esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Validation
#### 2.4.2 Comparison with In Situ Measurements from Weather Stations  
Temporal validity was performed by comparing all predicted scenes against daily Snow Depth (SD) data collected by weather stations (Table 3) present in the study area (Figure 1). These weather stations are owned and managed by the Piedmont Regional Agency for Environmental Protection (ARPA Piemonte). Meteorological stations data are accessible through a dedicated portal (available online at [[https://www.arpa.piemonte.it/rischinaturali/accesso-ai-dati/annali_meteoidrologici/annali-meteo-idro/banca-dati-meteorologica.html](https://www.arpa.piemonte.it/rischinaturali/accesso-ai-dati/annali_meteoidrologici/annali-meteo-idro/banca-dati-meteorologica.html)]([https://www.arpa.piemonte.it/rischinaturali/accesso-ai-dati/annali_meteoidrologici/annali-meteo-idro/banca-dati-meteorologica.html](https://www.arpa.piemonte.it/rischinaturali/accesso-ai-dati/annali_meteoidrologici/annali-meteo-idro/banca-dati-meteorologica.html)), accessed on 12 April 2022).  
The daily SD data were collected for the time window of this study (i.e., from 30 July 2015 to 31 December 2021). The day is considered with snow for SD \(\geq\) 1 cm and as snow-free otherwise, as below this threshold it is unlikely to detect snow through remote sensing [80; 81; 82; 83].  
In addition to validating the prediction accuracy, the snow season parameters were also validated by comparing the First Snow Day (FSD), i.e., the first day of stable seasonal snow (i.e., lasting more than 15 consecutive days) [84] per pixel, expressed in DOY, the LastSnow Day (LSD), last day of stable season snow per pixel, again, expressed in DOY, and the Snow Cover Duration (SCD), i.e., the number of days of snow cover on the ground in the snow year per pixel, expressed in number of days, computed from 1 September to 31 August of the following calendar year [85], with the same parameters extracted from the weather stations.
## 4 Discussion
### Differences in Bacterial Inactivation  
Successful inactivation of total coliform bacteria was achieved in all samples subjected to thermal pasteurization, with a recorded decrease in total bacteria colony forming units from 1.54 log to 2.36 log. In the case of samples treated with pulsed electric fields (PEFs), we observed a reduction in total bacteria by 0.13 log, with a more significant reduction of coliforms by 0.48 log when compared to the control liquid whey. The removal of curds from raw milk contributed significantly to the reduction in bacterial load, accounting for a decrease of up to 1.03 log in total bacteria and 1.13 log in coliforms. This effect can be linked to several processes that occur following the removal of curd from raw milk with rennet. No additional lactic acid bacteria were introduced to the liquid whey. Nevertheless, mesophilic lactic acid bacteria were detected in both the liquid whey samples and those treated with PEFs. A notable reduction of 1.54 log in mesophilic lactic acid bacteria was observed after treatment compared to the liquid whey control. Mesophilic lactic acid bacteria, naturally present in raw milk, have been identified as beneficial to human health due to their roles in nutrient absorption, immune function, and digestive processes. They have also been associated with effects on mental health [71; 72; 73; 74; 75; 76]. The raw milk was not subjected to pasteurization prior to curd removal; only the liquid whey protein underwent pasteurization using various techniques. The PEF treatment may have effectively inactivated the majority of coliform bacteria, leading to the dominance of mesophilic lactic acid bacteria, which further contributed to the acidity of the liquid whey [77; 78; 79; 80]. Given that coliforms tends to proliferate optimally at a pH range of 6 to 7.5 [81; 82], the lower pH could potentially have inhibited bacterial growth [83; 84]. Physical separation of whey from curds might also contribute to the reduction of coliform bacteria, as the curd, due to its retention of more moisture and nutrients compared to whey, is likely to harbor more bacteria.
