esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Verify the SIF Anomaly Index  
#### 4.4.1 Correlation Analyses between Drought Index and Yield  
The response of different crops to drought has significant differences. In Henan Province, winter wheat and summer maize are the primary crops. Figure 7 shows the yield changes in wheat and maize in Henan Province. This study aimed to verify the 1 km spatial resolution SIF anomaly index by analysing the response of winter wheat and summer maize yields to drought.  
Figure 5: The correlations among the value of GOSIF (W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\)), downscaled SIF (W m\({}^{-2}\)\(\upmu\)m\({}^{-1}\) sr\({}^{-1}\)) and MODIS GPP (g C m\({}^{-2}\) a\({}^{-1}\)) (the black line denotes the fitting). (**a**) GOSIF and MODIS GPP; (**b**) downscaled SIF and MODIS GPP.  
The occurrence of drought in Henan Province exerts an impact on crop growth, consequently influencing crop yield. The SIF anomaly index values in Henan Province were averaged to represent the regional drought index value. Figure 8 illustrates the correlation between the average SIF anomaly value and crop yield from 2001 to 2021. In Henan Province, the SIF anomaly index has positive correlations with crop yield and the scattered points show a statistically significant correlation (\(p\)-value < 0.01). The correlation coefficients are 0.93 for wheat and 0.89 for maize, respectively. These high correlations between SIF and crop yields confirm that the SIF anomaly index is a feasible and effective tool for monitoring drought.  
Figure 6: The SIF (W m\({}^{-2}\) μm\({}^{-1}\) sr\({}^{-1}\)) results using RF, nearest neighbour, and bilinear methods. (**a**) RF-based method, (**b**) nearest neighbour method, (**c**) bilinear method, (**d**) GOSIF.
## Tool Description
### Configuration Set Up  
As regards to Configuration Set Up phase (Figure 2), three principal Routines can be defined. Each of them refers to an.xml database file that is able to contain all the choices made in that specific Routine by the user during the Input Configuration phase (if saved). These databases will be used to speed up the future operations of the Configuration Set Up. The Routines are the following ones:  
1. Routine Test Bed: routine used to insert all the information concerning the Test Bed instrumentation installed; it is composed of five SubRoutines: SubRoutine Air Nozzle, SubRoutine Engine Torque System, SubRoutine Fuel Analysis and Measurement, SubRoutine Emissions, and SubRoutine Humidity. Furthermore, the SubRoutine Fuel Analysis and Measurement refers to its own.xml database file, containing the chemical characteristics of the fuel used by the engine. In this way, the opportunity of monitoring the variation in terms of carbon, hydrogen, nitrogen, oxygen, and sulfur concentration of a fuel compared to another one is provided.
2. Routine Engine Characteristic: routine used to insert all the information concerning the engine;
3. Routine Turbocharger: routine used to insert all the information concerning the turbocharger.  
With these Routines, the user must make a series of choices necessary to define a Set Up Environment that is able to identify uniquely both the sensors and instrumentations list installed and the Border Conditions required for the determination of computability of the following mathematical functions.  
Among the choices, the user has also to answer about the presence on the SN of every sensor. If the sensor is not online, then the software requires manually inserting a constant value; otherwise, the variable name becomes available in the parsing phase for the matching with the relevant sensor.  
Figure 2: Configuration Set Up phase.
