esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Features for Transformer Model to Improve the Performance of Sound Event Detection
## 4 Experimental Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Validation Data**} \\ \hline  & & \multicolumn{2}{c}{**Baseline**} & \multicolumn{2}{c}{**Multi-Scale**} \\  & & \multicolumn{2}{c}{**Mean-Teacher Model**} & \multicolumn{2}{c}{**Mean-Teacher Model**} \\ \hline  & & \multicolumn{2}{c}{**F-Score (\%)**} & \multicolumn{2}{c}{**ER**} & \multicolumn{2}{c}{**F-Score (\%)**} & \multicolumn{2}{c}{**ER**} \\ \hline  & NEB = 1 & 31.43 & 1.69 & 36.15 & 1.57 \\ \cline{2-6} w/o & NEB = 2 & 35.95 & 1.57 & 37.49 & 1.50 \\ \cline{2-6} Post & NEB = 3 & 33.58 & 1.56 & 37.54 & 1.46 \\ \cline{2-6} Processing & Average & **33.65** & **1.61** & **37.06** & **1.51** \\ \cline{2-6}  & Relative Improvement & **-** & **-** & **10.1\%** & **6.2\%** \\ \hline  & NEB = 1 & 40.02 & 1.11 & 44.17 & 1.06 \\ \cline{2-6} w/ & NEB = 2 & 44.05 & 1.06 & 44.57 & 1.05 \\ \cline{2-6}  & NEB = 3 & 41.75 & 1.09 & 44.66 & 1.04 \\ \cline{2-6} Processing & Average & **41.94** & **1.09** & **44.46** & **1.05** \\ \cline{2-6}  & Relative Improvement & **-** & **-** & **6\%** & **3.7\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of event-based SED results on validation data between baseline mean-teacher model and the proposed mean-teacher model with multi-scale features.
# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 3 Results
### Multiple Drones for Aerial Surveillance
#### 3.2.1 DT Drones Application  
The DT Drones application is now employed to obtain the precise locations of five drones strategically positioned around a designated target with coordinates (29.452177, 47.761308). Leveraging the computational power of Delaunay triangulation, the application systematically distributes the drones in an optimized configuration, ensuring effective coverage of the specified area. Figure 7 below shows the result obtained from DT drones application for the distribution of five drones around the target.  
The drones have been successfully distributed around the target location, with their respective distances ranging from 32 m to 63 m. The strategic deployment ensures comprehensive coverage of the specified area, providing a well-distributed spatial arrangement. Each drone's precise distance from the target has been accurately determined. This strategic distribution, facilitated by the DT Drones application using Delaunay triangulation, optimizes the spatial efficiency of the drone network. The varying distances contribute to a comprehensive and systematic approach, enhancing the drones' ability to capture data effectively and ensuring a thorough survey of the target location. Details of the drones' positions are shows in Table 2 below:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Recognize Results** & **Confidence Level** \\ \hline Car & 97 \\ Pickup Truck & 97 \\ Person & 95 \\ Outdoors & 87 \\ Wheel & 77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of image captured of target using single drone.  
Figure 6: The Dronelink mission for the single drone.  
Utilizing this geometric algorithm, DT Drones orchestrates the spatial arrangement of the drones, enhancing their coordination for efficient surveying and data capture. This methodology aims to improve the overall effectiveness and precision of the drone deployment, allowing for comprehensive and well-distributed data acquisition around the specified target location. The distribution of drones around the target with corresponding distance is shown in Figure 8 below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coordinates** & **Longitude (°)** & **Latitude (°)** & **Distance to Target (m)** \\ \hline Drone 1 & 29.451793 & 47.761793 & 63.47 \\ Drone 2 & 29.451702 & 47.761088 & 56.95 \\ Drone 3 & 29.452174 & 47.762022 & 69.13 \\ Drone 4 & 29.452291 & 47.760406 & 88.25 \\ Drone 5 & 29.452417 & 47.761484 & 31.66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of drones’ positons and their distances from the target.  
