esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Vibration Reduction by the MRE-Based ATDVA under Variable-Frequency Excitation  
### Experimental Setup and Methods  
The MRE-based ATDVA works to reduce vibrations with variable frequencies excited. An electric current should be applied under the condition of frequency excitation so that the MRE stiffness is variable. Accordingly, this study describes the process of applying a current to the MRE-based ATDVA to detect and reduce the variable frequencies in real time.  
The short-time Fourier transform (STFT) of acceleration responses was performed to detect the frequency applied to a plane plate at the time of variable-frequency excitation.  
The frequency resolution was set to 1 Hz with a sampling rate of 0.004 s and an FFT block size of 128. If any peak excitation frequency is detected in a frequency range targeted for vibration reduction, an electric current is applied based on relationships between the peak frequency and the stiffness of the MRE-based ATDVA and between the MRE-based ATDVA and the vibration reduction frequency. If the current is applied depending on the excitation frequency, the MRE-based ATDVA can reduce vibrations in real time. As shown in Figure 18, an experimental setup was built to evaluate the performance of the MRE-based ATDVA.  
In this work, we used a function generator (SEINTEK G5100) for variable-frequency excitation and a power amplifier (Inkel MA 320) for signal amplification. For the frequency excitation, a plate was to be excited in its center. The adaptive tuned MRE-based ATDVA was placed in the center of the plate where displacement was greatest. Likewise, an accelerator (Endevco 65-100-X) was installed in the center where the greatest displacement occurred.  
The MRE-based ATDVA reduces vibrations at a variable frequency by applying an electric current to match the frequency. LabVIEW Compact-Rio 9204 was therefore used to measure frequencies under  
Figure 17: Optimized model of adaptive tuned MRE vibration absorber. (**a**) CAD model, (**b**) cross sectional view, (**c**) prototype.  
the condition of variable-frequency excitation and apply a current accordingly. Because LabVIEW's current application module (NI9265) allowed for the application of up to 0.02 A, an amplifier circuit as shown in Figure 19 was fabricated to amplify the current to 3 A.  
When attached, the current amplifier circuit in Figure 19 achieved a current amplification of 0.3 A for each channel of LabVIEW's current application module. Consequently, a current of 3 A was obtained when the connection of the circuit was extended to ten channels.
# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 3 Proposed Method
### Spatiotemporal Sensitive (SS) Module
Thus, the output of the \(c\)-th channel at height \(h\) can be formulated as  
\[avgpool\Big{(}x_{c}^{h}\Big{)}=\frac{1}{W}\sum_{0\leq i<W}x_{c}(h,i) \tag{5}\]  
Similarly, the output of the \(c\)-th channel at width \(w\) can be written as  
\[avgpool(x_{c}^{w})=\frac{1}{H}\sum_{0\leq j<H}x_{c}(j,w) \tag{6}\]Specifically, given the aggregated feature maps \(avgpool\left(x_{c}^{h}\right)\) and \(avgpool\left(x_{c}^{w}\right)\), we first concatenate them and then send them to a shared \(1\times 1\) convolutional transformation function \(F_{1}\), yielding  
\[f=\delta\left(F_{1}\left[avgpool\left(x_{c}^{h}\right),avgpool\left(x_{c}^{w} \right)\right]\right) \tag{7}\]  
where \([\cdot,\cdot]\) indicates the concatenation operation of the feature maps, \(\delta\) refers to the non-linear activation function, the intermediate feature \(f\in R^{\widehat{\Gamma}\times(W+H)\times 1}\) is derived from the process. We then split \(f\) along the spatial dimension into two separate tensors, \(f^{h}\in R^{\frac{\widehat{\Gamma}}{\tau}\times H\times 1}\) and \(f^{w}\in R^{\frac{\widehat{\Gamma}}{\tau}\times W\times 1}\). Another two \(1\times 1\) convolutional transformations, \(F_{h}\) and \(F_{w}\), are utilized to separately transform \(f^{h}\) and \(f^{w}\) to tensors with the same channel number to input x, yielding  
\[ \begin{array}{l}g^{h}=\sigma\left(F_{h}\left(f^{h}\right)\right)\\ g^{w}=\sigma\left(F_{w}\left(f^{w}\right)\right)\end{array}  \tag{8}\]  
where \(\sigma\) stands for the sigmoid activation function. The output \(g^{h}\) and \(g^{w}\) are then expanded and used as attention weights, respectively. Finally, the output of our coordinate attention block \(output(x_{c}(i,j))\) can be written as  
\[output(x_{c}(i,j))=x_{c}(i,j)\times g_{c}^{h}(i)\times g_{c}^{w}(j) \tag{9}\]  
The SS block utilizes grouped dilated convolutions and coordinate attention to encode spatiotemporal contextual information within different spatiotemporal coordinate ranges. This approach enables the spatiotemporal maps to contain more precise spatiotemporal contextual details, thereby effectively enhancing the robustness of physiological feature extraction.
