esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 4 Security Requirements and Performance Analysis
Consequently, 4G and future 5G systems provide no different performance than the current computer. Figure 3 illustrates the comparisons of computational efficiency based on the running time in milliseconds (ms) for each scheme. From the above comparison in (Tables 1 and 3, Figure 3) we found that most of these schemes aim to reduce the computational and communication costs due to the low efficiency of the old cellular systems, with little effort toward achieving basic security requirements. On the other hand, the proposed security schemes in the new cellular systems shown in (Tables 2 and 3, Figure 3) focus on security requirements as well as the computational and communication costs. Building on that, we consider the proposed schemes in References [42; 57] are relatively high secure protocols due to their robustness against MITM attacks, in addition to mutual AKA features and independence from trusted third parties. On the other hand, the proposed scheme in References [37; 38] are based on PKI cryptosystems, but mobile users only adopted symmetric cryptographic approaches with one-time use of key between mobile user and visited network which leads to low computational cost. However, these schemes and beside the reasonable computational efficiency provide low level of security. For instance, lack of perfect backward secrecy, forgery attack, and mutual authentication property. Nevertheless, the proposed PKI-based approaches suit high performance mobile systems to achieve a desired level of security because mobile networks can never be perfectly secure. The proposed PKI approaches for mobile networks calibrate between computational/communication costs and security requirements, but there are limitations in some aspects. Figure 3: Comparison of computational efficiency.
## 3 Experiments and Discussions
### FIL Simulation Experiments  
FIL is a hardware-in-the-loop simulation technique. In this paper, an ANFIS-PID controller implemented on an FPGA development board is used to control the RFA temperature system model in Simulink to evaluate the performance of the FPGA-implemented ANFIS-PID controller.  
In the FIL simulation, the model used is shown in Equation (15), where the range of parameter \(a\) is from 78.9 to 79. The model consists of two main parts [29]: \(G_{1}(S)\) is the gain of the self-designed power amplification of the RF signal and \(G_{2}(S)\) is modelled based on the temperature and applied voltage in the ablation region (Equation (14)) during the RF heating studied in [30], with minor adjustments. In order to make the system time-varying and, at the same time, not to spend too much time adjusting the parameters, the first-order coefficients in the denominator are replaced by the varying parameter \(a\) and the values of \(a\) are set based on the original coefficients.  
\[G(S)=\frac{-1.24}{s^{3}+12.75*s^{2}+78.96*s+248.05} \tag{14}\]  
\[G(S)=G_{1}(S)*G_{2}(S)=45.6*\frac{-1.24}{s^{3}+12.75*s^{2}+a*s+248.05}=\frac{-56.54}{s^{3}+12.75*s^{2}+a*s+248.05} \tag{15}\]  
Figure 3 illustrates the modelling of the ANFIS-PID controller with the PID controller. To simulate the temperature hysteresis of the RFA, a delay module was added to the model (as shown in the red block diagram in Figure 3); but, due to the complexity of the actual RFA, the exact hysteresis time could not be obtained. To ensure that the model was closer to the actual scenario, a dead-zone module with non-linearity was added to make the controlled system exhibit non-linear characteristics.  
A comparison of important parameters between the ANFIS-PID controller and the PID controller was carried out under three different conditions, such as simulating normal use, varying the target temperature, and external disturbance. And the artificially set parameters of both controllers were kept constant under these three conditions.
# Adsorption of Arsenic and Heavy Metals from Solutions by Unmodified Iron-Ore Sludge
## 2 Materials and Methods  
### Preparation of Adsorbent and Solutions  
The adsorbent applied in this study was collected from the Ban Cuon iron processing area, Cho Don District, Bac Kan Province, northern Vietnam (Ban Cuon iron-ore sludge, SBC). It is an iron-manganese ore formed by oxidation of the original ore-bodies (containing magnetite, siderite, pyrite, and pyrotine) or ultramafic rocks [28]. The ore mineral assemblage includes magnetite (63%), quartz (30.6%), and chlorite (6.3%) [29]. The iron content of ore ranges from 38.0% to 67.6% with an average of 59.3% [29].  
