esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 3 Case Study
### Data Processing  
The engineering data corresponding to 50-1000 rings of the right line interval from Wenjing Road Station-Chalukou Station were collected for experimental verification. The data were pre-processed according to the steps described in Section 2.2.2. First, the data for the parameters in Table 2 at the time of advancement were extracted from the cloud database. Then, to avoid the influence of the input outliers on the model, the outliers in each ring were identified and removed using Equations (3)-(6), with the original data of 1,846,752 sample points and the remaining 1,779,708 data after processing, with the outliers accounting for 3.7% of the total data. The comparison of penetration before and after processing is shown in Figure 6. It was found that the original penetration data suddenly increased. This may be because the sensor fails to accurately read the excavation speed or cutter head speed due to the influence of noise, resulting in the above problem. After processing the outliers, the penetration data are within a reasonable range.  
Figure 5: Geological profile of the section between Wenjing Road Station and Chalukou Station.  
After filtering the data, the different types of features were standardized using Equation (7) to eliminate the adverse effects of different magnitudes on the model. Finally, after interpolating the stratigraphic information using Equation (8), the stratigraphic information and construction parameters were spatially aligned to construct the input dataset. The experiments were designed to preserve the original data characteristics and temporal continuity by dividing the dataset into training, validation, and test with a ratio of 7:1:2, in chronological order.
Currently, research by scholars both domestically and internationally on the stability control of four-wheel-drive vehicles mainly focuses on direct yaw moment control [12; 13; 14], drive torque distribution technology [15; 16], and electronic differential control [17]. Additionally, for the sprayer chassis system, which exhibits strong nonlinear characteristics, the choice of an appropriate stability control algorithm is crucial. Currently, the model predictive control [18], sliding-mode variable structure control [19], fuzzy control [20], or a combination of multiple control algorithms [21] are commonly used. However, the current research on the stability control of sprayers is mostly limited to the control of a single index such as the wheel slip rate or lateral or longitudinal stability, and an in-depth discussion on the cooperative control strategy of multiple stability indicators is lacking. At the same time, a control strategy combined with the specific working scene of the sprayer is relatively scarce. In view of this, this study aims to fill the research gap by combining the structural characteristics and operational requirements of a self-propelled sprayer and proposing an innovative chassis control strategy, aiming to optimize the side deflection angle of the center of mass, yaw speed, and the wheel slip rate at the same time, so as to maximize the stability of the whole machine. Through the carefully designed torque distribution algorithm, the reasonable distribution of each wheel torque can be ensured to improve the performance and stability of the sprayer under various working conditions. Finally, the controller simulation model built in the MATLAB/Simulink (R2023a) environment fully demonstrates the effectiveness and practicability of the innovative control strategy through the simulation verification of four driving situations under different working conditions.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Internet Layer
#### 3.3.1 Low-Power Wireless Personal Area Networks  
6 LowPAN is an IETF standard facilitating the integration of low-power devices into the IoT communication paradigm by transmitting IPv6 packets over low-power wireless networks [77]. Providing support to address the challenges posed by resource-constrained devices by employing efficient packet and header compression techniques, effectively reducing overhead [78]. In addition, IPv6 incorporates adaptation and fragmentation mechanisms to handle packet loss and variable link quality in low-power nodes. By optimising data transmission and network efficiency, 6 LowPAN facilitates the seamless integration of IoT devices with limited resources into the larger IPv6-based network infrastructures. 6 LowPan is easily adaptable to an increase in the number of nodes in a network, making it a suitable, easy choice for V2I communications.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 2 Background  
