esa-sceva/satcom-chunk-collection huggingface dataset:
Effect of High Hydrostatic Pressure on the Quality Parameters of Wild Red Deer (_Cerrus eleplus_) Meat  
[PERSON]  
1 Doctorial School of Food Sciences, Hungarian University of Agriculture and Life Sciences, 1118 Budapest, Hungary; [EMAIL_ADDRESS] (M.E.;) [EMAIL_ADDRESS] (M.E.;) anamamaria@phd.uni-mate.  
In recent years, growing consumer concerns regarding food safety, combined with a demand for fresh-like quality in minimally processed foods, have driven the food industry to seek innovative preservation methods. Among these, high hydrostatic pressure (HHP) processing is an environmentally friendly and non-thermal method that enhances meat tenderness while simultaneously inhibiting the growth of pathogenic and spoilage bacteria [4]. Numerous studies have demonstrated the effectiveness of HHP as a tenderization technique for various meats, including pork [5], beef [6], and chicken breast [7]. These studies indicate that applying HHP at pressures ranging from 150 to 600 MPa results in significant enhancements in meat tenderness and water-holding capacity [8]. HHP also affects the structural properties of muscle proteins, particularly myofibrillar and sarcoplasmic proteins, which undergo conformational changes due to pressure-induced denaturation. These modifications impact texture, water retention, and overall meat quality, making HHP a critical factor in protein functionality [9]. A key advantage of HHP is its ability to alter non-covalent bonds without significantly impacting the nutritional quality of food products [10]. Consequently, the use of HHP in the food industry presents promising opportunities for improving product quality.  
While HHP has been widely studied in other meat products, such as beef [6; 11], pork [5; 12], and poultry [7; 13], its effects on game meats like wild red deer remain largely unexplored. Given the unique composition of deer meat and its sensitivity to spoilage, there is a need to evaluate how HHP affects its key quality attributes. In this study, wild red deer meat samples were subjected to HHP treatment to evaluate its effects on quality parameters, including pH, surface color, microbial load, texture, and protein structure. Additionally, it investigates the shelf-life extension of vacuum-packed wild red deer meat over a 14-day refrigerated storage period at 4 \({}^{\circ}\)C. By assessing these factors, this research aims to determine the potential of HHP to enhance the overall quality and shelf life of wild red deer meat, addressing both consumer and industry demands for safer, high-quality meat products.
## 2 Preliminaries and Notations  
### Notations  
Throughout this context, the following notation is adopted: For any \(x\in\mathbb{R}\) and a positive constant \(\alpha\), \(sig^{\alpha}(x)\) is defined as \(sig^{\alpha}(x)=\left[sign(x)|x|^{\alpha}\right]\). For a vector \(\mathbf{x}\in\mathbb{R}^{n}\), \(sig^{\alpha}(\mathbf{x})=\left[sign(x_{1})|x_{1}|^{\alpha},\ldots,sign(x_{n})|x_{ n}|^{\alpha}\right]^{T}\).
# Late Reverberation Synthesis Using Filtered Velvet Noise +
## 5 Design Examples
### RIR Modeling Using Advanced FVN  
The synthetic RIR produced using the advanced FVN model and its spectrogram are shown in Figure 12. As an objective comparison, Figure 13 shows the reverberation time \(T_{30}\) against octave bands for three RIRs (original, basic FVN, and advanced FVN). We decided to use \(T_{30}\) instead of \(T_{60}\), because the signal-to-noise ratio near the end of the RIR does not sufficiently measure 60-dB decay; \(T_{30}\) is the measured time of a 30-dB decay multiplied by two.  
All three RIRs in Figure 13 show the same tendency of lower reverberation time for higher frequencies than low frequencies. The octave-band reverberation times for the basic FVN algorithm stay within \(\pm\)7% of the reference in all octave bands. For the second algorithm this spread is within \(\pm\)12%. The increased deviation is in accordance with the assumption that the second algorithm is a rougher approximation due to the lower coloration filter order.  
An informal listening test comparing the two new reverb algorithms with a reference convolution reverb has been carried out using headphones. The reference RIR and its approximation with the basic FVN algorithm sound very similar even when comparing the impulse responses themselves. The approximation produced by the advanced FVN algorithm has a slightly more unnatural sound when listening to its impulse response. Results of a subjective test comparing the audio signal processed with the original RIRs and their FVN approximations are presented in Section 7 of this paper.  