## 2 Baseline Monte Carlo Analysis
The coarse-bin example shows how excessive smoothing can hide tail behavior and bias density estimates, while the over-refined example reveals the high random variability that arises when too little data fall into each bin. The Freedman-Diaconis (FD) width provides a balance: it is wide enough to suppress random noise yet narrow enough to preserve the essential shape of the distribution. Applying the same rule to our Monte Carlo output yields a stable PDF and ensures that subsequent uncertainty metrics are not artefacts of arbitrary bin selection. In the present study, a Monte Carlo analysis that uses the histogram method for post-processing constitutes the baseline probabilistic framework which is used as a reference for the proposed monitoring-weighted analysis concept. The flowchart for the baseline Monte Carlo analysis is shown in Figure 2.  
The numerical stability of the analysis is verified through convergence plots of the resulting distribution parameters (\(\mu\) and \(\sigma\) for normal distribution) versus \(N\). If the change in results is negligible after increasing the number of simulations, it is considered that the convergence has been achieved. Figure 1: Synthetic histogram example: (a) 2 bins, (b) 300 bins, (c) FD rule bin width. Figure 2: Process of the baseline Monte Carlo analysis.
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis
### Peak Gain  
The peak gain analysis of the antenna, shown in Figure 10a, reveals key characteristics. Operating from approximately 4.08 GHz to 18.92 GHz, the DGS technique, featuring L-shaped and semi-circular slots, facilitates this broad range. A maximum gain of about 7.4 dB occurs at 15.5 GHz, demonstrating the design's effectiveness at higher frequencies. Although gain fluctuates across the spectrum, it remains above 4 dB for most of the bandwidth, ensuring reliable performance. At 5.5 GHz, the gain is approximately 4.1 dB, indicating successful optimization for the intended application. Overall, the DGS design enhances gain characteristics and operational capabilities, making it suitable for various wireless communication applications. The maximum gain of the antenna was measured at 0\({}^{\circ}\) elevation.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Ablation Study
#### 4.5.2 Effect of DSFM  
The integration of surface-level features and high-level semantic knowledge is vital for the accurate detection of objects in RSI. This is because complex backgrounds in remote sensing images often lead to significant information loss due to multiple downsampling operations, thereby significantly compromising the model's capacity to develop discriminative feature representations through optimized representational learning. To address this issue, we introduce the deep semantic fusion module (DSFM). When only DSFM is used in the baseline model, yielding mAP scores of 95.08% on the RSOD benchmark and 94.47% on the NWPU VHR-10 validation set, achieving improvements of 1.44% and 1.17%, respectively. These results suggest that DSFM effectively extracts high-level deep semantic features and enhances detection accuracy even in the absence of SDAM's complementary advantages.
## 2 Materials and Methods
Such attacks on the sensor network are protected using AI techniques such as the hierarchically efficient Intrusion Detection System (IDS) proposed by the authors of [37; 38; 39; 40]. This approach relies on the interchange of control packets between the base station and the sensor node. However, its security remains unresolved. Good studies have been completed on Blockchain's support of the use of this type of security. When processing a huge number of transactions, validating multiple signatures incurs unnecessary overheads on the nodes of the Blockchain [41]. Open communication between doctors and patients is another benefit of the direct mode. However, this raises concerns about privacy and security due to vulnerabilities such as man-in-the-middle, sniffing, and pursuit attacks [42; 43]. Despite all this work in the literature, existing systems have many limitations. First, most of the existing protocols send the data in plain text form without encryption. Second, the existing systems do not check for the presence of the malicious nodes that perform black-hole attacks. Third, energy modeling is not considered in all the studies. Finally, clustering and load-balancing issues are not considered in the existing works. Therefore, this paper proposes a cluster-based energy-efficient secure routing protocol that encrypts the packets on the sender side and decrypts them on the receiver side to improve the security, energy efficiency, and reliability of communication in WBAN.
