esa-sceva/satcom-chunk-collection huggingface dataset:
# ML-AKA: An Authentication Protocol for Non-Standalone 5G-Based C-IoT Networks
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors and Years** & **Method and Approach** & **Contribution (s)** & **Limitation(s)** \\ \hline [PERSON] et al. [4] (2015) & DHKE method for LTE network & Secure from potential DDOS attack & Prone to MIMT attack \\ \hline [PERSON] et al. [8] (2015) & Multilevel security protocol for device-to-device communication & Secures IoT devices from attacks that occur at the network layer & Developed protocol provides protection against physical layer attacks \\ \hline [PERSON] et al. [10] (2016) & Group-based secure (GBS) secure key verification and validation method & User blog for each group uses a designated encoded header with each data packet. & Group header enhances required bandwidth and processing time \\ \hline [PERSON] et al. [6] (2016) & Distributed key exchange (DKE) technique for IoT application & Reduces data injection attacks at the end user IoT devices & Key distribution is complex \\ \hline [PERSON] et al. [5] (2017) & Universal key exchange mechanism for cellular network & Secures the packet transmission over transmission channels & Increases communication overhead due to use of extra headers \\ \hline [PERSON] et al. [11] (2019) & Elliptical curve method for 5G cellular network & lightweight public key-based crypto-system & Implementation complexity is high \\ \hline [PERSON] et al. [12] (2023) & EGHA protocol for cellular network & A temporary ID-based authentication protocols for 5G-based V2X. & Increases computational complexity with the added TID \\ \hline [PERSON] et al. [13] (2023) & AGMA Code to reduce handover signalling in 5G-V2X & Inter- and intra-AMF handover secure solution & Other core elements are not considered \\ \hline [PERSON] et al. [14] (2024) & RSA and ECC encryption method for migrated data & Service Migration Security Framework (SMSF) for 5G network & Not useful for heterogeneous network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of the contributions and limitations of security protocols for C-IoT and D2D applications.
# Study on the Influence of Different Factors on Pneumatic Conveying in Horizontal Pipe
## 2 Mathematical Model  
### Particle Suspension Velocity  
Particles can be transported by air only when they meet a certain airflow velocity, that is, suspension velocity [37; 38; 39]. However, in a horizontal pipeline, particles are easy to be deposited at the lower part of the pipeline under the action of gravity, so in horizontal pneumatic conveying, the airflow velocity is generally greater than the suspension velocity. There are a variety of calculation formulas for suspension velocity in pneumatic conveying, the most widely used of which is the partitioned suspension velocity formula and its applicable particle size method, which is as follows:  
1. Stokes zone  
\[u_{b}=\frac{d_{b}^{2}(\rho_{b}-\rho_{a})g}{18\mu} \tag{1}\]  
and \(d_{b}\leq 1.225\left[\frac{\mu^{2}}{\rho_{b}(\rho_{b}-\rho_{a})}\right]^{ \frac{1}{3}}\)  
2. Allen zone  
\[u_{b}=1.195d_{b}\left[\frac{\left(\rho_{b}-\rho_{a}\right)^{2}}{\rho_{a}\mu} \right]^{\frac{1}{3}} \tag{2}\]  
and \(2.2\left[\frac{\mu^{2}}{\rho_{a}(\rho_{b}-\rho_{a})}\right]^{\frac{1}{3}}\leq d _{b}\leq 20.4\left[\frac{\mu^{2}}{\rho(\rho_{b}-\rho_{a})}\right]^{\frac{1}{3}}\)  
3. Newton zone  
\[u_{b}=5.45\sqrt{\frac{d_{b}(\rho_{b}-\rho_{a})}{\rho_{a}}} \tag{3}\]  
and \(20.4\left[\frac{\mu^{2}}{\rho_{a}(\rho_{b}-\rho_{a})}\right]^{\frac{1}{3}}\leq d _{b}\leq 1100\left[\frac{\mu^{2}}{\rho_{a}(\rho_{b}-\rho_{a})}\right]^{\frac{1}{3}}\)  
Where: \(u_{b}\) is the airflow velocity when the particles are suspended, m/s; \(\rho_{a}\) is air density; \(\rho_{b}\) is particle density; \(d_{b}\) is particle size; \(\mu\) is the aerodynamic viscosity.  
