esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Meat Quality  
Some parameters relating to meat quality studied and data are reported in Table 5. It is widely accepted that lipid and protein oxidation, in particular myoglobin oxidation, greatly affect meat quality, but the impacts of oxidation reactions on meat colour and discoloration are still controversial. As for the pH values, they were similar to those reported by [PERSON] et al. [50] in rabbit meat and higher than those determined in raw rabbit hamburgers by [PERSON] et al. [51]. The differences occasionally detected between the dietary groups and at the various storage times were not relevant and were hardly ascribable to a diet effect. However, the pH values observed for the VE100 dietary group appeared to be the lowest during the time period considered.  
Instrumental measurements of colour only occasionally showed significant differences, not always easily correlated to the different dietary treatments or to the storage time, especially for the L* values, which in fact remained essentially the same over time in all the dietary groups with the exception of the control group; in that group, starting from t2, a slight lowering was observed.  
A decreasing trend in a* values at t4 was observed in all the dietary groups, independent of diet supplementation. As for the b* values, a similar decreasing trend was observed. The variations in the colour parameter, both influenced to redox state of heme pigment, could have been related to the oxidation of the hamburger, as also reported by [PERSON] et al. (2015), since it paralleled the strong increase in MDA content. In particular, the variations in the a* values observed in this study were similarly reported by [PERSON] et al. (2016) in beef hamburgers and were attributed to the gradual oxidation of myoglobin and the simultaneous accumulation of metmyoglobin over time ([PERSON] et al., 2016; [PERSON] et al., 2017). Similar trends and observations were reported by [PERSON] et al. (2017), who clearly demonstrated that myoglobin and lipid oxidation inversely impacted redness. At least up to t2, the data of the colour parameters appeared compatible with an acceptable product quality level and seemed to parallel the TBARS values, which were above 1 and close to 1.5 only at t4, denoting the rancidity of the product ([PERSON] et al., 2016).
## 1 Introduction
In this scenario, infrared thermography could provide a solution to enhance diagnostic performance. IR is a tool that captures the infrared radiation emitted by the human body through the lens of an infrared camera and converts it into temperature data via photoelectric conversion. This technique is non-invasive, contact-free, cost-effective, reliable, and allows for rapid examination. It is safe for both patients and healthcare professionals. Artificial intelligence (AI), particularly deep learning, offers significant advantages over traditional statistical analysis in thermographic image interpretation for diagnosing DPN and PAD. While statistical methods rely on predefined features and thresholds, AI models learn complex, non-linear patterns directly from data. This enables higher sensitivity and specificity, especially in early-stage or subclinical cases. Furthermore, AI can integrate spatial and temporal information across images, enhancing diagnostic accuracy. This study aims to develop a deep learning-based methodology for the identification of diabetic peripheral neuropathy (DPN) and peripheral arterial disease (PAD) using infrared thermographic features extracted from the plantar surface of the feet. To achieve this, we trained and evaluated multiple convolutional neural network (CNN) architectures and a vision transformer (ViT) model, specifically adapted to small datasets. The models were designed to automatically classify patients as either healthy or affected by DPN and/or PAD based on subtle temperature distribution patterns. Ultimately, this approach seeks to support the development of non-invasive, accessible, and objective diagnostic tools that can enhance early detection and the personalized management of diabetes-related foot complications such as DPN and PAD.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 5 Experimental Results
### Experimental Setup  
The real experimental environment is shown in Figure 7. Qbot is a differential drive wheeled mobile robot, equipped with two motors, a wireless communication module, an infrared and sonar sensor array and a Logitech Quickcam Pro 9000 USB camera. Moreover, the wireless modules use the ZigBee communication protocol. An electromagnetic module is used as a signal source, shown in Figure 8. At the same time, we employ the OptiTrack system to accurately locate the position of the Qbot. For the robot communication, the Qbots can build a local area network to communicate with each other and establish links with the computer host.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Robots** & \(\mathbf{fre_{i}}\) & \(\mathbf{LE_{i}}\) \\ \hline Robot 1 & 1.37 (0.54) & 1.07 (0.44) \\ Robot 2 & 8.55 (0.50) & 1.69 (1.08) \\ Robot 3 & 8.07 (0.59) & 0.70 (0.71) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean (standard deviation) results in communication frequency (%) and localization error (m) based on 30 runs for Case 2.  
Figure 6: The curves for the localization errors for Case 2.  
Figure 7: Experimental environment.  
Figure 9: Movement trajectories of three robots where the red, blue and green lines denote the trajectories of three robots. (**a**) \(t=0\) s; (**b**) \(t=4\) s; (**c**) \(t=8\) s; (**d**) \(t=12\) s; (**e**) \(t=16\) s; (**f**) \(t=20\) s.
