esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 4 System Model
### Service Request Model from MS  
A request \(r\) that generates from the MS m features various NF types \(t\in T\) as per the specific service, where T is the total number of NF types. Each NF \(f_{t}\) requires computation and memory resources that are denoted by \(q_{prc}(f_{t})\) and \(q_{me}(f_{t})\), respectively. This accumulates to \(Q_{r}\) in total processing and memory requirements for the request as per Equation (1). Moreover, the variable \(b_{r}\) accounts for the link resources.  
\[Q_{r}\ =\ \sum_{\forall f_{t}\in\mathbf{F}_{r}}q_{prc}(f_{t})+\sum_{\forall f _{t}\in\mathbf{F}_{r}}q_{me}(f_{t}) \tag{1}\]  
Along with this, an incoming request to the fog nodes in the F-RAN has different requirements in terms of delay bound, lifetime, resources, and service types. The request is received by a fog node that is termed as the source node, _src_. Thereafter, it is traversed to the destination node \(dst\), which can be the same MS, another MS, or a fog node. The intermediate nodes that interconnect the _src_ and _dst_ nodes must have sufficient resources to route the data and the NFs. Along with this, each request r can be modeled by a 6-tuple \(r\) = _csrc_, \(dst\), \(\mathbf{F}_{r}\), \(Q_{r}\), \(b_{r}\), \(\delta_{r}\)>. The variable \(\mathbf{F}_{r}\) defines the set of NFs \(\mathbf{F}_{r}\) = \(\{f_{t}\}\). The variable \(\delta_{r}\) defines the delay bound for the request.
# DTN Trustworthiness for Permafrost Telemetry IoT Network
## 4 Trustworthiness Model Specification
### Network Trustworthiness Layer  
This layer is responsible for assuring that a packet reaches its destination on time and unaltered despite the adversities (e.g., link failure, network congestion). We measure this layer's trustworthiness with the packet delivery ratio (\(PDR\)), defined in Equation (3) as the quotient between the total number of packets correctly received (\(Pr\)) by all nodes and the total number of packets sent (\(Ps\)) by all nodes in the same time slot. The higher the \(PDR\) is, the better the network's trustworthiness.  
\[PDR=\frac{Pr}{Ps}. \tag{3}\]  
At the network trustworthiness layer, transmission coding techniques [66] are used to increase the robustness of the transmitted signal. Routing protocols and quality of service (QoS) mechanisms are used to find the best path from a source to a destination by quantifying the quality or performance of each link in the network [55,56]. Congestion control algorithms and other mechanisms of transport protocols [11] can also improve network trustworthiness. In the case of challenge networks, DTN overlay architectures and protocols, such as the Bundle Protocol [8], can also improve network trustworthiness (see Figure 6).  
Figure 6: Trustworthiness model goals and countermeasures relationship [7].
# A Temperature-Robust Envelope Detector Receiving OOK-Modulated Signals for Low-Power Applications
## 3 ED and PTAT Block Description
### ED Differential Approach  
The addition of the PTAT block causes its own noise, which includes both thermal and flicker--unlike that of the ED--to propagate through the signal chain. In order to prevent a heavy degradation in the achievable chain sensitivity, a differential approach was chosen: the implemented ED features two identical chains, each of which has \(N\) diodes, connected to the PTAT cell instead of one, as shown in Figure 2. The noise due to the PTAT cell is then seen as common-mode at the output of the ED and, thus, gets canceled out by a subsequent ideal differential amplifier. The differential circuit has an output signal  
\[V_{ED}=\frac{2 NV_{M0}^{2}}{4 nV_{T}}, \tag{18}\]  
an input resistance \(R_{in}=r_{DS}/2N\) and an input capacitance \(C_{in}=2 NC_{gs}+C_{pad}\), which yields the same SNR at the ED output as in the single-ended case. Sample simulated waveforms are shown in Figure 4. The envelope of the incoming OOK-modulated signal is extracted with both polarities, \(V_{ED}^{+}\) by the positive diode chain and \(V_{ED}^{-}\) by the negative diode chain. The output is then read differentially, \(V_{ED}^{+}-V_{ED}^{-}\).  
