esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Pre-Processing of Speech Signals
#### 4.1.3 Frame Analysis and Windowing  
The speech signal is quasi-stationary and slow-varying, which means the signal needs to be segmented in terms of short-time analysis. The segmentation process is called framing. Framing spills the speech signal into a fixed length of frames; each frame is around 20-40 milliseconds. The speech signal segments must overlap to capture all the signal behaviors [24]. In our experiments, we set the frame size to 30 milliseconds, with ten milliseconds overlap. After framing the speech signal, the frame needs enhancement to fix the cut-off of the signal. That windowing step is necessary. The framing of the signal is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Minimum Duration** & **Maximum Duration** & **Average Duration** & **Standard Deviation** \\ \hline
**Dataset before Detection** & 2.619875 s & 7.699875 s & 3.94195568 s & 0.47355293 \\ \hline
**Dataset after Detection** & 1.40775 s & 3.881375 s & 2.16832302 s & 0.25270021 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for speech detection.  
Figure 3: Detected speech plot.  
carried out in the time domain, but windowing could be conducted either in the time or frequency domain. We apply a Hamming window in each frame. Equation (2) shows the Hamming window equation [22]:  
\[\mathrm{w_{H}[n]}=\left\{\begin{array}{ll}0.54-0.46\mathrm{cos}\big{(}\frac{2 \pi n}{N-1}\big{)}\\ 0\,,&\mathrm{n}\,\,\,\mathrm{otherwise.}\end{array}\right.,\quad\mathrm{n}=0,1, \ldots,\mathrm{N}-1. \tag{2}\]
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 2 Proposed ADC Architecture and Pre-Quantization Timing
### Working Timing Strategy
#### 2.2.3 Clock Generation Circuit of PQT  
As PQT pointed out in Section 2.2.2, the 1 GSps 12-bit ADC must generate the timing: \(\phi_{1}\), \(\phi_{1e}\), \(\phi_{2}\), \(\phi_{2e}\), \(\phi_{3e}\), \(\phi_{3e}\), \(\phi_{4e}\) and \(\phi_{4e}\).  
The clock generation circuit is shown in Figure 4. The high-speed data interface uses current-mode logic (CML) as the receiving end of the clock, and then converts the output voltage of the CML to the CMOS logic voltage. In the non-overlapping schematic, the timing can be improved by adding more buffers. A clock with a 50% duty cycle is provided for SHA and MDACs with a frequency of 1 GHz. A comparator requires a clock with a duty cycle of 25%, which is common in time-domain interleaving mode based on an external frequency of a 2-GHz clock. In this ADC, DLL (blue frame) is applied to generate the clock of the comparator, solving the problem of two master clocks.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 3 Proposed Methodology
### Hybrid Fusion Mechanism
#### 3.2.2 Iterative Learning Strategy (ICFE Module)  
To further strengthen the memory of inter-modal and intra-modal complementary information and improve the model's performance, the ICFE module is introduced. Basedon the CFE, this module deepens the network's memory for cross-modal features by iteratively reusing shared parameters. Compared to simply stacking multiple CFE modules, the ICFE progressively optimizes features within cyclic iterations using the same parameters. This approach not only reduces the risk of overfitting caused by an overly deep model but also fully exploits complementary modal information with minimal additional parameter overhead. Taking iterations as an example, the process can be expressed as follows:  
\[\{T_{n}^{T},R_{n}^{R}\} =\mathcal{F}_{ICFE}\big{(}\{T_{R},T_{T}\},n\big{)}\] \[=\underbrace{\mathcal{F}_{CFE}\big{(}\cdots\mathcal{F}_{CFE}}_{n} \big{(}\{T_{R},T_{T}\}\big{)}\big{)}. \tag{7}\]  
Here, \(\{T_{n}^{T},R_{n}^{R}\}\) represents the output sequence obtained after \(n\) iterative operations, and \(\{T_{R},T_{T}\}\) denotes the input sequence to the ICFE module. The output of each iteration serves as the input for the next iteration, with parameters shared across all iterations. Additionally, the input sequences and output sequences from the ICFE module are first converted into feature maps and then rescaled to the original feature map size using bilinear interpolation. This iterative learning strategy progressively optimizes cross-modal complementary information without increasing the number of parameters, providing an effective solution for enhancing multimodal target detection models.  