Figure 12: (**a**) Synthetic RIR produced using the advanced FVN method and (**b**) its spectrogram. Cf. Figure 4.
## 4 Discussion
### Pigments, Binders, and Painting Techniques  
Red and yellow ochres were found as the main pigments in the deeper pictorial layers in most samples (layers B, C: samples SG9-SG15, SG16, SG17, SG25, SG39B, SG46, and SG47). These layers were likely applied \"a fresco\", as indicated by the analysis of the SG16 sample. C black was also detected in many of these inner layers. External pictorial layers (C, D) often presented more recent pigments of different colors, sometimes applied \"a secco\". For example, malachite and maybe Pb chromate were detected in SG16, ultramarine and Ti white in SG25, ultramarine and azurite in SG39B, and ultramarine and Pb white in SG40. In many samples, the occurrence of Ca sulfate, likely an alteration product, was inferred by the presence of S and Ca in the SEM-EDS analysis (Section 4.3). In addition, the presence of polymeric organic compounds was supposed by the Raman analyses in two samples (SG40, SG25). It is worth noting that SG9 and SG10 have a layer containing Ca phosphate (bone black) and Zn oxide present as probable materials from later interventions. In SG35, the EPR analysis detects signals due to C black radicals and copper alteration products; in SG36, the EPR signal was detected due to Mn (II) in Ca carbonate.  
The frequent presence of ochre and C black in fresco layers may be attributed to the primary design made with only red and yellow ochre (e.g., sample SG47), to which layers of other colors are superimposed (blue, green, yellow, black, and white), made with an \"a secco\" technique. In particular, the use of C black is typical for the dusting technique, for the realization of the sketch of the drawing, or to darken the hue of other colors.  
Some microchemical characteristics observed on the stratigraphic thin sections allow for making hypotheses on [PERSON]'s preferred painting technique when working on his frescoes in Tornimparte. For example, the identification of frequent \"a secco\" layers, evident from the lack of blending between pigment grains and plaster and the presence of sharp but plain discontinuities, points out a two-step working technique, with the artist tending to apply color corrections on the dry plaster. In others (e.g., sample SG39B), pigment traces localized only in specific areas or covered by further, different paint films, may point out mistakes or reworks, or even second thoughts on the pigment choice--as for azurite, detected only in such contexts.
## 1 Introduction
The main innovations are the following: (1) Far-field high-uniformity beam shaping: A composite beam homogenization scheme using a multimode fiber and apodizer achieves far-field 85.8% irradiance uniformity over a 200 mm aperture; (2) Spectral power co-optimized filter design: A joint power-spectrum optimization model improves spectral consistency across the communication band, achieving a match rate of 78%; (3) Stray light modeling and suppression: A _BRDF_-based simulation framework identifies dominant scattering paths, sets a 2 nm roughness threshold for key optics, and uses a U-shaped light trap to reduce background stray light to 5.13 \(\times\) 10\({}^{-12}\) W.
# MDIFL: Robust Federated Learning Based on Malicious Detection and Incentives
## 3 Materials and Methods
### Incentive Mechanism
#### 3.2.2 Optimal Contract Design  
The contract theory uses the self-disclosure mechanism under information asymmetry. In order to make the contract feasible, each contract must meet the following constraints to ensure that each participant receives a fair and appropriate incentive reward.  
* Individual Rationality (IR): Participants will only participate in the task of federated learning model training if their maximum utility is not less than zero, i.e., \[U_{n}=R_{n}-G_{n}+A_{n}\geq 0\] (14)
* Incentive Compatibility (IC): Each participant of type m can maximize utility only if he chooses the contract \(R_{m}\) designed for his type, i.e., \[R_{n}-G_{n}+A_{n}\geq R_{m}-G_{n}+A_{n},\forall n,m\in\{1,\dots,N\},n\
eq m\] (15)According to the above definition, we have the following lemma.  
