esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of USM Processing Parameters
#### 3.2.4 X-Ray Diffraction Patterns of P3 HB-Processed Samples  
The X-ray diffraction (XRD) profiles of the processed samples matched the known crystalline structure of P3 HB in its \(\alpha\)-form, which is characterized by an orthorhombic unit cell with parameters \(a\) = 0.576 nm, \(b\) = 1.320 nm, and \(c\) = 0.596 nm. This structure belongs to the \(P2_{1}2_{1}2_{1}\) space group and exhibits a TGGT molecular conformation [31]. The main peaks, corresponding to the 020 and 110 reflections, were observed at 0.660 nm and 0.445 nm, respectively (Figure 6a). The structure was stabilized through intermolecular hydrogen bonding between the C=O and CH\({}_{3}\) groups of adjacent chains along the \(a\)-axis. Additionally, a planar zig-zag conformation, known as the \(\beta\)-form, was identified in samples subjected to stretching, distinguishable by reflections at 0.480 nm and 0.460 nm. However, no traces of these reflections were found in the diffraction patterns of the processed samples.  
Variations between diffraction profiles were minimal due to their similar and high crystallinity, as later confirmed by DSC analysis. Nevertheless, variations in peak sharpness, attributable to differences in the size of crystalline domains, were noted. This effect was more pronounced in the \(q\) range (i.e., \(q\) = 2\(\pi\)/\(d\), with \(d\) representing the Bragg spacing) from 14 nm\({}^{-1}\) to 18 nm\({}^{-1}\) (Figure 6b). The comparison between the profiles of the initial P3 HB pellet (the extruded sample) and the USM specimen highlighted a higher crystallinity and crystal size for the former sample. However, the aging effect produced during the storage of the pellet should be considered. For example, the resolution was accentuated when the USM specimen was aged by heating to 60 \({}^{\circ}\)C.  
Figure 5: FTIR spectra of raw P3 HB (green line) and molded P3 HB specimens obtained by USM technology under optimal (red line), non-optimal conditions (black line), and conventional microinjection (blue line). Black ellipsoids point out the appearance of new carbonyl bands and intensity variation around 1200 cm\({}^{-1}\).
# Genetic Parameters for Selected Traits of Inbred Lines of Maize (_Zea mays_ L.)
## 2 Materials and Methods
### Genetic Parameters  
The following formula estimated the additive (_a_) gene effect:  
\[\widehat{a}=\frac{1}{2}\left(\overline{L}_{max}-\overline{L}_{min}\right), \tag{1}\]where \(\overline{L}_{min}\) and \(\overline{L}_{max}\) are the means for the extreme groups (minimal and maximal lines, respectively). The group of minimal (maximal) lines consists of the lines which contain, theoretically, only alleles reducing (increasing) the value of the trait [16]. Groups of extreme lines were identified by the quantile method [17] in which lines with mean values smaller (bigger) than 0.03 (0.97) quantile of the empirical distribution of means are assumed as minimal (maximal) lines.  
The following formula estimated the additive \(\times\) additive (_aa_) epistasis interaction effect [18,19]:  
\[\widehat{aa}=\frac{1}{2}\big{(}\overline{L}_{max}+\overline{L}_{min}\big{)}- \overline{L}, \tag{2}\]  
where \(\overline{L}\) is the mean of all inbred lines.  
The additive \(\times\) additive \(\times\) additive (_aaa_) three-way epistasis interaction effect was estimated by the following formula [15]:  
\[\widehat{aa}=\frac{1}{2}(L_{max}+L_{min})-\overline{L}, \tag{3}\]  
where \(L_{min}\) and \(L_{max}\) are the lines with minimal and maximal mean value, respectively.  
The test statistics to verify hypotheses about genetic parameters different than zero are given by:  
\[F_{a}=\frac{MS_{a}}{MS_{e}},\ F_{aa}=\frac{MS_{aa}}{MS_{e}}\ \text{and}\ F_{aaa}= \frac{MS_{aaa}}{MS_{e}}, \tag{4}\]  
where \(MS_{a}\), \(MS_{aa}\), \(MS_{aaa}\), and \(MS_{e}\) are mean squares for parameter \(a\), epistasis _aa_, epistasis _aaa_, and residual, respectively.