Moreover, switching to a differential approach causes no increases in power consumption due to the zero-current biasing of the diodes. Since the two chains work independently, the system does not get slowed down either.  
Finally, it shall be proven that the noise factor \(NF\) of the amplifier is the same when adopting a differential approach as it is in the single-ended case. The same assumptions as those in Section 3.2--a standard baseband amplifier, for instance, a common-source with an active load, operating in the subthreshold region with negligible residual flicker noise and transconductance \(g_{m}\)--are supposed to hold. In the differential case and assuming the comparison to be carried out at total current consumption parity, (7) results in \[NF_{diff}=1+\frac{4\times\frac{4k_{B}T}{g_{m}/2}\Big{(}\frac{g_{m}R_{out}}{2} \Big{)}^{2}f_{S}}{\Big{(}\frac{g_{m}R_{out}}{2}\Big{)}^{2}\times 4k_{B}T(2N)^{2}R_{ in}f_{N}}=1+\frac{2}{g_{m}N^{2}R_{ in}}, \tag{19}\]  
which is the same as in the single-ended case (11).  
This shows that no degradation occurs in the overall SNR at the amplifier output by adopting the differential approach.
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
## 4 T-Move
### Mobility Support in T-Move
#### 4.2.1 Producer Behavior  
Consider a content producer P with the two data objects _x/y/z/a_ and _x/y/z/b_, and is initially connected to edge routers R1 and R2, as in Figure 1. Initially, the data is only at the producer and not cached at any router. Eventually, R1 and R2 get interests for _x/y/z/a_ separately, get the data object from P and store it in their cache. When further interests for the data object are received, R1 and R2 just return the data from their caches and increment the frequency and trendiness of the data object. Let these values for data object _x/y/z/a_ be (\(f_{1,1}\), \(t_{1,1}\)) and (\(f_{2,1}\), \(t_{2,1}\)) at routers R1 and R2 respectively. In addition, let R2 also have data object _x/y/v/c_ in its cache, with frequency and trendiness values (\(f_{2,2}\), \(t_{2,2}\)).  
When P starts moving away within the same domain, it broadcasts a control message \(GETT\) (short for _GET Trendiness_) with the names of data objects it has. Each edge router receiving this message does the following for each data object listed in the \(GETT\) message:  
* It checks if that data object is already in the cache. If so, it discards the \(GETT\) and does nothing else. In Figure 1, both R1 and R2 do nothing for data object _x/y/z/a_.  
Figure 1: Example scenario for producer mobility.  
* If the data object is not cached, the router calculates the trendiness of the data object returns this value to P on the reverse path in a _REPT_ (short for _REPort Trendiness_) message. In the above example, R1 and R2 return the trendiness of _x/y/z/b_, because it is not cached.  
The mobile producer P collects all the _REPT_ messages from all its neighboring routers and sends a copy of the data object to be cached at the router that reports highest trendiness for that data object (say R2 in the above example). The data object is now cached at the router (R2) and the producer. This behavior of a producer is described in Algorithm 1.  
```
1://*Run by a producer moving away from the edge router*/
2://*D is list of data objects at the producer*/
3:Send \(GETT(D)\)
4:Collect \(REPT(D)\) for \(\tau\) seconds
5:Choose router \(R_{i}\) that reports maximum trendiness for each data object \(d_{i}\)
6:Send each \(d_{i}\) to the corresponding \(R_{i}\)
7://*All data objects are sent to edge routers that report maximum trendiness*/
```  
**Algorithm 1**Proactive_data_push (\(D\))
## 5 Practical Applications and Future Research Perspectives  
The results obtained in the presented experiment may be used for the tissue culture initiation of _Aldrovanda vesiculosa_ worldwide, which may lead to active conservation of the species through in vitro propagation and reintroduction of the obtained plants into their natural habitat.  
