esa-sceva/satcom-chunk-collection huggingface dataset:
# Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water
## 3 Simulation Calculation  
The isotropic assumption of the ambient noise is usually not satisfied in the ocean environment. Furthermore, the surface noise model is more widely used. According to the Equations (9) and (10),Figure 1 shows the correlation coefficient curves of the isotropic noise and the surface noise with the change of element interval-to-wavelength ratio \(d/\lambda\), respectively. Curves indicate that the first zero points of the correlation coefficient curve for the isotropic noise appears at the location of \(d=0.5\lambda\) and for the surface noise appears at the location of \(d=0.37\lambda\). The noise correlation coefficient in the isotropic noise field is 0.31 When \(d=0.37\lambda\) is satisfied. Moreover, correlation coefficients of noise oscillate with the variation of \(d/\lambda\).  
Section 2 derives the relationship between the array gain and correlation in theory. Next, we conduct a simulation to intuitively validate the effect of variation with signal correlation and noise correlation on the array gain.  
In this event, the VLA is deployed in the isotropic noise field, and the element number ranges from 1 to 100. The element interval-to-wavelength ratio is set to be 0.37, 0.5, and 0.7, corresponding to the noise correlation coefficient of 0.31, 0, and \(-0.22\) between adjacent hydrophones, respectively. The correlation coefficient of the signal between adjacent elements is set to be 0.8 and 1, respectively. In the isotropic noise field, the array gains with different noise correlation coefficients and different signal correlation coefficients are calculated and shown in Figure 2. It can be seen that AG decreases as the \(\rho_{s}\) decreases, and AG no longer increases when the number of elements increases to a certain quantity. When the \(\rho_{s}\) keeps at a fixed value, AG ascends with \(\rho_{n}\) descending. When the ambient noise is isotropic, and the signal is completely correlated, AG is equal to \(10\log M\), as presented in the purple solid line. In some cases, AG even exceeds the normal value of \(10\log M\) as the correlation coefficient of noise between adjacent elements is less than zero.  
Figure 1: Vertical correlation coefficients in the isotropic noise field and surface noise field.  
Figure 2: Array gains with variable correlation coefficients of signal and noise.
# Combinatorial Olfactory Signaling in Short-Distance Determines Host Plant Recognition in Locust
## 3 Results  
### Establishment of Olfactory Feeding Behavior Responses of Palps  
In this study, we established the duration of palpation-like behavior as a new quantitative parameter (duration of excitatory response, DER) to characterize the olfactory discrimination of the locust to volatiles under a free-moving state (Figure 1A). We observed that locusts were active in visiting both sides with wheat grinds and the solvent (Figure 1B). We analyzed additional parameters of the recorded trajectory response to wheat volatiles. Walking speed, movement distance, and duration between the wheat extraction and solvent were not significantly different, whereas locusts visited the plant extraction side longer than the control side, regardless of whether it was wheat or cotton (Figure S2A-D). We examined the locust actions near the stimulus in greater detail. After approaching the host odors, the locusts vibrated their palps and moved around the stimulus more frequently than the control solvent, which mimicked palpation under a natural situation (see Video S1).  
Locusts responded to the negative control (solvent in both arms) with a fairly low PI_DER index; however, wheat volatiles elicited a high index when paired with the solvent, which was not related to the extraction method (juice and grinds) (Figure 1C). Later in the assay, ground wheat seedlings (wheat grinds) were used as the host-plant stimulus. First, an antenna was not required in short-range palpation because antennae-amputated locusts still showed a high PI-DER to the stimulus. However, intact palps were indispensable to inducing active searching toward the stimulus (Figure 1D). Second, using DER, volatiles between host plants (wheat and corn) and nonhost plants (cotton and soybean) could definitely be discriminated in the pairing with the solvent (Figure 1E). Third, locusts were not capable of distinguishing volatiles between two host plants or between two nonhost plants (Figure 1F), but they sensitively preferred host wheat odors over nonhost cotton odors (Figure 1G). Furthermore, the palps of locusts responded to volatiles in a dose-dependent fashion using DER (Figure S3). Overall, the DER paradigm was not only feasible to quantify the olfactory preference for host plants but also the aversion to nonhost plants at short range. Thus, we established a novel behavioral paradigm to determine the olfactory roles of the palps in food choice.