The suspension velocity is calculated as 10.8 m/s. In the actual conveying process, due to the energy loss caused by collision and friction, as well as the air flow nonuniformity in the pipeline, the actual conveying wind speed is generally 1.5-3.0 times of the particle suspension velocity [40]. Therefore, considering the reliability of the test device, the actual air velocity used in this study is 18 m/s, 22 m/s, 26 m/s, and 30 m/s.
## 5 Future Prospects
### AloT for Hydrogen Energy Systems  
Incorporating artificial intelligence (AI) into hydrogen energy systems (AIoT) is a crucial step toward generating cleaner, more effective, and safer energy solutions. By combining the power of AI algorithms with the interconnection of IoT devices, AIoT technology advances every stage of the hydrogen energy lifecycle, from production and storage to distribution and usage (Figure 8).  
Figure 7: The Architecture of AIoT for Hydrogen Energy Systems.  
Because of the AIoT's real-time monitoring and prediction capabilities, proactive maintenance is possible, minimizing risks and downtime while providing rapid responses to safety hazards. Real-time data from renewable sources is used to optimize hydrogen manufacturing operations, resulting in more efficient energy consumption and reduced environmental impact. Furthermore, the AIoT systems' adaptability to changing conditions assures that hydrogen energy systems may evolve in parallel with improvements in technology and energy demand.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 1 Introduction
** We discovered several important outcomes from our study:  
* We conducted a series of experiments with over 80 speakers and found that using DNN speech synthesis tools to create PF speech can effectively deceive modern public and commercial speaker recognition systems with a success rate of 95% to 97%;
* A survey of 148 participants revealed that humans can only distinguish PF speech from real speech with a very low accuracy: 16% for unknown voices and 17.5% for known voices;
* Our study, which involved 148 participants, found that humans can only identify partially fake speech within a completely authentic video with a 24% accuracy when the speaker's face is close to the camera and an 11% accuracy when the speaker's face is far from the camera;
* A detailed evaluation of three state-of-the-art defence algorithms revealed an inability to prevent and detect PF speech, highlighting the need for new defences.
## 3 System Model
### Scenario Description  
As shown in Figure 1, we consider a wireless communication system in which a legitimate base station (BS) intends to send confidential information to a legitimate receiver (UE), while there are \(K\) eavesdroppers (Eves) who are distributed in the system randomly. Different from traditional RIS-assisted secrecy communication systems, we consider equipping the RHS on the base station to achieve beamforming by using holographic rules so that useful signals can be effectively transmitted to the user, avoiding eavesdroppers.
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 5 Question 1: Which mPLM Is Best for QE Tasks?
### Revisiting the QE Input Structure  
In this section, we investigate the differences between the input structures used for QE training. Existing QE studies generally construct an input using the following shapes: _[BOS] Source sentence [EOS] [EOS] MT output [EOS]_ or _[BOS] MT output [EOS] [EOS] Source sentence [EOS]_, where the beginning of sentence (BOS) and end of sentence (EOS) tokens can be viewed as _[CLS]_ and _[SEP]_, respectively, depending on the pretraining methods that were used. Previously, [PERSON] et al. [13], [PERSON] et al. [14], [PERSON] et al. [35] adopted a prior structure as an input, while [PERSON] et al. [4], [PERSON] et al. [33] adopted a posterior structure. Although decent performances can be achieved by adopting these structures, sufficient investigations concerning the selection of an input structure have not been conducted. In other words, clear criteria for constructing an adequate input structure have not yet been presented. Here, we focus on the inconsistent input structures utilized in current QE studies and quantitatively analyze the differences derived from adopting different input structures.
## 5 The Proposed Method
### Fitness Functions
#### 5.3.1 Mean Squared Error (MSE)  
MSE is considered to be one of the most common fitness functions that are used in MHOANNs and ENNs [41,42]. The value is the mean of the summation of the differences between the predictions and the ground truths, as described in Equation (7), where \(i=1,2,\ldots,n\), \(n\) is the number of samples, \(y_{i}\) is the actual or ground truth value, and \(\hat{y_{i}}\) is the prediction.  
\[cost_{MSE}=\frac{1}{n}\Sigma_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2} \tag{7}\]
###### Abstract  
This paper experimentally investigates the blast-resistant characteristics of hybrid fiber-reinforced concrete (HFRC) panels by contact detonation tests. The control specimen of plain concrete, polypropylene (PP), polyvinyl alcohol (PVA) and steel fiber-reinforced concrete were prepared and tested for characterization in contrast with PP-Steel HFRC and PVA-Steel HFRC. The sequent contact detonation tests were conducted with panel damage recorded and measured. Damaged HFRC panels were further comparatively analyzed whereby the blast-resistance performance was quantitively assessed via damage coefficient and blast-resistant coefficient. For both PP-Steel and PVA-Steel HFRC, the best blast-resistant performance was achieved at around 1.5% steel + 0.5% PP-fiber hybrid. Finally, the fiber-hybrid effect index was introduced to evaluate the hybrid effect on the explosion-resistance performance of HFRC panels. It revealed that neither PP-fiber or PVA-fiber provide positive hybrid effect on blast-resistant improvement of HFRC panels.  