Ten kilograms of iron-ore sludge were selected to be used as an adsorbent and dried using a NIIVE OVER KD200 oven at 80-105\({}^{\circ}\)C until constant weight was achieved. The dried materials were ground using an MRC Laboratory Equipment Manufact User and sieved to obtain particles ranging in size from 0.16 to 0.25 mm [30].  
Four divalent cationic metals (Mn\({}^{2+}\), Pb\({}^{2+}\), Zn\({}^{2+}\), and Cd\({}^{2+}\)) and As (V), were selected as target adsorbates to determine the adsorption characteristics of the adsorbent. These metals were prepared individually by diluting Mn(NO\({}_{3}\))\({}_{2}\), Cd(NO\({}_{3}\))\({}_{2}\)\(\cdot\)4H\({}_{2}\)O, Pb(NO\({}_{3}\))\({}_{2}\), Zn(NO\({}_{3}\))\({}_{2}\), and Na\({}_{2}\)HAsO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O (Kanto Chemical Co. Inc., Japan) with Milli-Q water to obtain the desired concentrations for the batch experiments.
## 4 The Bassnet Model
### Model Input: CQT Log Magnitude Spectrograms  
The BassNet model works with spectral representation of audio data. The monaural mix and bass signals are converted to the spectral domain while using the Constant-Q Transform (CQT), a non-invertible audio transform [55] that is often used in music applications because of its logarithmic frequency scale, which allows for the frequency bins to coincide with the chromatic scale.  
As is common in music applications, we discard phase information from the CQT and only use its magnitude. Furthermore, we anticipate that the pitch--and therefore the harmonics--of the mix and bass will play an important role in learning relations between the two. Because of this, we choose a relatively high frequency resolution for the CQT, computing 36 frequency bins per octave (three bins per semitone), over a range of 88 semitones, starting from 27.5 Hz (the pitch A0), yielding 264 frequency bins in total. Because higher harmonics of musical sounds often have low energy as compared to the lower harmonics, we take the log of the CQT magnitude spectrogram, so that the overtone structure of sounds is emphasized. For computing the CQTs, we use an adaptation of the CQT implementation  
Figure 1: The BassNet model architecture and information flow. The input and target data of the model are shown as grey boxes. Functions are shown as blue rounded boxes. Dark blue boxes depict functions with trainable parameters. White boxes depict computed quantities. The \(\otimes\) sign denotes element-wise multiplication. The green and purple planes denote the parts of the graph that correspond to the encoder functions \(f_{\mu}/f_{\sigma}\), and the decoder function \(g\), respectively. The \(enc/dec\) boxes represent dilated residual encoders/decoders (Section 4.4.1). The CQT spectrogram computation, feature extraction, and multi-resolution processes are discussed in Sections 4.1–4.3, respectively.  
of the _nnAudio_ library [56]. Before computing the CQTs, we resample the mono mixes at 16 KHz. The CQT spectrograms have a hop time of 0.01 s.
## 2 Framework of DRP Strategy and Problem Formulation  
This section will delineate the configuration of signal-free intersections, introduce the specific framework of the DRP strategy, and establish mathematical models for centralized CSP and decentralized TOP. These efforts lay the foundation for the subsequent sections.  
Figure 1 illustrates a symmetrical cross intersection comprising two dual lanes, each with a width of \(S\). This intersection operates without signal control and features a coordination unit at its center, capable of interacting with adjacent CAVs, allowing only CAVs to pass through. In accordance with existing research perspectives [19; 20], the intersection area is divided into three subareas. The central square region, with a side length of \(S\), designated as the Merging Zone (MZ), serves as the primary area for CAV merging and is thus prone to collisions. Surrounding the MZ is the Control Zone (CZ), with a distance of \(L\) from its entrance to the MZ entrance, where the coordination unit transmits control information to CAVs within this area. The outermost zone is the Organizing Zone (OZ), with a distance of \(H\) from its entrance to the CZ entrance, primarily serving as a buffer zone for organizing the crossing sequence and travel trajectories of CAVs. In this study, we assume that all CAVs are configured as Series Hybrid Electric Vehicles (HEVs) with identical physical properties and enter the intersection area with the same initial velocity \(v_{0}\).
