esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application
## 2 GFDM Systems and Channel Model
### Channel Model  
This section describes the channel model that is used for the experiment. Overall, the channel model is taken from NYUSIM simulator. The channel is suitable for 5G network using mmWave. The simulator is typically used in a wide range of carrier frequencies from 500 MHz to 100 GHz, antenna beamwidths (7\({}^{\circ}\) to 45\({}^{\circ}\) for elevation and 7\({}^{\circ}\) to 360\({}^{\circ}\) for azimuth), radio frequency bandwidth up to 800 MHz and other applications (urban microcell, urban macrocell and rural macrocell) [18]. Furthermore, this channel simulator is important for the performance evaluation of communication systems. The power delay profile is obtained by setting the input parameters specified in NYUSIM simulator. Overall, the channel and antenna parameters need to be determined before generating power delay profile.  
Table 2 provides the parameters that need to be set. Figure 4 shows that the power delay profile for each scenario selecting scenario, such as Umi, Uma and Rma, can be adjusted in its input parameters, as specified in Table 2. Overall, the input parameters for these three scenarios are made to be same in order to satisfy the tactile internet requirements. In addition, the number taps of Umi, Uma and Rma are 23, 39 and 2, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Tactile Internet** \\ \hline Cell size (km) & 1 \\ Delay Spread (us) & 1 \\ Bc (kHz) & 200 \\ Doppler shift (Hz) & 10 \\ Tc (ms) & 50 \\ Subcarrier \(K\) & 64 or 128 \\ Subsymbols \(M\) & 5 \\ Receiver Type & Zero Forcing \\ Tcp (\(\mu\)) & 2 \\ Symbol duration (us) & \(\approx\)5 \\ Modulation order & 2 or 4 \\ Bw (MHz) & 100 (fragmented) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel and GFDM parameters for tactile internet application [4].  
Figure 4: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel Parameters** & **Antenna Parameters** & \\ \hline Frequency & 28 GHz & \\ Radio Frequency(RF) Bandwidth & 800 MHz & Number of Tx antenna elements & 1 \\ Scenario & Umi & \\ \hline Environment & NLOS & \\ Lower Bound of T-R distance & 10 m & Number of Rx antenna elements & 1 \\ \hline Upper Bound of T-R distance & 1000 m & \\ Tx Power & 30 dBm & Tx antenna azimuth HPBW & 10\({}^{\circ}\) \\ \hline \begin{tabular}{c} Barotmetric Pressure \\ Humidity \\ \end{tabular} & 1013.25 mbar & \\ \
\begin{tabular}{c} Humidity \\ \end{tabular} & 50\% & Tx antenna elevation HPBW & 10\({}^{\circ}\) \\ \hline Temperature & 20 \({}^{\circ}\)C & \\ Rain Rate & 0 mm/hr & Rx antenna azimuth HPBW & 10\({}^{\circ}\) \\ \hline Polarization & Co-Pol & \\
\begin{tabular}{c} Foliage Loss \\ \end{tabular} & No & Rx antenna elevation HPBW & 10\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input Parameters NYUSIM Simulator.
## 2 Propagation Modeling and Key Rate Estimation
### Propagation Modeling
#### 2.1.2 Beam Depolarization  
The value of cross-spectral density function has the same variation when a beam has the same coherence length in x/y directions. Therefore, we can also conclude that the rotations of the polarization state will not be seriously impacted in a turbulent channel since the values of the wave front phase distortion characterized by the cross-spectral density function caused by atmosphere turbulence, \(\psi(\mathbf{\rho}_{1},\mathbf{r}_{1})\), are the same. Nonetheless, the linearly polarized quantum light after transmission will be depolarized in the atmosphere turbulent channel [28]. However, in previous works [29; 30], the geometric model has been proposed to calculate the free-space channel loss, \(\eta\), which is regarded as the channel transmittance, without considering the depolarization effects. Since the distribution of polarized-photon number is still Poissonian [19], we modify the transmittance as \(\eta^{\prime}=\eta_{p}\eta\). Here, the ratio \(\eta_{p}\) of the effective polarized intensity component to the total intensity of the light beam is assumed as the degree of polarization (DoP), which can be succinctly described by [31]  
\[P(\mathbf{\rho},\mathbf{\rho};z)=\frac{\sqrt{s_{1}(\mathbf{\rho},\mathbf{\rho};z)^{2}+s_{2}( \mathbf{\rho},\mathbf{\rho};z)^{2}+s_{3}(\mathbf{\rho},\mathbf{\rho};z)^{2}}}{s_{0}(\mathbf{\rho}, \mathbf{\rho};z)},\ 0\leq P(\mathbf{\rho},\mathbf{\rho};z)\leq 1 \tag{7}\]  
