esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Augmentation Based on Pixel-Wise Attention for Rail Defect Detection
## 4 Results
### Detection Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Backbone** & **mAP@.5** & **mAP@.5.95** & **FPS** \\ \hline _8 defect classes_ & & & & \\ \hline YOLOv5 + FA (ours) & s & **0.757** & 0.365 & 34.36 \\ YOLOv5 + FA (ours) & s6 & 0.726 & 0.369 & 34.25 \\ YOLOv6 + FA (ours) & s & 0.740 & **0.377** & 52.99 \\ \hline YOLOv5 [42] & s & 0.657 & 0.317 & 52.36 \\ YOLOv5 [42] & s6 & 0.680 & 0.346 & 48.54 \\ YOLOv6 [43] & s & 0.674 & 0.325 & 72.51 \\ Faster R-CNN [4] & R50 & 0.719 & 0.333 & 8.49 \\ Faster R-CNN [4] & R101 & 0.726 & 0.322 & 9.00 \\ AL-MDN [37] & VGG16 & 0.744 & 0.362 & 5.98 \\ DyHead [38] & RetinaNet R50 & 0.741 & 0.367 & 38.41 \\ DyHead [38] & F R50 & 0.741 & 0.367 & 8.23 \\ DyHead [38] & F R101 & 0.741 & 0.367 & 7.36 \\ \hline _4 defect classes_ & & & & \\ \hline YOLOv5s + FA (ours) & s & 0.796 & 0.357 & 41.49 \\ YOLOv5s6 + FA (ours) & s6 & 0.818 & 0.391 & 32.79 \\ YOLOv6 + FA (ours) & s & **0.835** & **0.393** & 54.31 \\ \hline YOLOv5 [42] & s & 0.816 & 0.336 & 56.34 \\ YOLOv5 [42] & s6 & 0.804 & 0.386 & 56.18 \\ YOLOv6 [43] & s & 0.827 & 0.383 & 72.33 \\ Faster R-CNN [4] & R50 & 0.808 & 0.387 & 14.48 \\ Faster R-CNN [4] & R101 & 0.820 & 0.373 & 9.76 \\ AL-MDN [37] & VGG16 & 0.816 & 0.381 & 6.27 \\ DyHead [38] & RetinaNet R50 & 0.802 & 0.377 & 40.67 \\ DyHead [38] & F R50 & 0.809 & 0.381 & 7.75 \\ DyHead [38] & F R101 & 0.810 & 0.389 & 6.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detection results of different methods over the rail defect test dataset. In industrial applications, dirt and gap objects can be ignored, whereas unknown objects are usually regarded as potential defects and should be double-checked manually. Therefore, false detection may cause additional manual inspection time. The detection result revealed that Faster R-CNN is too sensitive and confident to identify many objects that did not exist or need to be ignored. On the contrary, YOLO missed some objects and is too rigid to doubt the training samples. The feature augmentation framework can give scores to help balance the sensitivity and robustness of the model. It can increase the confidence of objects with obvious features but not correctly labeled (e.g., the unlabeled gap in the third row of Figure 6), while reduce the confident of objects with features similar to certain class but are 'far' to selected support defects (e.g., small objects in the first row of Figure 6 that should be ignored).
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 2 Spatial Modulation Technologies  
In this section, the details of SMTs that can be used in the UWA are presented. First, the conventional SM is discussed in detail for underwater. Then, the underwater GSM and FGSM are explained in detail. Figure 1 shows the SMTs system model.
# A Review of the Synthesis and Applications of Polymer-Nanoclay Composites
## 1 Introduction
Due to their unique properties, polymer/nanoclay composites have been used in a number of industrial applications, such as construction (building sections and structural panels), automotive (gas tanks, bumpers, interior and exterior panels), chemical processes (catalysts), pharmaceutical (as carriers of drugs and penetrants), aerospace (flame retardant panels and high performance components), food packaging and textiles [47]. Limitations in fossil resources and an urgent need to protect the environment have led to new generations and applications of polymer/nanoclay composites. Innovative bioinspired polymer/nanoclay composite materials are expected to find applications in various scientific and technological fields [48]. This article presents a review of recent advances in the synthesis and novel applications of polymer/nanoclay composites. It provides an up-to-date summary of synthesis methods and applications with a focus on the surface-modified nanoclay fillers, and the potential future scope for composite materials.
