esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### System Model  
We model the power consumption, SLA and resource wastage of a data center in the following subsections. Following the earlier works of [1; 3], we exploit the resource wastage factor to devise the resource usage at the PM level. Table 3 includes the list of notations used in this paper for better readability.  
### System Model  
We model the power consumption, SLA and resource wastage of a data center in the following subsections. Following the earlier works of [1; 3], we exploit the resource wastage factor to devise the resource usage at the PM level. Table 3 includes the list of notations used in this paper for better readability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Utilization (\%)** & **ThinkSystem SR850 V3** & **ThinkSystem SD535 V3** \\ \hline
0 & 375 & 222 \\
10 & 580 & 449 \\
20 & 670 & 552 \\
30 & 758 & 634 \\
40 & 848 & 709 \\
50 & 947 & 773 \\
60 & 1058 & 842 \\
70 & 1164 & 920 \\
80 & 1436 & 999 \\
90 & 1879 & 1199 \\
100 & 1916 & 1321 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power usage in watts of different servers at different utilization levels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(\mathbb{H}\) & The set of servers or hosts \\ \(\mathbb{A}(\mathbb{H})\) & The set of active hosts, where \(\mathbb{A}\subset\mathbb{H}\) \\ \(\mathbb{V}\) & The set of VMs \\ \(\mathbb{Z}\) & The set of (VM,PM) pairs \\ \(\mathbb{M}(v)\) & Processing power in MIPS \\ \(h^{th}\) & The chosen server for hosting \\ \(\mathbb{R}^{\prime}(v)\) & Requested resources for VM \(v\) \\ \(\mathbb{R}^{\prime}(h)\) & Available/free resources of host \(h\) \\ \(\mathbb{P}^{e}(h)\) & Power efficiency of host \(h\) \\ \(\mathbb{E}^{e}(h)\) & Energy efficiency for host \(h\) \\ \(\mathbb{P}(h)\) & Current power consumption of host \(h\) \\ \(\mathbb{P}^{\prime}(h,v)\) & Expected power consumption of host \(h\) after hosting VM \(v\) \\ \(\mathbb{S}(h)\) & The current SLA violation of a host \(h\) \\ \(\mathbb{S}^{\prime}(h,v)\) & The expected SLA violation of host \(h\) after hosting VM \(v\) \\ \(\mathbb{R}^{v}(h)\) & Resource wastage factor of host \(h\) \\ \(U^{cpu}(h)\) & Normalized CPU usage of host \(h\) \\ \(U^{ram}(h)\) & Normalized RAM usage of host \(h\) \\ \(Y^{cpu}(h)\) & Normalized free/remaining CPU of host \(h\) \\ \(\mathcal{F}^{ram}(h)\) & Normalized free/remaining RAM of host \(h\) \\ \(\mathbb{P}^{max}(h)\) & Maximum power for a host \(h\) when it is 100\% utilized \\ \(\mathbb{P}^{idle}(h)\) & Power of host \(h\) when it is idle \\ \(\mathbb{P}^{df}(h,v)\) & The increase in power consumption of host \(h\) after hosting VM \(v\) \\ \(\mathbb{S}^{df}(h,v)\) & The difference between \(\mathbb{S}^{\prime}(h,v)\) and \(\mathbb{S}(h)\) \\ \(\mathbb{F}(h,v)\) & Denotes \(\mathbb{P}^{df}(h,v)\) multiplied by \(\mathbb{S}^{df}(h,v)\) \\ \(V_{h}(t)\) & SLA violation of host \(h\) at time \(t\) \\ \(A^{T}(h)\) & Total time in which host \(h\) is active \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of notations used in this paper.
exposure models to evaluate the impact of the new mm wave frequency bands. In References [10; 11; 12], the authors studied the exposure levels caused by specific devices in uplink and downlink scenarios but limited only on a few configurations, and in References [13; 14; 15], the authors faced the variability of the exposure scenario by using ray tracing and statistical techniques [13; 14; 15].  
It is undeniable that the introduction of new frequencies and new techniques of radio physical access will have an impact on human radio-frequency electromagnetic field (RF-EMF) exposure, and the design of new exposure assessments that take into account the heterogeneity of different link parameters (e.g., the number of array antennas, the dielectric properties of users, the 3D beamforming patterns) still represents a challenging task [16; 17; 18].  
