esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Preprocessing and Data Augmentation  
All audios are randomly cropped to 2 s in training, audios shorter than 2 s will be duplicated and concatenated to create a 2 s segment. And, the sampling rate is 16,000 Hz. Pre-emphasis with a coefficient of 0.97 is first applied. Then, 80-dimensional log Mel-filterbanks are extracted with a 25 ms hamming windows and 10 ms shift. The frequency is limited to 20-7600 Hz in Mel spectragrom. Since the downsampling requirements in the CNN encoder, the number of frames is cut into an integer multiple of 8. In addition, mean normalization in time dimension is applied.  
Training audios are randomly augmented for a robust model. Our augmentation methods are mainly in terms of MUSAN dataset [39] and RIR dataset [40]. MUSAN provides three types of additional noise that can be mixed with the original sounds to generate augmented data. RIR has a variety of simulated room impulse responses for reverberation augmentation, which enhances the robustness of SV systems against various environmental interferences encountered during recording. In addition, we apply SpecAugment [41] in the pre-training stage, with random time masking and frequency masking, respectively, for all spectrograms. Considering the computational cost, no speed perturbation [42] is adopted in our experiments.
## 7 Quadratic Adaptive Replication (QAR)
### Performance Evaluation
If we look carefully at the results, they show that the improvement is specially significant when ToW model decides to increase the number of replicas. Then, during the cases where ToW uses \(d\) = 2 (16 replicas) the results show a maximum extra communication cost of 7.28% compared with QAR. In the cases where ToW chooses \(d\) = 3 (64 replicas), QAR improves ToW in average a 5%, with a maximum of a 17%. Finally, the case when ToW selects \(d\) = 4 (256 replicas) demonstrates that applying ToW with high ratios is not good enough due to its low adaptivity. In this case the maximum extra cost is 32.3% being the average gain of QAR a remarkable 13.5%. It must be noted, that in some few cases ToW slightly outperforms QAR, but never goes further than a 2% improvement. This happens in very few cases where our model, which is a distance-based one, does not always provide the best number of replicas in terms of hops, but the one in distance that leads to a similar result in hops, but not always optimal. As a side note, the original DCS proposal having a single replica has an average cost 3 times (300%) greater than our solution. This further demonstrates that multi-replication when applying Data Centric Storage is a must. **Production Dominates Consumption (\(\lambda_{p}>\lambda_{c}\))**  
We use a scenario with the same parameters than in the previous case. Again, the inverse ratio \(\lambda_{p}/\lambda_{c}\) changes from 1 to 200. First of all, it must be noted that in this case increasing the number of replicas requires a higher \(\lambda_{p}/\lambda_{c}\) ratio than the \(\lambda_{c}/\lambda_{p}\) ratio required in the previous section. This is why in this case the ToW solution only goes up to 64 replicas (d = 3) while QAR reaches 49. Figure 10 shows the extra communication traffic that ToW generates when compared with QAR. In this case the average extra cost generated by ToW is a 4.8%. Analyzing the results for the different values of \(d\) chosen by ToW, when \(d\) = 1 the improvement is not very significant. However, when ToW moves to \(d\) = 2, it produces a maximum extra cost of 10.15%. In the case of \(d\) = 3, QAR improves ToW in a 11.45% in average, reaching a maximum of 17.5%. Again, there are some few cases when ToW outperforms QAR, but the difference is always lower than a 3%. The reason why this happens is the same as in the previous case. When comparing our solution versus the original DCS with a single home node, the average extra communication cost produced by GHT is 145%. We can conclude that the quadratic evolution of QAR is much more adaptive than previous solutions in the literature that follows a \(4^{d}\) grid structure replication like ToW or the original multi-replication GHT. QAR improves the average network overall communication traffic and reaches peak traffic reduction of 32% in front of ToW, which has been demonstrated to be less adaptive to network conditions. In addition, our solution does not add any additional complexity to the replication mechanisms proposed in the literature.
