esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Uncertainty Processing Based on Clustering Technique
### Validation of the Hybrid Clustering Method with a Wind Power Dataset  
A typical wind power dataset, which is derived from two wind farms in the EWITS study [19], is employed to validate the proposed clustering method. Figure 1 gives its scatter diagram:  
Figure 1: Scatter distribution of the wind power dataset.  
The identification of cluster number is implemented first. Here the parameter \(s_{n}\) is set as 40 by experiments with test databases [20]. Through analyzing the estimated stability degree of each cluster number in 40 trials, statistical results with the cluster number varying from 3 to 15 are illustrated in Figure 2.  
According to Figure 2, the cluster number 7, which has the maximum stability degree, is determined as the optimal cluster number. Then the power dataset is clustered into seven clusters as depicted in Figure 3. It can be seen that the clustering result accurately recognizes seven typical combinations of wind power output of the two wind farms. Moreover, there are no overlapping areas among different clusters, showing that the cluster divisions are totally obvious. Hence, the effectiveness of the hybrid clustering method is verified.
# Aerothermoelastic Analysis of Conical Shell in Supersonic Flow
## 2 Dynamic Model  
### Model Description  
As shown in Figure 1, a conical shell composed of functionally graded material in supersonic airflow is considered. The semi-vertex angle, length, thickness, and radius at both ends of the conical shell are, respectively, represented by \(\alpha\), \(L\), \(h\), \(r\) and \(R\). An orthogonal curvilinear coordinate system \((x,\ \theta,\ z)\) is located at the mid-surface of the conical shell, and the displacements in meridional \((x)\), circumferential \((\theta)\), and radial \((z)\) directions are represented by \(u\), \(v\) and \(w\), respectively. The airflow direction is along the \(x\)-axis.  
The functionally graded conical shell is made from two materials; the inner layer of the conical shell is a ceramic material, and the outer layer of the conical shell is a metal material. The volume fractions of ceramic and metal are represented by \(V_{c}\) and \(V_{m}\), respectively, and they can be written as follows:  
\[V_{m}=\left(\frac{2z+h}{2h}\right)^{p},V_{c}=1-V_{m}=1-\left(\frac{2z+h}{h} \right)^{p}, \tag{1}\]  
where \(p\) is the index of the volume fraction, which is a positive number. Functionally graded material properties are temperature-dependent functions that can be given by  
\[P(T)=P_{0}(P_{-1}T^{-1}+1+P_{1}T+P_{2}T^{2}+P_{3}T^{3}), \tag{2}\]  
where \(T\) is the temperature in the environment, and \(P(T)\) is the material performance, including [PERSON]'s modulus \(E\), [PERSON]'s ratio \(\mu\), density \(\rho\), thermal expansion coefficient \(\beta\) and heat conduction coefficient \(k\). \(P_{0}\), \(P_{-1}\), \(P_{1}\), \(P_{2}\) and \(P_{3}\) are temperature-dependent coefficients of the material properties. The effective properties of composite materials in a specific temperature can be given by  
\[P(z,T)=P_{c}V_{c}+P_{m}V_{m}, \tag{3}\]  
Then,  
\[E(z,T) =E_{c}+(E_{m}-E_{m})\Big{(}\frac{2z+h}{h}\Big{)}^{p},\ \ \mu(z,T)=\mu_{c}+(\mu_{m}-\mu_{m})\Big{(}\frac{2z+h}{h} \Big{)}^{p},\] \[\rho(z,T) =\rho_{c}+(\rho_{m}-\rho_{m})\Big{(}\frac{2z+h}{h}\Big{)}^{p},\ \ \beta(z,T)=\beta_{c}+(\beta_{m}-\beta_{m})\Big{(}\frac{2z+h}{h} \Big{)}^{p}, \tag{4}\] \[k(z,T) =k_{c}+(k_{m}-k_{m})\Big{(}\frac{2z+h}{h}\Big{)}^{p}.\]
## 2 Problem Description
### Optimization Objectives  
Two objectives equations are based on the characteristics of the data transmission tasks. The objective function \(F\) encompasses two objectives, \(f_{1}\) and \(f_{2}\) :  
1. Maximize task gain: The task gain is meant to maximize the product of ground station and satellite priorities, determined by the following Equation (11): \[f_{1}=max\sum_{i=1}^{n}p_{t_{i}}\cdot g_{t_{i}}\] (11)
2. Minimize transmission time: Reduce the total time needed to accomplish the data transmission tasks, as calculated by the following Equation (12): \[f_{2}=min\ (ET-ST)\] (12)  
The comprehensive objective function, denoted as \(F\), is formulated as shown in Equation (13):  
\[max\ F=\{f_{1},\ -f_{2}\} \tag{13}\]  
The proposed model effectively simplifies the complexities of large-scale satellite data transmission scheduling constraints to ensure that the scheduling of satellite data transmissions is efficient.
