esa-sceva/satcom-chunk-collection huggingface dataset:
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 2 A Preliminary Overview of the LoRaWAN\({}^{\text{TM}}\) System  
As for almost any communication network (see for example Chapter 1 of [5]), LoRaWAN\({}^{\text{TM}}\) networks can be divided in two parts:  
* The radio access network part, composed of terminals (which are called end devices in the context of LoRaWAN\({}^{\text{TM}}\) networks) and gateways (GW);
* The back-end part, that is, the \"core network\" of LoRaWAN\({}^{\text{TM}}\) networks, composed of the network server (NS). Actually, the back-end is much more complicated than a simple entity as the NS, and will be further examined in the next sections.  
As far as the radio access network part (i.e., the wireless links between end devices and GWs) is concerned, for the scope of this paper, which is dealing with the \"core network\" of LoRaWAN(tm), it suffices to say that (see Figure 1)  
1. packets are exchanged in both directions (uplink and downlink);
2. the GWs are transparent devices. This means that they receive/transmit the packets over the radio interface and they receive/forward these packets from the NS without any intervention on the packets themselves.  
The complete intelligence of the LoRaWAN(tm) network resides in the Network Server, which is, for example, in charge of de-duplicating the received packets: indeed, as shown in Figure 1, a single packet can be received by multiple GWs. More detailed descriptions of the radio interface and the medium access control (MAC) layer can be found in [6; 7], respectively.  
We want to remark that this paper refers only to the latest version (i.e., version 1.1) of the LoRaWAN(tm) system specification [8]. The previous versions of the specifications--to which, e.g., [7] is referring--did not include a complete and verified description of the roaming feature, which is the focus of this paper.
# Regolith-Rich PEEK Composite Bricks: Steps Towards Space-Ready Lunar Construction Materials
###### Abstract  
This study introduces a novel composite construction material composed of lunar regolith combined with PEEK in dry powder form. The work demonstrates significant advantages over alternative methods, primarily by reducing production power consumption and simplifying the manufacturing process. Building on previous research that explored binder optimization through process simplification and targeting predefined shapes, this work delves deeper into a comparative analysis of high-performance thermoplastics. Among the various options, PEEK demonstrates the most favorable properties. The study investigates key processing parameters and evaluates the effects of vacuum processing and temperature testing on mechanical properties. The research also evaluates the effects of vacuum processing and temperature testing to assess the material's performance under lunar conditions. Comparative analysis is performed with standard performance of various reinforced and unreinforced concretes and with standard requirements for construction bricks as per ASTM standards. This shows that the composite, with an organic binder content as low as 5 wt%, has great potential. Notably, the improvements achieved through vacuum curing ensure compliance with lunar environmental conditions and alignment with most Earth-based engineering standards. Samples compacted at 7.50 MPa with 10 wt% binder, and tested at room temperature, achieve a compression strength of 16.3 MPa, exceeding that of industrial floor bricks and matching that of building bricks used on Earth. Bending strength (7.4 MPa) aligns with steel fiber-reinforced and high-strength concretes. Vacuum curing further enhances these properties, with an observed increase of +66% in bending strength and +33% in compression strength.  
ISRU; lunar regolith; PEEK; moon; composite material; bricks; sustainability +
Footnote †: journal: Applied sciences
# A 100 Gb/s Quad-Lane SerDes Receiver with a PI-Based Quarter-Rate All-Digital CDR
## 1 Introduction
As shown in Figure 2c, as the data rate increases above about 10 Gb/s/lane, CDR and equalizer circuits are added to each data lane to improve signal integrity and provide much higher communication bandwidth [5; 22; 23; 24; 25; 26; 27; 28]. However, these conventional multi-lane receivers with CDRs [5; 22; 23; 28] generally have the disadvantages of a large area and high-power consumption. In particular, these multi-lane receivers have a problem that it is difficult to apply to burst mode applications due to the large CDR lock time. In particular, since [5; 22; 23] all use a PLL as a high-frequency clock generator, there are issues such as a very long lock time, jitter peaking, and jitter accumulation problems that may degrade the CDR performance. Among the various CDR architectures, phase interpolator (PI)-based digital CDRs offer a fast lock time with better jitter performance [5; 6; 29; 30; 31; 32]. In this paper, a new 100 Gb/s quad-lane SerDes receiver with a small area and low-power consumption is presented. The proposed all-digital quarter-rate PI-based CDR utilizes a multiplying delay-locked loop (MDLL) instead of the usual PLL to generate the eight-phase high-frequency clocks required for the PI operation [24; 33]. Through this, both frequency multiplication and eight-phase clock generation are performed at the same time, thereby achieving an area and power reduction and low-jitter characteristics. The proposed digital CDR uses a new initial phase tracker that uses a preamble to reduce the lock time. The CDR utilizes a quarter-rate 2x-oversampling architecture, and the PI controller is designed full custom to minimize the loop latency. The update rate of the CDR can be adjustable, resulting in an improved peak-to-peak (p-p) clock jitter performance. As a result,it has the advantage of having a fast lock time of about 12 ns and a constant output data sequence. Moreover, a new differential near-ground receiver with an adaptive continuous-time linear equalizer (CTLE) was adopted to reduce the power consumption and ensure high-frequency characteristics up to 25 Gb/s/lane. The rest of this paper is organized as follows. Section 2 presents the architecture and operation of the proposed quad-lane SerDes Receiver and the quarter-rate CDR. Section 3 shows the experimental results and Section 4 presents the conclusion.