where \(s_{0},s_{1},s_{2}\) and \(s_{3}\) are the Stokes parameters. Specifically, \(s_{0}\) represents the total light intensity, \(s_{1},s_{2}\) represents the linearly polarized light component in the \(x\) direction and in the 45-degree direction, respectively. \(s_{3}\) represents circularly polarized light in the right hand. Furthermore, based on the formula between DoP and CSDM, the DoP can be rewritten as  
\[P(\mathbf{\rho},\mathbf{\rho};z)=\sqrt{\frac{\left[W_{xx}(\mathbf{\rho},\mathbf{\rho};z)-W_{ yy}(\mathbf{\rho},\mathbf{\rho};z)\right]^{2}+4W_{xx}(\mathbf{\rho},\mathbf{\rho};z)W_{yy}(\mathbf{ \rho},\mathbf{\rho};z)}{W_{xx}(\mathbf{\rho},\mathbf{\rho};z)-W_{yy}(\mathbf{\rho},\mathbf{\rho}; z)}} \tag{8}\]  
As in the previous assumption, the DoP of the quantum light beam at the received detection can also be represented as \(P(\mathbf{\rho}_{0};z)\).
# Solving Nuisance Cyanobacteria Eutrophication Through Biotechnology
## 4 Results  
### Species Composition  
The biomass from the Lafri lagoon consisted mainly of cyanobacteria comprised more than 95% of the total bloom biomass. A total of four cyanobacterial taxa were identified: _Limnoraphis_ (_Lyngbya_) cf. _robusta_, _Limnoraphis_ (_Lyngbya_) cf. _birgei_, _Microystis aeruginosa_ and _Anabaena_ sp. _Limnoraphis_ (_Lyngbya_) cf. _robusta_ made almost all the cyanobacterial biomass (>95%). Filaments showed a wide spectrum of coloration changing from blue-green-green to orange-brownish when carotenoids cover the other pigments. The brownish color is well recognizable in massive water blooms and floating mats of the species (Figure 1A). Morphology of the bloom-forming cyanobacterium and dimensions are presented in Figure 1B-F. Associated to the cyanobacteria, a diverse community of epiphytic and benthic diatoms was identified that consisted of _Nitzschia_, _Navicula_, _Smedra_, _Cocconeis_, _Epithemia_, _Cymbella_, _Achnanthes_, and _Pleurosigma_. In addition, the planktonic diatoms _Biddulphia obtusa_ (also a facultative epiphyte) and _Cyclotella_ sp. were present, together with cryptophytes and prasinophytes.  
Figure 1: **(A)**: An overview of _Limnoraphis_ (_Lyngbya_) heavy bloom in Lafri coastal lagoon, in June. (**B–F**): Light micrographs of _Limnoraphis_ (_Lyngbya_) filament morphology and coloration in the bloom biomass. Scale bars 20 μm for (**B–D**) and 30 μm for (**E**,**F**).
# Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning
## 3 Methods
### Framework
#### 3.3.1 Multi-Agent Deep Deterministic Policy Gradients  
MADDPG is an actor-critic method and adopts the paradigm of centralized training and distributed execution. It extends the Deep Deterministic Policy Gradient (DDPG) algorithm to the multi-agent context. The extension facilitates decentralized execution where each agent takes actions based only on its local observations while still permitting centralized training with access to the observations and actions of all agents.  
In the MADDPG framework shown in Figure 3, each agent employs two primary components: a critic \(V^{i}\) and an actor \(\pi^{i}\). The actor takes the current observation \(o\) as input and outputs an action \(a\), dictating the policy the agent should follow. The critic, meanwhile, predicts the Q-value of a given state-action pair, estimating the expected return for taking an action in a particular state following the actor's policy.  
Unlike traditional DDPG, where the critic only evaluates the Q-value of a state-action pair from a single agent's perspective, in MADDPG, the critic of each agent takes into account the actions and observations of all other agents. Having global information, MADDPG can eliminate the non-stationary in MARL by explicitly conditioning on the actions of other agents. As we know the actions of other agents, and the environment is stationary even as the policies change, since \(P(s^{\prime}|s,a^{1},\ldots,a^{N},\pi^{1},\ldots,\pi^{N})=P(s^{\prime}|s,a^{1},\ldots,a^{N})=P(s^{\prime}|s,a^{1},\ldots,a^{N},\pi^{1},\ldots,\pi^{N})\) for any \(\pi^{i}\
eq\pi^{i}\). This design enables agents to learn policies that are aware of the actions of other agents and promote cooperative behavior.  
