esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 5 Experimental Simulation and Analysis
### Evaluation of Airspace Clustering Effect
#### 5.2.1 Definition of Experimental Index Operator  
The final experimental results and evaluation criteria are mainly quantized into the above-mentioned detection accuracy and detection speed ratio by comparing the results of traditional airspace conflict detection with the results of airspace conflict detection after airspace clustering. Through the method of controlling a single variable, a number of experimental environments are designed to conduct multiple groups of simulation experiments.  
Figure 21: Comparison of airspace conflict detection accuracy (the airspace safety margin distance change).  
Detection accuracy \(\psi\)  
The first step of the simulation is to detect all airspace within the wide area action area through the traditional airspace conflict detection method and record the detection results: the number of conflict airspace \(A\) and the conflict detection time \(T\). After the airspace clustering is completed, the airspace conflict detection method is used to detect the airspace conflict in each airspace cluster after the airspace clustering. Finally, the detection result \(\{a_{11},a_{12},a_{13},\cdots,a_{1k}\}\) in each airspace cluster is summarized to get the final airspace conflict detection result \(a_{1}=\sum\limits_{i=1}^{k}a_{1i}\). Define the accuracy rate of the evaluation index detection results as the ratio of the difference between the number of detection results after using the airspace clustering method and the number of conflict results obtained by the traditional airspace conflict detection method and the number of conflict results obtained by the traditional airspace conflict detection method to reflect the accuracy of the airspace conflict detection results after airspace clustering:  
\[\psi=\frac{\left|\sum\limits_{i=1}^{k}a_{ji}-A\right|}{A} \tag{6}\]  
Detection speed ratio \(v\)  
After the airspace clustering is completed, use the airspace conflict detection method to detect the airspace conflict in each airspace cluster, and record the time \(t_{j}\) consumed for complete airspace conflict detection. Define the evaluation index detection speed ratio as the ratio of the time consumed after using the airspace clustering method and the time consumed by the traditional airspace conflict detection method to reflect the change in the detection speed of airspace conflict detection after airspace clustering:  
\[v=\frac{t_{j}}{T} \tag{7}\]
# Performance Analysis of UAV-IRS Relay Multi-Hop FSO/THz Link
## 3 Performance Analysis
### Outage Probability  
Outage probability, a key performance metric in wireless communications, quantifies the likelihood of data transmission interruption when the end-to-end SNR \(\gamma\) falls below a specified threshold \(\gamma_{th}\)[30; 31]. Thus, the outage probability of the \(i\)-th FSO link of the \(i\)-th node can be obtained as  
\[P_{out}^{FSO,j}=Pr\Big{(}\gamma\leq\gamma_{th}^{FSO}\Big{)}=F_{\gamma_{SUMMSO,i}}\Big{(}\gamma_{th}^{FSO}\Big{)}. \tag{22}\]  
Furthermore, the end-to-end outage probability for FSO communication can be calculated as follows:  
\[P_{out}^{FSO}=1-\prod_{i=1}^{N}(1-P_{out}^{FSO,i}). \tag{23}\]  
Similarly, the outage probability for the \(i\)-th THz link, which connects the \(i\)-th node and the \(i\)-th node, can be calculated as follows:  
\[P_{out}^{THz_{2},i}=Pr\Big{(}\gamma\leq\gamma_{th}^{THz_{2}}\Big{)}=F_{\gamma_ {SUMHz_{2},i}}\Big{(}\gamma_{th}^{THz}\Big{)}. \tag{24}\]  
The end-to-end outage probability for the THz system can be calculated as follows:  
\[P_{out}^{THz_{2}}=1-\prod_{i=1}^{N}(1-P_{out}^{THz_{2},i}). \tag{25}\]  
Subsequently, we analyze the outage probability considering two distinct switching strategies:  
