esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Assessment of Attainment of Requirements by Proposed Mission Approach  
Assessment of the proposed mission includes evaluation of whether the mission requirements are effectively implemented by the proposed architecture, concept of operations and mission implementation approach. This process begins with an assessment of the implementation of the requirements by the architecture design. This is presented in Table 4, below.  
\begin{table}
\begin{tabular}{|p{56.9 pt}|p{113.8 pt}|p{113.8 pt}|} \hline
**Element** & **Description** & **Explanation** \\ \hline Subject & Earth and launch vehicle & Technologies required for space situational awareness, Earth sciences and planetary science missions like NEO assessment will be tested while in a low Earth orbit \\ \hline Payload & Visual camera GPS receiver & A visible light camera is required to image the Earth; a GPS receiver is required \\ \hline Spacecraft Bus & To be developed by students at the University of North Dakota & One goal of this mission is to demonstrate the ability of the University of North Dakota to develop and test space hardware and gain the flight experience required for larger missions \\ \hline Launch System & To be determined by NASA & \\ \hline Orbit & 60\({}^{\circ}\) inclination and altitude of 300 to 400 km & The orbit needs to overfly Grand Forks, ND ideally multiple times per day (with a period between overflights to allow rectification of any problems that may have affected the first overflight) and be high enough to avoid most atmospheric drag (which would decrease mission life) \\ \hline Ground System & University of North Dakota based student-operated ground station & The University of North Dakota maintains a student operations center for the International Space Station Agricultural Camera. A similar center will be created to manage the operations of the cubesat during the mission. \\ \hline Communications Architecture & Direct communications from the satellite to UND-based ground station via amateur radio bands & Existing ground station hardware exists and will be utilized \\ \hline \end{tabular}
\end{table}
Table 3: Mission Architecture.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Data Standardization  
As we are working with different sensors, normalizing the data to a [0, 1] scale facilitates direct comparison between sensors. Additionally, standardized data eliminates scale differences that could negatively impact the correlation analysis between results from different satellites. To standardize the data, the following equation was applied to BR:  
\[\text{BRstandardized}=\frac{\text{BR}-\text{min}(\text{BR})}{\text{max}(\text{ BR})-\text{min}(\text{BR})} \tag{1}\]  
where min(BR) is the minimum pixel value of the BR and max(BR) is the maximum pixel value of the BR.
## 2 Previous Studies  
The earlier IR models that employ TF-IDF rankings do not consider semantic information such as homonyms properly because it depends on token-matching methods. To resolve this problem, some IR models based on ANNs have been proposed. [PERSON] et al. [7] proposed a ranking model called KNRM (Kernel based Neural Ranking Model). It generates a translation matrix that uses similarities between queries and documents. In addition, KNRM used a kernel pooling method to effectively summarize the translation matrix and to generate scores for ranking learning. [PERSON] et al. [8] proposed a ranking model termed DRMM that was based on cosine similarities between query vectors and document vectors in a latent vector space generated by a multilayer perceptron (MLP). DRMM demonstrated superior performance in certain retrieval tasks. However, [PERSON] et al. [9] pointed out that DRMM returns inconsistent similarities based on the lengths of the query and document vectors. To resolve this issue, [PERSON] et al. proposed a cross-mapping function based on a convolutional neural network (CNN) with kernel pooling [7] that always returns fixed lengths of query vectors and document vectors. To overcome the limitation that several ANN models cannot properly reflect term frequency and document frequency, [PERSON] et al. [10] proposed a joint model in which a local model based on conventional term frequencies and a distributed model based on the distributed representation of words are co-trained. [PERSON] et al. [11] proposed a ranking model that was based on the bi-LSTM with a co-attention mechanism between a query and a document. In addition to co-attention, we also used self-attention mechanism on various word embeddings (e.g., word2 vec [12], GloVe [13], fastText [14]).