### Compressive Sensing  
Denote \(\mathbf{x}=\boldsymbol{\
abla}\mathbf{s}\), where \(\mathbf{x}\in R^{N\times 1}\) is the signal of interest, and \(\boldsymbol{\
abla}\in R^{N\times N}\) is a transformation basis. We say \(\mathbf{x}\) is \(K\)-sparse if \(\mathbf{s}\) has only \(K\left(K\ll N\right)\) dominant elements, while other elements are zero or close to zero. The theory of CS states that, under certain conditions, instead of periodically sampling \(\mathbf{x}\), we only need to acquire \(M\left(M\ll N\right)\) non-adaptive linear measurements \(\mathbf{y}=\mathbf{\Phi}\mathbf{x}\), where \(\mathbf{\Phi}\in R^{M\times N}\) is a carefully-chosen measurement matrix [15]. Combining the measurement process and sparse representation, we have \(\mathbf{y}=\mathbf{\Phi}\mathbf{x}=\mathbf{\Phi}\mathbf{y}\mathbf{s}=\mathbf{ A}\mathbf{s}\), where \(\mathbf{A}\) is called the equivalent sensing matrix. It was shown in [20] that \(\mathbf{x}\) can be exactly recovered from its measurement vector \(\mathbf{y}\) by solving the following constrained optimization problem:  
\[\min\ \left\|\mathbf{s}\right\|_{0}\quad\text{s.t.}\quad\mathbf{y}=\mathbf{A}\mathbf {s} \tag{1}\]  
as long as \(K<\frac{1}{2}\left(1+\frac{1}{\mu\left(\mathbf{A}\right)}\right)\). Additionally, \(\mu\left(\mathbf{A}\right)=\underset{i\
eq j,1\leq i\leq N}{\max}\left\{ \frac{\left\|\mathbf{a}_{i}^{T}\mathbf{a}_{j}\right\|}{\left\|\mathbf{a}_{i} \right\|\cdot\left\|\mathbf{a}_{j}\right\|}\right\}\) represents the mutual coherence of \(\mathbf{A}\), with \(\mathbf{a}_{i}\), \(\mathbf{a}_{j}\) denoting the \(i\)-th and \(j\)-th column, respectively. Another metric is the average mutual coherence \(\mu_{av}\left(\mathbf{A}\right)=\underset{i\
eq j,1\leq i\leq N}{\text{mean}} \left\{\frac{\left\|\mathbf{a}_{i}^{T}\mathbf{a}_{j}\right\|}{\left\|\mathbf{a} _{i}\right\|\cdot\left\|\mathbf{a}_{j}\right\|}\right\}\). They can also be determined according to the maximum or average value among the absolute off-diagonal elements in the corresponding Gram matrix \(\mathbf{G}=\mathbf{\tilde{A}}^{T}\mathbf{\tilde{A}}\), where \(\mathbf{\tilde{A}}\) is the column-normalized version of \(\mathbf{A}\). The mutual coherence \(\mu\left(\mathbf{A}\right)\) is by now a classical way of analyzing the recovery abilities of a measurement matrix. Furthermore, it can be proven that the solution to the problem in (1) is the same as the relaxed one to the \(l_{1}\)-based minimization below:  
\[\min\ \left\|\mathbf{s}\right\|_{1}\quad\text{s.t.}\quad\mathbf{y}=\mathbf{A} \mathbf{s} \tag{2}\]  
which can be solved efficiently using the algorithms such as basis pursuit (BP) [15].
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
###### Abstract  
Uniform quantization is widely taken as an efficient compression method in practical applications. Despite its merit of having a low computational overhead, uniform quantization fails to preserve sensitive components in neural networks when applied with ultra-low bit precision, which could lead to a non-trivial accuracy degradation. Previous works have applied mixed-precision quantization to address this problem. However, finding the correct bit settings for different layers always demands significant time and resource consumption. Moreover, mixed-precision quantization is not well supported on current general-purpose machines such as GPUs and CPUs and, thus, will cause intolerable overheads in deployment. To leverage the efficiency of uniform quantization while maintaining accuracy, in this paper, we propose sensitivity-aware network adaptation (SANA), which automatically modifies the model architecture based on sensitivity analysis to make it more compatible with uniform quantization. Furthermore, we formulated four different channel initialization strategies to accelerate the quantization-aware fine-tuning process of SANA. Our experimental results showed that SANA can outperform standard uniform quantization and other state-of-the-art quantization methods in terms of accuracy, with comparable or even smaller memory consumption. Notably, ResNet-50-SANA (24.4 MB) with W4A8 quantization achieved 77.8% top-one accuracy on ImageNet, which even surpassed the 77.6% of the full-precision ResNet-50 (97.8 MB) baseline.  
