esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Neural network output: profiles from ECMWF data  
The targets for the neural network training and the references for the following independent test are the dry and wet refractivity profiles and the dry pressure profiles. These profiles were obtained by processing data provided by the ECWMF belonging to the \"ECMWF 91 model levels\" data set, representing the atmosphere from 75 km to the ground with a vertical resolution spanning from 5 km to 25 m in the high and low atmosphere, respectively [9].  
First, we have computed the water vapour partial pressure profile and then the dry pressure profile by subtracting the partial pressure of water vapor from the total pressure profile.  
Hence, we have computed the atmospheric refractivity at microwave wavelength by [10]:  
\[N=77.6\frac{P_{d}}{T}+72\frac{P_{w}}{T}+3.75\cdot 10^{5}\frac{P_{w}}{T^{2}} \tag{2}\]  
where \(P_{d}\) is the dry air pressure in mbar, \(P_{w}\) the water vapour partial pressure in mbar, \(T\) is the atmospheric temperature in Kelvin. Finally, we have vertically interpolated these profiles at the same altitude levels of the neural network input profiles.  
In conclusion, the neural network outputs are the dry \(N_{d}\) and wet \(N_{w}\) refractivity profiles and the dry pressure profiles \(P_{d}\), where \(N_{d}\) is:  
\[N_{d}=77.6\frac{P_{d}}{T} \tag{3}\]  
while \(N_{w}\) is the remaining part of eq. (2):  
\[N_{w}=72\frac{P_{w}}{T}+3.75\cdot 10^{5}\frac{P_{w}}{T^{2}} \tag{4}\]  
The choice of these output profiles is justified as follows: to solve the atmospheric profiling problem, i.e. to retrieve \(T\), \(P_{d}\) and \(P_{w}\) given \(N\), the additional constraints of ideal gas and hydrostatic equilibrium laws are conventionally used in literature [2], [5]. Such an approach introduces another unknown, the air density \(\rho\), leading to a system of three equations and four unknowns (\(T\), \(P_{d}\), \(P_{w}\) and \(\rho\)). Therefore it is necessary to have an independent knowledge of one of the four parameters to solve the atmospheric profiling problem [5], usually the temperature profile derived from independent observations or weather analysis [2]. Therefore, employing neural networks with inputs and outputs as described above, the atmospheric profile retrieval from refractivity can be solved using only eq. (2), overcoming the need of knowing the temperature profile at each GPS occultation.  
Also, the networks act as a virtual equation describing the statistical information of the input and output data. It is worth noting that even if the GPS LEO retrieved refractivity profiles (input of the neural network) are affected by errors, the neural network outputs (dry and wet refractivity) are more consistent with the ECMWF profiles assumed as the truth, showing a kind of multivariate calibration ability of the trained networks.  
In the next section, different training techniques will be proposed and compared to evaluate the neural network performances.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs for High-Performance Satellite Communication Systems
#### 3.6.2 Challenges  
The challenges to be solved are designing an antenna with high gain (ref. [118]), low sidelobe levels (refs. [118; 119]), and compactness and scalability (refs. [120; 121; 122; 123]), as well as enhanced efficiency (ref. [123]) and beam-steering with a wide-angle coverage (refs. [123; 124; 125]).
# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 4 Experiment
### Model Analysis  
To evaluate the computational overhead of the proposed MAFE framework, we measured its parameter count and the number of floating-point operations (FLOPs) required during inference. With an input size of \(224\times 224\), the total number of parameters in the model is approximately 72 million, and the FLOPs is about 50.59 GFLOPs.  
Although MAFE incurs a higher computational cost compared to traditional CNN architectures, considering its significant performance improvement on both standard and occluded datasets, this computational cost remains within an acceptable range. In the future, we will further optimize its deployment efficiency through methods such as pruning and replacing with lightweight backbones.  
In addition, we recorded the F1 scores on different datasets: 87.13 on the RAF-DB dataset and 86.02 on the FERPlus dataset, indicating that MAFE maintains good generalization ability when facing different data distributions and annotation styles.  
We further evaluated the generalization ability of the model across different datasets.  
As shown in Table 6, the model's accuracy drops significantly on the test datasets, especially under occlusion, indicating its limited generalization capacity. This is primarily due to the substantial distributional differences between datasets, which may cause the model to overfit to dataset-specific features during training, resulting in degraded performance on unseen data. In future work, we plan to incorporate domain generalization techniques to enhance the model's robustness in complex environments.  
