esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Bearing Fault Diagnosis Method using Acoustic Emission Signals
### Stochastic Line Search Optimizer  
Gradient Descent (GD) is one of the most well-known and standard algorithms to optimize CNN models [19]. There are many GD-based variants, such as Stochastic Gradient Descent (SGD), Adam, and Momentum [19]. However, these optimizers are used as black boxes. In general, different optimizers need to be applied to find out the most compatible one based on their strengths and weaknesses. The Adamax optimizer was utilized to strengthen the training process after trying various  
Figure 4: Scalability of EfficientNet.  
optimizers (SGD, Adam, and Adamax) [20]. Therefore, we can know that the convergence ability of CNN models can be enhanced when a compatible optimizer is adopted.  
Stochastic gradient descent (SGD) and its variants are highly effective in modern machine learning models [21]. In any case, the gradient methods are antagonistically influenced by two challenges: Firstly, their execution depends significantly on the setting of learning rate. Secondly, frequently updating the steps taken towards the minima shows higher probability to lead the gradient descent into other directions compared to across all training samples (due to noisy steps, it can take longer to converge to the minima of loss function).  
A stochastic line search was developed based on traditional line-search methods to find a suitable step size (learning rate) without pre-setting for SGD while training over-parametrized models. To do that, the line search approach seeks a downward trend directory of the objective function, then infers the step size, which corresponds to the length to move along that direction.  
SGD computes the gradient of the loss function randomly fed by one sample or by a minibatch of chosen training samples \(i_{k}\) in iteration \(k\). It then performs an update step as \(w_{k+1}\ =\ w_{k}\ -\ \eta_{k}\
abla f_{ik}(w_{k})\) corresponding to loss function descent, where \(w_{k+1}\) and \(w_{k}\) are the SGD iterates, \(\eta_{k}\) is the step size, and \(\
abla f_{ik}(\cdot)\) is the average loss function gradients computed at iteration \(k\). Each stochastic gradient \(\
abla f_{ik}(w)\) is assumed to be equivalent (e.g., \(E_{i}[\
abla f_{i}(w)]\ =\ \
abla f(w)\) for all \(w\)).  
The Armijo line search is a criterion for deterministically setting the step size of gradient descent [13]. At iteration k, the Armijo line search performs computations to choose a step size that satisfies the following condition, where c \(>\) 0 is a hyperparameter:  
\[f_{ik}(w_{k}-n_{k}\
abla f_{ik}(w_{k}))\leq f_{ik}(w_{k})-c\cdot\eta_{k}\big{\|} \
abla f_{ik}(w_{k})\big{\|}^{2} \tag{3}\]  
The implementation of the SLS process in our proposed bearing fault diagnosis method can be summarized as follows.  
1. Compute the gradients \(\
abla f_{ik}(w_{k})\) for a given training batch.
2. Search for a step size \(\eta_{k}\) that satisfies the stochastic Armijo line search condition.
3. Use the step size and update the model parameters with SGD: \[w_{k+1}\ =\ w_{k}-\eta_{k}\
abla f_{ik}(w_{k})\] (4)
## 3 Results
### Noise Analysis  
In order to explore the performance of the trained PSCNN, we assumed typical thermal and shot noise at the photodiodes of the electro-optic synapses, as the dominant perturbing mechanism. We varied the level of noise and evaluated the impact to the classification error when the network was inferring a set of 100 images which consists of all the four digits ('5', '6', '7' and '8'). Simulations provided evidence that even for low Signal-to-Noise Ratio (SNR < 10 dB) and for the above-described simple classification task, there was no impact in classification error. This resiliency can be attributed to the integrate-and-fire nature of the VCSEL-neurons and the relative, large integration time. In particular, even though the instant input power of the pixel may exhibit significant variation, the average input power remains approximately the same. Consequently, the timing of the produced spikes from CDL's receptive field neurons is not severely affected.  