**Lemma 1**.: _Monotonicity._  
For any feasible contract, when \(\theta_{n}\geq\theta_{m}\), we obtain \(R_{n}\geq R_{m},\forall n,m\in\{1,\dots,N\},n\
eq\ m\). Therefore, it should be satisfied  
\[\begin{cases}0<R_{1}<R_{2}<\dots<R_{m}<\dots<R_{n}\\ 0<s_{1}<s_{2}<\dots<s_{m}<\dots<s_{n}\end{cases} \tag{16}\]  
**Lemma 2**.: _The other individual rationality constraints also hold if the individual rationality constraints of type 1 participants are satisfied, where type 1 refers to the type of participants with the worst data quality._  
**Lemma 3**.: _Local downward Incentive constraint._  
According to the monotonicity of Lemma 1, the incentive compatibility condition can be simplified as a local downward Incentive constraint (LDIC), which can be expressed as  
\[R_{n}-\tau E_{n}^{cmp}+A_{n}\geq R_{n-1}-\tau E_{n-1}^{cmp}+A_{n-1},\forall n \in\{2,\dots,N\} \tag{17}\]  
Under the constraints of individual rationality and incentive compatibility, we can obtain rewards [25], which can be expressed as  
\[R_{n}=\tau E_{1}^{cmp}+E_{n}^{com}+\sum_{k=1}^{|S|}\Delta_{k} \tag{18}\]  
\(\Delta_{k}=\tau E_{k}^{cmp}-\tau E_{k-1}^{cmp}\), and \(\Delta_{1}=0\). We can rewrite the utility function to obtain  
\[\begin{split} maxU_{n}=&\tau E_{1}^{cmp}+E_{n}^{ com}+\sum_{k=1}^{|S|}\Delta_{k}\\ &-\gamma(\tau\zeta_{n}s_{n}f_{n}^{2}+\frac{\sigma\rho_{n}}{Bln(1+ \frac{\theta_{n}h_{n}}{N_{0}})})+A_{n}\\ s.t.& R_{1}-G_{1}+A_{1}>0\\ & R_{n}-G_{n}+A_{n}\geq R_{m}-G_{m}+A_{m}\\ &\sum_{n=1}^{|S|}R_{n}\leq R_{max}\end{split} \tag{19}\]  
\(R_{max}\) is the total reward budget.
The Potential of Satellite Sounding Observations for Deriving Atmospheric Wind in All-Weather Conditions  
[PERSON] and [PERSON]  
1 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China; [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (H.H.)1
## 5 Experimental Results and Discussion
### Discussion  
We emphasize here that blockchain-based secure datastore should not be regarded as a secure version of traditional database system. Unlike traditional database systems, which could offer a transaction rate higher than 1000 per second, blockchains are designed to have limited throughput due to the need to allocate sufficient time interval for reaching decentralized consensus. There are two conflicting factors: (1) target block interval, and (2) upper limit on the block size. A smaller block interval and a larger block size would be conducive to achieving higher system throughput. Unfortunately, the use of a smaller block interval would inevitably necessitate the use of a smaller block size for system stability [47].  
For example, Ethereum currently has a target block gas limit of 15 million gas with maximum limit of 30 million gas. The minimum gas consumption for a transaction is 21,000 gas. This means that the target number of transaction per block is 714 (maximum number of transactions is 1428). The current target block interval is 12 s. This means that the target system throughput would be 59 transactions per second (maximum 118 transactions per second). Considering that Ethereum has thousands of users, we recommend that users of our proposed datastore generate less than one transaction per minute for contract write. If users must handle data generated with higher frequency, we recommend aggregating the raw data first, as we have proposed in [13]. If aggregating data is not possible, then our proposed secure datastore is not suitable for the application because making too-frequent contract write calls would create a backlog and severely increase the latency to an unacceptable level.  
Another limiting factor for our secure datastore is the financial cost. Even with infrequent contract write and using the smart contract to record IPFS hash only, the system would still incur significant financial cost in the long run. Using the gas cost reported in Figure 15, a contract write call costs about \(0.2\times 10^{6}=200,000\) gas. If one contract write call is made per hour, the total daily gas consumption would be \(200,000\times 24=4,800,000\). Again, using the gas price of 14.3 gwei and the ETH price of USD 1640, the daily cost would be USD 2.57 and the annual cost would be USD 41,087.9. Not all organizations can sustain such high financial cost.