hybrid fiber-reinforced concrete (HFRC); blast resistance; contact explosion; damage mode; hybrid effect +
Footnote †: journal: Applied Sciences
# Determination of Mesh Stiffness of Gear--Analytical Approach vs. FEM Analysis
## 2 Analytical Models of Mesh Stiffness  
In this chapter, two different possibilities of how to model the course of mesh stiffness can be analytically approached. The mesh stiffness is based on the geometric and material properties of the gear teeth and is time-varying due to the meshing of the gears. This is the relationship between the load of the tooth and its deformation, while the tooth is considered as a cantilever beam with a variable cross-section, where a force acts in the direction of the line of action. The mesh stiffness of single pair of teeth and double pairs of teeth in engagement alternates during the meshing of the gears (in this case, it is a spur gear with straight teeth).  
In general, the stiffness of single pair of teeth can be described by the Equation (1):  
\[c=\frac{w}{\delta}, \tag{1}\]  
where \(c\) is the stiffness of single pair of teeth in engagement, \(w\) denotes the load along the face width in the direction of the line of action and \(\delta\) is the deformation of single pair of teeth, which corresponds to the load \(w\).  
Similarly, the stiffness of double pairs of teeth can be defined by Equation (1), with the load \(w\) being the sum of the load of the teeth of the first pair \(w_{1}\) and the load of the teeth of the second pair \(w_{2}\), which corresponds to the deformation \(\delta\).
# Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
## 2 Multicarrier Modulation Schemes Based on the VLC System
### Aco-Ofdm  
The ACO-OFDM framework with N subcarriers is illustrated in Figure 2. In this system, the input bit stream is transformed into complex symbols, \(S(W)\), where \(W\) ranges from 1 to \(\frac{N}{4}\), based on the chosen mapper scheme, such as M-QAM [26]. H.S is enforced on the OFDM subcarriers to ensure the real-valued nature of the time-domain signals, as dedicated in Equation (1). Specifically, only odd subcarriers are modulated to enable direct  
Figure 1: DCO-OFDM block diagram based on a VLC system.  
clipping of the time-domain signals at zero. The organized symbols for the ACO-OFDM signal are described as follows [27]:  
\[X(K)=\begin{bmatrix}0,\ S_{1},\ 0,\ S_{2},\ldots,S_{N/4},0,\ S_{N/4}^{*},\ldots,S_{2 }^{*},\ 0,\ S_{1}^{*}\end{bmatrix}, \tag{6}\]  
By utilizing an IFFT procedure, the time-domain ACO-OFDM signal is generated, as described:  
\[x(n)=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1}X(K)\ exp\left(j\frac{2\pi}{N}kn\right),\ n=0,1,\ldots,N-1, \tag{7}\]  
Following a symmetrical pattern over half a wave, as:  
\[x(n)=-x(n+N/2),\ \ \ n=0,1,\ldots,N/2-1, \tag{8}\]  
Therefore, it is possible to clip the negative portion without any corruption in the information.  
\[\lfloor x_{c}(n)\rfloor=x(n)+i(n)=\begin{cases}x(n),&x_{ACO,n}\geq 0;\\ 0,&x_{ACO,n}\leq 0;\end{cases} \tag{9}\]  
For \(n=0,1,\ldots,N-1\), \(i(n)\) indicates ACO-OFDM negative clip distortion.  
Following the IFFT operation, the signal is serialized. Subsequently, zero-clipping is applied, and a CP is added at the onset of each OFDM symbol to eradicate ISI at the receiver. This final signal is then utilized to modulate the LEDs [2].  
After analyzing the frequency domain at the receiver, it was observed that the negative clipping noise only affected the even subcarriers. Consequently, after removing the CP, the signal is parallelized, and the transmitted signal on the odd subcarriers is readily retrieved at the receiver by a simple FFT process [28].
