esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Theory  
The characteristics of a beam diffracted from a grating depend not only on its orientation and spatial frequency (photonic structure), but also on the angles and wavelength of the incident beam.  
The classical grating equation for transmission [21] can be expressed by Equation (1):  
\[k_{i}\ sin\theta_{0}+k_{d}sin\theta_{+1}=\frac{2\pi m}{n\Lambda} \tag{1}\]  
where, \(k_{i}=\frac{2\pi}{\lambda_{0}}\) and \(k_{d}=\frac{2\pi}{\lambda_{1}}\) are the propagation vector's modulus for the incident and diffracted beams, m = order of diffraction (\(m\) = 1 for volume HOE), \(\theta_{0}\) = the angle of incidence in air, \(\theta_{+1}\) = angle of diffraction in air, \(n\) = refractive index of the medium, and \(\Lambda\) = period of the refractive index modulation (spatial period).  
Volume HOEs work on the principle of Bragg's law [22], and at the Bragg condition, the angle of incidence equals the angle of diffraction (\(\theta_{+1}=-\theta_{0}\)_and_\(\lambda_{0}=\lambda_{1}\)). Equation (2) defines the wavelength at the Bragg condition.  
\[\lambda=2n\Lambda sin\left(\frac{\theta_{+1}-\theta_{0}}{2}\right) \tag{2}\]  
In applications where the re-direction of illumination is important, volume diffractive optics are preferred because of their potential for high diffraction efficiency, meaning that a large proportion of the incident light is diffracted into a single diffraction order in a direction defined by 2. The diffraction efficiency achieved also depends on the material characteristics of the grating, such as refractive index modulation and thickness. The Kogelnik Coupled Wave Theory (KCWT) [1] is a well-established tool for modeling the efficiency of such elements and can predict how the efficiency of Volume HOEs depends on many parameters, such as the illuminating beam incidence angle, replay wavelength, index modulation and grating thickness.  
KCWT shows that at Bragg incidence, the diffraction efficiency for the volume grating can be modeled using the following:  
\[\eta=Sin^{2}\left(\frac{\pi n_{1}d}{\lambda Cos\theta_{B}}\right) \tag{3}\]  
where \(\theta_{B}\) = Bragg angle inside the layer, d is the thickness of the grating and n\({}_{1}\) the refractive index modulation.  
The Q factor is used to check whether the grating will function as a volume Bragg grating [23].  
\[Q=\frac{2\pi\lambda d}{n\Lambda^{2}} \tag{4}\]  
With the parameters defined as above, a Q factor of > 10 is considered to be in the 'volume' regime, and therefore is expected to have a single diffraction order and behavior that can be predicted using KCWT.
# Biological Decontamination by Microplasma
## 1 Introduction
It is a highly non-equilibrium characteristic that recommends microplasma for various applications [33]. This, combined with the portability, makes microplasma the ideal technology for biological decontamination. In this research, we have investigated the microplasma treatment of _Escherichia coli_, _Pseudomonas aeruginosa_ and _Staphylococcus aureus_.
## 3 Cooperative Perception Information-Sharing
### Cooperative Perception Information-Sharing Network
#### 3.1.2 C-V2X  
C-V2X was standardized by the 3 rd Generation Partnership Project (3 GPP), which includes long-term evolution (LTE)-V2X and 5G-V2X (NR) technologies and is gradually enhanced through the evolution stages shown in Figure 6[67; 68]. In addition, C-V2X defines a PC5 interface that enables direct V2X communication through the sidelink and a Uu interface that realizes communication between the terminal and base station utilizing the uplink/downlink. It has high network capacity and wide coverage and can better support autonomous driving cooperative perception in the IoV environment, improve the reliability of data transmission, decrease transmission delay, and reduce frequent horizontal switching in the network. C-V2X offers performance advantages over DSRC in terms of its additional link budget, higher resilience to interference, and better non-line-of-sight (NLoS) capabilities [69]. Based on [65], [PERSON] et al. [70] further analyzed this concept based on DSRC and 4G cellular networks, which are not enough to support the large-scale sharing of raw sensor data. Three types of vehicle networks, including 5G cellular, a modified version of IEEE 802.11 ad, and a dedicated new standard, were proposed. In addition, it was pointed out that these three communication methods can effectively avoid the channel congestion problem caused by a large amount of data transmission. On the other hand, they can improve the real-time performance of the vehicle networking perception system. At present, C-V2X is in the actual deployment and application test stage, and the system is gradually improving. The latest release of New Radio (NR) V2X was specified in Release 16 (Rel-16) as a complementary access technology defined to better serve sophisticated applications and use cases with more stringent requirements (e.g., platooning, advanced driving, etc.). Meanwhile, the standardization and testing work of Rel-17 is actively promoted [71].  
