esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Large-Scale Path Loss Models
This model did not consider the anchor point, and therefore it was not physically-based. The path loss in dB based on the FI model as a function of the 3D T-R separation distance \(d\) is given by [47]. \[PL^{FI}(d)[dB]=\alpha+10\beta\log_{10}(d)+\chi_{o}^{FI} \tag{7}\]  
where \(\alpha\) denotes the FI in dB, \(\beta\) refers to a coefficient referred to as line slope that characterized the distance dependency of the path loss, \(\chi_{o}^{FI}\) is defined as a zero mean Gaussian random variable that stands for shadow factor, and \(\sigma^{FI}\) signifies the shadow factor standard deviation in decibels. In order to minimize the standard deviation, \(\sigma^{FI}\), using the MMSE technique, it is required to solve for \(\alpha\) and \(\beta\) values to determine the best minimum error fit, as follows:  
\[\alpha=\frac{\sum 10\log_{10}(d)\sum 10\log_{10}(d)PL^{FI}(d)[dB]-\sum\left(1 0\log_{10}(d)\right)^{2}PL^{FI}(d)[dB]}{\sum\left(10\log_{10}(d)\right)^{2}-N \sum\left(10\log_{10}(d)\right)^{2}} \tag{8}\]  
\[\beta=\frac{\sum 10\log_{10}(d)\sum PL^{FI}(d)[dB]-N\sum 10\log_{10}(d)PL^{FI}(d)[ dB]}{\sum\left(10\log_{10}(d)\right)^{2}-N\sum\left(10\log_{10}(d)\right)^{2}} \tag{9}\]  
\[\chi_{o}^{FI}=PL^{FI}(d)[dB]-\alpha-10\beta\log_{10}(d) \tag{10}\]and the standard deviation is calculated as:  
\[FSPL(f,d_{0})=20\log_{10}\left(\frac{4\pi d_{0}f}{c}\right) \tag{2}\]0
# Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network
Due to the increased focus in the ASVspoof 2021 PA test set on false audio in real-world scenarios, traditional DNN models cannot achieve outstanding performance on the new track test set. The reason for this is that the generalization ability of traditional DNN models is limited in cross-domain scenarios, resulting in poor adaptation to situations with different training and testing distributions. The HFN model in the field of image anti-spoofing extracts information from different feature levels by fusing the original RGB image information and the corresponding meta-pattern information, thus effectively improving the generalization capability of face anti-fraud systems [27]. Similarly, in the field of speech recognition, using both the original speech signal and its time-reversed version as inputs can improve the robustness of the model [28]. This is because the time-reversed signal and the original signal have different features in the frequency domain, which can help the classification network adapt to different speech environments [29]. To improve the generalization ability of replayed speech anti-spoofing models, this paper introduces the HFN model architecture [27] into the replayed speech anti-spoofing system, and proposes using both original speech signals and time-reversed speech signals as inputs to the network. Advanced performance was achieved in the ASVspoof 2021 PA task. The main contributions of our work are as follows:  
1. We first introduce the HFN model into the replay attack detection system. The HFM in the model can more thoroughly fuse information from inputs at different levels, thereby improving the robustness of the system. 2. We propose using both the original speech signal and the time-reversed speech signal as inputs to the network, which increases the amount of information provided to the model and facilitates the learning and classification of the model. 3. Our proposed method achieves an EER of 24.46% and a min t-DCF of 0.6708 on the ASVspoof 2021 PA test set. Compared to the four baseline systems of the ASVspoof 2021 PA task, the min t-DCF values are reduced by 28.9%, 31.0%, 32.6%, and 32.9%, and the EER values are reduced by 35.7%, 38.1%, 45.4%, and 49.7%, respectively. The remainder of the paper is as follows. Section 2 illustrates our method of HFN and the input methods. Section 3 describes the dataset and evaluation indicators used in this study. Section 4 presents the experimental results and a comparison with other methods. In Section 5, we conclude the paper.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC-AES Hybrid PKE Protocol
#### 5.2.3 Security of Data Encryption (AES-256)  
Data encryption uses AES-256, which is well-established and understood.  
Security Assumption  
AES-256 is considered secure against classical attacks. The hybrid approach aims for defense-in-depth; even if the PQC KEM is broken, the attacker still faces the challenge of breaking AES-256.  
Formal Statement  
The data encryption phase utilizes AES-256 with a key size of 256 bits. AES-256 is widely considered secure against classical computational attacks, providing 256 bits of security. We assume that the AES-256 implementation is free from side-channel vulnerabilities.
