esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Comparison with Benchmarking Models  
The goal of comparing the proposed model to other benchmarking models is to evaluate its performance relative to existing or widely accepted solutions. This comparison can reveal specific strengths and weaknesses and provide a rationale for adopting the new model.  
Figure 3: Convergence of the loss function during training and validation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Obtained Value** \\ \hline Precision & 95.3\% \\ Recall & 94.8\% \\ F1-Score & 95.1\% \\ ROC-AUC & 0.989 \\ MSE & 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of metrics obtained by the model in the test set.  
Table 6 compares the proposed model against three widely accepted reference architectures: DNN, RNN, and Decision Tree. When looking at the precision metric, the developed model outperforms the others with 95.3%, suggesting more excellent reliability in its positive predictions. Recall remains consistently high at 94.8%, indicating that the model effectively identifies the most accurate positive samples. The best results of the proposed model are also reflected in the F1-Score and the ROC-AUC metric, achieving 95.1% and 0.989 values, respectively, that demonstrate an optimal balance between precision and recall and an ability to distinguish between classes. Finally, the MSE value for our model is the lowest at 0.027, indicating more insufficient errors in the predictions. Although the Deep Neural Network (DNN) and the Decision Tree offer competitive results, they do not reach the performance of the proposed model. As for the RNN, although it is competent in sequential tasks, in this context, its performance is slightly lower than that of the other models.  
This analysis suggests that the design and techniques used in the proposed model are practical and outperform some of the more traditional and recognized architectures in the field. Choosing the appropriate model will, of course, ultimately depend on the specific nature of the problem and the data in question.  
In binary classification, AUC is an essential tool to compare the discriminative ability of different models. This metric provides an aggregate measure of each model's ability to discriminate between positive and negative classes. The proposed model presents outstanding performance with an AUC of 0.989, indicating a discriminative capacity close to perfection. The DNN shows remarkable performance with an AUC of 0.975, although not as impressive as the proposed model. The RNN and the Decision Tree have AUCs of 0.963 and 0.971, respectively. Although both models demonstrate competent discriminative capabilities, they lag compared to the proposed model.  
To understand each model's performance, it is essential to consider the AUC value in conjunction with other metrics such as precision, recall, and F1-Score. Additionally, it is vital to consider the problem context and other factors when selecting a particular model.
# Techno-Economic Analysis of the Peak Shifting Strategy Based on Time-of-Use Tariff for Cold Stores
## 3 Methodology  
### Peak Shifting Calculation Flow  
The peak shifting strategy is mainly based on the TOU tariff, where the operation of the compressors is shifted from peak to off-peak periods, resulting in cost savings. As shown in Table 3, the 24 h is divided into five periods, with a total of three types including peak, flat, and valley periods based on the electricity consumption situation. Since there are two separate peak and flat periods, this paper distinguishes them by 1# and 2#. The valley period is 0-8 a.m., during which the average ambient temperature tends to be much lower and the condensing pressure of the compressor unit can be further reduced, thus increasing the efficiency of the compressor unit.  
Detailed calculation flow is shown in Figure 3. The collected historical operation data need to be pre-processed first, and are then used as the input for the cooling load prediction in the LSTM algorithm. Based on the prediction results, two different operation modes are proposed to be selected according to practical production, where Mode A aims at complete load shifting while Mode B is a load-balancing strategy. Both modes combine the compressor operation mode control with the temperature setpoint control, thus offering the possibility to avoid overshooting the warehouse temperature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time (\(h\))** & **0–8** & **8–12** & **12–18** & **18–22** & **22–24** \\ \hline Type & Valley & Peak\_1\# & Flat\_1\# & Peak\_2\# & Flat\_2\# \\ Price (CNT/\(kWh\)) & 0.358 & 1.061 & 0.686 & 1.061 & 0.686 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time-of-use tariff.  
Figure 2: Comparison of before and after suction/discharge pressure filtering.  