Figure 8: Distance between target and the five drones.  
Figure 7: Result from computing Delaunay triangulation around target with DT Drones application. The grey points indicates the drones while the green point indicated the targeted object.
# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 5 Experimental Data
### Experimental Setup  
To evaluate the effectiveness and efficiency of our proposed algorithm, we compared it with MDFS [54], GRRO [55; 56], MDDM [57], FSFL [23], LDFS [58], six advanced feature selection algorithms.  
When reducing the dimensions of the feature space, the experimental dataset needs to be adapted for different feature selection methods. For the label distribution feature selection method, a multi-label dataset must be converted into a label distribution dataset through label enhancement. Specifically, the experiments in this study used the label enhancement method to convert the data. Multi-label feature selection methods cannot directly use the real-world label distribution dataset. Therefore, we used the equal frequency strategy to discretize the label distribution dataset before dimensionality reduction.  
The experiment was conducted using a 10 -fold cross-validation strategy. To obtain the output, the learner was trained on the above 15 datasets using SA-BFGS after feature selec  
\begin{table}
\begin{tabular}{l l l l l} \hline
**No.** & **Dataset** & **Examples** & **Features** & **Labels** \\ \hline
1 & S-JFFE & 213 & 243 & 6 \\
2 & Natural\_Scene & 2000 & 294 & 9 \\
3 & Human\_Gene & 17,892 & 36 & 68 \\
4 & Yeast-alpha (alpha) & 2465 & 24 & 18 \\
5 & Yeast-diau (diau) & 2465 & 24 & 7 \\
6 & Yeast-dtt (dtt) & 2465 & 24 & 4 \\
7 & Yeast-elu (elu) & 2465 & 24 & 14 \\
8 & Yeast-heat (heat) & 2465 & 24 & 6 \\
9 & Yeast-spo (spo) & 2465 & 24 & 6 \\
10 & Yeast-spo5 (spo5) & 2465 & 24 & 3 \\ \hline \end{tabular}
\end{table}
Table 2: Main features of the 12 datasets.  
\begin{table}
\begin{tabular}{c c} \hline
**Name** & **Formula** \\ \hline Chebyshev distance (Chebyshev) \(\downarrow\) & \(\text{Dis}(\bar{D},D)=\max_{i}|\bar{d_{i}}-\bar{d_{i}}|\) \\ Clark distance (Clark) \(\downarrow\) & \(\text{Dis}(\bar{D},D)=\sqrt{\sum_{j=1}^{L}\frac{(\bar{d_{i}}-\bar{d_{i}})^{2}} {(\bar{d_{i}}+\bar{d_{i}})^{2}}}\) \\ Canberra distance (Canberra) \(\downarrow\) & \(\text{Dis}(\bar{D},D)=\sum_{j=1}^{L}\frac{|\bar{d_{i}}-\bar{d_{j}}|}{\bar{d_{i}}+ \bar{d_{i}}}\) \\ KL divergence (KL) \(\uparrow\) & \(\text{Dis}(\bar{D},D)=\sum_{j=1}^{L}\bar{d_{j}}\frac{\bar{d_{j}}}{\bar{d_{i}}}\) \\ Cosine similarity (Cosine) \(\uparrow\) & \(\text{Sim}(\bar{D},D)=\frac{\sum_{j=1}^{L}\bar{d_{j}}}{\bar{d_{i}}}\) \\ Intersection similarity (Intersection) \(\uparrow\) & \(\text{Sim}(\bar{D},D)=\sum_{j=1}^{L}\min\left(\bar{d_{i}},\bar{d_{i}}\right)\) \\ \hline \end{tabular}
\end{table}
Table 3: Algorithm evaluation indicators.  
tion, and the prediction performance was obtained in the test. Following [59], the number of training iterations of SA-BFGS was set to 10, and the regularization factor was 1.