## 4 Experimental Evaluation
### Time Performance of Varroosis Detection  
To examine the time performance of varroosis detection, we used 20 pictures of bees selected from the test set used for the experiment described in Section 4.2. Pictures had various sizes, resolutions and represented both healthy and infected bees to check if any of the above factors impacted the time performance. The results are shown in Figure 9. For presentation purposes, pictures were numbered as follows: numbers from 1 to 10 for pictures with healthy bees, 11 to 20 for pictures presenting the _V. destructor_-infected bees.  
For the obtained results, we calculated the average detection time, which took 182.15 ms with a standard deviation of 7.32 ms for all of the pictures, 181.1 ms with a standard deviation of 8.65 ms for pictures with healthy bees, and 183.2 ms with a standard deviation of 5.51 ms for pictures of infected bees. The average time was similar for both groups, and differences between standard deviations are acceptable. Moreover, the picture with the longest processing time (no. 1) had a resolution of 190 \(\times\) 146 pixels, whereas the picture with the shortest processing time (no. 14) had a resolution of 1446 \(\times\) 124 pixels. The largest picture (no. 18) with a resolution of 632 \(\times\) 496 pixels did not generate the longest processing time. This information led us to conclude that none of the following factors--resolution, size, and the content of pictures--directly influence the performance of the _V. destructor_ detection process.
# Interaural Coherence Estimation for Speech Processing in Reverberant Environment
## 3 Numerical Experiment and Results  
As mentioned in the previous section, accurate estimated IC means that it is close to one for direct speech and zero for diffuse speech. Therefore, estimation accuracy is judged based on the IC values of the time-frequency bin with direct speech and the bins without direct speech for reverberation speech. The reverberant speech data used for verification were generated by convolution of the binaural room impulse response to utterances recorded in an anechoic environment. The anechoic utterance data included 4380 male and 1920 female utterances from the Texas Instruments/Massachusetts Institute of Technology (TIMIT) database spoken by 630 native American English speakers [16]. is the dataset was divided into a training set with 4620 utterances and a test set with 1680 utterances. The training set was used for determining the optimal \(L_{\text{T}}\) and \(\beta\), and the test set was used for performance comparison between the conventional and proposed methods. The binaural room impulse response was based on the Aachen Impulse Response (AIR) database [17], which recorded impulse according to various reverberation environments and azimuth using a KEMAR dummy head. By selecting the binaural impulse response recorded in an office, stairway, and lecture room, reverberant speech with a sampling rate of 16 kHz was generated, and simulations were performed.
# Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems
## 2 Method
### Spectral Efficiency  
The spectral efficiency (SE) of an encoding/decoding communication system is defined as the average number of bits of data that can be accurately transmitted (measured in bit/s/Hz). Specifically, in this work, the enormous number of connected devices in the proposed irrigation system (UAVs, fog nodes, and actuators) increases the demand in terms of spectral efficiency. We adopt M-MIMO technology to deal with this challenge, since it significantly enhances the system's spectral efficiency. This motivated us to derive, in this subsection, the analytical expression of the spectral efficiency that can be achieved within the proposed UAV-based irrigation network. In this study, we consider two diversity combining techniques: the M-MMSE (Multicell Minimum Mean Squared Error) combiner and the MR (Maximum Ratio) combiner.  
