esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Laboratory Tests: GFZ-WSU _versus_ Earth Data PR6-24  
Preliminary tests of the GFZ-WSUs performance when connected to a standard 3D SM-PE-6/B 4.5 Hz external geophone consisted of comparing the signals recorded under laboratory conditions with contemporary ones acquired using a highly sensitive standard Earth Data PR6-24 (EDL), a 24 bit digitizer, and the same kind of sensor. The sensors for both stations are deployed on a concrete slab (Figure 4a,b), with the digitizer gain for each set to 10.  
Figures 4c-e shows the spectra for the three component of motion for the two instruments. Additionally, we show also the spectra of the GFZ-WSU self-noise, which allows for an estimation of the frequency range over which signals can be reliably recorded [26, 27]. Interestingly, under the noise  
Figure 3: MultiPoint Relays protocol for communication within a dense array, _i.e._, how messages from a central node are distributed throughout a cluster. In practice, only a subset of the central node’s neighbors (red) need to rebroadcast the message.  
conditions existing during the experiment, the GFZ-WSU's spectra is in excellent agreement with the EDL's ones down to 1 Hz. Below this frequency, due to the lower sensitivity of the GFZ-WSU relative to the EDL digitizer, the seismic noise approaches the instrumental's self-noise and the recorded signals represent only the self-noise itself.  
It is worth pointing out that, being a function of the noise conditions, it is not possible to state a priori during field measurements which will be the lower frequency limit for recording reliable signals. Therefore, in order to define the frequency range where signals may be properly recorded, we consider it mandatory, also in the case when standard instrumentation is used, to compare experimental spectra together with the self-noise obtained for the specific combination digitizer-level to gain-sensor.  
Figure 4: Results from the laboratory experiment. (a) GFZ-WSU and EDL digitizer. (b) 3D SM-PE-6/B 4.5 Hz external geophones deployed over a concrete slab. (c) Spectra from EDL (_blue_), WSU (_red_), WSU’s self-noise (_black_) for the vertical component of ground motion. (d) same as (c) but for the north-south component. (e) same as (c) but for the east-west component.
## 2 Dynamical Models
### Patched Three-Body Approximation  
The BR4 BP is among the simplest suitable models to design SALT trajectories, but it can be further simplified while still capturing the key dynamical effect of the Sun's gravity. The idea behind such a simplification is very similar to the patched conic approximation approach for high-energy trajectories: the configuration space close to a planet and/or a moon is encircled by the _region of prevalence_ (RoP) of this planet-moon three-body system. The dynamics in the RoP interior are well described by the planet-moon CR3 BP model, whereas outside the RoP, the BR4 BP or the Sun-planet CR3 BP model is applied (in case of the Earth and the Moon, their barycenter usually performs the role of the minor primary).  
The concept of the region of prevalence was first coined by [PERSON] [35] who defined the Earth-Moon region of prevalence as a region in the configuration space bounded by a closed curve of points where the error introduced in the right-hand sides of the equations of motion would be the same independently of which body we neglect in the Earth-Moon-Sun system, the Moon or the Sun. The region of prevalence is time-dependent: it is parameterized by the Sun's phase \(\theta_{S}\). The Earth-Moon RoP boundaries projected onto the \(xy\)-plane are shown for different solar phases by colored closed curves (Figure 3). To avoid time dependency, we define the mean-square averaged region of prevalence: its stationary boundary is chosen to have a simple elliptical form (the bold orange curve in Figure 3), with the principal axes parallel to the coordinate axes of the Earth-Moon rotating reference frame. In the dimensionless system of units specified by Equation (1), the points of this boundary in the \(xy\)-plane satisfy the equation  
\[\frac{(x+c)^{2}}{a^{2}}+\frac{y^{2}}{b^{2}}=1, \tag{8}\]  
where \(a=1.44\), \(b=1.05\), \(c=-0.25\). Further in the paper, the area with the boundary given by Equation (8) is referred to simply as the Earth-Moon region of prevalence.  
