esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sensory Analysis  
Sensory characteristics of different inoculated sturgeon fillets stored aerobically on ice were analyzed. As is shown in Figure 2B, the control was not considered to be spoiled throughout the 18-day incubation period. _Pseudomonas fluorescens_ inoculated group and _Pseudomonas mandeli_ inoculated group reached the end of shelf life at day 12, and the corresponding QI values were: 9.50 \(\pm\) 0.50 and 8.83 \(\pm\) 0.58, respectively. The samples of _Shewanella putfaciens_ inoculated group, _Shewanella baltica_ inoculated group, and _Pseudomonas fragi_ inoculated group reached sensory rejection point after 15 days. However, samples of the two other bacterial groups began to deteriorate at day 18. Similar studies have found that modified atmosphere packaged dehydrated pickled cod (Gadidae) can produce dark brown mucus under the action of microorganisms [35], while the meat of sterile bighead carp back connected to _Pseudomonas_ sp. and _Shewanella_ sp. is light green  
Figure 2: Growth of total bacteria (**A**), sensory (**B**), pH (**C**), total volatile base nitrogen (TVBN) production (**D**), thiobarbituric acid reactive substances (TBARS) (**E**), TCA-soluble peptides (**F**), and histamine (**G**) analysis of sturgeon fillets inoculated with different bacterial groups stored aerobically on ice.  
and light yellow respectively at the later stage of refrigeration [36]. Similar to our results, it shows that microorganisms are the main factor causing fish meat discoloration.
###### Abstract  
Genome-wide association studies (GWAS) have identified genetic markers associated with type 2 diabetes mellitus (T2 DM). Additionally, tissue-specific expression quantitative trait loci (eQTL) studies have revealed regulatory elements influencing gene expression in specific tissues. We performed enrichment analyses using spatial eGenes corresponding to known T2 DM GWAS signals to uncover T2 DM pathological pathways. T2 DM GWAS signals were obtained from the GWAS Catalog, and spatial eQTL data from T2 DM-associated tissues, including visceral adipose tissue, liver, skeletal muscle, and pancreas, were sourced from the Genotype-Tissue Expression Consortium. The eGenes were enriched in Kyoto Encyclopedia of Genes and Genomes biological pathways using the Benjamini-Hochberg method. Colocalization analysis of 2857 independent T2 DM GWAS signals identified 556 eGenes in visceral adipose tissue, 176 in liver, 715 in skeletal muscle, and 384 in pancreas (\(P_{\text{FDR}}<0.05\) where \(P_{\text{FDR}}\) is the false discovery rate). These eGenes showed enrichment in various pathways (\(P_{\text{BH}}<0.05\) where \(P_{\text{BH}}\) is the corrected \(P\) for the Benjamini-Hochberg multiple testing), especially the lysosomal pathway in pancreatic tissue. Unlike the mTOR pathway in T2 DM autophagy dysregulation, the role of lysosomes remains poorly understood. The enrichment analysis of spatial eGenes associated with T2 DM GWAS signals highlights the importance of the lysosomal pathway in autophagic termination. Thus, investigating the processes involving autophagic termination associated with lysosomes is a priority for understanding T2 DM pathogenesis.  
Authoring; enrichment analysis; expression gene; lysosome; type 2 diabetes
# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 3 Probability Prediction Combination Model  
The essence of the combination method is to find the respective superior information in a variety of models that can more fully describe the sample information than single model. Therefore, it, with a stronger anti-interference ability, can effectively reduce the influence of complex environmental factors [36]. It should be noted that the performance of any single model directly effects the accuracy of the combined model. Considering the calculation cost and effect comprehensively, the number of single models is often taken as two to five. In addition, the quantile regression neural network (QRNN) model has better performance in predicting PV output [37]. This model and the proposed two improved models will be used to construct the combined model in this paper. In this part, a piecewise optimal combination model is proposed that uses different combined weighting coefficients in different periods to make full use of different prediction models.
