esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Sentiment Lexicon-Enhanced Dynamic Modulation Layer
#### 3.3.4 Bayesian Optimization  
Bayesian optimization [42; 43] is a probabilistic model-based global optimization method, particularly suitable for optimizing black-box functions with high computational costs. LePB-SA4 RE needs to capture sentiment signals quickly and accurately in complex data scenarios. Parameters \(a\) and \(b\) directly affect the sensitivity of the model to the diverse features of sentiment signal inputs. Bayesian optimization employs the Gaussian process proxy model to efficiently explore high-dimensional parameter space, avoid falling into local optimality, and quickly find the global optimal parameter combination, thereby ensuring the model's precise adjustments and quick responses to sentiment signals. Additionally, the posterior distribution update mechanism in Bayesian optimization dynamically assesses the potential benefits of parameter selection, allowing it to accurately identify the optimal parameters in few-shot and high-noise data settings. Therefore, it is employed to tune the parameters in this study to ensure the model dynamically balances responses on both weak and strong sentiments. Assuming the target function is \(f(a,b)\), the process of Bayesian optimization can be represented as follows:  
_a. Initial parameter points_: Choose \(n\) initial parameter points \(\{(a_{i},b_{i})\}_{i=1}^{n}\) and evaluate their objective function values \(\{f(a_{i},b_{i})\}_{i=1}^{n}\).  
_b. Surrogate model construction:_ Based on the evaluated parameter points and their objective function values, construct a Gaussian Process model \(\mathcal{GP}\):  
\[\mathcal{GP}\sim\mathcal{N}(\mu(a,b),\sigma^{2}(a,b)) \tag{15}\]  
where \(\mu(a,b)\) is the predicted mean and \(\sigma(a,b)\) is the predicted standard deviation.  
_c. Acquisition function optimization_: Choose an acquisition function \(\alpha(a,b)\), such as accuracy (Acc):  
\[\alpha_{\text{acc}}(a,b)=\mathbb{E}[max(0,f(a,b)-f_{\text{best}})] \tag{16}\]  
where \(f_{\text{best}}\) is the current best objective function value.  
Figure 3: Visualization example for lexicon-enhanced dynamic modulation at token-level adjustments in review sentiment analysis.  
_d. Parameter update_: Evaluate the objective function at the parameter point \((a_{\text{next}},b_{\text{next}})\) that maximizes the acquisition function:  
\[(a_{\text{next}},b_{\text{next}})=\arg\max_{(a,b)}\alpha_{\text{acc}}(a,b) \tag{17}\]  
and update the surrogate model \(\mathcal{GP}\) to include the new evaluation point.  
_e. Iteration_: Repeat steps \(c\) and \(d\) until the stopping criterion is met.  
Through the above process, the model can ensure parameter optimization while maintaining dynamic balance of the input. This improves the robustness and adaptability of the model under Hard-prompt and few-shot learning settings while minimizing the loss of generalization.
# Effects of Long-Term Fertilization on Phosphorus Form and Availability in Black Soil
## 3 Results  
### Effects of Long-Term Fertilizer Application on Soil Total and Available Phosphorus Content  
As shown in Figure 1, the TP content of the soil varied from 0.48 to 1.93 g-kg\({}^{-1}\), while the effective phosphorus (AP) content varied from 8.23 to 252.1 mg-kg\({}^{-1}\) after a locational fertilizer application experiment that lasted for 27 years. The application of chemical fertilizers significantly increased the TP and AP contents. Compared with the CK treatment, the soil TP and AP contents following the NPK treatment were significantly increased by 70.83% and 362.0%, respectively (\(p<0.05\)). Simultaneously, the application of organic fertilizers increased the contents of TP and AP more significantly. Compared with the NPK treatment, the M1 NPK, M2 NPK, and 1.5M1 NPK treatments significantly increased the soil TP content by 84.1%, 92.4%, and 135.4%, respectively (\(p<0.05\)), and significantly increased the soil AP content by 432.3%, 479.7%, and 558.2%respectively (\(p<0.05\)).