Figure 2: Rotating reference frame in the Earth–Moon–Sun bicircular restricted four-body problem. The figure is drawn for the planar case (the \(z\)-coordinate is dropped), though also valid in the 3D case.
# An Improved Set-Membership Proportionate Adaptive Algorithm for a Block-Sparse System
## 2 Review of Corresponding Algorithms  
### The PNLMS Algorithm  
We assume the system input signal is \(\mathbf{x}(n)=\left[x(n),x(n-1),x(n-2),\cdots,x(n-N+1)\right]^{\mathsf{T}}\), and the impulse response of estimated system is \(\mathbf{w}(n)=\left[w_{0}(n),w_{1}(n),\cdots,w_{N-1}(n)\right]^{\mathsf{T}}\), whose length is \(N\). The observed output signal can be described as  
\[d(n)=\mathbf{x}^{\mathsf{T}}(n)\mathbf{w}(n)+u(n), \tag{1}\]  
where \(u(n)\) denotes the noise signal which is assumed to be uncorrelated with \(\mathbf{x}(n)\). The estimation error is  
\[e(n)=d(n)-\mathbf{x}^{\mathsf{T}}(n)\mathbf{\hat{w}}(n-1), \tag{2}\]  
where \(\mathbf{\hat{w}}(n-1)\) denotes the estimation signal. The updating equation of the PNLMS algorithm can be described as  
\[\mathbf{\hat{w}}(n)=\mathbf{\hat{w}}(n-1)+\frac{\mu\mathbf{x}(n)\mathbf{Q}(n- 1)e(n)}{\mathbf{x}^{\mathsf{T}}(n)\mathbf{Q}(n-1)\mathbf{x}(n)+\varepsilon}, \tag{3}\]  
where \(\mu\) is an overall step size, and \(\varepsilon\) is a small regularization parameter. \(\mathbf{Q}(n-1)\) is the step size assignment matrix, which is diagonal and can be described as  
\[\mathbf{Q}(n-1)=\text{diag}\left\{\,q_{0}(n-1),q_{1}(n-1),\ldots,q_{N-1}(n-1) \right\}\,. \tag{4}\]  
The elements in \(\mathbf{Q}(n-1)\) are calculated by  
\[q_{j}(n-1)=\frac{\alpha_{j}(n-1)}{\sum\limits_{i=0}^{N-1}\alpha_{i}(n-1)},0 \leq j\leq N-1, \tag{5}\]  
where  
\[\alpha_{j}(n-1)=\max\left\{\rho\text{max}\left\{\,\delta_{j}\left|\hat{w}_{0} (n-1)\right|,\left|\hat{w}_{1}(n-1)\right|,\cdots,\left|\hat{w}_{N-1}(n-1) \right|\right\}\,,\,\left|\hat{w}_{j}(n-1)\right|\right\}. \tag{6}\]Herein, \(\rho\) is a positive constant of which range is usually \(\frac{1}{N}\sim\frac{5}{N}\), and its purpose is to avoid \(\hat{w}_{j}(n-1)\) stalling in the case of it being much smaller than the largest element. \(\delta\) is a regularization parameter, and it is used to avoid the updating stopping when all of the taps are zeros at beginning  
The IPNLMS has the same update equation as the PNLMS, which is shown in Equation (3). The IPNLMS improves the elements in \(\mathbf{Q}(n-1)\) by introducing an adjusting parameter \(c\) into Equation (5)  
\[q_{j}(n-1)=\frac{1-c}{2N}+(1+c)\frac{|\hat{w}_{j}|}{2\sum\limits_{i=0}^{N-1}| \hat{w}_{i}|},0\leq j\leq N-1, \tag{7}\]  
where \(-1\leq c\leq 1\). When \(c=-1\), the IPNLMS behaves as the NLMS, while when \(c=1\), the IPNLMS works like the PNLMS. We usually choose the value of \(c\) as 0 or -0.5.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 4 Discussion
### Future Possibilities: Integration with AI and Machine Learning  
3D technology, combined with artificial intelligence (AI) and machine learning (ML), promises to further revolutionize the analysis of lip prints. AI algorithms trained to recognize specific patterns in 3D scans will be able to identify unique characteristics of the lips with high precision, accelerating the identification process and further reducing operator-dependent errors [10; 12]. The possibility of applying AI algorithms for the automatic recognition of lip impressions could also reduce the need for manual interpretations, improving the overall efficiency of the process [31].
