esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Time-Dependent Performance Analysis
#### 3.3.3 Adaptation Patterns and Movement Consistency  
Figure 10 illustrates the relative productivity advantage of using the exoskeleton across time intervals. The graph shows the percentage productivity benefit of the exoskeleton compared to non-exoskeleton conditions for each task type and intensity level throughout the 2 h work period.  
Notably, the productivity advantage of the exoskeleton increased progressively throughout the 2 h work period. During the initial 0-30-min interval, the average productivity advantage across all tasks was 23.9%, which increased to 29.0% during the 30-60-min interval, then to 34.9% during the 60-90-min interval, and finally reached 40.1% in the 90-120-min interval. This consistent upward trend demonstrates that the benefits of exoskeleton use become more pronounced as work duration extends.  
The data reveal different patterns of exoskeleton benefit across task types. The masonry task under high-intensity conditions showed the most dramatic increase in productivity advantage, rising from 32.7% in the first interval to an impressive 59.5% in the final  
Figure 9: Changes in work cycle consistency (CV%) over time.  
interval. This suggests that the repetitive bending motions required for ground-level masonry work particularly benefit from exoskeleton assistance during extended work periods. The scaffolding installation task under high-intensity conditions also showed substantial improvement, with the productivity advantage increasing from 22.8% to 43.0% over the work period.  
Even tasks with more modest overall benefits, such as the sack carrying task under low-intensity conditions, demonstrated a clear pattern of increasing advantage over time, with productivity benefits rising from 20.8% to 29.6%. This pattern was consistent across all task types and intensity levels, though the magnitude of improvement varied.  
High-intensity conditions consistently showed larger productivity advantages compared to their low-intensity counterparts for the same task type, particularly in later time intervals. This pattern suggests that the exoskeleton's fatigue-reduction benefits become increasingly valuable as physical demands increase. This increasing productivity advantage over time results from a combination of factors: workers' progressive adaptation to the exoskeleton, the cumulative effects of fatigue reduction, and the maintenance of more efficient movement patterns throughout the work period. The data provides strong evidence that exoskeletons offer substantial and increasing benefits for prolonged construction work, with the greatest advantages observed in physically demanding tasks and extended work durations.
## 2 Materials and Methods
### Instrumentation, Analysis and Method Validation  
Agilent Technologies GC-MS 7890B/597/A instrument (Santa Clara, CA, USA) was used for the phthalates analysis. The conditions of the instrument were as follows: injection temperature 280 \({}^{\circ}\)C, MSD 280 \({}^{\circ}\)C; oven: initial temperature 90 \({}^{\circ}\)C (held 1 min) to 210 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min (held 2 min), then at the rate of 5 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C (held 5 min), followed by an increase of 5 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C and then followed by an increase of 25 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C and held for 4 min. Phthalate identification was based on a comparison of the peak retention times and target ions in SIM mode with those obtained from a standard phthalate mixture. The quantification was according to [PERSON] et al. and [PERSON] et al. (2017, 2018). The verification of the peaks was carried out based on the retention times, and the target ions were compared with those of external phthalates. Some phthalates were found in the solvent blank, but the amount was lower than the limit of quantification (LOQ). Phthalates determination was performed in splitless mode. The carrier gas was helium with a velocity of 35.698 cm/s, and the pressure was 7.0 psi. The measurement uncertainties were in the range recommended by SANTE Document No 11312/2021 ([PERSON] et al., 2018). Retention time, limit of detection (LOD) and LOQ of the analyzed phthalates are presented in Table 1. Chromatogram of the standard reference material is shown in Figure S1 of the Supplementary Materials. Figure S2 shows a chromatogram of the solvent and salt blank, while in Figure S3, the calibration curves of the analyzed phthalates are presented. A chromatogram of a smoked fish sample can be seen in Figure 2.  
Figure 1: Smoked fish samples packaged in different materials.
# Optimal CNN-Hopfield Network for Pattern Recognition Based on a Genetic Algorithm +
## 7 Conclusions  
In this paper, a CNN architecture was combined with an HNN for pattern recognition. The aim of this proposal was to reduce the number of parameters of the CNN (for a classification task) while increasing or at least keeping the same accuracy. It was pointed out that CNN models have a large number of weights concentrated at the fully connected layers, so we proposed the replacement of these layers while maintaining the performance.  