The present work aimed to face the challenge of studying the exposure variability in presence of this heterogeneity. In particular, the novelty of the paper is the use for the first time stochastic (metamodeling) techniques combined with the classical computation techniques (deterministic dosimetry). Stochastic dosimetry was successfully applied previously for both low- and high-frequency exposure scenarios [19; 20], allowing to enormously reduce the associated computational costs by substituting an expensive computational model with a functional approximation of the original model, much faster to evaluate. In this way, starting from the results of a small number of expensive computational simulations and coupling them with surrogate models (metamodeling), it is possible to estimate the exposure in a large number of configurations and conduct statistics and sensitivity analyses [19].  
The heterogeneity afforded in this study was introduced by the beamforming capability of the antenna of an access point (AP) in an indoor environment. In detail, the AP antenna was modeled by a uniform planar array (UPA) with 64 patch elements at 3.7 GHz that is inside the licensed range of frequencies below 6 GHz that the first 5G network installations will use (in Italy the range is 3.6-3.8 GHz [16]). A child model was used in the simulations to obtain the human exposure levels in terms of the specific absorption rate (\(SAR\)), as specified in the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines [21]. The joint use of deterministic and stochastic methods allowed us to expand the exposure assessment from a few specific beamforming patterns of the UPA antenna (as investigated in our previous study [22]) to the entire space of variability of the antenna pattern.  
Details of the methodology used and a brief discussion on the surrogate models adopted are presented in Section 2. The numerical results of the simulation campaign interpolated by the use of the surrogate models are presented and analyzed by means of statistical and global sensitivity in Section 3. The results of statistical and sensitivity analyses are discussed in Section 4. Finally, the conclusions of the work are drawn in Section 5.
## 4 Experiments and Results
### Simulation Results  
#### 4.2.1 Performance of Deep Reinforcement Learning Policy  
The average reward curve was applied to evaluate the DRL performance. Figure 7 presents the average reward curve over AV penetration rates. The smoothing of the curve of entire cases illustrates that the DRL policy totally converged. Additionally, when the AV penetration rate was higher, it obtained a superior average reward. Full autonomy traffic performed better than other AV penetration rates. This indicates that full autonomy traffic achieved the highest average reward of 135.611.
## 1 Introduction
Experimental and field evidence has demonstrated that plants adapt to prevailing environmental conditions by reducing stomatal density under drought pressure [17], elevated atmospheric CO\({}_{2}\)[18], and low irradiance conditions [19]. Thus, understanding the drivers of changes in GPP is a complex task, but recent studies have made advances using terrestrial biosphere models (TBMs). TBMs provide a powerful tool to integrate our understanding of ecosystem functioning on site to global scales in response to multiple factors, which can be grouped into three categories: vegetation structure (e.g., land cover and LAI), environmental factors(e.g., atmospheric CO\({}_{2}\) concentration and temperature, solar radiation, and soil moisture and fertility), and biophysical parameters (e.g., photosynthetic capacity and stomatal conductance) [12; 20; 21]. Although the impacts of changes in external factors on GPP have been well studied, the impacts of changes in internal factors on GPP remain poorly understood. For example, the maximum rate of carboxylation at 25 \({}^{\circ}\)C (V\({}_{\text{cmax}}\)) is as a key biophysical parameter determining leaf photosynthetic capacity and largely determines the accuracy of GPP estimated by the photosynthesis model of [PERSON] et al. [22]. Although V\({}_{\text{cmax}}\) varies with space and time [16], it has been widely treated as a constant parameter that varies only with plant functional types (PFTs). TBMs with prescribed PFT-dependent V\({}_{\text{cmax}}\) values are theoretically inaccurate when simulating the carbon exchange between the terrestrial ecosystem and atmosphere [23]. With advances in remote sensing technology, spatially and temporally continuous global V\({}_{\text{cmax}}\) datasets have been derived from remote sensing data [24; 25; 26], providing a new opportunity to better understand the response of European GPP dynamics to multiple external and internal factors. The main objective of this study was to quantify the spatial and temporal dynamics in GPP across Europe and its drivers using a process-based model with consideration of spatial and temporal variations in V\({}_{\text{cmax}}\). We first estimated the long-term trend and spatial distribution of European GPP at a medium spatial resolution and 8-day temporal resolution. Then, we quantified the relative role of different factors on GPP. Specifically, we aimed to answer the following questions: (a) Can the observed greening across Europe result in a proportional increase in GPP? (b) How do land cover change and climate change contribute to European GPP? (c) Is the widespread assumption of V\({}_{\text{cmax}}\) as a constant likely to affect the magnitude and long-term trend of GPP estimations using the Farquhar model? Exploiting the availability of long-term multisource time series datasets of satellite observations, meteorological factors, and site-level flux observations, we performed a series of model simulations to achieve the above objectives.
