esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Reconstruction of Cultural Heritage Sites as an Educational Approach. The Sanctuary of Delphi
## 3 The Rationale and Design: An Educational Virtual Tour at the Sanctuary of Delphi
It allows instant transportation to designated destinations, provides access to the generic material, or displays a form for user-generated questions about the place and its history. In present work, archaeology students from two University departments (University of the Aegean and University of Peloponnese, Greece) explored the virtual space of the Sanctuary of Delphi in order to gain knowledge of its buildings and history. Innovation here was the concept of using virtual reality cultural heritage assets via ICT for educational purposes with the participation and interviewing of students in almost all stages of software implementation. Students involved in the research lacked in software design and programming knowledge. Although they were capable of the archaeology issues, just a few (10%) had visited Delphi. However, their involvement was continuous in the design and implementation stages of the software contributing to their critical thinking and included the following actions:  
1. At the beginning they identified and delineated their educational needs, with discussion and answers to questions about the knowledge they would like to gain during their virtual tour of the space. 2 They evaluated the usability of the trial version of the software with the aim of improving its functionality and enriching it with more educational material. 3. In the latest software release for the research, they explored the archaeological site through virtual browsing and answered multiple-choice questions to evaluate the knowledge they have acquired. 4. They imprinted their impressions and made suggestions for further improvement, as well as for extending similar applications to other educational subjects. 5. The questions posed in the present concept are as follows: (a) how was the interaction between tutor and apprentices in the design and implementation? (b) how accurate was the virtual environment made? (c) what was the student participation and evaluation in the whole process?
# Properties of Spatially Indirect Excitons in Nanowire Arrays
## 1 Introduction  
Unprecedented progress in the fabrication of semiconductor nanowires (NWs) has stimulated their application in various areas, such as nano- and optoelectronics [1, 2, 3, 4], photovoltaics (PV) [5, 6], random lasers [7, 8], etc. NWs have been used as building blocks for fabricating various nanodevices including FETs [1, 9], p-n diodes, and inverters [1, 2]. These NW devices can be assembled in a predictable way and methods exist for their parallel assembly [1, 10]. The control of NW sizes achieved by a number of growth mechanisms including the widely used vapour-liquid-solid (VLS) process [11, 12] also allows for the study of exciton properties confined in two dimensions, with various degree of confinement. The Coulomb interaction between an electron and a hole located in neighbouring NWs may at certain conditions result in the bound state called'spatially indirect exciton' (SIE) [13, 14, 15]. The controlled arrangement of NWs into three dimensional ordered structures might potentially allow generating arrays of SIEs, the properties of which would be very interesting to investigate.  
Spatially indirect excitons are thought to be more long-lived than the usual (direct) excitons because of the spatial separation of electrons and holes [16, 17, 18]. They have been discussed in conjunction with quantum Bose gas in quasi-2D systems [13, 19, 20], capacitance in layered transition metal dichalcogenide structures [18, 21], and as the information units in quantum posts [22, 23]. So far, SIEs have been considered either for flat interfaces (the 2D case), or in pairs of quantum dots, the so-called quantum posts (the 0D case). In this article, we consider SIEs formed in parallel NWs (the 1D case) and analyse their binding energy and lifetime as functions of the NW diameter.
# Uncertainty Analysis and Improvement of Propellant Gauging System Applied in Space
## 3 Measuring Uncertainty Analysis and the Modified Model
### Measuring Uncertainty Analysis
#### 3.1.2 Calculation Input  
In the experiments applied in aerospace research, the experimental conditions are usually distinguished as degreed and over-degreed. Considering the safety of high pressure and the cost, the degreed experimental conditions are chosen in this research. In this paper, the experimental degreed factor is approximately 20 times the base pressure and 1 times the injected pressure. The tank volume of 600 L used in the theoretical analysis is a certain value of a future spacecraft.  
The total tank volume is set to be 600 L, the fulfillment is 50%, and the gas volume in the tank is then 300 L. The initial pressure and temperature are 0.5 MPa and 20 \({}^{\circ}\)C. The initial pressure and temperature in the injection room are 0.9 MPa and 20 \({}^{\circ}\)C. The uncertainties of the tested pressure and temperature are set to approximately 1% FS. The liquid propellant is uncompressible. All the uncertainties are defined in full scale. We assumed that there is no heat exchange between the gas in the injection room and the tank. The whole process of the gas injection is isothermal.  
\[PV^{\gamma}=C \tag{12}\]  
where \(C\) is a constant and the process coefficient \(\gamma=1.667\).  