## 4 Experiment
### Evaluation Criteria  
To comprehensively evaluate the prediction performance of each model, this paper primarily employed the following four metrics to verify the accuracy of the proposed method: \(RMSE\), \(MAE\), \(MAPE\), and \(R^{2}\). These evaluation formulas were used in the same manner as in (22)-(25), where \(x_{i}\) denotes the true value of the flow, \(\hat{x}_{i}\) denotes the predicted value of the flow, \(\overline{x_{i}}\) is the mean value of the flow data sample, and \(n\) indicates the length of the flow time series.  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{(x_{i}-\hat{x}_{i})}^{2}}}}}, \tag{22}\]  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{{{\left|{{x_{i}-\hat{x}_{i}}}\right|}}}}, \tag{23}\]  
\[MAPE=\frac{{100\%}}{n}{\sum\limits_{i=1}^{n}{{{\left|{\frac{{{\hat{x}_{i}-x_{ i}}}}{{x_{i}}}}\right|}}}}, \tag{24}\]  
\[R^{2}=1-\frac{{\sum\limits_{i=1}^{n}{{{(\hat{x}_{i}-x_{i})}^{2}}}}}{{\sum \limits_{i=1}^{n}{{{(\overline{x_{i}-x_{i})}^{2}}}}}}. \tag{25}\]  
In the aforementioned evaluation indices, smaller values of \(RMSE\), \(MAE\), and \(MAPE\) indicate more accurate prediction data. When the \(R^{2}\) is approaching 1, it indicates a stronger fit between the predicted and actual values [29], thus demonstrating a superior performance.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 5 Discussion  
The above experiments verified the validity of our scheme. We can deploy our network scheme to the Jetson TX1 on the lightweight SAR satellite for on-board ship detection. The combination of the classic CFAR algorithm and YOLOv4-tiny lightweight deep learning network greatly improved the speed of ship detection. After extracting the ship targets, the coordinates of the upper left and lower right corners of the images are collected and latitude and longitude coordinates of the ship are obtained through coordinate conversion. Only the target image patches and geographic coordinates will be transmitted to the ground receivers. This on-board ship detection method greatly reduces the amount of data that must be transferred to the ground receiver, which helps to reduce the pressure on the ground receiver. However, if required for special missions, users on the ground can control the satellite and obtain images of a region of interest, and the useful information will be transmitted to the ground after on-board processing. With these data, ground users will be able to make better and faster decisions in emergency situations.  
In addition, as shown above, our method achieved a rapid and accurate ship detection in SAR images. In order to obtain better ship detection results, subsequent work will need to label the ship chips of original images that were downloaded from the satellite at an early  
Figure 11: The ship detection results on HISEA-1 image. The purple boxes are the detection results of our method and the white boxes are the target patches extracted by our network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Acquisition Time** & **Resolution** & **Width** & **Height** & **Polarization** \\ \hline
2020.12.25 & 3 m & 9980 & 6607 & VV \\ \hline \hline \end{tabular}
\end{table}
Table 5: The information of the HISEA-1 image.  
stage and train the model. Finally, the model can be updated or a new model can be uploaded to the satellite to achieve more accurate detection results. Furthermore, in addition to the role of the deep learning model in determining detection accuracy, the CFAR algorithm also impacts the detection accuracy. Therefore, our future work will continue to improve the CFAR algorithm.
## 4 Current Approaches to Open Challenges of F Estimations from Proximal to Airborne Scale
### Atmospheric Influences
#### 4.1.3 Ground-Based Sensing  
When measuring fluorescence using passive remote sensing techniques, even at a few meters above the canopy, atmospheric effects should be accounted for [117]. In particular, retrieving fluorescence in the oxygen regions in proximal sensing scenarios requires compensation for oxygen absorption effects (Figure 8).  
Although oxygen transmittance dominates both the O\({}_{2}\)-A and O\({}_{2}\)-B spectral regions, as seen from Figure 8, in the O\({}_{2}\)-B region and for a canopy-sensor separation distance of a few meters, oxygen absorption effects can be neglected. However, in the O\({}_{2}\)-A absorption band, neglecting the O\({}_{2}\) absorption optical path between the target and the sensor introduces significant errors to the estimated fluorescence signal. In [117], a theoretical analysis was carried out to determine how the estimated fluorescence is affected when ignoring oxygen absorption effects in the O\({}_{2}\)-A absorption region for proximal sensing scenarios and assuming different fluorescence retrieval techniques, e.g., FLD and SFM. The reported results in this work demonstrated that the estimated fluorescence in the O\({}_{2}\)-A band had relative errors ranging between 5% to 50%, depending on the sensor height (evaluated from 3 to 20 m), the sensor spectral resolution (evaluated from 0.1 to 1 nm) and the fluorescence retrieval technique (3 FLD and SFM).  