## 2 Materials and Methods
### Test Evaluation  
Surface integrity was evaluated in terms of geometrical properties. From the point of view of micro-geometrical properties, the roughness average (Ra) was measured. For its part, the kerf taper angle (T) was the variable chosen for the evaluation of macro-geometrical properties. For measuring roughness profiles, the following methodology was taken into account. The cutting area was divided into three zones, where Figure 2 shows the distance at which each measurement was taken.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**Levels**} \\ \hline WP (MPa) & 250 & 380 & 500 \\ AMFR (g/min) & 170 & 340 & 450 \\ TFR (mm/min) & 100 & 300 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cutting parameters: working pressure (WP), abrasive mass flow rate (AMFR), traverse feed rating (TFR).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Office Diameter** & **Nozzle Diameter** & **Nozzle Length** & **Abrasive Size** & **Abrasive** & **Stand-Off Distance** \\
**(mm)** & **(mm)** & **(mm)** & **(mm)** & **Type** & **(mm)** \\ \hline
0.30 & 250 & 380 & 500 & Garnet & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Constant parameters.  
Figure 2: Schemes of the cutting area, zones in the section of the cut, and distances at which measurements were performed.  
In this way, all micro-geometrical changes that take place during the cutting process were considered (Figure 2).  
To carry out the measurements, a Mahr Pertometer Concept PGK 120 roughness tester (Mahr, Gottingen, Germany) was employed. In addition, in the evaluation of the micro-geometrical properties, an optical inspection was also carried out via the use of a scanning electronic microscope (SEM). In this case, a Hitachi SU 1510 (Hitachi, Tokio, Japan) microscope was employed. Moreover, analysis by SOM techniques was performed using a SMZ-800 Nikon microscope (Nikon, Tokyo, Japan) which had a 5-megapixel Optikam B5 optical camera (Optika, Ponteranica, Italy).  
Parameter \(T\), which identifies the kerr taper angle, was calculated using the following equation, Equation (1) [12]:  
\[T=tan^{-1}\bigg{(}\frac{w_{t}-w_{b}}{2t}\bigg{)}. \tag{1}\]  
In this equation, and as can be seen in Figure 2, \(Wt\) is the width of the slot produced by the very first impact of the water jet, while \(Wb\) is the width generated by the outlet of the jet. To measure these variables (\(Wt\), \(Wb\)) in the machined pieces, the free software Image J was used. Figure 3 shows an example of measurement. The procedure followed before carrying out the measurement consisted of a previous image calibration process to determine the pixel/mm ratio. In this way, all the measurements made directly on the images returned a correct result. In addition, by processing the image, the area damaged by erosion caused by loss of coherence of the jet was identified (see Figure 3).
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 3 Results
### Simulations
The slopes from this experiment are \(45\) times lower for the internal group and \(25\) times lower for the external group. In a scenario with delays from \(50\) to \(550\) milliseconds (see Figure 9), the results show that more messages are generated in the system per second, more overhead is required to ensure the causal ordering. The overhead has the same growth stages as for the system with communication channel delays from \(0\) to \(50\) milliseconds and from \(50\) to \(250\) milliseconds. Analyzing the growth of the overhead in the internal group, the slopes are \(0.002177379\) for \(2500\) messages per second, \(0.00272099\) for \(2000\) messages per second, \(0.005812997\) for \(1500\) messages per second and \(-0.006493243\) for \(1000\) messages per second while in the original experiment the slope is \(0.990\). As for external group the slopes could not be calculated because the linear growth part was not reached but the same tendency as for other network delays can be observed. In this scenario, the slopes have a tendency toward zero which means that the overhead does not directly depends on the number of peers in the system, but it depends on the message density in the system. The results obtained from this experiment show that when the number of messages generated in the system per second remains constant, the overhead growth slope has a tendency toward zero. This means that there is no direct dependency between the number of peers in the system and the amount of control information required for causal message ordering. Thus, the number of peers in the system can be relatively high while the message generation rate of the system remains constant. In addition, it should be noted that the above experiments consider that all of the peers in the system equally participate in the communication. All of the peers that are passive receivers do not increase system overhead. For example, in a system with \(300\) peers where \(200\) peers are passive receivers and do not generate any messages, the overhead will be the same as in a system containing only \(100\) participating peers. We note that in real world P2P applications, such as video call/meeting, current solutions only support up to \(25\) peers participating equally in the communication. This means that with our protocol, applications could allow up to \(100\) peers exchange streams in an equitable participation, keeping a controlled overhead growth. This leads to the following. If several communication groups exist over the same peer-to-peer network, the communication and storage overhead will only depend on the number of peers participating in a group communication, not on the total number of peers in the system. For example,  
Figure 9: Internal (**a**) and external (**b**) group overheads in bytes for a system with delays from \(50\) to \(550\) milliseconds. the overhead will depend on the number of people participating in a video conference, and not on a total number of people using the software.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Performance of Machine Learning Models
#### 3.1.3 Coefficient of Determination  
The coefficient of determination, also known as the R\({}^{2}\) score, is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variables. In other words, it gives an indication of how well the model captures the underlying variation in the data [46; 47; 48].  
In this study, the RF model attained the highest R\({}^{2}\) score, implying that it was able to explain a greater proportion of the variance in water-quality indices in comparison to the MLR and ANN models. This is likely due to the RF model's ability to model complex, non-linear relationships between variables, and its robustness to overfitting.  
Overall, based on these evaluation metrics, the Random Forest model demonstrated superior performance compared to the Multiple Linear Regression and Artificial Neural Network models in predicting the water-quality indices of the urban lake. These results highlight the robustness and suitability of the Random Forest model in handling complex environmental datasets and predicting outcomes with high accuracy.
# eHealth Integrity Model Based on Permissioned Blockchain +
## 1 Introduction
### Motivation  
The motivation for this work was to create a solution that allows ensuring transactional transparency in distributed eHealth environments, so every patient or auditor can verify if given medical histories or logs are complete and unchanged. Such a service is necessary because eHealth systems are increasingly complex and distributed, and process rising amounts of data. For this reason, it is difficult to verify the integrity of distributed medical records using organizational measures or simple integrity mechanisms, implemented in individual databases. In distributed eHealth environments, the nodes at healthcare sites by default operate according to the law. However, due to numerous reasons, from which the most important are cyberattacks and rogue administrators, unauthorized changes might occur in a medical history or log. The detection of these changes allows restoring the data from database replicas or backups that are mandatory in eHealth systems.
## Objectives  
The aim of the study is to automatically distinguish four types of voice qualities by means of glottal area waveform (GAW) modeling.  
**Introduction**  
Voice quality typing is pivotal to the clinical care of voice disorders, because it is important for the indication, selection, evaluation, and optimization of clinical treatment techniques. Three of the selected voicing types are typical for pathological voices, i.e., (1) diplophonia, (2) voices with random extra pulses during quasi-closed glottal cycle phases, and (3) voices with random phase differences between the left and the right vocal folds. The fourth voice type is a control type, i.e., (4) normophonia.  
**Methods**  
To enable experimentation under controlled conditions, per voicing type 20 GAWs are generated synthetically. Synthesizer structures and parameters are identified based on qualitative observations made on an available database comprising laryngeal high-speed videos of 120 subjects. Automatic 4-way classification is proposed. It is based on analysis-by-synthesis, and uses as predictors the four root mean square model errors obtained with each of the synthesis structures. The predictors are input to a 4-way logistic regression model.  
**Results**  
The overall accuracy of the proposed 4-way logistic regression model is 56.5%. The classifier performs best for the random phase difference GAW type (74.1%), and worst for the extrapulsed GAW type (45.8%). The GAW type is predicted correctly in the majority of the signals. No confusions of the extrapulsed type with the random phase differences type are observed, although they are not easily distinguishable auditorily (Table 1).
