the USD concept are 105.0% higher than average USD scores in the UD concept.
# Research on Pupil Center Localization Detection Algorithm with Improved YOLOv8
## 3 YOLOv8n Model Improvement
### The Focaler-IoU Damage Function  
In the field of object detection, bounding box regression is crucial, with the accuracy of object detection largely dependent on the loss function for bounding box regression. Existing studies enhance regression performance by leveraging the geometric relationships between bounding boxes, yet they overlook the influence of the distribution of difficult- and easy-to-handle samples on bounding box regression. This paper analyzes the impact of the distribution of difficult- and easy-to-handle samples on regression outcomes and introduces a novel method, Focaler-IoU.  
Figure 6: Structural diagram of compact channel attention module.  
Focaler-IoU enhances detector performance in diverse detection tasks by focusing on distinct regression samples. It reconstructs the _IoU_ loss using linear interval mapping to address the distribution of samples, analyzing and considering the impact of sample distribution on regression outcomes, an aspect often overlooked in traditional _IoU_ loss functions. Through this innovative method, Focaler-IoU addresses the deficiencies of existing bounding box regression techniques, thereby significantly enhancing detection performance across various detection tasks. The specific expression is as shown in (5):  
\[IoU^{focaler}=\begin{cases}0&IoU<d\\ \frac{IoU-d}{u-d}&d<IoU<u\\ 1&IoU>d\end{cases} \tag{5}\]  
where _IoU_ represents the intersection-to-union ratio, which is the ratio of the area of the overlapping part of two bounding boxes to the area of their union part, while \(d\) is the lower threshold and \(u\) is the higher threshold.  
This design allows the loss function to be sensitive to _IoU_ values within a certain range, allowing it to focus more on samples with moderate overlap between predicted bounding boxes and true bounding boxes, which are neither too difficult nor too easy. This helps the model better learn to extract features from moderately difficult samples, rather than just focusing on the easiest or most difficult samples.
# A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
## 4 Experimental Results and Analysis  
The scheme requires environmental air pressure values to be transmitted to the consumers using a high-speed message processing framework to meet the real-time processing requirements of massive data. Kafka and ActiveMQ are two widely used message queuing systems [36]. Therefore, to compare the suitabilities of different SP tools in the scheme, we compare the throughput and transmission delays between Kafka and ActiveMQ to determine the system's capacity and end-to-end message transmission time. This analysis allows us to evaluate the real-time processing performance for the data and helps us to understand the performance of the system under different message transmission frameworks in order to select a messaging framework suitable for the scheme.
## 3 Results
### Benchmark Function Analysis

Table 6: Final results in terms of average (\(\mu\)) and standard deviation (\(\sigma\)) over 10 execution of each algorithm for the Schwefel benchmark function. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(n_{batch}\)} & \multicolumn{2}{c}{**TuRBO**} & \multicolumn{2}{c}{**KB-\(q\)-EGO**} & \multicolumn{2}{c}{**mic-\(q\)-EGO**} & \multicolumn{2}{c}{**MC-\(q\)-EGO**} & \multicolumn{2}{c}{**BSP-EGO**} \\ \hline \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline
1 & **1108** & 672 & 1538 & 735 & 1584 & 971 & 1362 & 665 & 1336 & 776 \\
2 & **210** & 174 & 817 & 248 & 708 & 272 & 960 & 387 & 1185 & 509 \\
4 & **99** & 93 & 461 & 169 & 409 & 217 & 878 & 327 & 497 & 123 \\
8 & **39** & 34 & 502 & 139 & 371 & 126 & 733 & 45 increasing the batch size. The latter observation is present for any approach, even TuRBO despite its good performance. Figure 2: Number of function evaluations as a function of the batch size. Solid lines indicate the average over 10 runs, and dashed lines of same colors indicate the standard deviation. It has to be noted that optimizing multi-point AF can be time-consuming in some situations, with large batch sizes. For example, for Rosenbrock and Schwefel functions, the AP time cost is much higher than the one from the [PERSON] study and dominates the learning time cost. We observe in Figure 2 a clear decrease in the number of simulations for MC-based \(q\)-EGO which is not or less visible for TuRBO possibly thanks to its local AP inside a trust region which makes the inner optimization faster. Only BSP-EGO managed to achieve better scalability for the three studied functions in terms of function evaluation with respect to the batch size (i.e., the number of used cores in this study) thanks to its parallel AP using multiple single point EI. However and despite performances comparable to \(q\)-EGO-like methods, TuRBO performs better and manages to obtain very good final cost, probably because of its good intensification in the trust region. For larger batch sizes, e.g., \(n_{batch}=16\), relying on sequential heuristics as in KB-\(q\)-EGO and mic-\(q\)-EGO or having a parallel AP such as in BSP-EGO can be a better choice for scalability compared to multi-points AF. The time cost of the inner optimization of \(q\)-EI is also unstable regarding the time needed to provide a batch of points, single point approaches are more predictable in terms of execution time as indicated by the smaller standard deviations in Figure 2a-c.