The pressure in the injection room and in the tank is calculated as 0.50245 MPa from the isothermal assumption after the pressure reached a balanced state after gas injection. In the same process, the temperatures in the tank and injection room are 20.6 \({}^{\circ}\)C and \(-166.8\) \({}^{\circ}\)C. A few moments later, the temperature in the injection room and tank reached the same as the environment and was 20 \({}^{\circ}\)C. The tank pressure became 0.50408 MPa and the injection room pressure reached 0.69605 MPa.
# Quasi-Static Load Sharing Characteristics of a Planetary Fear Set with Planet Journal Bearings
## 3 Load Sharing Analysis Method
### Load Sharing Factor  
When a planetary gear set with \(N\) planet gears is loaded through the sun gear, the total load should be equal to \(T/r_{bs}\). \(T\) is the input torque. However, due to various manufacturing and assembly errors, the planet gears could not mesh simultaneously and only one planet will be loaded first. As the torque increases, the other backlash decreases, and then all the planet gears got to be loaded. During the meshing process, the rolling arc length of the sun gear along the pitch circle is given by  
\[\delta=r_{bs}\theta_{s}, \tag{35}\]  
where, \(\theta_{s}\) is the rotation angle of the sun gear.  
Considering the cumulative meshing error and the floating component error, the load can be described by  
\[W_{spi}=k_{\sum}\Big{(}\delta-e_{spi}-\Delta_{si}\Big{)}. \tag{36}\]  
Therefore, the total load can be obtained by  
\[\sum\limits_{i=1}^{N}W_{spi}=N\delta k_{\sum}-k_{\sum}\sum\limits_{i=1}^{N}e_{ spi}-k_{\sum}\sum\limits_{i=1}^{N}\Delta_{si}. \tag{37}\]  
The load above should be equal to the load on the sun gear, as demonstrated by  
\[\sum\limits_{i=1}^{N}W_{spi}=T/r_{bs}. \tag{38}\]  
Substitute Equations (35) and (38) into Equation (37), as expressed by  
\[W_{spi}=k_{\sum}\Bigg{[}\frac{1}{N}\sum\limits_{i=1}^{N}\Big{(}e_{spi}+\Delta _{si}\Big{)}-\Big{(}e_{spi}+\Delta_{si}\Big{)}\Bigg{]}+\frac{T}{r_{bs}N}. \tag{39}\]  
According to the stress analysis of the sun gear, the force balance equations are given by  
\[\left\{\begin{array}{l}\sum\limits_{i=1}^{N}W_{spi}\cos\psi_{i}+k_{s}x_{s}= 0\\ \sum\limits_{i=1}^{N}W_{spi}\sin\psi_{i}+k_{s}y_{s}=0\end{array}\right.. \tag{40}\]For an \(N\)-planet gear set with \(N\geq 3\), \(3N+2\) equations can be established based on Equations (39) and (40) with \(3N+2\) variables. By calculating the load on each planet gear, the load sharing factor of the planetary gear set can be computed by  
\[\Omega=\underset{1\leq i\leq n}{\max}\Omega_{i}=\underset{1\leq i\leq n}{\max}( W_{spi}Nr_{bs}/T). \tag{41}\]  
The flowchart of the load sharing factor calculation procedure outlined above is illustrated in Figure 6.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 5 Adsorption Isotherm
### Adsorption Thermodynamics  
For metal ion sorption, the temperature is important as far as thermodynamic adsorption is concerned. Usually, endothermal and exothermic sorption processes are the two general types that exist. These are determined by the increase or decrease of the temperature throughout the adsorption process. Thermodynamic functions such as entropy, enthalpy and free changes in energy can be determined using the Van't [PERSON] equation during the adsorption process as given below:  
\[\mathrm{d(lnKeq)}/\mathrm{dT}=\mathrm{\Delta H}/\mathrm{RT}^{2} \tag{9}\]  
where \(\mathrm{Keq}=\mathrm{qe}/\mathrm{C}\). \(\mathrm{Keq}\) is equilibrium constant. The change in free energy could be evaluated with the help of Equation (10):  
\[\mathrm{\Delta G^{0}}=\mathrm{\Delta H^{0}}-\mathrm{T}\mathrm{\Delta S^{0}} \tag{10}\]  
Gibbs free energy change is related to equilibrium constant (Equation (12)):  
\[\mathrm{\Delta G^{0}}=-\mathrm{RT}\mathrm{InKeq} \tag{11}\]  
By combining Equations (10) and (11), Equation (12) is obtained:  
\[\mathrm{ln}\;\mathrm{Keq}=-\mathrm{\Delta H^{0}}/\mathrm{RT}+\mathrm{\Delta S^ {0}}/\mathrm{R} \tag{12}\]  
The change in enthalpy and entropy of the adsorption process could be evaluated from the slope and intercept of a line obtained by plotting \(\mathrm{ln}\;\mathrm{Keq}\) vs. \(1/\mathrm{T}\).  
