esa-sceva/satcom-chunk-collection huggingface dataset:
# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 2 Theoretical Background
### Domain Adaptation  
Domain adaptation [20] is a transfer learning method to solve the problem that the source domain data distribution is different from the target domain data distribution, but the two tasks are the same. The goal is to use labeled source domain data to learn a classifier so that the unlabeled target domain can also be classified. The source domain has a large number of label samples, denoted as \(D_{s}\). In the target domain, there are no label samples or a small number of label samples, denoted as \(D_{t}\). Domain adaptation methods mainly include three categories: sample adaptation, feature adaptation and model adaptation. At present, the feature adaptation method is widely used due to its excellent performance, which is mainly applicable to the situation where the sample distribution in the source domain is different from that in the target domain.  
Recent studies show that there are three main methods to realize feature adaptation in deep learning. The first method is based on differences, such as Kullback-Leibler (KL) divergence, Jensen-Shannon (JS) divergence and MMD. The second is an approach based on adversarial, with the help of GAN ideas. The final approach is to use batch-normalized statistics to align the source and target domain distributions into a canonical distribution. In recent years, the combination of these methods has become a hot spot for scholars from all circles to study.
## 5 Aggregate Privacy Attack  
An AP-Attack (Aggregate Privacy Attack) [2,6] is a reidentification method that aims to identify users by analyzing their movement patterns as shown in aggregated heat maps. This attack uses frequent locations and movement trends from aggregated heat maps to match individual known mobility profiles [6]. By examining how often a user visits different areas, the AP-Attack compares anonymized data to known patterns. This approach is especially effective on datasets where general movement trends are still visible, as it focuses on commonly visited areas rather than specific locations [2,6].  
Figure 4 shows the process of reidentifying users based on their mobility data by carrying out an AP-Attack. The process is divided into two main parts: the training phase and the reidentification phase.  
In the training phase [6], mobility data from a set of known users \(U\) is collected. This data contains their movement traces \(KD\), which shows where and how these users moved over time. For each user \(U_{i}\), a mobility profile \(p(KD_{i})\) is created. Each profile abstracts individual movement patterns of the mobility traces into a heat map.  
In the reidentification phase [6], a separate set of anonymized data \(UD\) is used. This anonymized data contains movement traces \(UD_{j}\) from users whose identities are unknown. For each trace, a profile \(p(UD_{j})\) is created in the same way as in the training phase. These anonymized profiles are then compared to the known user profiles \(p(KD_{i})\) using a distance metric. This metric calculates how similar the two profiles are. In the context of this paper, the Topsee Divergence is used as a distance metric. The anonymized trace \(p(UD_{j})\) is then matched to the known profile \(p(KD_{i})\) that is most similar.
# An Evaluation of the Security of Bare Machine Computing (BMC) Systems against Cybersecurity Attacks
## 5 Cyberattacks and Analysis
### Overview of Selected Cyberattacks
#### 5.1.5. Man-in-the-Middle (MITM)  
The following root causes and preventive methods are derived from [12, 78, 100]. Root Causes:  
1. Attacker using public Wi-Fi access points.
2. Using a secure socket layer provided by the OS.
3. Exploiting protocol vulnerabilities in ARP, DHCP, DNS, ICMP, and IP.
4. Using open-source automation tools.
5. Using open-source OSs.
6. Lack of proper authentication measures to validate users.
7. Freely available educational resources on cybersecurity vulnerabilities and attack techniques. Preventive Mechanisms:  
1. Enable two-factor authentication (2 FA).
2. ARP Spoofing Detection: cryptographic, voting-based, hardware, server-based, host-based solutions.
3. DNS Spoofing Detection: entropy-based, cryptographic, artificial neural network (ANN) solutions.
4. IP Spoofing Defense: router-based, host-based, hybrid solutions.
5. SSL/TLS Solutions: detecting forged certificates, certificate pinning, multipath probing, forcing SSL/TLS connections, friendly MITM, TLS extensions.