Figure 2: The framework of the proposed method that combines meta-learning and multi-agent reinforcement learning.  
Overall, MADDPG represents an efficient and effective method for training multi-agent systems, leveraging the power of deep learning and reinforcement learning within a framework specifically designed to handle the complexities of multi-agent environments.
# Epigenetic Signatures of Dental Stem Cells: Insights into DNA Methylation and Noncoding RNAs
## 3 Results
### Noncoding RNAs Annotation and Network Analysis  
Among the consistently hyper- or hypomethyllated probes across the three dental-derived stem cell lines, a significant number of elements associated with noncoding transcripts was identified. These probes were annotated across different regulatory contexts based on ENCODE and LNCipedia data, including promoter, enhancer, and gene body regions. Among the noncoding elements identified, some noncoding RNAs (_DANCR_, _MIR22 HG_, _ZFAS1_, and _MALAT1_) emerged as particularly relevant due to their known roles in tooth development and alveolar bone remodeling [22; 23; 24; 25; 26; 27; 28; 29].  
Next, this study focused on noncoding RNA loci that shared promoter-associated CpG sites--either consistently hypermethylated or hypomethyllated across the three cell lines--with nearby protein-coding genes. GO enrichment analysis of these protein-coding genes identified 19 statistically significant biological process (BP) terms (Table S7). From these, three BPs (GO:0048568, embryonic organ development, fold-enrichment = 1.63, \(p\)-adj = 5.3 \(\times\) 10\({}^{-5}\); GO:0045165, cell fate commitment, fold-enrichment = 1.75, \(p\)-adj = 0.046; GO:0048863, stem cell differentiation, fold-enrichment = 1.81, \(p\)-adj = 0.047) were used to build a gene-concept network (Figure 6), as they capture key biological programs shared by dental stem cells, including their origin in embryonic craniofacial development, their lineage-specific fate commitment, and their capacity for regulated differentiation into odontogenic and periodontal cell types. The network contained 205 nodes and 235 edges and exhibited a compactness index of 0.033, which falls within the range classified as a sparse network.  
Interestingly, several of these genes include elements in key developmental pathways regulating tooth formation, patterning, and mineralization (e.g., _PAX6_, _TEAP2A_, _GATA2_, _FOXC2_, _SALL1_, _NR2F2_, _LHX1_, _HOXB2_, _JINB_, _OSR2_, _BMP2_, _DLX5_, _GL3_, _WNT2_, _BMP7_, _PITX2_, _JAG1_, _GATA4_). In particular, _PAX6_, _FOXC2_, _NR2F2_, _SALL1_, _BMP7_, and _JAG1_ were among the top 10 most central nodes based on the betweenness centrality measure, indicating their involvement in multiple biological functions (Table S8).  
Figure 5: **Distribution of hypo- and hypermethylated probes across gene-related genomic regions.** Proportion of probes that are consistently hypomethyllated or hypermethylated across three samples, based on their annotation in the UCSC_RefGene_Group field of the manifest file. (**Left**) Unfiltered dataset; (**right**) filtered probe set.
## 4 Results and Discussion
### GIS Modelling Results  
A GIS model was applied to generate prediction maps at different times a day (Figures 6 and 7). The resultant spatial prediction maps showed that the concentrations of traffic CO increased more on weekdays than on weekends. These maps also showed that there was significant variation between traffic CO concentrations according to the time of day, with the traffic CO ranging from a high of 35.23 ppm per 15 min during the weekday morning to a low of 4.76 ppm per 15 min during the weekend night. The prediction maps showed that the highest values of traffic CO are located near the toll areas compared to other areas such as residential green areas, because of the traffic congestion at toll gates. The lowest values of traffic CO are concentrated near residential areas which reached zero values.
## 4 Discussion
### The Uncertainty of Using fIPAR as a Proxy for fAPAR
In order to more closely approximate the results of field measurements which are mostly conducted under the blue sky with both direct and diffuse illumination and are influenced by multi-scattering, the simulations in the current study account for the direct and diffuse radiation and multi-scattering. The multi-scattering effect explains why fIPAR would be influenced by the background as the radiance reflected from the background can be intercepted again by the canopy. The brighter concrete background will produce a stronger multi-scatter and increase the intercepted and absorbed PAR, leading to larger fIPAR and fAPAR (Figure 5).
