esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Feature Extraction Method--Dual Time-Frequency Images Fusion
### Data Preprocessing
#### 2.1.2 Hilbert-Huang Transform  
The Hilbert-Huang transform (HHT) is NASA's designated name for the combination of empirical mode decomposition (EMD) [24] and Hilbert spectral analysis (HAS). The HHT analysis process is implemented in two steps: the first step uses EMD to pre-process the data, and the original data are decomposed into a set of finite intrinsic mode functions (IMF); the second step is the Hilbert transform (HT) of the decomposed IMF to obtain energy-frequency-time distribution.  
The EMD decomposes any signal in the IMFs as follows: For any data sequence, \(x(t)\) and \(m_{1}\), the local maximum and local minimum of the raw signal, are interpolated by cubic splines to form the mean value of the upper and lower envelopes. The difference between the signal and \(m_{1}\) is defined as component \(h_{1}\):  
\[h_{1}=x(t)-m_{1} \tag{2}\]This process can be repeated \(k\) times until \(h_{1k}\) is a basic mode component:  
\[h_{1k}=h_{1(k-1)}-m_{1k} \tag{3}\]  
Then the first prototype component \(C_{1}\) obtained from the raw data is:  
\[C_{1}=h_{1k} \tag{4}\]  
After sorting out \(C_{1}\) from the raw data:  
\[r_{1}=x(t)-C_{1} \tag{5}\]  
Since \(r_{1}\) still contains information of longer period components, \(r_{1}\) is still treated as new data and processed as described above. This strategy can process all subsequent remaining components \(r_{1}\). The sifting process ends when the preset stoppage criterion is satisfied. The stoppage criterion can be set as follows: when the remaining component becomes smaller than a predetermined value or when the remaining component becomes a monotonic function. The EMD algorithm is based on the local characteristics of the signal, and the basic mode components and residual components are obtained by iterative screening of the local mean. It can be explained that the decomposition and reconstruction of the signal have advantages, such as reducing noise interference and eliminating data.  
According to the definition of HT, the HT of real function \(x(t)\) is the real-valued function, which is defined as:  
\[y(t)=H(x(t))=\frac{1}{\pi}P\int_{-\infty}^{+\infty}\frac{x(u)}{t-u}du \tag{6}\]  
Here, the \(P\) indicates the principal value of the singular integral. An analytical signal was \(z(t)\), consisting of \(x(t)\) and \(y(t)\) in the complex number of \(x(t)\).  
\[z(t)=x(t)+iy(t)=a(t)e^{i\theta(t)} \tag{7}\]  
Here, \(a(t)\) is the amplitude of the analytical signal, and \(\theta(t)\) is the phase of the analytical signal.  
\[a(t)=\sqrt{x(t)^{2}+y(t)^{2}} \tag{8}\]  
\[\theta(t)=\arctan(y(t)/x(t)) \tag{9}\]  
Therefore, according to the definition, the instantaneous frequency expression is:  
\[\omega(t)=d\theta(t)/dt \tag{10}\]  
The polar coordinate expression shown in Equation (7) further emphasizes the local characteristics. It can be seen from this expression that both amplitude and phase are functions of time, and these form the basis of signal expression in the time domain.