6. Provide consistent training and reviews.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 2 Materials and Methods  
### Study Area  
The Loess Plateau spans 640,000 km\({}^{2}\) and is situated in the upper and middle reaches of the Yellow River (100\({}^{\circ}\)54\({}^{\prime}\)-114\({}^{\circ}\)33\({}^{\prime}\)E and 33\({}^{\circ}\)43\({}^{\prime}\)-41\({}^{\circ}\)16\({}^{\prime}\)N) (Figure 1). It has a typical temperate continental monsoon climate, which means it is both hot and rainy at the same time. There are significant zonal peculiarities in the vegetation distribution in the Loess Plateau. The Loess Plateau has the biggest loess dispersion in the world, with certain regions having loess thicknesses of over 200 m. The Loess Plateau has an inland arid and semi-arid climate, and the precipitation has obvious seasonal characteristics: the annual average precipitation is 100-800 mm, concentrated in June to September. The annual average temperature is 7 \({}^{\circ}\)C, showing a decreasing trend from southeast to northwest. The Chinese government has implemented a number of ecological restoration initiatives to address the region's substantial soil erosion issues as a result of serious past ecological difficulties. Particularly, the \"Gain for Green\" project, which has been in place since 1999, has resulted in a notable increase in vegetation cover and efficient soil erosion control. However, in this context, the carrying capacity of the region's water resources has reached its limit [1]. Currently, the vegetation types of the Loess Plateau are mainly artificial forest and grassland, and the forest types mainly include evergreen needle-leaf forests (ENFs), mixed forests (MFs), deciduous broadleaf forests (DBFs) and evergreen broadleaf forests (EBFs).  
Figure 1: Forest types and spatial distribution in the Loess Plateau (evergreen needle-leaf forest (ENF), mixed forest (MF), deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF)) (forest cover data from [[https://data-starcloud.pcl.ac.cn/zh/resource/15](https://data-starcloud.pcl.ac.cn/zh/resource/15)]([https://data-starcloud.pcl.ac.cn/zh/resource/15](https://data-starcloud.pcl.ac.cn/zh/resource/15)) (accessed on 10 October 2023)).
# A Survey of Vehicular VLC Methodologies
## 3 Vehicular VLC Overview
### Reviews on Vehicular VLC
Another work that investigates the maximum achievable distance with various parameters is [76], which introduced the performance of vehicular VLC systems based on non-sequential ray tracing to obtain the channel impulse responses under different weather states. Moreover, the closed-form path loss expression is proposed for both geometrical and attenuation loss. It considers lateral shift; link distance; clear, rainy, and foggy weather types; receiver aperture diameter and transmitter beam divergence angle; and asymmetrical light. In [69], the effect of shadowing and weather disturbances on V2V VLC communication performance was studied, as shading causes blockage of the line-of-sight path. Furthermore, the system was analyzed using diversity-multiplexing tradeoff, as it provides an optimal tradeoff between multiplex gain and diversity gain. The authors in discussed the effect of external propagation characteristics on vehicle-to-vehicle (V2V) communications for both line-of-sight (LoS) and non-line-of-sight (NLoS) models, where the path loss was about \(\approx\)6 dB. The received power was obtained as 7 dB in [77] by using a non-sequential ray tracing approach to study the performance of the V2V VLC system, where the effect of different types of lenses in a receiver was studied, including the combined lens, Aspherical lens, and Fresnel lens, in addition to the impact of bandwidth and receiver diameters, taking into account the asymmetric headlights of the vehicle. In [78], multiple input and output (MIMO) systems were presented with the vehicle-to-vehicle VLC system, where the horizontal and vertical lateral shift was discussed, and the effect of weather conditions, artificial light sources, and optical channel modulation orders between vehicles were studied by examining the performance of SNR and BER, as MIMO technology can significantly enhance system performance. Most of the previous literature focused on studying individual technology. At the same time, in [79], millimeter wave (MMW) and vehicular visible light communications were used. A comprehensive comparison between MMW and VLC channels was presented, and the channel characteristics were investigated between two vehicles with side shift in different paths in the same direction, providing analysis of the density effect of neighboring vehicles.
# Research on Geometric Design Standards for Freeways under a Fully Autonomous Driving Environment
## 3 Elements of Design
### Stopping Sight Distance
#### 3.3.3 Stopping Sight Distance of Trucks on a Downgrade  
The braking distance of traditional vehicles varies with grades, and autonomous vehicles are no exception. The stopping sight distance in Table 5 does not consider the influence of grades. Its value is on the safe side for upgrades and dangerous for downgrades.  