## 4 Proposed Migration Approach and Its Evaluation
### Migration Approach
#### 4.1.3 Microservice Development
An example of one possible implementation is presented in the sequence diagram below (Figure 4). The REST API layer exposes a method _GetCustomer_, which can be called by a client application to obtain all the customer details. Once the call is received, it is routed to the business logic layer, which calls the repository twice to obtain different details about the customer: _GetCustomerInfo_ and _GetCustomerHistory_. _GetCustomerInfo_ obtains the general customer information such as name, surname, address, etc. _GetCustomerHistory_ obtains the customer's payment history. The repository layer is called twice because _CustomerInfo_ and _History_ data are stored in separate data models within a database and two separate calls to a database are needed. In the business layer, _CustomerInfo_ and _History_ data received from the repository layer are combined and mapped into one consistent domain data model--_Customer_, which is used as a response to a client's _GetCustomer_ request. To sum up, the repository layer is responsible for data manipulation within the database, it encapsulates all of the technical implementation peculiarities such as connection establishment, data mapping, etc. The REST API layer is responsible for data exposure to clients via the REST API interface and encapsulates all technical implementation peculiarities such as connection establishment, authorization, authentication, etc. The business logic layer is responsible for building a consistent domain data model.
## 1 Introduction
In [22], a 4 \(\times\) 4 distributed multi-layer oscillator network for THz beamforming based on 65 nm CMOS is proposed, and it can achieve a 2-D beam scanning of 60\({}^{\circ}\). In [23], a 12 \(\times\) 12 coupled-oscillator array based on 65 nm CMOS is proposed, and it can achieve a 1-D wide-angle scanning of 90\({}^{\circ}\) at 675 GHz. Decoupling surfaces are dielectric substrates with periodic metallic patches printed on them. The patches are placed above the antennas and are initially used for decoupling in-array antennas. The decoupling surface is first proposed in reference [24] to achieve decoupling between antenna array elements. In [25], a decoupling surface that can enhance port isolation in millimeter-wave MIMO arrays is proposed. The square-ring metasurface designed in [26] can also be considered a type of decoupling surface, capable of achieving decoupling between PAA elements. When the square-ring metasurface is applied to a 1 \(\times\) 8 PAA, the 1 \(\times\) 8 PAA can achieve a wide-angle scanning of up to \(\pm\)27\({}^{\circ}\). This paper presents a novel THz band PAA based on a decoupling surface with stable gain and 2-D wide-angle scanning capabilities. To diminish the strong coupling caused by the small spacing between the elements, a decoupling surface and metallized cavity are applied in the design of the element [24; 26; 27]. Additionally, the clever design of the slot layer achieves a wide bandwidth and low cross-polarization for the element [28]. The simulated S\({}_{11}\) bandwidth of the designed element with radiation boundary conditions is 107-117 GHz, with a stable gain of 5.4-5.64 dBi within the bandwidth, and the radiation pattern exhibits the characteristics of a wide beam and low cross-polarization. Under periodic boundary conditions, the element S\({}_{11}\) bandwidth is 106-119 GHz, with an active VSWR \(<\) 2 within the frequency range of 105.5-119 GHz. Considering the high costs of fabrication and testing, this paper only provides simulation results for the designed beam-scanning PAA, offering a reference for industry peers. The 8 \(\times\) 8 PAA is modeled and simulated in the electromagnetic simulation software CST2022, and the simulation results show that within a 3 dB gain variation range, a 2-D wide-angle scanning of \(\pm\)48\({}^{\circ}\) is achieved from 106 to 114 GHz, while in the 115-119 GHz band, the scanning range reaches \(\pm\)40\({}^{\circ}\) on the E-plane and H-plane. Due to its stable gain and 2-D wide-angle scanning capabilities, the proposed PAA has a broad application prospect in THz wireless-communication equipment.
## 5 Case Study: Smart Energy Use Case
### The Highest Abstraction Level-Level 0  
The highest level of abstraction models abstract processes that are needed to provide basic information for the following lower levels of the simulation scenario. Furthermore, the power generating entities are modeled on this high and abstract level. This is shown in Figure 5.  
The CarGenerator atomic model acts as a source to the rest of the modules and provides the information needed to simulate users and random cars at the lower abstraction levels. This information is then passed to the CarProcessor.  