From the point of view of the compressor properties, a low load will result in low adiabatic efficiency. This paper provides three efficient operation patterns for the compressor unit based on expert experience, corresponding to low, medium, and high load cases, as shown in Table 4. The load setting value is expressed as the sum of two numbers, representing the load of the first and second compressors, respectively.
## 4 Materials and Methods
### Sample Preparation and HPLC-ELSD Quantification Method  
Tubes produced by the methods described in Sections 4.2-4.5 were prepared and analyzed as follows. For extraction of the glycolipid and its quantification with HPLC-ELSD, the following procedure was applied. First, 1 mL of double-distilled water was added to the tube containing the reaction mixture and vortexed for 45 s. Then, 2.5 mL of ethyl acetate was added to the warm solubilized DES; subsequently, the extraction took place at 50 \({}^{\circ}\)C for 20 min with the use of the orbital shaker set on program U2 and 90 rpm (neoLab, Heidelberg, Germany). Then, 1 mL of the upper phase was aliquoted and dried on a centrifugal evaporator in order to be resolubilized in 1 mL of chloroform/methanol (75:25 \(v/v\)), and 100 \(\mu\)L was placed into an HPLC vial for further quantification.  
The method described by [PERSON] et al. [28; 79] was used with slight modifications as follows. Kinetex EVO C18 (2.6 mm, 250 mm \(\times\) 4.6 mm) column from Phenomenex (Aschaffenburg, Germany) with an accompanying guard column (4 mm \(\times\) 3.0 mm ID) of the same phase, using an Agilent 1260 series liquid chromatograph (Waldbronn, Germany) equipped with a quaternary pump, an autosampler and a column oven. An evaporative light scattering detector (ELSD) from BUCHI Labortechnik (Essen, Germany) was used for detection. The mobile phase, solvent A, was water and solvent B was acetonitrile. The flow rate was 1 mL/min, and a gradient was used for separation of product and substrates: starting from 40% A-60% B, then 0-10 min a linear gradient up to 35% A-65% B, followed by another linear gradient from 10 to 15 min up to 25% A-75% B. This gradient was held for 5 min, followed by a reconditioning step of the column with 40% A-60% B for 5 min. The injection volume was set to 10 \(\mu\)L. The column was operated at 50 \({}^{\circ}\)C. The detector was operated at 38 \({}^{\circ}\)C with a gas flow (air) of 1.5 mL/min. The gain was set to 1. The retention times were 2.1 min for sorbitol, 3.5 min for SL and \(\sim\)9.5 min for lauric acid (Supplementary Materials, Figure S1).  
\[\text{Yield}\;[\%]=\frac{\text{nSL}\;[\text{mol}]}{\text{nVinyl laurate}\;[\text{mol}]}\times 100\%. \tag{1}\]  
\[\text{Specific reaction velocity}\;[\mu\text{mol}/\text{h}/\text{g}]=\frac{ \text{nSL}\;[\mu\text{mol}]}{\text{mBiocatalyst}\;[\text{g}]\times\;\text{time} \;[\text{h}]} \tag{2}\]  
n: number of moles.  
m: mass.
## 5 Experiment Results
### Numerical Analysis
These results highlight D-GTOM's robust scalability and capability in maintaining high resource utilization under diverse and large-scale workloads, underscoring its effectiveness in dynamic satellite network environments. Figure 7b provides the average task delays, showing that D-GTOM maintains competitive delays, such as 0.165 s for smaller datasets and 0.185 s for larger datasets, demonstrating a clear advantage over TSA and AGA-ES in minimizing delay. As shown in Figure 7c, D-GTOM consistently achieves the shortest runtime, ranging from 0.406 s for 600 tasks to 2.621 s for 3600 tasks, significantly outperforming TSA (1.663 to 9.601 s) and AGA-ES (507.411 to 3078.633 s). Meanwhile, Table 7 highlights the execution time of scheduling schemes, where D-GTOM achieves substantial reductions, requiring 1534 h for 600 tasks and 12,019 h for 3600 tasks, compared to TSA's 2577 to 13,709 h. These results collectively validate the superior scalability, computational efficiency, and adaptability of D-GTOM in handling large-scale satellite network scheduling scenarios. Figure 6: Comparison of average task delay for different algorithms across 4 datasets. Figure 7: Comparison of evaluation metrics in large-scale satellite dataset with growing task quantity.
