esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Time Series of Statistics of \(T_{IS}\) vs. \(T_{Sat}\) and Satellite-Based Retrospective QC
time series of monthly fractions of matched \(T_{IS}\) before the QC and the numbers of available MDSs in Figure 1b), reaching \(\sim\)18-20% in 2010-2021 and \(\sim\)25% in 2020-2021. In Figure 15b, the time series of \(\Delta T_{SAT}\) = \(T_{IS}-T_{SAT}\) produced by current, EXP, and SAT QCs are very similar. However, the biases for the EXP QC are somewhat warmer, due to better preserving the daytime \(T_{IS}\)'s affected by the diurnal warming. Figure 15c shows the time series of SDs of \(\Delta T_{SAT}\) for the three QCs. The EXP QC produces larger SDs than the current QC. The SAT QC reduces SDs below the current QC levels by rejecting a small number of outliers in \(\Delta T_{SAT}\). Note that the removal of outliers by SAT QC does not suppress the DS in the \(T_{IS}\), as attested by the fact that in 1990-2021, the SAT QC produced biases comparable with or warmer than those produced by the EXP QC (cf. Figure 15b). Note that the SAT QC eliminated the artificial peak in SDs in 1991 due to inaccurate \(T_{IS}\)'s that survive both current \(i\)Quam and EXP QCs. Large variations in biases and SDs in the 1980s are mainly due to a relatively small number of matchups (several hundred per month, according to Figure 1a). Degraded quality \(T_{SAT}\) caused by instrumental problems of the AVHRR/2 radiometers on the early  
Figure 15: Time series of monthly (**a**) fractions of quality \(T_{IS}\) observations, (**b**) biases of \(T_{IS}-T_{SAT}\), (**c**) SDs of \(T_{IS}-T_{SAT}\), and (**d**) correlation coefficients of \(T_{IS}-T_{L4}\) vs. \(T_{SAT}-T_{L4}\), for (black) current \(i\)Quam, (blue) EXP and (red) SAT QCs. The overlaid numbers show mean values of the statistics for four periods. NOAA-07/09/11 satellites also contributes, as well as training SST equations for NOAA-07/09 against the combinations of DTM and ship SSTs [19]. Figure 15d shows time series of monthly correlations between \(T_{IS}-T_{L4}\) and \(T_{SAT}-T_{L4}\) with \(T_{L4}\) produced from the OISST [23; 24]. The EXP QC increases correlation coefficients compared to the current \(i\)Quam QC due to improved preservation of the daytime \(T_{IS}\)'s affected by the diurnal warming and consistent with \(T_{SAT}\). Rejecting additional outliers by the SAT QC increases the correlation even further. Although the numbers of \(T_{IS}\)'s identified by the SAT QC are relatively small, these are the very matchups used in the Cal/Val of the satellite products listed in Table 1. Filtering outliers in the MDS with the condition (5) will facilitate the Cal/Val during subsequent reprocessings of the satellite data.
# Optimization of Different Deployments and Resolution of Antenna Arrays in SWIPT
## 3 Evolution Algorithm  
SADDE was developed from the dynamic differential evolution (DDE) [25]. This added some mechanism to dynamically change the adjustment factor during the search. Figure 3 shows the SADDE algorithm flowchart.  
Figure 2: The spitting architecture for the SWIPT system.  
Step 1. In the first step, we randomly initiate the parameter of the population for the antenna array. There are \(d\) dimensional adjustment parameters in each population.  
Step 2. According to the \(p\)-th population's parameters, calculate the objective function value and update the best value of the population.  
Step 3. Mutate the trial vector based on the control vector and adjust the control vector for the next generation.  
Step 4. By the presetting probability, decide whether the crossover mechanism is needed to start. If starting the mechanism, we compare the crossover objective value with the former objective value. Step 5. Update the position of the global best population.  
Step 6. According to the total number of populations and the number of generations, go to step 2 or stop operating the algorithm.  