Figure 6: C-V2X standardization and evaluation.
# InMemQK: A Product Quantization Based MatMul Module for Compute-in-Memory Attention Macro
## 2 Transformer and CIM Accelerator  
### Transformer Model  
Transformer architecture is primarily composed of an encoder and a decoder [14], with the block serving as their main constituent module. As illustrated in Figure 1a, a typical block consists of fully connected layers, self-attention layers, and a Feed-Forward Network (FFN). The multi-head self-attention layer incorporates three trainable matrices within its linear layers: the query matrix \(W_{Q}\), the key matrix \(W_{K}\), and the value matrix \(W_{V}\). The query \(Q\), key \(K\), and value \(V\) are derived by multiplying the input sequence \(X=[X_{0},X_{1},\ldots,X_{N}]\) with the corresponding query, key, and value matrices. The computational expression is as follows:  
\[Q,K,V=X\cdot W_{Q},X\cdot W_{K},X\cdot W_{V} \tag{1}\]  
Self-attention is calculated using scaled dot-product attention, while the multi-head attention mechanism enables the model to focus on various positions, thereby enhancing performance. The computational expression is  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{Q\cdot K^{T}}{\sqrt{d_{k} }}\bigg{)}\cdot V \tag{2}\]  
Figure 1: Transformer model structure and MatMul methods in CIM: (**a**) Multi-head self-attention module. (**b**) MatMul calculations in ISAAC, Timely [15], etc. (**c**) MatMul calculations in ReTransformer and (**d**) proposed MatMul methods.  
The initial step in self-attention involves computing token correlations by multiplying \(Q\) and \(K\) to produce the score matrix (\(S\)). In contrast to linear layers with fixed model parameters, the intermediate MatMul layers in transformers utilize input operands that are generated during runtime.  
Traditional CIM accelerators (as shown in Figure 1b) use analog-to-digital conversion to transfer intermediate results from the analog domain to the digital domain, where digital matrix computation units are employed for correlation calculations. The literature [16] optimized MatMul via matrix decomposition (as shown in Figure 1c), which necessitates writing intermediate results \(K\) to the NVM array in real time, but it cannot circumvent the high power consumption associated with analog-to-digital conversion.  
To tackle the issue of the challenges associated with adapting traditional CIM accelerators' self-attention modules for in-memory computation, Figure 1d illustrates our proposed solution, which employs product quantization to cluster one operand of the MatMul into a codebook, pre-written to the NVM array. This approach mitigates the high programming power consumption and writes latency [17] associated with attention computation while significantly reducing AD conversions. It is worth noting that the MatMul between the softmax output and the value (\(V\)) in the attention module can also be approximated using this method.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 5 Simulation Analysis
Furthermore, we can also observe that the SCJ-OPA scheme achieves better performance in terms of the end-to-end delay than the other two schemes across the entire transmit power range of interest, which indicates the advantage of the SCJ-OPA scheme. Figure 6 plots the secrecy outage probability versus the buffer size \(L\) for the three proposed transmission schemes under the NCE and CE scenarios, respectively. As can be readily observed, as the buffer size increases, the achieved performance approaches the performance floor. Specifically, for both NCE and CE scenarios, the NJ and SCJ-OPA schemes outperform the SCJ scheme when \(P_{Total}\) is small. On the contrary, the SCJ and SCJ-OPA schemes are superior to the NJ scheme at the condition of the high transmit power, which matches the simulation above. Figure 4: Average Secrecy throughput vs. the secrecy rate \(R_{s}\) for the three proposed transmission schemes under the NCE (**a**) and CE (**b**) scenarios when \(M=2\), \(L=3\), \(K=2\), \(P_{Total}=10\), 20 dB. Figure 3: Average Secrecy throughput vs. the total transmit power budget \(P_{Total}\) for the three proposed transmission schemes when \(R_{s}=1\) (bit/s/Hz), \(M=L=2\), \(K=2\). Figure 7 shows the impact of the power allocation factor \(\alpha\) on the secrecy