Since, the PD Gaussian noise did not affect the performance of our network, we took into consideration a different noise source that corresponds to mean intensity variation at each pixel. In this scenario, the intensity of each pixel is perturbed compared to the nominal value resulting in a distorted version of the original image (Figure 9). This type of perturbation affects the contrast in each SW and thus alters the timing of spikes. So as to model such an effect we added to the mean intensity of each pixel a random variation, drawn from a normal distribution with different standard deviations (see inset of Figure 9). These intensity variations are mapped to the input power of the neurons at the CDL. The PSCNN is considered trained and in this case is set to an inference mode.  
Figure 8: (**a**) Four numbers used as inputs for the training of the network. Learned patterns of (**b**) CL1, (**c**) CL2 and (**d**) CL3. (**e**) The abstract version of the digits that the network identifies.  
The set of 100 images, which comprised all of equivalently distributed digits '5' to '8', was fed to the network and the classification error was computed in a set containing all possible digits. In Figure 9 the total classification error is presented (for all digits) versus the standard deviation of pixel noise. It can be seen that the classification error of our system remained low for perturbations with standard deviation up to 6% of the nominal value, while an abrupt increase can be observed for higher values.
## 2 Methodology  
All images applied are collected by a surface cleaning robot. One of the key tasks of the robot is to detect floating objects in different water areas. Algorithm 1 is the framework of our floating object detection algorithm for each video frame.  
```
Input: \(I\) Output: \(B_{1}\), \(B_{2}\), \(B_{3}\)
1:\(B_{1}\leftarrow\)TextureDetection(I)
2:\(B_{2}\leftarrow\) SaliencyDetection(I)
3:\(B_{3}\leftarrow\) Combination(\(B_{1}\), \(B_{2}\))
4: return B\({}_{1}\), B\({}_{2}\), \(B_{3}\)
```  
**Algorithm 1 Floating Object Detection**  
As shown in Algorithm 1, the input is a video frame \(I\), and the detection task is carried out in three parts: texture detection, saliency detection, and combination. The outputs are three vectors of bounding boxes, i.e., \(B_{1}\), \(B_{2}\), and \(B_{3}\), which are the results of the three parts, respectively. Note that the final output \(B_{3}\) can be an empty vector, indicating that there are no floating objects in this video frame. More details are given in the subsections below.
## 4 Results  
### Dataset X1  
#### 4.1.1 Scree Plot Analysis for Dataset X1  
The scree plot generated from the PCA results in Figure 8 provides insights into the variance explained by each principal component. In the case of Dataset X1, the scree plot revealed the following:Figure 8 illustrates the scree plot, providing insights into the variance explained by each principal component. Key observations for Dataset X1 include the following:  
* Dominant First Component: The first principal component elucidates a significant portion of the dataset's variance.
* Elbow Point: A distinct \"elbow\" in the scree plot suggests a clear cutoff point for dimensionality reduction.
* PCA Suitability: Given the substantial variance explained by the first component, PCA is deemed suitable for reducing the dimensionality of Dataset X1.
## 2 Channel Model and Main Results  
The symbol notations and description of the channel model are presented in Subsection 2.1. Subsection 2.2 characterizes the inner and outer bounds on the capacity-equivocation region of the wiretap channel with information embedding on actions.
## 1 Introduction
To this end, the TRIMIS technology taxonomy is used, which is built through a grounded theory approach, and comprises more than 850 technologies that fall under 45 technology themes. The top technologies in terms of funding in the period 2007-2020, are identified, belonging to the STRIA roadmaps on connected and automated transport, network and traffic management systems, and smart mobility and services. The technologies are then linked to the flagship ambitions of the 2020 European SSMS, which has set out an action plan for transport policy in Europe for the next four years. Focusing on the latest projects since 2014, a macro level technology maturity analysis is carried out, to identify those technologies that are at an early stage of research and those that can be pushed forward to demonstration and deployment. The principal merit of this study is linking technologies identified in a structured manner to transport policies, something that constitutes a rigorous and fair approach compared to the fragmented information usually provided in ad hoc technology analyses. This study intends to help policy makers support the future development of transport technologies as part of relevant policy actions, while at the same time, helping researchers to forge collaborations and identify research gaps. The paper consists of the following parts: after the introduction, the next section discusses the materials and methods, including an overview of the methodology used in TRIMIS for transport R&I assessment and technology analysis. Following that, Section 3 provides some key metrics on EU transport R&I, including relevant identified technologies and their maturity phase. On this basis, Section 4 links the identified technologies to the EU \"Sustainable and smart mobility strategy\" ambitions and provides a discussion of the findings towards the development of these technologies. Section 5 provides the conclusions.
