esa-sceva/satcom-chunk-collection huggingface dataset:
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 4 Proposed Method
### Dimension-Augmented TimesFM  
#### 4.2.1 TimesFM  
Pretrained Large Time-Series Models (LTSMs) are able to obtain universal temporal feature representations by pretraining on massive amounts of time series data [28]. The pre-training paradigm enables the model to effectively migrate prior knowledge to improve prediction performance when facing downstream few-shot tasks. Among the existing pretrained transformer-based temporal models, TimesFM has attracted much attention due to its excellent few-shot learning capability, wide range of applications, and ease of use.  
However, there are significant challenges in applying TimesFM directly to the cloud demand prediction task, which stems mainly from the inherent feature distribution characteristics of the cloud resource demand data. During the performance evaluation of the TimesFM prediction model, when the prediction window length was set to 128, approximately 38% of the test resource pools experienced prediction failure. In contrast, when the prediction window length was adjusted to 64, the proportion of test resource pools with prediction failure was approximately 23%. TimesFM exhibits severe performance degradation on the CDF task, which may be attributed to the significant distributional discrepancy between the forecasting data and the training data, resulting in a weak pool adaptation capability of the model. Additionally, it was observed that the severity of this issue escalates as the prediction length (pred_len) increases.Although we try to optimize the model performance through fine-tuning strategies, it is difficult to find an optimization solution that can effectively balance these distributional differences due to the high distributional heterogeneity of cloud resource demand time series data. This issue arises primarily from two factors: distribution discrepancy and error accumulation. TimesFM's pretrained model is optimized on a dataset with ample historical data, but in few-shot scenarios, the target resource pool's data distribution often differs significantly, making it difficult for the model to capture local demand patterns. Additionally, as the forecasting horizon increases, small errors compound over time, further exacerbating prediction failures. To address these limitations, DimAug-TimesFM incorporates two key innovations: Delivery Period Extracting and dimension augmentation.  
Figure 3: An example of delivery period extracting.
## 4 Discussion  
### WUE Changes in Different Vegetation Types  
Using the GPP and ET of GLASS, this study identified an annual average WUE of 1.57 g C/m\({}^{2}\)/mm for ecosystems in the STC. This value is close to WUE values reported by [PERSON] and [PERSON] et al. for ecosystems in Southwest China, which belongs to the STC [40; 41], which were slightly lower than the those of ecosystems in the mixed subtropical and tropical regions of South Asia [25; 42], and higher than those of ecosystems in temperate regions, such as the Loess Plateau of China [43]. From the perspective of different landscape types, EBF had the highest WUE, followed by DBF and ECF; shrubland and grassland had consistent WUE, while cropland had the lowest WUE. Differences in WUE among landscape types may be due to differences in carbon uptake and water consumption [44]. In the STC, the GPP of EBF was much higher than that of other landscapes, and the gap in ET between different landscapes was smaller; therefore, the WUE of EBF was the largest. The GPP of DBF had obvious seasonal variation; therefore, the WUE of this landscape on an annual scale was not high. Previous studies have generally shown that the WUE of forest ecosystems is higher than that of grassland ecosystems [35], which is consistent with our findings. Woodlands have denser canopies and more developed root systems and thus can intercept and utilize a greater quantity of solar radiation and soil moisture, thereby contributing to greater ecosystem WUE and plant growth [35; 45].  
From 1982 to 2018, global ecosystems generally experienced climate warming. Based on increases in regional precipitation combined with the influence of human activities, such as returning farmland to forests, the ET and GPP in most regions of China have shown upward trends to varying degrees [46; 47]. A comparison of the trends of the normalized GPP and ET is shown in Figure S6. The upward trend of ET, which was slightly higher than that of GPP, was the direct cause of the decline in the overall WUE of the STC. Among the different landscape types, the ET of ECF, DBF, and shrubland showed an upward trend that slightly exceeded that of GPP, leading to a decline in WUE; moreover, the ET and GPP of EBF showed upward and downward trends, respectively, resulting in the most significant decline in WUE. Second, the ET and GPP of grassland had similar upward trends, which led to a relatively stable WUE, and the GPP of cropland had a higher upward trend than ET. Therefore, cropland WUE had an upward trend. [PERSON] et al. (2016) and [PERSON] et al. (2021) conducted global WUE studies from 1982 to the present, and showed a decreasing trend of WUE in the STC [48; 49], which is consistent with the findings of this study. While afforestation can slow climate warming by sequestering carbon, the decline in WUE means that on top of that, a higher proportion of water needs to be consumed to maintain the same level of vegetation carbon sequestration. Therefore, the planning and management of ecological protection and restoration requires more thinking and trade-offs between water resources and hydrology [50].