While the CFE and ICFE modules significantly improve multimodal information fusion, further enhancements are required to strengthen the model's ability to capture complex features. Inspired by DMFF and considering the specific needs of this study, we innovatively arrange the CAFM attention mechanism after adaptive aggregation mean pooling and maximum pooling. This novel arrangement aims to further optimize the feature fusion process and improve the model's capability to process diverse modal features, enabling it to effectively handle challenges in multimodal target detection. The following section will detail the principles and advantages of the CAFM attention mechanism.
## 4 Polyphenol-Based Systems and Their Unveiling Potential for Wound Care
### Antimicrobial Polyphenol Wound Dressings
of methacrylic acid (MAA), followed by subsequent crosslinking and silver reduction under irradiation. Rapid healing was observed in wounds treated with this nSnH gel on scars compared to the other groups. Histological studies have shown that aloe vera dressings are the most suitable. These results suggest that nSnH, together with aloe vera dressing material, may be promising candidates for wound dressings. [PERSON] et al. [78] prepared curcumin-loaded chitosan/cellulose microcrystals composite films by vapor-induced phase inversion (VIPI) method. This study revealed that the Ch/CMC films loaded with curcumin demonstrated a controlled release of Cur for a period of 36 h. The amount of curcumin released showed a negative dependence on the concentration of cellulose crystals dispersed in the chitosan matrix. The scattered CMC produced additional physical cross-links and slowed the release rate. The films showed reasonable antimicrobial activities against different bacteria and fungi. Despite all promising properties, polyphenols have certain limitations, including instability, low aqueous solubility, weak bioavailability, light sensitivity, and limited membrane permeability [79]. Consequently, polyphenols are easily oxidized by light exposure, which makes their use and handling limited, also due to their reactivity and their limited absorption and delivery into targeted tissues. Moreover, the concentrations of phenolic compounds that are effective in vitro tests are not often enough for in vivo studies [80]. Thus, as a means to overcome these drawbacks, encapsulation of polyphenols as an efficient delivery system is believed to be the solution to overcome this problem, improving the stability of polyphenols and controlling their delivery and release [81]. Different studies that compared the activity of the encapsulated polyphenol to its free form concluded that effects were greater for encapsulated compounds [82; 83; 84]. Curcumin incorporated in hydrogel for diabetic wound application was studied. Because curcumin has both low bioavailability and in vivo stability, as do most polyphenols, gelatin microspheres were used to encapsulate the phenolic compound in order to overcome its limitations. Through microencapsulation, obtained results revealed the capacity to significantly promote skin wound healing of the delivery system with curcumin. Similarly, despite the potential health benefits of resveratrol, polyphenol is a difficult compound to be incorporated into commercial pharmaceutical products due to its poor water solubility, low bioavailability, and chemical instability. In this sense, nanoparticulate systems using pectin as a wall material were investigated in order to protect and deliver the phenolic compounds. Antioxidant results showed higher values of antioxidant activity encapsulated resveratrol compared to free resveratrol.
## 4 Classification of Routing Protocols
### Cluster-Based Routing Protocols
#### 4.3.3 Comparative Study of Cluster-Based Routing Protocols  
Limited energy is the main constraint in WBSNs as well as in other WSN applications. In WSNs, efficient cluster mechanisms are used to reduce the number of direct communications of the sensor  
Figure 12: Hierarchical structure of cluster-based routing.  
nodes with the base station, to decrease the power consumption, enhance the link quality and thus increasing the lifetime of the network [62]. The comparison of both cluster-based routing protocols for WBSNs with other state-of-the-art schemes is summarized in Table 3, in terms of delay, packet delivery ratio, security and energy consumption.  
