esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service
## 3 Processing and Results  
In this section, the performances of the updated PPP software are evaluated in several cases of remote clock comparison. To focus on the performance of the software themselves without the effect of the quality of the compared clocks, two kinds of experiments were built: 1. Common clock difference (CCD), where the two GNSS stations are driven by the same external clock; 2. \"PPP-OPT\", which means the difference of clock comparison results from PPP and optical fiber link.  
The purpose of both experiments was to cancel the effect of the clocks themselves. The CCD results were assumed to be around zero if both GNSS stations were perfectly calibrated since it is the common clock that was compared. Two CCD experiments were set: 1-month common clock comparison usingstations GR01 and GR02 in INRIM during the modified Julian date (MJD) 58591-58636 and another comparison using stations BRUX and RTBS in ORB during MJD 58583-58621. \"PPP-OPT\" removes the clock component by subtracting the clock comparison results from two individual techniques, in which the optical fiber link results are expected to be more stable and accurate than the GNSS results. The astrogedynamic observatory of the space research center (AOS) and the central office of measures (GUM) provide a continuous optical link comparison between their atomic clocks [33]. Meanwhile, the clocks are also compared through their GNSS stations AO_4 and GUM4 using NRCAN PPP (both measurements can be downloaded from the BIPM FTP [[ftp://tai.bipm.org/TimeLink/LKC/](ftp://tai.bipm.org/TimeLink/LKC/)]([ftp://tai.bipm.org/TimeLink/LKC/](ftp://tai.bipm.org/TimeLink/LKC/)) [6]). Two periods of GNSS observation data and available measurements from both the optical link and NRCAN PPP at AOS and GUM were collected for the \"PPP-OPT\" experiment: MJD 58417-58447 and MJD 58537-58572. For the stations in the CCD experiments, the satellite MGEX and GRG products from CNES/CLS IGS analysis center were used, and for station AO_4 and GUM4, which only receive GPS signals, the IGS rapid products were used for the study of constrained PPP, and the GRG products from CNES/CLS were used for the integer PPP clock comparison.  
Note that the accuracy of the GNSS station calibration is not within the scope of this paper, only the stability and precision of the clock solution are studied here. Actually, these calibration values are normally constant values during clock comparison, which introduces only an offset to the GNSS clock output from the software but does not affect the stability. In this section, all the clock comparison results were shifted towards zero to ease the comparison between different solutions.
## 3 Dataset and Methods
### AD Definition during ML Model Deployment (Substudy 3)  
The goal of this final substudy is to show a concrete deployment of the developed ML-QSPR models to new species (i.e., those not used for model training and validation), including the prediction and the analysis of the reliability of the predictions with respect to the defined AD. In this sense, the models resulting from substudy 2, based on the preprocessed data without outliers and with dimensionality reduction, are deployed to predict the properties of new species. These are classified into four different categories, namely Cat. A (13 species), Cat. B (5 species), Cat. C (45 species for enthalpy, 44 species for entropy) and Cat. D (12 species). Due to confidentiality issues, no further information about the chemistry of these species can be provided. The descriptor values for these new species were calculated by AlvaDesc (v2.0.8) on the basis of the optimized geometry, in MDL MOL format (containing information on atom coordinates/types and on bond types), calculated by the software Gaussian 09 (revision B.01) [57]. Calculations were performed at the CBS-QB3 level of theory [58] and conformational analysis was conducted in a systematic way to be sure to identify the structure with the lowest energy (this was performed at the B3 LYP/6-31G(d) level [59]). Raw data provided by the software Gaussian were then post-processed using the GPOP software suite from Miyoshi [60] to derive the thermodynamic properties (enthalpy and entropy). The contribution of internal rotations of moieties around simple bonds was corrected by considering the hindered rotor model rather than the harmonic oscillator one when the potential energy was less than 10 kcal/mol. Note that the calculations could not be performed for all species contained in Cat. C, resulting in 21 and 39 species with available Gaussian-calculated properties in Cat. C, for H and S, respectively.  
The reliability of the predictions is subsequently analyzed in relation to the distance of the new species to the AD. Again, the AD is visualized on AD plots, showing the species' standardized residuals (Model-outliers) and _RF confidence_-based anomaly scores (X-outliers). Note that, in practice, the properties of new species are unknown; hence, their standardized residuals are also unknown. In this work, the enthalpy and/or entropy of the new species were calculated by DFT on Gaussian to serve as reference values for comparison with the predictions. The availability of the standardized residuals for the new species is utilized to improve the understanding and analysis of the AD. Besides, similarly as in substudy 2, tSNE 2D representations are also provided to try to identify the potential sources of high prediction errors.  