Then, the main idea was to exchange those FC layers with associative memories (Hopfield neural networks): a new architecture composed of multiple networks in parallel, where the training patterns were split into subsets by applying a local search approach. Furthermore, the assignment problem was formulated as a knapsack problem and solved via a genetic algorithm. The efficiency of the proposed CNN-Hopfield architecture on the Noisy MNIST Dataset was demonstrated experimentally. In this work, it was shown that the increase in the storage capacity of the associative memory (Hopfield network) considerably improved the performance on the classification application.  
The generalization of this approach to other applications and datasets is in progress.  
**Author Contributions:** Data curation, F.E.K.; writing--original draft preparation, A.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **AWGN** & **Motion** & **Contrast** \\ \hline Lenet & 0.8368 & 0.7228 & 0.9427 \\ Our approach & **0.7954** & **0.7020** & **0.8632** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the classification accuracy on the three versions of the Noisy MNIST Dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **AWGN** & **Motion** & **Contrast** \\ \hline Dropconnect [55] & 96.02\% & 98.85\% & 93.24\% \\ [PERSON] et al. [55] & 97.62\% & 97.20\% & 95.04\% \\ PCGAN-CHAR [57] & 98.43\% & 99.20\% & 97.25\% \\ Our approach (parallel networks) & **97.52\%** & **97.72\%** & **94.84\%** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance (accuracy) comparison between the Lenet-like architecture (with FC layers) and our hybrid Lenet–Hopfield architecture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **\# of Parameters** & **Accuracy** & **Accuracy** & **Accuracy** \\  & **AWGN** & **Motion** & **Contrast** \\ \hline Lenet5-Like (with FC Layers) & 324,858 & 97.12\% & 96.50\% & 93.82 \% \\ Our approach (parallel networks) & **124,344** & **97.52\%** & **97.72\%** & **94.84\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance (accuracy) comparison between the Lenet-like architecture (with FC layers) and our hybrid Lenet–Hopfield architecture.
###### Abstract  
The apricot storability is one of the largest challenges, which the apricot industry has to face all over the world; therefore, finding options for prolonging fruit quality during cold storage (CS) and shelf-life (SL) will help to decrease postharvest losses of apricot. The aim of this apricot fruit work was to study the temporal changes and correlations of 10 quality parameters (quality losses, antioxidant properties and enzyme activities) in the postharvest treatments of methyl jasmonate (MeJA) and salicylic acid (SA) under 1 \({}^{\circ}\)C CS (7, 14 and 21 days) and 25 \({}^{\circ}\)C SL (4 and 8 days after the 21-day CS) treatments. MeJA and SA significantly decreased the quality loss of chilling injury (CI) and fruit decay (FD) at all dates for both storage conditions. MeJA- and SA-treated fruits increased total antioxidant capacity (TAC), total soluble phenolic compounds (TSPC) and carotenoids contents (TCC) at all dates of both storage treatments. In contrast, the ascorbic acid content (AAC) increased only until days 14 and 4 in the CS and SL treatments, respectively. Among enzyme activity parameters, the activities of phenylalanine ammonia-lyase (PAL), peroxidase and superoxide dismutase (SOD) were significantly increased in the MeJA and SA treatments in all dates of both storage treatments. Catalase (CAT) activity increased in the SA and control treatments, while it decreased in the MeJA treatment in both storage conditions. In both the MeJA and the SA treatments, six pair-variables (FD vs. CI, PAL vs. CAT, PAL vs. SOD, TAC vs. SOD, TAC vs. FD, and AAC vs. CI) were significant in Pearson correlation and regression analyses among the 45 parameters pairs. Principal component analyses explained 89.3% of the total variance and PC1 accounted for 55.6% of the variance and correlated with the CI, FD, TAC, TSPC, TCC, PAL and SOD, indicating strong connections among most parameters. In conclusion, MeJA and SA are practically useful and inexpensive techniques to maintain quality attributes of CI, FD, TAC, TSPC, TCC, PAL, POD and SOD in apricot fruit during both CS and SL conditions.  