The SE of the proposed network in the uplink transmission, i.e., from the \(k\)-th UAV (or the fog device) to \(n\)-th GBS, is denoted as \(\mathbf{S}\mathbf{E}_{kn}^{UL}\) (for \(1\leq k\leq K_{U}+K_{F}\) and \(1\leq n\leq N\)) and is expressed as [29]:  
\[\mathbf{S}\mathbf{E}_{kn}^{UL}=\frac{\tau_{u}}{\tau_{c}}\mathbf{\mathrm{E}}\Big{\{}\log_{ 2}\Big{(}1+\mathbf{S}\mathbf{IN}\mathbf{R}_{kn}^{UL}\Big{)}\Big{\}}, \tag{3}\]  
where:  
* \(\tau_{u}\) (respectively, \(\tau_{c}\)) is the data sample in uplink (respectively, the total number of samples) for every coherence block;* \(\mathbf{SINR}_{kn}^{UL}\) represents the signal to interference plus noise ratio (SINR) of the \(k\)-th UAV (or the fog device) in uplink of the \(n\)-th cell. Likewise, the SE of the proposed network in the the downlink transmission, i.e., from the \(n\)-th GBS to the \(k\)-th IoT device (UAV or the fog node or actuator), is denoted as \(\mathbf{S}E_{nk}^{DL}\) (for \(1\leq k\leq K_{I}\) and \(1\leq n\leq N\)) and is expressed as [29]: \[\mathbf{S}E_{nk}^{DL}=\frac{\tau_{d}}{\tau_{c}}\mathbf{E}\Big{\{}\log_{2}\Big{(}1+\mathbf{ SINR}_{nk}^{DL}\Big{)}\Big{\}},\] (4) where:
* \(\tau_{d}\) is the data sample in the downlink for every coherence block;
* \(\mathbf{SINR}_{nk}^{DL}\) represents the signal-to-interference plus noise ratio (SINR) of the \(k\)-th IoT device in downlink of the \(n\)-th cell.
## 3 Results
### Amoebicidal Activity of Associated TTO/EEO  
After 1 h of experimentation, the TTO/EEO association sample displayed a decrease in the cell viability of _A. polyphaga_ (\(p<0.0001\)) compared to the untreated sample. A very high amount of amoebicidal activity was observed for 1 xMIC and 2 xMIC (\(p=0.0001\) and \(p<0.0001\) compared to 0.5 xMIC, respectively); cells treated with the highest concentration of TTO/EEO were no longer viable (Figure 3a).  
Figure 3: The cell viability of _A. polyphaga_ trophozoites after **(a)** 1 h and **(b)** 24 h of exposure to the associated _M. alternifolia_ Cheel (tea tree) (TTO)/_E. globulus_ Labill. (EEO) essential oil at concentrations of 0.5 xMIC, 1 xMIC, and 2 xMIC. Each bar represents the mean \(\pm\) SD of the three determinations (error bar = S.D.; n = 3). \(p<0.01\) (**), \(p<0.001\) (***), and \(p<0.0001\) (***) were considered significant during _t_-tests and ANOVA with the Bonferroni correction. ns stands for not statistically significant.  
Figure 2: The cell viability of _A. polyphaga_ trophozoites after **(a)** 1 h and **(b)** 24 h of exposure to _E. globulus_ Labill. (EEO) essential oil at concentrations of 0.5 xMIC, 1 xMIC, and 2 xMIC. Each bar represents the mean \(\pm\) SD of the three determinations (error bar = S.D.; n = 3). \(p<0.05\) (*), \(p<0.01\) (**), \(p<0.001\) (***), and \(p<0.0001\) (***) were considered significant during _t_-tests and ANOVA with the Bonferroni correction. ns stands for not statistically significant.  
After 24 h of TTO/EEO exposure, the amount of viable cells of _A. polyphuga_ in the sample treated with the 0.5 xMIC concentration was only 7.0%, and the amount in the sample treated with the 1 xMIC concentration was 1.3%. No viable cells were observed following treatment with the association at the 2 xMIC concentration (Figure 3b).  
Figure 4 shows an example of the ability of the TTO/EEO association to negatively affect the survival of amoebae, which leads to the rapid destruction of almost all _A. polyphuga_ trophozoites and morphological changes in the residual cells.  
In the Supplementary Materials section, the morphological changes and reduction in viable count of _A. polyphaga_ trophozoites at TTO/EEO concentrations of 0.5 xMIC and 2 xMIC are shown in Figures S1 and S2, respectively.