# Forecasting Students Dropout: A UTAD University Study
## 3 Data and Methods
### Ensemble Methods
#### 3.3.1 Random Forests  
Decision Trees (DTs) are a predictive model used in machine learning. DTs are designated as classification trees when the output variable takes on a discrete range of values. These are defined by [PERSON] and [PERSON] [20], as a \"classification procedure that recursively  
Figure 1: Pairwise relationships between the most important features [0-Success, 1-Dropout].  
partitions a data set into smaller subdivisions on the basis of a set of tests defined at each branch.\" DTs have a hierarchical structure, formed by a root node, a collection of internal nodes, and the end nodes, which are called leaves. The leaves represent class labels, and each branch represents a combination of features that lead to these labels.  
The use of DTs has several advantages, such as the fact that they require low data pre-processing since they can handle both quantitative and qualitative data. One of the most prominent benefits of this model is that they are considered a _white box_ model, unlike ANN, as they are easy to understand and interpret. However, this model holds some limitations, offering low robustness, meaning that a small change in the data can produce a substantial shift in the results. DTs are also prone to suffer from overfitting, since generated trees might be overly intricate, with the inadequate capability to generalize to new data.  
Random Forests (RFs) are an ensemble of many decision trees. In RFs, each tree is trained independently by using a random sample of the training data. Each tree then makes its prediction, and the class with the most votes is considered the final prediction. The fundamental principle of this model consists of the wisdom of the crowd, where the aggregation of independent solutions outperforms individual solutions [21].
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 6 Mimblewimble Implementations
### Discussion
#### 6.3.1 Broadcasting Protocol  
Both Grin and Beam use the Dandelion scheme as broadcasting protocol [41]. We have formalized that a broadcasting protocol should hold Property 4 of Transaction Untraceability. It should not be possible to link transactions and their originating IP addresses, in other words, to deanonymize users. Broadcast with confusion, as we describe in Property 16, should be carried out to satisfy Transaction Untraceability. We have also described the steam and fluff phases of the Dandelion scheme.  
Grin's implementation, in the steam phase, allows for transaction aggregation (Coin-Join) and cut-through, which provides greater anonymity to the transactions before they are broadcasted to the entire network.  
In addition, in order to improve privacy, Beam's implementation adds dummy transaction outputs at the steam phase. Each output has a value of zero and it is indistinguishable from regular outputs. Later, after a random number of blocks, the UTXOs are added as inputs to new transactions, that is, they are spent and removed from the blockchain.  
In Section 4.5 we have specified the steam routine. Following, we extend the routine to capture Beam's behavior:  
\(\begin{array}{l}\textit{subroutine steam}(tx:\textit{Transaction})\{\\ \textbf{(*}\text{incubation period random choice *})\\ i\leftarrow\{min,max\}\\ \textbf{(*}\text{ create zero value output with incubation period i *})\\ \textit{zeroOut}\leftarrow\textit{createZeroValueOutput}(i)\\ \textit{addOutputTransaction}(\textit{UTXO}(\textit{zeroOut})\\ \textit{addOutputTransaction}(\textit{zeroOut},tx)\\ \ldots\\ \}\end{array}\)  
To capture that semantic, we have combined two Beam's features: incubation period on UTXO and aggregation of zero value transaction outputs. Firstly, we randomly choose \(i\) as an incubation period. Then, we create a zero value transaction output (_zeroOut_) with incubation period \(i\). The incubation period will ensure not to spend the dummy output before \(i\) blocks are confirmed on the chain. After that, _zeroOut_ is added to the UTXO set (which is maintained in the local state of the node) and to the transaction \(tx\) that is being broadcasted. Finally, the routine follows as we specified in Section 4.5.