## 2 YOLOv8-Pose Estimation Model
### Pose Estimation  
The most significant feature of pose estimation compared to other computer vision tasks is its ability to analyze the three-dimensional joints in a two-dimensional image through neural networks.  
When dealing with the problem of joint occlusion, the key for YOLOv8-pose lies in fully utilizing contextual information. Contextual information includes the positions of detected joints, structural constraints of human posture, and the relative positional relationships between joints.  
Any object has a specific anatomical structure, and these structural constraints can help the model perform accurate pose estimation even when some joints are occluded. Suppose we have an object model where the positions of the joints are represented as:  
\[J=\{j_{1},j_{2},\dots,j_{n}\} \tag{5}\]  
where \(ji\) is the position vector of the \(i\)-th joint point. The structure of human posture can be represented as a set of distance constraints between joints:  
\[D_{ij}=\big{|}|j_{i}-j_{j}|\big{|},\forall i,j\in\{1,2,\dots,n\} \tag{6}\]  
These distance constraints \(D_{ij}\) can be learned from training data and used to infer the positions of occluded joints during the pose estimation process.  
YOLOv8-pose uses a convolutional neural network (CNN) to extract feature maps from the input image. Suppose the input image is \(I\), and the feature maps extracted by CNN are \(F\):  
\[F=CNN(I) \tag{7}\]  
The feature map \(F\) contains multi-scale features and contextual information in the image. When predicting joint positions, the model relies not only on local features but also on global features and contextual information. For example, for an occluded joint \(j_{k}\), the model can infer its position through other detected joints and contextual information in the feature map:  
\[j_{k}=f(F,j_{1},j_{2},\ldots,j_{k-1},j_{k+1},\ldots,j_{n}) \tag{8}\]  
In some advanced models, graph convolutional networks (GCNs) can be used to further integrate and utilize contextual information. Suppose the joints form an undirected graph \(G=(V,E)\), where \(V\) is the set of joints, and \(E\) is the connections between joints. Through GCNs, message passing and feature updating for joints can be performed:  
\[H^{(l+1)}=\sigma\Big{(}\sum_{v\in N(u)}W^{(l)}H^{(l)}_{v}+b^{(l)}\Big{)} \tag{9}\]  
where \(H^{(l)}\) represents the feature representation of the \(l\)-th layer, \(W^{(l)}\) and \(b^{(l)}\) are the weights and biases, respectively, \(\sigma\) is the activation function, and \(N(u)\) represents the set of neighboring nodes of node \(u\), \(v\) represents a node in set \(N(u)\). Through the iteration of multiple layers of GCN, the feature representation of joints can continuously integrate contextual information, thereby improving the estimation accuracy of occluded joints.