## 3 Results and Discussion
### Monosaccharide Composition of Saccharide Fractions  
The monosaccharide composition was analyzed by GC-MS-EI and the results are summarized in Table 3. For uronic acids, only the molar ratio of mannuronic acid was defined for alginates and fucoidan. ALCM and ALSM were mainly composed of 74.61% and 87.59% of ManA, respectively, with minor impurities of fucose and glucose. FUCCM presented 53.39% of fucose and 45.70% of ManA, which indicates the contamination by alginate, confirming the colorimetric analysis presented in Section 3.1. In contrast to prior research, fucoidans isolated from _Cystoseira_ spp. and other species contained monosaccharides other than fucose, including galactose, glucose, mannose, xylose, rhamnose, and uronic acids [43; 48; 56]. On the other hand, sulfated homofucans were isolated from _C. crinita_, _C. compressa_, and _C. sedoides_, of which fucose constituted 43.4%, 61.5%, and 54.5%, respectively [50]. The monosaccharide composition of fucoidans may differ depending on the source of brown macroalgae. The FUCCM isolated from CM is a sulfated homogenous fucoidan made up mainly of fucose.  
({}^{1}\)Agn: sulfated glycoprotein (crude extract) extracted from CD; \({}^{2}\) AGP: sulfated glycoprotein (purified extract) extracted from CD; \({}^{3}\) FUCCM: fucoidan extracted from CM; \({}^{4}\) ALCM: alginate extracted from CM; \({}^{5}\) ALSM: alginate extracted from SM; \({}^{6}\) Nd: Not determined.
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 7 Experimental Results
### Efficiency of IoTBystander  
We note that the computation overhead of the DCA module is negligible compared to AER and TER, which involve complex operations such as data processing and neural network forward propagation. Therefore, to evaluate the efficiency of IoTBystander, we test the computational overhead of AER and TER in terms of execution time. Due to the different underlying recognition methods, we compute the average execution times of AER and TER in different manners. We tested the average time for AER to handle each audio spectrogram from end to end and for TER to handle each network packet. Moreover, we perform the aforementioned efficiency evaluation on two different computing platforms: a laptop (Intel i7-13700H 2.4 GHz CPU and 8 GB memory) and a Raspberry Pi Model 4B (Aarch64 Cortex-A72 1.8 GHz CPU and 4 GB memory). The results shown in Table 11 demonstrate that IoTBystander is very efficient. On the laptop, the AER module identifies the event types for each 0.96-second audio frame within 0.0152 s, while the TER module recognizes the event types for each traffic packet in 0.04 s. On the Raspberry Pi, these processing times increase to 0.0289 s for AER and 0.08 s for TER, respectively. That is to say, it is practical to deploy IoTBystander on common home devices such as PCs, tablets, gateway devices, etc.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 2 Methodology
### Problem Formulation
#### 2.1.2 The Channel Division Method  
In the design of channelized receivers, the prototype filter is not ideal, and there is a transition zone. According to whether the sub-channels overlap or not, the channel dividing methods can be divided into non-overlapping channel dividing and overlapping channel dividing, as shown in Figure 3. The non-overlapping channel dividing will form a receiving blind zone. When the signal is in the transition zone, the signal cannot be effectively received. Moreover, the cross-channel broadband signal may be misjudged as multiple narrowband signals due to the signal in the transition zone being lost. The overlapping channel division method overlaps different channels, which solves the problem of receiving blind spots. However, under critical sampling conditions, the channel sampling rate is \(2\pi/K\). When overlapping channel dividing is used, the filter coverage bandwidth is greater than the channel sampling rate. The signal spectrum within the channel is aliased. The formula is derived as follows.  