# Current Uncertainties and Future Challenges Regarding NAD+ Boosting Strategies
## 9 Potential (In)Direct Harmful Effects of Increased NAD+
NNMT catalyzes the N-methylation of nicotinamide, using S-adenosyl-L-methionine (SAM) as a methyl donor, thus yielding N1-methylnicotinamide (MNA) as a product and releasing S-adenosyl-L-homocysteine (SAH) [167]. NNMT activity can thus affect NAD+ biosynthesis and ATP production, as well as drive epigenetic modifications and impact gene expression by modulating the intracellular SAM/SAH ratio [167]. Further, the enhanced NNMT activity and increased NNMT reaction products due to the conversion to nicotinamide from NAM, NR, or NMN supplementation might potentially be related to cancer development [166]. There is active research on discovering the inhibitors of NNMT enzyme activity with some small molecule inhibitors showing promising results [167; 168; 169; 170; 171; 172; 173]. Although NNMT may exert a primary role in the first step of carcinogenesis by irreversibly methylating NAM, thus generating MNA [166], the effective role of this enzyme as well as NAD+ boosters on cancer development still need to be fully elucidated.
## 5 Simulation and Evaluation Results
### DRTO Algorithm Performance Evaluation
#### 5.1.3 The Runtime Consumption of DRTO  
Finally, we evaluate the runtime performance of DRTO. Since pure TC computing and pure SatEC computing are static, we compare DRTO with other three dynamic benchmarks. Specifically, we respectively record the total runtime consumption of different algorithms running on 30,000 time frames, and compute the average runtime per time frame. The runtime comparison is shown in Figure 8.  
Although four dynamic algorithms achieve similar offloading cost performance (in Figure 7), DRTO consumes the lowest runtime with about 42.6%, 87.3% and 96.6% reductions compared to DDLO, CD and enumeration when \(N=7\). In addition, the runtime consumption of DRTO or DDLO does not explode when the network scale increases. This is because DNN can accurately fit the complex mapping from the channel states to the offloading location; compared with traditional CD or enumeration methods, the action space of DRTO or DDLO is significantly reduced, resulting in much fewer iterations. In the comparison with DDLO, at the very beginning of the learning process, the action space of DRTO is the same as that of DDLO. With the improvement of the offloading policy,  
Figure 8: Average execution latency by different algorithms.  
Figure 7: Average offloading cost by different algorithms (\(N=5\)).  
the number of quantized candidate offloading locations in DRTO is dynamically adjusted, and thus the action space of DRTO is further reduced.  
Actually, the channel coherent time is extremely short due to the high-speed movement of satellites. DRTO can quickly generate the offloading location and bandwidth allocation without compromising the offloading cost performance, which better adapts to the fast channel fading in satellite-terrestrial edge computing networks.
## 2 System Model and Problem Definition
### Problem Definition  
The main objective is to minimize the worst-case-delay-to-main-lobe-rendezvous (WCDMR) without coordination or information for TS1. We consider a pair of neighbor UAVs, denoted as \(a\) and \(b\). We assume that UAV \(a\) is located in the sector \(P_{b}\in\mathbf{u}\), and UAV \(b\) is located in the sector \(P_{a}\in\mathbf{u}\). UAV \(a\) points towards \(I_{a}\in\mathbf{u}\), and UAV \(b\) points towards \(I_{b}\in\mathbf{u}\) in initial state. The pair of neighboring UAVs, \(a\) and \(b\), can only discover each other if and only if they steer their main lobes towards each other as illustrated in the following scenario.  
In Figure 5, \(N=6\), \(\mathbf{u}=\{1,2,3,4,5,6\}\), \(P_{a}=1\),\(P_{b}=4\), \(I_{a}=4\), and \(I_{b}=3\).  
UAV \(a\) and UAV \(b\) switch their main beam to the next sector in one slot counterclockwise. The antenna scan sequence, denoted as \(S\), is designed by different algorithms. The scan sequences of UAV \(a\) and UAV \(b\) are \(S_{a}=\{4,5,6,1,2,3\}\) and \(S_{b}=\{3,4,5,6,1,2\}\), respectively. The antenna scan sequences and the discovery process are illustrated in Figure 6. In Figure 6, the main lobes of the two UAVs cannot simultaneously point towards sector 1 and sector 4, so the pair of the UAVs cannot discover each other through this switching method.  