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Agrivoltaics  
Agrivoltaics are a multi-use land application enabling the production of food and energy at the same time and on the same piece of land [13; 14]. Policies on clean energy, energy transition, sustainable agriculture, food security, biodiversity, rural development, and research and innovation, that are directly supporting the goals of the European Green Deal (EGD) [15; 16], are strongly associated with agrivoltaics.  
Currently, the installed capacity of agrivoltaic systems is 2.8 GWp in Europe [17] and 14 GWp globally [18].  
Agrivoltaics in the EU have a notable potential. Considering an installed capacity per land area equal to 0.6 MWp/ha and deployment on only 1% of Utilized Agricultural Area (UAA), the agrivoltaic potential installed capacity in the EU can reach roughly 944 GWp, (half of the approx. 1809 GWp that can be achieved by traditional ground-mounted PV systems). The EU's agrivoltaics potential of 944 GWp is close to four times more than the EU installed capacity in 2023 and the electricity generated could cover over 42% of the EU's total electricity consumption in 2023. As far as Technology Readiness Level (TRL) (Technology Readiness Level (TRL) is a type of measurement system used to assess the maturity level of a particular technology) is concerned, agrivoltaic applications stand between TRL 3 and 8, depending on the agricultural context [19]. Further studies regarding the crop suitability identification and implementation of water management optimization will substantially contribute to the application's development.
# Frequency-Dependent Schroeder Allpass Filters
## 2 Schroeder Allpass Filters
### Filter Operations  
Here, we recap some useful operations on filters for the upcoming derivations. In this manuscript, we assume that the filter coefficients are real-valued. Let \(b(z)\) be an FIR filter with order \(l_{b}\) in the Z-domain, i.e., \(b(z)\coloneqq\sum_{i=0}^{l_{b}}b_{i}z^{-i}\). Here, \(\coloneqq\) denotes a definition. The flip operation \(\stackrel{{\rightharpoonup}}{{\rightharpoonup}}\), which effectively flips the sequence of the filter coefficients, is then defined as  
\[\stackrel{{\rightharpoonup}}{{b}}(z)\coloneqq z^{-l_{b}}b(z^{-1}) \tag{1}\]  
and the flip operation for an IIR filter \(g(z)\coloneqq\frac{b(z)}{a(z)}\) is  
\[\stackrel{{\rightharpoonup}}{{\rightharpoonup}}(z)\coloneqq \frac{\stackrel{{\rightharpoonup}}{{b}}(z)}{\stackrel{{ \rightharpoonup}}{{a}}(z)}. \tag{2}\]  
A common notation of the flip operation is \(\widetilde{\cdot}\), e.g., in [2]. We propose \(\stackrel{{\rightharpoonup}}{{\rightharpoonup}}\) to be a more intuitive alternative. The flip operation causes the zeros of a FIR filter to mirror at the unit circle, i.e., for any complex zero \(p\in\mathbb{C}\), we have the equivalence  
\[b(p)=0\iff\stackrel{{\rightharpoonup}}{{b}}(p^{-1})=0. \tag{3}\]  
Consequently, for an IIR filter \(g(z)\), only either \(g(z)\) or \(\stackrel{{\rightharpoonup}}{{g}}(z)\) can be stable (except the marginally stable case where all poles are on the unit circle).  
We define the complex conjugation operation \(\cdot^{*}\) of a filter \(b(z)\) with real filter coefficients as  
\[b^{*}(z)\coloneqq b(z^{-1})=z^{l_{b}}\stackrel{{\rightharpoonup }}{{b}}(z) \tag{4}\]  
such that we can write for the corresponding frequency response  
\[b^{*}(e^{\mu\omega})=b(e^{-\imath\omega})=(b(e^{\mu\omega}))^{*}, \tag{5}\]where \(t=\sqrt{-1}\) and \(\omega\) is the angular frequency in radians per sample. Thus, the complex conjugate filter has a complex conjugated frequency response. Please note that such a complex conjugated FIR filter is non-causal for \(l_{b}>0\).  