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 2 Related Work
### Gateway Based Approaches
In our model, the number of gateway nodes and cluster head are not fixed. [PERSON] _et al._[PERSON] et al. (2016) have also proposed multi-hop cluster routing. The authors have used normal sensor nodes as the gateway nodes. They have not provided an unconstrained power supply to the gateway nodes. To avoid quick energy depletion of gateway nodes, each gateway node is associated with only one cluster head. The main drawback of this protocol is the one-to-one relationship between the cluster head and the gateway node. If the number of cluster heads is more than the gateway nodes then the algorithm will fail. Moreover, after some rounds the energy of all gateway nodes will deplete, but there is no provision of a selection of new gateway nodes. In our model, gateway nodes have an unlimited power supply and one gateway node can be associated with any number of cluster heads of its region. [PERSON] _et al._[PERSON] et al. (2016) have proposed the gateway-based distributed energy efficient clustering (G-DEEC) protocol in a heterogeneous sensor network. The authors have used rechargeable and stationary gateway nodes, which are placed at the center, corners, and the middle of the sensor field. Although the number and position of gateway nodes facilitate the communication between each cluster head and the gateway nodes, but the algorithm does not specify the process of gateway node selection. A cluster head can transmit data to any gateway node. If a cluster head selects a distant gateway node, transmission energy consumption will be high. In our method, a cluster head can only transmit to the gateway node of its region. In our method, the number of gateways can increase as the size of area increase. Thus, we use gateway nodes and clustering techniques to avoid energy holes and improve the network's lifetime. Gateway-based sensor networks are also suitable in real-life scenario as shown in [PERSON] et al. (2016).
# Virtual Fire Evacuation Drills through a Web-Based Serious Game
## 5 Results and Discussions
### Evacuation Path  
Using the developed serious game, the evacuation paths of the above 105 trainees were recorded, as shown in Figure 9. It can be observed that the evacuation paths change with the addition of drills. In detail, evacuation paths reflect the following spatial characteristics.  
1. Less and less trainees chose Path 1 for evacuation (see the red line in Figure 9), which is a wise choice for an efficient evacuation. The rooms are located on both sides of Path 1, but only on one side of Paths 2 and 3. Consequently, Path 1 is more crowded than Paths 2 and 3 because more people exit the rooms into the corridor. Therefore, the choice of Path 1 is not ideal for trainee evacuation.
2. Less and less trainees switched paths (see the yellow area in Figure 9), which benefits the evacuations. From the yellow area, it can be observed that the cross lines become increasingly smaller. The cross lines reflect the switch between Paths 2 and 3. However, the switch path generally takes a longer distance and more time; therefore, a smaller switch will cause faster evacuation.
3. Less and less trainees (see the blue area in Figure 9) moved the wrong way, which also benefits the evacuations. The blue area represents the dead end. When the trainees move to the dead end, it will waste evacuation time, which is detrimental.  
In summary, the above characteristics indicate that the evacuation performance of trainees improves through the drills.