###### Abstract  
Content caching and recommendation for content delivery over the Internet are two key techniques for improving the content delivery effectiveness determined by delivery efficiency and user satisfaction, which is increasingly important in the booming Internet of Things (IoT). While content caching seeks the \"greatest common denominator\" among users to reduce end-to-end delay in content delivery, personalized recommendation, on the contrary, emphasizes users' differentiation to enhance user satisfaction. Existing studies typically address them separately rather than jointly due to their contradictory objectives. They focus mainly on heuristics and deep reinforcement learning methods without the provision of performance guarantees, which are required in many real-world applications. In this paper, we study the problem of joint optimization of caching and recommendation in which recommendation is performed in the cached contents instead of purely according to users' preferences, as in the existing work. We show the NP-hardness of this problem and present a greedy solution with a performance guarantee by first performing content caching according to user request probability without considering recommendations to maximize the aggregated request probability on cached contents and then recommendations from cached contents to incorporate user preferences for cache hit rate maximization. We prove that this problem has a monotonically increasing and submodular objective function and develop an efficient algorithm that achieves a \(1-\frac{1}{e}\approx 0.63\) approximation ratio to the optimal solution. Experimental results demonstrate that our algorithm dramatically improves the popular least-recently used (LRU) algorithm. We also show experimental evaluations of hit rate variations by Jensen-Shannon Divergence on different parameter settings of cache capacity and user preference distortion limit, which can be used as a reference for appropriate parameter settings to balance user preferences and cache hit rate for Internet content delivery.  
2025157986
## 3 Methods and Results
### Comparison between Refuters
#### 3.2.2 Label Frequency  
A user portrait analysis was conducted based on the refuter label information. From the word frequency count, it was possible to roughly depict the refuter features and preferences.  
As can be seen in Table 1, economics-related rumor refuters showed high interest in IT, Dig, and investment, with most being young practitioners in the internet or finance industries. The economic-related and politics-related rumor refuters had some common interests (i.e., military, investment, Finance and IT, and Dig), and could be the same group of people. For the military-related rumor refuters, the label \"military\" was third ranked, with interest also being shown in design and history. The society-related and disaster-related rumor refuters were also both interested in education, with the former group having a specific \"campus\" label and the latter group having a specific \"employment\" label. Based on this information, we infer that, for these groups, college students should account for a relative large proportion of refuters.
## 2 Materials and Methods
### Experimental Setup  
Experimental setup protocols were adapted from [PERSON] and [PERSON] [5] and [PERSON] [7]. Figure 2 presents the experimental setup employed in this study. Three 1-L [PERSON] bottles used as bioreactors were coupled with UV-light (T8 blacklight-blue bulbs 400 nm, Philips, Netherland), a hotplate magnetic stirrer (temperature regulator), and biogas collection system. Each duplicated reactor was charged with 80% working volume of inoculum to substrate ratio of 3:5 seeded with or without a magnetic photocatalyst of 2 g of Fe-TiO\({}_{2}\) NPs. Herein, these bioreactors, defined as control (AD system with no NPs), biophotoreactor (BP system with NPs) and biophotomagnetic (BPM system with NPs), were operated at a hydraulic retention time (HRT) of 30 days at a temperature of 40 \({}^{\circ}\)C and intermittent mixing at 15 rpm. Two magnetic bars (GMX model 400) used on the BPM system was obtained from Chino, California, USA. The daily biogas was monitored by downward displacement techniques, while its composition was characterised by Gas Chromatography (GC 2014, Shimadzu). The degree of efficiency of the contaminant removal and the modified Gompertz kinetic model for the biogas production were respectively estimated by Equations (1) and (2).  
\[Reactor\;efficiency=\left(\frac{C_{i}-C_{f}}{C_{i}}\right)X\;100 \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline pH & 7.5 \\ Chemical oxygen demand (mg COD/L) & 1600 \(\pm\) 16.2 \\ Turbidity (NTU) & 155 \(\pm\) 2.6 \\ Colour (Pt.Co; 465 nm) & 85 \(\pm\) 3.6 \\ Total solids (mgTS/L) & 135 \(\pm\) 12 \\ Volatile solids (mgVS/L) & 94 \(\pm\) 6.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of wastewater sample.  