outage probability for the SCJ-OPA scheme. The curves shown in Figure 7 are calculated by using the grid-search or the straightforward search methods. It is clearly seen that the optimal power allocation factor is decreased with the increase of \(P_{Total}\). That is to say, more power is allocated to transmit the useful information at the source sensor when \(P_{Total}\) is not large. This is because, when \(\alpha\) is too small, only few packets can be sent from the source sensor to relay sensors in the first hop. Thereby, not enough data packets can be forwarded to the controller. Even if the jamming power is large in the second hop, it cannot improve the secrecy performance of the whole network. On the other hand, with the increase of \(P_{Total}\), the dominant factor that affects the secrecy performance of the considered system changes from the information transmit power to the jamming transmit power at the source sensor, which is the exact reason the optimal power allocation factor becomes smaller gradually. Figure 5: End to end delay vs. the total transmit power budget \(P_{Total}\) for the three proposed transmission schemes when \(R_{s}=0.6\) (bit/s/Hz), \(M=L=2\), \(K=3\). Figure 6: Secrecy outage probability vs. the buffer size \(L\) for the three proposed transmission schemes under the NCE (**a**) and CE (**b**) scenarios when \(R_{s}=0.5\) (bit/s/Hz), \(M=2\), \(K=3\), \(P_{Total}=5\), \(15\) dB.
## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review  
As a result of the semi-automatic analysis, which combined Natural Language Processing (NLP) techniques with expert review, the contributions have been organized into four main categories:  
1. Energy Efficiency and Sustainability: Research focusing on reducing energy consumption, optimizing resources, and improving industrial productivity--highlighting the positive impact of predictive maintenance strategies on process sustainability [51, 52, 53].
2. Sectoral Applications and Monitoring of Critical Failures: Studies targeting early detection and forecasting of failures in high-impact industrial systems, such as mining, energy, and manufacturing, featuring advances in vibration analysis, real-time diagnostics, and IoT integration [4, 6, 54, 55, 56, 57].
3. Digital Twins and Asset Management: Exploration of virtual replicas (digital twins) to simulate and predict asset behavior, enhancing maintenance planning and efficiency--emphasizing the combination of digital twins with machine learning algorithms and IoT [58, 59, 60, 61].
4. Foundational Technologies and Advanced Methodologies: Developments in AI algorithms (deep learning, optimization, LLMs), Big Data applications in industry, and the integration of chaotic systems. The role of sensors and data acquisition platforms is also highlighted, enabling more accurate and efficient predictive maintenance [10, 11, 12, 13, 15, 32, 33, 34, 62].
## 2 Preliminaries and Problem Formulation  
### Preliminaries on Signed Digraph Theory  
Throughout this paper, the networked system topology is represented by a signed digraph \(\mathcal{G}=(\mathcal{V},\varepsilon,\mathcal{A})\), where \(\mathcal{V}=\{v_{1},v_{2},\ldots,v_{N}\}\) is the node set, \(\varepsilon\subseteq\mathcal{V}\times\mathcal{V}\) represents the directed edge set, the pair \((i,j)\in\varepsilon\) means that node \(v_{j}\) can receive information from node \(v_{i}\), and \(\mathcal{A}=(a_{ij})_{N\times N}\) denote the adjacency matrix, whose entry \(a_{ij}\) denotes the connection and weight between agents \(v_{i}\) and \(v_{j}\). If the communication between node \(v_{j}\) and node \(v_{i}\) is cooperative, then \(a_{ij}>0\); else, if the communication between node \(v_{j}\) and node \(v_{i}\) is competitive, then \(a_{ij}<0\). Otherwise, \(a_{ij}=0\). For simplicity, it is generally assumed that the graph \(\mathcal{G}\) has no self-loop, that is, \(a_{ii}=0\). The Laplacian matrix \(L=(l_{ij})_{N\times N}\) is defined as \(l_{ij}=-a_{ij},i\
eq j\), and \(l_{ii}=\sum_{1,j,i\
eq i}^{N}|a_{ij}|\). Different from the Laplacian matrix for unsigned directed graphs, it is important to highlight that the Laplacian matrix for signed directed graphs may not have zero row sums. This deviation poses challenges in derivation. To address this, a set of essential lemmas is introduced for a solution.  