## 6 Future Outlook and Recommendations
### Scaling Beyond 5G: 6G and Blockchain  
Early studies on sixth-generation (6G) connectivity emphasize near-zero latency and terahertz bandwidth, pointing to holographic telepresence and more solid real-time simulations [82]. Complementary research on blockchain-based ledgers suggests tamper-proof credentialing, decentralized data governance, and advanced identity verification for distance education [78]. Although both 6G and blockchain remain largely experimental in the school context, incremental pilots can validate feasibility and address nascent security and privacy concerns.
# A Systematic Literature Review on Military Software Defined Networks
## 7 Discussion and Recommendations for Future Work
### Service  
Traditional networks are designed for static environments with cabled connections, hence there are some challenges when deploying IP networks in dynamic military operations. To cope with some of the challenges, Tactical Communications (TACOMS) have defined a set of services; two services which solve similar task but have different dependencies are the Autoconnectivity and Service Announcement (SA) and Border Gateway Protocol (BGP) service. Federated Networking Service Engine (FNSE) is a more generic approach for adding network services to NATOs Federated Mission Network (FMN) [25]; however, there are still many areas that require further invstigation, these include:  
* Evaluation of alternative East-West protocols;
* How to add anonymity to the authentication phase;
* Measurements of the overhead for secure channel establishment;
* Investigation of how management across Autonomous System (AS) borders can be achieved.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 5 Titanium-Based Nanomaterials
### Nanosheets  
TiO\({}_{2}\) nanosheets (TNS) are two-dimensional structures with a thickness of 1-10 nm and a lateral length in the range of \(\upmu\)m. The shape of these nanomaterials results in a smooth surface and low turbidity. TNS exhibit photocatalytic properties as the photocatalytic degradation of organic compounds and superhydrophilicity when irradiated with UV radiation. TNS are studied for their advantageous properties of carrier mobility and high adherence with various substrates. TNS do not have great adsorption properties in the visible light range due to their wide band gap and so their applications in indoor environments are limited. Due to their high adherence, TNS is usually used for anti-fouling applications that involve self-cleaning surfaces where photocatalytic reactions happen at the solid-gas interface [113].  
To enhance the photocatalytic activity for the degradation of pollutants, high-energy facets TiO\({}_{2}\) can be used. It has been demonstrated that [001] facets TiO\({}_{2}\) are more reactive, adsorb more photons, and have a higher photocatalytic activity than the thermodynamically more stable [101] facets. Therefore, different studies identified nanosheets with high-energy facets in order to enhance the adsorption of pollutants and photocatalytic oxidation [119].  
TNS can be doped or form heterojunctions in order to enhance its photocatalytic efficiency. For instance [120], research synthesized N-doped TiO\({}_{2}\) nanosheets/WO\({}_{3}\) composites to observe the photocatalytic removal of hexane. From the UV-vis adsorption spectra, it has been observed that the N doping and the introduction of WO\({}_{3}\) have successfully enhanced the adsorption properties of TiO\({}_{2}\) nanosheets. The photoluminescence spectrum of the composite exhibited a lower intensity indicating a lower recombination rate compared to the TiO\({}_{2}\) nanosheets. Finally, the photocatalytic oxidation of hexane has been observed andthe efficiency of TNS increased due to the N doping and the heterojunction with WO\({}_{2}\) and it has obtained a maximum photocatalytic efficiency of about 44%. The enhancement of the photocatalytic activity is due to the increase in the specific surface area.