HIT [60] performs better in reducing the energy consumption as compared to LEACH [63], PEGASIS [64] and direct transmission for small number of nodes, however it consumes more energy in dense networks. Furthermore, HIT also reduced the network delay to gather the data. The main advantage of AnyBody [61] is that the number of clusters remains almost constant by increasing number of nodes, while in LEACH with the increase in nodes results increase in number of clusters (5% of the nodes). This results in a bigger cluster size of AnyBody as compared to LEACH. Furthermore, AnyBody also reduces the cost of setting up the cluster.  
HIT [60] results in low packet delivery delay and energy consumption and does not consider packet delivery ratio. On the other hand, AnyBody [61] improves the packet delivery ratio and does not consider the packet delivery latency and energy consumption.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 5 Attack Description and Explanation
### Explanation of DoS Attack  
A Denial-of-Service (DoS) attack aims to make a network or system inaccessible to its intended users. This is achieved by overwhelming the target with an excessive volume of data or sending malformed packets that disrupt normal operations, ultimately causing system failure.  
In the context of AMQP, DoS attacks exploit the protocol's reliance on messaging brokers, such as RabbitMQ, by overwhelming them with connection requests or data packets. This prevents legitimate messages from being processed, leading to system crashes or unresponsiveness.  
For our experiments, we used the **hping3** tool to execute SYN flood attacks on the AMQP server. The attacker sends a high volume of SYN packets to port 5672, which is designated for AMQP communication. These packets initiate a TCP handshake but do not complete it, exhausting the server's resources and rendering it inaccessible.  
**Command for Hping3 Attack**:  
hping3 -d 90 -p 5672 -S -flood 192.168.180.230  
**Options**:  
* -d: Data size (in bytes).
* -p: Target port number.
* -S: SYN flag.
* -flood: Continuous packet sending.
* 192.168.180.230: Target IP address.
## 2 Applications of Opportunistic Networking Protocols
In addition, here we can observe some dependency between area and number of nodes, but the general node density is higher than for the rangers scenario. Concert/crowded event/disaster: Here, a crowded event such as a soccer game or concert is assumed. Such scenarios can be very dense, e.g., more than 50,000 people can fit into a large stadium. Of course, space is limited and some dependency between area and number of people/nodes inside can be observed. This scenario also includes the disaster case, as usually many people in small areas are affected and panic can cause further density. Smart City: Here, people and devices in a city are connected together, e.g., street lights, traffic lights, parking lots, vehicles and pedestrians. These scenarios are relevant when the relative density is quite high to cover the complete city. From experience, smart city concepts are applied mostly to bigger cities with larger populations than to smaller towns. Smart County: This scenario extends the Smart City application to even larger areas, such as San Francisco Bay Area or Berlin/Brandenburg Metropolitan Area. The relative density is similar or lower than for Smart Cities, as the county includes also less-populated areas. However, the absolute area and number of devices/people are greater. Industry/Internet of Things: Another significant scenario for OppNets is the internet of things or Industry 4.0. Here, millions of devices are installed in relatively small areas (e.g., factories or warehouses) to fully control industrial processes. In this scenario, fewer people and more devices are present, which makes the relative density very high. Disaster alert: This scenario is probably the one most referenced to in OppNets. Here, potentially many people over large areas need to be warned about an upcoming danger. The areas can be potentially as big as Kruger park (e.g., a volcano eruption, earthquake or big storm) and as small as a neighborhood (car accident). The number of people is also unlimited--from few people around an accident to the whole population of California. This application is not shown in Figure 1, since it would cover the complete area. _Take-home message: Similar to the conclusions in [3], we summarize that there are many different applications with great potential in OppNets, which cover different absolute areas and number of people/devices and relative densities. However, there are also some areas in Figure 1, which we consider non-realistic, i.e., very high densities (over 100 million devices per square kilometer) and very low densities (fewer than one device per square kilometer). While such scenarios are interesting from scientific point of view, experiments need to consider also the more realistic ones._
# Research on Real-Time Model of Turboshaft Engine with Surge Process
## 3 Real-Time Surge Model of Turboshaft Engine
### Surge Dynamic Process Simulation at the Design Point  
Step 1. Give the model input. (Altitude: \(H=0\) km, Mach number: \(Ma=0\), Fuel: \(W_{f}=0.092556\), \(B=0.6\), \(\gamma_{T}=0.65\))  
Step 2. The \(\gamma_{T}\) is decreased from 0.65 to 0.55 at \(t=5\) s, which makes the model into surge;  
Step 3. The \(\gamma_{T}\) is increased from 0.55 to 0.65 at \(t=15\) s, which makes the model out of surge.  