## 3 Numerical Simulation  
To assess the impact of the rock quality and the initial stress conditions on LDP around the tunnel, FLAC 3D numerical simulation was performed. For variable geology under different stress conditions, it was obvious to choose the FLAC 3D continuum model to study displacements at locations around the tunnel. The numerical simulations were done to present the LDP considering the rock mass conditions and the initial stress state. The overall shape of the analysis model is shown in Figure 5. The modeling dimensions are X = 27.5 m, Y = 80.0 m, and Z = 55.0 m, and the tunnel diameter is 5 m.
## 4 Discussion
### Gamma Irradiation Effect on Polyphenolic Content  
Polyphenol compounds are common secondary metabolites in plants. They are produced in plants as a response to biotic and abiotic environmental stress conditions. The bioactive compounds, having antioxidant properties present in horse chestnut seeds, could vary depending on their genotype, geographical and environmental conditions, used part of the seed (coat, endosperm, or whole seed), treatment of the sample, extraction procedure, and used solvent, etc. [1, 7, 8, 24]. In previous study, the dependence of total phenolic content and DPPH radical scavenging capacity on the maturation process of the seeds was determined [23]. However, the exert of gamma irradiation on polyphenic content has not been studied. The present investigation shows that the effect of gamma irradiation depends on the absorbed dosage of high-energy radiation and also on the type of sample. The observed slight decrease in TPC of irradiated peeled seeds is probably due to the degradation of some phenolic compounds compared to those with a non-phenolic structure or with tannins, decomposed as a result of cleavage of glycosidic bonds by the gamma rays [30; 31]. Some studies also described the negative effect of irradiation on the polyphenol content of irradiated products [31].  
The opposite effect is observed in the shell seeds, as the phenolic content increases, most significantly after irradiation by 5 kGy. The probable reason for it is that more polyphenic substances are being formed upon irradiation as a result of the breakdown of some larger phenolic molecular structures into smaller ones. A similar behavior of enhancement of phenolic content and antioxidant capacity after irradiation of almond skin extracts was reported by [PERSON] and [PERSON] [32]. On the other hand, in defatted almonds irradiated by 10 and 25 kGy, an increase in total polyphenols was observed only at 25 kGy [11].  
Significant differences were found in polyphenic compounds between peeled and shell chestnut seeds by HPLC analysis. The plant seed is a multicomponent system in which substances interact with each other as a result of the absorbed additional energy, in the case of the gamma radiation. In the process of irradiation, when a certain amount of ionizing energy is introduced into the system, some polyphenic structures are degraded with the intermediate participation of free radicals. It is possible that new phenolic compounds could be formed as a result of the degradation of higher molecular ones or to be formed as a result of the recombination of radical fragments, depending on the applied radiation dose. This is the case for the formation of morin and luteolin after irradiation of PS by certain doses of high-energy radiation, as well as for caffeic acid, p-comamaric acid, ferulic acid, hesperidin, and morin in SS.
## 2 Centralized Treatment Plants
### Industrial Wastewater Remediation
#### 2.3.12 Industrial Laundry  
An effective treatment of WW from industrial laundries must solve the problems of its high turbidity and COD values. The performance of different treatments was compared: coagulation with iron (III) sulphate, Fenton, photo-Fenton and biofiliter (BF) plus GAC. Concerning the treatment effects, the results indicated the following:  