If \(\mathrm{H^{0}}\) is positive, the reaction is endothermic, which means that as the temperature rises, so does the adsorption efficiency. If \(S^{0}\) is positive, however, the randomness at the moment of adsorption rises, and if \(G^{0}\) is negative, adsorption is spontaneous or favorable [92]. There are two types of adsorption process: spontaneous and non-spontaneous.  
\[\mathrm{Spontaneous\;reactions}:\mathrm{\Delta H^{0}}<0\quad\mathrm{\Delta S^{0}}>0 \quad\mathrm{\Delta G^{0}}<0\]  
\[\mathrm{Non-spontaneous\;reactions}:\mathrm{\Delta H^{0}}>0\quad\mathrm{\Delta S^ {0}}<0\quad\mathrm{\Delta G^{0}}>0\]  
Gibb's free energy, enthalpy, and entropy changes are important design factors for assessing the performance and predicting the mechanism of an adsorption separation process, as well as one of the fundamental needs for characterisation and optimisation. Table 1 show typical values for thermodynamic parameters for adsorption of heavy metal ions on adsorbents [93].
# Achieving Sender Anonymity in Tor against the Global Passive Adversary
## 4 The Proposed Protocol
### Set-Up Phase
#### 4.2.1 Forward Key Exchange  
Each node \(r_{i}\) receives from RM the set \(Q\) of the public keys of the nodes of the ring it belongs to, \(addr(next(r_{i}))\), and among \(Q\), the information about which public key is associated with \(next(r_{i})\) (the associations of the other keys with the proper network address remain unknown to \(r_{i}\)). The address of the next node will be used to forward tokens.  
Initially, each node \(r_{i}\) exchanges a symmetric key called _forward key_ with its next node. This key is used only to encrypt the token hop-by-hop. In detail, as the exchange of keys occurs between the OP and the first OR in Tor (see Section 3), each node \(r_{i}\) generates a public DH parameter \(y_{i}\) and encrypts it with the public key \(PK_{next(r_{i})}\), obtaining \(C=E_{PK_{next(r_{i})}}(y_{i})\). \(C\) is sent to \(next(r_{i})\) (we recall that \(r_{i}\) knows \(add(next(r_{i}))\). The latter decrypts \(y_{i}\), generates the _forward key_\(k_{r_{i}}\) and replies to \(r_{i}\) with its public DH parameter \(\tilde{y}_{next(r_{i})}\) along with the hashed value \(H(k_{r_{i}})\) (in plaintext). In summary, each node \(r_{i}\) shares a forward key \(k_{r_{i}}\) with its next node, and the tokens can be properly encrypted hop-by-hop.
## 2 System Model
### Scenario  
We consider a UAV mounted BS to provide coverage to \(n_{\rm u}\) ground users that are distributed over a geographical area of size \(a\times b\) square-meter, Figure 1. Let \(U=\{1,2,3,...,n_{\rm u}\}\) be a set of \(n_{\rm u}\) users, and the UAV can move in the (\(x\), \(y\), or \(z\)) direction to cover maximum number of ground users based on user density. The total time of service \(T_{\rm t}\) (in mins) is divided into consecutive time-slots with equal duration of \(T_{\rm d}\) (in mins), such that \(n_{\rm bs}=T_{\rm t}/T_{\rm d}\) is the number of time slots, and \(\vec{T}\) becomes a vector containing the consecutive time slots as \(\vec{T}=[t_{0},t_{1},...,t_{n_{\rm bs}}]\). The location of a user is represented by \((x_{\rm u},y_{\rm u},z_{\rm u})\), where \(x_{u}\in\mathbb{R}^{+}\) is in the range of \(x_{\rm u}=[0,a]\), and similarly \(y_{u}\in\mathbb{R}^{+}\) is in the range of \(x_{\rm u}=[0,b]\). \(z_{\rm u}\in\mathbb{R}^{+}\) is assumed to be a constant number as \(z=h_{\rm u}\), since we consider the conventional mobile handsets, which are carried around a similar height. In this work, we assume the height of UEs to be \(h_{\rm u}=1.5\) m.  