In addition, given the key role of oxygen absorption effects in retrieving F, the dependence of oxygen absorption on air temperature (T) and pressure (p) conditions is also a key aspect to be considered when measuring long temporal fluorescence data series, especially at latitudes subject to strong T and p seasonal effects. Of importance, therefore, are the seasonal dynamics in terms of T and p. Not appropriately considering these variables as part of the retrieval strategy will result in erroneous F seasonal trends that mimic those of known dynamics for temperature-dependent physiological responses of vegetation.
# Comparison of Amsler-Krumeich and Sandali Classifications for Staging Eyes with Keratoconus
## 1 Introduction  
Keratoconus (KC) is a corneal disorder characterized by ectasia and thinning of the cornea, which often causes high and irregular myopic astigmatism [1]. Therefore, KC patients experience suboptimal to poor vision despite wearing spectacles or contact lenses, especially if corneal scars or elevated irregular astigmatism are present. The diagnosis and classification of the disease have evolved in last decades thanks to the continuous advances in technology [2]. The initial classifications were based on the detection of irregular corneal astigmatism by means of a placid disc or the Javal ophthalmometer, along with the presence of characteristic signs in the biomicroscopic examination of the cornea [3]. Placido disc-based corneal topography is a sensitive and specific diagnostic tool that examines the anterior surface of the cornea but not the posterior corneal curvature, which is crucial in the early detection of KC. With the advent of the rotating Scheimpflug imaging and slit-scanning topography, anterior and posterior elevation measurements as well as curvature analysis have become available in the clinical setting [4, 5]. With the height data, the anterior protrusion and the corneal shape parameter changes can be assessed, since they are different from the curvature map assessments of the relative distortions of the cornea, and can provide useful diagnostic information for the detection of KC [6].  
The Amsler-Krumeich classification is currently still one of the most widely used system for identification of keratocnus and assessment of disease progression. This grading system is based on topographic analysis of the anterior corneal surface, corneal thickness, refraction and biomicroscopy [7]. However, this system does not consider the morphologic changes in the corneal microarchitecture of keratocnus eyes.  
Thanks to the use of anterior segment Optical Coherence Tomography (AS-OCT), a non-contact technique based on the principles of low-coherence interferometry [8; 9], [PERSON] and coauthors described a new classification based on structural corneal changes occurring in keratocnus throughout the evolution of the disease [10]. Based on the structural changes that occur in the corneal microarchitecture of keratocnus eyes, this system also allows grading of the disease severity.  
To date, there is no information about the possible agreement between these two different KC classifications. Therefore, the aim of this study is to compare the stratification of consecutive KC patients according to disease severity using the Amsler-Kumeich and Sandali classifications, and to further ascertain KC cases in which the use of one classification is preferred over the other one.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 4 Results
### Fractional Cover  
The scatter plots in Figure 6 show the effect of the differences in reflectance when used with a model of a fractional vegetation cover. The comparison presented here is for Sentinel-2A with Landsat-8. The Landsat-7 case is omitted here for the sake of brevity, but exhibits nearly identical effects.  
Over the three vegetation cover fractions, the ODR slope statistics range from 0.97 to 1.07, and RMSE ranges from 0.040 to 0.054. Since the cover fractions must sum to 1, obviously an increase in one fraction produces a corresponding decrease in one of the others. There is noticeably more scatter in these plots than in the reflectance plots (with \(r\) values as low as 0.95), possibly as a result of the strongly non-linear nature of the fractional cover model. However, there is still a systematic between-sensor variation in all three fractions.  
Figure 7 shows the same comparisons, but when the reflectances have first been adjusted using Equation (1), before being used in the fractional cover model. With this adjustment, the ODR slope  
Figure 5: Comparison of surface reflectance from Sentinel-2A and Landsat-8, for same-day acquisitions after adjusting Sentinel-2A to estimate Landsat-8, using Equation (1). Darker regions represent larger numbers of points. Solid line is 1-to-1, dashed line is the ODR regression fit showing proportional bias.  
Figure 6: Comparison of fractional vegetation cover from Sentinel-2A and Landsat-8, for same-day acquisitions. Darker regions represent larger numbers of points. Solid line is 1-to-1, dashed line is the ODR regression fit showing proportional bias.  
statistics range from 0.99 to 1.00, for all three cover fractions, and all of the RMSE values are reduced by the adjustment.
