esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### LIPLES Method for the Assessment of Hydrological and Morphological Pressure Levels  
In order to quantify the level of hydrological pressures experienced by the waterbody, both the water abstractions and consequent reductions in the normal volume, and the non-natural contributions, as well as over damming by heightening of the waterbody basin, are considered. To define the pressure levels, in the case of abstractions and transferences, the volumes extracted or transferred are compared to the maximum volume of the waterbody (Equations (6) and (7)). For heightening, the height of the over damming is compared with the maximum depth, as indicated in Equation (8). Data for these direct alterations at the studied sites were obtained for the sites listed in Table S1, with information gathered from the Spanish inventory DATAGUA [37].  
\[\text{Abstractions = (Volume extrac., in Hm}^{3})/(\text{Max waterbody volume, in Hm}^{3}) \tag{6}\] \[\text{Transferences = (Volume transferred, in Hm}^{3})/(\text{Max waterbody volume, in Hm}^{3})\] (7) \[\text{Heightening = (Height of over damming, in m)/(Max waterbody depth, in m)} \tag{8}\]  
To homogenize the scale of the hydrological pressure levels to the other types of pressures, the results of this quantification were normalized to percentage form. To do this, data obtained from the quotient of the corresponding formula were divided by the highest data reached for abstractions, transferences and heightening, respectively, in all of the studied sites. Then, the three homogenized data were added up in order to obtain a joint value for the hydrological pressure, ranking from 0 to 100%, as shown in Equation (9).  
\[\text{Hydrological pressures level = Abstractions + Transferences + Heightening} \tag{9}\]  
Morphological pressures were calculated directly from satellite image viewing tools, more specifically, using Google Earth Pro. These tools were especially useful for the identification and quantification of the anthropogenic alterations of the waterbody margins and banks. To visualize and determine the percentage of altered shore areas, orthophotos and the geometrical calculation tools of the Google Earth Pro viewer were used. Through this free viewer, the altered areas were identified with respect to the total perimeter of the waterbody. The percentage of altered margins was calculated from the perimeter by shoreline area altered, by modifications or constructions in the shore of the waterbody (Equation (10)). Sites studied were mostly the same as in the hydrological pressures section, all being waterbodies from Spain.  
Morphological pressure level = (Longitude of modified margin, in m)/(Total waterbody perimeter, in m)*100 (10)
## 3 Materials and Methodology
### Surface Subsidence Prediction and Impact Analysis
#### 3.4.2 Equivalent Mining Height (EMH)  
The EMH theory is a useful tool for analyzing the influence of rock movement control on solid backfilling mining. In solid backfilling mining, the gangue fills the goat, reducing the subsidence gap of the overlying strata. It can also be explained another way; the equivalent mining height is the mining height of the filling working face minus the height of filling material after compaction. Equation (6) can be used to determine the equivalent mining height [1, 26, 27, 28, 29, 30]:  
\[m_{e}=m(1-\rho) \tag{6}\]  
where \(m_{e}\) refers to the EMH, influenced by such factors as the backfill body's compression ratio; \(m\) is the mining height; and \(\rho\) is the filling ratio.  
When using the probability integral method based on the equivalent mining height to predict the subsidence of gauge filling mining, the subsidence prediction parameters can be taken as the corresponding parameters of the longwall caving method used for thin coal seams in the mining area. The subsidence prediction parameters used in this subsidence prediction process are listed in Table 6.