The altitude of the UAV \(h_{\rm d}\in\mathbb{R}^{+}\) is in the range of \([h_{\rm min},h_{\rm max}]\) where \(h_{\rm min}\) and \(h_{\rm max}\) are the minimum and maximum allowed altitude (According to the regulations of concerning country/region) of the UAV, respectively. For instance, according to the European regulations for flying UAV, \(h_{\rm min}\) and \(h_{\rm max}\) are 30 and 120 m, respectively (Easy Access Rules for Unmanned Aircraft Systems (Regulation (EU) 2019/947 and Regulation (EU) 2019/945) by EASA (European Union Aviation Safety Agency)).  
Specifically, the association between the UAV and the ground users is based on time division multiple access (TDMA). Although orthogonal frequency division multiple access (OFDMA) or nonorthogonal multiple access (NOMA) are the most used approaches for the UAV networks, we adopt TDMA to fully exploit the above-mentioned time slots for the total time of service.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## 2 The PCA NN Anomaly Detection Approach
### Anomaly Localization Step  
Once the time series containing anomalies have been identified, the second step of our approach aims to localize an abnormal observation among each contaminated time series. Again, we use the reconstruction errors defined in (3) as the inputs of this second step. The difference lies in the transformation these reconstruction errors undergo before labelling the different time stamp observations. We now consider the following transformation of the reconstruction errors:  
\[F(\epsilon^{i})=|\epsilon^{i}|=|X^{i}-\widehat{X}^{i}|,\]  
with \(|\cdot|\) meaning component wise. The model input \(F(\epsilon^{i})\in\mathbb{R}_{+}^{p}\) is thus assigned to each time series \(X^{i}\in\mathbb{R}^{p}\). The time stamp of the occurrence of the anomaly is given by  
\[\widehat{L}^{i}:=\arg\max_{j}\Big{\{}F(\epsilon^{i}_{t_{j}}):j\in\{1,\ldots,p \}\Big{\}}.\]  
Algorithm 4 recaps the anomaly localization model.  
Note that anomalies are not necessarily extrema. For this reason, the above PCA feature extraction step is necessary. Building on the reconstruction errors, the model identifies these extrema anomalies and also abnormal observations that are not necessarily extrema. This subtlety of the nature of the observations underlines the importance of going further than taking the index of the highest observed value of the contaminated time series \(X^{i}\).  
The flow chart in Figure 3, along with Algorithm 5, summarize our approach. When a time series \(X\) is given to our model, we suggest a new representation of \(X\), namely \(\epsilon\), through a feature engineering step involving PCA. The resulting representation \(\epsilon^{i}\) feeds the first component of the model, i.e., the identification step, which evaluates the time series propensity to be contaminated. The optimal parameter \(\widehat{\Theta}\) of the identification model solves  
\[\widehat{\Theta}=\arg\min_{\Theta=(\boldsymbol{W},\boldsymbol{b},\epsilon)} \sum_{X\in X}\mathcal{L}_{\Theta}\big{(}A,\hat{A}\big{)},\]  
where \(\hat{A}=\mathds{1}_{F\big{(}(\Omega\cap^{\top}-I_{p})X\big{)}>\text{s}}\) and \(\mathcal{L}_{\Theta}\) is the loss function defined in (5). Then, if the model considers the time series contaminated, the localization model takes over to localize the abnormal value in \(X\). For this second step of the model, the time stamp of the occurrence of the anomaly is given by  
\[\arg\max_{j}\quad\Big{\{}\Big{(}X\Big{(}\Omega\cap^{\top}\Omega-I_{p}\Big{)} \Big{)}_{j}:j\in\{1,\ldots,p\}\Big{\}}\]Finally, the anomaly is imputed. As our approach integrates the computation of a reconstruction of the time series, we could replace the anomaly with the corresponding reconstructed value. The model will be then able to detect the anomaly and suggest an imputation value. However, the numerical tests described in Section 6.1 show that imputations with naive approaches perform better.