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 3 Polyphenols: Properties and Significance in Food  
### Polyphenol Classification  
Polyphenols are natural chemical compounds abundant in various plant-based foods and beverages, including fruits (e.g., berries, blackcurrants, grapes), vegetables (such as onions, peppers, and broccoli), tea (particularly green and black varieties), coffee, red wine, olive oil, and nuts. According to their chemical structure, they can be divided into groups of flavonoids, phenolic acids, and non-flavonoids, including tannins, stilbenes and lignans [11]. Flavonoids form the largest group of polyphenols, one which includes subclasses of compounds such as flavonoids, flavones, flavanones, flavanols, isoflav, and anthocyanins. Their main sources include spices, vegetables, fruits, tea, and wine. Phenolic acids are the second largest group of polyphenols. They are the derivatives of hydroxybenzoic acid and hydroxycinnamic acid. They are contained mainly in coffee, cereals and some fruits. Tannins are a group of polyphenols present in tea, red wine, dark chocolate and certain fruits such as berries and grapes. Due to their high ability to combine with proteins, they cause a characteristic astringent taste [12; 13]. Stilbenes, including resveratrol, are found mainly in red wine and certain berries such as blueberries and cranberries. Lignans are present in large quantities in flaxseed, as well as in seeds, nuts, whole grains and vegetables [14; 15]. The main polyphenic compounds and their food sources are presented in Figure 1.
## 2 Materials and Methods
### Mechanical Testing  
Tensile experiments at ambient temperature were conducted on miniature size specimens (thread size: M5, I = 42 mm, \(l_{0}\) = 20 mm and d\({}_{0}\) = 3 mm) according to DIN EN ISO 6892-1 (constant strain rate \(\dot{e}=10^{-3}\) S\({}^{-1}\)) [41] utilizing an Instron 1381 type testing machine with 10 kN of load capability. At 650 \({}^{\circ}\)C, tensile tests were carried out according to DIN EN ISO 6892-2 [42]. In accordance with the standard, a strain rate of \(\dot{e}_{el}=8.33\times 10^{-5}\) S\({}^{-1}\) was applied in the elastic deformation range, while in the plastic range, a strain rate of \(\dot{e}_{el}=8.33\times 10^{-4}\) S\({}^{-1}\) was utilized. All tensile specimens were electrically discharge machined (EDM) from the HiperFer\({}^{\text{AM}}\) LMD samples in the building (Z) direction (Figure 1).  
Stepped increasing stress (70, 100, 120, 130 MPa) compression creep experiments were performed on miniature cylindrical specimens (d: 3 mm, h: 3.5 mm) at 650 \({}^{\circ}\)C by applying an Instron 1381 testing machine (Instron, Darmstadt, Germany) that was equipped with all-ceramic loading and strain measurement set-ups. The testing stress was incrementally increased upon entry of a quasi-secondary creep stage or after 150 h of holding at each stress level. Temperatures were controlled to an accuracy of \(+/-1\)\({}^{\circ}\)C using type S thermocouples attached to the specimens' gauge length in the creep experiments. The strain measurement was performed with a digital 5 mm strain transducer (Solartron, accuracy: 1.1 \(\upmu\)m (peak to peak error)). The compression creep specimens were EDM from the longitudinal (L)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Process Parameter** & **Low Power Density (\({}^{\text{*}}\)LPD\({}^{\text{*}}\))** & **High Power Density** \\  & & **(\({}^{\text{*}}\)HPD\({}^{\text{*}}\))** \\ \hline Area-specific laser power & 195 Wmm\({}^{-2}\) & 1050 Wmm\({}^{-2}\) \\ Feed speed & 400 mm/min & 1500 mm/min \\ Laser power & 1375 W & 360 W \\ Powder mass flow rate & 11.3 g/min & 2.4 g/min \\ Track offset & 2.3 mm & 0.35 mm \\ Height offset & 1.4 mm & 0.3 mm \\ Beam diameter & 3 mm & 0.66 mm \\ Deposition strategy & Unidirectional & Unidirectional \\ Nozzle type & 3-jet nozzle & 3-jet nozzle \\ Shielding gas & Argon & Argon \\ \hline \hline \end{tabular}
\end{table}
Table 2: HiperFer\({}^{\text{AM}}\) LMD process parameters.  