Figure 4: Directional multichannel communication in Slot1.  
Figure 5: The communication of a pair of UAVs, \(a\) and \(b\).  
Figure 6: Antenna scan sequences of the above scenario.  
For probabilistic neighbor discovery strategies, the period of \(S\) may be infinite; therefore, neighbor discovery delay cannot be guaranteed. Hence, the deterministic method is proposed.  
Overall, the criteria for the deterministic method of main lobe blind rendezvous are shown as follows:  
1. Guaranteed rendezvous: any two UAVs can achieve main lobe rendezvous in a certain period of time.
2. Full rendezvous diversity: UAVs can rendezvous on any combination of \((P_{a},P_{b})\in[1,N]\times[1,N]\) and \((I_{a},I_{b})\in[1,N]\times[1,N]\).
3. Asynchronous environment: in FANETs, it is difficult to employ highly tight time synchronization among users, and each user may start their scan sequence at a different time during initial access.
4. Without extra expenditure: no additional expenditures, such as a coordination channel and prior information, should be required.  
The blind rendezvous problem for neighbor discovery with directional antennas is defined as follows:  
\[\begin{split}\min& T_{\text{br}}\\ s.t.&\forall P_{a}\in[1,N],P_{b}\in[1,N],\\ \forall I_{a}\in[1,N],I_{b}\in[1,N]\\ \exists t\leq T_{\text{br}}&,\mathbf{S}_{\text{a}}( \text{t})= P_{a},\mathbf{S}_{\text{b}}(\text{t})=P_{a}\end{split} \tag{3}\]  
To clarify, the blind rendezvous problem with directional antennas for neighbor discovery is used to design antenna scan sequences \(\mathbf{S}\) that minimize the worst-case discovery delay \(T_{\text{br}}\), while guaranteeing neighbor discovery between any pair of neighbor UAVs \(a\) and \(b\) for any \((P_{a},P_{b})\) and \((I_{a},I_{b})\).
## 2 Study Area and Data Analysis  
### Study Area  
Except for the summer, with its heavy precipitation, during spring, autumn, and winter, it is known that a large amount of yellow sand dusts, raised from the Gobi Desert, arid Inner Mongolia and dry regions in northern China by a strong northwesterly wind or northerly wind, are transported to the downwind regions, such as Korea, and greatly affect the local air quality of Gangneung city, showing very high concentrations of particulate matters during such dust periods [33; 35; 37].  
As a clean city, Gangneung consists of a basin with an elevation of about 25 m above mean sea level, with high mountains over 900 m height in its west and the East Sea of Korea in its east (Figure 1). It has complicated characteristics of marine and continental climates. As this city has no special factories to emit a large amounts of air pollutants, the main atmospheric pollution sources are vehicles on the road and heating boilers in the resident area during both winter and the early spring; thus, it always maintains very low concentrations of PM, more or less 40 \(\upmu\)g/m\({}^{3}\), except for during the dust period.
# Context-Based Energy Disaggregation in Smart Homes
## 4 Disaggregation Algorithm
### Context-Based Disaggregation
#### 4.3.1 Timing Usage Statistic Conditioning  
The Tracebase dataset provides active power measurements and their relative sampling instants. This information has allowed us to evaluate the timing-usage statistics of the different devices. From the available daily measurements, the number of turning-ON events (OFF-ON transitions) of each device in time intervals of 30 min has been derived. Then, the occurrence histograms of the turning-ON events (in 24 h evaluation periods) have been generated.  
The analysis of the histograms has led us to extract the information for the conditioning which is the higher or lower probability that a device has been turned ON in a specific time of the day.  
For example, Figure 2a shows the occurrence histogram of a refrigerator. Relevant trends for the conditioning are not visible, due to the \"always ON\" nature of the device. _Vice versa_, a washing machine (Figure 2b) shows a very low turning-ON probability during night hours; this information can thus be employed to modify the state transition probabilities of this device.
