esa-sceva/satcom-chunk-collection huggingface dataset:
# Value Stream Analysis in Military Logistics:
## 2 Background
### Lean Methodologies and Tools  
Lean thinking promotes a continuous-improvement culture with its tools and practices widely applied in different sectors and organizations. Lean is one of the most influential recent paradigms in manufacturing, also considered in relation to another promising trend as Industry 4.0 [29]. It has expanded beyond the original application to other areas and sectors, either public or private. As before mentioned, the origin of lean philosophy generally attributed to the practices developed from the Toyota production system [8; 9]. One of the first studies mentioning lean concepts comparing automotive manufacturing plant performance in Japan, the United States, and Europe was [30]. Since then, lean practices have been developed and extended worldwide. The lean approach, its techniques, and limitations were revised in the literature [31; 32], remarking the necessity of an adequate implementation sequence [33] and the effect of large-scale strategic management in lean deployment [34]. Several efforts were focused on the measurement of leanness by considering different dimensions [35; 36]. The evolution of lean was also addressed in [37] not only as a concept but also in terms of its application.  
The link between lean and the supply chain evolved from the value stream concept [38], and the concept of pull was extended beyond single manufacturing facilities to include the up- and downstream partners. Focusing on supply chain management, different methodologies have been applied for logistics optimization purposes. Combined approaches of lean and agile methodologies applied to SCM are also found in [39] for the textile and apparel sector, where short product lifecycles, high volatility, low predictability, and a high level of impulse purchase have paramount importance. In addition, in [40], the existence of hybrid supply chain strategies with a mixed portfolio of products and markets where neither pure agile or lean strategies apply is remarked. Lean works best in high volume, low variety, and predictable environments; meanwhile, agile suits for less predictable environments, with volatile demand and high requirements for variety. An integrated proposal of both methodologies is proposed in [41] and in [42] supported by a personal computer (PC) supply chain case study.  
In relation to lean logistics in defense, there is scarce literature available. The concept of pull systems applied to military logistics was questioned by the authors in [43]. A large accumulation of stocks in intermediate distribution points supposes the reduction of effectiveness maneuverability of the deployed combat forces. On the other hand, the \"Just-in-Time\" approach could bring an associated risk of late or even null deliveries, given the possible actions of the enemy. [PERSON] et al. [44] discussed the inventory management in the uncertain environment of military support. Considering stocks, additional problematic aspects are related to damage, degradation, and obsolescence of the material, which mean monetary losses at the end.
# SCS-JPGD: Single-Channel-Signal Joint Projected Gradient Descent
## 2 Related Works  
### Application of Deep Learning in Fault Diagnosis  
Deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have achieved remarkable success in the field of fault diagnosis. By automating feature extraction, deep learning models can effectively identify potential fault patterns from complex signals, significantly improving the accuracy and efficiency of fault detection. For instance, CNN-based models have demonstrated excellent performance in vibration signal analysis, capturing temporal features in the signals and accurately identifying fault types under different operating conditions, yielding outstanding results in fault diagnosis. For example, [PERSON] et al. [1] leveraged the physical characteristics of bearing acceleration signals to guide CNN design for model optimization. [PERSON] et al. [2] proposed a 2-D hierarchical CNN (HCNN) hardware accelerator, implemented with 40 nm CMOS technology, for bearing fault diagnosis on the CWRU dataset. [PERSON] et al. [3] introduced a fault diagnosis model based on Bayesian optimization (BO-CNN-LSTM). [PERSON] et al. [4] acquired raw vibration and acoustic signals at different speeds and used Constant-Q Nonstationary Gabor Transform (CQ-NSGT) to convert them into time-frequency spectra for fault diagnosis. Simultaneously, RNNs, particularly Long Short-Term Memory (LSTM) networks, have been widely applied in dynamic system fault diagnosis due to their ability to model long-term and short-term dependencies in sequential data. They can accurately capture dynamic changes in equipment and perform anomaly detection, providing reliable support for early warning. For example, [PERSON] et al. [12] proposed an RNN-based model for time-varying operating condition fault diagnosis, while [PERSON] et al. [9] considered a quasi-periodic, LSTM-based method for the weak fault diagnosis of rolling bearings. [PERSON] et al. [10] proposed an automatic feature learning neural network that uses two Convolutional Neural Networks with different kernel sizes to automatically extract signal features from raw data at different frequencies. Through these technologies, deep learning models can effectively handle large-scale data processing needs and demonstrate strong generalization capabilities, adapting to various operating conditions and complex noise environments.  