Figure 2: 1: Temperature controller; 2: mixing controller; 3: hotplate magnetic stirrer; 4: purging port; 5: silicon tube; 6: gas sampler; 7: water displacement gas collector; 8: water trough.  
where, \(C_{i}\) = Substrate influent and \(C_{f}\) = Substrate effluent. The cumulative biogas production data obtained were evaluated by the modified Gompertz Equation (2) [5,7].  
\[Y=ym.exp\left(-exp\left[\frac{R_{max.~{}Ce}}{ym}[\lambda-t]\right]+1\right) \tag{2}\]  
where, \(Y\) = cumulative specific biomethane (mL/g COD) at time \(t\) (days), \(ym\) = the methane potential (mL/g COD), \(\lambda\) = lag phase of producing biogas (day), \(Ce\) = mathematical constant (2.718282), \(Rmax\) = the maximum specific substrate uptake rate per maximum biogas produced (mL/g COD.day) and \(k\) is the methane production rate constant (1/d), expressed as \(k=\frac{R_{max.~{}Ce}}{ym}\).
## Appendix A Evaluation and Assessment Metrics
### Results of the ANOVA Analysis  
The F statistic in the analysis of variance (ANOVA) was calculated according to the equation:  
\[F=\frac{MS_{between}}{MS_{within}}\] (A5)  
where the following are used:  
* \(MS_{between}\): Mean squares between groups--a measure of the variance between groups. \[MS_{between}=\frac{SS_{between}}{k-1}\] (A6)
* \(MS_{within}\): Mean squares within groups--a measure of variance within groups. \[MS_{within}=\frac{SS_{within}}{N-k}\] (A7)
* \(SS_{between}\): Sum of squares between groups--measures the difference between each group's average and the global average. \[SS_{between}=\sum_{i=1}^{k}n_{i}(\bar{X}_{i}-\backslash\text{bar}X)^{2}\] (A8)
* \(SS_{within}\): Sum of squares within groups--measures the difference between individual observations and the group mean. \[SS_{within}=\sum_{i=1}^{k}\sum_{j=1}^{n_{i}}(X_{ij}-\bar{X}_{i})^{2}\] (A9) where the following are used:  
* \(x_{ij}\) is the value of observation \(j\) in group \(i\).
* \(\bar{X}_{i}\) is average for group \(i\).  
* \(k\) is the number of groups.
* \(n_{i}\) is the number of observations in group \(i\).  
The results of the ANOVA analysis for precision indicate that the differences between the roof types are on the borderline of statistical significance, as evidenced by the values F = 2.89 and \(p\) = 0.07 (Table A1). The precision of the trained YOLO model for gable roofs is 0.79, which is slightly higher compared with the other roof types (0.76). This suggests that the model is more effective in detecting objects of this class.  
A similar result was obtained for the recall metric, with an ANOVA statistic of F = 2.65 and a \(p\)-value of 0.08. The model performs better at detecting gable roofs (recall = 0.78) than other roofs (0.75). These values confirm the slightly better ability of the model to detect objects with more regular geometry.  
The F1-score measure also showed differences between the groups (F = 2.77, \(p\) = 0.07), with a score of 0.78 for gable roofs and 0.74 for others. This indicates an overall higher effectiveness of the model in detecting gabled roofs, a result of a balance between precision and sensitivity for this class of site.  
The ANOVA performed allowed a detailed evaluation of the YOLO model, revealing differences in the detection performance of the two roof types. These results suggest the need for further fine-tuning of the model and the need to supplement the training data with examples of the other roof types. Marginalizing these differences would produce more consistent results and improve the overall performance of the model.