# DenseZDD: A Compact and Fast Index for Families of Sets +
## 3 Data Structure
### DenseZDD
#### 3.1.1 Zero-Edge Tree  
We construct the _zero-edge tree_ from a given ZDD \(G\) as follows. First of all, we delete all the 1-edges of \(G\). Then, we reverse all the 0-edges, that is, if there is a (directed) edge from \(v\) to \(w\), we delete the edge and create the edge from \(w\) to \(v\). Note that the tree obtained by this procedure is known as the left/right tree of a DAG whose nodes have two distinguishable arcs, originally used for representing a context-free grammar [13]. We also note that the obtained tree is a spanning one whose root node is the 0-terminal node. Next, we insert _dummy nodes_ into 0-edges so that the distance from the 0-terminal to every node is \(\mathit{index}(v)\). Specifically, for each node \(w\) that is pointed by 0-edges \((v_{1},w),\ldots,(v_{k},w)\) in \(G\), we add \(c\) dummy nodes \(d_{1},\ldots,d_{c}\) and edges \((w,d_{1}),(d_{1},d_{2}),\ldots,(d_{c-1},d_{c})\) and \((d_{b_{1}},v_{1}),\ldots,(d_{b_{k}},v_{k})\) to the tree (and remove \((v_{1},w),\ldots,(v_{k},w)\)), where \(b_{j}=\mathit{index}(v_{j})-\mathit{index}(w)-1\) for \(j=1,\ldots,k\), \(c=\max_{v\in\{v_{1},\ldots,v_{k}\}}\mathit{index}(v)-\mathit{index}(w)-1\) and \(d_{0}=w\). If \(c=0\), we add no dummy node for the 0-edges pointing at \(w\). For example, see Figure 5.  
We call the resulting tree the zero-edge tree of \(G\) and denote it by \(T_{Z}\). To avoid confusion, we call the nodes in \(T_{Z}\) except for dummy nodes _real nodes_. We construct the BP of \(T_{Z}\) and denote it by \(U\). We let \(U\) be the first element of the DenseZDD triplet (described in the beginning of this section).  
We define the _real preorder rank_ of a real node \(v\) in \(T_{Z}\) (and the corresponding node in \(G\)) as the preorder rank of \(v\) in the tree before adding dummy nodes and edges connecting nodes.  
On BP \(U\), as we will show later, introducing dummy nodes enable to simulate the _index_ and _topset_ operations in constant time by using the _depth_ or _level_ancestor_ operation of BP. The length of \(U\) is \(\mathcal{O}(mn)\) because we create at most \(n-1\) dummy nodes per one real node. The dummy nodes make the length of \(U\)\(\mathcal{O}(n)\) times larger, whereas this technique saves \(\mathcal{O}(m\log n)\) bits because we do not store the index of each node explicitly.  
Figure 5: Example of the construction of the zero-edge tree from a ZDD by inserting dummy nodes and adding/deleting edges. A black and white circle represents a dummy and real node, respectively. The number in a circle represents its index. A dotted arrow in the left figure represents a 0-edge.  
An example of a zero-edge tree and its BP are shown in Figure 6. Black circles are dummy nodes and the number next to each node is its real preorder rank.
## 2 Seismological Data and Results
### Seismicity Results
Further north, Corfu Island is characterized by very low seismicity onshore throughout the study period. Sparse seismic activity was recorded offshore to the south and a bit denser distribution of epicenters is identified to the north, near the shores of Albania (group #5, purple; with a background rate of 0.1-0.2 events/day), related with events of magnitude \(4.0\leq M<5.0\). West of Corfu Island, at NW Epirus, group #10 (blue; background rate of 0.2-0.4 events/day) included an \(M=5.6\) thrust event that occurred on 21 March 2020 near Kanallaki [68,69]. Group #11 (beige; background rate of 0.1 events/day) contains an M5.3 event that occurred on 15 October 2016 near Ioannina. At the northern edge of the study area, group #12 (gray; background rate of 0.1 events/day) near the Greco-Albanian border, contains an M5.3 event that occurred on 1 June 2019. The most significant bursts of earthquake activity at the Ionian Islands region during the study period comprise the 2014 Cephalonia (groups #1 and #2), the 2015 Lefkada (groups #3 and #2) and the 2018-2019 Zakynthos mainshock-aftershock sequences (group #4), which are described in more detail in the following subsections.