## 6 Conclusions  
In this work, DP-QPSK modulation with the MDM-WDM scheme has been employed to analyze the performance of the FSO link under fog, rain, and haze weather conditions. Real-time data for different months of 2023 and 2024 have been obtained for four diverse regions. Unified empirical models have been developed with the capability to predict overall attenuation due to multi-impairments of different regions. The minimum values of SNR and received power have been reported in the case of the New Delhi region. SNR and received power values above 38 dB and \(-\)45 dB have been obtained for the other three regions, respectively, due to the effect of multi-impairments. RF, KNN, MLP, and GB have been employed for the performance prediction of the MDM-WDM FSO link using DP-QPSK modulation under real weather conditions in the DP-QPSK system for SNR. The highest R squared (0.99) and lowest MSE (0.11), MAE (0.25), and RMSE (0.33) values have been reported in the case of the GB ML model for the estimation of the performance of the system. The work may be investigated further for cutting edge modulation techniques and real-time performance estimation and monitoring of impairments in optical networks.  
Conceptualization, T.K. and S.K.; methodology, T.K. and S.K.; validation, S.K. and M.I.; formal analysis, M.I.; resources, S.K.; writing--original draft preparation, T.K., S.K.; writing--review and editing, M.I.; Supervision, M.I.; project administration, M.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data can be made available upon reasonable request from the authors due to ongoing research.  
The authors declare that they do not have any conflicts of interest, and nor do they have any financial or non financial interest that are directly or indirectly related to the work submitted for publication.
## 3 Results
### Descriptive Analyses Results
Similarly, the period from 6 p.m. to 12 a.m. (0.023) shows a moderate positive correlation, possibly reflecting increased accident severity during evening hours when visibility may decrease, but drivers may still be inclined to drive at high speeds. Seasonal factors such as summer (0.025) and autumn (0.023) also show mild positive correlations, suggesting that accidents during these times of the year are more likely to result in severe outcomes. This could be related to higher traffic volumes during vacation seasons or more frequent long-distance travel. Glare from the sun (0.010) contributes to a smaller positive correlation, which might be due to reduced visibility affecting driver reactions. On the other hand, winter (\(-0.580\)) shows the most significant negative correlation with severe casualties. This strong inverse relationship suggests that accidents occurring in winter conditions are less likely to result in severe injuries, likely due to slower driving speeds and increased caution on icy or snow-covered roads. Similarly, snow (\(-0.067\)) and rain (\(-0.019\)) show negative correlations, which further supports the idea that adverse weather conditions encourage safer driving behaviour, leading to less severe accidents. Variables such as fog (\(-0.004\)), nail or freezing rain (\(-0.007\)), and limited visibility (\(-0.008\)) exhibit slight negative correlations, indicating that these conditions may reduce the risk of severe casualties. This might be because drivers are more cautious and reduce speed when faced with these challenging conditions. The analysis of situation-related variables suggests that clear weather and certain times of the day (e.g., nighttime) are more strongly associated with severe casualties, whereas winter conditions and precipitation tend to reduce the severity of accidents. Figure 6: Phi coefficient of situation-related variables.
## 3 General Results and Discussion
### Pure Materials
#### 3.2.1 CsSnX\({}_{3}\) (X=I, Cl)  
By observing the band structures of CsSnX\({}_{3}\) groups and comparing the two halide perovskites, we determined that CsSnI\({}_{3}\) obtained a narrower bandgap than CsSnCl\({}_{3}\). The distance between conduction band minimum (CBM) and valence band maximum (VBM) was 1.1 eV to 0.4 eV. The bandgap, electronic properties and bonding intensity can all be tuned using various halogens. For example, the orbitals around CsSnCl\({}_{3}\)'s Fermi level namely, the Sn-p and Cl-p orbitals which occupy the VBM and CBM respectively are distinctly positioned in the respective ranges of 0 eV to 7.0 eV and \(-2.0\) eV to \(-7.0\) eV. The PDOS plots of CsSnCl\({}_{3}\) revealed a Cl-p peak on \(-4.0\) eV and an Sn-p peak on 3.5 eV. Similar to the orbitals of CsSnCl\({}_{3}\) above, the Sn-p and I-p orbitals of CsSnI\({}_{3}\) were distinctly occupied on VBM and CBM respectively: the former was positioned between 0.0 eV to 4.0 eV, while the latter was located between \(-1.0\) eV to \(-4.0\) eV.  
