esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Multi-Scale Demodulation Method
### Description of the Experimental System  
The planetary gearbox (PGB) experimental test rig is shown in Figure 8. It consisted of two planetary gearboxes, two motors, an industrial computer and a control box, which were arranged symmetrically. The detailed design parameters of the planetary gearbox are given in Table 1, and the schematic of PGB is depicted in Figure 9. It contained four planet gears, a standstill ring gear, a sun gear and a carrier. In PGB 1, the sun gear was the input and the carrier was the output; in PGB 2, the carrier was the input and the sun gear was the output. The seeded sun gear tooth spalling fault was located in PGB 1; the schematic diagram and the photograph of the spalling fault are shown in Figure 10. The spalling fault's length equalled 35 mm, the width equalled 4 mm and the depth equalled 2 mm. The power of the two Siemens three-phase induction motors was 15 kW; motor 1 is for driving and motor 2 is for loading.  
Figure 8: The planetary gearbox (PGB) experimental test rig.  
Four ICP accelerometers were mounted in the housing of PGB 1 to acquire vibration signals, denoted as \(\sharp\)1, \(\sharp\)2, \(\sharp\)3 and \(\sharp\)4, respectively. \(\sharp\)1 was placed in the housing of the input bearing, \(\sharp\)2 was placed in the planetary gearbox base, \(\sharp\)3 was placed in the ring gear of the planetary gearbox and \(\sharp\)4 was placed in the housing of the carrier bearing, as illustrated in Figure 8. An LMS SCADAS SC305 (a Siemens data acquisition instrument) was applied for data acquisition; the sampling frequency was set as 20,480 Hz, and the sampling length was set as 11 s. Three datasets were acquired with rotating speeds equal to 100 rpm, 300 rpm and 500 rpm, respectively; it is noted that the vibration signals of \(\sharp\)4 are analyzed in this study.  
The corresponding characteristic frequencies of the planetary gearbox are calculated with the equation in [34] and given in Table 2.  
\[f_{c}=\frac{Z_{s}}{Z_{r}+Z_{s}}f_{sun} \tag{14}\]  
\[f_{p}=\frac{(Z_{p}-Z_{r})Z_{s}}{(Z_{r}+Z_{s})Z_{p}}f_{sun} \tag{15}\]  
\[f_{m}=\frac{Z_{r}Z_{s}}{Z_{r}+Z_{s}}f_{sun} \tag{16}\]  
\[f_{ds}=\frac{f_{m}}{Z_{s}}N_{p} \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Sun Gear** & **Planet Gear** & **Ring** \\ \hline Number of teeth & 16 & 33 & 84 \\ Modulus/mm & 4 & 4 & 4 \\ Pressure angle/(\({}^{\circ}\)) & 20 & 20 & 20 \\ Planet number & & 4 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the planetary gearbox.  
Figure 10: The seeded spalling fault: (**a**) schematic diagram, (**b**) photograph.  
Figure 9: Schematic of PGB.  
where \(Z_{s}\), \(Z_{r}\) and \(Z_{p}\) indicate the tooth number of sthe un gear, ring and planet gear, respectively. \(N_{p}\) is the number of the planet gear. \(f_{sun}\), \(f_{c}\) and \(f_{p}\) are the rotating frequencies of the sun gear, carrier and planet gear, respectively; \(f_{m}\) refers to the meshing frequency; and \(f_{ds}\) is the fault characteristic frequency.
## 2 Related Works
The work in [17] exploits DWT to reduce the total amount of information to be transmitted to the sink when facing congestion, with a low impact on the overall quality of the application. In case of congestion, priorities resulted from the different relevancies of DWT subbandsare considered by intermediate nodes. The idea is to discard packets containing less relevant data when the congested node may choose what packets must to be discarded. All those works exploit DWT in different ways, achieving different optimization results. In a similar way, DWT image coding is exploited here for cross-layer optimization in video-based wireless sensor networks, but we propose an innovative retransmission service where the relevancies of DWT subbands are considered by intermediate nodes for retransmission purposes, achieving energy saving by the cost of some loss of image quality.
## 1 Introduction  
Medium density fibreboard (MDF) is an important product in a variety of industries ranging from low-cost furniture to exterior protected panels. MDF is most commonly produced using wood particles produced as by-products from other operations but can be produced from a variety of other agricultural by-products. However, wood fiber is not always available, and there is a continuing search for alternative materials for these products.  