## 2 Regression Tree Ensembles
### Bagging Regression Tree  
In bagging regression tree, the base models are formed using multiple randomly drawn bootstrapped samples from the original dataset. This procedure is conducted numerous times until a substantial subset of training datasets are made, and the same samples can be gathered more than one time. On average, each formed bootstrapped training dataset holds \(N\Big{(}1-\frac{1}{e}\Big{)}\ \approx 0.63N\) instances, where \(N\) is the total number of samples in the original dataset. The left-out instances are known as out-of-bag observations, and it is used to assess the performance of the model. The final output of the bagging regression tree model is the average of the predicted output of the individual base models, which in turn is diminishing its variance and produces better stability [21; 22; 24; 30]. In bagging regression tree, the base model fits the training dataset \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{N},y_{N})\}\), attaining the tree's prediction \(\hat{f}(x)\) at input vector \(x\). Bagging averages this prediction over a collection of bootstrap samples. For each bootstrap sample \(D^{st}\), \(t=1,2,\dots,\ T\), the model delivers prediction \(\hat{f}^{st}(x)\). The bagged estimate is the mean prediction at \(x\) from \(T\) trees as presented by Equation (1).  
\[\hat{f}_{bag}(x)=\frac{1}{T}\sum_{t=1}^{T}\hat{f}^{st}(x) \tag{1}\]
# Action of Antimicrobial Peptides against Bacterial Biofilms
### Biofilm-Associated Infections  
Biofilms pose a serious threat to public health because of their potential to cause biomaterial-associated infections due in-part to the high resistance of biofilms to antimicrobials agents [33]. About 80% of bacterial infections in humans are caused by biofilms [1; 12; 23]. Biofilm mediated infection can be divided into two categories, non-device and device associated infections [34; 35].  
The first category involves biofilm formation on host tissues such as epithelial, mucosal surfaces, and teeth. These can cause infections associated with cystic fibrosis (CF) patients, foot ulcers in diabetic patients, chronic otitis media or rhinosinusitis, chronic prostatitis, recurrent urinary tract infections, and dental caries and periodontitis [36; 37].  
The second category of infections arises due to the microbial colonization of abiotic objects, for example indwelling medical devices such as central venous or urinary catheters, joint or dental prostheses, heart valves, endotracheal tubes, intrauterine devices, and dental implants [34; 35; 38]. Microbes can detach from these biofilms and disseminate to the surrounding tissues or to the bloodstream, further exacerbating the infection [39]. Worldwide production of biomedical devices and tissue engineering-related objects is approximately $180 billion per annum [37]. According to current estimates, over 5 million medical devices or implants are used annually in the U.S.A. alone [37]. About 60-70% of nosocomial infections are associated with biomaterials or implants [37]. Regardlessof the sophistication of the biomedical implant and tissue engineering constructs, all medical devices are susceptible to microbial colonization and can cause infections [40; 41; 42]. Biofilm growth on medical devices can be extremely difficult to eradicate, with only a few treatment options such as removal of the infected device or use of large doses of antibiotics [43]. However, this increases treatment costs and may increase the potential for the development of antibiotic resistance and cytotoxicity [44]. Moreover, removal may not be an easy option for patients with medical devices for critical care such as pacemakers. The clinical significance of biofilm-associated infections and their inherent resistance to antimicrobials urgently demand development of novel anti-biofilm compounds.
###### Abstract  
The load and angle of attack (AoA) for wing structures are critical parameters to be monitored for efficient operation of an aircraft. This study presents wing load and AoA identification techniques by integrating an optical fiber sensing technique and a neural network approach. We developed a 3.6-m semi-spanned wing model with eight flaps and bonded two optical fibers with 30 fiber Bragg gratings (FBGs) each along the main and aft spars. Using this model in a wind tunnel test, we demonstrate load and AoA identification through a neural network approach. We input the FBG data and the eight flap angles to a neural network and output estimated load distributions on the eight wing segments. Thereafter, we identify the AoA by using the estimated load distributions and the flap angles through another neural network. This multi-neural-network process requires only the FBG and flap angle data to be measured. We successfully identified the load distributions with an error range of \(-1.5\)-\(1.4\) N and a standard deviation of \(0.57\) N. The AoA was also successfully identified with error ranges of \(-1.03\)-\(0.46^{\circ}\) and a standard deviation of \(0.38^{\circ}\).  