Complex conjugated IIR filters can be realized by reversing the coefficient orders and compensating the order difference between numerator and denominator filters. Because  
\[(g(e^{i\omega}))^{*}=\left(\frac{b(e^{i\omega})}{a(e^{i\omega})}\right)^{*}= \frac{e^{i\omega l_{b}}\overset{\leftarrow}{b}(e^{i\omega})}{e^{i\omega l_{a} }\overset{\leftarrow}{a}(e^{i\omega})}=e^{i\omega(l_{b}-l_{a})}\overset{ \leftarrow}{g}(e^{i\omega}), \tag{6}\]  
where \(l_{b}\) and \(l_{a}\) denote the filter order of the numerator and denominator, respectively, we define  
\[g^{*}(z)\coloneqq z^{l_{b}-l_{a}}\overset{\leftarrow}{g}(z). \tag{7}\]  
In the following, we review general allpass filters.
# Study on the Thermospheric Density Distribution Pattern during Geomagnetic Activity
## 2 Date and Methodology
### Correlation between Dst, Ap, and Atmospheric Density
Therefore, the correlation level between the Dst index and thermospheric density significantly negatively correlates most of the time. In contrast, the correlation between the Ap index and thermospheric density only reaches a highly positive correlation. Figure 2: Relationship between thermospheric density and geomagnetic index in three geomagnetic storm events. (**a**) thermospheric density and Dst index on 20–26 June 2015; (**b**) thermospheric density and Ap index on 20–26 June 2015; (**c**) thermospheric density and Dst index on 4–10 October 2015; (**d**) thermospheric density and Ap index on 4–10 October 2015; (**e**) thermospheric density and Dst index on 5–11 September 2015; (**f**) thermospheric density and Ap index on 5–11 September 2017.
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
## 2 Spacecraft Dynamics
### Coordinate Systems  
#### 2.2.1 Earth-Centered Inertial Reference Frame  
The Earth-Centered Inertial (ECI) reference frame is centered at the CoM of the Earth, its associated coordinate system has origin at the CoM of the Earth and is denoted by the basis vectors \(\{\mathbf{\hat{X}},\mathbf{\hat{Y}},\mathbf{\hat{Z}}\}\), where \(\mathbf{\hat{X}}\in\mathbb{R}^{3}\) is the unit vector aligned with the vernal equinox direction, the unit vector \(\mathbf{\hat{Z}}\in\mathbb{R}^{3}\) is aligned with the rotation axis of the Earth and \(\mathbf{\hat{Y}}\in\mathbb{R}^{3}\) completes a right-handed Cartesian coordinate system, see Figure 2.  
Figure 1: Drag Maneuvering Device schematic.  
Figure 2: Coordinate systems.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 4 Protocol
### Distance-Bounding Protocols
It is worth noting that these protocols are well suited for RFID authentication because communications are end-to-end (from the physical layer perspective) and the computations performed by the RFID tag are lightweight, which implies that the RTTs are very stable. However, it is important to raise that distance-bounding protocols do not actually detect relays: they detect abnormally long RTTs, and that there is a risk of a relay attack. The key difference between Internet communications and RF communications is that the former involves physical relays, namely routers, and routes dynamically evolve over time. In spite of that, RTTs are pretty stable, as shown previously in this article. Consequently, instead of comparing RTTs with a reference time bound as performed with RF communications, our protocol compares RTTs with a reference profile defined during a learning phase.
## 6 Testing and Results
### Testing HSFW3  
Landing on a moving vessel was tested in two ways. The first used the IR beacon landing method, whereas the second used a QR code (the code was made in the form of a contrasting black-and-white image (50 \(\times\) 50 cm\({}^{2}\))). The purpose of the experiments was to determine a more reliable and faster method of landing, which would simultaneously provide the possibility of landing on an object moving at a higher speed. Previously, the landing options for both methods were simulated using the GAZEBO simulator.  