# Experimental Seaborne Passive Radar
## 5 Conclusions  
The presented results clearly show that seaborne passive radar using nearby DAB or DVB-T transmitters can detect airborne targets of different kinds. This allows the system to be used for monitoring the airspace over coastal areas. Such a system can be relatively inexpensive, as it can be built using commercial off-the-shelf VHF antennas, general-purpose SDR receivers, and a computer. Better detection performance can be obtained with more advanced equipment (e.g., more sensitive receivers, higher-gain antennas) and more sophisticated signal processing (e.g., integrating signals from more receiving channels and extending the integration time, which necessitates the use of range/Doppler migration correction techniques).  
Maritime targets (such as the fishing boat) were not detected in the conducted experiment. This resulted from the fact that the bistatic velocity of the target was small and the target echo was inseparable from the clutter, which was spread in the velocity dimension. To detect slow targets, such as other ships or boats, longer integration times should be employed, which will increase the velocity resolution. In addition, more advanced clutter removal techniques may be employed, such as STAP (space-time adaptive processing), which provides higher selectivity for unwanted signal attenuation. In general, the problem of clutter removal in seaborne platforms is much more complicated than in the case of stationary ground-based radars. This complication results from the fact that the platform is moving, and an additional Doppler shift is introduced. The clutter characteristics will change depending on the geometry of the transmitter, clutter, and radar, which should be taken into account when the algorithm for clutter cancellation is designed.  
Conceptualization, G.M. and K.K.; methodology, K.K.; software, G.M., K.K.; validation, K.K. and M.M.; formal analysis, K.K.; investigation, G.M.; resources, K.K. and A.D.; data curation, A.D.; writing--original draft preparation, G.M. and M.M.; writing--review and editing, G.M.; visualization, G.M. and M.M.; supervision, K.K. and M.M.; project administration, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.  
Figure 28: LOT4 CG detection visible with integration time increased to 524 ms (scene in Figure 26).
## 3 Results  
### Prototype Structure Analysis  
The diameter, width, and intervals of the four stainless steel rings in the four\(-\)ring probe significantly influence the measurement output of the conductivity sensor. To determine the optimal combination of these dimensions, 16 ring probes with varying specifications were prepared, encompassing four sizes for each of the three parameters. A three\(-\)factor, four\(-\)level orthogonal test was designed and conducted [30]. Measurements were performed in a conductivity standard solution of 545 \(\upmu\)S/cm [31]. The parameters of the probes and the experimental results are detailed in Table 1, with the excitation frequency of the conductivity probe set at 100 kHz.  
