esa-sceva/satcom-chunk-collection huggingface dataset:
# Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning
## 2 Literature Review  
### Temporal Aggregation  
Temporal aggregation for forecasting has been extensively researched in the last two decades and may be utilized using two different approaches; either by selecting the \"best\" temporal aggregation level where the forecasts should be produced or by combining the forecasts produced at multiple levels in an \"optimal\" manner. [PERSON] et al. [19] addressed the duality of temporal aggregation, arguing that \"each school of thought presents different advantages and has different limitations\".  
Aggregating time series data to a certain lower frequency can become beneficial both for slow- and fast-moving series [32; 33; 34]. With regard to fast-moving data, [PERSON] et al. [35] aggregated monthly and quarterly series from the tourism industry and observed that aggregated yearly forecasts were more accurate than forecasts derived from higher frequency data. Similarly, when analysing slow-moving inventory data, [PERSON] et al. [8] found that forecasts produced from lower frequency data resulted in better accuracy on average.  
Nevertheless, selecting the \"best\" aggregation level is not trivial and, as a result, no universal approach has been identified for addressing this task. For instance, [PERSON] et al. [8] proposed a heuristic rule corresponding to the lead time plus one review period to determine an appropriate level of aggregation for slow-moving data, while [PERSON] et al. [10] introduced a method that aggregates the original time series based on \"demand buckets\" instead of time windows. Another heuristic method was proposed by [PERSON] et al. [31] who approached the selection of the \"best\" aggregation level as an optimization problem, focusing on the minimization of in-sample forecast errors and information criteria.
# Sigma Routing Metric for RPL Protocol
## 6 Results
### Lifetime  
The network lifetime is defined as the time during which the network is operational. It ends when a node runs out of energy and can no longer send packets.  
It is clearly shown in Figure 16 that with the optimization of SIGMA-ETX, the lifetime of the network is higher than with the other metrics. This is due to the selection process of SIGMA-ETX, which tries to identify the best route by preferring paths without long hops, thus avoiding extra packet loss and latency. The prevented packet losses result in less retransmissions and in turn, lower power consumption. Particularly, the results in a 100-node scenario shows evidence that OF0 only achieves 70.83% of SIGMA-ETXs lifetime performance, MRHOF only 77.08%, and PH-ETX 87.50%.
## 1 Introduction
[34] provided accurate evapotranspiration data for the days without precipitation of an unirrigated grass commonly used on weather stations. [PERSON] et al. [35] studied wheat crop evapotranspiration partitioning with two SFLs of 30 cm diameter and different depths, one of 300 mm and other of 900 mm. The evapotranspiration estimated by the lysimeters were quite consistent with other methods, except in very wet or dry conditions. When it happened, the lysimeters slightly overestimated or underestimated the values. Using tractors with agricultural machinery in the area around the lysimeters reduces hand labour and is usual in commercial farms. These tractors introduce new loads that must be supported by the lysimeters. These loads can be particularly important in small lysimeters as their lightweight structure might suffer deformations that affect the weighing system. [PERSON] et al. [36] studied the influence of a footpath in two weighing lysimeters showing different evapotranspiration results during harvest period. The data illustrated that the exposure effect can modify the water balance. However, no studies have been carried out to analyse the possible effect of the machinery. It is probably due to the fact that their use is often restricted to research centres and not in agricultural farming. The design provided in this article allows farmers to incorporate them into their crops for an optimal water resource management using available technologies. Among the required lysimeter characteristics to achieve farmer needs, the following ones stand out: (i) small dimensions in order to be installed and integrated in the field within the plantation framework; (ii) easy to be transported and located, at the end of a crop cultivation season, to another production area, (iii) an affordable cost for a rapid amortization and (iv) good performance under tractor operations. One of the main difficulties in carrying out the development of the prototype, known as LP1, consists of designing equipment of reduced dimensions and weight for its transport, which incorporates a container for the crop that can withstand the stresses caused by the soil, the loads of which are supported by a weighing system, without being affected by the surrounding soil. This article shows the design of the prototype carried out to meet the previously established needs, its development, installation and field evaluation. It provides a high-precision quantification of evapotranspiration by weight change over short intervals of time and, therefore, an improvement of water management in intensive farms.
# Modeling and Simulation Tools for Fog Computing--A Comprehensive Survey from a Cost Perspective
## 2 Basic Concepts
### Fog Computing Architecture  
There is no standard architecture for fog computing, and research works often rely on different architectures. Figure 1 shows a high-level overview of the fog computing structure. In the most common scenario, it consists of three key layers: cloud, fog and end devices:  
* _End devices layer:_ This ground layer represents the end devices such as sensors, actuators or things, mobile smartphones and tables, smart meters, aircrafts and smart vehicles, desktops PCs, laptop computers accompanied with applications [2; 22; 23]. End devices may be considered as human-operated resources [23] and they provide a range of computing capabilities. All these elements form a communication network, and their data are transmitted to the cloud through the fog layer.
