esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 5 Dataset Construction and Experimental Results
### Dataset Construction  
Considering the limited availability of real MEO SAR data, LEO SAR ship target data with equivalent resolution is employed to build the dataset for model training and testing. The MEO SAR ship rotational motion errors are added to the LEO SAR data, generating an equivalent coarse-focused MEO SAR ship dataset for the first-level classification network. Local region slice extraction is performed on these real data to build fine-focused local region slice dataset for the second-level classification network.  
Phase errors induced by ship rotations, as analyzed in Section 2.3, can be employed to construct the dataset for the coarse-classification network. Let \(L\) and \(N\) be the number of pixels along the azimuth and range directions for the real SLC SAR image of a ship target. Transform the image into the azimuth frequency domain. Let \(f_{s}\) be the range sample rate. The slant range displacement between two adjacent range pixels is \(c/2f_{s}\), and the azimuth frequency interval between two adjacent azimuth pixels is \(PRF/L\). The phase errors to be added in the discrete distance-Doppler domain can be expressed as  
\[\begin{array}{l}\theta_{d}(n,l)=\exp\left\{-\mathrm{j}4\pi\left(n-\frac{N}{2 }\right)\frac{c}{2f_{s}}\frac{A\cdot\cos\gamma}{\mathrm{i}\cdot\sin\gamma} \sin\left(\frac{2\pi}{T}\frac{1}{K_{e}}\left(l-\frac{L}{2}\right)\frac{PRF}{L }-\phi\right)\right\}\\ n=0,\;1,\;\ldots,\;N-1\;\;\;\;\;\;\;l=0,\;1,\;\ldots,\;L-1\end{array} \tag{33}\]  
By taking the MEO SAR system parameters and the ship roll parameters into the above equation, we can derive the equivalent phase errors in the MEO SAR ship image.  
Figure 12: Flowchart of the proposed multi-level focusing-classification method.  
Figure 13 presents the equivalent MEO SAR coarse-focused ship image, which is generated by introducing these phase errors into the real LEO SAR image in Figure 14.  
Simulated MEO SAR ship data are also generated to further evaluate the generalization performance of the proposed multi-level focusing-classification strategy. Taking real ship SAR images as the intensity values of target scattering points, MEO SAR parameters are employed to generate simulated echo signals. Classical ship target motions are also injected during the echo simulation process. Range compression is performed on the simulated echo data. The range-compressed results are then utilized for coarse focusing and coarse classification. Subsequently, based on the coarse classification results, ship targets' local region slices are extracted to validate the fine-refocusing method and the fine-classification network. The workflow for dataset construction and experimental design is illustrated in Figure 15.  
Figure 14: Real LEO SAR images of ships.  
Figure 13: Equivalent MEO SAR coarsely focused images of ships.  
Figure 15: Dataset construction and experimental design.
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Supervised Method
#### 3.1.2 ShuffleNet  
ShuffleNet, a lightweight network with high computational efficiency, uses two new methods of pointwise group convolution and channel shuffle to ensure the computational accuracy and reduce the computational cost effectively.  
Based on Shuffle Net V2 framework, literature [43] proposed a novel in-line inspection solution for code on complex backgrounds for plastic containers, the proposed algorithm can also deal with images on complex backgrounds and be used into a practical industrial inspection system. Literature [44] proposed a ShuffleDefectNet defect detection system based on deep learning, which achieved an average accuracy of 99.75% on the NEU dataset.
# Implementation of a Fast Link Rate Adaptation Algorithm for WLAN Systems
## 3 The Link Adaptation Firmware
### Functions snr2 mi_bpsk, snr2 mi_qpsk, snr2 mi_16 qm, and snr2 mi_64 qm  
The input to the snr2 mi_bpsk function is the snr float data type that is the SNR loop variable per subcarrier, for which the MI is to be calculated. The snr2 mi_bpsk function receives the subcarrier SNR loop variable and computes the MI. To apply the BPSK SNR-to-MI table, particularly at which location the current subcarrier SNR lies in the SNR axis of the table is detected by using conditional statements. Here, a linear interpolation function, interp, is employed to compute a more accurate MI value from the SNR lying between two given entries on the SNR axis of the table. The interp function is briefly explained in Section 3.7. The SNR-to-MI tables according to the modulation schemes are shown in Figure 11.  