# From Nearly Zero Energy to Carbon-Neutral: Case Study of a Hospitality Building
## 5 Results
### Environmental Impacts Results
#### 5.2.3 Carbon Offset  
Although extremely performing, the CNB still reports emissions equal to 5016.2 tonCO\({}_{2}\)-eq along its service life. Since the building performance may not be further improved to pursue the carbon-neutral target, a carbon offset strategy needs to be established. An effective way to compensate for emissions is via local actions, visible to the citizenship and producing local advantages [74]. To this purpose, an area of 9850 m\({}^{2}\) adjacent to the building has been identified, and a project of requalification has been proposed. The area is currently a boulevard made of two roadways for cars and a central parking lot. The new project considers a redesign of the local mobility, reducing the section of the roadways, imposing low-velocity limits, installing pervious paving, and limiting the parking area. The intervention includes a green area of about 2400 m\({}^{2}\), with about 90 new trees selected among indigenous species, with limited need for watering, and a high-carbon capture capability. Calculations, run with i-Tree Eco v6 software [75], report that, in 30 years of development, the 90 trees will be able to locally capture about 300.4 tonCO\({}_{2}\)-eq and, moreover, they will provide collateral benefits, such as outdoor air quality and thermal comfort improvement, and other ecosystem services.  
Because of the limited space, a whole local carbon offset is not feasible (to do so, about 1400 additional trees would be required). Thus, the remaining 4715.8 tonCO\({}_{2}\)-eq emissions may only be offset via the acquisition of voluntary carbon founds on the carbon market, and the provision of energy supply contracts with guaranteed renewable origins.
## 2 Materials and Methods
### Experimental Design  
The working variables for the HS-SPME procedure, namely equilibration time, time and temperature for analyte adsorption into the fiber, time and temperature for analyte desorption, as well as weights of sample and background electrolyte (i.e., sodium chloride), were optimized by using an experimental design methodology. To this end, a fractional factorial design (2\({}^{(7-3)}\)) with three center points was applied to evaluate the main effects of the SPME variables on the recovery of volatile compounds from strawberry samples. Study ranges for these variables were as follows: equilibration time, 5-30 min; extraction time, 20-30 min; extraction temperature, 25-65 \({}^{\circ}\)C; desorption time, 1-5 min; desorption temperature, 240-280 \({}^{\circ}\)C; weight of sample, 2.5-10 g; weight of background electrolyte, 10-25%. All the extraction tests were performed using a headspace volume in the range 30-50% of the total vial volume, and keeping constant the height of the fiber inside the vial, to ensure reproducibility and extraction efficacy, according to previous literature [26]. In total, 19 trials were conducted in random order using a pooled strawberry sample. To study the effects of the factors on the response variables (i.e., number of peaks and peak areas), a Pareto diagram was used. In this representation, the length of each bar is proportional to the standardized effect (i.e., estimated effect divided by its standard error), and the vertical line can be used to judge which effects are statistically significant at 95% confidence. Subsequently, a response surface methodology (RSM) based on centralcomposite design (CCD) was developed to optimize those extraction factors that elicited significant effects on dependent variables [27]. CCD consisted of a two-level factorial design, a star design in which experimental points are at a distance \(\alpha\) from its center, and the central point. Each factor was studied at five levels (\(-\alpha\), \(-1\), \(0\), \(+1\), \(+\alpha\)). The relationship between dependent and independent variables was fitted by a second-order polynomial model. The validation of the quadratic model obtained by RSM was accomplished by analysis of variance (ANOVA).
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Data  
We utilized the Brats 2021 dataset [1; 2; 16] for our study. This dataset comprises multi-parametric MRI scans of glioma patients, featuring four modalities: T1-weighted, T1 with contrast enhancement, T2-weighted, and FLAIR (see Figure 2). These provide varied insights into brain anatomy and tumor characteristics. It includes segmentation labels for tumor sub-regions (necrotic core, edema, enhancing tumor, whole tumor). The data are co-registered, isotropically resampled, and skull-stripped for consistency across scans [2].