## 2 Materials and Methods
### Downscaling SMAP TB
#### 2.2.1 Collocating SMAP TB and CYGNSS Reflectivity Data  
We collocated SMAP TBs and CYGNSS reflectivity values in both space and time. We spatially collocated the fine-scale variables using the 3 \(\times\) 3 km EASE-Grid 2.0. We spatially collocated the coarse-scale variables using 33 \(\times\) 33 km boxes, centered on the 9 \(\times\) 9 km EASE-Grid 2.0, as shown in Figure 2. Each 33 km box consists of 11 \(\times\) 11.3 km grid cells [2; 3].  
We masked out all 3 \(\times\) 3 km grid cells containing semi-permanent open water. SMAP enhanced TB data are water body corrected, which result in higher TBs for adjusted grid cells [21]. Consequently, matching SMAP and CYGNSS data over regions of open water will introduce error in the downscaled SMAP/CYGSS TBs. Using 30 m surface water data derived from Landsat [22], available through the Global Surface Water Explorer, we calculated the percentage of each 3 \(\times\) 3 km grid cell that contained open surface water for at least six months in 2018. We then implemented a water mask over regions that exceeded 5% semi-permanent open water.  
The temporal collocation scheme we used is \(\pm\) half the time between successive SMAP observations. Since the time between SMAP observations is variable, the temporal collocation period varies from approximately 1-6 days. As can be seen in Figure 3a, the \"between-SMAP\" temporal collocation scheme captures all CYGNSS data without reusing any CYGNSS data.  
Figure 2: We merged 9 km SMAP TBs with CYGNSS reflectivity data, gridded at 3 km, to create 3 km SMAP/CYGNSS TBs. 9 km SMAP TB data have a native resolution of approximately 33 km. To collocate SMAP and CYGNSS data, we derived 33 km boxes for each SMAP 9 km grid cell [2; 3].
## 2 Study Area  
Four counties in western and middle Hetao Irrigation District (HID) were selected as the study region, covering an area of 0.91 million ha with 44% being cropland (Figure 1). The HID is the third largest irrigation district and an important food production base in China located in the western part of the Inner Mongolia Autonomous Region in North China. The HID lies in a typical arid area and mainly relies on the Yellow River water for irrigation, while a water-saving rehabilitation program has been carrying out since 1998 to reduce water diversion from the Yellow River and slow down the rise of the groundwater table caused by irrigation [53; 54]. Consequently, the decrease of available irrigation water influenced the crop planting structure and crop yield in HID.  
The dominant crops include maize, sunflower and wheat, and the crop planting structure has changed significantly in recent years due to economic and irrigation factors. According to the statistics of the crop planting area provided by the local government, the proportion of maize planting area increased from 28% to 40%, sunflower increased from 31% to 40%, and wheat decreased from 22% to 13% from 2010 to 2015. Since maize and sunflower accounted for about 80% of the crop planting area in recent years, only maize and sunflower were considered in this study. According to field  
Figure 1: Location and cropland distribution of the study area and sampling points in 2014 and 2015.  
investigation in HID, the growth period of maize is generally from the 120 th to 260 th day in a year, while the sunflower from the 160 th to 260 th day. Moreover, maize and sunflower both reached their peak growth periods at about the 220 th day in the year [49].
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 4 Proposed Methodology
### Fault Tree Analysis and Data Output  
The Fault Tree analysis is totally performed by the SHARPE tool, facilitating the time-consuming tasks of describing and evaluating large and complex systems. SHARPE receives a text document with the Fault Tree description, the related CTMCs and the dependability attributes to be assessed for the evaluation period \(T\), considering the discrete time step, \(t_{s}\). First, the SHARPE tool evaluates the individual CTMC models of hardware, battery and link and obtains their respective availabilityfunction. These functions are then used as input events of Fault Tree gates, according to the described model. At the end, SHARPE evaluates the FT and returns another text document with the values of the dependability metrics (e.g., availability) for each instant of time. The integration with the SHARPE is fully automated by the developed framework.  
The source code of the algorithms presented are available in [67].
# In-Band Medium-Frequency R-Mode Signal Quality Estimation +
## 4 Simulation
However, for \(C/N_{0}\) below 7 dB-Hz the signal should not be considered in the positioning algorithm; hence, it is not important to focus on that part of the results, and they are only shown for the sake of completeness. Above 42 dB-Hz, the CRB and the RMSE for the single tone overlap very well, whereas the result is slightly different when the full R-Mode signal is used. In such a case, we can observe that the RMSE of the phase estimate is a bit better then the predicted curve. Therefore, the predicted curve appears to be like an upper bound of the true RMS phase error. The RMS of the phase error does not follow the CRLB at high \(C/N_{0}\) due to the interference of the MSK. Nevertheless, the accuracy level at this point is in the sub-meter range domain, which is far above the target accuracy of the MF R-Mode system and, in reality, difficult to achieve due to the presence of additional source of error such as the residual error of the atmospheric and ground-wave delay factors (AGDF) [12; 13].