###### Abstract  
(1) Problem Statement: The development of clustering algorithms for neural recordings has significantly evolved, reaching a mature stage with predominant approaches including partitional, hierarchical, probabilistic, fuzzy logic, density-based, and learning-based clustering. Despite this evolution, there remains a need for innovative clustering algorithms that can efficiently analyze neural spike data, particularly in handling diverse and noise-contaminated neural recordings. (2) Methodology: This paper introduces a novel clustering algorithm named Gershgorin--nonmaximum suppression (G-NMS), which incorporates the principles of the Gershgorin circle theorem, and a deep learning post-processing method known as nonmaximum suppression. The performance of G-NMS was thoroughly evaluated through extensive testing on two publicly available, synthetic neural datasets. The evaluation involved five distinct groups of experiments, totaling eleven individual experiments, to compare G-NMS against six established clustering algorithms. (3) Results: The results highlight the superior performance of G-NMS in three out of five group experiments, achieving high average accuracy with minimal standard deviation (SD). Specifically, in Dataset 1, experiment S1 (various SNRs) recorded an accuracy of 99.94 \(\pm\) 0.01, while Dataset 2 showed accuracies of 99.68 \(\pm\) 0.15 in experiment E1 (Easy 1) and 99.27 \(\pm\) 0.35 in experiment E2 (Easy 2). Despite a slight decrease in average accuracy in the remaining two experiments, D1 (Difficult 1) and D2 (Difficult 2) from Dataset 2, compared to the top-performing clustering algorithms in these categories, G-NMS maintained lower SD, indicating consistent performance. Additionally, G-NMS demonstrated robustness and efficiency across various noise-contaminated neural recordings, ranging from low to high signal-to-noise ratios. (4) Conclusions: G-NMS's integration of deep learning techniques and eigenvalue inclusion theorems has proven highly effective, marking a significant advancement in the clustering domain. Its superior performance, characterized by high accuracy and low variability, opens new avenues for the development of high-performing clustering algorithms, contributing significantly to the body of research in this field.  
Gershgorin circle theorem; nonmaximum suppression; clustering; neural data analysis; signal sorting 2024
## 4 Discussion
### R2C Model for Co-Production of Sustainable Solutions in Environmental Resilience Technology
Through the development of this R2C model, we have learned three key lessons and present some best practices. First, thought needs to go into how to delineate intellectual property from the start [81] and how a team can co-produce a project together while still maintaining each institutions' rights. A key challenge arises when negotiating contracts and maintaining rights around intellectual property. Legally, \"co-production\" muddies the water around who ownswhat based on contributions [82; 83]. A best practice would be to clearly communicate this between all participants and to use multiple modalities for communicating such as the use of diagrams, legal contracts, Gantt charts, and documentation of roles and responsibilities in advance. Second, agreements on sharing knowledge and data are essential. Such agreements help hold the tension between open science defined as \"transparent and accessible knowledge that is shared and developed through collaborative networks\" [84] and proprietary information sold as value-added analytics. This requires consideration for establishing data sharing standards [85] accounting for FAIR (Findable, Accessible, Interoperable, and Reproducible) and CARE (Collective Benefit, Authority to Control, Respectful, and Ethical) data principles [86; 87] as well as supporting trustworthy AI [50]. Trustworthy AI has 10 considerations. One is the objectivity of data that promotes fairness and mitigates harmful bias. Two is how AI is secured and protected against unauthorized access use, disclosure, disruption, modification or destruction. Three is how AI protects safety and does not cause unacceptable risk. Four is how AI protects privacy. Five is how explainable or transparent an algorithm is. Six is how accurate AI emulates human intelligence. Seven is how reliably AI performs as expected. Eight is how resilient AI is by its adaptive capability. Finally, nine is how accountable AI is for tracing a record of events in the decision-making process. Third, we recommend developing and implementing a communication plan between institutions to overcome variations in meeting and work cultures. A communication plan [85] that includes a management structure with roles and responsibilities as well as reporting structure within teams [81] can alleviate confusion and enable work management within teams relying on each other for fulfilling their responsibilities of the partnership. Such a plan would alleviate tensions between the different incentive structures and deliverable timelines [88]. For example, a faster cadence of deliverables is required in industry, while academia has a slower turn due to the exploratory nature of the work. Clear technical deliverables, timelines, and inter-team meetings that create \"tie-points\" between industry and academic workflows enable each team to manage as is culturally appropriate while still collaborating toward the same end purpose [89].