## 2 Materials and Methods
### Article Identification, Screening, and Quality Assessment  
After performing all searches, duplicate results were removed. The remaining articles were screened by title and abstract to exclude irrelevant results. After full-text screening, 16 eligible articles were identified [9; 11; 13; 15; 16; 17; 19; 22; 29; 30; 31; 32; 33; 34; 35; 36]. These articles' references were examined to identify five secondary articles [12; 14; 37; 38; 39]. One further article was identified serendipitously during a manual search [40]. Altogether, 22 articles were identified for inclusion in the review (Figure 1). The quality of each study was assessed using the \"Quality Assessment Tool for Studies with Diverse Designs\" (QATSDD), developed by [PERSON] et al. [41], which evaluates the methodological and reporting quality of the studies by ranking them against 16 quality criteria. The QATSDD is an established tool designed specifically to assess diverse evidence within systematic reviews in healthcare [41] and is, therefore, highly applicable to the literature included in this review. Reference searching and data extraction was conducted by one assessor ([PERSON]), with results validated by another assessor (A.W.).
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 3 System Design  
In urban traffic management, one of the main challenges is optimizing traffic signal control at intersections to minimize congestion and reduce waiting time. However, traditional traffic signal control systems often rely on fixed timing plans, which may not be efficient under constantly changing traffic conditions, leading to increased delays and congestion.  
To address this limitation, we propose traffic signal control using a Deep Q-network combined with fuzzy logic. The system response varies according to the level of traffic congestion at the intersection. This allows traffic lights to automatically change phases through learning with a Deep Q-network to facilitate the passage of vehicles at intersections. In addition, the signal duration is appropriately adjusted using fuzzy logic by applying a prototype method developed based on the existing studies [29] and serves as the comparison target. The content of the system design is divided into the following subsections.
## 4 Discussion
UWiA exhibited the highest exposure levels of 4.7 V/m (E\({}_{max}\)), nearing the levels measured at the AP, while mobile phones showed the lowest levels, which were 1.5 V/m (E\({}_{max}\)) due to their optimized antenna designs and lower transmission power. Ref. [17] evaluated the maximum exposure levels and DC for Wi-Fi 6 devices, including AP and a mobile phone, under varying frequency bands, bandwidths, and output power conditions. Unlike this study, they used the iperf3 application to control traffic (up to 400 Mbps) and forced maximum channel utilization to assess exposure levels. Their findings indicated that DC proportional to data rate, with DC reaching up to 100% at the 5 GHz band. Regarding exposure levels, they reported that for an AP operating at 80 MHz bandwidth and 100% output power, the power density measured 35 cm from the AP was approximately 0.1 W/m\({}^{2}\) (6.1 V/m). For a mobile phone operating at 25% output power, the power density measured 30 cm from the device was approximately 0.02 W/m\({}^{2}\) (0.87 V/m). In comparison, the corresponding values reported in this study are 4.3 V/m for the AP and 1.5 V/m for the mobile phone. Although both results are of the same order of magnitude, the differences arise from measurement uncertainties, variations in device output power (devices in this study did not necessarily operate at their maximum or predefined power levels), and differences in experimental conditions. These results underscore the crucial role of device design, e.g., the number and layout of antennas, as well as the transmission power, in influencing exposure characteristics and the necessity of device-specific evaluations in exposure studies. Multi-user scenarios led to increased DC values compared to single-user cases, resulting in proportionally higher average exposure levels. The rise in exposure levels was most pronounced for applications like video streaming and web browsing, which rely on intermittent high data-rate transmissions. However, the overall electric-field strength remained in compliance with ICNIRP guidelines, with the highest DC-weighted average exposure level (4.2 V/m) representing only 6.9% of the ICNIRP limit. These results demonstrate Wi-Fi 6's ability to manage multi-user environments efficiently without exceeding safety limits, even under increased traffic loads. The experiments were conducted in a controlled laboratory environment, which, while minimizing interference, may not fully reflect real-world conditions. Future studies should expand to more complex environments with varied user densities and interference sources, e.g., residential, urban, suburban, office, and industrial, etc. Additionally, the investigation of long-term exposure patterns and their correlation with DC dynamics under dynamic network loads warrants further exploration.