**Keywords:** apricot; methyl jasmonate; salicylic acid; antioxidant property; enzyme activity; postharvest quality
## 2 Materials and Methods
### Experimental Testing System and Procedure
#### 2.4.3 Destructive Tests  
Destructive tests were performed using a SHIMADZU Autograph AGS-X 100 kN compression-tensile machine. The flexural test followed the European standard EN 12390-5 [26] and the compression test EN 1015-11 [27]. A constant displacement of 0.001 mm/s was applied to the specimen, and the computer registered the force (N) needed to generate this displacement. The test ended when no greater load could be sustained (collapse). The maximum loads recorded are the outputs needed to determine the flexural and compression strengths, following normalised equations' respective standards. The experimental testing system (Figure 2) consisted of a compression-tensile machine of 100 kN capacity (a), two types of support systems (b.i and b.ii) for each test, a load cell (c), a computer (d) equipped with the machine's software for data acquisition, and 160 mm \(\times\) 40 mm \(\times\) 40 mm (e.i) and 80 mm \(\times\) 40 mm (e.ii) test specimens in the flexural and compression tests, respectively. In addition, a schematic representation of both support systems (f.i and f.ii) is shown for better visualisation.
# Experimental Research on Bond Behavior between GFR Bars and Stirrups-Confined Concrete
## 2 Testing Program
### Specimens, Setup and Testing Equipment  
According to CSA S806-02 [29], the bars were pulled from 150 mm concrete cubes. The embedment length was 5d (d is the bar diameter). The diameter of the stirrup was 8 mm and the spacing was 40 mm. In order to be closer to the actual situation in engineering, longitudinal steel bars were configured in concrete cube and then tied with stirrups. The assembled steel cages are shown in Figure 2 and the tested specimens are shown in Figure 3. Two nominally identical specimens without stirrups (N) and three nominally identical specimens with stirrups (Y) were tested for each specimen type to obtain some measure of uncertainty. Each specimen has been identified with a five-part code. For instance, R1-C1-8-N-1 indicates the surface characteristic of GFRP in the specimen is helical wrapping, concrete strength is C1, the bar diameter is 8 mm, no stirrup is configured in the concrete cube, and the specimen is numbered as 1 in the identical specimens.  
A pullout load was applied using a servo-hydraulic testing machine (1000 KN/SHT4106-G) with displacement control with the loading speed set at 1.0 mm/min, as shown in Figure 4. Contact between the concrete and the bar along the debonded length was broken using a Polyvinyl chloride (PVC) tube to minimize the stress concentration that is near the loaded end. A spherical seat was installed between the steel plate and the base plate at the bottom of the steel frame of the fixed concrete specimen. This enabled the specimen to spin freely within a narrow range during the loading process in order to prevent the GFRP bar or the steel bar from bending and torsion due to the load. The free (unload) end slips of the bar relative to concrete were measured with two linear variable differential transformers (LVDTs) that were fixed on the surface of the bar. Outputs from the servo-hydraulic testing machine and the LVDTs were recorded while using an automatic data acquisition system.  
\begin{table}
\begin{tabular}{c c c} \hline
**Concrete Mix** & **C1** & **C2** \\ \hline Water (kg/m3) & 185 & 195 \\ Cement (kg/m3) & 429 & 542 \\ Coarse aggregate (kg/m3) a  & 1250 & 1164 \\ Fine aggregate (kg/m3) b  & 536 & 499 \\ Air content (\%) & 2.5 & 2.5 \\ \hline \end{tabular}
\end{table}
Table 2: Composition and characteristics of concrete.  
Figure 2: Assembled steel cages.
## 2 Finite Element Analysis of the Electromagnetic Track Launcher
### Finite Element Calculation Results
#### 2.2.2 Inductive Gradient  
The skin effect is a physical phenomenon that occurs in conductors in alternating electromagnetic fields. In the armature launching process, the state of motion of charge through the launcher changes due to the strong magnetic field, which causes the uneven distribution of the current density, and the inductance gradient is affected. The current density distributions at different time are calculated by LS-dyna as shown in Figure 6.  