During the surge process, the model's compressor outlet mass flow, pressure, rotor speed and exhaust gas temperature are shown in Figure 10. It can be seen that the turboshaft engine model enters into the surge at \(t=5\) s meanwhile the compressor outlet mass flow and pressure signal of the model represent the typical characteristics of low-frequency and large-scale pulsation during the surge. Besides, the overall performance parameters of the engine show a gradual decrease in speed and a gradual increase in exhaust temperature, which shows that the model successfully reflects the rotor inertia and thermal inertia of the turboshaft engine. When the model returns to the stable state at \(t=15\) s, each parameter gradually returns to the level during normal operation. Comparing the changes in rotor speed, compressor outlet pressure, and exhaust temperature given by the actual test data in [2] shown in Figure 11, after the model in this paper into the surge, the macro change characteristics of the parameters of the model are consistent with the test data.  
Fast Fourier transform is performed on the compressor outlet pressure signal, and its frequency component is analyzed as shown in Figure 12. Its main frequency component is 12.0977 Hz, which conforms to the pressure pulsation law of 0-15 Hz in [4] when the real turboshaft engine goes into surge.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\Phi_{0}\) & 0.6 \\ \hline \(\Psi_{0}\) & 0.6 \\ \hline \(l_{c}\) & 2 \\ \hline \(\Psi_{C0}\) & 0.3 \\ \hline \(U\) & Related to Ng in CLM \\ \hline \(H\) & Related to Ng in CLM \\ \hline \(W\) & Related to Ng in CLM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the extended MG model.  
Figure 11: Trend of engine’s parameters change in surge test.  
Figure 12: Fast Fourier transform results of P3.  
Figure 10: Simulation of surge dynamic process at design point: (**a**) The mass flow and pressure at compressor outlet; (**b**) Engine’s rotor speed and exhaust temperature.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 3 Experiment
### Experimental Setup
#### 3.2.2 Data Groups  
The parameter information is shown in Table 2. The data were evenly divided into 10 groups of data with different states, including five normal states (Group 1-5) and five fault states (Group 6-10). In the experiment, in order to validate the performance of the methodology, comparison models were introduced, i.e., a Medium Neural Network, Wide Neural Network, Bi-layer Neural Network, Tri-layer Neural Network, and Narrow Neural Network. The validation of the method accuracy was determined under 5-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & **Training Segments** & **Testing Segments** & **Samples in Each Segment** \\ \hline Normal & 9600 & 2400 & 64 \\ Fault & 9600 & 2400 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of data.  
Figure 4: Data collection platform. (**a**) Wind turbine generator; (**b**) acceleration sensor.
# Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
## 5 Experimental Setup  
Our target is to demonstrate that the proposed dynamic information retrieval system can work effectively on the Collaborative Filtering (CF) problem and, in particular, the \(k\)NN search problem. As real-time querying of the similarity content of audio/images is a common problem at present, we tested our system with a data set comprised of millions of audio fingerprints.  