Figure 11: SNR-to-MI tables for modulation schemes.  
In a similar fashion, the input to the snr2 mi_qpsk function is the SNR loop variable per subcarrier, for which the MI is calculated. This function computes the MI from the single subcarrier SNR and the only difference from the snr2 mi_bpsk is that a QPSK SNR-to-MI table is applied rather than the BPSK SNR-to-MI table. The simple linear interpolation function interp is employed here as well. How the MI value for each modulation scheme was obtained from the SNR is provided in Appendix A in equations. Likewise, the snr2 mi_16 qm function and the snr2 mi_64 qm function are constructed to behave as desired.
# Application of Modern Research Methods for the Physicochemical Characterization of Ion Exchangers
## 3 Results and Discussion  
The physicochemical characteristic of Dowex M 4195, Amberlite IRA 743 and Purolite Arsen X\({}^{\rm np}\) are presented in Tables 1 and 2. The SEM images for Dowex M 4195 and Purolite Aren X\({}^{\rm np}\) were obtained for 10,000\(\times\) magnification and Amberlite IRA 743 1000\(\times\).  
These ion exchangers are of ST-DVB macroporous type. The bead size is also of significant importance. It is well known that loading rates decrease as the resin bead sizes increase so that a smaller resin bead will load much quicker than a large one. For the analysed ion exchangers, it changes in the range: 0.300-0.8500 mm, 0.500-0.700 mm and 0.300-1.200 mm, respectively. Based on Tables 1 and 2, it can be stated that the macroporous resins are opaque and have a permanent porosity even in the dry state. Generally, they are characterized by the smaller capacity, surface area in the range 20-300 m\({}^{2}\)/g (in our case 3.2-14.5 m\({}^{2}\)/g), total pore volume of about 1 cm\({}^{3}\)/g and an average pore diameter of about 20 nm.  
Figure 6: Samples mounted on a carrier.  
Contrary to the gel ion exchangers, they have a higher degree and nonuniform cross-linking which decreases kinetics, capacity and operating efficiencies, but maximizes strength and resistance to fouling. In the solvated state, the ion exchanger beads contain mainly macropores. The pore dimension depends on the swelling degree. In the dry state, the resins are characterized by high porosity and the beads are not translucid [58]. The small particles associate in clusters, forming macropores. The clusters aggregate and the voids form the third family of macropores with the diameters of 50-1000 nm. Although the majority of the internal pores are mesopores according to IUPAC classification, they are called macroporous (in the case of Amberlites ion exchangers they are called macroreticular) [59]. Particularly, Dowex M 4195 is regarded as a material intermediate between the ion exchanger and the non-porous one that is with a not completely functionalized matrix [60; 61; 62; 63; 64]. As was mentioned in [63], such resin is characterized by a large operating capacity (higher regeneration efficiency), smaller leakage, and greater physical and chemical stability than the conventional and fully functionalized resins of the same particle size. Within the structure, the mechanically stable core and film of the bispicolylamine functional groups can be distinguished. Dowex M 4195 was used to control the Ni(II) levels from 0.80-0.90 g/dm\({}^{3}\) to below 0.16 g/dm\({}^{3}\) in the Co(II) electrolyte (40 g/dm\({}^{3}\)) in the INCO, Port Coulbourne, Ontario, Canada and ENRC's Chambishi Metals in Zambia, Africa [59]. Moreover, Metals Finance Africa used it for the recovery and purification of nickel sulphate from the Cu(II) electrolyte in Rio Tinto's Palabora Mining Company, Phalaborwa, South Africa.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 2 Related Works
In this research, we provide an experimental realization of the communication spaces concept by applying the XMPP, MQTT, and SAFAX technologies so that specific applicable capabilities of them are adapted to the needs of CPS. The special novelty of our contribution arises from the combined trust and communications process when dynamic resources owned by different stakeholders are exchanging information.