# The Arrangement of the Osteons and [PERSON]'s Conjecture
## 3 Results
### The Interosteonal Area and Its Theoretical Calculation  
As mentioned before, all the surface not occupied by secondary osteons is considered an interosteonal area. While the H\({}_{1}\) interosteonal area is very extensive and widely communicating to surround isolated osteons and more or less numerous groups of osteons, in the H\({}_{2}\) and H\({}_{3}\) subtypes, the interosteonal area is limited and forms small islets surrounded by osteons. More precisely, the shape of these islets is quadrangular with concave sides in H\({}_{2}\) and triangular with concave sides in H\({}_{3}\). In the H\({}_{1}\) subtype, the interosteonal area can be easily calculated as the difference between the area considered and the sum of osteonal areas. In contrast, in H\({}_{2}\) and H\({}_{3}\) subtypes, the interosteonal area could be mathematically calculated besides the method for the H\({}_{1}\) subtype. In detail, in the H\({}_{2}\) subtype, the interosteonal area can be considered the difference between the square drawn on the central point  
Figure 2: Dense Haversian tissue. (**Left**) Original microphotos; (**Right**) osteons are highlighted with circles to evaluate the common points with neighboring osteons. (**a,b**) H\({}_{2}\) subtype, where each osteon is in contact with 4 other osteons ((**a**) _Capra hircus_; (**b**) _Sus scrofa_). (**c,d**) H\({}_{3}\) subtype, where each osteon is in contact with 6 other osteons ((**c**) _Ovis musimon_; (**d**) _Equus caballus_). The circles correspond to osteons. In H\({}_{3}\) subtype an osteon (red circle) is surrounded by 6 osteons (blue circles). Bar = 40 \(\upmu\)m.  
of the Haversian canal of four adjacent osteons and the area of one osteon (corresponding to four cloves of a quarter of osteon) (Figure 3a). With r indicating the radius of the osteon, the quadrangular interosteonal area can be easily calculated with this mathematical procedure:  
\[(2\mathrm{r})^{2}-\pi\mathrm{r}^{2}=4\mathrm{r}^{2}-\pi\mathrm{r}^{2}=(4-\pi) \mathrm{r}^{2}\]  
In the H\({}_{3}\) subtype, the interosteonal area can be considered as the difference between the equilateral triangle drawn on the central point of the Haversian canal of three adjacent osteons and the area occupied by three cloves of a sixth of an osteon (Figure 3b). Firstly, the formula to determine the area of an equilateral triangle knowing Side 1 (that is \(2\mathrm{r}\)) can be applied:  
\[1^{2}\,1/4\,\,\sqrt{3}=(2\mathrm{r})^{2}\,1/4\,\,\sqrt{3}=\mathrm{r}^{2}\, \,\sqrt{3}\]  
The area occupied by three cloves of a sixth osteon is  
\[3\,1/6\,\,\pi\mathrm{r}^{2}=\
icefrac{{1}}{{2}}\,\,\pi\mathrm{r}^{2}\]  
Thus, the interosteonal area in the H\({}_{3}\) subtype is  
\[\mathrm{r}^{2}\,\sqrt{3}-\
icefrac{{1}}{{2}}\,\pi\mathrm{r}^{2}=(\sqrt{3}- \
icefrac{{1}}{{2}}\,\pi)\,\mathrm{r}^{2}\]  
It can be noted that the interosteonal area of the H\({}_{3}\) subtype is smaller than that of the H\({}_{2}\) subtype, since:  
\[\sqrt{3}-\
icefrac{{1}}{{2}}\,\pi<4-\pi\]
## 3 Results and Discussion
### Characterization of Encapsulated Pumpkin Oil in Carbohydrate Shells
#### 3.3.2 Size, Colour and Microstructure of Prepared Capsules  
The powders obtained by spray drying were characterized by similar particle sizes of D (4,3) and D (3,2) (Table 3). The highest mean diameters were detected for particles of encapsulated pumpkin oil with trehalose. The colour of the bulk capsules was comparable for most of variants. Additionally, whiteness index (WI) was only slightly differed between samples. The greater colour difference compared to capsules with maltodextrin (\(\Delta\)E) was found for the capsules obtained with the use of wheat starch, which were found to be lighter and less yellow than other ones.  
Figure 4 presents micrographs of encapsulated pumpkin oil in used carbohydrate shells. These powders were composed of particles with a similar external topography, characterized generally by rounded shapes and a continuous wall with no apparent cracks or fissures on the outer surface. This globular morphology results from the fast water evaporation during the spray drying process [47]. It was observed that capsules prepared with maltodextrin and native starches showed larger depressions on the outer surface than those prepared with lactose and trehalose. Additionally, the capsules of powders with rice and maize starches had visible small particles (starch granules) taped to the surface, which probably resulted from the high content of surface oil in these samples.