## 2 Measurement Method and Experimental Setup Design
### Optical Measurements System
The signal from the output of the resonator is collected by the integrating lens and it effectively contains the information about every pass through the resonator. Hence, in the ICOS cell, we continuously register integral signal, accumulating information about each effective optical pathlength and, consequently, about each respective quantity of absorption in the investigated air sample. This integral signal is proportional to the average value of effective optical pathlength, which is calculated for a definite wavelength by the following equation:  
\[L_{eff}=\frac{L}{(1-R+\alpha L)} \tag{1}\]  
where \(R\)--mirror's reflection coefficient, \(L\)--geometric length of the resonator, \(\alpha\)--per-pass intracavity absorption. In case of an empty cavity (\(\alpha L=0\)) the gain factor for effective optical pathlength is defined by geometric parameters of the cavity. As the distance between mirrors is 220 mm then, according to the Equation (1), the effective optical pathlength is 110 m and 55 m for 2.656 \(\upmu\)m and 2.808 \(\upmu\)m respectively. Absorption medium in the cavity decreases the effective optical pathlength and if the per pass intensity of absorption is much less than cavity transmission (\(\alpha L<<(1-R)\)), ICOS absorption spectra can be described by the Beer-Lambert law. Otherwise, when these parameters are compatible (\(\alpha L\approx(1-R)\)), the absorption line contour becomes wider with the absorption line magnitude depressed relative to the continuum level, the Beer-Lambert law doesn't work and more complicated approach is required. The DL frequency scan rate is also an important parameter, which should be described before the spectrum analysis. According to the principle of operation, the ICOS is based on measurements of the steady-state power from the cavity, so establishing this state takes time \(\tau\), which is defined by the cavity parameters and is called a cavity time constant:  
\[\tau=\frac{L}{c(1-R+\alpha L)} \tag{2}\]  
Actually, \(\tau\) is an average time interval within the light is captured by the optical cavity. If the time of DL frequency scanning over full width at half maximum (FWHM) of the spectral line is compatible with the cavity time constant (\(\tau_{\text{FWHM}}\approx\tau\)), it leads to a significant skewing of the line profile. In our case the DL scans the spectral range of 1 cm\({}^{-1}\) within 11.4 ms, thus the condition \(\tau_{\text{FWHM}}>>\tau\) is met and the M-DLS measures cavity transmittance spectra without line profile distortion.
## 5 System Implementation
### LOD Model Loading  
Given that the size of the model uploaded to the server and the screen size of the user's terminal device are different, the developed system applied user-defined parameters to determine the mesh simplification degree of each LOD model, as well as the threshold distance between viewpoint and instance. According to the edge folding algorithm, since the small components (e.g., bolts) are relatively sharp in the steel structural model, some large plate components might be unexpectedly deleted because of their thin thickness. In addition, it is necessary to ensure that users can understand the meaning of the interactive parameters in the visualized model. Therefore, the length of the folded edge can be defined by users and be used as the error measurement. Figure 10 shows the geometric representation of the same model in different LOD models.  
Figure 10: LOD models under different simplification levels with the shortest edge of (**a**) 0 mm, i.e., original model, (**b**) 10 mm, (**c**) 100 mm, (**d**) 200 mm, and (**e**) 400 mm. In these figures, components marked by circles will disappear in the next LOD model.  
Figure 9: BIM models (**a**) before redundancy removal and (**b**) after redundancy removal.  
In the developed system, when receiving the request to create LOD models, the server-side carries out the program of model transformation and mesh simplification and then stores the transformation results. On the browser side, the loaded LOD models are sorted according to the user-defined distance, and different meshes are switched in real time according to the viewpoint during rendering. Because of the loading of multiple models with different degrees of simplification, the consumption of memory and bandwidth will increase, but the simplified models generally have small volumes, such that little impact is imposed on the overall performance of the system. Moreover, the number of instances that need to be simplified is greatly reduced after redundancy elimination. Therefore, the display speed can meet the requirements of quasi-real-time analysis.  
When the user views the model in the roaming mode and changes the camera position, the distances from the camera to the instances will change dynamically. Therefore, different LODs can be switched dynamically according to the changing distances. As shown in Figure 11, the instances with different distances from the viewpoint are displayed by mesh models under different LODs. There are three mesh models in different LODs in Figure 11, whose shortest sides are 0 mm (not simplified model), 10 mm, and 50 mm, respectively. There are three instances, A, B, and C, whose distances from the camera are \(d_{1}\), \(d_{2}\), and \(d_{3}\), respectively. Since the distances between the instances and the camera are different, the mesh models adopted by the three instances can be dynamically adjusted. The current LOD model adopted by the instance is marked with \"\(\bigstar\)\" in Figure 11.