* _Fog layer:_ It is the layer above the end devices layer. According to Reference [2], any device which is able to process, store and connect to the network can be considered as a fog processing devices. In this sense, some devices can be considered as both IoT and fog devices, smartphones begin a characteristic example. The fog layer is a collection of processing devices, gateways, and networked devices (routers and switches) that are deployed between network edges and clouds. Fog resources are interconnected and used to deliver a number of services to users such as computing, storage and network services. Fog devices are shaping a distributed system that offers services for a certain set of end devices in a specific location and handle data which are transmitted by such devices [24].
* _Cloud layer:_ Physical data center nodes are placed in the cloud layer. Each node has CPU(s), main memory and network bandwidth and is used to satisfy user requests for resources. Control strategies allow the management and scheduling of cloud resources according to their load demands [22]. Clouds are connected to Wide Area Networks (WANs) and provide economic benefits, elastic services, data-intensive analysis for end-users [13], quality of the offered services and high degree of fault tolerance [25]. However, clouds suffer from high latency and bounded capacity due to current WAN connections. Moreover, as cloud computing works in a centralized manner, it is incapable of supporting context-aware computing for IoT applications [13].
# Seismic Random Noise Attenuation via Low-Rank Tensor Network
###### Abstract  
Seismic data are easily contaminated by random noise, impairing subsequent geological interpretation tasks. Existing denoising methods like low-rank approximation (LRA) and deep learning (DL) show promising denoising capabilities but still have limitations; for instance, LRA performance is parameter-sensitive, and DL networks lack interpretation. As an alternative, this paper introduces the low-rank tensor network (LRTNet), an innovative approach that integrates low-rank tensor approximation (LRTA) with DL. Our method involves constructing a noise attenuation model that leverages LRTA, total variation (TV) regularization, and weighted tensor nuclear norm minimization (WTNNM). By applying the alternating direction method of multipliers (ADMM), we solve the model and transform the iterative schemes into a DL framework, where each iteration corresponds to a network layer. The key learnable parameters, including weights and thresholds, are optimized using labeled data to enhance performance. Quantitative evaluations on synthetic data reveal that LRTNet achieves an average signal-to-noise ratio (SNR) of 9.37 dB on the validation set, outperforming Pyseistr (6.46 dB) and TNN-SSTV (6.10 dB) by 45.0% and 53.6%, respectively. Furthermore, tests on real field datasets demonstrate consistent enhancements in noise suppression while preserving critical stratigraphic structures and fault discontinuities. The embedded LRTA mechanism not only improves network interpretability, but also reduces parameter sensitivity compared to conventional LRA methods. These findings position LRTNet as a robust, physics-aware solution for seismic data restoration.  
Keywords:low-rank approximation (LRA); deep learning (DL); total variation (TV); weighted tensor nuclear norm (WTNN); random noise
# Evaluation of Metal-Organic Frameworks as Potential Adsorbents for Solar Cooling Applications
## 2 Thermally Activated Cooling Systems  
The use of thermal energy for heating and cooling applications is one of the feasible options for the effective utilization of renewable energy sources such as solar, waste heat, biomass, etc. [11; 12]. This can help to reduce the dependence on fossil fuels and to achieve the goals of sustainable development.  
Figure 3: Coefficient of performance (COP) as a function of temperature [10]. Reprinted with permission from [10]. Copyright from 2007 Elsevier.  
The naturally available energy sources can be used to drive thermally activated cooling systems such as adsorption [13; 14] and desiccant cooling [4]. Apart from that, absorption cooling is also an option that can make use of these low-grade energy sources effectively [15]. A classification of thermally activated cooling is presented in Figure 4.  
The working of thermally activated cooling technology is based on two processes, which are adsorption and regeneration as illustrated in Figure 5. During the process of adsorption, process air is passed through a dehumidification unit composed of some adsorbent material. The adsorbent material adsorbs the moisture from the process air depending upon its adsorption capacity. The adsorbed moisture by the adsorbent material is removed during the regeneration process. Thermal energy, depending upon the required regeneration temperature is provided during this step. In this way, the process of adsorption and desorption continues in a cyclic loop [16].  