The experiments were carried out in the daytime with a weak wind (no more than 4 m/s) and in the presence of a stable GNSS connection. The initial convergence was carried out using a GNSS signal, which came from a moving platform. The drone descended to a height of 2 m and then continued approaching until the IR beacon radiation was captured by a camera or until the QR code was captured by computer vision, with a distance of about 10 m between the drone and the platform. HSFW3 made the decision on its own to start boarding during testing, without interacting with HSFW Priority.  
The main landing criteria in this case were the distance between the drone and the landing site (about 15 cm) and the location of the drone relative to the center of the landing site. In the case of autonomous landing using an IR beacon, the maximum speed was 1.3 m/s for stable landing. The maximum speed was 2 m/s for landing using the QR code. Autonomous landing on a moving vessel, even at such speeds, greatly facilitates the work in real monitoring missions and makes it more efficient.  
Testing of drone landing and takeoff from the water surface was carried out in the absence of waves and at wind speeds of about 3 m/s. The decision of HSFW Priority regarding the necessity of landing was simulated. Then, HSFW3 analyzed the lidar data and issued a command to the controller to carry out a water landing.
## 3 DNN-Based Demodulation Evaluation
### Four Level Supervised Model Analysis  
From the results presented in Table 6, it is clear that the VGG16 model again performed the best, however, the VGG16 model only performed over 90% when the activation function of the output layer of the model was set to Softmax. Interestingly, the pattern of the VGG16 model performing the best regardless of the activation function of the output layer did not hold for this dataset as it did with the previous two datasets. There was about a 20% reduction in accuracy when Sigmoid was used as the activation function for the output layer of the VGG16 model, whereas for the two previous datasets, which are the 2 Level and 3 Level full-color frame datasets, the accuracy reduction was roughly 5% or so between Softmax and Sigmoid. This is probably because Sigmoid is not meant to handle multiclass classification. Similar to 3 Level and 2 Level data, the second-best model was the three layer convolution model. Moreover, similar to 3 level and 2 level model evaluation, full frames did better than cropped frames, with the exception of the 3 layer convolution. Similarly to 2 Level and 3 Level data, Softmax in the output layer performed better than Sigmoid in the output layer on average across all models. In terms of quantization, 4 level data will need to be further reviewed and refined in order to guarantee adequate data transmission.
## 3 Discussion
### Dynamics of AsA Content Acompanying Fruit Development and Ripening of A. latifolia  
AsA plays a plethora of roles in biological functions of both plants and humans [2; 3; 4; 5]. Besides cultivar-dependent differences [13; 14; 15; 16], kiwifruits also show tissue- and developmental-specific variability in AsA content [23]. The highest accumulation of AsA was recorded at four weeks after anesthesia in _A. chinensis_ (50-200 mg 100 g\({}^{-1}\) FW) and at six weeks after anthesis in _A. eriantha_ (800 mg 100 g\({}^{-1}\) FW) and _A. deliciousa_ (80 mg 100 g\({}^{-1}\) FW) [23]. _A. chinensis_ var. _deliciousa_ 'Qinmei' synthesizes AsA primarily during the early fruit development stage [24]. [PERSON] et al. [25] reported that _A. chinensis_ var. _chinensis_ 'Hongyang' kiwifruit exhibited a maximal AsA level at its immature green stage due to the high biosynthesis rate, which decreased as it ripened and then remained fairly stable until complete ripening.  
In the present study, we quantified the dynamic changes in AsA by HPLC during different stages of fruit development and ripening of _A. latifolia_ (Figure 2). Although no significant changes in AsA content were detected by [PERSON]'s HSD test during the fruit ripening stage (90-170 DAF), there was a rapid decreasing profile during the initial expansion (30-60 DAF) and veraison stage (60-90 DAF), followed by a progressive, albeit not significant, increasing tendency (Figure 2b). Overall, the accumulation dynamic of AsA in _A. latifolia_ positively correlated with fruit developmental stages, as in other kiwifruit varieties [23; 24; 25]. On the other hand, our results demonstrated the extremely high levels of AsA content of _A. latifolia_ (1108.76 \(\pm\) 35.26 mg 100 g\({}^{-1}\) FW, Figure 2b), which were consistent with previous studies [13; 14; 15; 16].