* The coagulation and Fenton processes produced large amounts of sludge and failed to meet the required standards for water reuse. * The BF completely removed the turbidity and highly removed the COD but not enough to meet the COD for water reuse, and thus a GAC filtration was added. * The photo-Fenton process alone did meet the criteria for water reuse. The cost to treat 1 m\({}^{3}\) of WW was EUR 6.72 for photo-Fenton and EUR 0.71 for BF + GAC. Moreover, this second option causes a much lower environmental impact than photo-Fenton, underlining once more the sustainability of nature-based treatments, when applicable [114]. Laundry wastewater shows high contents of fibers and microplastics (MPs). Their effective removal would greatly reduce the environmental impact of this WW, and membrane-based technologies have been considered as an effective replacement for conventional treatments with various advantages: lower energy consumption, smaller environmental impact and efficient separation capability. In particular, an MF membrane, made by silicon carbide (SiC), and a UF membrane, made by zirconia (ZrO\({}_{2}\)), were compared on a semi-pilot scale under real conditions in the treatment of WW from the cleaning of industrial tents made from polyvinyl chloride (PVC) textile. The MF membrane manifested a greater decrease in permeability (95%) compared to the UF membrane (37%), which removed higher amounts of total solids, turbidity and microplastics (99.2% versus 98.55% MF removal efficiency). A negligible reduction in the TDS was observed for MF and a 20% reduction for UF ceramic membranes, which can be used as a polishing step to remove MPs before WW reuse or discharge, but to reduce the TDS, treatments such as RO or FO are necessary [115]. An electrochemical method (electroflotocoaulation, EFC) combined with an adsorption treatment was applied for the purification of industrial laundry WW in a pilot plant which made possible the simulation of the processes for large WW volumes. The coagulant was prepared in a separate block and fed into the coagulation chamber, where it was mixed with the WW. Soluble aluminum electrodes were used, since aluminum hydroxide is better separated from the treated water. After EFC, the flow went into contact with a polymer nanocomposite based on walnut-shell AC and polyaniline (PAN/WAC). After 11 min, the maximum removal of surfactants (93%), color and turbidity (94%) was obtained, and the best cleaning performance was observed at pH 6. The maximum adsorption is reached at 25 C, and the cycle can be repeated for many times, saving a lot of water. EFC should be taken into consideration when synthetic surfactants must be removed from WW [116]. * _et al._ (2025)_ _et al._ 2025, 2448
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 2 Materials and Methods
### Information Sources and Search Strategy  
We conducted a systematic search across three major healthcare-focused databases: PubMed, Scopus, and Web of Science (WOS). The search was performed in December 2022, focusing on English-language articles published between January 2010 and December 2022. To ensure research quality, we included only peer-reviewed journal articles while excluding conference proceedings, book series, and articles without abstracts. The search strategy utilized key terms \"discrete-event simulation\", \"health-care\", and \"hospital\" in the title and/or abstract fields (\"tiab\").  
The specific search strings employed for each database are detailed in Table 1, which resulted in initial identification of 2547 publications across all databases (PubMed: 291; Scopus: 1237; WOS: 1019).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Search** & **Publications** \\ \hline \multirow{2}{*}{PubMed} & \"discrete-event simulation\"[tiab] AND & \multirow{2}{*}{291} \\  & (\"health care\"[tiab] OR \"hospital\"[tiab]) & \\ \hline \multirow{3}{*}{Scopus} & TITLE-ABS-KEY ((“discrete-event simulation”) AND (hospital\" OR (“health care”))) AND PUBYEAR \textgreater{} 2009 AND PUBYEAR \textgreater{} 2023 AND & \multirow{2}{*}{1237} \\  & (LIMIT-TO(LANGUAGE, \"English\")) & \\ \hline WOS & (discrete-event simulation) AND (hospital\" OR “health care”) & 1019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search strings used in the literature review.
# Impact of Multiple Beams and Mobility-Based Beam Alignment Error on Millimeter Wave Communication +
## 3 Analysis of SINR Coverage Probability  
SINR coverage probability is given by \(P_{c}(T)=\mathbb{P}[\text{SINR}>T]\). Given that the link between typical UE and reference BS is LOS, the received SINR at BS at a random distance \(r\) is  
\[\text{SINR}=\frac{|h_{0}|^{2}G_{0,n}G_{0,k}l(r)}{\sigma^{2}+\sum_{z\in I}|h_{ z}|^{2}G_{z}l(D_{z})} \tag{6}\]  