The orthogonal test data were analyzed using SPSS software (SPSS 17.0). The results obtained are shown in Table 2:  
The analysis results indicate that the \(p-\)values associated with the three geometrical parameters of the probe are as follows: diameter (0.001), width (0.006), and spacing (0.000). The order of influence on the conductivity prototype's output, from most to least significant, is spacing, diameter, and width. The measured impedance amplitude is directly proportional to the ring's interval and width and inversely proportional to the ring's diameter. Considering the measurement accuracy, prototype practicality, and minimal disturbance to the surrounding soil, the optimal dimensions for the prototype's ring probe are set to a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Diameter (mm)** & **Width (mm)** & **Intervals (mm)** & **Impedance Amplitude (Ohm, 100 kHz)** \\ \hline
16 & 4 & 4 & 112.74 \\
**16** & 6 & 6 & **119.20** \\
16 & 8 & 8 & 131.24 \\
16 & 10 & 10 & 151.68 \\
18 & 4 & 6 & 107.46 \\
18 & 6 & 4 & 103.02 \\
18 & 8 & 10 & 142.23 \\
18 & 10 & 8 & 136.54 \\
20 & 4 & 8 & 104.57 \\
20 & 6 & 10 & 127.16 \\
20 & 8 & 4 & 99.78 \\
20 & 10 & 6 & 115.06 \\
22 & 4 & 10 & 111.47 \\
22 & 6 & 8 & 108.65 \\
22 & 8 & 6 & 104.37 \\
22 & 10 & 4 & 98.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orthogonal test table for ring probes of different sizes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Type III** & **Degrees of** & **Mean** & **F** & **Statistical** \\  & **ANOVAs** & **Freedom** & **Square** & **F** & **Significance** \\ \hline Correction model & 3804.917 a  & 9 & 422.769 & 26.910 & 0.000 \\ Intercept & 219,377.483 & 1 & 219,377.483 & 13,963.721 & 0.000 \\
**Diameter (mm)** & **1286.606** & **3** & **428.869** & **27.298** & **0.001** \\
**Width (mm)** & **582.594** & **3** & **194.198** & **12.361** & **0.006** \\
**Intervals (mm)** & **1935.716** & **3** & **645.239** & **41.070** & **0.000** \\ Inaccuracy & 94.263 & 6 & 15.711 & & \\ Total & 223,276.663 & 16 & & & \\ Revised total & 3899.180 & 15 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: SPSS analysis of orthogonal test of ring probe.  
diameter of 16 mm, a width of 6 mm for the stainless\(-\)steel electrodes, and an electrode spacing of 6 mm.
## 2 Related Work
Compared to existing models that mainly focus on single-server or homogeneous multi-server scenarios, our model is designed specifically for heterogeneous server environments. For example, most prior studies overlook the impact of different transmission power, computing capabilities, number of CPU cores, and waiting queue lengths among MEC servers on network performance. Our model comprehensively considers these factors, enabling a more accurate and detailed analysis of task offloading in complex MEC networks. Task offloading efficiency is critical to edge computing service quality, as it directly impacts system responsiveness and resource utilization, so it is of great significance to model and evaluate the task offloading process. MEC task offloading performance has been the subject of extensive research. Studies in this area have primarily focused on delay analysis [41; 42; 43] and energy consumption analysis [33; 44; 45], with heuristic and machine learning algorithms proposed for optimizing these metrics. Moreover, while average performance metrics receive considerable attention, there is a lack of focus on real-time indicators, such as immediate service probability and blocking probability. Immediate service probability measures the probability that users can have their needs met without waiting, while blocking probability refers to the probability that the number of user tasks exceeds the edge server's capacity, which reflects the probability of the task being discarded. We conduct research on task offloading performance evaluation under heterogeneous edge computing network architecture. Utilizing random geometry and queuing theory, we develop models to analyze key metrics, including average task offloading delay, immediate service probability, and blocking probability. This comprehensive approach enables a detailed assessment of system performance and contributes to the development of more efficient and stable MEC systems. Table 1 summarizes the research scenarios and evaluation metrics of the cited literature.
## Methods and Data  
### Methodology  
The flowchart shown in Figure 1 is designed to demonstrate the methodology steps, presenting the sequence of the work as follows:  
1. Data Collection: The required dataset of COVID-19 in Saudi Arabia provided by the Ministry of Health.
2. Pre-Analysis: Some analysis steps were made on the data to discover hidden patterns.
3. Processing: Processing data and cleaning missing values or unimportant variables.
4. Time Series Extraction: Extraction of a day, month, and year from collected data and making them into separate attributes for the analysis.
5. Data Scaling: Scaling data is very important to get good performance when applying models.
6. Building Models: It is the core step, where three models are built based on three different algorithms; LSTM, CNN, and ARIMA.