The CarProcessor then determines if the received information is used to simulate a scenario with a random visitor of the parking facility or with a user of the smart parking mobile APP. In the latter case, information about the desired parking slot is generated and used in an attempt to make a reservation  
Figure 5: Smart energy use case: Level 0.  
via the model of the smart parking APP. If this reservation fails, the information of the APP user is treated as information about a random visitor of the facility and sent further to the ParkingFacility.  
Once entering the ParkingFacility model, the received information is used to model the abstract behavior of both random visitors and smart parking service users competing for available parking slots, parking and subsequently leaving the facility again.  
Users of the APP that succeed with a reservation will directly target their desired parking slots while random arrivals and users that failed to reserve their desired slot will choose the first free available parking slot. When arriving at the slot, it will be determined if it is still free or in the meantime has already been reserved by a user or taken by another random car that arrived first. If it has already been taken, they will head for the first free parking opportunity again. If they do not succeed in finding one, the car will leave the parking facility. If the parking process succeeds, the car will occupy the chosen parking slot for a while and then subsequently leave the parking facility again.
## 2 Background
### Pallet Exchange Systems Analyzed  
Four distinct pallet exchange systems were evaluated:  
* Scenario A (Standard Deferred System): This system involves four trips and two trucks. The first trip delivers goods on pallets from the trademark industry to the large-scale retailer. After delivery, the truck returns empty to the company. A second truck is then dispatched empty to the retailer to collect the pallets and return them to the trademark industry.
* Scenario B (Standard Direct System): This scenario involves two trips using a single truck. Goods are delivered on pallets to the retailer during the first trip, and the same truck returns the unloaded pallets to the trademark industry during the second trip.
* Scenario C (Nolpal Deferred Exchange): This scenario optimizes the process described in Scenario A by involving Nolpal as a third party. After the goods are delivered to the retailer and the truck returns empty, a second empty truck is sent from the nearest Nolpal Point to the retailer. The pallets are collected and routed to the Nolpal Point instead of being returned to the original sender.
* Scenario D (Nolpal Direct Exchange): Similar to Scenario B, this scenario involves two trips with one truck. However, once pallets are delivered and unloaded at the retailer, they are directly routed to a nearby Nolpal Point instead of being returned to the trademark industry.  
These scenarios were analyzed using life cycle assessment (LCA) methodology to evaluate and compare their carbon footprints.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 8 Submerged HF Membrane Integrity and Failure
### Ageing  
Membrane ageing of commonly used composite membranes is defined as the deterioration of the surface layer and sub-layers of membranes due to the irreversible deposition of foulants or by frequent exposure to chemical cleaning agents, which leads to the deterioration of the membrane performance [308; 309]. While the active layer of the membrane has been found to be chemically modified, pore reduction has been found in the intermediate sublayer (i.e., between the active layer and the porous support) [308]. Membrane material also affects ageing; for example, polyethylsulfone (PES) membranes were found to be more resistant to acid than alkali [310].  
To control membrane fouling caused by the retention of dissolved salts, organics, microorganisms, and suspended solids after extended operation, the industry employs routine chemical cleaning protocols involving specific concentrations, temperatures, and extended cleaning times; in some cases, with submerged HFs, strong oxidants such as sodium hypochlorite (NaOCI) are used to control fouling, causing membrane ageing to be exacerbated after repeated cleanings. Prolonged filtration and cleaning cycles not only have an adverse effect on membrane integrity, but can also lead to the internal fouling of membranes, which is irreversible, detrimental to membrane performance, and also reduces the lifespan of the membrane by increasing the likelihood of membrane failure. For submerged HFs, physical cleaning by backwashing (Section 6.1) can also cause changes in the membrane properties. For example, it has been shown that backwashing can cause a change in the pore-size distribution by increasing the diameters of the largest pores [311]. This could make the membranes more susceptible to fouling as these larger pores become blocked.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 4 Proposed System Overview
### Evaluation Criteria  
In consonance with the security threats that we recognize in the previous section and the shortcoming of analogous systems, we can now define a set of requirements that can serve as evaluation criteria for our system.  
* User privacy against the Broker and Ad-Dealers: Neither the Broker nor his/her Ad-Dealers should be able to obtain any information that could be used to link a user's true identity to his/her advertising interests.
* User privacy against other users: Users should not have precise knowledge of the advertising interests of other users within their social circle.
* Protection from fake or harmful content: Attackers should not be able to infect the system with adverts that have not been distributed from a valid Ad-Dealer.
* Protection against impersonation attacks: Attackers should not be able to impersonate the identity of a user or an Ad-Dealer.
* Resource conservation: As mobile devices offer limited resources, the system needs to be conservative in the consumption of memory and battery power.