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 5 Performance Analysis
### Simulation Analysis  
The performance of the FDW code was simulated using OptiSystem version 15. The test was carried out with a bit rate of 2.5 Gb/s for a 40-60 km distance with single-mode optical fiber. The spectral width for each chip is set to 0.8 nm. The dark current value is 1 nA, and the thermal noise coefficient is \(1\times 10^{-22}\) W/Hz for each photodetector. The performance of the FDW code is based on the BER obtained for each receiver. Figures 11-13 in the Appendix A section show eye pattern results for 40-60 km optical fiber length.  
Based on the simulation results, the eye pattern result degrades as the distance increases. The system can go up to 60+ km before the signal is distorted. The BER for 60 km optical fiber distance is \(2.11951\times 10^{-11}\), which is lower than \(10^{-9}\). The acceptable amount for BER is less than or equal to \(10^{-9}\).  
To establish the reliability of the FDW system, tests were conducted based on the effect of different bit rates with distance. Figure 5 shows the graph of BER vs. distance for different bit rates applied on the FDW system. The graph provides a clear view on FDW system performance. On 10 Gb/s, the system can go up to 40 km, which has the BER \(1.15\times 10^{-10}\). 1 Gb/s bit rate able to travel 70 km, while the 5 Gb/s bit rate is capable of reaching up to 50 km. Based on this observation, FDW can support high data rates and can cover long-distance applications such as a metropolitan area network (MAN).  
Figure 4: Performance of OCDMA code signal and noise power (W) versus received power, Psr (dBm).
## 5 Conclusions  
Shield tunnels exhibit obvious discontinuities in the longitudinal direction. The conventional longitudinal equivalent continuous model can only consider the longitudinal stiffness of the shield tunnel when it is loaded by a single degree of freedom, which cannot well characterize its longitudinal deformation under the combined action of multiple internal forces. This paper derives and analyzes the longitudinal nonlinear equivalent continuous model for shield tunnels, considering both longitudinal axial force and bending moment. This study confirms that the traditional equivalent continuous model is a specific instance of the proposed longitudinal nonlinear equivalent continuous model when the axial force is zero.  
According to the contact state of the joint interface of shield tunnels when longitudinal bending moments and axial forces co-exist, the longitudinal deformation of shield tunnels can be categorized into three modes, i.e., total separation of the joint interface, total contact, and partial contact. The ratio of axial force to bending moment \(e\) can be used as a variable to distinguish each mode. The longitudinal equivalent bending stiffness is constant in modes 1 and 2, and in the \(EI-e\) curve, modes 1 and 2 behave as straight lines at both ends of the curve. The longitudinal equivalent bending stiffness decreases with increasing \(e\) in mode 3, and mode 3 is smoothly connected to modes 1 and 2 in the \(EA-e\) curve. The longitudinal tension-compression stiffness is also constant in modes 1 and 2. In mode 3, there is a negative stiffness region in the longitudinal tensile stiffness, with an abrupt change at point \(e_{\varphi 0}\).  
**Author Contributions:** Conceptualization, X.G. and C.C.; methodology, C.C. and Z.X.; software, Q.C. and X.G.; validation, X.G., C.C. and Z.X.; formal analysis, Z.X.; investigation, X.G. and Q.C.; resources, X.G.; data curation, C.C.; writing--original draft preparation, C.C. and Z.X.; visualization, Q.C.; supervision, C.C.; project administration, X.G.; funding acquisition, X.G. and Q.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 52308400, No. 52378342) and the Funds of Scientific and Technological Plan of Fujian Province (No. 2022Y4015).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** Author [PERSON] was employed by the company CCCC First Highway Consultants Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.