* Initially, we conduct a performance analysis focusing on the system outage probability under the hard-switching strategy. In this setup, the THz link is activated when the condition \(\gamma_{THz_{2},i}\geq\gamma_{th,i}\) is met. In contrast, when conditions \(\gamma_{THz_{2},i}<\gamma_{th,i}\) and \(\gamma_{FSO,i}\geq\gamma_{th,i}\) are met, a feedback mechanism activates the FSO link and deactivates the THz link. Consequently, the end-to-end outage probability of the hybrid THz/FSO IRS-UAV system, utilizing this specific switching strategy, can be expressed as [32]\[P_{out}^{1}=1-\prod_{i=1}^{N}\Big{(}1-P_{out}^{FSO\_i}\times P_{out}^{ THz\_i}\Big{)} =1-\prod_{i=1}^{N}\Big{(}1-F_{\gamma_{SUMMSO,j}}\Big{(}\gamma_{th}^{FSO} \Big{)}\times F_{\gamma_{SUMTH\_i},j}\Big{(}\gamma_{th}^{THz}\Big{)}\Big{)}. \tag{26}\]  
* In this section, we assume that a soft-switch solution is used. Furthermore, the end-to-end outage probability using selective combination (SC) technology is expressed as \[P_{out}^{2}=P_{out}^{P1\_i}\Big{(}\gamma_{th,l}^{FSO},\gamma_{th,u}^{FSO} \Big{)}\times P_{out}^{THz\_i}\Big{(}\gamma_{th}^{THz}\Big{)},\] (27) where \[P_{out}^{P1\_i}(\gamma_{th,l}^{FSO},\gamma_{th,u}^{FSO})=P_{Low}^{FSO\_i}+P_{ Med}^{FSO\_i}\cdot\frac{P_{Low}^{FSO\_i}}{P_{Hi}^{FSO\_i}+P_{Low}^{FSO\_i}},\] (28) \[P_{Low}^{FSO\_i}=P_{out}^{FSO\_i}(\gamma_{th,l}^{FSO}),P_{Hi}^{FSO\_i}=1-P _{out}^{FSO\_i}(\gamma_{th,u}^{FSO}),\] (29) \[P_{Med}^{FSO\_i}=P_{out}^{FSO\_i}(\gamma_{th,u}^{FSO})-P_{out}^{FSO\_i}( \gamma_{th,l}^{FSO}).\] (30)
## 4 Discussion
### Child-Speech Data  
As previously discussed, collecting child speech presents with many difficulties. The type of child speech typically available in corpora does not always provide complete coverage of all the speech units in the language, and it is often inconsistently read and imperfectly recorded [19, 23]. According to a number of researchers [19, 37, 43], increasing the amount of training or adaptation data results in lower word-error rates, indicating greater intelligibility, even if imperfect data are included [23]. This is in contrast to another study, where researchers concluded that using fewer data of superior quality is preferred in adult-speech-synthesis models, as opposed to using more data of inferior quality [16]. Although fewer data are available, the high-quality data reportedly result in adult voices of improved naturalness and intelligibility [16].  
Comparably, when adapting adult-voice models to resemble child speakers, some researchers suggest clustering the data (i.e., age, sex, max fundamental frequency, etc.) so as to develop an average-voice model that closely matches the target child speaker to some degree [17, 64], while other researchers suggest adaptation can be performed after the average-voice model has been trained, with adaptation techniques such as VTLN [44, 50, 55]. In a study by [PERSON] et al. [22], it was seen that the greater the difference between the average-voice model and the target speaker, the poorer the resulting target voice quality would be. This finding suggests that simply using an average adult voice for adaptation to a child target speaker will yield poor results. However, it is quite clear from the review that this method has been used on numerous occasions [5, 23, 24, 25, 26, 37, 44]. In order to pre-empt the decreased quality due to the mismatch between adult and child, researchers have suggested selecting training speakers that are closest to the child target speaker. The resulting quality could be improved by training a large amount of pre-selected data, aided by a neural-network classifier, to better match the children's speech [40]. Training speakers to resemble the child's vocal quality to some degree could either be done in the initial training phase by selecting similar training speakers, or after data normalisation has occurred. In addition, if the average adult voice is further augmented with a small amount of children's speech, a closer match may be found [37, 43].