All the ML models of this work were implemented using the Scikit-learn library v1.0.2 of Python v3.9.12.
In this paper, the intended and unintended emission spectrum data mentioned above were divided into three kinds of components, which are peak components, broadband components, and trend components, to reflect the inherent properties of the equipment under test (EUT), inspired by [36]. By comparing the distance between the radiation emission components of different equipment for multiple groups of data tested in the same batch, the classification of different pieces of equipment can be realized through our method based on radiation emission components. On the other hand, after the identification model of multiple pieces of equipment is completed using radiation emission components, the identification of new test data can be realized. The novelty of this paper is embodied in two major aspects: (1) the proposed identification method of interference emission sources is mainly based on radiation emission components inspired by BEWT. Unlike previous works, both the characteristics of point frequency and the envelope shape of signals in the frequency domain are taken into account. This greatly expands our selection range of features to provide a more sufficient amount of data for identification and classification. (2) In this study, we attempted to reduce the number and randomness of features through a series of methods to avoid the use of complex mathematical tools, such as neural networks. Therefore, the amount of test data can be compressed to an acceptable level. The remainder of this paper is organized as follows. Section 1 is mainly about the background and progress in the corresponding fields of this area of research. In Section 2, the theoretical basis and implementation scheme of the emitter classification and identification technology is described in detail. In Section 3, the data collection and verification of the classification and identification model are discussed in detail. Finally, in Section 4, we conclude this paper with a summary of the proposed methods and some follow-up research outlooks.
## 2 Background and Related Work
### Deep-Learning Background
#### 2.1.4 RNN  
The assumption of a traditional neural network is that all the inputs are independent of each other, which makes them ineffective when dealing with sequential data and varied sizes of inputs and outputs [24]. The RNN is an extension of the conventional feedforward neural network and is well suited to sequential data, such as time series, gene sequences, and weather data.  
An RNN has memory loops and handles a variable length of input sequence by having a recurrent hidden state [25]. It is known to have a shortcoming of a significant decrease in learning ability as the gradient gradually decreases during back-propagation when the distance between the relevant information and the point is long, which is called the vanishing gradient problem [26]. Errors from later time steps are difficult to propagate back to previous time steps, which results in difficulty in training deep RNNs to preserve information over multiple time steps because the gradients tend to either vanish or explode as they cycle through feedback loops [27]. To address this problem, [PERSON] and [PERSON] [26] proposed the LSTM, which is capable of solving the vanishing gradient problem using memory cells.
# Performance Qualification of Automatic System for Antineoplastic Preparation
## 2 Materials and Methods  
### Compounding and Formulation System  
The automated system is composed of a negative pressure laminar air flow chamber, with ULPA-U15 filters, classified as ISO Class 5, according to EN ISO 14644-1 [23] corresponding to class A-GMP [24]. It is equipped with precision scales, which control the weight accuracy of a set-up product, considering the specific weight of the drug and reconstitution liquid, peristaltic pumps for liquid transfer, a dissolution station for powders, barcode and RFID identification systems for consumables (kits) and a label reader for bottles. The internal view of the machine chamber is shown in Figure 2.  
Figure 2: Internal view with technical details of the PHARMODUCT® automated system. There is also an ozone decontamination tool to ensure proper cleaning of the working chamber and waste areas are isolated with thermally sealed bags. The automated system thus helps the pharmacist to prepare drugs, according to NBP standards (Italian Pharmacopeia); in particular, it ensures proven rather than assumed dosing accuracy (with an accuracy of \(\pm 10\%\)), without risk of microbiological contamination, significantly reducing, as our study showed, operator exposure, as well as waste production and dispensing and administration time also. To evaluate the performance of the aseptic process of this automated system, three antiplastic drugs, the most representative preparations in clinical practice, were used in clinical practice: cyclophosphamide, available in powder form, 5-fluorouracilacil and paclitaxel, both available in solution for infusion. The correct mixing of powders and the correct execution of the various steps in the multidose bag preparation cycle, the filling cycle of the final containers and the operator safety were verified. For each drug included in this study, three preparation and dispensing sessions were performed using the PHARMODUCT(r) machine built by Bioduct s.r.l. Firenze 50141, Italy. After disinfection and subsequent bio-decontamination of the instrument, the dispensing module was prepared for the execution of a processing cycle by inserting the final kits, accessories and containers inside the chamber, which were necessary to perform proper mixing of the powders and final dispensing. Subsequently, the antiplastic drug powder container was introduced. For each work section, concerning each antiplastic, five infusion bags with different concentrations have been prepared, as specified in Tables 1-3. Two of the five infusion bags for each run per type of antiplastic were sampled and sent to a laboratory to perform the quantitative assay analysis.