# Enhancement of Metabolite Production in High-Altitude Microalgal Strains by Optimized C/N/P Ratio
## 3 Results
### Production of Proteins under Different C/N/P  
In the case of protein synthesis, when sodium carbonate (Sc) is used, the Pareto chart (Figure 2a) shows no significant effect for any of the analyzed variables; however, according to the surface response (Figure 2b), high NaNO\({}_{3}\) (>0.25 g/L) and low concentrations of the carbon source (<1.76 g/L of Na\({}_{2}\)CO\({}_{3}\)) might increase the total content of proteins. In the case of sodium bicarbonate (Sb), the phosphate buffer and the carbon source significantly affect the synthesis of proteins (Figure 2c). The surface response (Figure 2d) shows that medium concentrations of K\({}_{2}\)HPO\({}_{4}\) and KH\({}_{2}\)PO\({}_{4}\) (0.0375 and 0.0875 g/L, respectively), and medium concentrations of the carbon source (1.39-3.49 g/L of NaHCO\({}_{3}\)) positively affect the synthesis of proteins up to 40% \(w/w\) of the total biomass. Finally, the Pareto chart of sodium acetate (Sa) (Figure 2e) shows that the phosphate buffer, sodium nitrate, and the interaction between NaNO\({}_{3}\) and sodium acetate affect the synthesis of proteins. The surface response (Figure 2f) shows that high NaNO\({}_{3}\) (>0.25 g/L) and low concentrations of the carbon source (<1.5 g/L of C\({}_{2}\)H\({}_{3}\)NaO\({}_{2}\)) might increase the total content of proteins in _Desmodesinus_ sp UFPS021.  
Figure 2: Pareto charts and surface response of protein production using Na\({}_{2}\)CO\({}_{3}\) (**a,b**), NaHCO\({}_{3}\) (**c,d**), and C\({}_{2}\)H\({}_{3}\)NaO\({}_{2}\) (**e,f**).
# Deep Class-Guided Hashing for Multi-Label Cross-Modal Retrieval
## 3 Methodology
### Problem Formulation  
Notations.Without loss of generality, sets are denoted by math script uppercase letters (e.g., \(\mathcal{D}\)). Scalers or constants are signed by uppercase letters (e.g., \(C\)). Matrices are represented in uppercase bold letters (e.g., \(A\)), and the \((i,j)\)-th element of \(A\) is denoted by \(A_{ij}\). Vectors are denoted by lowercase bold letters (e.g., \(a\)), and the \(i\)-th element of \(a\) is represented by \(a_{i}\). A transpose of a matrix or a vector is denoted by a superscript, T (e.g., \(A^{\text{T}}\)). Functions are denoted by calligraphy uppercase letters (e.g., \(\mathcal{L}\)). The frequently used mathematical notations are summarized in Table 1 for readability.  
Problem Definition. This work focuses on two common modalities: image (denoted by \(x\)) and text (denoted by \(y\)). Given a dataset, \(\mathcal{D}=\left\{\mathbf{d}_{i}\right\}_{i=1}^{N}\), where \(\mathbf{d}_{i}=\left[\mathbf{x}_{i},\mathbf{y}_{i},\mathbf{I}_{i}\right]\) represents the \(i\)-th sample, \(\mathbf{x}_{i}\) and \(\mathbf{y}_{i}\) represent the \(i\)-th image and text, respectively. \(\mathbf{I}_{i}=\left[l_{1i},l_{2i},\ldots,l_{iC}\right]\) is the multi-label vector for \(\mathbf{d}_{i}\), where \(C\) is the number of categories. The goal of cross-modal hashing is to learn two hashing functions, \(\mathcal{H}^{x}\) and \(\mathcal{H}^{y}\), to generate binary codes \(B^{x}\in\left\{-1,1\right\}^{K}\) and \(B^{y}\in\left\{-1,1\right\}^{K}\), ensuring that the Hamming distance between similar samples is smaller and the distance between dissimilar ones is larger. Since binary optimization is a typical NP-hard problem, the continuous relaxation strategy is employed to obtain the binary-like codes.