fiber Bragg grating (FBG); load identification; machine learning; neural network; optical fiber sensor; structural monitoring +
Footnote †: journal: Applied Physics Letters
# Quantitative Characterization of _Geotrichum candidum_ Growth in Milk
## 3 Results
### Primary Modelling of G. candidium Growth in Milk
candidum_ growth in milk with added salt was less obvious as it was in milk without added salt. There were no significant (_p_ < 0.05) differences in growth rate of the isolate at 6 and 8 \({}^{\circ}\)C or at 12 and 15 \({}^{\circ}\)C. However, the specific growth rate of the isolate significantly (_p_ < 0.05) increased with the increase in cultivation temperature from 15 to 25 \({}^{\circ}\)C.  
Figure 2: Growth curves of _G. candidum_ isolate J in UHT milk with 1% NaCl in the temperature ranges of 6–18 \({}^{\circ}\)C (**a**) and 21–37 \({}^{\circ}\)C (**b**). The points represent the observed values of counts of _G. candidum_. The continuous lines represent fitted growth curve according to DMFFit modelling program. Higher temperatures than 30 \({}^{\circ}\)C caused a decline in the growth rate in both cases--_G. candidum_ growth in milk without and with 1% NaCl. As already mentioned, at the cultivation temperature of 37 \({}^{\circ}\)C, the _G. candidum_ log counts continuously decreased after lag phase durations that were shorter than 24 h, regardless of the salt content in the milk. This result is in accordance with that of [PERSON] and [PERSON] [44], who did not observe any growth of _G. candidum_ isolate I on the surface of skim milk agar at 37 \({}^{\circ}\)C. The studies of other cheese-related yeasts demonstrated similar patterns of response to temperature [16; 45; 46]. In addition, [PERSON] et al. [47] reported _Deharyomyces hansenii_ MM6 1194 and PCF1 1148 or _Candidia zeylanoides_ 7 as unable to grow at 37 \({}^{\circ}\)C. Although we observed significantly (\(p<0.05\)) slower inactivation of _G. candidum_ cells in milk with 1% NaCl in contrast to the rate of devitalisation of the isolate in milk without salt, more experiments would be needed to understand the protective effect of salt against the higher temperature in this specific case. However, the effect of decreasing water activity, which increases the microbial tolerance and even the resistance to higher temperatures, is generally known [48]. In contrast to the lag phase duration and growth rate of _G. candidum_ J, counts of the isolate in the stationary phase were almost equal in all experiments (excluding the growth curves at 37 \({}^{\circ}\)C) at the level of 10\({}^{4}\)-10\({}^{5}\) log CFU\(\cdot\)mL\({}^{-1}\). In addition, the pH of the milk growth medium was stable during all of the experiments performed, with an average initial value of 6.54 \(\pm\) 0.05 (\(CV\) = 0.8%, \(n\) = 10) and average final value of 6.53 \(\pm\) 0.08 (\(CV\) = 1.2%). Similarly, the pH of milk with 1% NaCl had the average initial value of 6.50 \(\pm\) 0.02 (\(CV\) = 0.3%) and final value of 6.52 \(\pm\) 0.04 (\(CV\) = 0.6%). This may result from the fact that _G. candidum_ is unable to ferment lactose [2; 7]. Although the optimal pH for _G. candidum_ growth was reported to be in the range of 5.5-6.0 [4; 5; 49], according to [PERSON]
# A Geospatial Framework of Food Demand Mapping
## 2 Materials and Methods
### Integration with Population Data  
During the initial data preprocessing phase, participants' ages were categorized into predefined brackets. These age brackets, along with other demographic information, were meticulously aligned with the Lithuania National Statistical Department's population survey data. It was noted that the national statistical department did not cover some elderly municipalities in their \(1\times 1\) km grid data. For respondents from these areas, or those who did not specify their elderly municipality, points were generated based on the broader municipality region. The \(1\times 1\) km grid from the Lithuanian Statistical Department's population census served as a foundational dataset [38]. This grid, which detailed demographic distributions like age and gender, allowed for a seamless fusion of the survey data, thereby ensuring that the spatial representation mirrored the demographic dispersion of the population accurately. To represent respondents spatially without violating privacy, an innovative approach to generating respondent coordinates was devised. Initially, the grid data were filtered based on each respondent's age and gender. Subsequently, relevant boundaries were identified depending on the provided municipality and elderly municipality names. Within these boundaries, grids were selected randomly. The selection's randomness was weighted primarily by the age demographic to ensure a representative selection. However, the methodology also boasts versatility by allowing for the incorporation of other demographic statistics as additional weights, if required. For each selected grid, a random point was generated to symbolize the respondent.