The conducted experiment has shown that AgNPs added to tissue culture media inhibited the growth and development of waterwheel plants. In the case of that carnivorous species, the lack of traps might prevent the plants from catching their prey. Therefore, further research should be conducted concerning the effect of AgNPs and other nanoparticles used at different concentrations on the development of both aldrovanda and other plant species in order to find a safe way to use these novel substances.
# High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression +
## 3 Proposed Method
### Edge Effect  
When embedding a watermark in an image larger than the \(128\times 128\) resolution of the encoder input image, it is necessary to divide the input image into fragments that match the encoder input. During the watermark embedding process, an edge effect may occur (Figure 3), consisting of a perimeter marking of the encoded image section, which is noticeable upon zooming in.  
This effect can be present with a well-trained ANN (correctly working over most of the image area with a negligible edge effect up to a few pixels in width) and can be transparent and therefore not present in practice for a correctly trained ANN that has a very high level of image reproduction relative to the original image as in the case of the network trained for the purposes of the research presented in this article.  
The edge effect can be easily eliminated by re-encoding the parts of the image where it is visible. This increases the time needed to embed the watermark, however, it ensures high image quality without any visible distortion in the form of an edge effect.
## 1 Introduction
These models use detailed information about building materials, their thermal properties, occupancy patterns, and systems. However, when generating these models at the neighborhood scale and beyond, developing highly detailed models may not be cost-effective in terms of time and expense [16]. Thus, the urban building energy models (UBEMs) were developed. These models use simplified building physics to simulate large groups of buildings and are generated to cover large building sets within reasonable time and cost constraints [17]. Typically, an UBEM covers a spatial scale ranging from a group of buildings to a district, neighborhood, or city [18]. However, this scale can be expanded as data availability allows. In this paper, we propose a national-scale model to analyze the entire residential building stock of a country. We call this model a national-scale urban building energy model enhanced with AI (nUBEM). It is a country-scale model which allows us to obtain certain progress indicators required by the 2024 EPBD recast and is helpful in generating valuable information for developing large-scale decarbonization strategies, enabling data-driven policy decisions. This model is a substantial improvement over a previous one [9], which generated a powerful country-scale model mapping and characterizing the energy performance of buildings that have energy performance certificates (EPCs), which is based on open data and automatically updates. However, it could only provide data for buildings with EPCs, which, in Spain, represent 10% of existing buildings. In this paper, we propose an enhanced model that provides the energy performance of all buildings in a country and evaluate it in comparison with other existing models in the scientific literature. The improved model achieves greater precision with shorter computation times by employing a hybrid approach that combines data from heterogeneous sources and AI to predict the energy performance of buildings. It incorporates geospatial techniques to enrich existing data and artificial intelligence algorithms to predict the energy performance of all residential buildings across a country. In this paper, Spain is taken as a case study. As this model is created using open data from public information sources established through European directives, it can be applied to all EU MSs that have incorporated the data frameworks set by these directives.
## 4 Security Attacks
### Consensus and Incentive Layer
#### 4.3.2 Replay Attacks  
Replay attacks [28; 31; 32; 46] primarily occur during hard forks, when the blockchain splits into two distinct versions, one following the older protocol and the other adopting the upgraded protocol. In this scenario, a transaction that was valid before the fork remains valid on both chains. A malicious attacker can exploit this situation by broadcasting a pre-fork transaction on both chains, effectively causing the same funds to be transferred to the attacker's wallet on one chain while duplicating the transaction effect. Although hard forks are relatively infrequent, the potential financial losses resulting from replay attacks can be substantial.  
To mitigate these risks, several countermeasures have been proposed. For example, transactions can be marked so that they are only valid on one specific chain; timestamps can be incorporated to prevent the reuse of transactions; and session keys can be employed to add an additional layer of security.
# Analysis of Vertical Stiffness Characteristics Based on Spoke Shape of Non-Pneumatic Tire
## 3 Results and Discussion
### Vertical Stiffness Characteristics for Each Spoke Shape  
Table 5 presents the vertical stiffness characteristics of each model. The vertical stiffness is defined [3; 6] as shown in Equation (1), the load condition is 4000 N, and \(\delta_{y}\) is the result of directional deformation.  
\[\text{K}=\frac{F}{\delta_{y}} \tag{1}\]Compared with the reference model and the fillet-applied model, for the asymmetric-spoke-division (Model Type (c)) and the symmetric-spoke-division (Model Type (d)) models, the vertical stiffness was relatively small due to the significant directional deformation. This is attributable to the divided spoke and the weakened resistance to the load in the longitudinal direction.