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 2 Motivation and Method Overview  
### Surface Reconstruction Planning Description  
The existing satellite photography focuses on observing point or area targets [15], and the planning for this kind of task is to provide the maximized fulfillment of target observations. Because there are usually many possible imaging opportunities for satellites, represented as time windows in their orbits, the role of planning is to choose the time windows and provide the final imaging schedule which maximizes the overall profits. Though image quality is also considered as one of the objectives, such as in [16; 17; 18], they still tackle traditional satellite imaging tasks. The authors in [19] formalize the quality of stereo imaging tasks as one objective and its variance as another. However, the image quality directly affected by satellite attitude is considered as the 3D reconstruction quality, i.e., calculated as the result of the pitch and roll angles of the satellite, which is similar to reference [18], where the satellite attitude is converted to one imaging angle to represent quality.  
Assuming the images captured for all possible visible time windows \(VTW\) are denoted as \(IM\), each image \(im_{i}\) has its attributes of \(a_{i}\) including off-nadir angle, solar elevation angle, solar azimuth angle, etc., where \(1\leq i\leq N\) and \(N\) is the total number of imaging opportunities. Without losing generality, the single-target 3D reconstruction from two images is a function \(f(im_{i},im_{j}):IM\times IM\to S\) to map from an image pair to a stereo reconstruction. The task of planning here is to search for optimal pairs of images which lead to high-quality 3D reconstructions. Because planning is a decision making process which can only be implemented before imaging is actually fulfilled, the quality of reconstruction from an arbitrary image pair should be estimated accurately beforehand, according to their attributes of \(a_{i}\) and \(a_{j}\) which can be calculated according to the satellite orbit.  
However, the influence of different attributes and their intersections on reconstruction quality is unclear and hard to quantify. Even with the predicted quality, how to search for optimized stereo combinations from a large number of imaging opportunities is not addressed. This is especially challenging for 3D reconstruction with large constellations, to which the quality-driven planning method proposed in this paper can be applied.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
###### Abstract  
In the era of data deluge, Big Data gradually offers numerous opportunities, but also poses significant challenges to conventional data processing and analysis methods. MapReduce has become a prominent parallel and distributed programming model for efficiently handling such massive datasets. One of the most elementary and extensive operations in MapReduce is the join operation. These joins have become ever more complex and expensive in the context of skewed data, in which some common join keys appear with a greater frequency than others. Some of the reduction tasks processing these join keys will finish later than others; thus, the benefits of parallel computation become meaningless. Some studies on the problem of skew joins have been conducted, but an adequate and systematic comparison in the Spark environment has not been presented. They have only provided experimental tests, so there is still a shortage of representations of mathematical models on which skew-join algorithms can be compared. This study is, therefore, designed to provide the theoretical and practical basics for evaluating skew-join strategies for large-scale datasets with MapReduce and Spark--both analytically with cost models and practically with experiments. The objectives of the study are, first, to present the implementation of prominent skew-join algorithms in Spark, second, to evaluate the algorithms by using cost models and experiments, and third, to show the advantages and disadvantages of each one and to recommend strategies for the better use of skew joins in Spark.  