To evaluate whether the performance improvements of the MAFE method over other approaches are statistically significant across different datasets, we computed the \(p\)-values for each set of results, as shown in Table 7. The results indicate that all \(p\)-values are below 0.1, suggesting marginal statistical significance and supporting the robustness and generalization capability of MAFE.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Train Dataset** & **Test Dataset** & **Accuracy (\%)** \\ \hline RAF-DB–92.11\% & FERPlus & 26.81 \\  & Occlusion-FERPlus & 25.84 \\ \hline FERPlus–90.15\% & RAF-DB & 37.06 \\  & Occlusion-RAFDB & 30.98 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cross-dataset evaluation results.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **Region** & **Accuracy (\%)** \\ \hline \hline \multirow{6}{*}{RAF-DB} & Lt & 91.53 \\  & Rt & 91.95 \\  & Rb & 91.78 \\  & Lb & 91.51 \\  & Image & 91.72 \\  & MAX & 92.11 \\ \hline \multirow{6}{*}{Occlusion-RAFDB} & Lt & 87.79 \\  & Rt & 88.74 \\ \cline{1-1}  & Rb & 88.47 \\ \cline{1-1}  & Lb & 88.20 \\ \cline{1-1}  & Image & 88.33 \\ \cline{1-1}  & MAx & 89.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of the different regions on model performance.
## 3 Drone Swarms as Networked Control Systems
### Interactive Deployment Strategy  
In an interactive deployment strategy, drone swarms can be deployed in a completely autonomous manner, making their own decisions (e.g., about path adjustment) based on their perception of the environment including neighbor drones. In an interactive deployment strategy the drone swarm will be able to coordinate their operation based on a cooperative approach towards sensing, monitoring as well as information sharing, allowing them to get to a consensus about the best way to execute a mission while facing detected obstacles. In what concerns the exchange of information, an autonomous drone swarm must rely on a networking system that does not depend on the operation of a ground controller, with the exception of emergency situations.  
In a full autonomous system, swarms rely on airborne networks with potentially strong line of sight attributes, which facilitate data transmissions, as well as with the capability to generate suitable routing tables allowing drones to take decisions based on a good understanding of the nearby neighbor. This localized operation, typical of self-organized systems, does not require a networking system capable of creating a full image of the complete drone swarm, which is an advantage in dynamic settings.  
Independently of the considered network topology for the airborne network, the design of a reliable wireless communication system allowing for autonomous drone interaction under dynamic propagation conditions is essential for achieving efficient swarm operations [32]. Hence, an interactive deployment strategy leads to a more flexible swarm with higher density and range than a non-interactive strategy.
###### Abstract  
Urban areas located on complex geological formations, such as dune deposits, require detailed seismic risk assessments that extend beyond standard seismic codes. This study focuses on the city of Concon, Chile, where a significant portion of the urban area is situated on a coastal dune deposit. The research integrates seismic microzonation with a three-dimensional finite element model (3D FEM) to comprehensively evaluate the regional seismic response. Field data from 208 strategically distributed points were collected and combined with geotechnical and geomorphological information to construct a detailed 3D model of the region. This model allowed for the simulation of seismic behavior under various conditions, highlighting the limitations of general seismic codes in capturing local variations in seismic response. The results underscore the importance of considering local geological conditions in structural design, particularly in areas with irregular topography and complex subsurface conditions. This study concludes that incorporating microzonation into urban planning and seismic analysis can significantly enhance infrastructure resilience and disaster preparedness, providing a replicable approach for other cities facing similar geological challenges.  
seismic microzonation; seismic response; dune deposit; 3D finite element modelling Article
## 3 Results and Discussion
### Variation in Moisture and Acidity during the Tugu Fermentation Process of Hunan Light-Flavor Baijiu  
As shown in Figure 3A, from the initial stage of fermentation, the moisture content increased sharply from 65.04% to 73.41% and remained stable. During the prefermentation period, the source of moisture is mainly the water added during production as well as the water produced by the interaction between microorganisms [3]. During the fermentation process, volatile flavor substances such as ethanol, acids, and esters are produced along with water, thus increasing the moisture content of the fermented grains [16]. There was a slight decrease in moisture content at 2-10 days, and then it slowly increased again. This was due to the effect of gravity; the water flowed to the bottom of the cellar, becoming the main component of the yellow water, resulting in a decrease in moisture. During the fermentation process, with the progress of fermentation, yeast and other microorganisms conducted ethanol fermentation to produce free water, so the water of the fermented grains increased.  