# Joint Resource Allocation in Secure OFDMA-Based Networks Taking a Base Station as a Two-Way Relay
## 2 System Model
\begin{array}{l}\underset{\boldsymbol{\pi},\boldsymbol{\rho}, \boldsymbol{\eta},\boldsymbol{P}}{\text{maximize}}\left(\sum\limits_{k=1}^{K} \sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\pi_{i,j}p_{k}^{i,j}R_{\text{sec},T_{ k}}^{i,j}+\sum\limits_{m=1}^{M}\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\eta_{i,j}p_{m}^{ i,j}R_{\text{sec},m}^{i,j}\right).\\ \text{s.t.}\sum\limits_{j=1}^{N}\left(\sum\limits_{m=1}^{N}P_{R,(A_{m},B_{m})}^ {j}+\sum\limits_{k=1}^{K}P_{R,T_{k}}^{j}\right)\leq P_{totalB}.\\ \rho\in\{0,1\},\,\eta\in\{0,1\}.\\ \sum\limits_{T_{k}\in\boldsymbol{\
abla}}\rho_{k}^{i,j}+\sum\limits_{(A_{m},B_ {m})\in\Gamma}\rho_{m}^{i,j}=1,\forall i\in 1,2,...,N;k=1,2,...,K;m=1,2,...,M.\\ \sum\limits_{T_{k}\in\boldsymbol{\
abla}}\rho_{k}^{i,j}+\sum\limits_{(A_{m},B_ {m})\in\Gamma}\rho_{m}^{i,j}=1,\forall j\in 1,2,...,N;k=1,2,...,K;m=1,2,...,M. \end{array}  \tag{19}\]  
\(\boldsymbol{\pi}=\{\pi_{i,j}\}\) and \(\boldsymbol{\eta}=\{\eta_{i,j}\}\) are the sets of all possible subcarrier pairings; \(\boldsymbol{\rho}=\{\rho_{k}^{i,j},\rho_{m}^{i,j}\}\) is the set of all possible subcarrier-user assignments; and \(\boldsymbol{P}=\{P_{R,T_{k}}^{j},P_{R,(A_{m},B_{m})}^{j}\}\) is the set of all possible power allocations for the given subcarrier pairing and subcarrier assignment, which satisfies \(P_{R,T_{k}}^{j}\geq 0\), \(P_{R,(A_{m},B_{m})}^{j}\geq 0\) for \(\pi_{i,j}\rho_{k}^{i,j}=1\), \(\eta_{i,j}\rho_{m}^{i,j}=1\) and \(P_{R,T_{k}}^{j}=0\), \(P_{R,(A_{m},B_{m})}^{j}=0\) for \(\pi_{i,j}\rho_{k}^{i,j}=0\), \(\eta_{i,j}\rho_{m}^{i,j}=0\). This is a mixed integer programming problem, the solving process of which is very complicated. However, we can relax \(\rho\in[0,1]\) and \(\eta\in[0,1]\) to get the near the optimal solution. In the next section, the dual method is used to solve this optimization problem.
## 3 Proposed Method
### Multi-Granularity Embedding Module
#### 3.1.2 Spectral Multi-Granularity Embedding Module  
The spectral multi-granularity embedding module adopts a three-branch architecture to extract multi-scale and multi-level semantic features from spectral information. Each branch's first convolutional layer utilizes a 3D convolution with varying dilation rates and a kernel size of \(1\times 1\times 7\). The second layer comprises a pointwise convolution with a kernel size of \(1\times 1\times 1\). This module processes the dimensionality-reduced 3D feature maps to effectively capture the multi-scale and multi-level characteristics of the spectral information. The specific algorithmic flow for this module is described below.  
Figure 2: Illustration of multi-granularity embedding module.  
For an input feature map \(X\in\mathbb{R}^{B\times c\times h\times w\times d}\), where \(B\) represents the batch size, \(h\) and \(w\) denote the spatial dimensions of the feature map, \(d\) indicates the spectral dimension, and \(c\) is the number of channels.  
First Branch: The operation \(Conv_{1,1}\) is performed initially, employing a \(1\times 1\times 7\) 3D convolution with a dilation rate of 1, followed by batch normalization (BN) and the ReLU activation function. Subsequently, the operation \(Conv_{1,2}\) is executed, which utilizes a \(1\times 1\times 1\) convolution along with BN and ReLU, leading to the output \(X_{1,2}\).  