## 2 Materials and Methods
### Datasets
#### 2.2.3 Nitrogen (N) Deposition Dataset  
The database of atmospheric N deposition fluxes is from [PERSON] et al. (2023) [45], with a spatial resolution of 0.25\({}^{\circ}\) (\(\sim\)27.75 km at the equator). The dataset integrates multi-source satellite observations (OMI NO\({}_{2}\) columns and IASI NH\({}_{3}\) columns), atmospheric chemical transport model (GEOS-Chem) simulations, ground-based monitoring data, and meteorological data to predict the spatiotemporal distribution of atmospheric N deposition. The temporal extension of the dataset is from 2010 to 2020 with a yearly step.
## 3 Results and Discussion
### SIMCA Calibration Model  
As a result of the highly collinear nature of the mean bacterial cell spectra, a large amount of data benefited the robustness of the SIMCA's prediction capability. It was found that increasing the _Salmonella_ serotype numbers and serotype repetitions began to incorporate sufficient robustness over time, and that the model could predict the _Salmonella_ HMI collected several years later. In Figure 5A, the distribution of the PCA score plots can be seen, and as more data points are added to the calibration model, the distribution across PC1 and PC2 becomes more normally distributed. The plots shown in Figure 5 were indicative of a robust model that could offer unsupervised classification of _Salmonella_ cells. Figure 5B shows the loadings vectors for PCs 1-4. PC1 shows the strongest loading vectors in the red color bands, while PCs 2, 3, and 4 appearing to be strongest in the green color bands, and PC 4 represented the strongest of the blue color bands. The explained variance of PCs 1-4 is detailed in Figure 5C, with 95% of the _Salmonella_ calibration model's explained variance described in the first four PCs. The error matrix plotting [PERSON]'s T\({}^{2}\) values against the F-residuals is shown in Figure 5D.  
There are over 2500 known serotypes of _Salmonella_[25]. As new serotypes are added to this calibration model, it would be assumed that some serotypes may skew the spread of these scores in the principal component space, but with enough HMI repetitions, the PCA scores will progress towards filling the multivariate space representative of _Salmonella_. Bacteria share many physiological traits, especially those of the same Enterobacteriaceae taxonomical family, including common foodborne pathogens such as _Salmonella_, _E. coli_, _Sligella_, _Enterobacter_, and _Klebsiella_[26]. These microbes tend to share many common traits such as lipopolysaccharide-accharide cell wall structures, porins, and other features that make for a single pixel differentiation between cells virtually impossible under the given conditions. For this reason, a mean spectrum was calculated per cell. For example, the pixelwise classification of _E. coli_ cells resulted in many pixels misclassified as _Salmonella_ pixels because of the common physiological characteristics of the two _Enterobacteriaceae_ species. Single cell mean spectra offer an overview of the cellular characteristics, while maintaining the representation of the inherent biological variability between bacterial species.
## 4 Computational Experiments  
To verify the performance of the proposed DILA, two sets of experiments were conducted. The first set investigates the effects of the controlled probability and the phase termination time. The second set compares the performance of the DILA with the best parameters found in the first set against several metaheuristics reported in the literature.  
We coded the DILA in C++ and executed it on an Intel Core i7 PC with a 3.4 GHz CPU and 4 GB RAM. A series of computational experiments were conducted to compare the results of all the combinations of the algorithms with the benchmark instances provided by [PERSON] and [PERSON] [9]. The problem instances are characterized by four factors: the number of jobs \(n\), number of families \(F\), due-date range \(r\), and setup-time range \(\eta\). The number of jobs has three levels, which are set at 60, 80, and 100. The number of families has four levels, which are set at 2, 3, 4, and 5. The due date of jobs is generated from integer numbers uniformly distributed in the interval \(\left[0,\ r\sum_{j=1}^{n}p_{j}\right]\) (recall that \(r\) is the due-date range). The due-date range r has four levels: 0.5, 1.5, 2.5, and 3.5. Three setup-time ranges \(\eta\)--[11; 20], [51; 100], and [101; 200]--are used to generate job setup times. The processing time for a job is generated from a discrete uniform distribution [1; 99]. From each of the 144 combinations of the four factors, 10 problem instances, each involving a processing time in the interval [1; 99], were generated. Therefore, a total of 1440 instances were used to evaluate the proposed algorithms.  