Thermally driven cooling technology provides many advantages over conventional HVAC devices which are based on the vapor compression cycle [17]. The direct use of thermal energy and low heat of regeneration are some of the advantages offered by this technology [18; 19; 20]. A summary of the potential advantages offered by this technology is summarized in Figure 6. Silica gel is widely used as adsorbent material for different cooling applications [21; 22; 23] such as trigeneration [24; 25], air conditioning for automotive [26], and solar cooling [27; 28]. However, adsorption at higher pressure is one of the major problems of silica gel utilization as adsorbent. This means that water adsorbed/desorbed by silica gel will be a part of its total adsorption capacity at lower pressure or normal operating conditions of the cycle. This will result in a large amount of adsorbent material required to achieve the desired  
Figure 4: Classifications of thermally activated cooling.  
Figure 5: Two-step process of thermally activated cooling.  
output and bulky systems having high capital cost [29; 30; 31]. Therefore, new and advanced adsorbent materials are required which can exhibit better properties according to the system requirements. The performance improvement of these systems is the only way forward for the development and promotion of this technology.
## 4 Experimental Process and Result Analysis
### Comparative Experiments  
To better demonstrate the performance of the presented method, two comparative experiments are carried out. The first comparative experiment is between the presented method and other common methods, and the second comparative experiment is between the presented method and methods studied by other scholars.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 6 Results Using the Spoofing Detection Algorithm  
After observing the correct working of the ECADLL for the estimation of the signal parameters using the TEXBAT dataset in Section 5, in this section, we present the results for the spoofing detection algorithm, along with a comparison versus another technique. Numerical results highlighting the detection capabilities and the improvement brought by the inclusion of RM are also presented.
## 4 Application to an Additive Manufacturing Benchmark Test
### Polynomial Chaos Expansion  
Along with the DNN, we used a PCE model to construct a surrogate model for our SLM process. We conducted this comparative study to determine the best surrogate model for our problem, one that can simultaneously reduce the computation time and increase the accuracy of the predictions. Thus, similar to the DNN, the PCE was trained and tested with outputs of 360 samples calculated using the Ansys additive software. For the comparison, we generated a data set of 6000 input samples with the Sobol sampling technique, and then used our trained PCE and DNN surrogate models to predict normal strains as outputs for the points at z = 9.536. Figure 17 shows a comparison between the DNN and PCE models, and we can conclude that the results are very close to each other. The mean square error between the PCE and the DNN is 7.17 \(\times 10^{-11}\), proving that both models are equally adequate for prediction. In Table 3, we present a comparison of the computational cost for both models, and Table 4 presents the error between experimental and predicted values. The results show that the PCE is faster than the DNN in training g, but almost three times slower than the DNN in prediction. Considering the overall time, the DNN is faster than the PCE. Therefore, in conclusion, we proceeded with our investigation using a DNN instead of a PCE.  
Figure 16: (**a,b**) represent the convergence of standard deviation with the increase in sample size at x = 15 mm and 45 mm for x strains, respectively, whereas (**c,d**) represent the y strains at x = 15 mm and 45 mm. Similarly, (**e,f**) represent the convergence plots for z strains at x = 15 mm and 45 mm respectively.  
Figure 17: The (**a,b**) represents the comparison of PCE and DNN models with x and y strains respectively.
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 2 Signal Model  
In this paper, one sound source is considered, and \(M\) microphones are used to collect the observation signal containing the reverberant and noisy speech signal. By performing a short-time Fourier transform (STFT), the received signal of the \(m\)th microphone can be expressed as \(y_{m}\) (\(k\),\(l\)) in the frequency domain, where \(k\) and \(l\) are the indices of the frequency bins and frames, respectively. The entire stacked microphone signals vector is denoted as follows:  
\[\mathbf{y}(k,l)=[y_{1}(k,l),\cdots,y_{m}(k,l),\cdots,y_{M}(k,l)]^{T} \tag{1}\]  
where superscript \"\(T\)\" indicates the transpose of the matrix. Thus, the multimicrophone signals corrupted by reverberation and noise can be written as follows:  
\[\mathbf{y}(k,l)=\mathbf{x}(k,l)+\mathbf{v}(k,l) \tag{2}\]  
where the vectors \(\mathbf{x}(k\),\(l\)) and \(\mathbf{v}(k\),\(l)\) are the STFT results of the speech signal containing reverberation and the noise signal, respectively. Here, the speech and noise are assumed to be mutually independent. Since the algorithm is finished independently in each frequency bin, the index \(k\) is omitted for the simple expression in the subsequent parts.