It can be seen that the current is mainly concentrated in the armature center and the contact of the armature tail and track due to the skin effect, rather than a uniform distribution, which causes the inductance gradient of the launcher to change dynamically with time.  
The inductance gradient curve is calculated by LS-dyna as shown in Figure 7.  
In the finite element simulation software, multiple sets of finite element simulations are set up with a fixed track conductivity \(\sigma_{\rm g}\) and armature conductivity \(\sigma_{\rm d}\), and fixed armature conductivities \(\sigma_{\rm d}\) and track conductivities \(\sigma_{\rm g}\). The inductance gradient calculated results are obtained and shown in Figure 8.  
It can be seen that, when the track conductivity \(\sigma_{\rm g}\) is fixed, the inductance gradient increases with the armature conductivity \(\sigma_{\rm d}\). This is due to the fact that the greater armature conductivity \(\sigma_{\rm d}\) causes a more uniform magnetic field distribution, and a larger inductance gradient. Conversely, the inductance gradient decreases with the increase of the track conductivity \(\sigma_{\rm g}\). This is due to the fact that the greater track conductivity \(\sigma_{\rm g}\) increases the skin depth, which reduces the diffusion speed of the magnetic field in the track. Thus, the inductance gradient decreases.  
Figure 5: Curves of armature velocity with time at different conductivities. (**a**) Armature velocity at different armature conductivities \(\sigma_{\rm d}\); (**b**) armature velocity at different track conductivities \(\sigma_{\rm g}\).  
Figure 6: Current density distribution at different time points. (**a**) \(t\) = 0.10 ms; (**b**) \(t\) = 0.25 ms; (**c**) \(t\) = 0.40 ms; (**d**) \(t\) = 0.55 ms.  
Figure 7: Inductance gradient curve with time.
## 6 Experiments, Results, and Analysis
### Dailing with Anomalous Fallback Forecasts (Experiment 3 and Experiment 5)  
Assuming that more data is beneficial to global model performance and that the naive forecast can provide a reasonable estimate when the GM(GBMR) issues an anomalous forecast, we conducted two additional experiments (Experiment 3 and Experiment 5), removing only the train instances that were similar to feature vectors for which both, the GM(GBMR) and the naive forecast provided an anomalous forecast. For Experiment 5, we enlarged the reference vector with the predicted value (or the target value for the vectors in the train set). In both experiments, we observed an improved MASE score regarding Experiment 2 and Experiment 4 and a reduced discrepancy between values considered anomalous for GM(GBMR) predictions and target values. In particular, we found that the best results for all metrics in both datasets were obtained for Experiment 5, reducing outliers discrepancy regarding the original model for at least seven percent in both datasets.  
From the results that we obtained, we consider the vector structure used to measure the similarity between the instances (using the most relevant features either with the target (or predicted) value or not), did not influence the results. We confirmed that improving the global model performance is possible by removing particular data instances from the train set. When searching for such instances, it is crucial to ensure that the least possible data is removed. Since the M4 CD dataset comprises a wide variety of time series and the same architecture and methodology were used to replicate the findings on the KWWTCD dataset, we expect that they can be generalized to other domains. Moreover, though the research was applied to time series datasets, we consider that the findings can be ported to regression problems in general, due to how we formulated the forecasting problem.
# A Low Phase Noise Dual-Loop Dual-Output Frequency Synthesizer in SiGe BiCMOS
## 2 Design and Implementation of the Dual-Loop Dual-Output Frequency Synthesizer
### Voltage Controlled Oscillator using Cap Bank and Ka-Band Doubler  
Figure 4a shows the schematic of the Ku-band Colpitts VCO with 3-bit cap bank (in Loop2). The Ku-band Colpitts VCO with single varactor pair (in Loop1) uses the same topology without cap bank. The 3-bit cap bank is utilized to lower the VCO gain. The NMOS transistor is used as RF switch, which is controlled by Vsw. The capacitance in the 3-bit cap bank is binary weighted. In the Colpitts VCO, the LC tank consists of C1, C2, L2, Cvar, Le, and the 3-bit cap bank. Cvar is capacitance of the varactor, which is biased with tuning voltage, Vt. Bipolar transistor T1,2 generates the desired negative resistance to sustain oscillation. The biasing condition for T1\(\sim\)T4 are optimized for noise and output power. The emitter lengths for T1,2 and T3,4 are 18 \(\upmu\)m and 10 \(\upmu\)m, respectively. The switching signal, Vsw, is controlled by on-chip SPI. Figure 4b shows the designed Ka-band doubler, using differential Cascode core to generate second harmonic signal with sufficient gain and output power. The emitter lengths for T5,6,7,8 are all 16 \(\upmu\)m.