As a comparison with CsSnCl\({}_{3}\), the peak appeared to be similar to the approach heights. These results show that the two halide perovskites, CsSnCl\({}_{3}\) and CsSnI\({}_{3}\), likely undergo hybridization due to Sn-halide bonding, although CsSnI\({}_{3}\) appears to have a stronger hybridization effect.  
Figure 1: (**a**) Cubic structure of formal perovskites. The spheres in the figure are the atoms. The gray atom in the center represents A atoms, the green atoms represent the B atoms, and the blue atoms are the X atoms. (**b**) First Brillouin zone of cubic crystal structure [42]. High symmetry k-points are \(\Gamma\) (0,0,0), R (0.5, 0.5, 0.5), X (0, 0.5, 0), and M (0.5, 0.5, 0).  
Figure 2: \(Cont.\)Figure 3: _Cont._  
Figure 2: Electronic band structures of four cubic perovskites (**a**) CsSnCl\({}_{3}\), (**b**) CsSnI\({}_{3}\), (**c**) SrTiO\({}_{3}\), (**d**) SrRhO\({}_{3}\), (**e**) band structure of CsSnCl\({}_{3}\), (**f**) band structure of CsSnI\({}_{3}\). (**g**) band structure of SrTiO\({}_{3}\), (**h**) band structure of SrRhO\({}_{3}\). The Fermi level is set to 0 for all band structures.  
Figure 3: Total DOS (left) and projective (right) DOS of four kinds of cubic perovskites **(a,b)** and CsSnCl\({}_{3}\), **(c,d)** CsSnI\({}_{3}\), **(e,f)** SrTiO\({}_{3}\), **(g,h)** SrRhO\({}_{3}\). We calculated the A-s, B-p, B-d, X-s, and X-p orbitals of ABX\({}_{3}\) cubic perovskites.
## 3 Dynamic Modelling  
The dynamic equations of a general system of motor(s)-mechanism-effector(s) type describe the system motion under the action of actuation and resistance forces/torques. The dynamic equations of a wind turbine under the premises mentioned in Section 2 are homogeneous linear differential equations of second order. The angular acceleration and speed of the wind rotor interfere with and define the independent motion of the mechanical system.  
Figure 2: Flowchart of the proposed dynamic modelling for single-rotor wind turbines with counter-rotating electric generator.  
The dynamic modelling method uses the equations of the Newton-Euler formalism and implies the prior isolation of each body by replacing the connections between the bodies by the corresponding reaction forces. It is followed by the description of the Newton-Euler equations for each body, according to the general expression when the origin of the coordinate system attached to the body coincides with the body centre of mass cm [37]:  
\[\left(\begin{array}{c}\mathbf{F}\\ \mathbf{T}\end{array}\right)=\left(\begin{array}{cc}m\mathbf{I_{3}}&0\\ 0&\mathbf{J_{cm}}\end{array}\right)\left(\begin{array}{c}\mathbf{a_{cm}}\\ \varepsilon\end{array}\right)+\left(\begin{array}{c}0\\ \boldsymbol{\omega}\times\mathbf{J_{cm}}\boldsymbol{\omega}\end{array}\right) \tag{7}\]  
where \(\mathbf{F}\) is the resultant force vector acting on the centre of mass, \(m\)--the body mass, \(\mathbf{I_{3}}\)--the \(3\times 3\) identity matrix, \(\mathbf{a_{cm}}\)--acceleration vector of the centre of mass, \(\mathbf{T}\)--resultant torque vector acting on the centre of mass, \(\mathbf{J_{cm}}\)--moment of inertia defined about the centre of mass, \(\boldsymbol{\omega}\)--angular speed vector of the body, and \(\varepsilon\)--angular acceleration vector of the body.  
In the case of symmetrical bodies with a fixed axis of rotation (i.e., the centre of mass cm is a fixed point on the axis of rotation), Equation (7) can be simplified in the following scalar form:  
\[T=J\cdot\varepsilon \tag{8}\]  
where \(T\) is the resultant torque acting on body, \(J\)--moment of inertia around the body motion axis, and \(\varepsilon\)--angular acceleration of the body.