The acid in the fermented grains is mainly produced by microbial metabolism and is an important flavor substance in the Baijiu body, which can play a role in flavor enhancement, and the right amount of acid can increase the after-taste of liquor and make the Baijiu body fuller [17]. As seen in Figure 3B, the overall acidity showed an upward trend. As fermentation proceeded, anaerobic and partly anaerobic bacteria proliferated to produce a variety of organic acids, such as lactic acid and acetic acid, to increase the acidity.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Fermentation** & **Alpha Amylase** & **Acid Protease** & **Glucoamylase** & **Cellulose** & **Lipase** \\
**Days (d)** & **Activity/(U/g)** & **Activity/(U/g)** & **Activity/(U/g)** & **Activity/(U/g)** & **Activity/(U/g)** \\ \hline
0 & 28.1 & 7.6 & 1027.1 & 118.9 & 31.1 \\
2 & 6.5 & 30.6 & 809.8 & 473.8 & 34.3 \\
4 & 4.2 & 25.7 & 786.2 & 607.7 & 34.8 \\
6 & 3.5 & 20.5 & 691.8 & 603.1 & 33.5 \\
8 & 4.9 & 14.7 & 732.5 & 607.1 & 35.1 \\
1  
index is, the higher the diversity [7]. As shown in Figure 4, the Chao1 index of F4 was higher than that of the other sample groups, which indicates that the total number of bacterial species in the F4 group was higher than that in the other sample groups. The Shannon index was higher in the F0 group than in the other sample groups, which indicates that the diversity of bacterial species was higher in the F0 group than in the other sample groups. The Shannon index decreased continuously as the fermentation proceeded, which indicates that the unfavorable conditions of low oxygen, low pH values, and high ethanol caused many bacteria to be inhibited. The coverage of all samples was greater than 0.996, and the \(p\) value of the Chao1 index was 0.069, which indicates that the library coverage of each sample was high, the sequences were basically completely sequenced, and the sequencing results were reliable.
## 3 Results
### Case Study in Slovakia
#### 3.1.7 Resulting Headway Times  
Resulting headway times were determined based on procedures in the previous section and are shown in Figures 6 and 7.  
Figures 8 and 9 show that headway times for individual train sequences are significantly shorter in comparison with the current state. The most significant savings in the headway time is, for example, in the case of a train sequence Mn-R, where this interval is shortened to 0.8 min.  
Figure 6: Marked chosen railway section.  
Figure 7: Kilometric and speed profile of the track section in the Galanta–Senec direction, ETCS L3.
## 4 Cache-Supported ICN Router
### Implementation
#### 4.2.1 Packet Processing Flows  
Firstly, we briefly summarize the packet processing flows in our implemented ICN router. When a data packet arrives, the Forward core looks up the counting bloom filter (CBF) to see whether the corresponding chunk has been cached. If not, the packet is sent to the CE by putting it on the virtual queue while being forwarded according to its flow rules in flow table. To achieve the load balance of flows among threads, the Distribute core in the CE distributes packets to assemble cores for chunk assembling afterwards. After the chunk has been assembled and stored in the disk, the CE notifies the TX core to send a register message to the NRS. Similarly, if a chunk is deleted due to the cache replacement policy, the TX core also needs to send a logout message.  
When a request packet is received, if the destination address points to the IP address of the router, the requested chunk must be cached in the router. Therefore, the packet is directly sent to the CE; otherwise, it is forwarded according to the flow table. If the request hits the L1(DRAM)/L2(SSD) level caching, the assemble cores or SSD I/O cores place the chunk on the I/O buffer queue via direct memory access (DMA). Finally, the TX core sends the chunk encapsulated with the ICN and IP header out from the port that the request packet comes from.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 6 Experimental Validation
### Clustering in Static Topologies  
In the following, CHaChA is investigated in static mesh topologies of different sizes in terms of clustering result, duration, and message overhead. First, Section 6.2.1 describes the estimation of the clustering duration depending on the parameters of the algorithm. After Section 6.2.2 explains the general experiment procedure, Section 6.2.3 discusses the clustering of a \(5\times 5\)-node grid topology for a conservative parameterization of CHaChA. Here, the focus is on confirming the reproducibility of the results as well as the previously estimated clustering duration. Subsequently, in Section 6.2.4, several grid topologies of increasing node count are examined for a second parameterization to determine the impact of network size on clustering duration and message overhead.