One possible alternative material would be feathers from poultry production. Global chicken production results in an estimated 40 million tonnes of feathers as a by-product [1]. A variety of methods have been proposed for processing feathers, including steam explosion to create new feed sources and composting, but most approaches are technologically difficult [1]. At present, there are relatively few practical avenues for processing of these materials. As a result, much of this material is sent to landfills or burned. Identifying processing pathways for feathers would reduce pressure on landfills while helping to extend global wood resources.  
Chicken feathers have been successfully incorporated into MDF as well as particle-board [2, 3], but the effects of feathers on fire performance have not been studied.  
Features are largely composed of keratin, which has some ability to limit flammability by creating a carbon layer on the surface, much in the same way as wood. Thus, supplementing wood panels with feathers may also behave similarly to wood in terms of flammability of the resulting product. At the same time, addition of low-cost fire retardants could help to further improve panel properties. One candidate is wollastonite (CaSiO\({}_{3}\)),a naturally occurring mineral that has previously been shown to enhance resin curing and improve fire performance [4; 5; 6; 7; 8]. Combining feathers and wollastonite might create a pathway for utilizing feathers while improving panel properties.  
The objective of this study was to explore the effects of incorporating small amounts of chicken feathers and wollastonite on subsequent fire performance of MDF panels.
## 6 Discussion  
This work has presented a signal processing method which allows for a precise Doppler measurement by multiple matched filters applied to the same set of data. The computational effort required by the many matched filters pays off by a significantly reduced illumination time of the target as compared to conventional Doppler determination by Fourier analyses. The requirements for the presented Doppler processing approach are three-fold:  
1. The waveform needs to have a (pseudo-)random modulation that covers the full signal bandwidth at all times.
2. The waveform has to be free of range-Doppler coupling, usually indicated by a thumbtack-like shape of the ambiguity function.
3. The target of interest has to be the sole target in the corresponding range cell.  
The first two requirements are inherently met by noise radar signals. The third requirement strongly depends on the radar application and is expected to be the decision-maker if the proposed processing scheme is useful in a particular application or not.  
The experiment shows an example in which the presented Doppler procedure applies well to real-world data. In the following, the experimental results are discussed with a Doppler measurement based on a conventional Fourier analysis. Section 5 stated that the duration of the data recording was deliberately limited to 12 ms. This maximum available coherent processing interval, according to (2), would limit the Doppler resolution to  
\[\Delta f_{D}=83\ \mathrm{Hz}\,\]  
using a Doppler filter bank (implemented by an FFT) to extract the Doppler information. This value is equivalent to velocity increments of  
\[\Delta v_{r}=4.8\ \mathrm{km/h}\]  
for the X-band radar used during the experiments. In noise radar technology, the cross-ambiguity function CAF [3] is a suitable signal processing approach for which the PRI can be set to the sampling rate of the signal and a Doppler analysis of a single range cell may be performed without Doppler-ambiguities by  
\[\mathcal{F}\{m_{D}(t)\}=\int\limits_{-T/2}^{T/2}m_{D}(t)e^{-2\pi ft}dt, \tag{26}\]  
where  
\[m_{D}(t)=s(t)g^{*}(t-\tau_{D}) \tag{27}\]  
is computed for a single target distance D, respectively for the corresponding delay value \(\tau_{D}\). Figure 9 shows the results of a Doppler analysis performed by the cross-ambiguity function and a coherent processing interval of 12 ms. The FFT, in the current example, has a corresponding length of 600,000 samples and shows a resolution of \(\Delta v_{r}=4.8\ \mathrm{km/h}\) as expected for this particular CPI.