big data analytics; skew join; MapReduce; Apache Spark +
Footnote †: journal: Applied  
Article  
Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce Datasets with MapReduce and Spark
# Experimental Study on the Features of Infrasonic Waves of Sandstone under Shear Load
## 4 Analysis of Experiment Results  
Three groups of typical experiment data were selected to separately plot the diagrams of the rock sample's normalization of the infrasound event over time, normalization of the energy accumulation rate over time and shear force over time (Figure 6). Since the straight shear load in the experiments was loaded with a constant change rate of displacement, the process of rock shear failure can be divided into four stages [30]: the compression stage, the elastic stage, the plastic stage, and the destruction stage. The last three stages correspond with the three key moments of the sandstone landslide [31]: forecast, early warning and warning (the turquoise vertical dashed line, purple vertical dashed line, and yellow vertical dashed line in Figure 6).  
Overall, under straight shear loading, infrasound events always occur during sandstone destruction. In the experiment, the rock sample was first under normal stress, and its original opening structure plane or microfissures closed up gradually. When the stress was increased to shear stress, the rock sample was further compacted, and the grains inside the sample were squeezed and embedded into each other. Then, the shear stress increased slowly with the increase in strain, and the normalized energy accumulation coefficient also increased. When the shear stress increased further, the rock sample entered the elastic stage, and the microfissures in the sample underwent elastic deformation and propagated steadily. If the shear stress stopped at this point, the rock sample would stop breaking. The shear stress-strain curve in this stage is like a straight line after a sharp increase of the gradient. The normalized energy accumulation coefficient correspondingly rose sharply and then steadily. Therefore, the point that the normalized energy accumulation coefficient rises sharply can be taken as the start point of the forecast. With the increase in shear stress, the shear stress entered the yield point. The rock sample had qualitative changes, including microfissures, large deformation, cumulative damage, and the rapid increase of axial strain. When the sandstone is loaded in the yield stage, it simulates the imminent sliding stage of the sandstone landslide. The acoustic data and mechanical data of this experiment are jointly analyzed. The normalized energy accumulation coefficient at this stage is between 0.6 and 0.75, and the normalized infrasound changes between 0.89 and 0.93. Therefore, the values of these two acoustic parameters can be used to determine whether the sandstone is in the near-failure stage. At the same time, this feature can be used as a precursor feature of the impending sliding of a rock slope.  
Figure 6: _Cent._  
Figure 6: _Cont._  
Figure 6: Relationships of normalized infrasound event rate, normalized energy accumulation rate, and shear force-time diagram of typical samples. (**a**) Sample a1-1; (**b**) sample b2; (**c**) sample c3.
# Enhancing Robotic-Assisted Lower Limb Rehabilitation Using Augmented Reality and Serious Gaming
## 3 Serious Gaming Application Developed for LegUp
### Hardware
#### 3.1.2 HoloLens 2  
A product of Microsoft ([PERSON], WA, USA), HoloLens 2 [32], is an augmented reality device used for creating interactive holographic experiences. It features a wider field of view, a resolution of 47 pixels per degree, and a gesture recognition system that tracks 25 hand points for natural interactions. Advanced voice tracking facilitates easier instruction, while eye-tracking ensures holograms are always displayed within the user's field of view. In the medical field, this device is commonly used for surgical navigation, Human-Computer Interaction (HCI) and Brain-Computer Interface (AR-BCI), gait analysis and rehabilitation, medical training, virtual teaching/telementoring, and teleconsulting [33].