## 5 Conclusions  
This study demonstrates that kinematic handwriting analysis effectively captures psychomotor retardation in youth with depression, as reflected in significantly reduced writing velocity. By leveraging three velocity-based features, selected through statistical methods and recursive feature elimination (RFE), the proposed logistic regression model achieved a classification accuracy of up to 82.5%. These findings underscore the potential of a simple, accessible, and objective screening tool for detecting psychomotor slowing associated with depressive disorders. Future research should aim to validate specific handwriting metrics as reliable indicators of depressive symptoms. Longitudinal studies could help determine how handwriting dynamics evolve over time in response to treatment, offering critical insights into therapeutic efficacy and informing the development of personalized treatment plans.  
V.D.: writing--original draft, writing--review and editing, visualization, software, methodology, investigation, data curation. N.I.: writing--review and editing, conceptualization, investigation. S.R.: writing--review and editing, visualization, software, formal analysis. B.N.: investigation, resources, data curation. D.S.: writing--review and editing, conceptualization, data curation. J.J.: writing--review and editing, resources, conceptualization. M.M.J.: writing--review and editing, supervision, conceptualization, validation, funding acquisition, methodology. All authors have read and agreed to the published version of the manuscript.  
This work was partly financially supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, under contract number 451-03-137/2025-03/200103. The research was partially conducted in the premises of the Palace of Science, Miodrag Kostic Endowment.  
This study was approved by the Ethical Committee of the Clinic for Neurology and Psychiatry for Children and Youth, Belgrade (no. 21-79/02, approved on 16 July 2018).  
Written informed consent and permission were obtained from all participants and the parents or guardians of minors.  
The authors will provide the raw data supporting the conclusions of this article, with no unnecessary restrictions or reservations. For further information about the data, please contact [EMAIL_ADDRESS].  
The authors declare that there are no conflicts of interest.  
The following abbreviations are used in this manuscript:  
YLDs Years Lived with Disability  
DD Depressive Disorder  
SSRI Selective Serotonin Reuptake Inhibitors  
TCA Tricyclic Antidepressants  
EHI Edinburgh Handedness Inventory  
SMFQ Short Mood and Feelings Questionnaire  
SCARED Screen for Child Anxiety-Related Emotional Disorders  
LOOCV Leave-One-Out Cross-Validation  
RF Recursive Feature Elimination  
LR Logistic Regression  
SVM Support Vector Machine  
RF Random Forest
# RF Energy Harvesting Wireless Communications:
## 5 Intermittent Connection Problem in RF-EHWC Networks
### Impact of Intermittent Connections
#### 5.2.1 Frequent Changes of Network Topology  
In real applications, the status of an RF-EHWC node may continuously switch between alive and dead, thereby causing frequent changes of the network topology. Compared with mobile ad hoc networks, the topology changes in an RF-EHWC network are faster and at a higher frequency due to the intermittent connection problem. Therefore, the routing protocols and topology management methods designed for mobile wireless networks may not work efficiently in RF-EHWC networks [81].
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 5 OpenFOAM FVTM Comparison
### Boundary Conditions (BC)  
As stated in Section 3.2, in order to reproduce the test radiative environment in the numerical simulation, a radiation shield has been placed around the payload. In this way, by knowing the temperatures at the walls of the shield through the TCs and the known emissivity of the Kapton placed inside it, is possible to properly set the BC of the simulation.  