## 7 Results
### Energy Consumption
#### 7.1.3 Energy Cost Comparison  
Figure 14 presents a comparison of the energy costs between our proposed MFHS approach and the FOFSA, OLB, and ATAT algorithms discussed in [15], with respect to the number of packets. The chart clearly illustrates that our MFHS approach achieves superior energy cost results compared to the existing methods, particularly as the number of packets increases. This indicates that the MFHS algorithm outperforms its counterparts in handling extensive data analysis, which is essential for IoT networks.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 4 Prototype Assembly and Testing
### Circuit Tests
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Threshold** & **Average Voltage** & **Duty Cycle** \\ \hline
250 & 3.00 V & 97.99\% \\
230 & 2.78 V & 90.00\% \\
210 & 2.53 V & 81.62\% \\
190 & 2.29 V & 74.10\% \\
150 & 1.80 V & 58.01\% \\
100 & 1.24 V & 39.99\% \\
70 & 889 mV & 28.00\% \\
50 & 627 mV & 19.61\% \\
30 & 402 mV & 12.00\% \\
5 & 89.5 mV & 1.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relationship between threshold and average impulse voltage values. Figure 16: (**A**) EMG signal of the forearm muscle and (**B**) EMG signal of the genioglossus muscle. The characteristics of this signal, amplitude, and duration, can be changed through the application, and the threshold value is directly linked to the average voltage of the signal, in this case, the amplitude. This value can only be between 0-255, and a value entered in the application of, for example, 230, is equivalent to a pulse of 2.78 V.  
Figure 19A represents a PWM signal, set with a threshold value of 200. As can be seen, it has an average voltage of 2.48 V and a duty cycle of 78%. Figure 17: Forearm electromyographic signal. Figure 18: Process of sending the information about an occurrence to the database. Figure 19B shows a PWM signal with a duration of 250 ms and a threshold value of four. Through an analysis of the images presented, it was possible to verify that the circuit fulfilled the function for which it was developed. Through these tests, it was clear that the circuit was able to acquire and analyze the electromyographic signal, detecting possible apneic events through these data. When one of these events arises, the system successfully sends these data to the database, as well as the stimulation impulse. This impulse, generated by PWM, can be modulated in both the amplitude and duration to be applied to the muscle.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
This structure maintains the global consistency of extracted information through a self-attention mechanism that is beneficial to cross-view image retrieval. A piecewise soft-margin triplet loss function is further developed to prevent the model parameters from being trapped in suboptimal sets caused by the lack of constraint on positive samples in the original soft-margin triplet loss function. This novel loss function allows the model to learn as many correct image-retrieving pairs as possible while accomplishing sufficient distinction between correct and incorrect retrieving pairs. To eliminate the interference from the color inconsistency between satellite and UAV images, we modify the color distribution of drone images based on the color distribution of satellite images. The color transfer method reduces the possibility of learning interference information and improves the image retrieval accuracy. Moreover, data augmentation strategies are implemented to overcome the disproportionate numbers of UAV and satellite images for the Unversity-1652 benchmark. Our main contributions are summarized as follows:  
* In this paper, a single-stage training cross-view geolocation image retrieval method is proposed, which simultaneously extracts cross-view features and accomplishes image retrieval. Our approach achieved the highest retrieval accuracy while reducing the complexity and time consumption of model training among other existing methods. * A novel piecewise loss function is first proposed. Compared with the loss function used by the existing methods, it can overcome the problem of a small proportion of positive samples in training. The Loss functions, together with the color transfer modules and data augmentation techniques, enable the model to learn drone and satellite image information effectively, contributing to extracting cross-view consistent attention features and improving geo-localization accuracy. * Our method achieves state-of-the-art results on the benchmark University-1652. All codes will be publicized to promote reproducibility. ([[https://github.com/zfcui33/CCA](https://github.com/zfcui33/CCA)]([https://github.com/zfcui33/CCA](https://github.com/zfcui33/CCA)) (accessed on 14 September 2023)).
## 6 Evaluation Experiments
### Experimental Conditions  
Subjective evaluation experiments were conducted to confirm the effectiveness of the proposed method for comfortable sound design and reducing discomfort. Table 5 shows the experimental conditions, and Table 6 shows the evaluation indices for comfort and discomfort reduction.  
For the dental treatment noises, four types of dental treatment noise were used in this experiment, as described in Section 2. The evaluation sounds were assessed as follows:  
* DT: dental treatment noises only.
* CM: conventional method [46] (dental treatment noise + Canon progression sound).  
\begin{table}
\begin{tabular}{l l} \hline \hline Sampling frequency & 48 kHz \\ \hline Quantization & 32 bit \\ \hline FFT frame length & 8192 samples \\ \hline Frame shift length & 4096 samples \\ \hline Subjects & 10 males \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental conditions.  
Figure 12: Comfort scores for the evaluation style palettes of melodies.  
\begin{table}
\begin{tabular}{l l} \hline \hline Comfort score & 5: Very comfortable sound \\  & 4: Quite comfortable sound \\  & 3: Neutral \\  & 2: Slightly comfortable sound \\  & 1: Not so comfortable sound \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation index in preliminary experiments.  