The relative deviation index (RDI) and number of best solutions (NBS) produced are the two criteria used to evaluate the performance of the proposed algorithms. The RDI is calculated as follows:  
\[\text{RDI}\%\ =100\times\begin{cases}\frac{S_{b}-S_{b}}{S_{w}-S_{b}}&\text{ if }(S_{w}-S_{b})\
eq 0,\\ 0&\text{ otherwise.}\end{cases}\]  
_Sa_ is the solution obtained by method \(a\), and \(S_{b}\) and \(S_{w}\) are the best and the worst solution values, respectively, among the solutions obtained by all the methods included in the comparison.
## 5 Experiments and Results
When comparing Figure 5 with Figure 7 (simple LSTM with stacked LSTM on the removed punctuation) and Figure 6 with Figure 8 (simple LSTM with stacked LSTM on the separated punctuation), the stacked LSTM encoder demonstrates similar results with the fastText encoding; however, BERT encoding is recommended with the removed punctuation for both languages, but it is definitely not the choice with separated punctuation in the English language. Tables 4 and 5 demonstrate a similar trend to Figures 7 and 8, respectively. The BLUE scores with the BiLSTM encoder and decoder model applied with different vectorization types to the datasets with removed and separated punctuation are presented in Figures 9 and 10, respectively. The ROUGE-2 and ROUGE-L precision/recall/f-score values are presented in Tables 6 and 7, respectively. With the BiLSTM encoder, BLUE values in Figures 9 and 10 are the highest with the BERT vectorization in both languages, especially when BERT vectorization is used for both encoder and decoder units. However, fastText vectorization for both encoder and decoder is not recommended for any of the used languages. When comparing BiLSTM encoder values with the appropriate simple LSTM or stacked LSTM values in Figures 5-8, the recommendation would be to use more sophisticated architectures. If choosing the appropriate vectorization, stacked LSTM works better using the English dataset and BiLSTM works better using the Lithuanian dataset. As presented in Tables 6 and 7, BERT vectorization is the best choice with the BiLSTM encoder. The conclusions from ROUGE-2 and ROUGE-L values in Tables 6 and 7 are consistent with the conclusions drawn from the appropriate BLUE values in Figures 9 and 10, respectively.
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
###### Abstract  
To solve the fundamental problem of excessive consumption of classical resources and the simultaneous security vulnerabilities in semi-quantum dialogue systems, a multi-party controlled semi-quantum dialogue protocol based on hyperentangled Bell states is proposed. A single controlling party is vulnerable to information compromise due to tampering or betrayal; the multi-party controlled mechanism (Charlie\({}_{1}\) to Charlie\({}_{n}\)) in this protocol establishes a distributed trust model. It mandates collective authorization from all controlling parties, significantly enhancing its robust resilience against untrustworthy controllers or collusion attacks. The classical participant Bob uses an adaptive Huffman compression algorithm to provide a framework for information transmission. This encoding mechanism assigns values to each character by constructing a Huffman tree, generating optimal prefix codes that significantly optimize the storage space complexity for the classical participant. By integrating the \"immediate measurement and transmission\" mechanism into the multi-party controlled semi-quantum dialogue protocol and coupling it with Huffman compression coding technology, this framework enables classical parties to execute encoding and decoding operations. The security of this protocol is rigorously proven through information-theoretic analysis and shows that it is resistant to common attacks. Furthermore, even in the presence of malicious controlling parties, this protocol robustly safeguards secret information against theft. The efficiency analysis shows that the proposed protocol provides benefits such as high communication efficiency and lower resource consumption for classical participants.  
hyperentangled bell states; Huffman compression coding; multi-party controlled; semi-quantum dialogue +
Footnote †: journal:  
Article
## 1 Introduction  
The correct assessment of the maximum height (MH) in the vertical jump depends on an accurate and reliable measurement tool [1; 2; 3]. The validation of this new instrument related to the MH estimation method is commonly based on the comparison of this tool with the force platform considered the gold standard [2; 4; 5]. In particular, the analysis of the displacement of the jumper's center of mass (CoM) obtained after the double integration of the acceleration data vs. time as a result of the ground reaction force measurement enables a very accurate determination of the MH [6; 7; 8; 9]. Moreover, using the force platform, the MH can be estimated based on the flight time according to [PERSON]'s formula [10] and the velocity of the CoM at take-off [7; 11].  