## 5 Conclusions  
A new control method for the three-phase shunt active power filters has been proposed for harmonic and reactive power compensation. Over the years, many control strategies based on artificial neural networks have been tested. However, the simple and extended feedforward or multilayer perceptron ANN could not be successfully applied as the core element of an APF control. In this work, the search for an appropriate MLP application strategy and an in-depth training with distorted waveforms allowed us to prove that this ANN topology is a feasible alternative. An MLP containing two hidden neuron layers (10-10-2 neurons) was used for inputs consisting of the samples of every waveform cycle and outputs consisting of the fundamental harmonic Fourier coefficients, \(A_{1}\) and \(B_{1}\). Six identical ANNs estimate the coefficients of fundamental currents and voltages. The computation method described in the work allows us to obtain the compensation currents from these coefficients. The 100 thousand waveform patterns for the training were generated through random harmonic combinations, including high order harmonics and variable fundamental frequency. The MLP learning, with _mse_ around 2\(\cdot 10^{-7}\), was enough to reach accurate fundamental harmonic waveforms of currents and voltages in any practical three-phase system, even in the case of frequency deviations in the interval from 47 to 52 Hz, in compliance with the IEC standards. The compensation strategies were applied in practical cases implemented in a first stage with MATLAB-Simulink. After that, the method was also tested in an experimental prototype. Features such as source _ITHD_ below 5%, unit power factor, good dynamic performance, and balanced source currents validate the proposal.  
Conceptualization, P.S. and J.L.F.-G.; methodology, J.L.F.-G. and P.S.; software, J.L.F.-G.; validation, P.S. and J.A.G.-G.; investigation, P.S. and J.L.F.-G.; laboratory resources, P.S. and J.L.F.-G.; writing--original draft preparation, J.L.F.-G.; writing--review and editing, J.A.G.-G., P.S., and J.L.F.-G.; supervision, P.S. and J.A.G.-G. All authors have read and agreed to the published version of the manuscript.  
This work is part of the project \"Connection of microgrids to the network by means of active conditioners with high performance of electric power quality\", UHU-1256532, funded by the Programa Operativo FEDER Andalucia 2014-2020, Spain.  
The authors declare no conflict of interest.
## 3 Results  
### Assistance Force  
Example trajectories for two patients with overlaying density plots indicating the location and amount of the offered assistance are shown in Figure 9. These two patients were specifically selected to illustrate two types of patients.  
Patient 1 is the case of a patient capable of performing the exercise with a very low assistance level, as observed in the local and remote therapist assistance modes. However, in the fixed assistance mode, higher assistant forces are applied.  
The results of patient 2 illustrate the most common type of patient of this study. They suggest that the fixed assistance mode provided higher assistance forces than the local and remote therapist assistance modes. We also observe that the therapist assisted more remotely than locally. Therefore, the local therapist assistance was the mode in which the lowest assistance level was applied.  
Mean and maximum assistance forces values in every condition for all patients were computed (Figure 10a). The mean assistance force results show significant differences between groups (one-way repeated measure ANOVA \(p<0.0001\)). Paired comparisons  
Figure 9: Example trajectories for two users, with overlaying density plots indicating the location and amount of the offered assistance. Darker areas indicate higher amounts of assistance.  
show that the fixed assistance mode is significantly different concerning local and remote therapist assistance modes (\(p=0.0002\) and \(p=0.002\), respectively). In the assistance modes in which the therapist is involved, the mean assistance forces are similar (\(p=0.18\)). The analysis also shows significant differences between modes for the maximum assist forces (one-way repeated measure ANOVA \(p<0.0001\)). Paired comparisons show that all modes are significantly different, except in the fixed assistance mode concerning the local therapist assistance mode, where a reliable trend toward significance is observed (\(p=0.05\)).  