7. Predicting Outcomes: For each model, we predict the outcomes of different cases in cities.
8. Results of Analysis: After applying the models, the results of each algorithm are analyzed separately.
# Machine Learning for Network Intrusion Detection--A Comparative Study
## 4 Background
### Algorithms and Techniques
#### 4.2.3 Gaussian Naive Bayes  
Bayesian classifiers are statistical classifiers. They are able to forecast the probability that the given model fits a particular class. Gaussian Naive Bayes (GNB) is a type of Naive Bayes algorithm that is based on [PERSON]'s theorem. The hypothesis that these algorithms are  
Figure 1: Graphical representations of used ML algorithms.  
based on is that, for a given class, the attribute value is independent of the values of the attributes. This theory is called class-conditional independence.  
It is mostly used for datasets with continuous data. This algorithm assumes that classes follow a Gaussian distribution. A Gaussian distribution, or normal distribution, can be analyzed by the following formula. Some of the advantages of using GNB are that it is a quick algorithm to train, is suitable for datasets with many classes, and is primarily used for categorical problems. The main disadvantage is that this algorithm treats every feature independently, which does not always happen in real life. This makes the algorithm less suitable for real-world cases.
# SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
## 1 Introduction
The average recognition rate was about 94% when the knee joint was flexed and extended, the ankle joint was dorsiflexed, and the tibia and femur were rotated. In the field of sensor signal research, [PERSON] et al. used gyroscope signals to obtain joint angles and proposed the dynamic positioning system [14]. [PERSON] et al. collected sensing information of the lower limb exoskeleton itself, such as acceleration, angular velocity and pressure data. They manually selected features and used linear discriminant analysis classifier to solve the recognition problem of five movement modes and transformations of five modes [15]. The recognition rate reached 93.9%. Compared with bio-sensory sensors and physical interaction sensors alone, the method of multi-source information fusion can effectively increase the identification accuracy and reduce the prediction delay. Therefore, [PERSON] et al. proposed to use the dynamic Bayesian network method to integrate the sEMG signals and Inertial Measurement Unit (IMU) signals in the characteristic layer to identify the basic locomotion modes on the basis of the existing research. Subsequent studies that included a time history of exercise data reduced the steady-state pattern error recognition rate by 50% under deep belief network (DBN). It has a certain hysteresis, because the collected data is between the stance state before the locomotion transition and the swing state after the transition [16]. In addition, [PERSON] et al. innovatively proposed and verified that 6 movement patterns can be recognized by collecting capacitive signals on the affected limbs of patients [17], the average identification accuracy can reach 93.4%. The image-based recognition method has scene limitation. EEG and sEMG sensors are not easy to wear and are easily affected by sweat, body temperature and other factors. Although the sensor information of the exoskeleton robot can intuitively judge the human locomotion pattern, the recognition result is not satisfactory. In order to solve the above effects, this paper proposes a locomotion pattern recognition method of exoskeleton robot based on SA-SVM. SA is one of the earliest and most popular meta-heuristic algorithms, which can effectively solve the parameter optimization problem of the SVM model. It introduces the natural mechanism of solid annealing as well as the appropriate random elements. The value of randomness in the whole solution neighborhood can avoid the algorithm falling into local optimum and improve the ability of global optimization. In order to identify the locomotion pattern stably and accurately, the SA-SVM model is adopted in this paper based on traditional SVM. Firstly, lower limb IMUs information, joint angle information and plantar pressure information were collected. Then, IMUs information, joint angle information and force sensing resistors (FSRs) data were combined as the input of SA-SVM model to further improve the overall locomotion pattern recognition model. The model effectiveness and accuracy of the multi-mode machine learning model are also verified.
## 4 Sum-Rate Performance with SQ-Based CSI Feedback
### Complexity Comparison of RVQ and SQ  
To compare the implementation complexity quantificationally, we adopt the number of floating-point operations (FLOPs) as the computational complexity measure in SQ and RVQ. A FLOP is either a complex multiplication or a complex summation, and the FIOP numbers for matrix-scaling operation \(a\mathbf{A}\) and inner product operation \(\mathbf{a}^{H}\mathbf{b}\) are \(MN\) and \(2N-1\), respectively [31], where \(\mathbf{a},\mathbf{b}\in\mathbb{C}^{N}\) and \(\mathbf{A}\in\mathbb{C}^{N\times M}\).  