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 5 Conclusions and Discussion  
### Discussion  
Despite the improvements achieved, the proposed algorithm still exhibits several limitations. 1. Extreme haze density: In scenarios with near-zero visibility (heavy sandstorms or dense fog), Retinex preprocessing may amplify noise due to a severely degraded signal-to-noise ratio, leading to artifacts in the recovered image. 2. Night-time or extremely low-light scenes: The reliance on the dark channel prior (DCP) is compromised under low-light environments, as the DCP assumes minimal illumination in local patches--a condition often violated by artificial light sources at night. This may cause inaccurate transmission estimation and color shifts. 3. Highly dynamic scenes: Rapidly moving objects (vehicles in traffic) may introduce motion blur during the sequential processing steps. The current pipeline lacks explicit motion modeling, potentially degrading edge preservation. Future work will integrate adaptive noise suppression for extreme haze, explore fusion with low-light enhancement networks for night-time defogging, and investigate temporal consistency mechanisms for video-based dynamic scenes.
## 3 Sensor Node Position Estimation
### The Position Packet Validation Algorithm
#### 3.2.2 Circle Limit Algorithm  
When using the hybrid algorithm, a sensor node combines the validation rules of the position distance and circle limit algorithms. Upon receiving a new position packet, the sensor node checks whether the source position of the received packet is within the circle of each stored packet, and calculates the distance between the source position of the received packet and the source position of each stored packet. If either of these conditions is satisfied, the packet is considered invalid and dropped. Otherwise, if all of the calculated distances exceed the predefined threshold and the source position of the received packet is not within any of the circles, the packet is accepted and the sensor node stores its information to be used in future estimations. Figure 6c shows a sensor node that received, validated, and stored two position packets at times \(t1\) and \(t2\). The shaded regions represent the circles calculated based on the received signal strength, and the dashed line represents the threshold for the distance validation. If the received position packet is within the shaded area or its circle contains one of the validated source position packets, or the distance between the received packet and the stored packets is less than the threshold, it is considered invalid. Otherwise, the received packet is accepted.  
\[\forall p\in rp:r\
otin c_{p}\;\;\text{and}\;\;p\
otin c_{r} \tag{5}\]  
where \(rp\) represents the set of received packets, \(r\) is the newly received packet, \(c_{p}\) and \(c_{r}\) are the circles of \(p\) and \(r\), respectively.
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 7 Discussion  
A new step-up/step-down converter with a step-up characteristic steeper than that of the classical SEPIC and hybrid SEPIC is introduced. As a result, the new topology is suitable in applications where an output voltage much higher than the input voltage is needed. Inductor coupling introduces an additional degree of freedom, and this allows for the minimization of certain stress values. After deriving the steady state equations and sketching the main waveforms, design equations were provided, and the simulations validated the theoretical concepts. In order to prove the feasibility and usefulness of the proposed converter, a PV solar system was built around it. It is shown that, using the P&O algorithm, the system can be maintained on the MPP and the maximum power capabilities of the PV panel can be used. In addition to its higher step-up capability, the converter has the advantage of offering a non-inverting output voltage and a non-floating transistor, which simplifies the control.  