###### Abstract  
In this work, a novel signal processing method is proposed to assist the Receiver Autonomous Integrity Monitoring (RAIM) module used in a receiver of Global Navigation Satellite Systems (GNSS) to improve the integrity of the estimated position. The proposed technique represents an evolution of the Multipath Distance Detector (MPDD), thanks to the introduction of a Signal Quality Index (SQI), which is both a metric able to evaluate the goodness of the signal, and a parameter used to improve the performance of the RAIM modules. Simulation results show the effectiveness of the proposed method.  
Global Navigation Satellite System (GNSS); signal processing; Receiver Autonomous Integrity Monitoring (RAIM); integrity +
Footnote †: journal: Sensors
## 4 Experiments
### Settings
#### 4.1.2 Implementation Details  
The model used in this study follows a basic CNN architecture, as outlined in Section 3.1.2 of the Methodology. The input to the model is image data, and the output is the corresponding classification of the image into one of the three groove levels. The loss function of the model is the cross-entropy loss function, as shown in Table 1.  
The first convolutional layer of the model outputs 64 channels, with a 3 \(\times\) 3 kernel size and a stride of 1, ensuring that the spatial dimensions of the input are preserved through'same' padding. For the subsequent three convolutional layers, the kernel size and stride remain the same, but the number of channels doubles with each layer, progressively extracting more complex features from the input data. The model also includes two fully connected (dense) layers. The first fully connected layer consists of 2048 neurons, and the second fully connected layer consists of 1024 neurons, providing the capacity to learn intricate feature representations. For more details on the network architecture, please refer to Figure 1.  
The model was implemented using the PyTorch framework (version 2.0.1) in Python (version 3.8.17), an open-source deep learning library widely used in research and industry. The training was conducted on a machine running Windows 10 with 32 GB of RAM. The model leveraged GPU acceleration for faster training, utilizing an NVIDIA RTX 4090 GPU with 24 GB of VRAM. All software frameworks and training hardware used in this study are open-source or commercially available and do not require any additional licenses for research or academic purposes.  
The specific parameters used during the iterative training process are detailed in Tables 1 and 2. Each experiment for the different algorithms was repeated 10 times to ensure consistency and reliability of the results. For each iteration, all parameters--including weights, biases, and the order of sample inputs--were randomly initialized to avoid any biases in the training process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & \\ \hline Loss function & \(L=-\frac{1}{H}\sum\limits_{S=1}^{H}y_{S}^{\text{T}}\text{log}(O_{S})\) \\ Learning rate: & 0.001 \\ Batch size: & 8 \\ Initial weight: & \(w\in[-0.5,\ 0.5]\) \\ Initial bias: & \(b\in[-0.5,\ 0.5]\) \\ Number of iteration: & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of FOCNN and DE-FOCNN.
## 3 Results  
### Results of the BCT Tests  
The results of the BCT tests are presented in Table 3 and Figure 7. Table 3 shows the maximum and average of compression force for each cutout size. The table shows the length-to-width ratio for each box type as well. The maximum compression force was 2842 N for the box with dimensions of 400 \(\times\) 300 \(\times\) 300 mm and 0% cutout. In each cutout  
Figure 5: Setup of the box compression tests (BCTs): (**a**) control sample and (**b**) cutout sample.  
Figure 6: Typical BCT force–deformation diagram.  
case, the maximum compression force was observed for the box with dimensions of 400 \(\times\) 300 \(\times\) 300 mm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Length (mm)** & **Width (mm)** & **Length-to-Width Ratio** & **Cutout(\%)** & \
\begin{tabular}{c} **Maximum Compression** \\ **Force (N)** \\ \end{tabular} &
\begin{tabular}{c} **Average Compression** \\ **Force (N)** \\ \end{tabular} \\ \hline  & & & 0 & 2461 & 2261 \\
200 & 300 & 0.66 & 20 & 2364 & 2217 \\  & & & 40 & 2037 & 1851 \\  & & & 60 & 1447 & 1346 \\  & & & 80 & 685 & 614 \\ \hline  & & & 0 & 2594 & 2367 \\  & & & 20 & 2382 & 2275 \\
300 & 300 & 1 & 40 & 2197 & 1981 \\  & & & 60 & 1470 & 1373 \\  & & & 80 & 814 & 734 \\ \hline  & & & 0 & 2842 & 2651 \\  & & & 20 & 2608 & 2537 \\
400 & 300 & 1.33 & 40 & 2351 & 2291 \\  & & & 60 & 1771 & 1656 \\  & & & 80 & 993 & 946 \\ \hline  & & & 0 & 2666 & 2402 \\
500 & 300 & 1.66 & 20 & 2316 & 2203 \\  & & & 40 & 2158 & 2066 \\  & & & 60 & 1713 & 1603 \\  & & & 80 & 1003 & 877 \\ \hline  & & & 0 & 2624 & 2339 \\  & & & 20 & 2371 & 2189 \\
600 & 300 & 2 & 40 & 2082 & 1980 \\  & & & 60 & 1745 & 1591 \\  & & & 80 & 915 & 862 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary table of measurement results.  