The assessment of the jump height obtained from the flight time is made easier by the installation of the My Jump app on a smartphone, which is much cheaper than laboratory devices [12; 13]. Importantly, the My Jump app was validated by comparing this application with the force platform as a criterion. Analyzing the MH calculated based on the flight time in the counter-movement jump (CMJ), some researchers demonstrated a very high agreement between the gold standard and these measurement tools [2; 14; 15; 16]. Furthermore, similar results for the MH of the CMJ and very high correlation coefficients between the force platform and the My Jump app for the take-off velocity method [6] and for the impulse-momentum method [17] were observed.  
The calculation of the MH based on the flight time is also provided by the contact plate [3; 4], Optojump photoelectric cells [4; 5; 9] and high-speed cameras with the Kinovea 0.8.15 software [18; 19]. In addition, the motion capture (mo-cap) system enables the determination of the MH value based on the recorded displacements of the reflective markers [9; 20; 21]. An accelerometer sensor that measures vertical acceleration can also be used to calculate the velocity and MH [4; 22; 23]. Considering the Vertec system, the MH is estimated as the difference between the MH and the standing reach height of the jumper [20; 22; 24].  
Validity tests of the alternative measurement tools used for MH estimation should include a very accurate method of the jumper's CoM trajectory associated with the force platform. Previous papers did not use this method to assess the agreement between the My Jump 2 app and the force platform, mainly comparing the MH results obtained from the flight time for both measurement tools. In this study, a new method was adopted to calculate the MH of the CMJ based on the displacement time of the reflective marker placed on the jumper's sacrum. Therefore, the main aim of the study was to determine the concurrent validity and reliability of the My Jump 2 app for this method of estimating the MH of the CMJ in recreationally active male adults.
# Improved Calculation of Load and Resistance Factors Based on Third-Moment Method
## 2 Computation Procedure of Load and Resistance Factors
### ASCE Method for the Computation of Load and Resistance Factors  
In the ASCE method, load factors are well approximated by:  
\[\gamma_{Q}=(\mu_{Q}/Q_{n})(1+\alpha_{Qi}\beta V_{Q}) \tag{24}\]  
where \(\beta\) is the reliability index; \(\alpha_{Q}\) is a sensitivity coefficient that is approximately equal to 0.8 when \(Q\) is a principal action and 0.4 when \(Q\) is a companion action. This approximation is valid for a broad range of common probability distributions used to model structural loads. The load factor is an increasing function of the bias in the estimation of the nominal load, the variability in the load, and the target reliability index.  
Similarly, the resistance factor that is consistent with the aforementioned load factors are well approximated for most materials by:  
\[\phi=(\mu_{R}/R_{n})\exp(-\alpha_{R}\beta V_{R}) \tag{25}\]  
where \(\alpha_{R}\) is the sensitivity coefficient equal approximately to 0.7.
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
Algorithm development and sensor miniaturization potentiated a transition from terrestrial laser scanning (TLS) to mobile laser scanning (MLS) [20; 21; 22; 23] and handheld personal laser scanning (PLS\({}_{\text{hh}}\)) [22]. These technologies allow for precise data collection on a relatively small scale compared to conventional DAP and ALS data collection, albeit still on a significantly larger scale than what is feasible with a topographic survey. Until recently, only a few studies discussed the evaluation of a DTM derived using close-range remote sensing data in a forest area [1; 24; 25; 26; 27]. However, most of them have compared only two close-range remote sensing datasets, e.g., UAV\({}_{\text{image}}\) and ULS. The typical accuracy achieved with ULS and UAV\({}_{\text{image}}\) without the presence of vegetation is <15 cm [1; 24]. In an international TLS benchmark study, [PERSON] et al. [27] reported that TLS multi-scan achieved high accuracy in easy forest conditions (5 cm < RMSE < 20 cm), slightly lower accuracy in medium-difficult forest conditions (5 cm < RMSE < 25 cm) and low accuracy in difficult forest conditions (5 cm < RMSE < 40 cm). The single-scan approach, in general, had twice the range of errors as compared with the multi-scan approach. This paper aims to benchmark the accuracy and reliability of the DTM produced from various close-range remote sensing data in lowland deciduous forest. In total, four different state-of-the-art close-remote sensing data sources (UAV\({}_{\text{image}}\), ULS, PLS\({}_{\text{hh}}\), and TLS) were evaluated in reference to a large number of high-quality topographic checkpoints collected using total station measurements.