## 4 Experimental Results and Discussion
### Algorithm Complexity Comparisons  
Table 4 summarizes the complexity comparisons between the proposed SFO estimation method and some conventional SFO estimation algorithms. Since the SFO value is obtained by averaging multiple SFO estimation values in these schemes, and the computational complexity is mainly determined by a single SFO estimation, Table 4 lists the complexity of one SFO estimation. In Table 4, \(N_{d}\) represents the number of data-carrying subcarriers and \(M\) is the number of the pilot symbols. In [26], the phase-shift slope on each OFDM symbol can be acquired by the least square (LS) method after extracting the phases on \(M\) pilot subcarriers, and the results of each OFDM symbol are averaged to obtain the accurate SFO value. In [30], the training sequences at the frame head and frame tail for SFO estimation are used, and the LS method is also applied to improve the estimation accuracy where the computational complexity depends on the number of data-carrying subcarriers. A blind SFO estimation scheme for QPSK and 16-QAM modulation is proposed in [22] by extracting two symbols in each frame to perform a fourth-power algorithm, which needs 4 multipliers. For the proposed SFO estimation method, we only need 2 multipliers in each frame for SFO estimation. Therefore, compared with the other SFO estimation and compensation methods, the proposed scheme has lower computational complexity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SFO (ppm)** & **0** & **200** & **1000** \\ \hline EVM (dB) & \(-21.4\) & \(-21.9\) & \(-19.4\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The EVM performance of different SFO.  
Figure 7: Physical device diagram. (**a**) Oxygen pump; (**b**) Water surface with bubble volume of 16 L/min.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 4 Results and Analysis
### Light Use Efficiency  
In 2009, the light use efficiency for winter wheat in the Tongzhou and Shunyi districts of Beijing remained relatively constant over each given month, although slight differences appeared due to differences in climate and environment. As shown in Figure 4, the \(\varepsilon\) in March ranged from 0.67 to 0.71 g C MJ\({}^{-1}\), and the average monthly light use efficiency was 0.69 g C MJ\({}^{-1}\). In April, the \(\varepsilon\) increased to reach 0.80-1.0 g C MJ\({}^{-1}\), and the average monthly \(\varepsilon\) was 0.92 g C MJ\({}^{-1}\). Environmental conditions such as sunshine, temperature, and precipitation in May provided improved growing conditions for winter wheat, so the \(\varepsilon\) in May ranged from 1.06 to 1.15 g C MJ\({}^{-1}\), with an average of 1.11 g C MJ\({}^{-1}\). Figure 4 also shows that the \(\varepsilon\) in March and April was approximately the same in Tongzhou District as  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Vegetation** **Indices**} & \multirow{2}{*}{**Maximum and** **Minimum**} & \multicolumn{8}{c}{**Months 3**} \\ \cline{3-11}  & & **10** & **11** & **12** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{3}{*}{**NDVI 1**} & MAX & 0.557 & 0.684 & 0.687 & 0.548 & 0.430 & 0.493 & 0.757 & 0.854 & 0.687 \\  & MIN & 0.254 & 0.265 & 0.210 & 0.224 & 0.179 & 0.190 & 0.246 & 0.433 & 0.239 \\ \hline \multirow{3}{*}{SR 2} & MAX & 3.519 & 5.337 & 5.386 & 3.426 & 2.512 & 2.943 & 7.236 & 12.665 & 5.395 \\  & MIN & 1.681 & 1.720 & 1.531 & 1.578 & 1.437 & 1.469 & 1.651 & 2.528 & 1.629 \\ \hline \end{tabular}  
* The full name of NDVI is Normalized Difference Vegetation Index; 2 The full name of SR is Ratio Vegetation Index; 3 “Months” refers to the winter wheat growing season from October 2008 to June 2009.  
\end{table}
Table 1: NDVI and SR maximum and minimum values for winter wheat for each month of the growing season.  
Figure 3: Spatial distribution of winter wheat fPAR from March to May, 2009: (**a**) March; (**b**) April; (**c**) May.  
in Shunyi District. In May, however, the \(\varepsilon\) in Shunyi District was significantly greater than in Tongzhou District. This result reflected the better environmental conditions for winter wheat growth, such as temperature and precipitation, in Shunyi District from March to May, which allowed superior growth and development of winter wheat.