However, the application of deep learning models in fault diagnosis also faces certain challenges. The \"black-box\" nature of these models makes their decision-making process difficult to interpret [19; 20], lacking transparency. Particularly when faced with noise, disturbances, and data missing issues, the models may lead to unstable diagnostic results, impacting the reliability and safety of the system. For instance, when equipment experiences atypical faults or significant changes in operating conditions, deep learning models may struggle to accurately identify these new types of faults, leading to misdiagnosis or missed diagnosis.
## 2 2D Wave Field Reproduction in 3D Space Using a Single Circular Array
### Wave Field Reproduction Error with Radii \(R_{mic}\) and \(R_{s}\)  
As described in the previous section, we explained how \(r_{ref}\) affects wave field reproduction. When \(r_{ref}=R_{mic}\), it indicates an acceptable reproduction performance. In addition, \(R_{mic}\) and \(R_{s}\) affect wave field reproduction. In the process of extracting the circular harmonics coefficients, the Bessel zero problem occurred, and the frequency of the Bessel zero problem differed according to the change of \(R_{mic}\). Figure 5a,b respectively show the average reproduction error and variance according to \(R_{mic}\) changes when \(r_{ref}=R_{mic}\) and \(R_{s}\) were fixed at 2 m. In contrast, Figure 5c,d show the reproduction error and variance according to \(R_{s}\) when \(r_{ref}=R_{mic}\) and \(R_{mic}\) were fixed at 0.8 m.  
As shown in Figure 5a, the smaller the value of \(R_{mic}\) (up to a frequency of approximately 160 Hz), the smaller will be the reproduction error. However, at frequencies greater than 160 Hz, an irregular pattern was observed. Similarly, as shown in Figure 5b, the smaller the \(R_{mic}\) value is for frequencies lower than 150 Hz, the more uniform the wave field reproduction is. As indicated, the relationship is reversed at frequencies greater than 150 Hz. As shown in Figure 5c,d, when \(R_{mic}\) is fixed, the reproduction error decreases as \(R_{s}\) increases, but the variance is irregular.  
Figure 5: Wave field reproduction error and variance with respect to the radii of the microphone and loudspeaker array. (**a,c**) Average wave field reproduction error. (**b,d**) Variance.  
The part with a large Bessel zero effect was observed as a peak in the wave field reproduction error graph. In the case of Figure 5a, as \(R_{mic}\) decreases, the peak was pushed back, which was attributed to the Bessel zero in the coefficient extraction process. As shown in Figure 5c, a peak occurred at the same frequency because \(R_{mic}\) is the same. However, even at the peak, it showed a wave field reproduction error of less than \(-10\) dB. This means that the noise reduction performance exceeds \(10\) dB when ANC using a single circular array is performed, suggesting that the Bessel zero problem can be tolerated. Furthermore, these results provide information for determining the radii of the microphone and loudspeaker arrays for the dominant frequency component of the noise source when constructing an ANC system.