## 2 Materials and Methods
### E-Nose Measurement  
In accordance with the method of [PERSON] et al. [24], E-nose (PEN3, German AIRSENCE company, Schwerin, German) was applied with a detection time of 120 s, cleaning time of 80 s, pre-injection time of 5 s, injection flow rate of 400 mL/min, and carrier gas (clean air) flow rate of 400 mL/min. At the beginning of measurement, the sensor fluctuated with time and began to flatten after 110 s. The sample was diluted 1:40, and 10 mL was then added to a test tube (20 mL), which was subsequently sealed with plastic wrap. After 30 min in a water bath at 50 \({}^{\circ}\)C, headspace gas of the samples was analysed by E-nose. Data at 114, 115 and 116 s were used to calculate the average for analysis. Each sample was determined three times in parallel. The 10 sensor values corresponding to nine samples (S1-SJ3, SW1-SW3, DL1-DL3) were used for stacking bar chart (Origin 9.0 software, OriginLab Co., Northampton, NC, USA). Principal component analysis was used to analyse the differences of the overall odour among the three groups of samples (SIMACA-P software, Umetrics Co., Malmo, Sweden). Furthermore, the correlation analysis (R software, The Universityof Auckland, Auckland, New Zealand) between the response value of different sensors of electronic nose and the content of flavour substances in GC-MS was carried out to establish the relationships between E-nose data and key VOCs to provide a basis for the rapid detection and flavour optimisation of SW.
# Generalization of ALMM Based Learning Method for Planning and Scheduling
## 7 Reinforcement Learning vs. ALMM Based Learning  
ALMM Based Learning exhibits certain similarities to a general and much more well known method, namely Reinforcement Learning (RL) [6,8,9,11,15,27]. Let us attempt to review these similarities as well as differences. It is worth remembering, though, that these remarks do not constitute an entire comparison of the two methods. Such a comparison would not be possible anyway due to the large variety of RL method variants available. A more comprehensive comparative discussion will be the subject of a separate study. Similarities may be noticed more clearly when uniform terminology is used, which is why this paper attempts to provide equivalent terms for both methods.
# Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
## 2 Interpreting DGA  
Thermal and electrical stressors are the two primary factors that lead to gas production in a functioning transformer. Every defect produces a different amount of dissolved gas and breaks down the oil or paper in a different way. The significance of the quantity varies according to how the specific issue is severed. Information about the kind of stress, its intensity, and the kinds of materials impacted may be found in the nature of the gases that are produced and their relative amounts [18]. Methane and ethylene are created in trace amounts along with significant amounts of hydrogen and acetylene during an electric arc discharge. Acetylene usually makes up 20% to 70% and hydrogen 30% to 90% of the total hydrocarbons in such a failure. In addition, cellulose existing at the fault site may lead to the formation of carbon dioxide and carbon monoxide. The oil may carbonize in some situations [19]. It is thermal flaws that cause oil and paper to deteriorate. Small quantities of hydrogen and ethane are produced along with ethylene and methane when oil overheats. In the event that the malfunction is severe or includes electrical connections, traces of acetylene may occur. When heat defects attack cellulose, large amounts of carbon dioxide and carbon monoxide are created. If there is an oil-impregnated structure involved in the fault, hydrocarbon gases such as ethylene and methane are produced [20]. The transformer state is ascertained using these observed gas concentrations in the standards with various interpretation techniques. The Doernenburg ratio method (DRM), the Rogers ratio method (RRM), and the Duval triangle method (DTM) are such techniques. The gas ratios and gas percentages from gas concentrations employed in these approaches are listed below, as shown Tables 1-3 [21,22]. All datasets can be found at [[https://github.com/ahmedtariq71/Dataset1](https://github.com/ahmedtariq71/Dataset1)]([https://github.com/ahmedtariq71/Dataset1](https://github.com/ahmedtariq71/Dataset1)) (accessed on 13 December 2024).  