In Figure 10b, the mean assistance force value in every condition for each patient is shown. In all cases, the mean assistance force value is higher in the fixed assistance mode. Furthermore, we can also see that in most cases, the therapist assists more remotely than locally.  
Results suggest that in fixed assistance mode, the robot assists equally towards all targets. However, this is not the case of the assistance modes where the therapist is involved where the assist forces seem to be better adapted to the patients.  
The percentage of targets successfully reached was also computed as a measure of exercise performance (Figure 10). A very high level of performance was obtained in all modes.  
These results indicate that the assistance level provided to the patients in all modes is sufficient to perform the exercise correctly.
###### Abstract  
Demanding requirements in automotive and aerospace applications promote the growing need to obtain materials and advanced technology capable of combining low weight with high mechanical properties. Aluminum matrix nanocomposites could be great candidates to respond to such needs. In this sense, this investigation aims to study the mechanical properties of nanocomposites of aluminum matrices reinforced with carbon nanotubes (CNTs). The nanocomposites were produced by powder metallurgy with 1.00 vol.% of reinforcement and ultrasonication as the dispersion method. Tensile, Vickers microhardness and nanoindentation tests were carried out in different sections. Microstructural characterizations were conducted in scanning electron microscopy (SEM) and electron backscattered diffraction (EBSD) to understand and relate to the mechanical properties. An increase in the yield strength of 185% was observed for the nanocomposites, which can be attributed to the load transfer mechanism. However, the CNTs observed at the grain boundaries promote a decrease in the ductility of the nanocomposites. The mechanical behavior of the nanocomposites was further investigated by EBSD observation. The results revealed that the nanocomposites have a less extensive area of plastic deformation than the Al matrix, which is consistent with the tensile results. The presence of reinforcement affects the lattice rotation during the tensile test and the active slip systems, thus affecting their deformation behavior.  
metal matrix nanocomposites; carbon nanotubes; aluminum; tensile test; hardness; mechanical properties
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 2 Related Works  
### Qilc and HTTP/3  
QUIC [1] is a transport layer protocol based on the User Datagram Protocol (UDP) [5]. In contrast to the Transmission Control Protocol (TCP) [6], the UDP includes relatively few functions, such as packet reordering, error detection, retransmission, congestion control, and handshaking. QUIC takes advantage of this, absorbs the above TCP features, and reimplements them in a single protocol that is integrated with Transport Layer Security (TLS) [7]. Moreover, QUIC implements multiplexing of connections that are newly created by HTTP/2 [8], and it reduces the responsibilities of the application layer in achieving thorough efficiency. HTTP/3 is a new major version of HTTP, the specification which is developed on the premise of QUIC, and it is simpler than the old HTTP. The basic syntax is compatible, and the fundamental difference between the improved and old HTTP lies in the use of the TCP or QUIC for the transport layer protocol. As the transport layer protocols differ, the incompatibility with the network environment is significant, and the connectivity problem, which is the subject of our study, is increasingly important. An overview of the new and old HTTP protocols is depicted in Figure 1.
# Non Data-Aided SNR Estimation for UAV OFDM Systems
## 2 Signals Model of UAV OFDM  
### UAV Channel Model  
The UAV OFDM system model is shown in Figure 1. We consider the NDA SNR estimation for UAV OFDM with ground station in multipath propagations. The transmitted OFDM signals can be reflected by the urban barriers, such as buildings, and OFDM subcarrier signal bandwidth is relatively small and generally smaller than channel coherent bandwidth, which belongs to narrowband signal. Thus, multipath channel model can be used as a UAV downlink channel model where the OFDM narrowband signal experiences multiple impulse responses at ground station. The channel impulse response [24] is given as  
\[h=\left[h_{0},h_{1},\ldots,h_{l}\right], \tag{1}\]  
where \(h_{l}(l=0,1,\ldots,L)\) and \(L\) are the the gain of the \(l\)-th path and the channel order, respectively. And \(l\)-th is assumed to be equal to the maximum delay of the multipath channel.