## 4 GenSQLi Framework
### SQLi Clustering  
To cluster and analyze SQLi attacks, two machine-learning algorithms were used: TF-IDF with Hierarchical Agglomerative Clustering (HAC) and SequenceMatcher with DBSCAN. Each method captures unique aspects of SQLi attack patterns. **TF-IDF + HAC:** TF-IDF evaluates term importance in SQLi queries by emphasizing unique terms and de-emphasizing common ones [29]. Queries are represented as TF-IDF vectors, and HAC clusters them based on vector similarity [30]. For example, SELECT 1 = 1 and SELECT 1 = 1 OR 0 \(\times\) 61 = 0 \(\times\) 61 are grouped due to shared terms. **SequenceMatcher + DBSCAN:** SequenceMatcher calculates character-level similarity scores between queries, while DBSCAN clusters queries with high similarity density [31]. For instance, SELECT 1 = 1 and SELECT 0 \(\times\) 61 = 0 \(\times\) 61 are grouped due to their structural similarity.  
Advantages of the Selected Clustering Approaches  
The combination of TF-IDF + HAC and SequenceMatcher + DBSCAN are highly effective for clustering SQLi payloads due to their complementary strengths in handling the complexities of SQLi patterns. TF-IDF emphasizes rare, attack-specific terms while reducing the weight of common ones, ensuring meaningful feature extraction, and HAC effectively handles irregular, hierarchical relationships without assuming cluster shapes or predefined counts. Similarly, SequenceMatcher captures string-based similarities, and DBSCAN excels in managing overlapping clusters, tolerating noise, and dynamically identifying the number of clusters. Together, these approaches outperform alternatives like k-means, regex-based clustering, or topic modeling by offering superior accuracy, robustness against noisy data, and adaptability to diverse and irregular SQLi datasets. From a computational perspective, TF-IDF + HAC is suitable for smaller, structured datasets with clear hierarchical relationships, while SequenceMatcher + DBSCAN is more scalable and robust for larger, unstructured, and noisy datasets [32]. Experimental evaluation of the clustering algorithms is presented in Section 5.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 3 In-Orbit Payload Performance  
The HSI onboard HYPSO-1 is a push-broom hyperspectral imager with a transmissive grating design, assembled using Commercial-Off-The-Shelf (COTS) components, as described in [23]. The main design specifications are a spectral range of 400 to 800 nm, theoretical spectral resolution of 3.33 nm at 600 nm, spatial resolution of 100 m \(\times\) 100 m (defined from the HYPSO-1 orbit) and a Top-of-Atmosphere (ToA) SNR of 100 [5; 23]. From pre-launch calibration, the full spectral range imaged on the sensor is 226 to 961 nm, but only the 400 to 800 nm range is used for nominal operations due to a low signal below 400 nm and second-order diffraction effects appearing above 800 nm. The spectral resolution was found to be less than 4.5 nm for all wavelengths, and the spatial focus was shown to be best around 600 nm, which is approximately in the center of the image [24].  
The instrument was designed and built for the HYPSO mission, based on the following Goals from [5]:  
**Goal 3:** Images should have spatial resolution better than 30-100 m per pixel [3; 11];
**Goal 4:** The raw hyperspectral data should have a spectral resolution of about 5 nm, or better, for VIS-NIR wavelengths [3; 11];
**Goal 5:** The SNR at ToA should be greater than 400 in visual wavelengths for open ocean water [33], and atmospherically corrected SNR of water-leaving signals should be between 40-100 [34].  