## 2 Related Work
### Topic Modeling for Natural Language Processing
#### 2.3.1 Text Representation  
Text representation is the form in which the text can be understood and quantified, which is called a vectorized form. The most classic form of text processing in the early stage is the bag-of-words (BoW) model [62]. It directly uses the frequency of each word as the input feature of the classifier. However, the disadvantage of the BoW model is that it only counts word frequency or existence, but ignores word order and grammar, which makes the vector representation it generates unstable [63]. The TF-IDF algorithm [64] is optimized based on BoW and takes into account both word frequency and inverse document frequency as indicators for calculating text relevance. As shown in Formulas (1)-(3) \(f_{t,d}\) is the number of times \(t\) appears in document \(d\). \(N\) represents the total number of documents, and \(n_{t}\) represents the number of documents containing term \(t\).  
\[\text{TF}(t,d)=\frac{f_{t,d}}{\sum_{t^{\prime}\in d}f_{t^{\prime},d}} \tag{1}\]  
\[\text{IDF}(t)=\text{log}\bigg{(}\frac{N}{n_{t}}\bigg{)} \tag{2}\]  
\[\text{TF-IDF}(t,d)=\text{TF}(t,d)\times\text{IDF}(t) \tag{3}\]  
Compared to BoW, TF-IDF solves the problem of the BoW model, which is overly sensitive to high-frequency words and ignores word differentiation by weighting word frequency and inverse document frequency. This approach can better distinguish different categories and extract keywords. However, as an upgraded version of the BoW model,it still does not have the limitation of relying on a predefined corpus to capture word order or semantics. With the development of deep learning, BERT (Bidirectional Encoder Representations from Transformers), as a context-aware pre-trained language model, has been widely used to improve the text vector representation problem in topic modeling tasks. The structure of the BERT model is shown in Figure 1. It consists of three parts: word embedding, sentence embedding, and position embedding, which form a new comprehensive embedding. The word embedding maps each word into a high-dimensional vector. The sentence embedding enables BERT to distinguish and process a single text or text pair. The position embedding provides sequential information and marks the position of a word in a sentence. Such an encoding structure can contain semantic information about words and sentences while retaining position information.
## 3 Methodology
### Repair Time Estimation Model  
The repair time for critical components in sparse road networks, such as road segments, bridges, and tunnels, is influenced by both disaster type and disaster scale. To quantify the repair time for different infrastructure components, previous studies have employed triangular fuzzy numbers [48] or uniform distribution models [49] to estimate the expectedrecovery time of damaged links. Following the existing research [20], this study assumes that the repair time of a damaged link (or node) follows an exponential distribution, considering that repair time is positively correlated with disaster scale, whereas disaster scale is negatively correlated with disaster probability. The corresponding calculation formula is presented in Equation (15).  
\[P\left(d_{j}^{l}=c\right)=\frac{1}{\pi_{j}^{l}}e^{-\frac{c}{\pi_{j}^{l}}} \tag{15}\]  
In the formula, \(d_{j}^{l}\) denotes repair time required (in days) for a road link to recover from damage caused by the \(j\)th type of disaster event. \(\pi_{j}^{l}\) denotes the average repair time of a road segment affected by the \(j\)th type of disaster event, calculated based on historical data. \(c\) is a positive random variable.  
Using Equation (13), the recovery time of different components under disaster events is estimated and denoted as \(S_{j,l}^{T}\). If no disaster event occurs on road segment \(l\) on day \(T\), then \(S_{j,l}^{T}=0\). Otherwise, \(S_{j,l}^{T}\) equals the required recovery time for the disaster event occurring on road segment \(l\) on day \(T\).
## 2 Theoretical Foundations  
### Support Vector Regression  
Support Vector Machine (SVM) is a machine learning algorithm based on statistical learning theory proposed by [PERSON]'s research team [PERSON] (1966), which is mainly applied to pattern recognition and solving highly nonlinear regression problems. Support Vector Regression (SVR) is a method for solving regression problems based on SVM by introducing an insensitive loss function \(\varepsilon\).  
SVR maps low-dimensional data that is not amenable to regression into a high-dimensional space to obtain data that is amenable to regression and determines the hyperplane which minimizes the distance from the samples. The SVR method creates a \"spacing band\" where samples inside the spacing band are not counted as loss and samples outside are counted as loss and optimizes the model parameters by minimizing the \"spacing band\" and the total loss. The principle of SVR is shown in Figure 1.  