Figure 7: Maximum force–compression diagrams according to cutout ratios: (**a**) 0% cutout ratio; (**b**) 20% cutout ratio; (**c**) 40% cutout ratio; (**d**) 60% cutout ratio; (**e**) 80% cutout ratio.  
Figure 7 shows those curves for the samples where the maximum force was observed on different box sizes during the compression tests. Observing the compression force as a function of the length-to-width ratio, the maximum compression force occurred for the box size with a ratio of 1.33. This length-to-width ratio exceeded the values of the other specimens for all sizes, regardless of the cutout. The values of maximum compression forces for the other samples were, in decreasing order, 1.66, 2, 1 and 0.66, so the worst result in all cases was obtained by the box with a base size of 200 \(\times\) 300 mm. The maximum compression force decreases as the cutout area increases. A significant decrease in compressive force occurs at a cut-off ratio of 80% (Data from all measurements are available from the authors).
# Development of Multi-Channel Seismic-Electrical Combined Rolling Coverage Measurement System
## 1 Introduction
Multi-channel, multi-functional, multi-parameter, high-power, and wide-range capabilities will become a trend in the development of geophysical instruments [27]. This paper improves upon the limitations of current traditional geophysical instruments by proposing a multi-functional, high-power, multi-channel, rolling, fast measurement electrical-seismic combined measurement system. By integrating both seismic and electrical methods for joint exploration, the system breaks away from the traditional approach of relying on a single geophysical method. It enables high-precision, multi-parameter geophysical data acquisition, effectively reducing ambiguity and enhancing the accuracy of exploration results. When measuring electrical signals, the power can exceed 10 kW. The instrument supports rolling coverage measurements, significantly improving work efficiency.
# Molecular Imaging with \({}^{68}\)Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
## 4 Biomedical Applications of \({}^{68}\)Ga-Labeled Nanoparticles  
Molecular imaging (MI) is defined as the ability to visualize and quantitatively measure the biochemical processes in a living organism at cellular and molecular level. Imaging techniques, such as optical imaging (OI), magnetic resonance imaging (MRI), magnetic particle imaging (MPI), computed tomography (CT), ultrasound (US), photoacoustic (PA), positron emission tomography (PET) and single photon emission computed tomography (SPECT), provide a deeper understanding of fundamental biological processes, allowing to relate their structure and function (Figure 3). Furthermore, MI enables longitudinal monitoring of subjects, facilitating long-term observations that allow elucidating specific behaviors, efficacy and failure causes of treatments.  
Nevertheless, the need for improvement of spatial resolution along with the temporal resolution and the signal-to-noise ratio (SNR) of images has encouraged the development of innovative contrast agents. Research focusing on the use of nanomaterials in MI, for this purpose, has broadly increased during the past few years [75; 76]. This growing interest can be explained by several factors involving nanoparticles exceptional properties. Firstly, due to their physical and chemical tailorability, these materials constitute highly flexible agents. Properties (size, shape, surface charge, etc.) governing critical parameters such as the pharmacokinetics of the probe or the ligand payload, can be easily customized. Secondly, given the available variety in their composition and size it is possible to produce probes for almost every imaging modality [77; 78]. Finally, the intrinsic multifunctional character of most nanomaterials, greatly eases the performance of hybrid molecular imaging experiments. In this context, the radiolabeling of different NP types with \({}^{68}\)Ga seems an optimal approach for the development of multifunctional probes for hybrid imaging of diverse conditions and diseases (Table 2).