All authors contributed to this research. Conceptualization and study design I.-M.P.-C. and D.L. Formal analysis, I.-M.P.-C. and D.L. MATLAB program development, I.-M.P.-C. and D.L. Performed the Caspoc simulations for the new proposed SEPIC-based converter, I.-M.P.-C. Performed the Caspoc simulations for the MPPT algorithm, S.P. Software algorithm, S.P. and D.L. Experiments, I.-M.P.-C., D.L. and S.P. Validation, I.-M.P.-C., D.L. and S.P. Writing original draft, I.-M.P.-C. Writing-review and editing, D.L., I.-M.P.-C. and S.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI--UEFISCDI, project number PD76/2020, within PNCDI III.  
The authors declare no conflict of interest.
## 4 Methodology
### PPR Sampling via Super Nodes  
In the constructed contextual graphs, the geographical and temporal influences are preserved within the graph-structured data. To effectively learn POI representations from these data, we initially utilize the PPR sampling method to extract pivotal node information for each POI across diverse contextual graphs, thereby capturing interrelations among POIs across various contextual features. However, direct application of the PPR algorithm tends to sample nodes from adjacent areas, potentially limiting the exploration of pertinent POI nodes. To mitigate this, we introduce a novel super node-based PPR sampling strategy.  
First, we leverage different strategies to add super nodes into geographical and temporal contextual graphs. In the geographical contextual graph, POIs are categorized into groups based on their geographical attributes, typically delineated by administrative regions within the city. Each POI is then associated with a specific super node corresponding to its geographic location. Similarly, in the temporal graph, timestamps of check-ins are segmented into 24 h intervals, each represented by a dedicated super node. A POI becomes linked to the appropriate super node whenever it has been visited during the corresponding time interval. By incorporating super nodes, we can enhance the connections between POI nodes on the graph, thereby facilitating the identification of related POI nodes.  
Next, we utilize the following PPR sampling strategy [30] to conduct node sampling from the graph-structured data:  
\[\mathbf{r}=\alpha\cdot\overline{\mathbf{A}}\mathbf{r}+(1-\alpha)\cdot\mathbf{m} \tag{1}\]  
where \(\mathbf{r}\) denotes the PPR score vectors, \(\overline{\mathbf{A}}\) denotes the propagation matrix, and \(\mathbf{m}\) denotes the initial vector. Using Equation (1), we employ PPR scores to sample POI nodes that exhibit high correlation with each given POI. Taking the geographical contextual graph as an example, for a given POI \(p\), we use PPR sampling to obtain a set of highly correlated POI nodes \(N_{p}^{L}=\{p_{1},\ldots,p_{nl}\}\) where \(nl\) denotes the sampling size.  
Inspired by the recent study [31], we further transform the sampled node set into a grid-like feature matrix. This matrix serves as the input feature matrix for the CNN-based backbone:  
\[\mathbf{M}_{p}^{L}=\varphi(N_{p}^{L},\mathbf{X}) \tag{2}\]  
where \(\mathbf{X}\) denotes the raw feature matrix of POIs and \(\mathbf{M}_{p}^{L}\) denotes the grid-like feature matrix of POI \(p\), \(\varphi(\cdot)\) denotes the transformation function. In this paper, we rank all sampled POIs based on their PPR scores to generate the grid-like feature matrix \(\mathbf{M}_{p}^{L}\). Following the same operation, we can obtain the feature matrix \(\mathbf{M}_{p}^{T}\) from the temporal contextual graph. This approach ensures that the grid-like feature matrix effectively preserves contextual  
Figure 1: The overall framework of MCGRec.  
information. Furthermore, leveraging such structured input data enables the application of advanced neural networks to extract comprehensive POI representations.