* PM w/o melody: proposed method without melody (dental treatment noise + chord progression sound).
* PM: proposed method (dental treatment noise + chord progression sound + melody sound).  
We calculated the match rate of the peak frequency and the nearest neighbor harmonics as follows:  
\[PFMR[\%]=100\cdot\frac{MFN_{\text{es}}}{ARN}, \tag{16}\]  
where PFMR is the peak frequency match rate, \(AFN\) is the number of all frames, and \(MFN_{\text{es}}\) is the number of matched frames by the evaluation sound \(es=\{\text{DT},\text{CM},\text{PM w/o melody, PM}\}\). The number of matched frames was counted under the condition that the frequency bandwidth between the peak frequency and the nearest neighbor harmonics was smaller than the critical bandwidth. In the experiment, the subjects wore headphones (SONY, MDR-CD900 ST) in a soundproof room. The subjects listened to the dental treatment noises and evaluation sounds, in which control sounds were added to the dental treatment noises, and then evaluated these sounds using the evaluation indices shown in Table 6.  
Subjects rated their comfort and discomfort reduction on a 5-point scale, as shown in Table 6. The mean opinion score was calculated as follows:  
\[Score=\frac{1}{I}\sum_{i=1}^{I}Ans_{i}, \tag{17}\]  
where \(I\) is the number of subjects, and \(Ans_{i}\) is the opinion score (1\(\sim\)5) of the \(i\)-th subject. To evaluate the degree of comfort, assessments were made based on the presented evaluation sounds. To evaluate the degree of discomfort reduction, subjects compared the dental treatment noises with the composite sounds.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Determining the Perpendicular Distance from a Node to the Trajectory
#### 3.3.3 Determining the Perpendicular Distance by Comparing Curve Similarities  
We need compare the similarity between \(\vec{\mathbf{R}}\) and each \(\overline{\mathbf{R}}_{i}\), find the \(\overline{\mathbf{R}}_{i}\) that is most similar to \(\vec{\mathbf{R}}\), and determine the \(d_{i}\) corresponding to the \(\overline{\mathbf{R}}_{i}\) as the perpendicular distance from the node to the trajectory. Before calculating the similarity, it is necessary to normalize the elements of the vector to avoid the weight of the elements with larger absolute values in the similar calculation results being too large. We combine the \(\vec{\mathbf{R}}\) with all \(\overline{\mathbf{R}}_{i}\) to construct a new matrix  
\[\mathbf{A}=\left[\vec{\mathbf{R}},\overline{\mathbf{R}}_{1},\overline{ \mathbf{R}}_{2},\ldots,\overline{\mathbf{R}}_{n}\right]^{\mathsf{T}} \tag{40}\]  
\(x\) is the element at the \(i\)th row and \(j\)th column of the matrix \(\mathbf{A}\), and the normalized value of \(x\) is  
\[y=\left(x-x_{mean}\right)\times\left(y_{std}/x_{std}\right)+y_{mean} \tag{41}\]  
where \(x_{mean}\) is the mean value for the \(j\)th column of the matrix \(\mathbf{A}\), \(x_{std}\) is the standard deviation for the \(j\)th column of the matrix \(\mathbf{A}\), \(y_{mean}\) is 0, and \(y_{std}\) is 1. The normalized vector \(\vec{\mathbf{R}}\) is represented as \(\vec{\mathbf{R}}^{{}^{\prime}}\), the normalized vector \(\overline{\mathbf{R}}_{i}\) is represented as \(\overline{\mathbf{R}}_{i}^{{}^{\prime}}\), We compare the similarity of \(\vec{\mathbf{R}}^{{}^{\prime}}\) and \(\overline{\mathbf{R}}_{i}^{{}^{\prime}}\) by calculating the Euclidean distance:  
\[E_{i}=\left\|\vec{\mathbf{R}}^{{}^{\prime}}-\overline{\mathbf{R}}_{i}^{{}^{ \prime}}\right\| \tag{42}\]  
The smaller is the value of \(E_{i}\), the higher is the similarity between vectors. The reference RSSI curve with the smallest euclidean distance is most similar to the predicted RSSI curve. According to Equation (27), the perpendicular distance corresponding to the reference curve is determined as the perpendicular distance from the node to the trajectory.