###### Abstract
Recently, the MEMS switch aroused attention due to its small size and linearity [26; 27], but it has a relatively slow switching time compared to the PIN switch [24]. In addition, many switch components operate at voltage levels of 0 V and 5 V, which are standard voltages for controllers and electronic components that simplify the realization of the DC biasing circuit. However, the discrete nature of the switch state prevents the switches from being used in applications where a continuous phase change is required. The switches do not meet our basic requirement since it is impossible to achieve high-resolution beam steering, let alone the possibility of continuous tracking for surfaces that are not unusually large. A varactor diode allows for a continuous and high-tuning-capacitance ratio and fits the continuous reflection phase method in the proposed MS reflector [8; 14; 15; 17; 18; 19; 28; 29; 30]. The varactor diodes allow for high-frequency work up to 70 GHz, a conventional PCB assembly, and extremely low consuming power [31], and thus leads to a focus on the varactor diode in this study. However, the varactor diode suffers from relatively high losses compared to the switches. The losses effect is reduced by decreasing \(Q\)[11; 17; 18; 19]. The reduction in \(Q\) is justified due to the very high tunability of the varactor diode capacitance value with a high \(TC\) parameter. In addition, using the quantization reflection phase method compared to the continuous reflection phase method adds quantization losses for beam steering of about 4 dB for 1-bit digital coding MS and about 1 dB for 2-bit digital coding MS [32; 33]. Thus, considering the total losses budget for a beam-steering application, using the varactor diode seems better, but additional research is required. Therefore, considering the total losses budget for a beam-steering application, using the varactor diode seems better, but further research is required. A reconfigurable MS reflector based on varactor diodes as a tuning element is well known with many published implementations operating at frequencies of up to 6 GHz [8; 15; 28; 29], with very few at X-band and above [18; 30; 34], where one of them [34] is categorized as a sparse MS and not as a classic MS. Such implementations in the X-band and above will improve satellite communications' quality of service (QoS) in the X-band and Ku-band [35]. Furthermore, an MS reflector with a high range and accurate beam steering enables the coverage of shaded areas and improved quality of service, including beam tracking. Such continuously reconfigurable MS reflectors for high-resolution beam steering operating above the X-band frequency are needed to improve satellite communication. However, the implementation of reconfigurable MS above the X-band, based on a varactor diode as a tuning element, is challenging due to the constraint to reduce unit cell dimensions and increasing the diodes' density on the surface while maintaining the performance. In addition, the varactors require many DACs, complicating the realization of the DC biasing circuits in small dimensions.
## 5 Experimental Results and Simulations
### Evaluation of Deep Learning Approaches

Table 14: Comparison of the proposed CNNLSTM model with other previous studies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Optimizer** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **AUC** \\ \hline \multirow{2}{*}{RNN} & SGD & 0.80500 & 0.83664 & 0.81454 & 0.82498 & 0.88372 \\  & [PERSON] & 0.68875 & 0.87596 & 0.66615 & 0.75631 & 0.75468 \\ \hline \multirow{2}{*}{CNN} & SGD & 0.73095 & 0.85229 & 0.72718 & 0.78475 & 0.80924 \\  & Adam & 0.89375 & 0.
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 4 Experiments
### Ablation Study  
#### 4.4.1 The Individual Module in NaST  
To verify the contribution of each component in our model, we conducted exhaustive ablative experiments on both the ETH and UCY datasets. Specifically, we removed one of the components including the sparse Spatial Transformer Encoder (STE), sparse Temporal Transformer Encoder (TTE), and Transformer decoder (TD) in NaST each time and computed predictions, and the results are shown in Table 2. Detailed experiments are introduced in the following.  