The spatial resolution Goal may be achieved either before or after applying corrections and image capture in a slewing maneuver combined with imaging enhancing techniques such as super-resolution [5]. This has not yet been fully explored for the HYPSO-1 data, and the spatial resolution is estimated based on the raw data. Imaging with the slewing maneuver [5] leads to a larger number of scan-lines over a smaller area. With image processing, this extra information over a given area is expected to improve the spatial resolution. For Goal 4, the spectral resolution should be 5 nm preferably for all data collected. For HYPSO-1 nominal data, a spectral binning factor of 9 is used, aligning the spectral sampling with the theoretical spectral Full-Width at Half-Maximum (FWHM). This will affect the estimated spectral resolution. Both binned and non-binned data will be investigated here. For Goal 5, related to the noise characteristics of the data, the ToA SNR is estimated using a nominal image cube with binned data from HYPSO-1. The binned cube is compared to data from other sensors.
## 6 Results Analysis and Discussion
### Performance of Throughput across Varying AQM Algorithms  
**Scenario 1 (Bandwidth = 10 Mbps, Delay = 20 ms)**:  
We evaluated the throughput performance of FQ-CoDel, FQ-PIE, and L4S across two scenarios: Case 1 with ECN enabled and Case 2 with ECN disabled. Figures 28 and 29 illustrates the results for both cases.  
Our findings show that FQ-CoDel achieves fairness across all TCP connections, whereas FQ-PIE and L4S exhibit significant unfairness. Subsequently, FQ-PIE and L4S exhibit elevated unfairness when ECN is enabled rather than disabled. Enabling ECN leads NewReno to detect congestion sooner, causing an earlier reduction in its _cvond_ threshold and quicker entry into congestion avoidance mode. This behavior sustains higher _cvond_ levels, resulting in more data transferred, as indicated in Table 2 when ECN is enabled, especially L4S. L4S exhibits higher throughput than FQ-PIE when ECN is enabled, showing superior performance. We can also observe that the maximum throughput attained when ECN notification is enabled is higher compared to when ECN is disabled.  
Figure 27: Predicted queue delay vs. actual queue delay for all workers with varying reward scaling factor in units of 100 \(\upmu\)s. (**a**) Predicted QDelay with varying reward scaling factor Agent Worker 1; (**b**) Predicted QDelay with varying reward scaling factor Agent Worker 2; (**c**) Predicted QDelay with varying reward scaling factor Agent Worker 3; (**d**) Predicted QDelay with varying reward scaling factor Agent Worker 4.  
**Scenario 2 (Bandwidth = 1 Mbps, Delay = 20 ms)**: We evaluated the performance of FQ-CoDel, FQ-PIE, and L4S in two cases: Case 1 with ECN enabled and Case 2 with ECN disabled for comparison. Figures 30 and 31 showcase these two cases' results. Similarly to Scenario 1, FQ-PIE and L4S exhibit higher unfairness in throughput when ECN is enabled due to NewReno's early congestion detection, which sends New Reno directly into the congestion avoidance phase. This leads to higher sustained _cwnd_ values and higher total data transferred, at least in the case of FQ-PIE. L4S shows higher data transfer when ECN is disabled; refer to Table 2. L4S has the same buffer size, but its lower bandwidth causes excessive delays. We can also attribute it to the uneven distribution of packets into their respective classic and L4S queues. The low-bandwidth environment has resulted in FQ-CoDel showing high variations in throughput, though it seems to have maintained fairness across its multiple TCP connections.  
Figure 30: Case 1: (ECN enabled) Throughput Scenario 2 with Bandwidth = 1 Mbps, Delay = 20 ms. (a FQ-CoDel (Throughput)); (b) FQ-PIE (Throughput); (c) L4S (Throughput).  
Figure 28: Case 1: (ECN enabled) Throughput Scenario 1 with Bandwidth = 10 Mbps, Delay = 20 ms. (a) FQ-CoDel (Throughput); (b) FQ-PIE (Throughput); (c) L4S (Throughput).