\(|h_{0}|^{2}G_{0,n}G_{0,k}l(r)\) is desired signal power, \(\sigma^{2}\) is the noise power, \(z\) denotes the interfering UE, and \(\sum_{z\in\Phi_{z}}|h_{z}|^{2}H_{z}G_{z}l(D_{z})\) is the aggregate interference power. The obtained expression for coverage probability is given below,  
\[P_{c}(T)=\sum_{g=1}^{4}G_{0}\sum_{n=1}^{N_{L}}{(-1)^{n+1}\binom{N_{L}}{n}}\int _{0}^{\infty}{\Bigg{(}e^{(-\mu_{L}\sigma^{2})}\prod_{i\in(LOS,NLOS)}\mathcal{ L}_{I}(\mu_{L})\Bigg{)}f_{R}(r)dr} \tag{7}\]  
\(\mu_{L}=\eta_{L}nT/\big{(}G_{0}C_{L}r^{-\kappa_{L}}\big{)}\) and \(f_{R}(r)\) are the distribution of the distance (\(r\)) between UE and LOS serving BS [12],  
\[f_{R}(r)=2\pi\lambda rP_{LOS}(r)exp\Bigg{(}-2\pi\lambda\int_{0}^{(C_{N}r^{ \kappa_{L}}L/C_{L})^{1/\kappa_{N}}}uP_{LOS}(u)du\Bigg{)}exp\Bigg{(}-2\pi \lambda\int_{0}^{(C_{N}r^{\kappa_{L}}L/C_{L})^{1/\kappa_{N}}}u(1-P_{LOS}(u))du \Bigg{)} \tag{8}\]  
The Laplace transform (LT) of interference can be obtained using the similar steps used in our previous work [14]. The derived expression for LT for \(s\in\{LOS,NLOS\}\) is given below:  
\[\mathcal{L}_{I_{s}}(\mu_{L})=exp\Bigg{(}-2\pi\lambda\sum_{G\in G_{z}}{P_{G_{ z}}\Bigg{[}\int_{r}^{\infty}{\Bigg{(}1-\frac{1}{(1+\mu_{L}G_{z}C_{s}t^{- \kappa_{s}})^{N_{L}}}\Bigg{)}\Bigg{]}P_{s}(t)dt}}\Bigg{)}x \tag{9}\]
# Evaluation of Satellite Imagery for Monitoring Pacific Walruses at a Large Coastal Haulout
## 4 Discussion
### Optical Imagery
For a retrospective study like this one aimed at detecting and delineating walrus aggregations, the combined efficacy of PlanetScope's daily archive and Sentinel-2's public archive provided an informative time series of optical imagery, but it was often compromised by periods of persistent cloud cover. Landsat imagery yielded considerable disagreement among observers in estimating the area occupied by walruses. In 2 of 9 Landsat images classified as having walruses present, observers delineated walrus aggregations at dissimilar locations, resulting in near-zero 3-way overlap among digitized areas (Figure 4). The discrepancies apparently arose in images that had discolorations along the beach, which some observers interpreted to be walruses while others considered the coloration to reflect previous walrus occupancy. This ambiguity of observer interpretation, combined with the sensor's coarse spatial resolution, long revisit interval, and more than two-week latency between image collection and availability (which was generally one day or less for all other imagery sources), indicates Landsat imagery to have limited value for walrus monitoring.
# Forward and Inverse Studies on Scattering
## 3 Inverse Problem by Modified BEM
### Numerical Examples
#### 3.2.4 Reconstruction Results of Double-Canyon Type Flaws  
In order to examine the validity of our inversion approach in multiple flaw cases as seen in Figure 4b, we introduce here two models with double-canyon type flaws. For each model, two canyons with different geometric parameters are cut on the surface of the half-space with a distance l in between. The geometric parameters are set as (i) normalized radius: \(R_{1}=R_{2}=1\); (ii) normalized depth: \(d_{\max,1}=0.2\), \(d_{\max,2}=0.1\); (iii) normalized edge-to-edge distance between two flaws: \(\mbox{\textcircled{1}}\)\(L=1/r=2.0\), \(\mbox{\textcircled{2}}\)\(L=1/r=0.2\).  
The inversion results for the two models are plotted in Figure 11, in which input data with frequency ranges \(\Omega=0\)\(\sim\) 1.0, \(\Omega=0\)\(\sim\) 2.0, and \(\Omega=0\)\(\sim\) 5.0 are shown in different curve types. It can be seen that, when the two canyons are distant (see Figure 11a), even the data with low frequency range can give correct locations of both two flaws and rough estimations of their depth and width. As the input frequency range increases to \(\Omega=0\)\(\sim\) 5.0, the geometric parameters of both two flaws are reconstructed with better accuracy.  
On the other hand, if the two flaws are close with each other (see Figure 11b), the vague image reconstructed from low frequency range data cannot distinguish the two canyons. As the frequency range increases, the resolution becomes higher, and the images of two canyons are clearly separated. Thus, it is concluded that the frequency range \(\Omega=0\)\(\sim\) 5.0 is necessary for reconstruction of multiple flaws.  
Figure 11: Inverse reconstruction results of double-canyon flaws (**a**) \(l/r=2.0\); (**b**) \(l/r=0.2\).