## 3 Some Key Issues on the Use of SLNs and NLCs as Oral Nanovehicles for Nutraceuticals or Bioactives  
The development of food-grade nanovehicles, as delivery systems for improving water solubility (or dispersibility) and bioavailability of poorly soluble bioactive compounds, has become an intense research focus during the past decade, in both the food science and colloid fields. In general, an ideal food-grade nanovehicle, as a bioactive or nutraceutical, should meet some basic requirements, as follows: (1) high capacity to load significant amounts of bioactives; (2) site-targeted delivery; (3) high stability under stressed environmental conditions (e.g., temperature, oxygen, and light) and during consumption (e.g., pH, ionic strength and enzymes); (4) easy to prepare and scale-up with low cost; (5) high safety and no use of organic solvents; (6) commercial availability of raw materials in abundance; (7) good suitability to industrial drying processes (e.g., spray-drying or freeze-drying), ensuring the versatility of applications in food formulations [18].  
The selection of a lipid matrix is a determining factor when developing an appropriate delivery system, based on SLNs or NLCs, for a specific bioactive. The solubility of bioactive in the lipid matrix and the interaction between the molten lipid and bioactive are two important parameters that must be considered when choosing the lipid matrix of SLNs. Both the encapsulation performance and loading capacity of bioactives in these nanovehicles and the stability of the encapsulated bioactives are affected by the selection of lipids. On the other hand, the selection of the lipid matrix is also, to a certain extent, determined by the nature of bioactives, e.g., lipophilicity, charge, molecular weight, structure, and functional groups.
# Laser Observations of GALILEO Satellites at the CBK PAN Astroegodynamic Observatory in Borowiec
###### Abstract  
The laser station (BORL) owned by the Space Research Centre of the Polish Academy of Sciences and situated at the Astroegodynamic Observatory in Borowiec near Poznan regularly observes more than 100 different objects in low Earth orbit (LEO) and medium Earth orbit (MEO). The BORL sensor's laser observation range is from 400 km to 24,500 km. The laser measurements taken by the BORL sensor are utilized to create various products, including the geocentric positions and movements of ground stations, satellite orbits, the components of the Earth's gravitational field and their changes over time, Earth's orientation parameters (EOPs), and the validation of the precise Galileo orbits derived using microwave measurements, among others. These products are essential for supporting local and global geodetic and geophysics research related to time. They are crucial for the International Terrestrial Reference Frame (ITRF), which is managed by the International Earth Rotation and Reference Systems Service (IERS). In 2023, the BORL laser station expanded its list of tracked objects to include all satellites of the European satellite navigation system GALILEO, totaling 28 satellites. During that year, the BORL laser station recorded 77 successful passes of GALILEO satellites, covering a total of 21 objects. The measurements taken allowed for the registration of 7419 returns, resulting in 342 normal points. The average RMS for all successful GALILEO observations in 2023 was 13.5 mm.  
satellite laser ranging; MEO; GALILEO; BORL station 2024162862[[https://doi.org/10.3390/rs16152862](https://doi.org/10.3390/rs16152862)]([https://doi.org/10.3390/rs16152862](https://doi.org/10.3390/rs16152862))
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
## 4 On-Demand Wi-Fi Connection to Nearby Mobile Devices
### Discovery and On-Demand Wi-Fi Activation  
In our mechanism, the Wi-Fi radio is activated purely 'on-demand', i.e., only when it is needed for accessing or serving content. To accomplish this on-demand activation of Wi-Fi, the mechanism uses a proxy server on the Internet. Every mobile device periodically uploads its location as well as its received signal strengths (RSSI) from neighboring cellular base stations to the proxy server over its cellular data (3G/4G LTE) connection. Since users keep their cellular data connection on most of the time, for applications such as push e-mail, this data can be sent to the server at little extra energy cost. We quantify this overhead in Section 7.  
Figure 2 shows the _T-Wing_ protocol for discovery and on-demand Wi-Fi activation. A mobile device can query for a list of nearby mobile devices that are hosting content. This query is first routed to the proxy server on the Internet. The server then uses an algorithm to predict if direct Wi-Fi connection is possible between the querying mobile platform and any nearby mobile device based on the RSSI values reported by the mobile devices. If a mobile device wants to access data from such a nearby mobile device, it activates its own Wi-Fi card, and the proxy server on the Internet notifies the mobile device hosting the content to turn on its Wi-Fi connection as well.
## 3 Results and Discussion
### DIC and Poisson Ratio as Damage Indicators
After a stress of 1000 MPa, a decreasing trend was monitored coinciding with the appearance of delaminations, as indicated in Figure 15. On the other hand, in the [0\({}^{\circ}\)/60\({}^{\circ}\)]\({}_{\text{2s}}\) laminates, the Poisson's ratio was constant in the initial part of the static test and it started decreasing at a stress value close to the point that matrix cracking occurred, having lower values than the [0\({}^{\circ}\)/30\({}^{\circ}\)]\({}_{\text{2s}}\) laminates. The above proves that even global mechanical properties, like the Poisson's ratio, can qualitatively indicate the transition from moderate to severe damage.