## 3 Results
### Computational Complexity  
FLOPs represent the number of floating point operations and are used to measure the computational complexity of a model, shows in Table 5. They are often used as an indirect measure of the speed of the neural network model. Compared to the baseline model, the proposed model increases FLOPs by 0.85 G due to the addition of certain modules. However, with a slight increase in the amount of computation, the OA of the model is significantly improved on different splits of the two datasets. Compared to the recently developed T-CNN method, the FLOPs of the proposed method are only 0.32 G higher, but there was a large improvement in accuracy. In conclusion, the proposed method can improve the classification accuracy without significantly increasing the computational complexity.
# A Transfer Learning and Optimized CNN Based Maritime Vessel Classification System
## 3 Materials and Methods
### Proposed Method  
In this research, a unique optimized CNN and transfer learning-based ITS is proposed as a means of detecting a wide variety of different kinds of ship targets in interior waterways. We proposed a new classification model in order to tackle the concerns that were discussed above, regarding the accuracy of the classification system.  
Figure 1 provides a high-level representation of the ship classification system that is being considered. First, the data from different classes of the ship's images required for classification are collected. Next, the images are transformed into a training set by balancing the ship's different classes of images. The training dataset may be expanded with the help of additional data augmentation, which is conducted in order to increase the performance of the model and its capacity to generalize.  
The following step involves training eight cutting-edge CNN models (VGG16, Xception, Inception, Inception, ResNet-50, DenseNet121, MobileNet, EfficientNetB0, and InceptionRes-net) using the generated image set in order to develop basic learners.  
Figure 1: Proposed architecture of modified ensemble model.  
PSO, an HPO approach that is capable of automatically tuning the hyper-parameters, is used in the optimization process for the CNN models. Following that, the three CNN models with the best performance are chosen to serve as the foundation for the ensemble learning models that will be constructed. In the end, two ensemble techniques, called confidence averaging and concatenation, are used to make ensemble models for final detection.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 3 Methods
### Network Overview  
#### 3.1.1 Encoders  
Following the STCN [2], the image encoder plays a crucial role in extracting image features from a query frame with dimensions \(H\times W\). Additionally, we utilize a mask encoder to encode the mask, and then store it in the memory bank. Similar to the STCN [2], we select the res4 features of the ResNet50 [27] model as the backbone features for the image encoder, with a stride of 16, while excluding res5. For the mask encoder, we opt for the ResNet18 [27] model for implementation. Subsequently, we generate key and value embeddings using two projection headers, referred to as key (\(\mathbf{k}\in\frac{H}{16}\times\frac{W}{16}\times\mathbb{R}^{C_{k}}\)) and value (\(\mathbf{v}\in\frac{H}{16}\times\frac{W}{16}\times\mathbb{R}^{C_{v}}\)). In this article, we set \(C_{k}\) to 64 and \(C_{v}\) to 512.
## 3 Data and Methods  
### Satellite Images  
Multi-temporal satellite imagery including 27 4-band PlanetScope images of high spatial resolution (3.125 m) were downloaded from Planet Labs ([[https://www.planet.com](https://www.planet.com)]([https://www.planet.com](https://www.planet.com))) for the period from 9 March 2016 to 19 October 2017. Images were downloaded in two formats: as PlanetScope images and as Ortho Scene Product images, both with four spectral bands in the blue, green, red, and near infrared spectrum (Table 1).  
In addition, 29 images of the Sentinel-2A (Sentinel-2 MSI L1C) product were download from Sentinel's Scientific Datahub ([[https://schiub.copernicus.eu/](https://schiub.copernicus.eu/)]([https://schiub.copernicus.eu/](https://schiub.copernicus.eu/))). Sentinel-2A images have also four spectral bands in the blue, green, red, and near infrared for the 10-m product (Table 1).  