## 5 Experiments
### Experimental Results
#### 5.3.2. Rounds-to-Accuracy Performance  
Figures 6 and 7 display the rounds to accuracy curves during 1000 rounds. These curves provide a more precise visualization of the overall accuracy trends. It is evident that the MB_ResNet34 + Oort model, represented by the red line, achieved the highest accuracy compared to the other models. On the other hand, the models trained without the selection algorithm, which correspond to blue and orange lines,, exhibited lower accuracy levels.
# Photodegradation Assessment of Calicotriol in the Presence of UV Absorbers by UHPLC/MSE
## 1 Introduction
Meradimate, a UVA filter exhibiting maximum absorption at 385 nm, was also included in the study [25; 26; 27]. The aim of this study was to assess the photostability of calcipotriol under radiation conditions. The UHPLC/MS\({}^{\text{E}}\) method used for the determination of calcipotriol was thoroughly validated in accordance with international guidelines, ensuring its accuracy, precision, specificity, and reliability for the assessment of photostability under the tested conditions. The photostability of calcipotriol was tested in the presence of selected chemical UV filters that have been approved for use in cosmetic products in Europe and the United States. Methanol solutions of calcipotriol were tested both with and without the addition of various UV filters, including oxybenzene, dioxybenzene, meridamate, sulisobenzene, avobenzene, and homosalate. Molecular docking simulations were employed to assess the binding potential of the calcipotriol degradation products to the vitamin D receptor (VDR), providing a predictive evaluation of their possible interactions at the molecular level.
## 1 Introduction
### Feature Extraction Method for an ECG  
However, the ECG signal identification technology is limited by noise reduction and feature extraction, which complicates the improvement of effective ECG signal recognition. The ECG feature extraction is a key technique for heartbeat recognition. Feature extraction selects a representative feature subset from the raw ECG signal. These feature subsets have better generalization capabilities and can improve the accuracy of the ECG heartbeat classification. Underlying feature extraction mainly revolves around the extraction of the time-domain features, frequency-domain features, or morphological features of the signal, such as by FT [11], discrete cosine transform (DCT) [12], and wavelet transform (WT) [13]. Some high-level feature extraction methods are also available, including dictionary learning [14] and CNNs [15; 16]. With the increase of the number of patients, the accuracy of the classification will be decreased due to the large pattern variations of the ECG signals among different patients, and the preprocessing methods like PCA [9] and Fourier transform [8] may increase the complexity and the time of the computing as well. To enhance the heartbeat classification performance, selecting a suitable feature is of paramount importance.
# Seeking a Definition of Digital Twins for Construction and Infrastructure Management
## 5 Defining and Standarising the Concept of a Digital Twin for the Built Environment
### Creation of a European Standardisation WG for Digital Twins
In parallel, other related standardisation projects are using BIM models or DTs with sensors to analyse buildings, gathering real-time data from sensors to propose cost-effective, long-term maintenance and renovation plans. The Chronicle (GA 101069722) is developing a methodology, based on real buildings, to improve the performance of buildings to increase energy efficiency, comfort, and well-being. The pilots are in Denmark, Ireland, Greece, Spain, and Switzerland. This project continues the work developed in D'2 EPC and is developing standardised methodology for the operational energy assessment of buildings based on DTs; considering the occupants behaviour should improve the energy efficiency of buildings [59], contributing to the ecological transition. Thus, the Chronicle is working with SmartLivingEPC (GA 101069639) developing a European standard for the operational energy performance assessment of buildings in CEN/TC 371/WG 5, with [PERSON] (SmartLivingEPC) as Convenor and [PERSON] (Chronicle) as Secretariat. Another important element to be integrated in the DT is the Digital Product Passport (DPP) defined in the sustainable products Regulation (ESPR) and in the new construction products regulation (CPR). CEN/CLC/JTC 24 is developing European standards for the DPP system of the ESPR and CEN/TC 442/WG 12 is developing a European standard for the BIM data templates for construction products, which can be used in the future DPP system for products covered by the CPR. The DPP will contain LCA-based environmental indicators for construction products, communicated using Environmental Product Declarations based on ISO 21930 (at international level) or EN 15804 (in Europe). This information can be included in the DT using the data templates defined in EN ISO 22057. However, this standard has several limitations for computer-interpretability [60], which should be solved in the next revision of the document.