# Observer-Based Distributed Fault Detection for Heterogeneous Multi-Agent Systems
## 2 Preliminaries and Problem Formulation  
### Graph Theory  
A graph \(\mathcal{G}=\{\mathcal{V}(\mathcal{G}),\ \mathcal{E}(\mathcal{G})\}\) could represent communication links among one leader agent and several follower agents, where \(\mathcal{V}(\mathcal{G})=\{\mathcal{V}_{0},\mathcal{V}_{1},\ \cdots,\ \mathcal{V}_{N}\}\) and \(\mathcal{E}(\mathcal{G})=\{(\mathcal{V}_{i},\ \mathcal{V}_{j}):\mathcal{V}_{i}, \mathcal{V}_{j}\in\mathcal{V}\}\subseteq\mathcal{V}\times\mathcal{V}\) represent the vertex and edge sets of \(\mathcal{G}\), agent \(i\) could receive information from agent \(j\) if \((\mathcal{V}_{i},\ \mathcal{V}_{j})\in\mathcal{E}(\mathcal{G})\), where \(\mathcal{V}_{j}\) is also called the neighbour of \(\mathcal{V}_{i}\). If \((\mathcal{V}_{i},\ \mathcal{V}_{j})\in\mathcal{E}\) means that \((\mathcal{V}_{j},\ \mathcal{V}_{i})\in\mathcal{E}\), graph \(\mathcal{G}\) is called undirected. A path from \(\mathcal{V}_{i}\) to \(\mathcal{V}_{j}\) is a sequence of distinct nodes \(\{\mathcal{V}_{k_{0}},\ \mathcal{V}_{k_{1}},\ \cdots,\ \mathcal{V}_{k_{l}}\}\), where \(k_{0}=i\), \(k_{l}=j\) and \((\mathcal{V}_{k_{r}},\ \mathcal{V}_{k_{r+1}})\in\mathcal{E},0\leq r\leq l-1\). An induced subgraph \(\mathcal{G}_{s}\) is a graph such that \(\mathcal{V}(\mathcal{G}_{s})\subset\mathcal{V}(\mathcal{G})\), and \((\mathcal{V}_{i},\ \mathcal{V}_{j})\in\mathcal{E}(\mathcal{G}_{s})\) indicates that \((\mathcal{V}_{i},\ \mathcal{V}_{j})\in\mathcal{E}(\mathcal{G})\). Subgraph \(\mathcal{G}_{s}\) with the vertex set \(\{\mathcal{V}_{1},\ \cdots,\ \mathcal{V}_{N}\}\) represents the communication relationship among follower agents. The adjacency matrix \(\mathcal{A}=[a_{ij}]\) associated with \(\mathcal{G}\) is defined as \(a_{ii}=0\) and \(a_{ij}=1\) if \((\mathcal{V}_{i},\ \mathcal{V}_{j})\in\mathcal{E}(\mathcal{G})\). What is more, define the in-degree of each agent as \(d_{i}=\sum_{j=0}^{N}a_{ij}\geq 1\), and define the Laplacian matrix associated with \(\mathcal{G}\) as \(\mathcal{L}=[l_{ij}]\), where \(l_{ii}=d_{i}\) and \(l_{ij}=-a_{ij},j\
eq i\).  
For the convenience of analysis, denote \(\mathcal{L}_{s}\) as the Laplacian matrix associated with \(\mathcal{G}_{s}\), and define \(\mathcal{A}_{0}=\mathrm{diag}(a_{10},a_{20},\cdots,a_{N0})\) and \(\mathcal{H}=\mathcal{L}_{s}+\mathcal{A}_{0}\). Furthermore, define \(\mathcal{N}_{i}=\{\mathcal{V}_{j}\in\mathcal{V}(\mathcal{G}_{s}):(i,j)\in \mathcal{E}(\mathcal{G}_{s}),i\
eq j\}\) as the neighbour set of node \(\mathcal{V}_{i}\in\mathcal{V}(\mathcal{G}_{s})\) in \(\mathcal{G}_{s}\), and define \(\bar{\mathcal{N}}_{i}=\{i\}\cup\mathcal{N}_{i}\), where \(|\bar{\mathcal{N}}_{i}|\) is the cardinality of \(\bar{\mathcal{N}}_{i}\), and \(\{\bar{i}_{1},\cdots,\bar{i}_{|\bar{\mathcal{N}}_{i}|}\}\) are sequence numbers of nodes in \(\bar{\mathcal{N}}_{i}\) from small to large.  