Second Branch: The input is enhanced by adding \(X_{1,2}\), and the operation \(Conv_{2,1}\) is performed using a \(1\times 1\times 7\) 3D convolution with a dilation rate of 2, accompanied by BN and ReLU. Following this, \(Conv_{2,2}\) is applied via a \(1\times 1\times 1\) convolution with BN and ReLU, yielding the output \(X_{2,2}\).  
Third Branch: The input is further enriched by incorporating both \(X_{1,2}\) and \(X_{2,2}\). The operation \(Conv_{3,1}\) is then conducted, which employs a \(1\times 1\times 7\) 3D convolution with a dilation rate of 3, followed by BN and ReLU. Finally, \(Conv_{3,2}\) is executed using a \(1\times 1\times 1\) convolution with BN and ReLU to achieve the output \(X_{3,2}\).  
Fusion and Transformation: The outputs from the three branches, \(X_{1,2}\), \(X_{2,2}\), and \(X_{3,2}\), are concatenated and subsequently processed through a \(1\times 1\times 1\) convolution, followed by BN and ReLU, resulting in \(\widehat{X}\). The dimensions of the feature map are then transformed through a Rearrange operation to yield the spectral embedded token \(X_{spe\_Token}\). Consequently, the output \(X_{spe\_Token}\in\mathbb{R}^{B\times d\times(w\times h\times c)}\), where \(d\) indicates the number of spectral embedded tokens, reflects the size of the feature map's spectral dimension, while \((w\times h\times c)\) denotes the token dimension of spectral embedding, as illustrated in the lower part of Figure 2.
## 2 Materials and Methods  
### Study Area  
Three urban agglomerations in eastern coastal China were taken as study areas including BTH, YRD, and PRD (Figure 1). BTH, with an area of 58,789 km\({}^{2}\), is the largest economic zone in northern China. Characterized by a temperate monsoon climate, the region boasts diverse vegetation types including grasslands, deciduous broadleaf forests, croplands, and savannas. According to \"The Yangtze River Delta Urban Agglomeration Development Plan\", the YRD encompasses 26 cities across Jiangsu Province, Zhejiang Province, Anhui Province, and Shanghai Municipality, covering a total land area of 84,779 km\({}^{2}\). This subtropical humid monsoon climate zone is predominantly flat, with pockets of mountains and hills in the west and south. The vegetation in YRD is mainly composed of croplands, woody savannas, and mixed forests. Defined according to the \"Outline of the Pearl RiverDelta Reform and Development Plan (2008-2020)\", the PRD, with an area of 63,710 km2, is situated in southeastern China. Boasting a subtropical humid monsoon climate, the region enjoys warmth and humidity throughout the year. It boasts a terrain that includes both expansive plains and undulating hills, while its vegetation types are predominantly evergreen broadleaf forests, woody savannas, savannas, and cropland/natural vegetation mosaics. These three agglomerations have undergone a rapid urbanization process in recent decades, and their drastic land use change has severely impacted the vegetation. Therefore, exploring the impact of regional land use changes as well as climate change on vegetation greenness and productivity holds the utmost importance for promoting sustainable development in these regions.
## 2 Materials and Methods
### E-Nose Analysis  
The E-Nose analysis was conducted using a commercial E-Nose (PEN3, Airsense Analytics GmbH, Schwerin, Germany, Figure 3) with 10-MOS sensors to acquire the volatile compound profile of the ham samples. The details of the MOS sensors are shown in Table 3[24].  
\begin{table}
\begin{tabular}{c c c} \hline
**NO.** & **Aroma Group** & **Main Characteristics** \\ \hline
1 & Floral & Aromatic, cologne, violet, subtle, rose, sweet \\
2 & Woody/Resinous & Musty, strong, waxy, herbal, fatty, pungent, mushroom \\
3 & Fruity, other than citrus & Sweet, strawberry, pineapple, banana, subtle, cucumber \\
4 & Sickening & Putrid, sour milk, sweaty, excrement, strong, disgusting \\
5 & Chemical & Anesthetic, cleaning fluid, medicine, paint, alcohol, pine oil \\
6 & Minty & Cool, sweet, fennel, spice, medicine, aromatic \\
7 & Sweet & Vanilla, caramel, chocolate, subtle, warm, malt \\
8 & Popcorn & Burning, warm, strong, nutty, oil, peanut butter \\
9 & Pungent & Onion, strong, household gas, burning, tartness, sulfide \\
10 & Lemon & Citrus, sweet, cool, herbal, aromatic, subtle \\ \hline \end{tabular}
\end{table}
Table 2: Details of the 10 aroma groups.  