Unlike conventional railways, maglev systems rely on electromagnetic levitation and guidance, making them highly sensitive to bridge deformations--particularly lateral and torsional vibrations. Poorly optimized pier stiffness can lead to excessive lateral displacements, ride discomfort, or even safety risks, yet current design codes provide limited guidance on stiffness thresholds for maglev applications. Existing commercial finite element packages, while suitable for traditional rail systems, prove insufficient for maglev applications due to their inability to efficiently handle the parametric studies required for comprehensive system evaluation. This limitation stems primarily from the following two factors: the computational overhead of repeated analyses and the architectural constraints that limit flexible parameter modification. The present study overcomes these challenges through implementation of the General Simulation and Analysis Platform (GSAP), a specialized computational environment developed by Professor [PERSON]'s research group at Central South University. GSAP's computational core features an advanced explicit-implicit hybrid integration algorithm that achieves solution speeds 3-5 times faster than conventional methods while maintaining an equivalent numerical precision. More significantly, the platform implements a sophisticated parametric modeling framework that enables the direct manipulation of key system parameters--including pier top stiffness profiles, track geometry definitions, and vehicle configuration parameters--without requiring structural discretization. This parametric architecture, combined with the platform's computational efficiency, has permitted the extensive numerical investigations underlying this study's findings regarding pier top stiffness effects. Using a long-span bridge on a dedicated maglev line as the engineering case study, this paper focuses on a 10 \(\times\) 25 m simply supported beam bridge segment as the research object. The finite element model was developed using self-developed vehicle-bridge coupled dynamics simulation software, through which the natural vibration characteristics were analyzed and the three-dimensional coupled vibrations of the vehicle-bridge system were computed. The accuracy and validity of our self-developed software for dynamic analysis were experimentally confirmed through comprehensive field tests measurements in our prior work [34]. Based on the numerical results, structural performance compliance with design specifications was verified. Furthermore, from the perspective of the lateral linear stiffness at pier tops, the spatial dynamic responses of the vehicle-bridge system under multiple operational scenarios were investigated, aiming to elucidate the influence of pier top lateral stiffness on the dynamic behavior of typical medium-low-speed maglev train-bridge systems. This research provides technical foundations for the design of medium-low-speed maglev bridges and offers valuable references for related engineering designs.
## 2 The Mechanical Structure of the Intelligent Drying Apparatus
### Modeling the Working System  
Conventional static drying methods result in prolonged contact between crop grains and the drying tray, often leading to localized burning or scorching. To address this issue, intermittent separation between the grains and the tray can be achieved by applying controlled external force. In this study, the proposed grain-processing tray system comprises four main components: a power unit, a transmission mechanism, an actuator, and a control unit. As illustrated in Figure 3, the transmission and actuator subsystems are critical to the operational efficiency of the entire mechanism.  
Figure 2: Vibratory piezoelectric detection device for crop seed heating.  
The transmission device modulates the parameters of the seed support plate and regulates the vibration frequency of the actuator to optimize drying performance and enhance process quality. Key technical specifications of the system include the structural design of the actuator's vibration block, input voltage, operational frequency, dimensions of the drying unit, and motor power, as shown in Table 1.  
This study adopts a dynamic vibration-based drying approach. A vibration block is positioned beneath the seed support plate and configured to operate at a predetermined frequency. During operation, the vibration block induces oscillatory motion in the otherwise static seed plate, intermittently lifting the grains and enabling momentary separation from the tray surface. The entire tray assembly, including the weight sensor, is placed inside the drying chamber. Intermittent vibration is controlled through the precise adjustment of frequency and duty cycle. Real-time weight measurements are taken during static intervals to ensure accurate mass data acquisition while minimizing the influence of vibration. This setup effectively balances the need for grain-tray separation with high-precision weight monitoring during the drying process.