## 3 Results and Discussion  
### Properties of the Plastic Waste Material  
In this work, the PEX waste is the new material applied, originally, in asphalt mixtures. The results of its experimental characterization are presented below, according to the methodology presented in Section 2.2.  
The particle density of PEX waste at 25 \({}^{\circ}\)C was evaluated as being 938.6 kg/m\({}^{3}\). This value is slightly lower than that of bitumen (nearly 1030 kg/m\({}^{3}\)), but is clearly lower than that of mineral aggregates (nearly 2650 kg/m\({}^{3}\)) that PEX is partially substituting. The knowledge of this value was crucial to calculate important volume and weight proportions between the different materials used in the asphalt mixtures. Furthermore, the grading curve of the ground PEX waste particles, previously presented in Figure 1a, was also essential to adjust the grading curve of the new asphalt mixtures with PEX within the standard limits of an AC 14 surf mixture.  
The ability of PEX particles (Figure 4a) to partially melt under application of heat (180 \({}^{\circ}\)C) and pressure, specifically because they are not totally cross-linked, can be observed in Figure 4b. However, the cross-linked part of PEX is not changed by the presence of solvents, heat or pressure.  
The GEL content (or degree of cross-linking) of the PEX waste used in this work was determined to be 54%. This percentage of PEX will certainly not melt, working only as aggregate substitute, but the other part of PEX waste (nearly half of it) can melt and modify the asphalt binder during the production of asphalt mixtures, thus improving the mixture performance.  
Figure 4: PEX waste particles: (**a**) Before being heated; (**b**) After applying heat and pressure at 180 \({}^{\circ}\)C.  
The calorimetry results (DSC) of PEX waste, presented in Figure 5, can be used to evaluate the melting temperature of this material. The melting temperature of PEX particles obtained in the DSC was nearly 130 \({}^{\circ}\)C, for both heating cycles, which is within the typical melting temperature range of polyethylene materials. This value is clearly lower than the temperature used for asphalt mixture production (180 \({}^{\circ}\)C). Thus, the PEX waste, introduced in the asphalt mixture by the dry process, as aggregate substitute, could easily interact with the asphalt binder at those temperatures.  
Therefore, the binder properties should be assessed, before and after interaction with PEX, in order to quantify the binder modification attributed to this waste material.
## 3 Results
### Senna
#### 3.4.3. Senna podocarpa  
([PERSON] & [PERSON].) Lock  
_Senna podocarpa_ (Synonym: _Cassia podocarpa_ [PERSON] et [PERSON]), called _Asuwon_ (Yoruba) and _Agelo-Oglala_ (Igbo), is one of the six medicinally useful species of _Senna_. It is a smooth almost hairless shrub that grows up to five metres high and is widely distributed in West Africa, especially in the Savanna forest area of the region [157]. The plant is readily available in Nigeria in ancient farmlands in the western and northern parts. _S. podocarpa_ has traditionally been used to treat several health conditions including skin diseases including, eczema, scabies and ringworm. It has also been used in folklore as a labour inducer, purgative, anti-gonorrhoea medicine and guinea worm expeller. The leaf infusion or decoction is also given as a mild laxative while a large dosage of it acts as a purgative [158].  
[PERSON] et al. [157] produced an emulgel formulation of the plant extract, which offered superior pharmacological activity in comparison with the _S. podocarpa_ poultice used traditionally for wound healing. The 7.5% emulgel formulation demonstrated an optimal effect in the inhibition of oedema and burns in addition to promoting wound healing. In addition to its wound-healing property, _S. podocarpa_ has also been reported for its antimicrobial, antiviral and anti-inflammatory activities [159].  
_Senna podocarpa_ contains phytochemicals such as saponins, tannins, anthraquinones, phlobatannins, phenolics, flavonoids and alkaloids, among others [160]. Some chemical compounds identified from the leaves and pods of _S. podocarpa_ include chrysophanol, rhein (**40**), emodin (**41**), sennoside A (**42**) and sennoside B (**43**) [161]. The chemical structures of the isolated compounds are presented in Figure 6.  
Figure 6: Some compounds isolated from the _Senna_ genus.