**Lemma 1** ([26]): _If the subgraph \(\mathcal{G}_{s}\) is undirected, and each follower agent has paths to the leader in the graph \(\mathcal{G}\), \(\mathcal{H}\) is positive definite._
## 2 Methods
### Simulation Frame  
The transport simulations were applied in the small section shown below the channel in Figure 1. The gas is transported from the bottom to the top, towards the air channel. For the purpose of characterization of the GDL material, the gas flow in the channel of an operating fuel cell is not considered, avoiding the composition of different effects that could influence the results.  
The microstructure of the GDL is specified by a series of binary images, as shown in Figure 2. In this way, 130 images of size 512 \(\times\) 512 define a 512 \(\times\) 512 \(\times\) 130 lattice. With a resolution of 1.5 \(\upmu\)m, this defines a section of 768 \(\upmu\)m \(\times\) 768 \(\upmu\)m of a GDL with a 195 \(\upmu\)m thickness, consisting of 26 fiber layers, in accordance with the stochastic geometry model of [PERSON] et al. [8]. A series of images specifies an irregular porous structure. A bounce back condition is applied at the fiber surface, representing no-slip conditions. The definition of relevant boundary conditions is enabled by free space added upstream and downstream, as was already used in previous studies [4; 5]. At the inlet, a Dirichlet boundary condition is specified, implemented as a fixed velocity that corresponds to a total mass flow of steam produced by an average current density of 1 A/cm\({}^{2}\). At the outlet, a Neumann boundary condition, also known as constant pressure, is specified. At the side walls, a slip condition was applied. The goal is the detection of the relationships between well-known volumetric properties like permeability or tortuosity and the characteristics of the surface flow. Single-phase, single-component flow is simulated to obtain characteristics related to the pure GDL material without being affected by physical effects from outside the GDL. In Table 1, the values are completed with the amount of hydrogen and oxygen needed for the electrochemical conversion of 1 A/cm\({}^{2}\) as a reminder of [PERSON]'s law.  
Compared to earlier work [4; 5; 26], the binary images representing the microstructure are used in reverse order. For the calculation of average volumetric values and statistical evaluation, this is unnecessary, and the only reason is to avoid local inconsistencies with the evaluation of the other interface (GDL/electrode) presented by [PERSON] et al. [26].  
Geometries with 30% compression were created by merging adjacent fiber layers as mentioned in Section 2.1 and described in more detail by [PERSON] et al. [4]. In this case, 92 images represent a GDL of 138 \(\upmu\)m in thickness.
## 3 State of the Art
### Regional Regulations  
In order to enable fair coexistence among LTE and Wi-Fi, research has been focusing on designing coexistence techniques that will allow LTE to operate in the unlicensed spectrum, respecting the different regional regulations. Concurrently, these techniques aim to fairly share the medium with other well-established technologies like Wi-Fi. For instance, the European Telecommunications Standards Institute (ETSI) defines requirements [10] that should be fulfilled by each technology that operates in the unlicensed spectrum. These requirements among others include:  
* CCA before transmission together with timing requirements for each CCA phase
* Maximum antenna gain
* Transmission power limitations  
Table 1 summarizes the regional regulations that must be obeyed by a technology that operates in the unlicensed spectrum. In the table, DFS stands for Dynamic Frequency Selection and TPC stands for Transmit Power Control.  
Figure 4: The 802.11 CSMA/CA procedure.