neural architecture adaptation; automated machine learning; uniform quantization; model compression; efficient deep learning +
Footnote †: journal: _Applied_  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 5 Experiment and Analysis
### Experiment Settings  
The anchor is equipped with a circular antenna array and multiple Decawave UWB chips [46]. To verify the lowest performance of single anchor localization, the antenna number is set as 3 in order to realize omnidirectional angle measuring. In particular, all antennas are utilized for receiving the Resp messages while only one antenna is used to send the Init and Sync messages. According to the IEEE 802.15.4 UWB channel standard [45], the pulse repetition frequency (PRF) is set as 64 MHz, the preamble symbol length is set as 128, and the preamble code index is set as 9 both for transmitting and receiving and the channel 1 is adopted. An illustration of the scenario with three agents and one anchor is shown in Figure 4.  
We conduct two experiments: one is in an indoor hall environment E1 and the other is in an indoor lab classroom environment E2, where the CAD maps are shown in Figures 5a and 6a, respectively. The E1 is a circular area with the inner and the outer ring radius of 7 m and 15 m, respectively. In E1, there are various reflection objects and scattering objects which have a negative impact on the results, such as walls, tables, chairs, indoor plants, and walking people, and the walking people will normally pass into the experimental environment, but will not block the LOS propagation. Besides, there are no more than three people in the scene at the same time. E2 is a rectangular room with a 6 m \(\times\) 8 m = 48 m\({}^{2}\) rectangular area. There are also many reflectors and scatters in the area, such as desks and chairs. In both environments, the anchor, represented by the red ring, is placed nearly in the center of the environment in order to provide better service for the agents. Both the anchor and the agents are placed at the height of 1.3 m, so that they are on the same two-dimensional plane. The root mean squared error (RMSE) and the Euclidean Distance are chosen as the performance metrics, and the Euclidean Distance between position \(\mathbf{p}_{1}\) and \(\mathbf{p}_{2}\) is given by \(||\mathbf{p}_{1}-\mathbf{p}_{2}||\).  
Figure 4: Experimental environment illustration. Three agents and a single anchor are shown as an example in an indoor environment.
###### Abstract
The objective was to reduce operating costs and production time in the industry, increase the productivity of an existing system, improve asset maintenance, facilitate accessibility, or create a safer work environment [53]. DT represents a breakthrough in digital transformation which applies to other industries and not only to the construction field, and in all of them, Information and Communication Technologies (ICTs) are a key driver. It is part of the so-called Product Life cycle Management (PLM). It is increasingly applied in more sectors, such as smart manufacturing, building management, smart cities, healthcare or oil and gas. DT integrates multiple engineering disciplines [54]. Nevertheless, even considering all the BIM profits, implementation barriers have also been detected in the Architecture, Engineering and Construction (AEC) sector. One important barrier in terms of information exchange is the great difference of workflow considering the type of infrastructure [55]. Data interoperability is another outstanding BIM implementation barrier [56] with the need for powerful hardware to run BIM infrastructure models [57]. Moreover, the lack of standards and legal issues related to BIM have been reported by some published research [58; 59; 60; 61; 62; 63]. Lastly, the BIM skills needed for the methodology implementation are one of the most repeated implementation barriers shown in published research [4; 8; 18; 19; 64; 65; 66]. Beyond the BIM model, there are new challenges to implementing DT. DT is a complex system in which the main problems arise in integrating between virtual entities with real objects [67], the complexity of processing and data flows [68]. Thus, having a DT to interact with an infrastructure is essential to optimise its FM. However, most authors approach BIM-IoT integration to monitor the built environment, and only the most advanced research includes its management and predictive maintenance [49]. However, most of this research has been carried out in the construction sector, but only a few studies have examined transport infrastructure [35]. In the AEC industry, linear infrastructure is the work with the fewest BIM-FM studies. Future contributions should aim at solving these issues using not only BIM methodologies but also implementing it with other technologies to promote an easy BIM implementation in the sector. This review has been performed with the aim of gathering the most relevant procedures and advances in the use of BIM, especially focussed on the possibilities of BIM together with other digitalisation technologies such as Big Data, IoT or Cloud Computing. After analysing and discussing the data, guidance is provided as regards the alternatives to be used in civil engineering projects. This combined use of digital technologies together with physical terminals such as sensors, smart devices, GPS receivers or wireless communication networks is commonly known as Smart Construction (SC) [69; 70]. Additionally, improvements are identified which provide some new perspectives for the use of BIM in the construction and civil engineering field.