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 5 Conclusions  
In this paper, we present Non-autoregressive Sparse Transformer (NaST) networks for Pedestrian Trajectory Prediction. NaST captures the sparse directed spatial and temporal interactions by sparse spatial and sparse temporal transformer encoders separately, which generate trajectory predictions by a transformer decoder in a non-autoregressive pattern and could effectively avoid error accumulation and reduce computational cost. Beyond that, we decoded predictions from a query sequence generated in advance by using the last observed time step for making more accurate predictions. It was found that the sparse spatial transformer could effectively model the directed social interaction in real scenes. The sparse temporal transformer could capture the motion dependency. By combining sparsity and the self-attention in the transformer, we constructed the transformer encoder to extract the features for future trajectory prediction. Beyond that, we investigated the use of non-autoregressive inference with transformer neural networks to avoid error propagation. Extensive experimental studies illustrated that our proposed model achieved better performances than previous methods. The information that NaST used was only the past trajectories of pedestrians who might have failed to predict unexpected sharp turns. Additional information such as roads, vehicles, buildings, and surrounding environment could be considered to solve this issue. We leave it for future study.  
Conceptualization, D.L. and S.L.; methodology, D.L.; software, D.L.; validation, D.L., Q.L.; formal analysis, M.Q.; investigation, Q.L.; resources, J.K.; data curation, S.L.; writing--original draft preparation, D.L.; writing--review and editing, Q.L.; visualization, D.L.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China, grant number 62272096, and the Fund of Jilin Provincial Science and Technology Department, grant number 20210201077 GX.  
Not applicable.  
[[https://data.vision.ee.ethz.ch/cvl/aem/ewap_dataset_full.tgz](https://data.vision.ee.ethz.ch/cvl/aem/ewap_dataset_full.tgz)]([https://data.vision.ee.ethz.ch/cvl/aem/ewap_dataset_full.tgz](https://data.vision.ee.ethz.ch/cvl/aem/ewap_dataset_full.tgz)) (accessed on 25 December 2022).  
The authors declare no conflict of interest.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 4 Communication Systems in Oceans
### Wireless Ad Hoc Network (WANET)
#### 4.6.1 Nautical Ad Hoc Network (NANET)  
Vessels and facilities on the water surface capable of wireless communication can be used to construct a nautical WANET (NANET) [14; 15] to provide vessel-to-vessel and vessel-to-shore communication. For example, a WiMAX-based mesh network is used to provide onboard Internet broadband access for vessels in the Mediterranean without using satellites [63], and to enable high rate ship-to-ship and ship-to-shore communication [13]. In marine Internet, the network nodes of a NANET include any objects on the water surface that can be used for networking such as floating platforms and buoys besides vessels.  
NANETs are practically feasible in near water areas due to high vessel densities along the coastline as illustrated in Figure 2, by jointly using buoys and various floating platforms as mentioned in Section 3. The performance of NANETs can be further improved by exploiting some favorable features of NANETs. For example, the availability of the information on the positions and speeds of vessels, which can be provided by AIS or GPS. This information can be used to set directional antennas and improve routing performance.
This paper is organized as follows: Section 2 discusses the key factors limiting PET spatial resolution, including detector geometry, scintillator design, and electronic signal processing. Section 3 explores advanced detector designs achieving ultra-high resolution (\(<\)2 mm), focusing on pixelated and monolithic scintillator configurations as well as photon depth-of-interaction (DOI) techniques. In addition, this section also provides reviews of the selected ultra-high-resolution PET systems, including a comparison of commercial scanners with research prototypes described in the recent literature. Section 4 provides in-depth details on a prototype 1 mm\({}^{3}\) resolution clinical PET system developed at Stanford University's Molecular Imaging Instrumentation Laboratory (MIIL). This comprehensive review highlights key technological advances and provides practical information on advancing the capabilities of molecular imaging scanner designs.  