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 2 Materials and Methods
### Dynamic Models of EHPS System
#### 2.1.4 Steering System Characteristic Analysis  
In order to analyze the characteristics of the EHPS system, the mechanical and hydraulic dynamic should be fully derived. Regardless of the friction of the steering column, the mechanical system is linear, but the hydraulic system is nonlinear due to the hydraulic assistance. Equations (1) and (2) can be rephrased as follows.  
\[J_{s}\ddot{\theta}_{s}+B_{s}\dot{\theta}_{s}+K_{t}\theta_{s}-K_{t}R_{t}r_{cs} \theta_{f}=T_{i} \tag{8}\]  
\[M_{p}r_{cs}\ddot{\theta}_{f}+B_{p}r_{cs}\dot{\theta}_{f}+\Big{(}K_{r}+K_{t}{R_ {t}}^{2}r_{cs}\Big{)}\theta_{f}=K_{t}R_{t}\theta_{\omega}+K_{\Psi}\cdot\Psi(T _{t}) \tag{9}\]  
Take the Laplace transform of Equations (8) and (9) and get the following:  
\[J_{s}s^{2}\theta_{s}(s)+B_{s}\theta_{s}(s)+K_{t}\theta_{s}(s)-K_{t}R_{t}r_{cs} \theta_{f}(s)=T_{i}(s) \tag{10}\]  
\[M_{p}r_{cs}s^{2}\theta_{f}(s)+B_{p}r_{cs}\theta_{f}(s)+\Big{(}K_{r}+K_{t}{R_{ t}}^{2}r_{cs}\Big{)}\theta_{f}(s)=K_{t}R_{t}\theta_{s}(s)+K_{\Psi}\cdot\Psi(T _{t}) \tag{11}\]  
where \(\theta_{s}(s)\), \(\theta_{f}(s)\) are the Laplace transforms of \(\theta_{s}\), \(\theta_{f}\); \(T_{i}(s)\) is the Laplace transform of \(T_{i}\).  
The transfer function of EHPS from the front wheel to the steering wheel can be described as:  
\[\frac{\theta_{f}(s)}{\theta_{s}(s)}=\frac{A_{p}\cdot K_{t}\cdot K_{A}+R_{t} \cdot K_{t}}{M_{n}s^{2}+\big{(}B_{n}-K_{q}\cdot A_{p}\big{)}s+\Big{[}K_{f}+R_{ t}\cdot K_{t}(R_{t}+K_{A})\Big{]}}\cdot\frac{1}{r} \tag{12}\]  
The transfer function of EHPS from front wheel to steering torque can be described as:  
\[\frac{\theta_{f}(s)}{T_{i}(s)}=\frac{\theta_{f}(s)}{(J_{s}s^{2}+B_{s}s+K_{t} \theta_{s}(s)-K_{t}\cdot K_{t}\cdot r} \tag{13}\]  
Take the parameter values in Table 1 and set \(\theta_{f}=\delta\), the Equations (12) and (13) can be expressed as:  
\[\frac{\delta(s)}{\theta_{s}(s)}=\frac{7020}{0.4035s^{2}+1764s+40010} \tag{14}\]  
\[\frac{\delta(s)}{T_{i}(s)}=\frac{7020}{0.0112s^{4}+45.83s^{3}+2404s^{2}+282287 s-2.25e8} \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline \(J_{s}/(\)kg\(\cdot\)m\({}^{2})\) & 0.0258 & \(R_{t}\) & 459 \\ \(B_{s}/(\)Nm\(\cdot\)s/rad\()\) & 0.742 & \(M_{p}/\)kg & 8.07 \\ \(K_{t}/(\)Nm\(/\)rad\()\) & 143.2 & \(B_{p}/(\)Nm\(/\)s\()\) & 35,283 \\ \(r_{cs}/\)m & 0.05 & K\({}_{R}/(\)Nm\(/\)rad\()\) & 5730 \\ \(C_{d}\) & 0.5 & \(\rho/(\)kg\(/\)m\({}^{3})\) & 880 \\ \(\beta/(\)N\(/\)m\({}^{2})\) & \(1.4\times 10^{5}\) & \(A_{p}/(\)m\({}^{2})\) & \(9.4\times 10^{-3}\) \\ \hline \end{tabular}
\end{table}
Table 1: The parameters used in the EHPS system dynamic model.
## 6 Experiments and Discussion
### Semi-Physical Simulation Experiments
#### 6.2.1 Measurement Noise Variance Estimation  
To verify the reliability of RMNCE, we compared the RMNCE results with those obtained by RAE. Figure 5 shows the results of satellite #24 which was always visible throughout the simulation. The comparison indicates that RMNCE is more robust and accurate than RAE, particularly when the true value of the measurement noise variance changes suddenly.  