* **Doernenburg's Ratio Method (DRM)** \[\text{CH}_{4}/\text{H}_{2},\text{C}_{2}\text{H}_{2}/\text{C}_{2}\text{H}_{4}, \text{C}_{2}\text{H}_{2}/\text{CH}_{4},\text{C}_{2}\text{H}_{6}/\text{C}_{2} \text{H}_{2}\]  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**R1** & **R2** & **R3** & **R4** & **R5** & **Fault\_DRM** \\ \hline
0.188976 & 2.53125 & 3.375 & 0.000123 & 3200 & ARCING \\ \hline
0.000185 & 0.0025 & 1 & 1 & 400 & Normal \\ \hline
0.174797 & 2.52381 & 1.232558 & 0.000189 & 2100 & ARCING \\ \hline
0.004224 & 1.983669 & 324.7626 & 0.02716 & 18.56091 & Normal \\ \hline
0.255319 & 0.00125 & 0.000833 & 1 & 800 & PD \\ \hline \end{tabular}  
* **Rogers Ratio Method (RRM)** \[\text{C}_{2}\text{H}_{2}/\text{C}_{2}\text{H}_{4},\text{CH}_{4}/\text{H}_{2}, \text{C}_{2}\text{H}_{4}/\text{C}_{2}\text{H}_{6}\]  
\end{table}
Table 1: A sample from the DRM dataset.
## 4 Conclusions and Prospects  
This paper provides a comprehensive review of the research progress on fracture evolution, deformation mechanisms, and the development of mechanical constitutive models for fractured rock masses under the THMC coupling effects. It highlights the critical challenges in rock mechanics research under multi-field coupling and identifies key areas for future research and exploration. The following points outline the primary directions and focal areas of future work in this field:  
_I. Mathematical models and analytical methods for multi-scale coupling:_  
Future research in multi-field coupling must account for the behavior of rock masses at different scales, from the microscopic to the macroscopic. To solve real-world engineering challenges, it is essential to develop mathematical models and analytical methods that consider the complex interactions across these scales. A deeper understanding of scale-dependent coupling mechanisms can be achieved through advanced simulations that replicate the physical field environments of rocks at various scales. This multi-scale perspective will help engineers develop more reliable and accurate models for predicting rock behavior in various practical applications, such as deep mining and underground construction.
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas
### Applications and Benefits of PMWIs
#### 2.1.7 Soil Moisture and Drought  
Microwave sensors are extremely useful for retrieving soil moisture information from near surface layers of the Earth, offering quantitative measurements of water content within a shallow surface layer, specifically at 1 to 10 GHz frequencies [99; 100]. At the L-band (1.4 GHz), the soil moisture within the top few centimeters of soil has a substantial effect on the observed Tb, with approximately a 2 Kelvin (K) change for every 1% increase in volumetric soil moisture over bare soil [100]. L-band microwaves are one of the most promising remote sensing techniques for monitoring soil moisture over land surfaces at large scales since they are not sensitive to the effects of the Earth's atmosphere and clouds [101]. Another satellite equipped with an L-band sensor is the Soil Moisture and Ocean Salinity (SMOS) satellite, launched in November 2009. It carries the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), as shown in Table 1[25]. Similarly to SMOS, NASA's Soil Moisture Active Passive (SMAP) radar, launched in January 2015, has used an L-band radiometer to measure soil moisture starting in July 2015, thus ensuring more precise monitoring and measurements [102]. In contrast, most currently used sensors operate at frequencies above 10 GHz. For instance, the SMMR on Nimbus-7 used frequencies of 6.6 GHz and higher [103], the SSM/I operated at frequencies of 19 GHz and above [104], the AMSR-E aboard the Aqua satellite covers frequencies from 6.9 to 89 GHz [105], and WindSat operates between 6.8 and 37 GHz [106].  