In the degraded model (a), the sparse Temporal transformer Encoder (TTE) is removed, and we fixed the model with the sparse Spatial Transformer Encoder (STE) and the Transformer Decoder (TD). We observed that without the temporal transformer, model (a) suffered from performance reduction compared with the NaST model, especially on HOTEL and ZARA1. We inferred that because the scenes in HOTEL are relatively less crowded, and the spatial interaction in ZARA1 is much simpler. Hence, temporal dependency is more important during the inferring procedure. This illustrated that the temporal transformer can provide effective temporal modeling ability. In model (b), the sparse Spatial Transformer Encoder (STE) is removed and the sparse Temporal Transformer Encoder (TTE) and Transformer Decoder (TD) are kept fixed. From the results, we can see that model (b) accrues much worse results on UNIV, which contains dense crowd scenes, which suggests that the spatial transformer is important on modeling social interactions under crowd scenarios. Model (c) removes the Transformer Decoder (TD) and keeps the STE and TTE retained. It also receives a performance decrease. In conclusion, according to the experiment results in Table 2, we can see that removing any component from our model will result in a large performance reduction. The results clearly validate the contribution of each module to NaST for trajectory prediction.
## 2 Theoretical Aspect and Design of the 1 \(\times\) 2 Demultiplexer
IL can be calculated by using the formula:  
\[\text{IL}(\text{dB})=-10\log\left(\frac{\text{P}_{\text{out}}}{\text{P}_{\text {in}}}\right) \tag{4}\]  
where P\({}_{\text{in}}\) is the power calculated at the input and P\({}_{\text{out}}\) is the power calculated at the output. Crosstalk losses can be calculated by using the formula:  
\[\text{CT}_{\text{n}}\text{= }10\ \log\left(\frac{\text{P}_{\text{m}}}{\text{P}_{ \text{n}}}\right) \tag{5}\]  
where P\({}_{\text{n}}\) represents the power in the desired port and P\({}_{\text{m}}\) indicates the port power that interferes with the other ports. The S-bend area size dimensions were carefully selected to reduce bend loss. [PERSON] and [PERSON] stated that 5 \(\mu\)m is the ideal S-bend offset for Si [43]. As a result, in our situation, the offset is approximately 5 \(\mu\)m, and the following equation can be used to determine the radius of the S-bend:  
\[\text{R=}\frac{1}{\text{O}}\left(\frac{\text{L}^{2}+\text{O}^{2}}{4}\right) \tag{6}\]  
where L is the length of the S-bend and O is its offset. The MMI coupler was solved using BPM. BPM is generally a very efficient method and has the characteristic that its computational complexity can be optimal in most cases; that is to say, the computational effort is directly proportional to the number of grid points used in the numerical simulation. A commonly used boundary condition is the transparent boundary condition which was used in the BPM simulations for solving the MMI coupler. The back reflection was solved using the FDTD method. The FDTD method is a rigorous solution to [PERSON]'s equations and does not have any approximations or theoretical restrictions. This method is widely used as a propagation solution technique in complex integrated optics, especially in situations where solutions obtained via other methods such as the BPM cannot cope with the structure geometry or are not able to calculate the light in the reverse direction. Since FDTD is a direct solution of [PERSON]'s curl equations, it therefore includes many more effects than other approximate methods. The boundary condition used in the FDTD simulations was the perfectly matched layer, in which both electric and magnetic conductivities are introduced in such a way that the wave impedance remains constant, absorbing the energy without inducing reflections.
# An Overview of CubeSat Missions and Applications
## 5 CubeSats as Astronomical Exploration and Research Enabling Platforms
### UV/VIS/IR Telescopes
Cropped version of the original image [213]. Image credit: NASA/JPL. (**b**) ASTERIA CubeSat lens alignment. Image credit: NASA/JPL [214]. using a vacuum ultraviolet (VUV) telescope called SOL, which captures images in the 17.1 nm (Fe IX/X) and 30.4 nm (He II) wavelengths [216]. These spectral lines are critical for studying solar flares, coronal mass ejections, and coronal heating, offering data important for space weather monitoring.The VUV telescope occupies 2U of the CubeSat and uses multilayer-coated optics and aluminum filters for narrowband imaging, with special attention to thermal control, contamination resistance, and optical alignment. Successfully deployed into a Sun-synchronous orbit, Norbi-2 has returned operational data and confirmed downlink performance via networks like TinyGS [217].