## 1 Introduction
The research results suggest that numerical simulation methods show a certain degree of reliability compared with the probabilistic integration method results. [PERSON] et al. [16] used FLAC3D to simulate surface subsidence induced by mining activities at the Wutong coal mine, investigating the extent of the subsidence's impact. [PERSON] et al. [17] analyzed the influence of surface subsidence caused by mining activities on the surface infrastructure using FLAC3D, providing insights into the extent of the subsidence's effects. [PERSON] et al. [18] established a three-dimensional finite difference numerical model for the surface topography, ore body, vertical shafts, and major faults of the Jinfeng Gold Mine. They used FLAC3D to assess the stability of vertical shafts and surface deformation caused by the transition from open-pit mining to underground mining. [PERSON] et al. [19] conducted simulation analyses using FLAC3D to assess surface and surrounding rock damage caused by deep high-dip coal seam mining. [PERSON] et al. [20] employed FLAC3D to simulate mining-induced subsidence and obtained corresponding results. [PERSON] et al. [21] utilized FLAC to predict surface subsidence induced by mining activities and evaluate the effects of different mining methods on dam stability. They also optimized mining schemes. In addition, there are other scholars who have carried out analyses of numerical simulations for a variety of scenarios [22; 23; 24; 25; 26; 27]. This study of the impact of new mining operations at the Prang copper mine in Yunnan province was the first of its kind and is relevant to this region. This study utilized FLAC3D software for three-dimensional numerical simulations, exploring the impact of new mining activities at the Prang Copper Mine in Yunnan Province on surface subsidence and crack development. Through the simulation, we aimed to investigate the degree, temporal evolution, and underlying mechanisms of these impacts, providing a scientific basis for future surface engineering and underground mining activities. This research also aimed to conduct risk assessments to ensure the safety of mining operations.
## 5 Conclusions  
In this paper, dynamic simulations of the seasonal coefficient of performance SCOP of ASHPs have been presented. Simulations were conducted by employing the software package TRNSYS and by investigating three different heat pump systems coupled with the same building located in three different Italian municipalities: S. Benedetto del Tronto (Central Italy, 42\({}^{\circ}\)58\({}^{\prime}\) North, 13\({}^{\circ}\)53\({}^{\prime}\) East), Milan (North of Italy, 45\({}^{\circ}\)28\({}^{\prime}\) North, 9\({}^{\circ}\)10\({}^{\prime}\) East), and Livigno (North of Italy, 46\({}^{\circ}\)28\({}^{\prime}\) North, 10\({}^{\circ}\)8\({}^{\prime}\) East). Real weather data (i.e., outdoor air temperature and humidity as well as solar radiation) available for the three abovementioned cities for a period of 8 years (2013-2020) were introduced within numerical models, and a comparison with the standard test reference year for each location was conducted as well. Attention was paid to the modeling of defrost cycles in order to evaluate its influence on the heat pump SCOP. The main outcomes of the obtained simulations can be summarised as follows:  
* Referring to different years, the thermal energy demand displays huge variations (in some cases it can even double its value);  
Figure 12: Annual electric energy demand (EE) with defrosting (DEF) and without defrosting (NO DEF). The red labels above the blue bars represent the percentage increase of electric energy demand compared to the simulation conducted for the same year neglecting defrosting (represented by yellow bars).  
* On the contrary, the SCOP displays small variations with respect to its mean value. SCOP also displays small variations considering defrost vs. no defrost cases (Table 7);
* SCOP values obtained from the test reference year often tend to overestimate or underestimate the SCOP value, and in the special case of Milan, CTI TRY seems to be more reliable than Meteororm;
* The electrical energy absorbed by the HP considering defrost is higher with respect to the case with no defrost for every year; it can be observed that the percentage increase is higher for Milan (mean value +10.7%) and San Benedetto del Tronto (mean value +9.5%) than for Livigno (mean value 4.6%). Indeed, if one neglects the effect of defrosting, there is a tendency to underestimate the electric energy consumption of the HP up to 10%.  
To sum up, frosting/defrosting cycles have to be taken into account in order to correctly evaluate the electric energy consumption of a HP. The use of TRY instead of real data tends to underestimate the number of defrosting cycles, and a revision of TRY should help manufacturers and technicians.  
Data curation, V.B., M.D. and P.V.; Methodology, E.R.d.S., V.B., M.D. and P.V.; Supervision, M.D.; Validation, E.R.d.S. and P.V.; Visualization, V.B. and M.D.; Writing--original draft, E.R.d.S., V.B., M.D. and P.V.; Writing--review & editing, E.R.d.S., V.B., M.D. and P.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.