In order to handle two different requirements at the same time, we designed two objectives for optimization by algorithms and we should solve the unit inconsistent problem. One objective is the BER and the other is the total harvesting power. Since the BER usually have a minimum quality requirement, we can divide the minimum limitation of BER by the required BER to solve the inconsistency of the unit. The different objective can be optimized into one objective function. The \(BER_{W}\) with the BER constrained condition of \(10^{-5}\) is defined as  
\[BER_{W}=\left\{\begin{array}{cc}\frac{10^{-5}}{BER}&,BER>10^{-5}\\ 1&,BER<10^{-5}\end{array}\right. \tag{8}\]  
The total power harvesting can be expressed as  
\[HP_{T}=\sum_{m=1}^{M_{R}}HP_{m} \tag{9}\]  
Figure 3: SADDE flow chart.  
where \(M_{R}\) is the total number of receivers in the environment. The multiple objective function is:  
\[1/(BER_{W}\times HP_{T}) \tag{10}\]
# Edge Computing Approach for Vessel Monitoring System
## 2 State of the Art and Edge Computing Approach  
Research work on a cloud computing approach has been used in data processing to extract knowledge for further assessment of the fishing impact and find illegal activities [9; 10; 11]. These studies range from the processing of time and space collected data to the identification and evaluation of the environmental impacts of the fishing activity [12; 13]. VMS also has similarities to fishing reports files about the caught fish [14], and a series of tools can be applied for data processing towards knowledge extraction with two main software libraries: VMStools [15] and VMSbase [16], for R.  
Several efforts have been performed in a centralized approach at cloud servers with data processing to extract knowledge, but it is possible to start at edge computing at VMS or AIS systems in a similar approach that has been applied in an IoT system to avoid the transmission of billions of data files [17]. Some approaches can be applied to the current case to save communication that is expensive due to the use of the satellite at sea (in remote sea zones, only satellite communications are available).  
Most VMS or AIS lack processing power, storage capacity, and memory [14], because they are embedded systems and associated transmitted costs lead to data transmission every 10 min. In some cases this frequency is not enough, as in the example of the detection of purse-seiner fishing, in which the vessel operation is done in less than 5 min.  
Edge computing is based on processing data near the edge of the specific network, where the data is being generated, instead of in a centralized data-processing location. Edge computing reduces the volumes of data that must be transmitted and, consequently, provides lower latency and reduces transmission costs. A survey about the current development and status of edge computing is found at [18], which also discusses future research directions of edge computing. This technology approach allows giving highly responsive cloud services for mobile computing, scalability, and privacy-policy enforcement for IoT [19].  
The vessel monitor system was developed based on the paradigm of cloud computing with the transmission of data to a central system. Because of the high communication costs at sea (most cases are based on satellite communications, which is the only one available at high sea), the data collection process is in general around once every 10 min. In some cases, this time span can be a problem because, for the monitoring process, there is a loss of reaction time and the ability to identify some fishing activities such as purse-seine.  
Figure 1: Image of a MONICAP vessel installation; image from a Portuguese fishing boat.
## 5 Performance Evaluation
### Effect of the Size of Encoding Information Block \(N\) on the Network Performance
#### 5.1.2 Network Throughput  
Throughput is another measure of network performance. A protocol with network encoding is expected to excel in throughput when compared to one without network coding, subject to the choice  
Figure 6: Impact of \(N\) on network delay.  
Figure 7: Impact of \(N\) on the number of required packets for decoding.  
of \(N\). Figure 8 compares the throughputs of DBR (without network coding), GPNC (\(N=3\)) and GPNC (\(N=5\)). With the increasing number of network nodes, throughput increases and gradually levels off for all three protocols, which is mainly because the increase of the overall network throughput is restrained by the competition among nodes. Figure 8 further reveals that the choice of \(N\) in GPNC has a clear impact on network throughput. When \(N=3\), the throughput of GPNC is similar to that of DBR because its opportunity of encoding packets is low. When \(N=5\), the throughput of GPNC is 22% better than that of DBR and the advantage of network coding is obvious. In conclusion, as \(N=5\) yields an optimal network performance shown in Figures 6 and 8, we choose \(N=5\) in later experiments.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 4 Conclusions  
The results obtained demonstrated the feasibility of using HSI as a non-destructive technology to classify the bitterness of almonds analysed in bulk. The potential application  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual Category**} & \multicolumn{2}{c}{**Classified as**} & \multirow{2}{*}{**Samples Correctly**} \\ \cline{2-2} \cline{4-4}  & **Sweet** & & **Bitter** & **Classified** \\ \hline Sweet & 95 & 1 & 99\% \\ Bitter & 0 & 61 & 100\% \\ Sensitivity = 0.99 & Specificity = 1.00 & NER = 0.994 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discriminant models for classifying almond batches by bitterness using the most significant wavelengths selected by VIP scores.  
Figure 4: VIP scores for the discrimination of almonds by bitterness.  
of HSI technology at a single-pixel level would enable us to identify each individual bitter almond present in the sweet almond batches. This approach could be extremely useful at an industrial level as it could eliminate each bitter almond included in the processed sweet product, reducing the cost and increasing the commercial value. However, further studies are needed, in particular, related to the use of other mathematical approaches to achieve the object-wise segmentation that enables the isolation of the bitter fruits.  