The BC of the numerical model are set according to the experimental values from the thermocouples, as reported in Figure 21. With the same approach used for the walls, the temperature BC of the aluminum base is determined. As reported in Section 3.2, it is assumed that the side panels of the shield have the same temperature, except for the top that is not bolted to the others. In any case, as reported in Table 5, the temperature difference between base plate and shield panels is less than 1 \({}^{\circ}\)C which is below the TC accuracy.  
The description of the numerical schemes, the view factors calculation and the numerical setup is described in detail in [7], to which the interes  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Material** & \(\varepsilon_{IR}\) & \(k\) [W \(\mathbf{m^{-1}}\) K\({}^{-1}\)] & \(c_{p}\) [J kg\({}^{-1}\) K\({}^{-1}\)] & \(\rho\) [kg m\({}^{-3}\)] \\ \hline Structure (black coating) & 0.86 & 220 & 910 & 2700 \\ Structure, heaters, dummies & 0.03 & 220 & 910 & 2700 \\ PCB & 0.80 & 20 & 560 & 2200 \\ Connector & 0.90 & 30 & 560 & 2200 \\ FEE copper strips & 0.03 & 400 & 385 & 8900 \\ Radiation shield + Kapton & 0.81 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Material properties used in the simulation.  
Figure 21: Simulation domain. Pale gray walls represent the shroud around the DM.
## 2 E-Health
### Main Categories of E-Health Systems  
The numerous ways digital and mobile technologies are used to meet health system demands are classified as e-health interventions. This classification framework, aimed primarily at public health audiences, attempts to create an accessible and bridging language for health program planners to explain the functions of e-health systems. This classification method is based on the concept of an e-health intervention, which is a distinct function of digital technology used to achieve healthcare goals [1; 5]. Figure 1 provides the recent classifications of e-health systems according to technological evolution. E-health systems can be classified into wireless body area networks (WBAN), Internet of Medical Things (IoMT), and Healthcare 4.0.  
WBANs typically comprise small nodes that record body health indicators, including pulse rate, blood pressure, body temperature, and glucose level. Such networks aim to provide patients with long-term health surveillance under the supervision of medical authorities without interfering with their daily lives. WBAN is a wireless network of small, low-power, and minimally invasive sensors attached to or implanted in a person's body to monitor vital signs or other physiological data. The sensors collect and wirelessly transmit data to a central monitoring system, i.e., the sink node [16].  
The IoMT is a network of medical devices and applications connected to the Internet and communicating with one another. This technology provides healthcare professionals with real-time diagnostic data and data that can be used to make more informed decisions. The IoMT consists of dense medical devices, including wearable devices, remote monitoring systems, and medical implants [8]. It works by gathering data from numerous medical devices, transmitting it to a central system or database, and analyzing it to provide insights into patient health or to enhance healthcare workflows. IoMT is viewed as a tool to enhance patient outcomes, lower healthcare expenses, and boost the effectiveness of healthcare delivery [17].  
WBAN focuses specifically on monitoring physiological parameters and collecting data directly from the human body. However, IoMT involves a broader ecosystem of medical devices and technologies beyond the body itself [18].  
Healthcare 4.0 or Health 4.0 is the term used to describe the fourth industrial revolution in healthcare, involving integrating advanced digital technologies, e.g., AI, and IoT devices to enhance healthcare delivery systems [19]. It is an advanced form of healthcare that incorporates digital and networking technologies, leading to enhanced patient care.  
Healthcare 4.0 is the next evolution of the healthcare industry that has the potential to reshape the way healthcare is delivered, making it more efficient, effective, and accessible to everyone. Healthcare 4.0 goes beyond just technology and focuses on transforming the entire healthcare delivery system. It encompasses IoMT as a part of its larger framework [20].