## 3 The Hysteresis Current Controller
### Effects of the Power Rating of the MC on Inverter-Switching Frequency  
The effects of the MC power rating on the maximum instantaneous switching frequency of the VSC was analyzed with MC ratings of 10 kW, 50 kW and 100 kW. Each had a 6 kHz frequency on the trapezoidal signal, as it closely represented the MC current shape. For the above converter rating, the appropriate MC input current, _If_, was obtained with Equation (34). For the MC rated capacity, \(\textsl{S}_{\text{MC}}\), of 10 kW, the MC input current and the maximum rate of change in the reference current can be obtained from Equations (34) and (35).  
\[I_{f}=\frac{S_{\text{MC}}}{400\sqrt{3}}=14.43\text{ A} \tag{34}\]  
\[\frac{di_{f}^{*}}{dt}=\frac{8\sqrt{2}}{\pi^{2}}\,I_{f}^{*}\omega\left(1+\frac{ 1}{3}-\frac{1}{5}\right)=7.06\times 10^{5}\text{ A}/\text{s} \tag{35}\]  
The instantaneous frequency of the VSC can be calculated from Equation (35), and the rate of change in the rising and falling inverter current of Equations (30) and (31) is shown as Equation (36).  
\[\begin{array}{l}\frac{di_{f}^{+}(t)}{dt}=\frac{(700-400)}{0.077 mH}=3.88 \times 10^{6}\text{ A}/\text{s}\\ \frac{di_{f}^{+}(t)}{dt}=-\frac{(700+400)}{0.77 mH}=-1.47\times 10^{7}\text{ A}/\text{s}\\ f_{inst}=\frac{1}{2\varepsilon}\left(\frac{1}{\frac{399\times 10^{6}-70\times 10^{5}} {\varepsilon}\frac{1}{7.96\times 10^{6}+1.47\times 10^{7}}}\right)\\ f_{inst}=\frac{13\varepsilon}{\varepsilon}MHz\end{array} \tag{36}\]  
Table 3 summarizes the deduction above with a different matrix converter power, \(\textsl{S}_{\text{MC}}\) at 6 kHz, which is expressed as a ratio of the hysteresis band current. From the table, it can be concluded that the VSC switching frequency is directly proportional to the power of the MC. Hence, an increment in the MC power increases the VSC switching frequency, which proves the theory.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\textsl{S}_{\text{MC}}\)/\&/\Finally, the effects of the hysteresis current error, \(\varepsilon\), and the maximum instantaneous switching frequency are analyzed. From Equation (21), the relation between \(\varepsilon\) and \(f_{inst}\) is inversely proportional. The error current was toggled between 0.2 A, 0.5 A, 0.7 A and 1 A at 10 kW MC rated power. Table 4 shows the obtained results.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Resistance to Noise and Data Loss  
Digital images transmitted over communication networks are often affected by noise and data loss, which can render cipher images undercyptable. Such distortions could also be introduced by attackers aiming to corrupt cipher images. These attacks are known as noise and occlusion attacks. To ensure the reliability of an image encryption algorithm, its ability to withstand such attacks and decrypt the plaintext image from the altered cipher image needs to be tested [60]. To evaluate an encryption algorithm's resilience against noise attacks, researchers introduce salt and pepper and Gaussian noise to the cipher image with variable densities and try to decrypt the affected cipher images. For resistance, against occlusion attacks, researchers evaluate the encryption algorithm's ability to decrypt a cipher image that had segments cropped out at varying degrees of severity. Furthermore, the PSNR metric (Section 3.5.3) could be utilized to quantify the quality of the encryption algorithms at decrypting a corrupted image by comparing the PSNR of the decrypted corrupted image to the plaintext image [61]. A higher PSNR of the decrypted image when compared to the original plaintext image indicates that the decrypted corrupted image is closer to the original plaintext image.