# Optimal Pricing in a Rented 5G Infrastructure Scenario with Sticky Customers
## 4 Operators' Costs, Revenues, and Profits
### The Demand Function
By imposing that condition in Equation (8), we obtain \[e^{-\xi}=\frac{1}{2}\rightarrow-\xi=\ln\frac{1}{2}\rightarrow\xi=\ln 2.\] (9)  
A stickiness factor deviating from that value signals an imbalance towards one of the two operators. When we have \(N_{R}>1\) MVNOs, we can similarly find the correct value of \(\xi\) when there is no stickiness effect. If all operators set the same price, the repartition function should assign the MNO a fraction \(\frac{1}{N_{R}+1}\) of the customers. In that case, solving Equation (8) provides us with the stickiness factor \(\xi=\ln(N_{R}+1)\), which is also valid for a single renter. We can call this value \(\xi_{0}\). When \(\xi>\xi_{0}\), the balance shifts towards the MVNOs, which obtain a larger fraction of customers than the MNO even offering the same price. The repartition function for the generic \(i\)-th renter can be derived by imposing the normalizing condition that  
\[f_{\text{MNO}}+\sum_{i=1}^{N_{\text{R}}}f_{\text{MVNO}}^{(i)}=1\rightarrow\sum _{i=1}^{N_{\text{R}}}f_{\text{MVNO}}^{(i)}=1-f_{\text{MNO}}. \tag{10}\]  
In order to obtain the share of the generic \(i\)-th renter, we can assume that shares distribute linearly with prices so that the operator offering a lower price obtains a higher share. The linear function describing the share of the \(i\)-th renter is  
\[ \begin{split} f_{\text{MVNO}}^{(i)}&=a_{i}\sum_{j=1 }^{N_{\text{R}}}f_{\text{MVNO}}^{(i)}=\left[1-(N_{R}-1)\frac{p_{\text{MVNO}}^{( i)}}{\sum_{j=1}^{N_{\text{R}}}p_{\text{MVNO}}^{(i)}}\right]\sum_{j=1}^{N_{ \text{R}}}f_{\text{MVNO}}^{(i)}\\ &=\left[1-(N_{R}-1)\frac{p_{\text{MVNO}}^{(i)}}{N_{R}\overline{p }_{\text{MVNO}}}\right](1-f_{\text{MNO}})\\ &=\left(1-e^{-\xi\frac{p_{\text{MNO}}}{p_{\text{MVNO}}}}\right) \left[1-(N_{R}-1)\frac{p_{\text{MVNO}}^{(i)}}{N_{R}\overline{p}_{\text{MVNO}} }\right].\end{split}  \tag{11}\]
# Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages
## 2 Hilbert-Huang Transform
### Empirical Mode Decomposition (EMD)  
IMFs must satisfy the following definitions  
(1) In a whole data set, the number of zero-crossing must either equal or differ at most by one.  
(2) At any point, the mean value of the envelope defined by local maxima and minima is zero.  
EMD decomposes the input signal \(x(t)\) to many IMFs, which help to eliminate noise and simplify the whole detection of bearing damage. The method profits by removing noise to make the bearing damage detection easier. The decompose process of IMFs is as follows, and the flowchart of EMD is shown in Figure 2[30].  
1. Identify all the local maxima and minima of \(x(t)\).
2. Obtain mean envelope \(m_{ik}\) by connecting upper and lower envelopes.
3. \(x(t)\) subtracts \(m_{ik}\) to obtain a new signal \(h_{ik}\).
4. Determine \(h_{ik}\) whether the IMF, such as the establishment of its deposit in the IMFs \(c_{i}(t)\), otherwise repeat Step 1 to Step 4.  
Figure 1: Structure of bearing damage detection using the proposed greedy empirical mode decomposition (GEMD).  
* Calculate the tendency \(r_{i}(t)\) by \(r_{i}(t)=x(t)-c_{i}(t)\).
* Complete decomposition if the tendency is a monotonic function or constant. Otherwise, repeat Step 1 to Step 6.  