It must also be considered that the percentage of correctly classified samples is influenced by key factors such as the characteristics associated with each cultivar (genotype, amygdala content and physical features) and the representativeness of each cultivar within the training set.  
Based on the results obtained, data selection using VIP scores enables to differentiate between sweet and bitter almond batches, but it is not an appropriate method to identify single bitter almonds in mixtures.  
Conceptualisation, D.P.-M., A.G.-V. and M.-T.S.; Data acquisition, I.T.-R., J.-A.E. and M.V.-C.; Methodology, I.T.-R. and D.P.-M.; Formal analysis, I.T.-R., J.-A.E. and M.V.-C.; Research, I.T.-R., M.-T.S., M.V.-C. and D.P.-M.; Resources, D.P.-M., A.G.-V. and M.-T.S.; Data curation, I.T.-R.; Validation, I.T.-R., M.-T.S. and D.P.-M.; Writing--Original draft preparation, I.T.-R., M.-T.S., J.-A.E., M.V.-C. and D.P.-M.; Writing--review & editing, I.T.-R., M.-T.S. and D.P.-M.; Visualisation, I.T.-R., J.-A.E., M.-T.S., M.V.-C. and D.P.-M.; Supervision, M.-T.S., A.G.-V. and D.P.-M.; Project administration, M.-T.S., A.G.-V. and D.P.-M.; Funding acquisition, M.-T.S., A.G.-V. and D.P.-M. All authors have read and agreed to the published version of the manuscript.  
This research was carried out as part of the research project P-12018024 'Measuring the quality of almonds grown in the Guadalquivir Valley (Cordoba)', funded by Desarrollo y Aplicaciones Fitotecnicas, DAFISA.  
The authors are grateful to [PERSON] from the Animal Production Department for her technical assistance.  
The authors declare that they have no known competing financial interest or personal relationships that could have influenced the work reported in this paper in any way.
## 3 Results
### Modeling of Daily Gross Primary Productivity
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**CA-Obs**} & \multicolumn{4}{c}{**CA-Oas**} & \multicolumn{4}{c}{**CA-Gro**} \\ \cline{2-13}  & \(\mathbf{R}^{2}\) & **NSE** & **Bias** & **RMSE** & \(\mathbf{R}^{2}\) & **NSE** & **Bias** & **RMSE** & \(\mathbf{R}^{2}\) & **NSE** & **Bias** & **RMSE** \\ \hline ANN1 & 0.880 & 0.854 & \(-\)0.358 & 1.046 & 0.753 & 0.742 & \(-\)0.243 & 2.279 & 0.736 & 0.682 & 0.573 & 1.705 \\ ANN2 & 0.482 & 0.472 & 0.003 & 1.989 & 0.428 & 0.420 & 0.203 & 3.417 & 0.554 & 0.529 & 0.395 & 2.076 \\ ANN3 & 0.001 & \(-\)0.088 & 0.311 & 2.854 & 0.062 & 0.062 & 0.022 & 4.347 & 0.110 & 0.072 & 0.483 & 2.914 \\ ANN4 & 0.811 & 0.788 & \(-\)0.305 & 1.259 & 0.831 & 0.830 & 0.118 & 1.848 & 0.795 & 0.778 & 0.340 & 1.425 \\ ANN5 & 0.903 & 0.838 & \(-\)0.273 & 0.937 & 0.808 & 0.806 & \(-\)0.069 & 1.975 & 0.804 & 0.763 & 0.488 & 1.473 \\ ANN6 & 0.932 & 0.917 & \(-\)0.317 & 0.789 & 0.871 & 0.870 & \(-\)0.087 & 1.618 & 0.861 & 0.825 & 0.430 & 1.267 \\
**ANN7** & **0.963** & **0.944** & \(\mathbf{-0.253}\) & **0.645** & **0.909** & **0.905** & **0.211** & **1.386** & **0.907** & **0.877** & **0.319** & **1.060** \\ GRNN1 & 0.876 & 0.825 & \(-\)0.488 & 1.145 & 0.752 & 0.720 & \(-\)0.524 & 2.377 & 0.734 & 0.699 & 0.486 & 1.660 \\ GRNN2 & 0.485 & 0.470 & 0.056 & 1.992 & 0.434 & 0.428 & 0.261 & 3.395 & 0.564 & 0.551 & 0.325 & 2.027 \\ GRNN3 & 0.000 & \(-\)0.108 & 0.367 & 2.881 & 0.063 & 0.058 & 0.082 & 4.357 & 0.125 & 0.090 & 0.527 & 2.886 \\ GRNN4 & 0.794 & 0.770 & \(-\)0.311 & 1.311 & 0.841 & 0.822 & \(-\)0.359 & 1.894 & 0.802 & 0.787 & 0.270 & 1.396 \\ GRNN5 & 0.904 & 0.882 & \(-\)0.282 & 0.941 & 0.786 & 0.78 Figures 1 and 2 illustrate the comparisons of daily GPP between observed and predicted using the data-driven models at the three sites in the form of scatter plot and time series graph, respectively. As clearly seen from Figure 1, the fit lines of the models (ANN, GRNN, ANFIS and SVM) during the testing period for CA-Oas site appear to be consistent with the ideal fit lines (y \(~{}=~{}\)x), whereas at CA-Obs site, these models have higher values of \(R^{2}\) and NSE than those of the corresponding models for CA-Oas and CA-Gro stands (Tables 4 and 6). Moreover, all the five models generate more scattered estimates at both CA-Oas and CA-  
On the other hand, as demonstrated in Figure 2, the over- and under-estimation of the best model for each site in predicting daily GPP among the training, validation and testing periods are examined. It is apparent from Figure 2 that most of the simulated values from the best ANN model in the testing period (the last 1 year) are substantially underestimated at CA-Obs site, while greatly overestimated at CA-Gro site. This is also confirmed by the aforementioned scatter plots in Figure 1.
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 3 Scheduling Algorithms and Their Performance
### Queueing-Theoretic Approaches
For example, LAS requires choosing a lease duration: if leases are too long, short jobs lose their responsive service; if too short, preemption overhead grows. A concrete instantiation--\"preempt every 30s to rebalance toward least-attained service\"--captures LAS behavior in practice and can be tuned analytically or via empirical evaluation [85]. Likewise, Gittins-style policies rely on accurate size distributions; misestimating a job as short may cause it to monopolize GPUs. Tiresias experiments show that both partially informed (Gittins-like) and agnostic (LAS-style) schemes outperform classical heuristics, suggesting that even simplified index rules can realize much of the theoretical benefit in realistic workloads. Predictive admission and QoS control: Beyond scheduling, queueing theory's formulas can drive admission control and service-level guarantees. A scheduler that estimates, say, a one-hour wait for newly submitted work can use that prediction to deny, defer, or reprioritize jobs based on user deadlines or QoS contracts. Simple metrics--Little's Law (\(L=\lambda W\)), mean response-time bounds, or \(M/M/1\) approximations--can trigger autoscaling or more aggressive preemption when saturation looms, helping to balance cluster utilization against developer expectations. Trade-offs vs. optimization-based methods: Queueing policies like LAS and Gittins Index optimize user-centric metrics (mean response time, slowdown) at the expense of additional preemption and decision overhead. In contrast, optimization-based schedulers typically focus on system-centric objectives (throughput, GPU utilization), often allowing long jobs to finish uninterrupted to avoid migration or checkpoint costs. For instance, an MILP solver may keep a nearly completed job running, boosting overall throughput but delaying short arrivals--precisely the opposite of SRPT-like fairness. Hybrid designs and practical deployment: Most production schedulers blend both worlds: they pack GPUs efficiently and layer in preemption or time-slicing to prevent starvation. Gandiva, for example, uses placement-optimization via migration while time-slicing hyperparameter searches for rapid feedback [56]. A common pattern is to treat each allocated GPU as a \"service chunk\" when computing priorities, enabling index or LAS heuristics to work atop any resource-packing framework. In summary, queueing-theoretic approaches bring a rich repertoire of priority rules, fairness guarantees, and predictive insights. Their core strength lies in optimizing responsiveness and equity--qualities highly valued by ML practitioners--while their main challenge is controlling overhead and ensuring system-wide efficiency. As demonstrated by [PERSON] and [PERSON], with careful adaptation and cluster-specific enhancements, queueing-inspired schedulers can substantially reduce average completion times and deliver fair, predictable GPU allocation in multi-tenant environments [74; 85].