This work constitutes a review of the literature focused on the design of ultra-high-resolution (\(<\)2 mm) clinical PET systems. A comprehensive search of peer-reviewed articles published between 2010 and 2024 was performed using databases such as PubMed, IEEE Xplore, Scopus, and Google Scholar. The search strategy used keywords including 'high-resolution PET', 'depth of interaction PET', 'pixelated detectors','monolithic PET detectors', and 'ultra-high-resolution PET systems'. Articles were selected based on their relevance to technological innovations in the design and implementation of clinical PET detector systems. The primary objective of this review is to synthesize recent advancements in PET detector technology and spatial resolution enhancement strategies, providing a consolidated overview for researchers and developers in the field of molecular imaging.
## 3 Materials and Methods
### Physics-Based Models  
Physics-based models are mostly one-dimensional leaching models, i.e., RZWQM [160; 161], WAVE [162; 163; 164] and DAISY [165], which simulate root zone processing. Numerous models are hybrid, i.e., process- and physics-based models, e.g., MODFLOW/MODFLOW-MT3D (see Section 3.1).  
Daisy simulated crop production and nitrogen and water balance in the root zone [102]. It includes modules and soil-water dynamics based on Richards' equation [78]. It incorporates nitrogen transformation, i.e., mineralization/immobilization, nitrification, denitrification and agricultural management practices.  
Numerous RZWQM components were developed to improve simulation accuracy regarding crop root zone. Therefore, RZWQM contains a soil water and heat transfer module [166; 167], a generic plant growth module [168], a nitrogen balance module [169], a soil equilibrium chemistry module [170], an evapotranspiration (PET) module [171], a management module [172] and a pesticide module [173].  
The modeling system MIKE SHE describes the distribution of solutes and water flow in a catchment using physical principles. This entails the numerical solutions of the coupled partial differential equations for the processes of evapotranspiration and snowmelt for overland (2-D), channel (1-D), unsaturated (1-D), and saturated (3-D) flow. A full modeling system for simulating the movement of water and nitrate within a watershed can be created by integrating Daisy with MIKE SHE [102].  
Phosphorus (P) is an important nutrient and, though less abundant in surface water compared to nitrogen, is still within a constant proportion, N:P, e.g., 14.7:1 in the sea column [174]. Ratio unbalancing brings about eutrophication phenomena in aquatic ecosystems [175]. Therefore, the study of phosphorus fate mechanisms and selective infiltration routes in terrestrial and aquatic ecosystems is of high priority. [PERSON] et al. (2020) [176] introduced a comprehensive review of phosphorus-soil interaction models, classified into multiple categories, such as temporal and spatial scale, surface/subsurface transport, matrix/macropore transport and mobile/immobile transition in dual-porosity models.
## 2 Power Converter Principle
### Designs of Output Capacitor and Inductor
#### 2.3.2. SBBC Inductor for Boost Mode  
In accordance with the study [16], the inductor for the SBBC's boost mode operation can be estimated as follows:  
\[L_{bt}=V_{in(min)}D_{bt}/(2f_{s}\Delta i_{L}). \tag{3}\]  
\(L_{1}\) can be replaced with \(L_{bt}\), \(D_{bt}\) is the duty cycle of the boost mode that can be estimated using \((V_{o(max)}-V_{in(min)})/V_{o(max)}\). \(V_{in(min)}\) is the HPLDDC minimum input voltage. Substitution of \(\Delta i_{L}=\beta l_{o}\) and \(D_{bt}=(V_{o(max)}-V_{in(min)})/V_{o(max)}\) into Equation (3) can rewrite the equation as follows:  
\[L_{bt}=V_{in(min)}(V_{o(max)}-V_{in(min)})/(2f_{s}V_{o(max)}I_{o}\beta). \tag{4}\]
## 2 Related Work
### Hierarchical Routing  
Hierarchical (clustering) technology is particularly promising and has received much attention in the research community. In a hierarchical network, the data gathered by sensor nodes is transmitted to CHs. The sensed data from nodes within one cluster usually exhibit high correlation, and therefore, a CH can aggregate data to remove redundancy and only send one packet to the sink.  