Figure 1: The filter banks for channelization.  
Figure 2: The complex signal down-conversion low-pass channelization structure.  
In discrete time, the instantaneous frequency can be approximated by the backward difference operation. The instantaneous frequency is  
\[\hat{\omega}_{k}^{i}[m]=\frac{\Delta\phi_{k}[m]}{D} \tag{1}\]  
where the \(\wedge\) denotes the estimate. In order to have an unambiguous frequency measurement  
\[-\pi\leq\Delta\phi_{k}[m]\leq\pi \tag{2}\]  
Thus  
\[-\pi\leq\hat{\omega}_{k}^{i}[m]D\leq\pi \tag{3}\]  
Suppose the channel overlap factor is \(\delta\). The frequency range of the signal in the channel can be expressed as  
\[-\frac{\pi}{K}\cdot(1+2\delta)\leq\omega_{k}^{i}[m]\leq\frac{\pi}{K}\cdot(1+2\delta) \tag{4}\]  
Using Equations (3) and (4), the IFM for each channel will be unambiguous if  
\[F=K/D\geq(1+2\delta) \tag{5}\]  
In addition, wideband signals can suffer from cross-channel problems [11].
## 2 The V2G Technology with Energy Resource
Despite the advances achieved thus far, a distinct imperative subsists and needs comprehensive exploration of the ramifications of this technology in existing socio-environmental frameworks. As for the vehicular exemplar chosen, the midsize electric car predominates within this discourse. The rationale for this selection is grounded in the pervasive market presence of the midsize electric car in jurisdictions that have embraced this technological trajectory. Noteworthy attributes of the chosen vehicle encompass a battery capacity of 40 kilowatt-hours, which concomitantly endows an estimated travel range of 389 km as per the New European Driving Cycle (NEDC) urban assessment. Moreover, the vehicle charging dynamics are underscored, signifying a maximum charging duration of 8 h, as elucidated in Table 2.  
Figure 1: Vehicle-to-Grid technology (Source: the authors). Orange represents the electricity flowing from the grid to the EV, while green symbolizes the electricity flowing from the EV back to the grid.
# Multi-Material Optimization for Lattice Materials Based on Nash Equilibrium
## 4 Numerical Examples and Tests
### Three-Point Bending Test  
In this section, we validate the optimization method proposed in this article through a three-point bending test. The structure, shown in Figure 16, is an MBB beam with a structural design domain of 180 mm \(\times\) 50 mm and thickness of 8 mm. The midpoint of the upper edge of the structure is subjected to a vertical downward load while being symmetrically supported at a distance of 140 mm from the lower edge of the structure.  
The design domain was discretized into 18 \(\times\) 5 elements, with volume constraints of 0.2 for each of the two materials. Figure 17 shows the optimized structure of multiple materials, the unoptimized structure of Material 1, and the unoptimized structure of Material 2.  
Figure 16: Messerschmitt–Bolkow–Blohm (MBB) beam structure for three-point bending test.  
Figure 17: Models for 3D printing: (**a**) optimized model, (**b**) Material 1 model, (**c**) Material 2 model.  
We can efficiently fabricate the optimized structure with the assistance of 3D-printing technology. As depicted in Figure 18, the specimens are produced using a 3D printer based on the Fused Deposition Modeling (FDM) technique [45]. Polylactic acid (PLA) is applied as the material, characterized by an elastic modulus of 3500 MPa, Poisson's ratio of 0.2, and density of 1400 kg/m\({}^{3}\). Considering the test requirements and printer precision, the design variables are confined within the range of 0.6 mm to 2 mm. Moreover, the post-processing techniques introduced by [PERSON] et al. [46] are utilized for further enhancement of the optimized structure. The weights of the fabricated specimens are 56.82 g, 57.30 g, and 57.83 g, respectively.  
The specimens are slowly loaded onto the MTS Exceed E43 Electronic Universal Testing Machine (10 KN) manufactured by MTS Systems Corporation in Shenzhen, China, at a longitudinal displacement speed of 1 mm/min. The test process is shown in Figure 19.  