For complex matrix \(\mathbf{H}\), its SQ operation is equivalent to a matrix-scaling operation, and therefore the computational complexity of one SQ operation is \(\mathcal{O}(MN)\). With \(B\) bits to quantize complex matrix \(\mathbf{H}\), one RVQ operation mainly includes three steps, i.e., vectorizing \(\mathbf{H}\) in (5), calculating the inner products of \(\mathbf{h}\) and all codewords in (6) and searching for the codeword with the largest inner product value. The vectorization of \(\mathbf{H}\) requires \(3 MN-1\) flops; the inner product of \(\mathbf{h}\) and \(2^{B}\) codewords requires \(2^{B}(2 MN-1)\) flops; the computational complexity of codeword searching is \(\mathcal{O}(2^{B})\). Therefore, the computational complexity of one SQ operation is \(\mathcal{O}(2^{B}MN)\).
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 3 Experimental Results and Analysis
### Experimental Environment  
In this paper, we conducted all experiments and training using the PyTorch framework on a desktop computer equipped with the operating system Ubuntu 20.04, a processor of 12 vCPU Intel(R) Xeon(R) Silver 4214R CPU @ 2.40 GHz, and a GeForce RTX 3080 ti GPU. For a fairer comparison, we directly used a publicly available detector, which was trained on MOT17 and MOT20 through ablation studies. For optical flow feature extraction, we used the trained detector to detect targets and extract optical flow within the target boxes. In all experiments, we used a two-stage tracking algorithm with a default detection score threshold of 0.6. In the linear assignment step, if the similarity distance between the  
Figure 4: The two-stage tracking framework  
detection box and the tracking box exceeds 0.5, the match is rejected. For lost tracks, we keep them for 30 frames in case they reappear.
## 3 Results
### Bmd  
#### 3.2.1 BMD and Type of Fracture  
Unimalleolar fractures were observed in 10 patients (19.6%), bimalleolar fractures were found in 20 patients (39.2%) and trimalleolar fractures in 21 (41.2%).  
The bone mineral density (BMD), expressed as a T-score for each patient, revealed that 12 patients (23.5%) were categorized as normal, 29 (56.9%) as osteogenic, and 10 (19.6%) as osteoporotic. Among the normal patients, 50% were women and 50% men; among patients classified as osteogenic, women represented 72% of cases (21 patients) while men 27% (8 patients). All 10 patients with osteoporosis were women (Figure 1).  
Considering only the women in the study (37), only 16.2% were classified as normal BMD; 56.8% were osteogenic, while 27% were affected by osteoporosis. Overall, 83.8% of women in the study were found to have reduced BMD compared to normal.  
The average T-score for men is \(-\)1.1 SD, while for women, it is \(-\)2.0 SD.  
The results of DXA were correlated with the type of fracture. In particular, the percentage of each type of fracture was calculated after dividing the patients into three groups based on BMD: normal, osteogenic, and osteoporotic (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Age Ranges** & **Sex** & **Tot (N\({}^{\circ}\))** & **Tot (\%)** \\ \hline \multirow{4}{*}{70–75} & M + F & 10 & 19.6\% \\  & M & 2 & 20.0\% \\  & F & 8 & 80.0\% \\ \hline \multirow{4}{*}{75–80} & M + F & 4 & 7.8\% \\  & M & 0 & 0\% \\  & F & 4 & 100\% \\ \hline \multirow{4}{*}{80–85} & M + F & 5 & 9.8\% \\  & M & 1 & 20.0\% \\ \cline{1-1}  & F & 4 & 80.0\% \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Classification of patients based on BMD T-scores stratified by gender.  
The most significant results concern trimalleolar fractures: these occur in 25% of patients with BMD classified as normal, 37% of fractures in osteogenic individuals, and increase to 70% in osteoporoptic patients.