All images went through radiometric and sensor corrections and were orthorectified to a cartographic projection. Sentinel-2A images were atmospherically corrected using the Sen2 Cor algorithm [(36)]. Vegetation indices were calculated from the spectral bands for Planet and Sentinel-2A (Section 3.2).
## 4 Design  
### The Advantages of the mHBFT Algorithm  
The proposed mHBFT algorithm in this paper is designed as a blockchain storage strategy based on the HBFT algorithm. We consider its advantages and features from the following perspectives:  
1. Strong fault tolerance: The HBFT algorithm can ensure the consistency and correctness of the entire system even in the presence of up to \((\mathrm{P}-1)/3\times(21-2)/\mathrm{L}\) Byzantine nodes (i.e., malicious nodes) in the network, thus demonstrating strong fault tolerance.
2. High performance: Compared to traditional BFT algorithms, the HBFT algorithm achieves higher performance in scenarios with fewer Byzantine nodes. This is because it employs a scalable design where nodes are distributed in a hierarchical manner and consensus is reached through multiple rounds of protocols. This approach allows for optimal utilization of system resources, thereby improving performance.
3. Security: The HBFT algorithm ensures the security of the entire system by employing cryptographic techniques to guarantee the confidentiality and integrity of messages, thereby preventing attacks from malicious nodes. We aim to enhance the security of the HBFT algorithm without compromising its core functionality.
4. Fairness: The HBFT algorithm ensures fairness among all nodes during the consensus process, where each node has an equal opportunity to participate and receive equal rewards. Our goal is to enhance fairness for all nodes, allowing them not only to participate in the consensus but also to have the chance to become leader nodes and gain higher privileges.
5. Scalability: We aim for the HBFT-RAFT algorithm to maintain the high scalability of the original HBFT algorithm while incorporating the benefits of the RAFT algorithm.
## 3 Results and Discussion
### Dissolved Trace Elements
#### 3.2.1 Manganese  
Dissolved Mn concentrations at site 1 showed extremely high temporal variation (Table 2). During the South African winter months, a maximum concentration of 178 mg/L dissolved Mn was recorded. However, Mn concentrations in the water phase of site 1 did not exceed 1 mg/L in 9 out of the 13 samples, leading to an extremely high standard deviation (mean: 21.6 \(\pm\) 49.1 mg/L). Previous publications examining site 1 found concentrations of dissolved Mn between 11.4 and 192 mg/L [19; 20; 33], which is well comparable to those measured in this study. Under AMD conditions, large quantities of Mn (II) are typically present in acidic MIW. Through limestone and aeration treatment and the resulting change in pH and redox conditions, the metal is oxidized, hydrolyzed, and precipitated as Mn (IV) oxides [34]. Therefore, high Mn concentrations in pre-treated acidic MIW were expected; nonetheless, finding similar concentrations in some of our post-treatment samples was unexpected. In June, July, and August the Mn concentration was much higher than in all other months. These South African winter months mark the dry season of the year, which would introduce evapotranspiration as a possible cause of elevated metal concentrations in the water phase [35]. However, looking at major cations and especially Ca\({}^{2+}\), a clear concentration decrease in the same period was observable, which speaks against presumptions of precipitation/ evaporation influences. Additionally, lower values of other physico-chemical parameters such as pH and EC were evident for the same months. The pH at site 1, for example (mean of 8.21 \(\pm\) 0.77), was lowest, and ranged between 6.81 and 7.17 from June to August. Sufficient removal of Mn requires a pH of \(\sim\)8 according to [PERSON] et al. [36], explaining the sharp increase of Mn during that time. The mean concentrations of dissolved Mn at sampling site 2 were not significantly different from site 1. One reason could be the geographical proximity of site 2 to mine dumps, abandoned mines, and the potential inflow of MIW in, e.g., rainy periods in the South African summer months. Significantly lower concentrations of dissolved Mn were found at sites 5-7 (versus sites 1-4). This can be attributed to dilution effects from confluences, as the Crocodile River (West) site 5 contained the lowest levels of dissolved Mn. The SA-WQG for aquatic ecosystems [25] by the Department of Water Affairs and Forestry provided information on dissolved Mn with a TWQR of \(\leq\)0.18 mg/L Mn, a CEV = 0.37 mg/L Mn as well as an AEV = 1.3 mg/L Mn. The maximum concentrations of dissolved Mn in sites 1-4 all exceeded the AEV, and the mean concentrations of these sites also surpassed the CEV. Only the concentrations at sites  
Figure 2: Temporal variation of dissolved trace elements at sampling site 1 and change in pH during the same period of time (second \(y\)-axis).  