## 2 Materials and Methods
### PGP Features  
#### 2.9.1 Phosphate Solubilization Capacity  
The Pikovskaya agar plate method was employed to estimate the bacterial strains capacity of solubilizing calcium tricalcium phosphate (Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)). The Pikovskaya medium was prepared dissolving in one liter of ultra-pure water 10 g glucose, 5.0 g Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\), 0.5 g (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 0.1 g MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O, 0.2 g KCl, 0.2 g NaCl, 0.0001 g MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O, 0.0001 g MgSO\({}_{4}\), 0.5 g yeast extract, 15 g agar. Petri dishes of 90 mm diameter were prepared with 5.0 mL of Pikovskaya medium to allow the halo observation. Bacterial smear from culture plate was resuspended in saline solution and then 5.0 \(\mu\)L of it at 0.5 OD\({}_{600}\) were spotted on the plates and then incubated for 7 days at 27 \({}^{\circ}\)C. Therefore, phosphate solubilizer strains were identified by observing the formation of a visible halo around the spotted bacterial strains [25].
# Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization
## 2 Literature Review
The objective function of the algorithm is to minimize the total cost, i.e., the total cost of charging the batteries minus the total reward earned from discharging the batteries, which is defined as:  
\[min\quad\sum_{k=1}^{K}\sum_{i\in V_{stall}}\sum_{t\in T}[r_{itk}P_{re}^{t}-d_{ itk}P_{dis}^{t}]+\sum_{k=1}^{K}P_{night}[B-b_{pk}-\sum_{t\in T}\delta(r_{ptk}-d_{ ptk})] \tag{3}\]  
where \(P_{night}\) is the cost of charging per unit of time at night, \(P_{re}^{t}\) is the cost of charging during period \(t\) and \(P_{dis}^{t}\) represents the reward for discharging during period \(t\). The first part of the objective function corresponds to the net cost during the planning horizon, while the second part is the cost of fully recharging all vehicles at night. The constraints of the algorithm contain served times of customer, no route ending at a customer or a station node, time feasibility of the edges leaving the customer and the depot nodes, the time windows, charge state, battery capacity and the demands. In the paper [26], the route optimization method to balance the needs of passengers and transport companies under urban traffic is studied and a simple multi-objective optimization (SMO) algorithm is proposed to modify the existing route to generate a new feasible route. The algorithm's optimization goal is to reduce passengers' travel time and the operators' travel distance. Moreover, there are studies that tackle artificial intelligence, the technology of multi-agent systems and applications for other industries in the route problems. The paper [27] applies Automated Vehicle Guide (AGV) to the milk-run of factory logistics and improves the efficiency and economy of logistics by setting the path of AGV. In the framework of the concept of Industry 4.0, the paper [28] uses multi-agent systems to improve the efficiency of the logistic supply chain, taking into account the heterogeneous, time-varying factors. Although there have been many studies on route planning, people's preferences are still less taken into account, which will greatly affect the optimality of planned routes. In addition, a single goal cannot meet the practicality of route planning under the condition of autonomous driving. Therefore, it is an urgent problem to build a route planning framework for autonomous driving based on various factors.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 2 Materials and Methods
### Promoter Activity Monitored by RT-qPCR  
_rrn_B P1 and _Preg_ promoters were fused to the marker _lacZ_ gene (LK134 and LK135), yielding identical transcripts. The strains were grown to exponential phase (OD\({}_{600}\)\(\sim\)0.5)--time point 0. Each culture was then divided into two flasks. Cells in one flask were treated with novobiocin (5 \(\upmu\)g/mL) and cells in the other flask were left non-treated. At time points 0, 10, 20, and 30 min, 2 mL of cells were withdrawn and treated with RNAprotect Bacteria reagent (QIAGEN, Hilden, Germany), pelleted and immediately frozen. RNA was isolated with RNeasy Mini Kit (QIAGEN) and recovery marker RNA (RM RNA) was added at the time of extraction to control for differences in degradation and pipetting errors during extraction. The RM RNA was prepared from _B. subtilis_ strain LK41 as for qPE. Finally, RNA was DNase treated according to manufacturers' instructions (TURBO DNA-free Kit, Ambion). Total RNA was then reverse transcribed to cDNA with reverse transcriptase (SuperScript(tm) III Reverse Transcriptase, Invitrogen, Waltham, MA, USA) using primer #2973 that targets _lacZ_ (both in the test mRNA and RM). This was followed by qPCR in a LightCycler 480 System (Roche Applied Science, Penzberg, Germany) containing LightCycler(tm) 480 SYBR Green I Master and 0.5 \(\upmu\)M primers (each). RM cDNA was amplified with primers #2974 and #2973, and the test lacZ cDNA with primers #2975 and #2973. Sequences of primers were originally published in [15]. The final data were normalized to RM and the amount of cells (OD\({}_{600}\)).