The NOAA Soil Moisture Operational Product System (SMOPS) integrates soil moisture data from multiple satellite platforms, including SMAP, Metop-B, Metop-C, GCM-W,and GPM. It combines observations from various sources, including PMWIs such as AMSR2, GMI, and the SMAP L-band radiometer, as well as active sensors and optical data. As an example, Figure 5 illustrates the blended soil moisture distribution on 11 January 2025, derived from these datasets.  
Using these soil moisture retrievals, studies have explored applications such as improving precipitation estimation, creating blended soil moisture products, and reconstructing soil moisture time series [25; 107; 108]. Another crucial use for these retrievals is for drought assessment; for example, [PERSON] and [PERSON] used precipitation data from TRMM, land surface temperature, and soil moisture data derived from AMSR-E to compute a drought index called the Microwave Integrated Drought Index (MIDI) [109]. Other studies utilize merged satellite precipitation products that have PMWI data integrated in them to compute Standardized Precipitation Indices (SPIs), indicators for assessing drought [110].
## 4 Experiment
### Results  
#### 4.5.1 CTR Prediction Results and Analysis  
The CTR prediction results are shown in Table 4. The MDAR model proposed in this chapter was compared with seven baseline models in terms of predicting recommendation algorithms. From Table 4, MDAR performs better than the other compared models on two indicators. In detail, on the MovieLens-1M datasets, compared with the MKR baseline model, MDAR achieves an improvement of approximately 1.2% in AUC and 1.40% in accuracy (ACC) on the test dataset. Similarly, on the MovieLens-20M datasets, MDAR surpasses the basic MKR with an increase of around 1.50% in AUC and 1.2% in accuracy (ACC) on the test dataset. This proves that MDAR is an effective enhanced recommendation of MKR, which can more effectively improve the utilization of the data in RS. In addition, on the BookCrossing dataset, MDAR exhibits notable enhancement, with an improvement of approximately 1.90% in AUC and 1.30% in accuracy (ACC) compared to the MKR baseline model on the test dataset. Similarly, on the Last.FM dataset, MDAR demonstrates a significant improvement, with an approximately 1.90% increase in AUC and a 1.30% increase in accuracy (ACC) on the test dataset when compared to the MKR baseline model. This is because, in a sparse environment, the user's behavior preferences can be more effectively understood by using the user's history of interaction records. Moreover, we compared the CTR prediction of MDAR with KGCN, KGAT, CKAN, and EMKR. MDAR performed well in prediction, thus indicating the added relation-fused multi-head graph attention network can effectively extract the deep relationships in KG, thus helping with recommendations.
## 2 System Model
### Channel Model  
In our model, we separate the two gains obtained from the channel simulation into channel gain obtained directly from the NYU channel simulation, and antenna gain obtained by determining the antenna radiation pattern for every scanning angle after each channel simulation. Using the NYUSIM channel simulator, we determine, in each simulation, the LOS link characteristics between the serving antenna and the receiving MT. From that link, we can determine the channel gain, and the angles of departure (AoD) from the BS towards the MT. The AoDs are then used to determine the array gain using the 3 GPP active array model [9], modeled in Section 2.1.2. The antenna gain for each subpath can be determined from this model, according to its AoD, as shown in the realization in Figure 1. We assume that the antennas are deployed in an urban microcell (UMI) scenario. The channel model is presented in Figure 2. The parameters used for the channel simulation are presented in Table 2. Running a large number of simulations, we empirically fitted the distributions of the gain components into statistical models. We chose the distributions for the channel and antenna gain by determining the best fit out of the common distributions using MATLAB. The distributions' parameters summarized in Table 3 were determined accordingly.  
Figure 1: 2D slice of the 3D antenna pattern realization of the 3 GPP active antenna array model with 256 antenna elements directed perpendicularly to the antenna array over the steering angles in the azimuth and elevation.