## 2 Materials and Methods  
### Study Design and Study Population  
We performed a retrospective chart review for all patients who visited our hospital for snoring, sleep apnea, or drowsiness during the day and underwent in-laboratory full-night PSG between March 2017 and February 2019. Hypertension and diabetes mellitus were determined based on patients' records at hospital visits, considering factors such as taking drugs prescribed for these conditions. Self-reported SB was determined based on questionnaires as to whether the patients had been told or noticed by themselves that they grind their teeth or clench their jaws when they are asleep. Inclusion criteria were as follows: Age \(\geq\)18 years, and an apnea-hypopnea index (AHI) \(\geq\) 5 based on in-laboratory PSG. Patient data were excluded if:  
1. they were aged \(<\)18 years;
2. AHI was \(<\)5;
3. they used any medications known to affect sleep or breathing;
4. In-laboratory PSG was performed for a therapeutic purpose, such as the titration of CPAP; or
5. the patient had neurological disorders, epilepsy, neuromuscular disease, a history of upper airway surgery, or any severe mental illness.  
Of the 379 subjects who underwent in-laboratory PSG, the data for 100 who met the above criteria were analyzed. The data were divided into groups of patients with and without SB episodes (Figure 1).  
Figure 1: Study flow diagram.
## 2 Urban Pipeline Gas Leak Sensor Placement Method  
This study presents an optimization method for the placement of sensors within urban natural gas networks, GIS technology, and the ACO algorithm. The specific steps of the methodology are illustrated in Figure 1. Initially, GIS technology was employed to analyze the city's two-dimensional network map, accurately extracting the distribution of natural gas pipelines. At each designated node location, system clustering analysis is performed based on nearby critical facilities, thereby assessing and determining the risk level of each node.  
These risk levels are established by evaluating the environmental sensitivity surrounding each node and analyzing historical leakage data. These factors influence the pheromone importance parameters within the ACO algorithm. Furthermore, soil type is incorporated into the risk assessment as it affects the gas diffusion patterns in the soil, subsequently impacting the pheromone concentration gradients.  
Utilizing the determined risk levels and soil diffusion characteristics, a sensor layout optimization model is developed. This model applies the ACO algorithm to adjust the distribution and number of sensors, ensuring that each area receives adequate monitoring coverage in alignment with its risk level. Additionally, the physical distances between nodes are factored into the heuristic importance parameters of the algorithm, further refining the pheromone gradient settings.  
Figure 1: Underground pipeline monitoring placement optimization method diagram.  
For sensor selection, a hybrid approach using both catalytic combustion sensors and open-path laser sensors is adopted. Catalytic combustion sensors are predominantly installed in wells, specifically designed to detect gas diffusion in parallel pipelines post-leakage. Conversely, open-path laser sensors are deployed outdoors to monitor gas diffusion from the soil surface into the open air.  
Ultimately, the sensor layout optimized through the integration of GIS and the ACO algorithm ensures comprehensive monitoring coverage tailored to each node's risk level and gas diffusion characteristics. This significantly enhances both the monitoring efficiency and safety of the urban natural gas network. This method effectively harnesses the strengths of GIS technology and bio-inspired algorithms, offering an innovative technical solution for urban gas safety management.
## 6 Security Analysis
### Preventing the Replay Attack  
The process of conducting a replay attack requires the attacker to tap into the authentication message that is normally transferred to the CC from the SB. After the SB-CC interchange, the attacker's attempts to have the same message replayed surpasses the system's verification process. In the present authentication procedure, it is only practical to transfer the secret parameter in the authentication message to the CC from the SB only once. Therefore, in case the attacker attempts to have the same request message intercepted or resent, the CC is then able to easily identify it by using the ID shadow's hash function of the SB's _Hsbj_. In the same method, if an attacker tries to copy an authentication message and resends it to the SS, then this action can be easily detected by the CC. For this approach, the suggested unidentified authentication protocol may hold against the replay attacks.
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Ablation Study
#### 3.3.1 SSE Module  
In Section 2.3 we introduce the structure of SSE module and its effectiveness, as well as the improvement compared with SE module. In order to verify the above content, we designed the experiment as shown in Table 2. As shown in Table 2, the experimental results show that the detection accuracy of SSE module is 1.44% higher than that of SE module. It is proven that SSE module with skip-connection strategy can effectively enhance network performance and improve the crack detection accuracy.