In the last few years, many hierarchical routing algorithms are proposed for WSNs. One pioneering work in the literature is LEACH (Low-Energy Adaptive Clustering Hierarchy) [11]. LEACH is an application-specific data dissemination protocol that uses clustering to prolong the network lifetime. However, the assumption that all nodes are capable of communicating with any node in the field does not allow the network to be scalable, and LEACH does not guarantee good distribution of CHs. To improve LEACH performance, [PERSON] _et al._ introduced chain into clustering (power-efficient gathering in sensor information systems, PEGASIS) [12]. In this work, all nodes are connected in a chain and communicate only with the nearest neighbor. Nodes take turns to be the CH and send aggregation data to the sink. Although PEGASIS outperforms LEACH in network lifetime, it assumes that all nodes have a global knowledge of the network. Thus, PEGASIS may not be efficient with closely deployed nodes in a specific area. In [13], the authors designed an ant-based algorithm (T-ANT) to cluster and achieved a uniform distribution of CHs in the network.
## 5 Spectroscopy Analysis
### Dielectric Properties and Absorption Characteristics
\begin{table}
\begin{tabular}{c c c c} \hline
**Material** & **State** & **Region (THz)** & **Parameters** & **Reference** \\ \hline Water & & 0.1–1.6 & \(\alpha,n\) & [47] \\ Water & & 0.3–3.6 & \(ATR\) & [48] \\ Water & & 0.5–12 & \(A\) & [49] \\ Aqueous glycine solution & & 0.5–12 & \(A\) & \\ Methanol & Liquid & 0.3–3.6 & \(ATR\) & [48] \\
2-Propanol & & 0.3–3.6 & \(ATR\) & \\ NaCl & & 0.2–1.1 & \(\alpha,n\alpha,\,n\) & [50] \\ NaI & & 0.2–1.2 & \(\alpha,n\) & [32] \\ L-(+)-tartaric acid & & 0.2–1.2 & \(\varepsilon\) & [47] \\ \hline InAs & & 1.0–2.1 & \(\varepsilon\) & [47] \\ Porcine tissues & & 0.01–1.00 & \(\varepsilon\) & [51] \\ Pyridoxine (vitamin B6) & & & \(ATR\) & \\ Riboflavin (vitamin B2) & & & \(ATR\) & \\ Thiamine hydrochloride (vitamin B1) & & 0.3–3.6 & \(ATR\) & \\ L-tartaric acid & & & \(ATR\) & \\ D-mannitol & & & \(ATR\) & \\ D-sorbitol & Solid & & \(ATR\) & \\ Xylitol & & & \(ATR\) & \\ glycine powder & & 0.5–12 & \(A\) & [49] \\ Azotetrazolate (explosive materials) & & & \(A\) & \\ Pentahydrate (explosive materials) & & & \(A\) & \\ Diammonium (explosive materials) & & & \\ azotetrazolate & & 0.2–3.2 & \(A\) & [52] \\ Guanidinium azotetrazolate & & & \(A\) & \\ (explosive materials) & & & \\ Triaminoguanidinium azotetrazolate & & & \\ (explosive materials) & & & \(A\) & \\ Hexanitrohexaazaisowurtzitane & & & \(A\) \\ (explosive materials) & & & \(A\) & \\ Tetraoxadinitroisowurtzitane & & & \(A\) \\ (explosive materials) & & & & \\ \hline \end{tabular} \(\alpha\): absorption coefficient, \(n\): refraction index, \(ATR\): attenuated total reflectance, \(\varepsilon\): dielectric constants, \(A\): absorbance.  
\end{table} 
Table 1: The optical and dielectric characteristics of certain substance obtained by THz-ATR spectroscopy reported in the papers. by using transmission and ATR from 0.2 to about 3.2 THz were investigated by [PERSON] et al. [52]. The positions of peaks showed a reasonable consistence to the transmission results, which suggested the feasibility of ATR for identification of these materials. Figure 4 shows the absorption spectra of 4 kinds of them through the ATR method. Since the ATR geometry is very sensitive to the change of the interface, the contact between the sample, especially for powder and the ATR prism could also affect the results. Thus, there is difference between these two measurements. Besides, the penetration depth at lower frequency is bigger than that in the higher region, according to Equation (2), so the absorption characteristic could be more distinguishable at lower frequency in the THz region.