# The Role of PET and SPECT Imaging in Prostate Cancer Targeted Alpha Therapy: When and How?
## 4 \({}^{223}\)Radium-dichloride (Xofigo)  
In 2013, \({}^{223}\)Radium-dichloride (\({}^{223}\)Ra-Cl2) obtained the Food and Drug Administration (FDA) approval for the treatment of metastatic castration-resistant prostate cancer (mCRPC) in patients with symptomatic bone metastases, no visceral metastases and without lymph nodes greater than 30 mm [36; 37]. \({}^{223}\)Ra is an alpha-emitter with a half-life of 11.4 days [38]; \({}^{223}\)Ra-Cl2 acts as a calcium analog and is taken up into sites of increased bone remodeling, such as bone metastasis, binding bone hydroxyapatite [39; 40]: in skeletal metastasis, \({}^{223}\)Ra induces apoptosis by means of double-stranded DNA breaks [41]. As regards posologyand the method of administration of this radiopharmaceutical, the European Association of Nuclear Medicine (EANM) recommends a dose of 55 KBq per Kg body weight administered every 4 weeks for 6 injections [42]. As described in the EANM guidelines, relative contraindications for the treatment with \({}^{223}\)Ra-Cl2 are represented by a Karnofsky score <50%, a limited bone marrow reserve, and fecal incontinence. The most common side effects are represented by thrombocytopenia, vomiting, diarrhea, and nausea. Pancytopenia, neutropenia, and leukopenia are common side effects as well [42]. Treatment with \({}^{225}\)Ra-Cl2 extends the overall survival in patients with mCRPC, as demonstrated in the ALSYMPCA trial [19], in which 921 patients with mCRPC (these patients presented symptomatic bone metastasis and no visceral lesions) received six injections of \({}^{225}\)Ra-Cl2 every 4 weeks: compared with placebo, treatment with \({}^{223}\)Ra-dichloride improved overall survival (median, 14.0 months vs. 11.2 months). Moreover, this type of treatment is effective, regardless of previous chemotherapies [43]. A retrospective analysis conducted by [PERSON] et al. demonstrated an important survival benefit especially when \({}^{223}\)Ra-Cl2 is used earlier in the treatment pathway of patients with mCRPC [44]. Factors associated with survival in the treatment with \({}^{223}\)Ra-Cl2 are represented by a baseline alkaline phosphatase <115 U/L, \(\leq\)5 bone metastases, and no prior chemotherapy [45,46]. Furthermore, the patients' quality of life (QoL) in the pretreatment phase is an important predictor for the overall survival [47].
# Frequency Division Multiplexing of Terahertz Waves Realized by Diffractive Optical Elements
## 3 Results
### Focal Spots
Nevertheless, it must be noted that, for these geometrical parameters and wavelengths, the focal spots are relatively long (high Rayleigh length) in comparison with the focal length shifts. The second thing is the rotation angle of the detector (optimized for 300 GHz). The farther the detector is moved from the original position, the higher is the angle between the normal of the scanning plane and the propagation direction of the THz beam. In such a case, the cross-section of the circular beam becomes elliptical, which can be observed in Figure 7. This effect is also stronger for smaller frequencies (higher shifts from the optical axis), as described in the simulations section. Figure 7: Intensity distributions of focal spots obtained for range of frequencies for DOEs with \(f=200\) mm and \(f=400\) mm. Presented intensities were cropped to size of 4 mm \(\times\) 4 mm to show the shape of focal spots. Each intensity distribution was normalized to the maximum value registered while scanning a particular focal spot. Power emitted from the source depends on the frequency; therefore, intensities of the focal spots cannot be compared directly.