# Detecting Out-of-Distribution Samples in Complex IoT Traffic Based on Distance Loss
## 4 Experiments
### Results
The results, presented in Tables 5 and 6, demonstrate that our method exhibits greater robustness across different datasets and achieves the best overall performance. Although the fixed-percentile threshold approach performs comparably to our method on the IoT Sentinel dataset, it proves less effective on the CIC IoT Dataset 2022, suggesting that fixed thresholds lack adaptability to varying data distributions. Other traditional threshold selection strategies suffer from similar performance degradation. Figure 5: t-SNE visualization of feature distributions for two OOD detection scenarios. (**a–d**) CIC IoT 2022 dataset with the Amcrest device as OOD. (**a**) Raw class features. (**b**) Group features after class mean-based clustering. (**c**) Group features after distance loss optimization. (**d**) Class features after distance loss optimization. (**e–h**) IoT Sentinel dataset with the grid TTU device as OOD, corresponding to feature representations in (**a–d**). The Otsu method performs best when using IoT Sentinel as the in-distribution dataset and Shenzhen Grid Flow TTU as the OOD data. However, its performance drops substantially in experiments using Shenzhen Grid Flow LMT as OOD, due to the positively skewed score distribution (skewness of 1.19). In contrast, our method exhibits minimal performance variation across different experimental conditions, demonstrating stronger robustness by better adapting to varying data distributions and leading to more appropriate threshold choices. To evaluate computational efficiency for edge deployment, we report the inference runtimes (in seconds) of all compared methods on both the CIC-IoT2022 and IoT Sentinel datasets, as shown in Table 7. Our proposed method (ODDL) achieved the lowest inference time on both datasets--0.0165 s on CIC-IoT2022 and 0.0200 s on IoT Sentinel--outperforming all baseline methods, including MSP, AE, ODIN, Energy, and Mahalanobis. These results demonstrate that our approach offers not only superior detection performance but also significantly reduced computational cost, making it highly suitable for real-time OOD detection in resource-constrained IoT environments.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 3 Model
### Attacker Model
In order to avoid such monitoring, attackers must disperse the total attack traffic to multiple uplinks to avoid excessive bandwidth surges on a single link. The maximum absolute bandwidth increment of this uplink attack traffic is defined as MaxUp to characterize the detectability of the attack. MaxUp is normalized to the bandwidth of an uplink. In the worst case, the attacker needs to fully saturate the satellite's uplink, and the MaxUp at this time is 1.
## 7 Experimental Results
### Test in a Pond  
We have also conducted outdoor experiments in a pond shown in Figure 13, which is 60 m long, 40 m wide and 1 to 3 m deep. Transmitting and receiving transducers are submerged in 0.5 m depth from the water surface. Figure 14 shows the output voltage of the receiving transducer \(V_{out}\) as a function of the input voltage of the transmitting transducer \(V_{in}\) and communication distance \(d\). Since the pond is very shallow when compared with its length and width, cylindrical spreading is assumed for the derivation of analytical results. According to the results, the experimental results do not exhibit a good match to the analytical ones. This is because the shape and structure of the pond is so complex that even the transmission loss model based on cylindrical spreading fails to reflect bottom topography.  
For the performance evaluation of the implemented modem, we have evaluated bit error rate (BER). Fifty frames where each frame contains the physical layer payload of 255 bytes are sent over the underwater channel. That is, in total 102,000 bits are transmitted for each experiment. The distance between transmitting and receiving modems and the data rate are set to 40 m and 200 bps, respectively.  
Figure 15 shows the bit error rate with respect to the input voltage \(V_{in}\). From the results, it is observed that the BER is improved with the increase of \(V_{in}\) for a fixed distance. This is because \(V_{out}\) is proportional to \(V_{in}\), which makes it easy to extract the original signal from noise and multipath. Meanwhile, we can achieve a BER of \(3.9\times 10^{-4}\) at \(V_{in}=40\)\(V_{pp}\). Since \(V_{out}\) by experiment is equal to 4.5 \(mV_{pp}\) at \(d=40\)\(m\) and \(V_{in}=40\)\(V_{pp}\) in Figure 14, we can interpret that the BER of \(3.9\times 10^{-4}\) is achieved if the transducer at receiver side acquires \(V_{out}\) of 4.5 \(mV_{pp}\).  
Figure 13: Experimental environment for the tests in a pond.
## 4 Empirical Study
### Objective Evaluations  
#### 4.1.1 Experimental Settings for Objective Evaluations  
The experimental data for objective evaluations were obtained from the Department of Information Management at Cheng Shiu University, which consists of 460 students and 17,438 course degrees. From the 17,438 course degrees, 6612 professional course degrees were selected, including 145 unique professional courses. From the 6612 degrees, 1539 degrees were randomly selected as testing degrees (unknown degrees) and the others were selected as training degrees (known degrees). The degrees ranged from 1 to 5, where 1 indicates an original score lower than 60, 2 indicates an original score between 60 and 69, 3 indicates an original score between 70 and 79, 4 indicates an original score between 80 and 89, and 5 indicates an original score greater than 89.