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 2 Materials and Methods
### Lightweight Sandglass Block
The size of the feature map is transformed from \(C\times H\times W\) to \(G\times C^{\prime}\times H\times W\), where \(G\) is the number of groups defined by the group pointwise convolution, and \(C^{\prime}\) is the number of channels in each group. 2. Transposing the first two dimensions, the output feature map dimension is \(C^{\prime}\times G\times H\times W\). 3. Merging the first two dimensions, the output feature map dimension is \(C\times H\times W\). It can be seen from the above process that if the output feature map is divided into the same number of groups as the input feature map, the information fusion between the channels of different groups can be completed in the next group convolution. Therefore, the channel shuffle can effectively solve the problem of the inadequate fusion of information between channels caused by group convolution. Furthermore, this study adopted _hard-svish_ in the sandglass block. The _svish_ from the automatic search technique is an activation function with better performance, and a previous paper [34] on _svish_ showed that its performance is better than that of ReLU on deeper models. However, subsequent studies found that the _svish_ function results in more computational consumption. Therefore, researchers proposed _hard-svish_[35], a hard analog of the _svish_. The _hard-svish_ does not differ significantly from _svish_ in terms of performance, but is less computationally consuming. Therefore, we adopt the _hard-svish_ activation function in the sandglass block. The _svish_ and _hard-svish_ activation functions are shown in Equations (6) and (7), respectively, and their function curves are compared, as shown in Figure 5. The function curves of _svish_ and _hard-svish_ almost overlap, which indicates that they can play almost identical roles. \[\textit{svish}(x)=x\cdot\sigma(x) \tag{6}\]  
\[\textit{hard-svish}(x)=x\frac{ReLU6(x+3)}{6} \tag{7}\]  
This study adopts the above-described group convolution, channel shuffle, and _hard-svish_ activation function to build a lightweight sandglass block with better performance, fewer parameters and less computation, named LightSandGlass. The structure of LightSandGlass is shown in Figure 3d, where RGConv and EGConv denote the group pointwise convolution used for dimension reduction and dimension expansion. Considering that too many group convolutions will increase the memory access consumption [36] and that _hard-svish_ performs better in deeper network layers, this study only replaces the residual blocks in the deep part of the network with LightSandGlass.
# Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond
## 4 Problem Formulation  
Let \(A_{i}\) be a zero-one indicator variable with \(A_{i}\) = 1 if an ABS is deployed at candidate location \(i\); otherwise, \(A_{i}\) = 0. Similarly, let \(B_{i,j}\) be a zero-one indicator variable with \(B_{i,j}\) = 1 if user \(j\) is connected to the ABS at candidate location \(i\); otherwise, \(B_{i,j}\) = 0. Let \(P_{i,j}\) be the power received by user \(j\) from the ABS at location \(i\). If user \(j\) is connected to the ABS at location \(r\), then the signal-to-interference noise ratio at user \(j\) will be:  
\[\text{SIR}_{j}=\frac{P_{r,j}}{\sum_{i\in L_{i}j\
eq r}P_{i,j}} \tag{3}\]The goal is to place the ABSs in candidate locations and to assign users to ABSs to maximize the sum of SIR for all users, that is,  
\[\underset{A_{i},B_{i,j},\forall i\in L,\forall j\in S}{\text{maximize}}\sum_{j\in S }\sum_{j\in S}\text{SIR}_{j} \tag{4}\]  
If all the ABSs use different frequency bands, then the goal will be to maximize the sum of received powers (i.e., received signal strength (RSSI)). Let \(P_{j}\) be the power received by a user \(j\) from the candidate location to which it is assigned; then,  
\[\underset{A_{i},B_{i,j},\forall i\in L,\forall j\in S}{\text{maximize}}\sum_{j \in S}P_{j} \tag{5}\]  
In Equations (4) and (5), \(A_{i}\) controls where to place an ABS and \(B_{i,j}\) controls to which location to assign a user such that the corresponding summation is maximized.  