# On Pseudorandomness and Deep Learning: A Case Study
###### Abstract  
Pseudorandomness is a crucial property that the designers of cryptographic primitives aim to achieve. It is also a key requirement in the calls for proposals of new primitives, as in the case of block ciphers. Therefore, the assessment of the property is an important issue to deal with. Currently, an interesting research line is the understanding of how powerful machine learning methods are in distinguishing pseudorandom objects from truly random objects. Moving along such a research line, in this paper a deep learning-based pseudorandom distinguisher is developed and trained for two well-known lightweight ciphers, Speck and Simon. Specifically, the distinguisher exploits a convolutional Siamese network for distinguishing the outputs of these ciphers from random sequences. Experiments with different instances of Speck and Simon show that the proposed distinguisher highly able to distinguish between the two types of sequences, with an average accuracy of 99.5% for Speck and 99.6% for Simon. Hence, the proposed method could significantly impact the security of these cryptographic primitives and of the applications in which they are used.  
pseudorandomness; cryptographic primitives; deep learning; cryptanalysis +
Footnote †: journal: _Applied Sciences_  
of the same cipher than to a truly random sequence (and vice versa), we will be able to de facto distinguish a similarity function.  
In the DL area, Siamese networks have shown to be a powerful tool for tackling problems of similarity, with several applications since their introduction [6; 7; 8]. To date, no application to cryptanalysis, and specifically to the randomness evaluation of cryptographic primitives, has been proposed except for the one provided in [9], to defeat the privacy features of the Gossamer protocol. In this work, we propose a DL-based distinguisher which exploits a Siamese network on cryptographic primitives such as the Speck and Simon block ciphers. As we will see in Section 5, preliminary experiments with different instances of Speck and Simon on two datasets of 5000 sequences, one of 2500 random 32-bit sequences and the other one of 2500 cipher outputs, have been conducted. The results obtained show that the proposed distinguisher is able to achieve a high ability in distinguishing between the two types of sequences, with an average accuracy of 99.5% for Speck and 99.6% for Simon, improving on [PERSON]'s earlier work [5]. For a description of Speck and Simon block ciphers, the interested reader is referred to Appendix A.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver
### Proposed Synchronization Algorithm
#### 3.3.1 Step 1--Preamble Detection  
The first step in our synchronization algorithm is the detection of a signal of interest through the search of the known preamble. To this end, the receiver must be in a listening mode, which is done by multiplying each block of \(M\) samples by the complex conjugate of the reference signal as written in (13). Then a FFT is calculated on each block of non-overlapping \(M\) samples, as in (8). To increase the certainty of the preamble detection, it is advantageous to average the FFT magnitudes of successive blocks before applying the \(argmax\) function. Indeed, since in the preamble the symbols are identical, this processing would average out the bin containing the noise, easing finding the correct one. To do this, authors in [14; 19] propose designing an IIR filter, such as \(y[n]=x[n]+xy[n-1]\) instead of averaging, with \(\alpha<1\) being the portion of the previous block to be remembered. In this work, we chose to average the FFT magnitude over each two consecutive blocks. In addition, as stated in [14], the performances are enhanced if a threshold value, according to the noise level, is set to determine the presence of the signal peaks in the FFTs. Subsequently, a preamble is assumed to be detected when in \((N_{p}-1)\) blocks of \(M\) samples, the maximum FFT absolute value is on the same FFT bin. However, due to the fractional STO and CFO, and the presence of a significant DR, the positions of the FFTs \(argmax\) would be shifted by several FFT bins from the beginning to the end of the preamble up-chirps. Hence, proper control procedures must be envisaged to take into account all these effects when searchingfor the preamble. In other words, we do not have to look for \((N_{p}-1)\) consecutive peaks at the same FFT bin. In the same context, authors in [14; 22] propose relaxing this constraint by searching for only \(\frac{N_{p}}{2}\) consecutive peaks at the same frequency, with a tolerance up to \(\pm 2\) FFT bins.  
After detecting the presence of a valid preamble, our receiver should identify in which \(T\)-long sequence the received packet begins. To do this, a sequence of up-chirps was applied to the \(T\)-long sections where the down-chirp of the SFD is expected. The module of the FFT having the highest maximum indicates the location of the \(T\)-long section of the SFD. Given the latter position, \(\hat{K}\) an estimation of \(K\) in (25) can be deduced.