5-7 were predominantly (<90%) in the TWQR and mean concentrations were below the CEV, indicating potential toxic effects for aquatic organisms, especially at site 1.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### High-Power Thrusters
#### 4.3.2 High-Power Gridded Ion Thrusters  
Unlike many forms of electric propulsion, high-power gridded ion thrusters were explored several decades ago, including a 130 kW mercury thruster [113], and thus, they have long since climbed their s-curve in Figure 13. Although many high-power GITs were constructed and tested, the complexity and power requirements of these designs far outstripped what was (and arguably still is) practical for flight. Thus, GIT research was refocused toward more moderate power levels, leading to the Deep Space 1 mission flying a 2.3 kW NASA Solar Technology Readiness (NSTAR) thruster in 1998 [114].  
Over the intervening decades, the advancements in high-power GITs (\(>\)20 kW) have been limited. The state-of-the-art NEXT thruster, for example, is designed for \(<\)7 kW [115]. Practical devices from the few modern forays into \(>\)20 kW GITs, such as the High Power Electric Propulsion (HiPEP) system shown in Figure 15 targeted for a Jupiter mission [116], were not advanced beyond laboratory models. There are several reasons for this relatively slow development of higher power GITs. First, as mentioned in Section 4.1.2, GIT beam current is ultimately space-charge-limited, and thus, high-power thrusters must be physically larger and are therefore more challenging to test and fly. For example, the 130 kW mercury GIT tested at Glenn Research Center in 1967 was a cumbersome 1.5 m in diameter, much larger than present day flight EP systems. Second, the successful ground testing of such large GITs in the past was in part accomplished by using easily pumped propellants like mercury and cesium; however, such toxic and reactive fuels are mostly avoided in modern EP systems for both concerns of health and spacecraft interaction. As with other high-power EP technologies, then, the high gas throughput of these GITs easily overwhelms most ground vacuum facilities.  
Figure 14: The X2, X3, and N30 nested Hall thrusters.  
Figure 15: The discharge chamber of the HiPEP ion thruster developed for the nuclear-powered Jupiter Icy Moons Orbiter.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 4 Robust Watermarking Algorithms Based on Original Videos
### Video Watermarking in Transform Domain  
Video watermarking algorithms in transform domain transform video frames into the frequency domain first, and then modify the coefficients in the frequency domain to achieve the purpose of watermark embedding. After embedding, the video is converted back to the spatial domain from the frequency domain to obtain the watermarked video. Common frequency domain transforms include DCT, discrete wavelet transform (DWT), singular value decomposition (SVD), etc. In addition, many watermarking algorithms in transform domain combine two or more transforms together to improve the performance using the advantages of different transforms.
## 4 System Architecture  
The output from specific single-output beamforming architectures can be seamlessly transformed back to its analog state using a DAC. Given that commercial receivers are predominantly configured to interface with the more common fixed reception pattern antennas (FRPAs), they inherently expect a singular input. Single-output multiple-beam beamformers, however, require the satellite position to calculate the direction of arrival (DOA) of the signals.  
The computation of the complex weight multiplications in beamforming can be performed in parallel by the FPGA, minimizing time delay between the input and output. This is critical to achieve the signal synchronization native to beamforming systems.  
The development platform chosen for this project is the Xilinx ZCU216 evaluation board, which uses the XCZU49 DR RFSoC. This platform contains 16 ADCs, allowing the direct sampling of up to 16 antenna element arrays.  