## 5 Implementation and Evaluation
### Parameters and Indicators  
The proposed method is influenced by random initial values, and even with multiple experiments conducted on the same dataset, there may be slight deviations in the results. To mitigate these biases and obtain more accurate results, all experimental outcomes are averaged from three repetitions conducted under the same dataset, parameters, and environment. The parameters involved in the experiments are shown in Table 2.  
In the practical implementation, we set \(Max_{edge}=1.8\mathcal{N}\) and \(Max_{Degree}=4\). Time slices are divided every 60 s for sampling, and the total number of time slices is set to 100. \(\rho\) is set to different values based on data to control the expected optimization rate of the program. Consider the optimization ratio \(\Phi=\frac{\tau_{L}}{\tau_{min}}\) as the optimized scale relative to the theoretical lower bound. \(\tau_{L}\) denotes the average delay sum of each time slice, \(\tau_{min}\) refers to the average delay sum without restrictions. Consider \(\mathcal{K}\) to denote the total number of iterations in simulated annealing and \(\mathcal{T}_{run}\) to denote the running time.
# A Comprehensive Review on Clay Soil Stabilization Using Rice Husk Ash and Lime Sludge
## 3 Laboratory Tests
### Standard Proctor Compaction Test
Figure 3 shows that by increasing the amount of LS content, the OMC of the soil increases due to the water-absorbing capacity of the admixtures. Additionally, it is evident that adding 10-15% of LS content escalates the OMC value in soil samples, and by adding more than 15% of LS content, a sudden drop in OMC value is observed. This is because, beyond a certain threshold of LS content (more than 15%), the soil structure becomes overly rigid, reducing its ability to retain moisture, thereby leading to a lower OMC since less water is required for compaction. Figure 4 reveals that incorporating RHA and LS into the soil samples enhances the OMC. It is observed that OMC increases progressively with the higher amount of admixtures added due to the high water absorption capacity of both materials, which increases the overall water demand for proper compaction. Additionally, the pozzolanic reaction between RHA, LS, and clay soil minerals consumes water, further increasing OMC. Figures 5-7 demonstrate a decrease in the MDD of the soil samples as the percentage of RHA and LS content increases. This reduction in MDD can be attributed to the lower specific gravity of the admixtures, which is less than the natural soil [113]. Additionally, reduced MDD indicates less strength of the matrix, which is compensated by the binding effect of the RHA and/or LS, increasing cohesion. The MDD decreases when RHA and LS are incorporated because both materials increase the void spaces within the soil matrix, making it less compact and requiring more water for lubrication, further reducing dry density. The pozzolanic reaction between RHA, LS, and clay soil also leads to the formation of cementitious compounds, which create a more open, flocculated soil structure, contributing to the reduction in MDD.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding  
The shortcoming of the mailman algorithm is the restriction that the wiring matrix must be a permutation. Thus, it does not do computations except for multiplying the codebook matrix to the output of the wiring. The size of the codebook matrix grows exponentially with the number of computations it executes. As a result, the matrix dimension must be huge to achieve even reasonable accuracy.  
We cure this shortcoming, allowing for a few additional entries in the wiring matrix. To keep the computational cost as low as possible, we follow the philosophy of the CORDIC algorithm and allow all non-zero entries to be signed powers of two only. We do _not_ restrict the wiring matrix to be a rotation, since there is no convincible reason to do so. The computational cost is dominated by the number of non-zero entries in the wiring matrix. It is not particularly related to the geometric interpretation of this matrix.  
The important point, as the analysis in Section 5 will show, is to keep the aspect ratio of the codebook matrix exponential. This means the number of rows \(N\) relates to the number of columns \(K\) as  
\[K=2^{RN} \tag{8}\]  
for some constant \(R\), which is \(\log_{2}T\) in the mailman algorithm, but can also take other values, in general. Thus, the number of columns scales exponentially with the number of rows. Alternatively, one may transpose all matrices and operate with logarithmic aspect ratios. However, codebook matrices that are not far from square perform poorly.