# Modelling of Evenness of Runways as an Element of Sustainable Airport Maintenance
## 2 Methods for Measuring the Evenness of Airport Runways
### Methods for Modelling the Evenness of Airport Runways
#### 2.1.3 Interpolation Based on Triangulated Irregular Network (TIN)  
A common TIN algorithm is Delaunay triangulation [58]. It attempts to generate a surface formed by triangles of the nearest points. To do this, circles are created around selected sample points and their intersections are connected to form a network of triangles that do not overlap and are as compact as possible [54].  
Besides the formation criteria of Delaunay triangulation TIN (empty circle principle), there are many other criteria, e.g., local equiangularity (max-min angle principle), minimization of the interior angle range, minimum-sum-distance principle, minimum circumscribing circle radius, and others.  
The main disadvantage of TIN interpolation is that the surfaces are not smooth and can have a jagged appearance. This is caused by discontinuous slopes at the triangle edges and sample data points. In addition, triangulation is generally not suitable for extrapolation beyond the area with sampled sample data points. Interpolation methods are part of many GIS applications. Linear interpolation (TIN1) and Clough-Tocher (cubic) interpolation (TIN2) have been evaluated as useful [59].
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 4 Methodology of Algebraic Point Projection
### Extension to NURBS Surfaces
#### 4.2.1 Projection in Physical Space  
Utilizing the linearity property (Equation (17)), one can rewrite \(\
abla\Gamma\) and \(\mathbf{H}\) in Equation (19) as follows (as before, superscript \(D\) is dropped for ease of reading):  
\[\
abla\Gamma=\left|\mathbf{M}\right|\begin{bmatrix}\operatorname{tr} \left(\mathbf{M}^{-1}\frac{\partial}{\partial x}\mathbf{M}\right)\\ \operatorname{tr}\left(\mathbf{M}^{-1}\frac{\partial}{\partial y}\mathbf{M} \right)\end{bmatrix}=\left|\mathbf{M}\right|\begin{bmatrix}\operatorname{tr} \left(\mathbf{M}^{-1}\mathbf{M}_{x}\right)\\ \operatorname{tr}\left(\mathbf{M}^{-1}\mathbf{M}_{y}\right)\end{bmatrix}= \Gamma\mathbf{\bar{g}} \tag{31}\] \[\mathbf{H}=\left|\mathbf{M}\right|\begin{bmatrix}\operatorname{tr} \left(\mathbf{M}^{-1}\mathbf{M}_{x}\right)&\operatorname{tr}\left(\mathbf{M}^ {-1}\mathbf{M}_{x}\right)\operatorname{tr}\left(\mathbf{M}^{-1}\mathbf{M}_{y} \right)&\operatorname{tr}\left(\mathbf{M}^{-1}\mathbf{M}_{x}\right)\operatorname {tr}\left(\mathbf{M}^{-1}\mathbf{M}_{x}\right)\\ -\operatorname{tr}\left(\left(\mathbf{M}^{-1}\mathbf{M}_{x}\right)^{2}\right)& -\operatorname{tr}\left(\left(\mathbf{M}^{-1}\mathbf{M}_{x}\right)\left( \mathbf{M}^{-1}\mathbf{M}_{y}\right)\right)&-\operatorname{tr}\left(\left( \mathbf{M}^{-1}\mathbf{M}_{x}\right)\left(\mathbf{M}^{-1}\mathbf{M}_{x}\right) \right)\end{bmatrix}\] \[=\Gamma\mathbf{\bar{H}} \tag{32}\]  
Next, as before, Equations (20), (23) and (24) can be exploited to obtain the physical footpoint \(\mathbf{x}_{f}\) in three-dimensional space. Earlier statements on efficiency of the algebraic point projection for rational parametric curves also apply to rational parametric surfaces except that the size of the Dixon matrix \(\mathbf{M}^{D}\) is \(2 pq\times 2 pq\), where \(p\) and \(q\) are the degrees of the rational parametric surface in each dimension. Algebraic point projection in three-dimensional physical space is demonstrated in Figure 13, where test points are projected onto a target Bezier surface using the proposed second-order algebraic point projection method and the Newton-Raphson iterations. Again, one can observe that the proposed method leads to accurate solutions for test points closer to the surface.