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 3 A GAN Based Channel Estimation Scheme
### Model Training Stage: Training A GAN Model to Capture the Distribution of Real Channel
#### 3.1.1 Basic Framework of GAN  
As shown in Figure 3, the general structure of GAN is depicted. It consists of two independent neural networks, which are named generator \(G\) and discriminator \(D\)[10], respectively. In particular, the generator aims to learn the mapping relationship from a latent variable \(z\) in low-dimensional space to the real data samples in high-dimensional space. In the ideal case, the trained generator can produce a large variety of different synthetic channel samples that follow the real data distribution. The key goal of discriminator is to distinguish whether the input data come from the real dataset. Generally, to achieve this goal, the output layer of the discriminator network is designed to be a sigmoid function such that a probability value \(p_{s}\) labeling the input data can be obtained, \(p_{s}\in[0,1]\). When \(p_{s}\) approaches 1, it means that the input data sample is most likely to be a real data sample, while when \(p_{s}\) approaches 0, it means that the input data sample, with high probability, belongs to the synthetic sample set.  
Figure 2: The procedure of the proposed GAN-based channel estimation scheme.  
Since the generator tries to generate data samples capable of confusing the discriminator while the discriminator aims to distinguish synthetic data samples produced by the generator from the real data samples, the key objectives of the generator and discriminator are in conflict. Therefore, a min-max game problem for training the GAN model can be formulated, which is expressed as [10]  
\[\mathcal{P}_{2}=\min_{\theta_{g}}\max_{\theta_{d}}\mathbb{E}_{x\sim p_{x}}\{ \log D(x;\theta_{d})\}+\mathbb{E}_{z\sim p_{z}}\{\log(1-D(G(z;\theta_{g}); \theta_{d}))\}, \tag{6}\]  
where \(\theta_{g}\) and \(\theta_{d}\) denote the parameters of generator and discriminator, respectively, \(G(\cdot)\) and \(D(\cdot)\) are the output of the generator and discriminator, respectively, and \(p_{z}\) and \(p_{r}\) are the probability distribution of the latent variable \(z\) and real dataset, respectively.
# An Improved Dynamic Model of the Vanadium Extraction Process in Steelmaking Converters
###### Abstract  
The production process from iron ore to steel can be divided into several stages, among which the processes of vanadium extraction and steelmaking are two key technological sections. The products of vanadium extraction are important strategic resources for modern industrial countries, and the remaining molten iron after vanadium extraction provides the material used in the subsequent steelmaking processes. In some mechanism models of vanadium extraction and the steelmaking process, the contact area of the reactions is considered to be constant in the empirical formula; furthermore, even the masses of the molten steel and slag are taken to be constants. This paper presents an important improvement to the existing models, in which the contact areas of the slag-metal interface and the emulsion system are considered to be non-constant. The improved model is simple and easy to analyze theoretically. Theoretical analysis of the model equations can be used to explain the competitive oxidation between each element, as well as the oxygen conservation of the system. The numerical simulations are consistent with existing production data, showing that the mass fraction of vanadium can be reduced to the specified threshold after about 3.5 min of blowing, which provides an important reference for production control. Furthermore, it is shown that the model captures the \"trapezoid\" structure of the decarburization rate. This paper also considers the relationship between FeO and O, the numerical simulation partly reflecting the dependence between the concentrations of FeO and O. The improved model can be used to describe and predict the change of the molten steel and slag composition in the process of vanadium extraction, which provides a mathematical foundation for the automatic control of the vanadium extraction process.  
vanadium extraction; steelmaking; decarbonization; dynamic model; contact area Article