Figure 4: Comparisons of stopping sight distance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Speed** & **Chinese Specified** & \multirow{2}{*}{**Calculated Value (m)**} & \multirow{2}{*}{**Suggested Value (m)**} \\
**(km/h)** & & & \\ \hline
180 & — & 392.65 & 400 \\ \hline
160 & — & 312.71 & 320 \\ \hline
140 & — & 241.85 & 250 \\ \hline
120 & 210 & 180.07 & 185 \\ \hline
100 & 160 & 127.36 & 130 \\ \hline
80 & 110 & 83.73 & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Stopping sight distance based on automatic driving technology.  
Especially for trucks, grades have an impact on their stopping sight distance, as shown in Formula (2) [35; 36]:  
\[S_{\text{st}}=\frac{V}{3.6}\cdot t+\frac{(V/3.6)^{2}}{2(a_{\text{max}}-i)}=\frac{ V}{3.6}\cdot t+\frac{V^{2}}{254(\frac{a_{\text{max}}}{9.81}-i)} \tag{2}\]  
where \(S_{\text{st}}\) is truck downhill stopping sight distance(m), \(i\) is the gradient (%), \(V\) is design speed (km/h); \(t\) is system reaction time, which is 0.5 s, and \(a_{\text{max}}\) is the maximum braking deceleration, which is 3.4 m/s\({}^{2}\).  
Referring to the standards on maximum grade of various countries in the world [3] and combined with the research results of the maximum grade of freeways [23], this paper takes the maximum grade at design speeds of 140 km/h, 160 km/h, and 180 km/h as 3, 2, and 2%. According to Formula (2), the stopping sight distance of trucks on a downgrade in an autonomous driving environment is calculated, as shown in Table 6. Considering the dynamic performance of trucks, their maximum speed is lower than passenger cars. Therefore, the stopping sight distance of trucks at the design speeds of 160 km/h and 180 km/h is not calculated and specified.
## 4 Numerical Results
### Case 3: Study with a Random Friction Coefficient and Disc's Material Properties
None of the stationary kriging results predict the stability of the response within the range of the given sample sizes, while nonstationary kriging yields similar results with the reference. The reason for this difference is that the oscillation phenomenon in stationary kriging is much larger than the tolerance of the indicator function in Equation (26). This implies that although the stationary kriging based metamodel seems to be applicable to the brake system in terms of 2-norm based error, it is difficult to investigate the propensity of the stability. However, in the case of nonstationary kriging, since spurious oscillation is alleviated by applying the step-wise hyperparameters, the obtained results are close to the reference. Figure 17: Evolution of the 2-norm and 1-norm error with the number of samples in case 3. Stationary and nonstationary kriging with polynomial order from 0 th to 3 rd are considered. Figure 18: Evolution of the stability with the number of samples in case 3. Stationary and nonstationary kriging with polynomial order from 0 th to 3 rd are considered. Finally, the computational cost and numerical efficiency of the proposed method are discussed. Given the finite element model, it takes about 3.7 s to solve the complex eigenvalue problem for one sample, and the total computation time is assumed to be linear for the given sample sizes. The total computation time results for complex eigenvalue problems, and stationary and nonstationary kriging, are given in Figure 19. The right and left \(y\)-axis are computation time in eigenvalue problems and kriging, respectively. The obtained results indicate that although the computation cost for nonstationary kriging is higher than that for stationary kriging, its additional cost can be relatively negligible when compared to solving the eigenvalue problems. Therefore, for the small number of random variables, the proposed nonstationary kriging can improve metamodel performance with acceptable additional computation.
# Voice Communication Systems Impact on Navigating Officers
###### Abstract  
The tasks of an officer of the watch (OOW) on complex ships during navigation in coastal areas may be very challenging. Almost all the tasks require substantial information processing and timely decision making. Every distracting element should be avoided during navigation. Every call, made through any communication system, activates a new process that the OOW needs to conduct. The new process may interrupt a previously started task. In case of too many incoming calls, the workload of the OOW may increase significantly, thus, their situational awareness may be compromised and the risk of errors may increase. The objective of this research was to analyze the impact of incoming voice calls on the OOW. The research methods used include a questionnaire survey and a series of interviews with experienced officers. The main outcomes refer to the average frequency of incoming calls, duration of conversations and subjective assessment of their influence. The results indicate that, during one watch, an officer needs to answer 14 calls that last 16.19 min altogether. However, the officers consider 45% of calls made during watch keeping as distracting. A possible call management system with the aim to reduce distractions made by low priority calls is proposed in this paper.  