The decomposition will be complete if the tendency is a monotonic function or constant. Thus, the signal \(x(t)\) consists of the IMFs and tendency, as shown in Equation (1).  
\[x(t)=\sum_{i=1}^{n}c_{i}(t)+r_{n}(t) \tag{1}\]
## 2 Methods and Principles  
### Response Displacement Method  
#### 2.1.1 Lateral Response Deformation Method  
The reaction displacement method is applicable for performing seismic analysis of underground pipe culverts. During the analysis, the interaction between soil layers and the tunnel is modeled by arranging stratum springs around the immersed tunnel. Meanwhile, the seismic load is imparted to the structure through the foundation springs, as illustrated in Figure 1.
## 3 Results and Discussion
### Proximate Composition and Cooking Loss  
Moisture and ash content in goose breast, along with ash retention and cooking loss (CL), are presented in the Supplementary Material (Table S1). The results indicate that the method of heat treatment significantly affects these meat quality parameters. Goose breast meat with skin exhibited significantly higher cooking loss compared to skinless samples. Heat processing also had a notable impact on cooking loss, with the highest values observed following grilling (G), oven roasting (OCR), and pan frying (PF), and the lowest after water bath cooking (WBC) (G, OCR, PF > WBC). Conversely, skinless goose breast meat retained significantly more moisture than meat with skin. Additionally, both raw and WBC-treated goose meat showed significantly higher moisture content compared to samples subjected to other thermal treatments. No statistically significant differences in ash content or retention were found between meat with and without skin. However, heat treatment itself did influence ash content, with higher levels observed after OCR and grilling compared to raw meat, PF, and WBC. In terms of retention, WBC resulted in the highest ash retention, followed by PF. A detailed discussion of these findings is available in [30]. This work, however, focuses on the content of selected trace elements.
# Screening of the Honey Aroma as a Potential Essence for the Aromachology
## 1 Introduction  
An aroma represents an effective marketing tool and is considered to be a new way of in-store communication with customers [1]. According to [PERSON] et al. [2], nowadays the usage of fragrance compounds and essential oils in many business sectors is increasing. Many companies are using their own branded aromas in their shops, offices, or even during some marketing events or campaigns. More and more companies are creating aroma logos or corporate scents to be used indoors. Even retailers apply specific aromas to create comfortable environment where customers would spend more time and money by purchasing more products [3].  
In general, these odors and aromas are distributed by installing aromatizers or diffusers in certain areas [4]. Moreover, aromatization may attract new customers; however, at first it is necessary to conduct aroma testing in order to select appropriate aromas prior to their implementation. For example, application of cappuccino aroma at store in Slovakia increased the volume of sales in confectionery category (desserts, chocolate bars, chocolates, and waffles) [5]. Furthermore; [PERSON] et al. [6] indicates that implementing aromatization in business spaces has positive impact on economic indicators, including the sector of services.  
Aroma, as a marketing stimulus plays, an important role even in the food industry and gastronomy [7]. Honey, as a food product, has (besides health benefits and nutritional values) very specific characteristics from an organoleptic point of view. It is used not only in the food sector, but also in the cosmetic and pharmaceutical industries [8,9]. Healing properties of honey are used for massages, therapy wraps, or for production of scented candles. Honey contains compounds with antioxidant [10], antibacterial, antifungal, anticancer [11], and anti-inflammatory effects [8,9,11]. Compounds with such properties are mainly polyphenols [8,11]. However, these effects were confirmed also for some volatile organic compounds (VOCs), particularly terpenes [12,13]. VOCs from honey were previously determined by GC-MC analysis of simultaneous extraction and distillation extracts [14,15], HS-SPME/GC-MS [16,17,18,19,20,21], HS-GC-MS [22], and HS-SPME/GC\(\times\)GC-TOF-MS [23,24] analyses. Even though honey diffusers are available on the market, there are no studies evaluating their impact on the business sphere.  
The aim of this study was to characterize the aroma profiles of Slovak honey samples, in order to (i) find differences between four kinds of monofloral honey, and (ii) to determine promising volatile candidates for production of inhaling aromas.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 3 Network Architecture Model  
To tackle the issues mentioned above, we present our network architecture model and the WBAN traffic categories, which are detailed below.