Besides, the error volatility of RMNCE is lower than RAE. We calculated the variances of the estimation errors during [1700 s, 1900 s], [1901 s, 2500 s] and [2501 s, 2700 s] intervals. The results of RMNCE are 0.0212, 0.2711, and 0.1651, and the corresponding results of RAE are 0.0304, 0.5828 and 0.3025. It is clear that RMNCE provides a more stable measurement noise variance estimation.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Performance** \\ \hline Gyroscope bias & 10 \({}^{\circ}\)/h \\ Angle random walk & 0.3 \({}^{\circ}\)/\(\sqrt{h}\) \\ Accelerometer bias & 1 mg \\ Velocity random walk & 1 mg/\(\sqrt{\text{Hz}}\) \\ Variance of barometer & 25 m\({}^{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: Sensor error settings employed in the simulation experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time(s)** & **Satellite Number** & \(\sigma_{p}\)**(m)** & \(\sigma_{p}\)**(m/s)** & **Special Settings** \\ \hline
730–750 & 7 & 1 & 0.01 & add additional large errors 1 & to the \#10, \#13 and \#24 satellite pseudo-range easurements \\
1900–2500 & 7 & 2 & 0.01 & increase \(\sigma_{p}\) of \#10, \#13 and \# 24 to 5m \\
2900–2960 & 1 & 1 & 0.01 & only \#24 is visible \\ other & 8 & 1 & 0.01 & — \\ \hline \end{tabular}  
* The additional large errors of the pseudo-range measurements of satellites #10, #13, and #24 are set as time varying, which can be expressed as error = \(a\cdot t+b\), where \(a=1\), \(b=100\), 90, and 80, respectively.  
\end{table}
Table 2: The GNSS measurement error settings.  
Figure 5: The estimated variances of the pseudo-range measurement noise obtained by RAE and RMNCE.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 3 Problem Statement  
An emerging requirement in the multimedia applications over the internet is the provisioning of the synchronized playout of real-time media streams. Nowadays many proposals of intra and inter-stream synchronization algorithms for different scenarios exist but there are very few proposals for group synchronization, such as [17; 18; 19; 20] have been implemented. All of them are receiver-based and, except the one in [17; 19] that uses RTP and sender-based solution, the others did not use a standard protocol and define specific control messages to interchange between sources and receivers. Most of the multimedia applications use RTP/RTCP protocol to transport media data over the internet [21]. IP network introduces loss and/or delay of media packets resulting in distortions (i.e., synchronization error) at receivers that can be perceived by the user. These distortions need to be removed before data is presented to the end user. There have been two main types of synchronization, i.e., intra stream synchronization, and inter-stream synchronization. There are lots of proposals to implement inter-stream and intra-stream synchronization in multimedia applications by using RTP/RTCP [22] but to the best of our knowledge, none of them addresses group synchronization issue.  
In this paper we have designed two use cases and implemented them in Ericson Network Research Center Lulea, Sweden to address the subjected issue of synchronizing multimedia data streams under multicasting environment. The two use cases are as under.
# Machinery Fault Signal Detection with Deep One-Class Classification
## 5 Conclusions  
In this study, we present a fault signal detection framework based on deep SVDD for the implementation of an efficient CBM strategy on machinery systems. To this end, we utilize the raw time-series signal because it directly reveals the health status of the machinery system. To handle the raw time-series signal data, the deep SVDD model is trained by one-dimensional CAE as a pre-training procedure, and the encoding part of the CAE structure is used as the initial network structure of the deep SVDD model.  
The pre-training procedure helps to more accurately detect the decision boundary and the fault signals because intrinsic properties of time-series signal data can be accommodated to the neural network structures of the encoding part. Then, in the fine-tuning procedure, the neural network structures for the deep SVDD model are updated to minimize the loss function for the anomaly detection tasks. Through the fine-tuning procedure, the latent features specialized to the fault signal detection can be generated, and it eventually helps the deep SVDD model to achieve superior fault signal detection performance. To demonstrate the efficacy of the deep SVDD model in fault signal detection, we used a widely used benchmark signal dataset (CWRU dataset) and both the vibration and acoustic signal datasets collected from our own rolling element experiment platform. In this experimental study with these datasets, the deep SVDD model outperforms other OCC methods, and these results confirm the applicability of the deep SVDD model in real fault signal detection problems.  
In spite of its superiority in fault signal detection problems, the decision boundary of the deep SVDD model might be corrupted by the noisy or outlying time series signals, which are generated by incomplete sampling or data transmission error [20; 56]. Thus, in further study, we will address the deep SVDD model's vulnerability against noisy or outlying time series signals. To this end, we will improve the deep SVDD model by incorporating the relative density of individual signals because these noisy or outlying time series signals tend to be located in sparse regions in feature space.  