# Working State of ECC Link Slabs Used in Continuous Bridge Decks
## 5 Characterization of the ECC Link Slabs' Working State
### Development of Structural Internal Forces  
The internal forces of the mid cross section of Type II ECC link slab can be determined using the data expanded by the response simulating interpolation method as well. The dependences of axial force/bending moment on the loading steps are plotted in Figure 14, where the two critical points (M1 and M2) are marked. It can be seen from Figure 14a that the growth of axial force gradually slows  
Figure 13: Strain fields of the mid cross section of Type II ECC link slab: **(a)** around the yielding point M1 and **(b)** around the initial failure point M2.  
down around M1 because compressive stresses are rising faster than tensile stresses at this time, which is consistent with the characteristics of structural strain fields. This plateau lasts until the 19 th loading step and a rapid change from tension to compression is observed at M2, demonstrating the occurrence of initial structural failure. As shown in Figure 14b, the level of bending moment is very low during the early period when this cross section is mainly influenced by axial tensile forces. There is a slight increase in the bending moment from M1 on, corresponding to the rise in compressive strains at the bottom as shown in Figure 13a. Moreover, the bending moment presents a considerable increase at M2. Generally, the developing trend of internal forces evidence the qualitative change of working state around the critical points.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 4 DiffServ-Based Dynamic Cooperative MAC Protocol
### MAC Sub-Protocol with Optimal Relay
#### 4.3.2 Optimal Relay MAC Procedure  
Suppose that the procedure for optimal relay initiates only if the direct transmission fails. First, we introduce two new control packets, namely Request to Cooperation (RTC) and Clear to Cooperation (CTC), with the same structure as RTS and CTS. The procedure can be elaborated as follows:  
* **Step 1:** Source node \(s\) sends an encoded RTC & RTS packet to its \(1\)-hop neighbors, this packet is encoded by RTC and RTS packets, and has the same structure as them. If node \(s\) knows the azimuth of node \(r\) based on prior knowledge, the packet will be sent in DT-OR mode; if not, in OT-OR mode with the basic data rate.
* **Step 2:** The \(1\)-hop neighbors, which meet conditions (i) and (ii) of Definition 3, will decode the RTC & RTS packet, calculate \(\gamma_{SR}^{i}\), and transmit RTS to node \(r\) after a SIFS.
* **Step 3:** Receiver \(r\) will decode the azimuth and position of node \(s\) after receiving RTS, and reply with CTS in DT-OR mode. The neighbors that satisfy conditions (i) and (ii) will be included into CRS. CRS members \(s_{\text{rly}}^{i}\) will calculate their \(\gamma_{SRD}^{i}\) by _Proposition 1_, and start the back-off timers. Hereafter, the optimal relay \(s_{\text{rly}}\) will access to the channel first and forward an encoded CTC & CTS packet to the source node. The CTC & CTS packet is encoded by CTC and CTS packets and has the same structure as them.
* **Step 4:** If the other relay candidates of CRS overhear the packet sent by \(s_{\text{rly}}\), they will freeze their timer and keep on listening to the channels.
* **Step 5:** If the source node \(s\) receives CTC & CTS correctly, it will decode the information with respect to its destination \(r\) and the CRS. Then the pair \((s,r)\) gets into the data transmission phase with the assistant of the optimal relay \(s_{rly}\), while other CRS members will delete the CTS after overhearing DATA, and then keep on listening to the channels.
* **Step 6:** If the other relay candidates do not overhear DATA after a SIFS, the cooperative communication fails for one time. Thus, the other relay candidates will reactivate their timers simultaneously. At this time, the suboptimal candidate, which has the channel quality next only to \(s_{rly}\), will expire its timer first, and will execute Steps 3-5.
* **Step 7:** If no node leads to successful negotiation phase, or the number of retransmission attempts reaches the retry limit, the source node will then obtain channel access rights again for another _round_ of packet transmission, and carry out the same procedures above.