Figure 3: Photo of the PEN3 E-nose.  
The experiments were conducted for 12 days in a clean, well-ventilated testing room (approximately 45 square meters) at a temperature of 25 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C and a humidity level of 40% \(\pm\) 2%. Each of the five ham samples was measured 10 times per day by a skilled operator, with fresh samples used for each daily experiment. To minimize baseline fluctuations and interference from other volatiles, volatile compound profiles were acquired in a controlled environment. Zero gas (a baseline) was generated using two active charcoal filters (Filter 1 and Filter 2 in Figure 4) to ensure consistency between reference air and sample air.  
The E-Nose analysis comprised two stages: collection and flushing. As illustrated in Figure 4 [24], one gram of the ham sample was placed into a 20 mL sampler for 180 s to allow the volatile compounds to disperse into the sampler. Before the collection stage, clean air was directed into the device in the direction of In 2 (flow rate: 10 mL/s, time: 100s) for automatic adjustment and calibration. This process is known as zero-point trim. Values relative to the zero-point values were recorded as a baseline. Following the zero-point trim, the volatile gases from the sampler were pumped into the device in the direction of In 2 (flow rate: 10 mL/s, time: 100 s). The conductivity of the E-Nose sensors changed in response to gas molecule absorption and stabilized at a constant value once adsorption was saturated. During the collection stage, the sampling continued at one sample per second and lasted for 100 s. The sensor chamber was flushed between measurements. In the flushing stage, clean air was pumped into the device in the direction of In 2 (flow rate: 10 mL/s, time: 100s) to remove residual analytes. This collection and flushing process was repeated to obtain the raw data of the five ham samples.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Multi-Speaker Scenario
#### 4.2.1 Baseline Comparison
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Task** & **Metric** & **Performance** \\ \hline \multirow{3}{*}{Speaker Count} & Accuracy (\%) & 73.20 \\  & Speaker Counting Error & 0.348 \\ \hline \multirow{3}{*}{Speaker Turn Detection} & Precision (\%) & 62.41 \\  & Recall (\%) & 46.31 \\  & F1-score (\%) & 53.16 \\ \hline \multirow{3}{*}{Overlap Speech Detection} & Precision (\%) & 64.90 \\  & Recall (\%) & 42.40 \\ \cline{1-1}  & F1-score (\%) & 51.30 \\ \cline{1-1}  & Accuracy (\%) & 87.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics for our baseline implementation after CH1 adaptation on the CH2 evaluation set. Results demonstrate the system’s capabilities across different aspects of the diarization task. The results suggest that while the system performs reasonably well in speaker counting, there is substantial room for improvement in detecting speaker transitions and overlapped speech segments.
## 3 Results
### Transponder and Antenna  
The most sensitive process of the RFID system is a proper transmission of an accurate signal from the bee (queen, drone, worker) carrying the transponder to the antenna fixed on top of the tunnel at the hive entrance (Figure 3a). The electromagnetic fields of the antenna can be significantly disturbed by metal objects. Therefore, it is recommended to avoid using nails and screws in close proximity to the antenna's electromagnetic field. Our optimization and testing results showed that the antenna used in this unit (small precision 5 dBi, Table 1) could only read transponders in a range of 0-1.5 cm and failed or lacked accuracy and consistency in detecting tags at >1.5 cm. Moreover, the antenna could adequately read the tagged bees through the plexiglass in its reading range, whether placed above or below the bee's path to the hive. The translucent plexiglass used on top of the tunnel is recommended, as it allows the user to visually check the proper positioning of the antenna and observe any abnormality in bee behavior while passing through. The tunnel described in Figure 3a can fit both five-frame nuclei and full ten-frame Langstroth hives. In case of overpopulated hives requiring larger entrances, two tunnels per hive can be used. Concerning the transponder itself, the average weight of the UHF Hitachi tags used to label queens in this study was approximately 5 mg, which represents \(\approx\)5% of the average weight of a honey bee worker (0.1 g) and <3% of a queen's weight, which may range between 170 and 230 mg.