## 6 Security Analysis
### Privacy Protection
#### 7.1.3 Accuracy  
Typically, model parameters are represented as floating-point numbers. But when it comes to encrypting these parameters, we need to convert them from floating-point to positive integers. As outlined in the method by [42], we effectively transformed floating-point numbers into positive integers using the following approach: In our study, each floating-point number \(w\) is scaled to a positive integer \(x=\alpha\times(w+L)\), where \(\alpha\) is a scaling factor, \(L\) is a sufficiently large positive integer, \(\alpha\) equals \(10^{k}\), and \(k\) represents the precision level. The choice of \(k\) influences the model's accuracy.  
Figures 3-5 highlight the discrepancy in model accuracy between the FedAVG [2] method using floating-point numbers and that quantizing model parameters to positive integers. Experimental findings reveal that when \(k\) is set to 4, the FedAVG method employing floating-point numbers outperforms the quantized approach in terms of accuracy. However, as \(k\) increases, the gap in accuracy between the two methods markedly narrows. Specifically, when \(k\) reaches 5, the difference in accuracy falls within an acceptable range; by the time \(k\) hits 7, the accuracy levels of both methods are virtually indistinguishable.  
\begin{table}
\begin{tabular}{c c} \hline
**Software and Hardware** & **Configurations** \\ \hline OS & Ubuntu 16.04 64 bit LTS \\ CPU & Intel(R) Xeon(R) Gold 6130 2.10 GHz \\ Memory & 256 GB \\ Storage & 24 TB \\ GPU & NVIDIA RTX 2080 Ti \\ Graphics memory & 11 GB \\ Hardware acceleration & 4352 CUDA Cores \\ \hline \end{tabular}
\end{table}
Table 2: Software and hardware configurations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Settings** & **MNIST** & **Fashion-MNIST** & **CIFAR-10** \\ \hline Model & CNN & CNN & CNN \\ Learning rate & 0.005 & 0.005 & 0.001 \\ Batch size & 16 & 16 & 128 \\ Local epoch & 1 & 1 & 1 \\ Optimizer & SGD & SGD & SGD \\ \hline \end{tabular}
\end{table}
Table 3: Experimental settings on different datasets.  
Figure 4: Accuracy comparison between our scheme and FedAvg under different \(k\) values on Fashion-MNIST. (**a**) \(k=4\); (**b**) \(k=5\); (**c**) \(k=6\); (**d**) \(k=7\).  
Figure 3: Accuracy comparison between our scheme and FedAvg under different \(k\) values on MNIST. (**a**) \(k=4\); (**b**) \(k=5\); (**c**) \(k=6\); (**d**) \(k=7\).
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Short Range Communication Technologies  
Telemedicine involves remote collection of human vital information; this can be carried out by establishing sensor network systems on the human body. This sensor network can be coupled with short-range communications technology to form a Body Area Network (BAN). A BAN is a network consisting of distributed nodes placed on the body to acquire physiological data [4].  
The typical issues to consider in implementing wireless BANs (WBANs) include the sensor type, data management, routing method, quality of service expectation, communication path, standard/technology, security, and simulation environment [4]. The initial WBANs had low bandwidth requirements, with data rates between 10 kilobits per second (kbps) to 10 megabits per second (Mbps) [4]. The most popular short range communication technologies to achieve the telemedicine system are Bluetooth, Zigbee, Wi-Fi, and UWB. In [87], the implementation of a medical monitoring system comprising sensor communication technology was described. The communication protocols used for the communication aspect were Bluetooth and ANT/ANT+, while WebSocket technology was used to establish the connection between the mobile client and remote server. In [88], the authors presented a telemedicine system with a network that utilised an optical fibre backbone. WBANs were used for vital sign collection with the aid of the sensor nodes. The network was set up with nodes arranged in clusters, and each microcontroller in a cluster was connected to a sink which could predict the disaster parameters for a particular individual. The sensor network relayed the aggregated data remotely to doctors via the internet.