## 1 Introduction
[37],which showed that the two models have similar results on the premise of stable material performance. In recent years, the Ramberg-Osgood model has been adopted in other research to describe the stress-strain constitutive relation of metallic materials [38; 39; 40; 41; 42]. In conclusion, numerous previous studies have shown that the Ramberg-Osgood model [43] has good applicability in describing the cyclic stress-strain relationship of metallic materials, but little related research has taken into account the damage accumulation in the hysteretic model and constitutive model. On the other hand, the study showed that there are obvious differences in the cyclic characteristics between the welding material and its parent material. For weld, [PERSON] et al. [44] investigated the behavior of welded T-joints under low-cycle fatigue though tests and nonlinear finite element analysis, which has contributed to the predicting model of fracture and fatigue behavior for welded joints. Some other studies [45; 46; 47; 48; 49; 50] relating to the constitutive model of the different materials of a weld also should be mentioned, but the research on the cyclic constitutive model for the welding material of steel tubular truss structure joints has still not been fully carried out, and there is also a lack of model parameters that can reflect the damage deterioration of the welding material. Thus, the cumulative damage evolution rule [51; 52; 53; 54; 55] of welding materials also should be paid attention to. Consequently, an experimental investigation was performed in this research on the welding material of steel tubular truss structure joints under law cyclic loading. The experimental details are presented and the observations of test, the stress-strain responses, as well as the hysteretic properties and energy dissipation capacity, were discussed. The cumulative evolution law of the welding material was obtained through experiments. Based on the experimental results, a damage cumulative evolution equation of welding material was established, and a hysteresis model of welding material considering damage cumulative effects was constructed. Then, the damage parameters of the hysteresis model were fitted by the experimental data. Finally, the results calculated by the hysteretic model were compared with the experimental results.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 3 Materials and Methods
### Polynomial Regression
#### 3.6.4 Noise Figure vs. Frequency  
Finally, the impact of channel frequency on the noise figure is evaluated. As indicated in Equation (9), frequency explicitly affects the noise figure. Additionally, from Equation (14), it is clear that frequency influences channel gain. Unlike other cases, the term \(10\log_{10}(hfB_{0})\), which was previously a constant, now represents an explicit dependency on frequency. However, this term shows nearly negligible variations relative to frequency. To assess the maximum excursion of this term within the frequency range of interest, we can use a differential approximation:  
\[\Delta\,NF=10\,log_{10}\,(hf_{MAX}\,B_{0})-10\,log_{10}\,(hf_{MIN}\,B_{0}) \tag{15}\]  
\[\Delta\,NF=\Delta\,f\cdot\frac{\delta}{\delta f}(10\,log_{10}\,(hf_{MID}\,B_{ 0})),\;f_{MID}=\frac{f_{MIN}\,+f_{MAX}}{2} \tag{16}\]  
\[\Delta\,NF=(f_{MAX}-f_{MIN})\frac{10}{f_{MID}\,ln(10)} \tag{17}\]  
By substituting values corresponding to the C-band for \(f_{MIN}\) and \(f_{MAX}\), the resultant change is approximately \(0.08\,\mathrm{dB}\). Given that the noise figure typically exceeds \(4\,\mathrm{dB}\), this corresponds to a relative contribution of, at most, \(2\%\). Therefore, this term alone does not significantly influence the noise figure's trend as a function of frequency. As discussed earlier, when the target tilt is positive, channel gain decreases with increasing channel frequency, and the reverse is true for negative tilt. ASE noise exhibits a similar behavior, forming a plateau in the middle for positive tilts and varying more at the band edges. SSE tends to be noisier at lower power levels, likely due to the instrument's sensitivity. \(P_{ASE_{AMP}}\) follows a pattern similar to channel gain, meaning the resulting noise figure will be influenced by the specific slopes and variations of each parameter.  
The results are summarized in Figure 11. It is immediately apparent that the noise figure's variation with frequency shows significantly greater variance compared to other parameters, making it more challenging to fit into a polynomial curve. This behavior is detailed in Figure 12. One advantage is that, unlike other models, overfitting is not a concern because the C-band remains constant, so the model does not need to predict accurately outside the tested frequency range. Therefore, increasing the polynomial degree to improve accuracy is a viable approach. The only consideration is that raising the polynomial's degree requires keeping the degrees of other polynomials low to maintain a manageable dataset size and ensure short training times for our models. An eighth-degree polynomial  
Figure 10: Noise figure vs. target gain tilt, at various input parameter configurations.  
was found to strike the best balance between training speed, numerical precision, and model accuracy.