Given a data set \(Z=\{(x_{1},y_{1}),(x_{2},y_{2}),L,(x_{i},y_{i})\},i=1,2,\ldots,n\), where \(x_{i}\) is the input data, \(y_{i}\) is the output data, and \(n\) is the number of samples. The feature vector obtained after going through the nonlinear mapping \(\varphi=R^{n}\to R^{m}(m\geq n)\) is \(\phi(x)\). The regression function of the samples in the high dimensional space can be expressed as follows:  
\[f(x)=\omega^{T}\cdot\varphi(x)+b \tag{1}\]  
where \(f(x)\) is the value of the prediction function, \(\omega\) is the weight, and \(b\) is the bias value.  
Introducing the slack variable \(\xi_{i},\xi_{i}^{*}\), problem of Formula (1) can be transformed into the following:  
\[\begin{split}\min_{\omega,b,\xi_{i}^{*}}&\frac{1}{ 2}\|\omega\|^{2}+c\sum\limits_{i=1}^{n}\left(\xi_{i}+\xi_{i}^{*}\right)\\ s.t.\left\{\begin{array}{l}y_{i}-(\omega^{T}x_{i}+b)\leq\varepsilon+ \xi_{i}\\ (\omega^{T}x_{i}+b)-y_{i}\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i},\xi_{i}^{*}\geq 0,i=1,2,\cdots,n\end{array}\right.\end{split} \tag{2}\]  
where \(\frac{1}{2}\|\omega\|^{2}\) is the regular term, and \(c\) is the penalty factor.  
Introducing the Lagrangian function to transform the problem into its dual form for ease of solution, and the solution to Formula (1) can be obtained as follows:  
\[f(x)=\sum\limits_{i=1}^{n}\left(a_{i}-{a_{i}}^{*}\right)K(x,x_{i})+b \tag{3}\]  
Figure 1: Support vector regression schematic.  
where \(K(x,x_{i})\) is the kernel function, and \(a_{i},a_{i}{}^{*}\) is a Lagrange multiplier.  
There are generally four types of kernel functions for SVR: linear kernel function, polynomial kernel function, gaussian kernel function and sigmoid kernel function. The Sigmoid kernel function, also known as the Radial Basis Function (RBF), has been chosen in this paper for its wide applicability and high prediction accuracy.  
\[K(x,x_{i})=\exp(-\frac{\|x-x_{i}\|}{2\sigma^{2}})=\exp(-g\|x-x_{i}\|^{2}) \tag{4}\]  
where \(\sigma\) is the band width of the Sigmoid kernel function, and \(g\) is a parameter that affects the predictive performance of SVR regression.
## 3 Results and Discussion  
### Distance between Accident Location and Nearest Rainfall Data  
Based on the number of traffic accidents that occurred under rainy conditions (24,797), the distance between the traffic accident location and closest point of each rainfall data source (radar grid point and AWS) was analyzed. The results are shown in Table 1 and Figure 2. Accident location and rainfall observation points did not directly match because of spatial differences. In Figure 2, the Accident IDs were sorted according to the nearest AWS and the high-resolution radar grid point. As shown in Table 1, the minimum distance between the traffic accident point and the AWSs location is 16 m, and the maximum distance is 5956 m. On average, AWSs are 1886 m from the accident site. The minimum, maximum, and average distances to the radar grid point are 1, 183, and approximately 99 m, respectively. Clearly, the use of AWS locations to assess accident-causing rainfall phenomena is not reliable given the range of distances involved, whereas the high-resolution radar ensures that any given accident location is no more than 183 m from a rainfall data point, resulting in much higher accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Type** & **Min.** & **1 st Qu.** & **Median** & **Mean** & **3 rd Qu.** & **Max** \\ \hline Rain gauges & 16 & 1221 & 1806 & 1886 & 2466 & 5956 \\ Radar & 1 & 74 & 103 & 99 & 126 & 183 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distance between rain gauges and radar grid with respect to the accident locations (unit: meters).