The diagram of the system, presented in Figure 3, is divided into three main blocks. The hardened data converters, represented in green, capture (ADC) and reproduce (DAC) L1/E1 frequency signals. The captured signals are transmitted by an AXI4-Stream containing their I/Q representation. The beamforming IP core, represented by the blue block in the center of the diagram, performs the complex weight multiplication and summing operation of the sampled signals. The single product of this core is then converted to an analog signal to replicate a traditional single antenna output that can be fed to a GNSS receiver. The hardened data converters were implemented using the Data Converter IP core from Xilinx [17]. Its configuration is explored in the next subsection. The beamforming IP core is a custom-made IP that receives the ADCs data from the Data Converter IP and outputs the beamforming solution back to one DAC of the Data Converter IP. Further explanation on the beamforming IP can be found below.  
Figure 3: Block diagram of the developed system.  
This hardware is controlled by the processor side using the system bus which relies on the AXI4-Lite protocol. The processing system can be programmed live by an Ethernet connection to a desktop to achieve fast testing of beamforming algorithms.
## 4 ASK Modulation and Demodulation
### ASK Modulation for Neural Implants  
An electronic ASK demodulator subsystem has been developed by [PERSON] and [PERSON] using a power-on-reset block, op/am E class, low drop-out regulator and hybrid charge redistribution ADC with 10-bit resolution [30]. However, its implementation suffers from inaccurate synchronization of the clock and data signals. [PERSON] designed a ASK demodulator by using a current edge detector circuit and two current mode comparators to stimulate the nerves and muscles or to measure and sense physiological signals [31]. This demodulator can process the input current with a very small modulation depth and is converted into voltage pulses to control the state of the ouput stage. However, the size of the demodulator is one of the issues. A new CMOS current mode ASK demodulator used to extract and detect digital data of current signals with very small modulation depth of 250 kHz and distinct levels of amplitude variation of 4 uA to 4.5 uA has been reported [22]. To solve the aforementioned problems, [PERSON] and [PERSON] [32] proposed an integrated low power clock and data recovery circuit without DLLs for neural recording microsystem. The circuit employs a ASK modulation scheme and pulse position modulation (PPM) with modulation index \(m\) = 1 to facilitate clock recovery and reduce power dissipation. Recently, a novel low power ASK-PPM receiver was designed using inductive peaking, clock and data recovery circuit with multiple charge pumps to boost up the gain and facilitate the time-to-voltage conversion [33]. In this system, the required voltage reference is generated adaptively to cover a large range of data rates.  
Figure 4: Principle of ASK modulation.
## 3 Experiments and Analysis  
### Dataset Description and Preparation  
The detailed description of the dataset of the ship-radiated noise called ShipsEar (available at [[http://atlantic.uvigo.es/underwaternoise/](http://atlantic.uvigo.es/underwaternoise/)]([http://atlantic.uvigo.es/underwaternoise/](http://atlantic.uvigo.es/underwaternoise/))), which contains a total of 91 records of 11 vessel types and one background noise class, is presented in the literature [16]. During 2012 and 2013, the researchers recorded the sounds of many different classes of ships on the Spanish Atlantic coast. The recordings were made with autonomous acoustic digitalHyd SR-1 recorders, manufactured by MarSensing Lda (Faro, Portugal). This compact recorder includes a hydrophone with a nominal sensitivity of \(-193.5\) dB re 1 V/1 \(\upmu\)Pa and a flat response in the 1 Hz to 28 kHz frequency range.  
To keep consistent with other classification methods, the \(11\) vessel types are merged into four experiment classes and the background noise type is as one class, as shown in Table 1.  
Before preprocessing, the number of the recorded sound clips with a duration of 5 s is 1956 by truncating the original records. The original signals are recorded at different sample rates. By preprocessing, each sound clip is further separated into 41 frames with an overlap of zero.