safety of navigation; human element; workload; situation awareness; ship's bridge +
Footnote †: journal: Marine Science and Engineering
# FACTMS: Enhancing Multimodal Factual Consistency in Multimodal Summarization
## 6 Results and Analysis
### Ablation Study  
Two ablation experiments are designed to analyze the effects of the textual knowledge graph and the visual knowledge graph on word overlap and faithfulness. The result is shown in Table 5. We can see that the model has better performances in multimodal factual consistency with the dual heterogeneous graph. Textual knowledge improves textual factual consistency more than visual knowledge, but it is the opposite in the consistency of visual facts. That is to say, by fusing visual knowledge information only, the model will generate more fluency and informativeness but less reliability and factual consistency. It demonstrates that integrating textual and visual knowledge information helps generate both fact-aware and fluency summaries.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **FactCC\({}_{avg}\)** & **FactCC\({}_{>0.9}\)** & **RAR** & **EAR** & **SICS\({}_{max}\)** & **SICS\({}_{avg}\)** \\ \hline PG [(46)] & 68.44 & 20.91 & 11.90 & 23.31 & 42.86 & 40.71 \\ FTSUM [(4)] & 66.84 & 25.01 & 12.10 & 23.19 & 42.77 & 40.62 \\ MAtt [(2)] & 47.71 & 11.14 & 7.43 & 17.93 & 42.96 & 40.82 \\ MSMO [(2)] & 67.90 & 27.20 & 11.35 & 22.20 & 42.63 & 40.47 \\ MOF [(43)] & 68.72 & 21.12 & 12.33 & 23.83 & 42.95 & 40.80 \\ MSE [(3)] & 70.22 & 27.51 & 13.20 & 24.22 & 42.99 & 40.86 \\ VMSMO [(44)] & 63.71 & 24.50 & 12.86 & 23.37 & 42.67 & 40.69 \\ HCSCL [(19)] & 68.71 & 31.76 & 12.27 & 23.81 & 42.79 & 40.63 \\ \hline FactMS & **70.69** & **32.92** & **19.41** & **24.92** & **43.01** & **40.90** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of FactMS with baseline on faithfulness evaluation metrics.
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 4 Results
### Impact of Typical Obstructions  
#### 4.2.1 Impact of the Catenary  
We use the parameters derived from the data collected on \(T_{1\&2}\) to analyze the impact of the Catenary. As we mentioned in Section 2, the LOS path can be blocked by the horizontal metal beam or the metal net at some testing points on the selected track, as shown in Figure 2, and these testing points were recorded in detail during the test and are listed in Table 6.  
From Figures 16 and 17, we can tell that there are no obvious changes with respect to the number of MPCs and the delay of the first effective path at the blocked positions, and the LOS path, which is the first effective path, can penetrate the metal net and reach the receiving end, even though penetration loss is introduced, which we will further analyze. However, from Figures 18 and 21, we can see that, in terms of the time-spread and received power, significant changes have taken place in these blocked positions.  
Figure 19: Fitting of time-spread.  
positions of 152.4 m and 202.4 m, the time-spread compared with the expected value is increased by 2970 ns and 2853 ns, respectively. This is because, as the distance between Tx and Rx increases, the number of Catenary between the Tx and Rx increases. The delay of the first path, which is the LOS path, is basically stable, but the reflection and scattering conditions of other paths reaching the blocked position are more complex, so the delay spread increases obviously.  
In Figure 21, at all the blocked positions, deep fading occurs; we can see at the positions of 37.4 m, 152.4 m, and 202.4 m that the penetration loss is 4.63 dB, 5.31 dB, and 10.33 dB, respectively. As mentioned above, as the distance between Tx and Rx increases, the number of Catenary between Tx and Rx increases, and the density of Catenary increases accordingly; therefore, the probability of being blocked by Catenary increases. We noticed that the penetration loss at the position of 202.4 m doubled compared with the former two positions; this is because, at this position, the path was blocked by Catenary twice, as we observed during the test. We can see that the penetration loss of the Catenary is approximately 5.07 dB.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 4 Uplink Transmission
### Partial Full Zero Forcing  
The Full-Pilot Zero Forcing (FZF) combining method allocates \(\tau_{p}\) degrees of freedom to address interference. However, since interference affecting User Equipment (UE) \(k\) primarily comes from a small subset of other UEs, we implement Partial-Pilot Zero Forcing (PFZF) combinations. This technique focuses on mitigating interference from UEs with strong channel gains while allowing for some interference from UEs with weaker gains. Consequently, Access Point (AP) \(l\) uses PFZF combining for strong UEs and Maximum Ratio (MR) combining for weaker ones.  