## 2 Differential Radar Interferometry for Deformation Monitoring in Cultural Heritage Sites  
In general, cultural heritage sites have their own particularity with regard to spatial scales, geometric characteristics, material properties and constituent structures. Different approaches of differential radar interferometry can be applied in deformation monitoring over cultural heritage sites according to their specific characteristics, as summarized in Table 1.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 3 Results
### Soybean_1  
The Figure 5 contain the results of the comprehensive evaluation of the entire test dataset using two fundamental metrics, namely MAAPE and NRMSE. Method C (conventional) displays a greater prediction performance over method A (augmented) by 54.54% in terms of MAAPE and 52.83% in terms of NRMSE. In summary, method C outperforms method A in terms of both metrics. However, when focusing exclusively on the top 20% of the complete test set (Figures MAAPE_20 and NRMS_20), the metric analysis reveals that method A exhibits a performance advantage over method C, with a reduction of 42.07% in MAAPE_20 and 39.72% in NRMSE_20 compared with MAAPE and NRMSE, respectively. These results reflect a substantial improvement in the performance of method A when using the top 20% of the data. For a more detailed exploration, refer to Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Metric** & **Average** & **SE** \\ \hline Maize\_4 & A & MAAPE & 0.3109 & 0.0038 \\ Maize\_4 & A & MAAPE\_20 & 0.0854 & 0.0013 \\ Maize\_4 & A & NRMSE & 1.8373 & 0.0048 \\ Maize\_4 & A & NRMSE\_20 & 1.4156 & 0.0087 \\ Maize\_4 & C & MAAPE & 0.1317 & 0.0016 \\ Maize\_4 & C & MAAPE\_20 & 0.1415 & 0.0015 \\ Maize\_4 & C & NRMSE & 0.8560 & 0.0033 \\ Maize\_4 & C & NRMSE\_20 & 2.2556 & 0.0172 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction accuracy results for dataset Maize_4 using conventional (C) and augmented (A) methods, with metrics MAAPE and NRMSE for the total testing and MAAPE-20 and NRMSE-20 for the best 20% testing.  
Figure 5: Prediction accuracy performance results for the Soybean_1 dataset using conventional (C) and augmented (A) methods in terms of mean arctangent absolute percentage error (MAAPE), mean arctangent absolute percentage error for the best 20% cultivars (MAAPE_20), normalizing the mean square error (NRMSE), and normalizing the mean square error for the best 20% cultivars (NRMSE_20).
## 5 Conclusions and Future Work  
This paper proposes a sentiment analysis model (UA-BFET) based on feature enhancement technology for the cross-modal fusion of text and video in unaligned data scenarios. Specifically, the forgetting problem of unimodal sequences in unaligned sequences is solved based on the cyclic memory enhancement network across time steps, and the unimodal loss is reduced. The enhancement technology based on CCA strengthens the correlation between different modalities at the important time steps and makes the obtained modal features tend to be true emotions. Experimental results on unaligned MOSI and MOSEI datasets show that the proposed UA-BFET model can enhance the weight of emotion-related unimodal features in unaligned data sequences and provide effective help for the  
Figure 7: Attention weight visualization of interactions between text and video cross-modal elements in CMU-MOSI and CMU-MOSEI data sets. It can be seen that in the unaligned scenarios, the UA-BFET model proposed in this paper can realize the association of emotive text words with facial expression changes in video modalities. In the three cases, the three-color scale of red, yellow, and green is used to represent the emotional polarity of specific cases; that is, the color tending to red corresponds to positive emotion, while the color tending to green corresponds to negative emotion. The darker the color, the greater the emotional value. The part circled by the black block diagram represents the final emotional representation of the interaction between text modality and video modality. In case (**a**), it can be seen the part circled by the black block diagram is biased towards green and the overall is biased towards negative emotion, while the overall color of case (**b**,**c**) is biased towards red and the overall is biased towards positive emotion.  
subsequent acquisition of correlation and interaction between different modalities, as well as the accuracy of cross-modal sentiment analysis.  
We believe that the UA-BFET model for cross-modal sentiment analysis in unaligned data scenarios provides more possibilities for specific areas such as cross-modal visual Q&A, image and text retrieval, and recommendation systems. Next, we will consider introducing more external knowledge to better handle sentiment analysis of irregular texts, satirical phrases, and comparative sentences in real-world corpora.  
Conceptualization, P.H. and H.Q.; methodology, P.H.; software, H.Q.; validation, S.W., J.C. and H.Q.; formal analysis, P.H.; investigation, H.Q.; resources, H.Q.; data curation, H.Q.; writing--original draft preparation, H.Q. and S.W.; writing--review and editing, P.H. and J.C.; visualization, H.Q.; supervision, P.H.; project administration, P.H.; funding acquisition, P.H. All authors have read and agreed to the published version of the manuscript.  
Scientific Research and Development Program Project of the Hebei University of Economics and Business (2022 ZD09).  
Not applicable.  
Data Availability Statement: Data can be provided by the authors upon request.  
The authors declare no conflict of interest.