## 4 Content and Results of Research
### Results of Research
#### 4.2.1 Results of the Experiment
There was a significant difference in academic performance between the experimental group and the control group, indicating that the digital twin system has a notable positive effect on students' academic achievement. Students in the digital twin system engage in virtual operations and field investigations, resulting in a higher performance in landscape effects from planting, transplant survival rates, and seedling emergence rates compared to the control group. However, the control group showed a lack of mastery in specific skills related to plant growth and maintenance. \begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Vaicance** & **Group** & **N** & **Mean** & **SD** & **t** & \(\mathbf{p}\) \\ \hline \multirow{2}{*}{Creative thinking} & Experimental & 35 & 10.46 & 1.874 & \multirow{2}{*}{0.343} & \multirow{2}{*}{0.645} \\  & Control & 35 & 10.23 & 1.133 & & \\ \hline \multirow{2}{*}{Critical thinking} & Experimental & 35 & 21.66 & 3.245 & \multirow{2}{*}{2.142} & \multirow{2}{*}{0.034 *} \\  & Control & 35 & 19.78 & 3.649 & & \\ \hline \multirow{2}{*}{Cooperative learning} & Experimental & 35 & 12.52 & 1.763 & \multirow{2}{*}{2.146} & \multirow{2}{*}{0.029 *} \\  & Control & 35 & 11.47 & 1.548 & & \\ \hline \multirow{2}{*}{Cognitive load} & Experimental & 35 & 10.45 & 1.678 & \multirow{2}{*}{-2.435} & \multirow{2}{*}{0.012 *} \\  & Control & 35 & 11.34 & 1.694 & & \\ \hline \multirow{2}{*}{Learning experience} & Experimental & 35 & 12.74 & 2.438 & \multirow{2}{*}{2.056} & \multirow{2}{*}{0.032 *} \\  & Control & 35 & 10.86 & 1.973 & & \\ \hline \multirow{2}{*}{Learning motivation} & Experimental & 35 & 11.56 & 2.134 & \multirow{2}{*}{1.237} & \multirow{2}{*}{0.178} \\  & Control & 35 & 10.98 & 2.087 & & \\ \hline \multirow{2}{*}{Academic record} & Experimental & 35 & 89.65 & 11.34 & \multirow{2}{*}{3.762} & \multirow{2}{*}{0.000 ***} \\  & Control & 35 & 72.14 & 13.65 & & \\ \hline \hline \end{tabular}  
* \(p<0.05\), *** \(p<0.001\).  
\end{table} 
Table 1: Statistical test results of learning effect of experimental group and control group.
## 7 The Influence of the Constraint Range on the Optimization Results
### Influence of the Constraint Range for the Cable Force on the Optimization Results  
Maintaining all of the other constraints, the influence of varying the cable force constraint from 1600 kN \(\leq T_{\mathrm{i}}\leq 0.4T_{\mathrm{pdi}}\) to 2000 kN \(\leq T_{\mathrm{i}}\leq 0.4T_{\mathrm{pdi}}\) on the optimization outcomes is examined. Figure 19 illustrates the variations in the structural states as the cable force constraint range is reduced. Changes in the cable force constraint range significantly impact the maximum sag of the main beam and the maximum tensile and compressive stresses at the top and bottom edges of the main beam, respectively; the impact on the other structural states is less than 5%. Specifically, the maximum compressive stress at the bottom edge of the main beam increases with a decreasing cable force constraint range. The maximum tensile stress at the top edge of the main beam initially increases and then decreases with a decreasing range. The maximum deflection of the main beam initially decreases and then increases with a decreasing range. Overall, a cable force constraint of 1800 kN \(\leq T_{\mathrm{i}}\leq 0.4T_{\mathrm{pdi}}\) results in a smoother structural line, smaller maximum positive and negative bending moments in the main beam and pylons, and a more uniform cable force distribution, suggesting that this constraint range is reasonable.  
Figure 19: Variation in values of selected structural responses with decreasing constraint range for cable force. (**a**) Structural bending energy; (**b**) structural displacement; (**c**) structural bending moment; (**d**) structural normal stress; (**e**) cable force; (**f**) vertical support reaction.
# Fronthaul Design for Wireless Networks
## 4 Fronthaul Design Study Results
### Network Assessment  
In order to assess the impact of the RRH density on the selection of cost-effective communication technologies for C-RANs, two distinct environments were considered, namely a rural and an urban environment [36], which had different RRH densities; the characteristics of these test environments are presented in Table 8. In addition, the costs of one BBU was assumed to be equal to 167,000 euros [33; 37], as well as it was assumed that each BBU had enough capacity to support as many RRH-BBU links as needed; furthermore, for each of these links, it was assumed that the BBUs could support the required bit rate, which was set equal to 1 Gbps for all cases. Moreover, the \(K\)-means clustering procedure was repeated enough times so that each solution using a certain number of BBUs converged to a minimum in terms of network costs.  
Following the previous settings, the network planning algorithm was run multiple times for each environment and considering, in a separate manner, the four aforementioned test scenarios (cf. Table 7), in order to evaluate the impact of different weather conditions on the fronthaul design and costs.