# A Novel Searching Method Using Reinforcement Learning Scheme for Multi-UAVs in Unknown Environments
## 2 Materials and Methods
### Effect of Collision Avoidance on Search Map  
When multiple UAVs perform tasks together, security is a top priority. This paper draws lessons from the idea of hormone information dissemination and diffusion, and the concept of the territory awareness information map [20] is used to establish a search map. When a UAV moves to the grid \((m,n)\) at time \(k\), the pheromone information is generated at the corresponding location of the search map, which affects the generation and updating of other UAVs' pheromones by diffusion. The diffusion of existing pheromones inhibits the generation of other kinds of pheromones, which is the role of the territory awareness information map. A new environmental search information map can be constructed, which can be used as the basis to solve the collision avoidance problem in multi-UAV cooperative control.  
\(H_{nm}(k)\) is defined as the total pheromone concentration at the grid \((m,n)\). The concentration is a function of the grid position and time; thus, the environment search map is rewritten as follows: \(P_{mn}^{*}(k)=[\begin{array}{cc}p_{mn}(k)&\chi_{mn}(k)&H_{mn}\;(k)\end{array}]\).  
When \(V_{i}\) searches grid \((m,n)\), it produces pheromone \(H_{i(mn)}(k)\), which can diffuse to other grids in the search map. Taking the grid \((a,b)\) as an example, the diffusion propagation function is  
\[H_{i(ab)}(k)=\frac{\beta}{\rho^{2}}\times e^{\frac{(a-m)^{2}+(b-n)^{2}}{2\rho^ {2}}}, \tag{4}\]where \(\rho\) and \(\beta\) are constants.  
When \(N_{v}\) UAVs perform search tasks, \(N_{v}\) kinds of pheromones are constantly generated and diffused. Taking the grid \((c,d)\) as an example, the concentration of a pheromone at the current time is the sum of the concentration of pheromones left by volatilization at the previous time and the newly produced pheromone diffusing to the grid. The updating equation is as follows:  
\[H_{cd}(k)=\tau_{H}H_{cd}(k-1)+\sum_{i=1}^{N_{v}}H_{i(cd)}(k), \tag{5}\]  
where \(\tau_{H}\in[0,1]\) is a volatility factor.  
When \(V_{i}\) detects a high concentration of other kinds of pheromones at grid \((m,n)\), it means that other UAVs' activities are frequent at grid \((m,n)\). That is, flying \(V_{i}\) into the grid not only reduces the search efficiency, but also has a high probability of collision. The concentration of other kinds of pheromones detected by \(V_{i}\) is as follows:  
\[\overline{H}_{imm}(k)=H_{mm}(k)-\sum_{j=0}^{k}H_{imm}(j). \tag{6}\]
## 2 Numerical Model and Adopted FE Techniques  
### Geometry and Material of Stiffened Plates  
To better understand the accuracy of common methods for simulating the collapse behavior and estimating the ultimate strength of individual structural members, ISSC'2000 VI.2 carried out a benchmark research, where 19 stiffened plates were formed by varying the combination of slenderness ratio of the plate and the stiffener and also changing the type and the size of the stiffener [11]. The 19 stiffened plates of various materials are employed in this study to investigate the effect of steel grades and the welding induced residual stress on the ultimate strength.  
In the ISSC'2000 VI.2 benchmark calculations, the stiffened plates with evenly spaced stiffeners of the same size were studied, and three types of stiffeners, namely flat-bar, angle-bar and tee-bar as shown in Figure 1, with various dimensions as described in Table 1 were considered. The dimensions of the local plate (\(a\times b\)) between the stiffeners were 2400 mm \(\times\) 800 mm and 4000 mm \(\times\) 800 mm, and five different thicknesses were considered, namely 10, 13, 15, 20 and 25 mm, respectively. The material of the stiffened plates was assumed to have a yield stress of 313.6 MPa and a Young's modulus of 205.8 GPa. The material properties are implemented into FE models via modelling by elastic-perfectly plastic formulation and no material hardening effect is considered [11; 25].  
Figure 1: Definition of cross-sectional dimension of stiffened plate.  