We aim to examine the impact of our proposed system on a large memory space with an enormous amount of data in the database. With the typical size of a HiFP2.0 feature being 512 bytes, for the test, we generated a set of 64 million HiFP2.0 features with a total size of 62 GB. To analyze the accuracy of both LSH and CUSagedLSH systems, we created numerous testing queries with different distortions from the data set and examined different numbers of hash functions in the generated family of hash functions.  
The query set contained 10,000 items that differ from items in the data set, where every query item had its own ground truth \(k\)NN set containing the indices of items in the data set (32 true NNs for each query). The accuracy of \(k\)NN was examined by the percentage of correct results compared to the ground truth sets (i.e., the sets of true NNs to the queries in the data set). The recall is an accuracy measurement for a group of queries, which can be calculated as:  
\[\text{Recall}_{k}(Q)=\frac{1}{m}\sum_{q\in Q}\frac{|k\text{NN}(q)\cap\text{GT} k\text{NN}_{k}(q)|}{k}, \tag{8}\]  
where \(k\text{NN}(q)\) is the \(k\)NN results of query \(q\) after searching and \(\text{GT}k\text{NN}_{k}(q)\) is the ground truth \(k\)NN set for \(q\). The precision was not important, as our method used the threshold to check the \(k\)NN candidates and ranked them in every probe.  
The specifications of the testing computer are shown in Table 2. Each testing node had two P100 GPGPU devices and was able to create 32 MPI parallel processes.
# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 4 Experiment and Analysis
### Comparison of Different Detection Models  
In order to further investigate the advantages of the ES attention module proposed in this study, we utilize the IMS spectrogram as the input feature and employed the MobileNetV3 network, ECA-MobileNetV3 network, and ES-MobileNetV3 network as the machine anomalous sound detection networks for the ablation study. The experimental results are presented in Figure 10. From Figure 10, it is evident that the ES-MobileNetV3 network proposed in this paper achieves the best results across the six different machine types. The AUC and pAUC reach 96.67% and 92.38%, respectively, which are 4.53% and 4.85% higher than the original MobileNetV3 network. Notably, for Pump and ToyConveyor, the AUC increases by 6.34% and 6.92%, respectively, and the pAUC increases by 6.64% and 8.26%, respectively. Despite the similarity in normal audio data from the ToyConveyor machine, the SoftPool layer in the detection network retains the fine features of the machine sound and enhances the recognition of the feature information. This demonstrates that the ES attention module proposed in this paper can capture sample features to the fullest extent and detect the key machine sound information. Comparing the results of the ES-MobileNetV3 network and the ECA-MobileNetV3 network, it can be observed that the AUC and pAUC for most machines are improved. However, the detection performance of the Valve machine decreased slightly. Reference [38] suggests that the sound signal of the Valve machine is non-stationary, so we speculate that the SoftPool method used in the ES-MobileNetV3 network may not be as effective in handling non-stationary sound signals. Through these experiments, it has been confirmed that the ES-MobileNetV3 network effectively optimizes the performance of the anomalous sound detection system. The reason is that the ECA network avoids the dimensionality reduction operation in the process of feature information processing and reduces information loss. Additionally, the SoftPool method preserves more feature information during the pooling process, addressing the issue of the insufficient feature extraction ability of the lightweight network.  
Figure 10: Performance comparison for different detection networks. (**a**) AUC. (**b**) pAUC.  
Furthermore, we conduct a comparative analysis to assess the detection accuracy, model parameters, and FLOPs (floating point operations) both before and after enhancing the network, using the machine anomalous sound dataset. Moreover, we also compare the results to those obtained using the MobileNetV2 network. As the predecessor of the MobileNetV3 network, the MobileNetV2 network is a well-established lightweight network and has been widely used in various mobile devices. The comparison results are presented in Table 1.