**Definition 1**.: _A signed digraph \(\mathcal{G}=(\mathcal{V},\varepsilon,\mathcal{A})\) is said to be structurally balanced if the node set can be partitioned into two subsets \(\mathcal{V}_{1}\) and \(\mathcal{V}_{2}\), which satisfy \(\mathcal{V}_{1}\bigcap\mathcal{V}_{2}=\emptyset\), \(\mathcal{V}_{1}\bigcup\mathcal{V}_{2}=\mathcal{V}\), such that if \(a_{ij}\geq 0\), then \(v_{i}\) and \(v_{j}\) are in the same subset, and if \(a_{ij}\leq 0\), then \(v_{i}\) and \(v_{j}\) are in different subsets separately. Otherwise, the digraph is said to be structurally unbalanced._
## 1 Introduction  
Despite the already quite long and quite solid \"history\" of the implementation of spacecraft missions to the Moon (both automatic and manned), the ballistic design of SC flight trajectories from low Earth orbit to near-lunar orbits or to the vicinity of the libration points still remains highly non-trivial. This is a direct consequence of the complex and \"implicit\" essentially non-linear mechanics of motion of the SC center of mass within systems consisting of three or more gravitating bodies. In addition, the generally \"weak\" controllability of the SC has a significant impact on the design of transfer trajectories (from the standpoint of considering the latter as a controlled dynamical system (i.e., a control object)) since the set of its possible state is extremely small in most cases. Nevertheless, it is known, that a usage of dynamic effects in the framework of the considered complex model of SC motion helps to significantly improve the energy characteristics of the flight trajectory. We are talking here about the possible implementation of the so-called low-energy spacecraft flight trajectories [1; 2; 3; 4; 5; 6; 7; 8; 9; 10] from a low Earth reference orbit to a given selenocentric one. It is quite natural to consider the problem of ballistic design of the trajectory of a low-energy Earth-Moon flight as an optimization problem. However, in itself the formalization of the latter is not so simple, even when using the simplest model of the motion of the SC center of mass. The difficulty here lies in the significant multi-extremality of the problem [1; 2; 5], due to, firstly, the complex interference and resonant nature of the effects of perturbations from the Moon and the Sun acting on SC trajectory; and secondly, to a very narrow region of admissible states of a SC that satisfy the final conditions in the vicinity of the Moon (entry into a circular orbit of a given radius, etc.). The latter has a particularly strong effect on the convergence of most numerical methods of conditional optimization using the space of local directions. For these reasons, the solution of the problem \"directly\" turns out to be a very complex and irregular process, overshadowed by significant computational difficulties, and therefore requires consideration of a number of auxiliary problems and analysis of their solutions, due to which a good initial approximation can be found (since the area of convergence of the main problem is too narrow). For the above reasons, the main substantive part of the present work is the description of the approach to finding a solution to the main problem, which is based on the analysis of planar auxiliary problem. Further, in the course of the presentation, their detailed formalization and the method of numerical solution will be presented.
## 1 Introduction
It can also cause frequent quality issues such as welding defects (pores and cracks) in the welds due to process problems. The type of joint applied in this study is a fillet joint where the side frame and the bottom frame meet, as shown in Figure 3. The side frame is made of extruded aluminum material with a hollow cross-sectional structure. As it is structurally impossible to insert the tool from the opposite side of the joint as shown in Figure 2b, FSW is performed on the fillet joint by tilting the tool at a 45\({}^{\circ}\) angle as shown in Figure 2c. The hollow cross-sectional structure of the side frame is a structure in which the inside of the base material is not full of solids. In this case, backing plates cannot be installed on the opposite side of the joint to support the downward force of the tool when performing FSW. Therefore, it is much more difficult to perform fillet joint FSW welding on hollow cross-sectional structures compared to specimen units. As shown in Figures 3 and 4, the structure is the main frame of the electric vehicle where the heavy battery packs will be assembled. It is designed with a hollow cross section to secure the rigidity of the vehicle body and a thin aluminum extrusion material is applied to reduce weight. Therefore, FSW should be used for this structure because applying MIG welding, which has been mainly used for fillet joints in conventional automotive manufacturing processes, causes deformations beyond the design specifications and quality defects such as pores. As mentioned above, although studies on T-joint specimens have been performed in the past, it cannot be applied to the cross-sectional shape  
Figure 3: Application type of fillet joint FSW. Figure 2: Types of FSW joint: (**a**) Butt FSW; (**b**) T-joint FSW; (**c**) fillet joint FSW. of the actual structure designed in this study. Moreover, data to predict the structural behavior are insufficient. Therefore, this study performed FSW by tilting the tool at an angle of 45 degrees for four filter joints where the bottom frame and the side frame were in contact, as shown in the solid line and the dotted line in Figure 4. The full frame structure and equipment were set as shown in Figure 5a, and the welding was performed with the optimum process derived through the specimen unit experiment using a specially manufactured angle head as shown in Figure 5b. In addition, finite element analysis was performed using the commercial analysis program Ansys Mechanical to predict the deformation and residual stress occurring in the entire frame structure. Finally, the accuracy of the analysis was verified by measuring the welding deformation of the actual structure and comparing it with the analysis result. Figure 4: Full schematic diagram of fillet joint FSW for main battery frame. Figure 5: Fillet joint FSW equipment: (**a**) Actual full-frame structure set; (**b**) actual fillet FSW tool set.