## 3 Simulated Results and Error Analysis
### Discussion and Analysis of Reconstruction Simulation Results  
The proposed MIARO algorithm was validated using both the axisymmetric and non-axisymmetric numerical models via simulation. In order to verify its superiority, MIARO was compared with ART, Landweber and MAART, three classical iterative reconstruction algorithms in terms of accuracy and robustness.
## Nomenclature  
\begin{tabular}{l l} \(u,v\) & Component of velocity along the axial and radial \\  & direction \\ \(v\) & Kinematic viscosity \\ \(n\) & Size of needle \\ \(a^{*},b^{*}\) & Concentration of chemical species \(A^{*}\), \(B^{*}\) \\ \(\epsilon\)= \(\frac{\kappa_{\rm cr}-\kappa_{0}}{\kappa_{0}}\) & Small parameter depends on the nature of fluid \\ \(k_{c},k_{s}\) & Rate constants \\ \(T\) & Temperature of fluid \\ \(T_{0}\) & Reference temperature \\ \(T_{\infty}\) & Temperature away from the surface \\ \(T_{w}\) & Constant wall temperature \\ \(u=\frac{1}{r}\frac{\partial\psi}{\partial r},v=-\frac{1}{r}\frac{\partial\psi} {\partial x}\) & Stream function in terms of component \\ \(C\) & heat sink/source w.r.t space \\ \(D\) & heat sink/source w.r.t time \\ \(C,D>0\) & Internal heat generation \\ \(C,D<0\) & Internal heat absorption \\ \(A^{*},B^{*}\) & Chemical species \\ \(\mathrm{Re}_{x}=\frac{Ux}{v}\) & Local Reynold number \\ \(Sc\) & Schmidt number \\ \(\kappa=\kappa_{0}(1+\in\theta(\zeta))\) & Thermal conductivity \\ \(U=u_{w}+u_{\infty}\) & Composite velocity \\ \(q_{w}\) & Heat flux at wall \\ \(K_{s}\) & Strength of heterogeneous reaction \\ \(u_{w}\) & Constant velocity of needle \\ \(u_{\infty}\) & Free stream velocity of fluid \\ \(S\) & Thermal stratification \\ \(\beta\) & Ratio of diffusion coefficient \\ \(\lambda=\frac{u_{w}}{U}\) & Ratio of needle velocity to composite velocity \\ \(\beta\) & Ratio of diffusion coefficients (mass) \\ \(C_{f}\) & Drag force coefficient \\ \(\tau_{w}\) & Surface shear stress \\ \(F_{r}\) & Coefficient of inertia \\ \(K\) & Strength of homogeneous reaction \\ \(\psi\) & Stream function \\ \(\alpha\) & Thermal diffusivity \\ \end{tabular}
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 2 Related Works
### Challenges in Mobility for Visually Impaired Individuals
#### 2.2.2. _Haptic Feedback via Wearable Devices for Street-Crossing_  
Compared to portable devices, wearable devices have gained broader applications due to their unique advantages. Tactile feedback via wearable devices does not occupy the user's hands, allowing visually impaired individuals to continue using a white cane for environmental exploration. Additionally, such devices can easily be integrated into everyday wearable items, providing users with a more natural and intuitive tactile feedback experience [29]. Despite significant advancements in obstacle detection and directional navigation, research into the application of tactile feedback for specific scenarios, such as street crossing for visually impaired individuals, remains insufficient. Notably, [PERSON] et al. found that, in scenarios like street crossing, where maintaining environmental auditory awareness is critical, visually impaired individuals subjectively preferred tactile feedback over voice prompts. This preference allows them to focus on perceiving surrounding sounds without interference [30]. In the exploration of assistive solutions for visually impaired individuals crossing streets, other forms of information cues are also worth investigating. For instance, [PERSON] et al. found that in navigation tasks for visually impaired users, visual feedback demonstrated higher efficiency, smoothness, and accuracy compared to tactile feedback [27]. Users also generally perceived visual feedback as more intuitive and user-friendly [27]. This finding opens up new directions for designing street-crossing assistance systems for visually impaired individuals.