## 3 Local Differential Privacy
### PrivKV
#### 3.2.3 Estimating  
Let \(f_{i}\) be the true frequency of key \(k_{i}\) and let \(f_{i}^{\prime}\) be the observed key frequencies among the perturbed vectors, for which \(k_{i}\) = 1. The frequency estimation using MLE is as follows:  
\[\hat{f}_{i}=\frac{n(p_{1}-1)+f_{i}^{\prime}}{2p_{1}-1}, \tag{5}\]where  
\[p_{1}=\frac{e^{e_{1}}}{1+e^{e_{1}}}. \tag{6}\]  
Let \(m_{i}\) be the true mean of key \(k_{i}\). Let \(n_{i}^{(1)}\) and \(n_{i}^{(-1)}\) be the counts of \(v_{i}=1\) and \(v_{i}=-1\) in the perturbed vectors. \(n_{i}^{(1)}\) and \(n_{i}^{(-1)}\) are calculated as \(\hat{n}_{i}^{(1)}\) and \(\hat{n}_{i}^{(-1)}\) in the same manner as in Equation (5).  
\[\hat{n}_{i}^{(1)} = \frac{n(p_{2}-1)+n_{i}^{(1)}}{2p_{2}-1}, \tag{7}\] \[\hat{n}_{i}^{(-1)} = \frac{n(p_{2}-1)+n_{i}^{(-1)}}{2p_{2}-1}, \tag{8}\]  
where  
\[p_{2}=\frac{e^{e_{2}}}{1+e^{e_{2}}}. \tag{9}\]  
Therefore, the mean estimation using Maximum Likelihood Estimation (MLE) is as follows:  
\[\hat{m}_{i}=\frac{\hat{n}_{i}^{(1)}-\hat{n}_{i}^{(-1)}}{n}. \tag{10}\]  
According to the composition theorem of DP [10], the sequential composition of randomization algorithms with privacy budgets \(e_{1}\) (for keys) and \(e_{2}\) (for values) results in \((e_{1}+e_{2},0)\)-DP.
## 4 Experimental Performance Evaluation
### Evaluation Results and Discussions
Table 3 shows an example output generated by the proposed SAT-ASR with a profile. The input was the mixed speech of one speaker saying Label-A and the other saying Label-B, and Result-1 and Result-2 were the output word sequences. The word sequences were generated using the token level text vocabulary of a Transformer-based language model [43], so there were several non-language tokens such as <eos>, meaning _end of sentence_. When comparing Label-A and Result-1, only a single error was observed: OF \(\rightarrow\) BUT. When comparing Label-B and Result-2, only a single error was also observed: TEN \(\rightarrow\) AND. In summary, the experimental results show that the proposed speaker-attributed training was highly effective in multi-speaker speech recognition tasks without adding significant storage and computational overheads.
# Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning
## 3 Framework for the Real-Time Classification of Acoustic Emission Data
### Analysis of Acoustic Emission Events Using Feature Extraction
#### 3.2.1 Instantaneous Frequency  
The IF of a nonstationary signal is a time-varying parameter that relates to the average frequencies of the signal [77; 78] and it is computed as the derivative of the phase of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & **Acronym** & **Domain** & **Expression** & **Description** & **Ref.** \\ \hline Instantaneous Frequency & IF & Time & \(IF(t)=\frac{1}{2\pi}\frac{d\theta}{dt}\) & Derivative of the analytic signal of the input & [73; 74] \\ \hline Spectral Entropy & SE & Freq. & \(-\frac{\sum\limits_{i=1}^{M}PDF(m)\log_{q}(PDF(m))}{\log_{2}M}\) & Measure of the spectral power distribution & [30; 75] \\ \hline Spectral Kurtosis & SK & Freq. & \(SK\equiv\kappa(f)=\ldots\) & Describes the resemblance or difference of the shape of the spectral distribution of a signal if compared to the shape of a Gaussian bell curve & [31; 76] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the statistical properties used in this study. We generally refer to these functions as event descriptors (EDs).  
Figure 4: (**a**) Time domain representation of three investigated signals representing tensile (red), shear (blue) and mixed-mode (green), respectively. (**b**) Frequency spectrum representation of the same signals.  
analytic signal of the input [59]. In order to extract it, we have performed the following calculations:  
* Compute first the analytic signal \(z(t)\) of the input, \(s(t)\), such that \(z(t)=s(t)+jH\{s(t)\}=A(t)e^{\phi(t)}\), where \(H(t)\) is the Hilbert Transform of \(s(t)\), \(A(t)\) is defined as the instantaneous power, whereas \(\phi(t)\) is the instantaneous phase;
* Estimate the instantaneous frequency from the following time derivative: \[IF(t)=\frac{1}{2\pi}\frac{d\phi}{dt}\] (3)  
A plot of IF as calculated from the signals of Figure 4a is shown in Figure 5a. Those data extend the information of the Fourier spectra showing the range of time where the modes are excited.  
Figure 5: Instantaneous frequency (**a**) for tensile (red line), shear (blue line) and mixed-mode (green line) as calculated from the signals represented in Figure 4a. Plot of spectral entropy (**b**) and spectral kurtosis (**c**) as calculated on the same signals.