## 2 Literature Survey
### Routing Protocols
#### 2.3.2 Ad hoc On-demand Distance Vector (AODV) (On-Demand)  
AODV is a combination of on-demand and distance vector hop-to-hop routing methodology [7]. A connection broadcasts a request when a node needs to know a route to a specific destination. The route request is forwarded by nodes, and records a reverse route for itself to the destination. When the request reaches a node with a route to the destination it creates a backwards message which contains the number of hops that are required to reach the destination.  
All nodes that participate in forwarding this reply to the source node create a forward route to the destination. This route created from each node from source to destination as a hop-by-hop state and not the entire route as in source routing [18].  
The main advantage of this protocol is having routes established on demand and that destination sequence numbers are applied to find the latest route to the destination. The connection setup delay is lower. One disadvantage of this protocol is that intermediate nodes can lead to inconsistent routes if the source sequence number is very old and the intermediate nodes have a higher but not the latest destination sequence number, thereby having stale entries. Also, multiple route reply packets in response to a single route request packet can lead to heavy control overhead. Another disadvantage of AODV is unnecessary bandwidth consumption due to periodic beaconing [18].
# Roller-Integrated Acoustic Wave Detection Technique for Rockfill Materials
## 2 Methodologies
### Theoretical Analysis
#### 2.1.2 Infinite Baffle Piston Radiation Acoustic Field  
Figure 6a shows a circular piston radiator on an infinite baffle. When the piston vibrates at the speed \(\mathbf{u}=u_{A}e^{i\omega t}\) of in the \(z\) direction, the radiator radiates the acoustic wave to the semi-infinite space in front of the baffle. In Figure 6b, a surface element with an area of \(dS\) and a distance of \(q\) from the center of the piston is selected. Since far field acoustic wave radiation is studied here, this surface element can be considered as a point source, and the entire piston radiator is made up of many point sources. Based on teaching materials and the literature, the far field radiation sound pressure is described as follows:  
\[p=i\frac{a\rho_{0}cu_{A}J_{1}(kasin\theta)}{2r_{P}sin\theta}e^{i(\omega t-kr_{ P})}. \tag{5}\]  
For an \(N\) order first class Bessel function, if \(n\) is a positive integer or zero, \(\Gamma(n+m+1)=(n+m)!\). In addition, considering the relation between the zero-order and the first-order Bessel functions, assign \(n=0,\ n=1\), the following formula is obtained:  
\[J_{1}(x)=\frac{x}{2}-\frac{x^{3}}{2^{3}.2!}+\frac{x^{5}}{2^{5}.2!3!}-\frac{x^ {7}}{2^{7}.3!4!}+\cdots+(-1)^{k}\frac{x^{2k+1}}{2^{2k+1}k!(k+1)!}+\cdots.\]  
Considering the characteristics of the acoustic far field, there following relations are obtained: \(r_{P}\gg a,r_{P}\gg a^{2}/l.\) According to the small quantity property of the Bessel function, when \(x\to 0\), substituting \(J_{1}(x)\) into Equation (5), the following formula is obtained:  
\[p(r_{P},t)=i\frac{ka^{2}\rho_{0}cu_{A}\left[\frac{1}{2}-\frac{1}{16}(kasin \theta)^{2}\right]}{2r_{P}}e^{i(\omega t-kr_{P})}\approx i\frac{ka^{2}\rho_{0} cu_{A}}{4r_{P}}e^{i(\omega t-kr_{P})} \tag{6}\]  
Figure 6: (**a**) Circular piston on infinite baffle; (**b**) radiated acoustic field of circular piston.