## 5 Conclusions  
We present in this paper a method for the identification of the optimal spectral bands of a commercial camera for the intraoperative monitoring of the brain hemodynamic and metabolic responses following neuronal activation. The method described in the report is based on Monte Carlo simulations of the light propagation in a volume of grey matter and incorporates a realistic modeling of the camera acquisition with the addition of Gaussian noises (experimentally measured with the cameras). We identified that an optimal spectral combination of our hyperspectral camera composed of 21 to 22 spectral bands can be used to compute accurate hemodynamic and metabolic brain maps. This configuration could be however reduced from 10 to 12 spectral bands while keeping fairly constant performances, which is consistent with the spectral configurations proposed in the literature. We also showed that RGB imaging is not a suitable technique to compute metabolic brain maps, but is very accurate to compute hemodynamic maps with the quantification of deoxygenated hemoglobin concentration changes. Our Monte Carlo framework needs to be improved, namely with the consideration of the perfusion of grey matter by blood capillaries.  
Conceptualization: C.C., L.M.-W., and B.M. Methodology: C.C., L.M.-W., R.S., M.S., and B.M. Software: C.C., L.M.-W., and M.S. Writing, original draft: C.C. Writing, review and editing: C.C., L.M.-W., R.S., M.S., J.G., and B.M. Supervision: R.S. and B.M. Funding acquisition: R.S. and B.M. Investigation: J.G. Resources: J.G. Project administration: B.M. All authors have read and agreed to the published version of the manuscript.  
These works were funded by LABEX PRIMES (ANR-11-LABX-0063) of Universite de Lyon, within the program \"Investissements d'Avenir\" (ANR-11-IDEX-0007), operated by the French National Research Agency (ANR); Canceropole Lyon Auvergne Rhone Alpes (CLARA) within the program \"OncoStarter\"; Infrastructures d'Avenir en Biologie Sante (ANR-11-INBS-000), within the program \"Investissements d'Avenir\" operated by the French National Research Agency (ANR) and France Life Imaging.  
We want to acknowledge the PILoT facility for the support provided for the image acquisition.  
No conflicts of interest, financial or otherwise, are declared by the authors.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HbO\({}_{2}\) & Oxygened hemoglobin \\ Hb & Deoxygenated hemoglobin \\ oxCCO & Oxidative state of cytochrome-c-oxidase \\ BOLD & Blood oxygenation level dependent \\ fMRI & functional magnetic resonance imaging \\ NIRS & Near infrared spectroscopy \\ SPM & Statistical parametric mapping \\ SNR & Signal-to-noise ratio \\ \end{tabular}
# Collision Study on New Aluminum Alloy W-Beam Guardrail
## 1 Introduction
Using the explicit dynamics software LS-DYNA, finite element simulations were conducted to analyze the dynamic deformation, energy absorption, and vehicle centroid acceleration of the new aluminum alloy guardrail during collisions, comparing it with traditional steel guardrails. This research aims to reveal the functional characteristics of the new aluminum alloy guardrail in terms of collision protection, buffering, and energy absorption.
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Log-Based Attack Detection  
Logs are the activity records of operating systems or application programs; they include system calls, alert logs, and access records. Logs have definite semantics. There are three benefits to using logs as a data source in IDSs. (1) Logs include detailed content information suitable for detecting SQL injection, U2R, and R2L attacks. (2) Logs often carry information about users and timestamps that can be used to trace attackers and reveal attack times. (3) Logs record the complete intrusion process; thus, the result is interpretable. However, one problem is that log analysis depends on cyber security knowledge. Additionally, the log formats of different application programs do not have identical formats, resulting in low scalability. The log-based attack detection primarily includes hybrid methods involving rules and machine learning, log feature extraction-based methods, and text analysis-based methods.