## 4 Effect of Surface Roughness on Hydrogen Resistance
### Hydrogen Hardening Effect Analysis
The presence of many dangling bonds on the surface and the atoms on the surface are saturated, exhibiting a higher chemical activity [35]. Therefore, under the same hydrogen environment, specimens with larger surface roughness tend to form more brittle hydrides, resulting in a higher hardness than the specimens with smaller surface roughness. As the depth increases, the permeating effect of hydrogen atoms gradually decreases. Furthermore, the surface damage induced by pretreatment is attenuated, and the proportion of hydrogen traps in the subsurface and interior of the material is higher than that on the surface. The permeation of hydrogen atoms is hindered by hydrogen traps in the interior of the material, leading to a decrease in hydrogen atoms permeating the interior of the material in deeper depth. The amount of hydride produced by the hydrogen atoms' permeation also decreases as the amount decreases of permeation, which presents a decreasing tendency in the depth direction for the hardness of each surface roughness specimen. Specimens with smaller surface roughness exhibit higher surface integrity, resulting in less contact area between hydrogen atoms and the specimen surfaces and weakening the permeation of hydrogen atoms. Meanwhile, the damage depth caused by pretreatment of the specimen subsurface and internal sections is relatively low. At the low depth section of the specimens, the proportion of hydrogen trap is higher than that with a larger surface roughness, which further reduces the permeation of hydrogen atoms to the deeper sections of the specimen. Therefore, the hardening depth of the specimens with smaller surface roughness caused by the permeation of hydrogen atoms is relatively low. It can be seen from the test results in Figure 5a that a lower precision in the surface pretreatment process results in a larger residual compress stress of the specimen. However, by comparing with Figure 8a, it can be seen that the specimen with a surface roughness of Ra 3.01 \(\upmu\)m exhibits a larger residual compress stress on the surface. However, this does not effectively prevent the process of hydrogen permeation because the hydrogen permeation effect induced by large specimen surface roughness is stronger than the hydrogen permeation resistance effect induced by surface residual compress stress. Consequently, the hydrogen-induced hardening effect on the specimen is more obvious.
###### Abstract  
In the recent years, multi-constellation and multi-frequency have improved the positioning precision in GNSS applications and significantly expanded the range of applications to new areas and services. However, the use of multiple signals presents advantages as well as disadvantages, since they may contain poor quality signals that negatively impact the position precision. The objective of this study is to improve the Single Point Positioning (SPP) accuracy using multi-GNSS data fusion. We propose the use of robust-Extended Kalman Filter (referred to as robust-EKF hereafter) to eliminate outliers. The robust-EKF used in the present work combines the Extended Kalman Filter with the Iterative ReWeighted Least Squares (IRWLS) and the Receiver Autonomous Integrity Monitoring (RAIM). The weight matrix in IRWLS is defined by the MM Estimation method which is a robust statistics approach for more efficient statistical data analysis with high breaking point. The RAIM algorithm is used to check the accuracy of the protection zone of the user. We apply the robust-EKF method along with the robust combination of GPS, Galileo and GLONASS data from ABMF base station, which significantly improves the position accuracy by about 84% compared to the non-robust data combination. ABMF station is a GNSS reception station managed by Meteo-France in Guadeloupe. Thereafter, ABMF will refer to the acronym used to designate this station. Although robust-EKF demonstrates improvement in the position accuracy, its outputs might contain errors that are difficult to estimate. Therefore, an algorithm that can predetermine the error produced by robust-EKF is needed. For this purpose, the long short-term memory (LSTM) method is proposed as an adapted Deep Learning-Based approach. In this paper, LSTM is considered as a de-noising filter and the new method is proposed as a hybrid combination of robust-EKF and LSTM which is denoted rEKF-LSTM. The position precision greatly improves by about 95% compared to the non-robust combination of data from ABMF base station. In order to assess the rEKF-LSTM method, data from other base stations are tested. The position precision is enhanced by about 87%, 77% and 93% using the rEKF-LSTM compared to the non-robust combination of data from three other base stations AJAC, GRAC and LMMF in France, respectively.  