The force-displacement characteristics of the three types of MBB beam structures in the test are illustrated in Figure 20. It is evident that among the three force-displacement curves, the optimized structure exhibits a steeper slope compared to the other two, indicating that the multi-material optimized structure possesses superior stiffness performance in comparison to the unoptimized single-material structures.  
Figure 18: The upper part is the optimized specimen, the middle part is the unoptimized Material 1 specimen, and the lower part is the unoptimized Material 2 specimen.
## 2 Literature Review
### Factors That Influence (the Perception of) the Added Value of Occupancy Sensing  
This research focuses on the added value of occupancy sensing for offices and the factors that influence the perceived added value. The added value of occupancy sensing can be that spaces that remain empty can be scaled down and being secluded. This leads to more efficient use of the room because cleaning is less needed intensively, lightning and climate control can be switched off there and better maintenance in the short and long term can be adjusted to the actual use of the spaces. This is pleasant for the user but can also save on real estate and service costs [32]. If the data results are not exploited well, and the organisation does not know how to reach the full potential with the systems, the effects can be adverse. Proper space utilisation is not always a critical factor for good space management practice, and therefore, the (utilisation) data are not always useful. The following reasons why it does not work are examples from professionals [33]:  
* Measures of utilisation provide information but not solutions.
* High utilisation rates do not mean that space is managed effectively.
* Collecting data costs time and money, and the data can be difficult to interrogate and analyse.
* Utilisation survey results may not be integrated with space need assessments or timetabling or linked with other management information systems.
* It is often used on a relatively small part of the total floor area.
* There is an overflow of options and therefore data.
* There are privacy issues.  
From the literature, it seems that three factors are conditional for successful occupancy sensing: data maturity of the client organisation, human behaviour and the quality of the sensors. These will be discussed next.
## 5 Discussion  
Compared with the widely used water vapor retrieval algorithm based on look-up table, the semi-empirical algorithm developed in this study has an extremely high retrieval efficiency because it does not need to use the radiative transfer model such as the MODerate resolution atmospheric TRANsmission (MODTRAN) computer code [41] to build the look-up table before retrieval and retrieve CWV by querying the look-up table [20]. Additionally, our algorithm has very strong portability. Although it is only used for PSAC at present, it can be easily used for CWV retrieval of sensors such as MODIS and MERSI-2 that have both a water vapor absorption band and a band unaffected by water vapor absorption in the spectral range of 850-1250 nm [11].  
Although PSAC CWV data developed in this work has a high accuracy and can effectively characterize the change trend of CWV, it still has some limitations. First, because the water vapor retrieval algorithm developed in this study needs to use historical PSAC data and AERONET data to determine the parameters of the algorithm model (\(A\), \(B\), and \(C\) in Equation (3)), it cannot be used for CWV retrieval immediately after the launch of HJ-2 satellites. However, HJ-2 satellites will undergo nearly one year of in-orbit testing before operational operation, a large amount of data will be accumulated during this period, which is sufficient for building the algorithm model. Therefore, it is feasible to use it as an operational algorithm.  
In addition, as shown in Figure 6, the retrieval error of PSAC CWV data generally increases with the increase in CWV. This is unavoidable mainly because the sensitivity of band 6 to water vapor gradually decreases with the increasing water vapor content [42]. Although the sensitivity of all NIR bands to water vapor decreases with increasing water vapor content, the sensitivity of different water vapor absorption channels to water vapor is different. This means that the retrieval error of remote sensing CWV data can be reduced by using a band that is more sensitive to water vapor at high water vapor content compared to band 6. For example, MODIS and MERSI-2 are equipped with three NIR water vapor absorption bands to improve the accuracy of CWV retrieval results. Therefore, to improve  
Figure 6: Day-to-day comparisons between PSAC CWV data and AERONET CWV data. (**a**) NEON_LENO. (**b**) SEDE_BOKER. (**c**) Tomsk. (**d**) Ussurijssk.  
the accuracy of CWV retrieval results at high water vapor content, two or more water vapor absorption bands are necessary for the subsequent PSACs.