For ease of description, the stiffened plates are hereafter referred to as '_Xijmm_', where \(X\) indicates the type of stiffener, \(i\) the size of stiffener, \(j\) the aspect ratio of a plate, and \(mn\) the thickness of plate, respectively, and each of them is defined as follows:  
\[X:=F\text{: flat-bar};=A\text{: angle-bar};=T\text{: tee-bar}\]  
\[i:=1\text{: Size 1};=2\text{: Size 2};=3\text{: Size 3}\]  
\[j:=3\text{: }2400\times 800\text{ (mm)};=5\text{: }4000\times 800\text{ (mm)}\]  
For example, F2510 denotes the stiffened plate with flat-bar stiffeners of Size 2, aspect ratio of 5, and thickness of 10 mm.
## 3 Materials and Methods
### 3D Euler Deconvolution  
The aim of 3D Euler deconvolution (3 DED) is to determine the geometry, field source location and depth estimation of gravimetric anomalies [26; 27; 28; 29]. The 3 DED has successfully been used in discovering fault lineaments distribution including the dip direction and extension below the subsurface [30]. This method was created by [PERSON] [31] and [PERSON] et al. [32] who developed the equivalent method which operated on magnetic data. Later, the method has been introduced to be used in gravity data such as in [PERSON] et al. [33] and [PERSON], et al. [34]. The 3 DED equation given by [32] is:  
\[N(\beta-g)=(x-x_{0})\frac{\delta T}{\delta x}+(y-y_{0})\frac{\delta T}{\delta y }+(z-z_{0})\frac{\delta T}{\delta z} \tag{3}\]  
where \(g\) is the gravity anomaly detected at \((x,y,z)\) and \(x_{0}\), \(y_{0}\) and \(z_{0}\) is the position of the gravity source. \(N\) is a constant defined as the structural index (SI) depending on the type and shape of the source body such as geological contacts, faults, cylinder, or sphere (Table 1). The 3 DED is commonly applied in magnetic [35] and gravity interpretation since it requires a general input of gravity source geometry and requires no information of the acceleration vector. However, several SI values are required to be tested to find the optimum input to give a good clustering solution with a particular size of window. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [32] stated that window size must be small enough to differentiate anomalies resulting from different sources yet large enough to incorporate deep sources and spatially extensive sources. [PERSON] et al. [36] also stated that a large window size could detect very deep basement sources.
## 2 Current Situation  
### Heat Pump Performance--The Expectation  
UK projections are predicated on the assumption that air and ground source heat pumps perform at efficiency levels of 250%-275% and 315%-385% respectively [6]. These levels of performance are consistent with the assumptions in widely used domestic energy models published by BRE, e.g., BREDEM [7], and the Governments own energy performance measure of dwellings, the Standard Assessment Procedure (SAP) [8] (SAP itself being a simplified BREDEM model) with values of 250% for air and 320% for ground source. Manufacturers tend to present more optimistic performance levels for air source of up to 350% [9] and up to 420% for ground source [10]. Furthermore, many energy models developed for academic research, particularly those focusing on UK housing stock and carbon emission scenarios, often use the BREDEM models at their heart, e.g., UKDCM, DeCARB, CDEM [11].  
Across all sectors (_i.e._, residential and non-residential) a predicted total of 66 TWh of heat could be generated from renewable sources by 2020 with 18 Mt of CO\({}_{2}\) abated as a result [6]. This projection gives no breakdown of the CO\({}_{2}\) abatement contribution from heat pumps; however, air source heat pumps (ASHP) are projected to contribute 40% of the total by 2030 in the residential sector for a medium abatement scenario [6].  
With 85% of heating installations currently being natural gas central heating [12], given the 2030 installation levels being considered, heat pumps will be displacing significant amounts of gas central heating. The UK's Energy Saving Trust (EST) states that ASHPs may emit more CO\({}_{2}\) than a baseline mains gas boiler system supplying both space and water heating, with Ground Source Heat Pumps (GSHP) being comparable with gas under real operational conditions study [13]. Therefore it is difficult to identify how heat pumps could contribute to such significant CO\({}_{2}\) reductions as forecast. On the other hand, this report confirmed that most heat pump installations had lower emissions than the other non-gas grid connected heating alternatives, hence the identified contribution that ASHP's can make to CO\({}_{2}\) abatement. In support of this, the aforementioned Office of Climate Change (OCC) report [3] also identified that the main opportunity for renewable heat, from an economic perspective,lies in off gas grid properties. Furthermore, other modelling studies have also identified ASHPs as yielding more CO\({}_{2}\) savings than other low-carbon micro-generation heating technologies examined in the same study [14].