## 5 Enhancing QoS and QoE in ICN
### Name Resolution and Routing
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \# & **Proposal and** & **Architecture** & **Experimental** & **Optimized QoS/QoE Metric** \\ \hline
1. & [PERSON] et al. [75], 2015 & PSIRP & Simulation, & QoE (mean opinion score) \\ \hline
2. & [PERSON] et al. [78], 2017 & NDN & NS-2 and Matlab & Delivery rate, average cost \\ \hline
3. & [PERSON] et al. [73], 2017 & NDN & ndnSIM 2.0 [84], & Data delivery time, cost, hop count, dropped packets, and mean hit ratio \\ \hline
4. & [PERSON] et al. [80], 2018 & NDN & NS-3 based NDN simulator & Packet loss, throughput \\ \hline
5. & [PERSON] and [PERSON] [81], 2018 & ICN & OMNeT++, wireless network & Throughput, delay \\ \hline
6. & [PERSON] et al. [79], 2018 & ICN & CERNET, CERNET2, WAN & Hop count, routing success rate, cache hit rate \\ \hline
7. & [PERSON] et al. [74], 2019 & NDN & ndnSIM and SUMO, VANET & Packet loss, retransmission rate \\ \hline
8. & [PERSON] and [PERSON] [83], 2020 & CCN & Cefore (Python) & Processing time \\ \hline
9. & [PERSON] et al. [82], 2020 & NDN & Mini-NDN [85] & Round-trip time, bandwidth \\ \hline
10. & [PERSON] et al. [86], 2022 & CCN & Python programming, WAN & Cache hit ratio, delay, forwarded interest count, time-out interest count, throughput, jitter, packet loss \\ \hline
11. & [PERSON] and [PERSON] [87], 2023 & ICN & Python programming & Delay \\ \hline
12. & [PERSON] et al. [88], 2024 & ICN & lcarus, WAN & Delay, link load \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Name resolution and routing-based strategies: architectures, experimental platforms, and QoS/QoE metrics. However, maintaining an accurate and up-to-date FIB can be resource-intensive, especially in highly dynamic networks. Cooperative forwarding strategies utilize collaboration between ICN routers to find cached content closer to the consumer, and the content may be delivered from either single or multiple sources. By distributing the load across multiple providers, cooperative forwarding can lead to reduced congestion. However, the effectiveness of these strategies relies on the network's up-to-date knowledge of available providers and accurate bandwidth estimation, and stale or incorrect information can lead to suboptimal decisions.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging
### Microelectromechanical scanners
The most common system architecture is raster scanning, where a low frequency, linear vertical scan (quasi-static) is paired with an orthogonal high frequency, resonant horizontal scan. For the raster scanner, the fast scanner will typically run at frequencies of some KHz (typically exceeding 10 KHz), and should provide a large scan angle [63; 64]. A detailed overview of MEMS laser scanners is provided in [62], where the topics covered previously may be found expanded. Extended discussions on accuracy issues in MEMS mirrors may also be found in [63]. Due to its promising advantages (in particular being lightweight, compact and with low power consumption) MEMS-based scanners for lidar have received increasing interest for their use in automotive applications. MEMS-based lidar imaging systems have in parallel shown the feasibility of the technology in different scenarios such as space applications and robotics. Currently, the automotive utilization of MEMS for lidar is in growing development by a number of companies [52; 65; 66; 67; 68] becoming one of the preferred solutions at present.