Figure 1: Schematic representation of electrical discharge machining of mechanical testing specimens from HiperFer\({}^{\text{AM}}\) LMD samples: creep specimens were taken from both the longitudinal and transversal directions, while tensile specimens were taken from the longitudinal direction only.  
and transversal (T) directions in relation to the build direction from the HiperFer\({}^{\text{AM}}\) LMD samples (Figure 1).  
Vickers hardness tests (HV1) were performed according to DIN EN ISO 6507 [43] by applying a Qness A10+ hardness tester (ATM Oness GmbH, Mammelzen, Germany).
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 4 Proposed A2C-Based Algorithm
### Problem Transformation  
In MECC systems, the ultimate goal is to improve the long-term QoS for users by implementing service migration and resource allocation policies. Given that tasks in time slot \(t+1\) depend on the execution status of tasks in time slot \(t\), and considering the dynamic nature, heterogeneity, and complexity due to cross-time-slot execution characteristics, we utilize reinforcement learning methods to address this problem.  
Prior to applying reinforcement learning, we first transform the problem into an MDP, represented by the tuple \(M=\{S,A,P,R,\gamma\}\). Here, \(S\) denotes the state space, \(A\) represents the action space, \(P\) is the state transition probability, \(R\) is the reward function, and \(\gamma\) signifies the discount factor. Within this MDP framework, the agent perceives the state \(s_{t}\) of the environment at time slot \(t\), selects an action \(a_{t}\) from the action space to execute migration and resource allocation, and receives a reward \(r_{t}\). The environment then transitions to the next state \(s_{t+1}\) based on \(P\)[35]. Further details of the MDP are as follows.  
State: For any time slot \(t\), the system's state is represented by an array reflecting the real-time status of tasks in the MECC system. This array includes information such as the remaining time for tasks, remaining task size, task type, the ES associated with the user, and the ES where the task is located, i.e.,  
\[s_{t}=[Rst_{t}^{k},V_{t}^{k},W,loc_{t}^{k},X_{t}^{k}]_{|task_{t}^{k}|*S}. \tag{18}\]  
Each row represents the state of a task in the system, with a total of \(|task_{t}^{k}|\) rows.  
Action: In time slot \(t\), actions for state \(s_{t}\) are represented by an array that includes migration decisions and resource allocation strategy, i.e.,  
\[a_{t}=[\Psi_{t}^{k},p_{\mathbf{v}_{t}^{k}}]_{|task_{t}^{k}|*2}. \tag{19}\]  
Each row indicates the migration target server \(s_{\mathbf{v}_{t}^{k}}\) for the task \(task_{t}^{k}\), and the computing resources assigned to \(task_{t}^{k}\) on that server.  
Reward: Reinforcement learning methods are typically employed to maximize the long-term reward of the system. By converting the objective of minimizing the average delay of all tasks into minimizing their cumulative response times, we define the reward for an action \(a_{t}\) as the negative value of the delay. Additionally, we assign specific negative rewards for tasks that fail to complete their upload to the cloud within the given time constraints.  
\[r_{t}=\left\{\begin{array}{c}-\sum\limits_{k=1}^{|task_{t}|}(Z_{t}^{k}T_{t}^{k}),\quad if\ Rst_{t}^{k}\geq 0\ and\ V_{t}^{k}\leq 0;\\ \\ (-10^{3})*\sum\limits_{k=1}^{|task_{t}|}(Z_{t}^{k}T_{t}^{k}),\quad if\ Rst_{t}^{k} \leq 0\ and\ V_{t}^{k}>0.\end{array}\right. \tag{20}\]
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 2 Data
### GNSS Data  
The tomography region in this work will mainly cover the Guangdong Province and the surrounding area in the southern China as shown in Figure 1. Observations from GPS and GLOMASS at 95 stations in Guangdong Continuously Operating Reference Stations (CORS) network with the sampling rate of 30 s on April 29 (day 119) 2017 is processed. Since this study will make an effort to exclude the influences of the discrepancy in SWD between the GNSS and the reference (reanalysis product), we will not use the GNSS-derived SWD as inputs in the tomography experiments, but only use GNSS to make comparisons with the reanalysis-derived slant delays.