The PFZF combining vector for UE \(i_{k}\) at AP \(l\), denoted as \(v_{i_{k},l}^{\text{PFZF}}\), is given by  
\[v_{i_{k},l}^{\text{PFZF}}=c_{i_{k}}\theta_{i_{k}}\bar{H}_{l}E_{S_{l}}\Big{(}E _{S_{l}}^{H}\hat{H}_{l}^{H}\bar{H}_{l}E_{S_{l}}\Big{)}^{-1}, \tag{94}\]  
where \(E_{S_{l}}=(e_{\tau_{l,1}},\dots,e_{\tau_{l,\tau_{l}}\tau_{l}})\in\mathbb{C}^{ \tau_{p}\times\tau_{S_{l}}}\) and \(e_{\tau_{l,l}}\) denotes the \(t\)-th column of \(\mathbf{I}_{\tau_{S_{l}}}\).  
Equation (94) is based on the derivation in [43]. In our study, we employ a similar formulation derived directly from the approach outlined in [43].
## 3 Experimental Analysis  
In this section, we present the analysis of the reflection power response of two rooms from their RIR datasets recorded using an em32 Eigenmike [38], which is a \(Q=32\) element rigid spherical microphone array of radius \(R=0.042\) m. Both the RIR datasets were measured using a source signal generated from a directional loudspeaker. The first RIR dataset available from the work in [39] is for a small audio laboratory room of size \(3.54\times 4.06\times 2.70\) m, hereafter referred to as Room-1. The second RIR dataset from [40]pertains to a larger classroom of size \(6.5\times 8.3\times 2.9\) m, hereafter referred to as Room-2. According to these datasets, the reverberation time (\(T_{60}\)) of Room-1 and Room-2 are 0.329 s and 1.12 s, respectively. From the datasets, we have selected the RIRs for different source positions in the XY plane, i.e., \(\theta_{o}=90^{\circ}\) at different \(\phi_{o}\) angles, and at 1 m distance from the microphone array center. The direct path component from the source arrives at the receiver around 0.0026 s and 0.0028 s for Room-1 and Room-2, respectively.  
From the selected 32-channel RIRs, we obtain \(H(x_{q},y_{o},t,k)\) using the STFT operation with a 1024-sample Hanning window with 50% overlap, 2048-point fast Fourier transform (FFT), and 48 KHz sampling frequency. We then follow the process described in Section 2.2 to generate \(P_{R}(t,k,\hat{y}|\mathbf{y}_{o})\) for 500 uniformly distributed \(\hat{y}\) directions derived from spiral-based sampling [41]\(\forall\)\(t,k\) bins in the frequency band of 20 to 1500 Hz. These 500 spiral sampled directions provide sufficient spatial resolution to assimilate the sound reflectivity variations across the room surfaces at a reasonable computation cost. Finally, we estimate \(P_{T}(t,k|\mathbf{y}_{o})\) for analyzing the time-frequency spectrum of the reflection power of the two rooms. While dealing with the temporal response in the following sections, the 0 s in the time-index indicates the moment of sound event occurrence. However, the reflection power response is calculated only from 0.01 s which is the center of the first STFT frame. This frame size was selected after considering a reasonable time-frequency resolution for proper spectral and temporal analysis of reflections in both rooms.
## 6 Conclusions  
Notwithstanding continuous progresses in the capacity of batteries, minimizing the energy dissipation still is one of the key objectives in the design of most sensor devices. In particular, transmission energy is a relevant component of the overall energy budget of a wireless sensor. This paper explores the use of LDPC codes to protect sent information against channel errors, thus allowing for a lower transmission energy. The energy that is saved at the transmission side depends on the coding gain of the selected code: more powerful the code, larger the saved energy. However a decoder is required at the receiver side to reconstruct the original information. The node to node communication throughput is low in wireless sensor applications and this enables the design of a fully serial decoding architecture, with limited implementation complexity and extremely low dissipated power. The additional energy consumed by the decoder has been evaluated by means of logical synthesis and layout generation. Final results prove that percentage saving as high as 80% can be achieved with the coded approach with respect to the usual uncoded transmission.