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
## 5 Discussion  
Time series of solar white-light running-difference images can be used to extract the morphology features of CMEs by tracking the bright structure in the coronagraph FOV [56]. EUV images of the whole solar disk provide information on CMEs' source regions with additional spatial and temporal coverage in eruption observations. Taking combinations of both observation images as input, our deep learning framework outperforms the state-of-the-art methods both on the geoeffectiveness and arrival time prediction of the CME events (see experimental results given in Section 4). Besides, it has the following features:  
1. This is the first time that making geoeffectiveness and arrival time prediction of CMEs in a unified deep learning framework.
2. This is the first time that the CNN method is applied to geoeffectiveness prediction of CMEs.
3. The only input of the deep learning framework is the time series images from synchronized solar white-light and EUV observation images that are directly observed.
4. Once we get the observation images, we can immediately get the prediction result with no requirement of manually feature selection and professional knowledge.
# The Paradigm Shift in Hyperspectral Image Compression: A Neural Video Representation Methodology
###### Abstract  
In recent years, with the continuous development of deep learning, the scope of neural networks that can be expressed is becoming wider and their expressive ability stronger. Traditional deep learning methods based on extracting latent representations have achieved satisfactory results. However, in the field of hyperspectral image compression, the high computational cost and the degradation of their generalization ability reduce their application. We analyze the objective formulation of traditional learning-based methods and draw the conclusion that rather than treating the hyperspectral image as an entire tensor to extract the latent representation, it is preferred to view it as a stream of video data, where each spectral band represents a frame of information and variances between spectral bands represent transformations between frames. Moreover, in order to compress the hyperspectral image of this video representation, neural video representation that decouples the spectral and spatial dimensions from each other for representation learning is employed so that the information about the data is preserved in the neural network parameters. Specifically, the network utilizes the spectral band index and the spatial coordinate index encoded with positional encoding as its input to perform network overfitting, which can output the image information of the corresponding spectral band based on the index of that spectral band. The experimental results indicate that the proposed method achieves approximately a 5 dB higher PSNR compared with traditional deep learning-based compression methods and outperforms another neural video representation method by 0.5 dB when using only the spectral band index as input.  
deep learning; hyperspectral image; neural video representation; deep learning compression methods
## 2 Materials and Methods  
### Patients  
We performed a retrospective analysis of a database acquired from prospective clinical trials conducted at the Research Center for Charged Particle Therapy, QST Hospital. This research was sanctioned by the institutional review board at QST Hospital (approval no. N21-005), and the study details have been disclosed on our institutional website; potential participants were given the chance to opt out of being enrolled. The patients in this study were diagnosed with high-risk PCa and treated with CIRT combined with ADT between April 2000 and May 2018. The inclusion criteria were pathologically confirmed prostate adenocarcinoma, stage T1-T3N0M0, no concomitant primary malignancies, and no historyof PCa treatment. TNM staging was conducted by digital rectal examination, ultrasound, computed tomography, magnetic resonance imaging, and isotope bone scanning, according to the TNM Classification of Malignant Tumors [24]. The definition of high-risk PCa at QST Hospital, which has been modified from those in conventional risk classifications [5; 6], is an initial prostate-specific antigen (PSA) level \(\geq\) 20 ng/mL, cT3a/b disease, and/or a Gleason score (GS) \(\geq\) 8, corresponding to high-risk and very-high-risk disease (excluding cT4 stage) based on the NCCN risk classification [6]. All prostate biopsy specimens were re-evaluated by a single pathologist (M.H.) to determine the percentage of positive biopsy cores (%PC) and assign a Gleason score. Of 929 patients in total, 785 (84.5%) were included in this study. Since %PC is a clinically important predictor, multivariate imputation by chained equations was used to supplement missing %PC values in this study. No other variable had missing values from the beginning.