[PERSON] is responsible for the whole part of the paper (Conceptualization, methodology, formal analysis, validation, and writing--original draft preparation), and [PERSON] is responsible for formal analysis, review and editing, supervision, and fund acquisition. All authors have read and agreed to the published version of the manuscript.  
The corresponding author ([PERSON]) of this research was supported by the Incheon National University (International Cooperative) Research Grant in 2021.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing several other studies with the datasets presented in this study. If all of these studies are published, we will release the datasets presented in this study in our research team's website.  
The authors declare no conflicts of interest.
## 1 Introduction
It is shown that the acquired performances with the recommended waveform are highly satisfactory from the computer simulation outcomes. The rest of our study is organized as follows: GFDM waveform construction is explicated in Section 2. Walsh-Hadamard Transform is summarized in Section 3. Lifting Wavelet Transform is described in Section 4. The construction of the proposed waveform is explained in detail in Section 5. Computer simulations are shown in Section 6. In the last section, the results of computer simulations are discussed comparatively.
# R&D of an Innovative OFDM Communication Payload for Small-Size AUV
## 4 Discussion  
The accuracy and availability of the proposed UWA OFDM communication payload are analyzed in this section. Initially, the Doppler estimation resolution proposed in this paper can be expressed as  
\[\Delta\sigma=\frac{1}{(n_{LFM}-1)\cdot L_{\mathrm{s}}}\cdot f_{c}, \tag{24}\]  
where \(n_{LFM}\) is the number of LFM signal contained in the preamble sequence, and \(L_{\mathrm{s}}\) represents the total length of one LFM signal and one blank interval. The Doppler estimation bias can be formulated as  
\[\begin{array}{l}\eta=|\varepsilon-\varepsilon_{d}|\cdot f_{c}\\ \leq\frac{1}{2}\Delta\sigma.\end{array} \tag{25}\]  
Figure 4: Doppler and BER with respect to SNR. (**a**) Doppler curve over SNR; (**b**) BER curve over SNR.  
where \(\varepsilon_{d}\) is the desired Doppler factor. From (24) we can notice that the Doppler estimation resolution depends on the length of preamble signal. In this paper, let \(n_{LFM}=10\) and \(L_{s}=2048\), the Doppler estimation resolution is \(\Delta\sigma\approx 1.36\) Hz with respect to \(\eta\leq 0.68\) Hz. In addition, due to the suppression ability of the time-frequency differential OFDM receiver on small-scale Doppler expansion, the interference of residual Doppler \(\eta\) can be effectively eliminated by differential detection to facilitate OFDM symbol demodulation.  
Furthermore, using \(I2 SDIV=2\) and \(ODD=0\) in the actual acquisition process, the required adjustable external clock frequency is less than 100 MHz; thus, the AD9954 chip can satisfy the adjustable frequency requirements of clock control signal due to its ability of generating up to 200 MHz analog sine wave. In addition, the frequency resolution of the clock control signal generated by AD9954 chip is 1 Hz, corresponding to the adjustable sampling rate resolution of about 0.001 Hz, and it can also meet the accuracy requirement of signal acquisition with an adjustable audio sampling rate.
## 3 Experimental Results  
This paper proposes a qshift DTCWT and MPCA in laplacian pyramid domain. Quality measures include \(SD\), \(Q^{AB/F}\), \(E(F)\), \(AG\), \(SF\), \(CC\), \(SSIM\), \(Q_{E}\), \(Q_{W}\), \(FMI\), \(L^{AB/F}\), \(N^{AB/F}\), \(RMSE\), and \(PSNR\) were employed to assess the algorithm's quality. These metrics are used to contrast the proposed technique to the methods that have been published in the past. The resemblance and robustness of the fused images against distortions are measured using these criteria. Source images for comparison are commonly used in MIF. Experiments are also carried out on many images from various areas and datasets [52]. In these images, the proposed approach yields good results. However, these images are not included in the paper because the techniques that are contrasted with the proposed approach do not produce outcomes for these images. Desk, balloon, book, clock, flower, lab, leaf, leopard, flowerpot, Pepsi, wine, and craft images are used to compare methodologies in the literature with [21; 22; 23; 24; 25; 26; 27; 28; 30]. In addition, the outcomes of the proposed technique for certain tried source images were presented. The images are of various sizes and qualities. The proposed method is applicable to any multi-focus images, not only those presented in this work.