## 5 Performance Evaluation and Results Discussion
### Comparison of Network Lifetime  
The protocols discussed in this paper are proven to be energy efficient with cluster topology. The assumption of a sink in the center is the best method to aggregate the data as the distance for the nodes will be reduced, but various applications (border security, environmental monitoring, _etc._) of the IoT do not consider this assumption. The IoT considers the various objects installed at the application layer where a user can interact and collect the data. This layer should have an unlimited power supply to collect data from various regions, so we have assumed this condition by forming the topmost layer consistingof the base stations which are interconnected and have unlimited power. Figure 3 shows the number of rounds _versus_ the number of the dead nodes graph. It can be noticed that all nodes are dead in LEACH after 3200 rounds (transmission of data by all the nodes completes one round), in ERP after 3370 rounds, in genetic HCR after 3305 rounds, in SEP after 3900 rounds, in MODLEACH after 1590 rounds, and in T-DDEC after 2401 rounds, but in EESAA 11 nodes are alive after 5000 nodes, andin ME-CBCCP 15 nodes are alive.  
Figure 3: Network lifetime comparison (1000 nodes, with the help of alive nodes, 200 m\({}^{2}\)).  
Figure 2: Simulation after 200 rounds of data transmission.  
To make it more clear, Figure 4 depicts the dead node graph after 5000 rounds. After 500 rounds of data transmission, 90 nodes are dead in SEP, 113 in LEACH, 368 in ERP, 303 in genetic HCR, 121 in MODLEACH, 543 T-DDEC, 14 in EESAA and 49 nodes in ME-CBCCP. After 1000 rounds of data transmission, SEP, LEACH, ERP, genetic HCR, MODLEACH, T-DEEC and EESAA lost 459, 493, 660, 625, 535, 726, and 129 of their nodes, respectively, but only 73 nodes are dead in ME-CBCCP. This shows that ME-CBCCP is an optimal solution for an energy efficient IoT.  
We increased the network scale and number of nodes to know about its efficiency for the IoT, and it is found that with the increase in the number of nodes and network area, ME-CBCCP still performs better as can be noted from Figures 5 and 6.  
Figure 4: Network lifetime comparison (1000 nodes, with the help of dead nodes in 200 m\({}^{2}\)).  
Figure 5: Network lifetime comparison (1500 nodes, with the help of alive nodes in 300 m\({}^{2}\)).
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 1 Introduction
* We conduct extensive simulations to evaluate performance of the proposed method under various environments and parameters, comparing with other existing methods such as OBSS/PD, PSR and Dual-CST [5]. The rest of the paper is organized as follows. In Section 2, we survey existing work on improving the efficiency of WLANs and discuss how the proposed scheme is different from other methods. In Section 3, we present the proposed method PSC-UL in detail with example scenarios to illustrate its operation. In Section 4, we evaluate performance of the proposed method comparing with other state-of-the-art mechanisms. Finally, in Section 5, we conclude the paper with remarks for possible future work.
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
## 2 Materials and Methods
### Interleaver Parameters Estimation for Deinterleaving Sequence  
The system configuration for estimating interleaver parameter estimation in a non-cooperative context can be represented as shown in Figure 1. For ECC, encoded information is transmitted after the encoded data is interleaved. In non-cooperative context, receivers indirectly collects the transmitted signals and they do not have prior information about encoded data or parameters of interleaver. Therefore, the deinterleaving process is essential for receivers to decode the collected data.  
In this paper, we focus on block interleavers and introduce some existing methods to predict the interleaver duration. Block interleaving refers to arranging encoded data columns in a certain block unit and then transmitting them by changing columns and rows. Figure 2 shows the difference in results according to whether or not block interleaving is applied when a burst error occurs. Through the block interleaver, it can be seen that the 7-bit continuous burst error occurring on the channel is spread evenly on the receiver side. In general, the linear dependence of codewords have been used to estimate interleaver parameters [2; 3; 4; 5; 6; 9; 10; 11].  
Figure 1: Illustration of the process of interleaver paramters estimation.