GNSS; data fusion; MM-Estimation; IRWLS; robust-EKF; LSTM; rEKF-LSTM +
Footnote †: journal: Applied Sciences
# Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept
## 3 Experimental Evaluation
### Overall Assessment
#### 3.2.3 Kinematic Assessment Results  
Joint angles were calculated, and displayed as in Figure 8A, based on the standards defined by the International Society of Biomechanics [105]. All kinematic data were plotted from 0% to 100% of gait cycle (0% = touch-down of heel, 100% = consecutive touch-down of the same heel). The estimate of the effective assistance provided by each actuator module (i.e., \(QPA_{kneeFlexion}\), \(QPA_{kneeExtension}\)) worn by the subject is reported in Figure 8B,C. As previously mentioned, the \(QPA_{kneeFlexion}\) is activated between 5% and 70% of the gait cycle and the \(QPA_{kneeExtension}\) is activated between 60% and 100% of the gait cycle. The effective elongation and torque generated by each actuator is also a function of the knee angle, thus, the final elongation trend starts later along the gait cycle with respect to each engagement event (shown in Figure 8B,C). The final abrupt drop of the torque is due to the system disengagement which is done deliberating to dissipate the potential elastic energy. Figure 8A shows the knee angles, averaged between right and left leg during the five walking tasks with (\(Angle_{Exo}\)), and without the exosuit (\(Angle_{NoExo}\)). The vertical line represents the toe off point for each cycle corresponding with the swing starting event. The figure shows how the exoskeleton modifies the trajectory of the knee joint reducing both flexion (between 50-80% of gait cycle) and extension angles (between 80-100% of gait cycle), which is due to the effect of the resistive force generated by the exosuit. Figure 8B,C represent the calculated torque and the elastic band elongation as a function of the joint angle, based on the Equation (2) presented in Section 2.2.1 and validated in [24]. The RoM without the exosuit is \(54.6^{\circ}\pm 8.0^{\circ}\), and with the exosuit it is \(44.0^{\circ}\pm 9.0^{\circ}\), which corresponds to a reduction of \(10.6^{\circ}\). The Peak Knee Flexion (PKF) angle in the sagittal is plane calculated as the maximum flexion angle within the gait cycle and is \(61.9^{\circ}\pm 5.3^{\circ}\) without and \(48.6^{\circ}\pm 4.7^{\circ}\) with the exosuit. These results show that the joint angle is affected by the use of the exosuit, in particular both RoM and PKF are reduced. Physiologically, the knee flexion should be greater than \(40^{\circ}\) with a knee RoM between \(40^{\circ}\) and \(50^{\circ}\), specifically a flexion less than \(40^{\circ}\) inadequately prepares the limb for swing [106; 107; 108]. Although the reduction of both RoM and PKF are about \(10^{\circ}\) and \(13^{\circ}\) respectively, the user is still able to accomplish the walking task with no increase in the risk of tripping due to the knee flexion reduction. In addition, the RoM remains large enough to allow the exosuit to work properly.
###### Abstract  
In Morocco, cereal production is below the expected potential. The adoption of best agricultural practices that reduce vulnerability to climate is a major requirement. No-tillage (NT) agriculture is a system that could improve cereal production by enhancing soil fertility. Some factors, in combination with no-tillage, can further improve cereal yields, especially the choice of variety the adequate fertilization. The objective of our study is to investigate the effect of no-tillage agriculture and nitrogen fertilization on soil fertility and the yield of five durum wheat varieties developed in Morocco in a long-term (18 years) NT trial at the INRA Merchouch experimental station, Morocco. The results show that tillage type had a significant effect on soil organic carbon and CEC (measured before the start of the experiment), as well as on ammonium and nitrates (measured at the end of the experiment), whereas nitrogen dose had a significant effect on total nitrogen and nitrates (measured at the end of the experiment). Regarding wheat yield, as measured at the end of the experiment during the 2020-2021 cropping season, the results show that, under NT, the varieties Nachit, Faraj, and Louiza had grain yields of 4.5, 4.3, and 3.4 t ha\({}^{-1}\) and straw yields of 9.8, 7.8, and 6.8 t ha\({}^{-1}\), respectively, whereas the LC and M.G germplasm had grain yields of 4.05 and 3.72 t ha\({}^{-1}\) and straw yields of 8.25 and 8.39 t ha\({}^{-1}\), respectively. These values are low for a favorable area and correspond to a semi-arid area. In addition, no effects of nitrogen dose were observed due to water stress, which reduced nitrogen use efficiency. Nachit is the most adapted variety under NT, with the highest yield (5.1 t ha\({}^{-1}\)) under a low dose of nitrogen (20 kg N ha\({}^{-1}\)), followed by Faraj (4.7 t ha\({}^{-1}\)), still under NT but with a higher nitrogen dose (40 kg N ha\({}^{-1}\)) and Nachit (4.5 t ha\({}^{-1}\)) with the minimum nitrogen dose but under both NT and CT. In conclusion, Nachit and Faraj wheat varieties performed the best under no-tillage conditions with the minimal nitrogen dose. However, grain yield values were reduced, owing to water stress, which reduced nitrogen use efficiency.  
conventional tillage; durum wheat; nitrogen fertility; no-tillage; soil