# A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data
## 4 Experimental Results  
Table 1 shows the LOO accuracy results for the classification problem together with the number of selected voxels (the approaches without FS used 100%). Despite using different initializations in the evaluated models, the results were stable across them with a negligible standard deviation, showing that the initialization hardly influences the results. For this reason, we did not include the standard deviation in Table 1. The results show that GPs, KSSHIBA+FS, and MKL-SVM obtained the worst classification accuracies, while SVM and KSSHIBA achieved the best performances among the baselines. However, DBL-FS and DBL-FS+EK still obtained an improvement of 5.7% in terms of accuracy over the best baseline while learning the most restrictive selection mask. From these results, we need to highlight that (1) KSSHIBA obtained a predictive performance similar to that of SVM while summarizing the information of the original data (distributed in more than \(10^{6}\) voxels) in only nine latent variables, and (2) MKL-SVM showed worse results than the standard SVM, probably due to the higher number of hyperparameters it needed to learn in order to perform the MKL, which may be causing overfitting.  
Figure 3 shows the brain areas selected by the methods with FS capabilities. As each voxel has an associated weight, the image masks represent the absolute value of these weight magnitudes for the selected of voxels as an indicator of the voxel relevance. In addition, since we have a model for each LOO iteration, Figure 3 displays the average values of these relevances (over all LOO iterations) and includes a normalization of their scales to the range \((0,1)\) to simplify their analysis. As a result, we can observe that the GP-FS selected meaningless voxels in neurobiological terms while KSSHIBA detected well-defined areas corresponding to only WM tissues. Finally,the DBL-FS and DBL-FS+EK approaches obtained well-defined regions in the GM and WM tissues and the CSF, which are interpretable in neurobiological terms. Although both methods provided similar selections, DBL-FS+EK selected a reduced set of voxels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Accuracy** & **\# Selected Voxels** \\ \hline GP & 67.9\% & 1,077,656 (100\%) \\ GP+FS & 67.9\% & 269,414 (25\%) \\ SVM & 71.6\% & 1,077,656 (100\%) \\ MKL-SVM & 67.9\% & 1,077,656 (100\%) \\ KSSHIBA & 69.8\% & 1,077,656 (100\%) \\ KSSHIBA+FS & 64.1\% & 269,414 (25\%) \\
**DBL-FS** & **77.3**\% & **287,996 (26.72\%)** \\
**DBL-FS+EK** & **77.3**\% & **242,754 (22.52\%)** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the different methods under study showing the model accuracy and the number of selected voxels (with their percentages with respect to the total). In addition, models with the best accuracy have been highlighted in bold and placed at the bottom of the table, which corresponds to the proposed DBL-FS approaches.
## 1 Introduction
Furthermore, in the current study, the performance resulting from an increase in the latent heat removal capacity was predicted on the basis of simulations by considering the cooling capacity of the HDCS to be sensitive to humidity [10]. For simulations, the transient system simulation tool (TRNSYS), which can include spatial and weather information of a conditioned space, was used. TRNSYS has been widely used to predict the performance characteristics of desiccant cooling systems. [PERSON] et al. [14] simulated a solid-desiccant-assisted hybrid space cooling system by using TRNSYS. [PERSON] et al. [15] used TRNSYS for thermodynamically assessing an HDCS, in which water for evaporative cooling was heated by solar thermal energy. [PERSON] et al. [16] examined whether TRNSYS could be used to investigate the sustainability, feasibility, and potential use of desiccant-powered thermal space cooling systems. Finally, [PERSON] et al. [17] modeled a solar-assisted desiccant cooling system by using TRNSYS and MATLAB. An HDCS driven by a gas engine CHP system was installed in an office building at a university in Naju, Korea. [PERSON] and [PERSON] [18] successfully predicted the transient behavior of temperature and humidity in an air-conditioned space through TRNSYS simulations. In the current study, during the experiments in summer, the indoor thermal environment was out of the ASHRAE comfort zone under certain outdoor conditions, and consequently, additional temperature or humidity control was required to meet the comfort zone conditions. The dehumidification capacity was increased since there was room for exhaust heat generated by the CHP [13]. The target humidity ratio of the indoor space was maintained at 10 g/kg, which is representative of the ASHRAE comfort zone. A series of TRNSYS simulations for 39 outdoor condition scenarios with increased desiccant capacity for humidity control was performed. In the CCHP system, if the heat used for cooling is less than that used for heating, there is scope for additional desiccant regeneration. For the effective use of this information for a hybrid system, a performance analysis based on the contributions of desiccant cooling and the heat pump should be performed. To date, many studies have been conducted on hybrid desiccant cooling, but there has been no study on the performance change when the desiccant regeneration capacity is increased. In this study, a CHP system and a TRNSYS model were developed, and simulated thermal indoor conditions were validated with experimental results. Subsequently, the change in the thermal comfort conditions resulting from an increase in the desiccant capacity was predicted. Performance data for different outside air conditions obtained from simulations were compared with the data of [PERSON] et al. [10] to investigate the performance change of the HDCS resulting from an increase in the desiccant capacity.