A user \(j\) can only be assigned to a candidate location \(i\) if there is an ABS placed at that candidate location, i.e., \(A_{i}\) = 1. This means:  
\[A_{i}B_{i,j}=1,\forall j\in S \tag{6}\]  
A user should be assigned to exactly one candidate location, which means:  
\[\sum_{i\in L}B_{i,j}=1,\forall j\ \in\ S \tag{7}\]  
As \(N_{u}\) is the number of ABSs, the following constraint should be satisfied:  
\[\sum_{i\in L}A_{i}=N_{u} \tag{8}\]
## 3 Results and Discussion
### Saturation Solubility  
The saturation solubility was demonstrated for GEF pure, F1 and F3 in different solvents having pH values simulating gastric fluid (pH 1.2), the slightly acidic microenvironment of cancers (pH 5.6) and small intestine fluid (pH 7.2). The pH of the microenvironment of cancers or malignant tumors is slightly acidic and varies between 5.6 and 6.8. The reason for this acidic microenvironment is attributed to glycolysis, inadequate blood flow and hypoxia in the tumor cells [41]. Table 2 represents the saturation solubility (in linear and logarithmic) of GEF (\(\mu\)g/mL) at different pH values. The results were suggesting the pH dependent solubility of GEF and GEF SDs. It was found that GEF was comparatively very much soluble at 1.2 pH (gastric environment) compared to pH 5.6 (tumor microenvironment) and pH 7.2 (intestinal environment). The relatively low saturation solubility at neutral pH is attributed to the aqueous-insoluble or poor aqueous soluble property of GEF, which was due to the fact that GEF is a dibasic molecule with two pKa values (5.4 and 7.2). The enhanced saturation solubility of GEF SDs at acidic pH is suggesting, such that the SDs of GEF prepared with TPGS (F1) will have better absorption of the drug through GIT (especially in the stomach part) as compared to the SDs of GEF prepared with SLS (F2). Among the three optimal SDs, the F3 (the complexation of F1 with HP \(\beta\)-CD) has shown the highest solubility (F3 \(>\) F1 \(>\) F2 \(>\) GEF pure) at 1.2 pH. Moreover, use of TPGS as a stabilizer would improve the oral bioavailability of GEF from SDs to overcome the multidrug resistance as TPGS is a potent P-glycoprotein (P-gp) inhibitor [38]. Thus, we can postulate that the complexation of GEF SDs prepared with TPGS will provide the highest absorption of GEF in the GIT, which in turn would improve the overall bioavailability of the drug.
###### Abstract  
Drastic improvements in transmission rate and system capacity are required towards 5 th generation mobile communications (5G). One promising approach, utilizing the millimeter wave band for its rich spectrum resources, suffers area coverage shortfalls due to its large propagation loss. Fortunately, massive multiple-input multiple-output (MIMO) can offset this shortfall as well as offer high order spatial multiplexing gain. Multiuser MIMO is also effective in further enhancing system capacity by multiplexing spatially de-correlated users. However, the transmission performance of multiuser MIMO is strongly degraded by channel time variation, which causes inter-user interference since null steering must be performed at the transmitter. This paper first addresses the effectiveness of multiuser massive MIMO transmission that exploits the first eigenmode for each user. In Line-of-Sight (LoS) dominant channel environments, the first eigenmode is chiefly formed by the LoS component, which is highly correlated with user movement. Therefore, the first eigenmode provided by a large antenna array can improve the robustness against the channel time variation. In addition, we propose a simplified beamforming scheme based on high efficient channel state information (CSI) estimation that extracts the LoS component. We also show that this approximate beamforming can achieve throughput performance comparable to that of the rigorous first eigenmode transmission. Our proposed multiuser massive MIMO scheme can open the door for practical millimeter wave communication with enhanced system capacity.  
massive MIMO; multiuser MIMO; first eigenmode; millimeter wave; channel estimation; channel time variation +
Footnote †: journal: Sensors
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.1. Backwards Compatibility  
Up till IEEE 802.11 ac, all the IEEE 802.11 systems have been designed to be backward compatible. However, for IEEE 802.11 ah, backward compatibility is not considered due to the use of a completely different frequency band.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 1 Introduction
### Motivation and Contribution  
In recent years, DL has significantly advanced the field of FAS, especially with the support of large-scale datasets. However, existing reviews often focus on single topics and need a more comprehensive analysis of different modalities, the generalization capabilities of algorithms, and spatiotemporal efficiency. Therefore, a systematic review of FAS based on DL holds academic and practical significance in exploring current research bottlenecks and future directions. Firstly, DL techniques, such as CNNs, GANs, and contrastive learning have achieved notable success in FAS. However, challenges remain in addressing their generalization to unseen attacks and cross-domain applications. A comprehensive review of these methods will facilitate an understanding of their strengths and weaknesses, promoting further development in the field. Secondly, integrating multi-modal information (e.g., RGB, depth images, infrared) shows potential for enhancing FAS detection capabilities, but it also increases computational complexity. Summarizing the pros and cons of multi-modal methods can guide future research toward balancing accuracy and efficiency. Finally, the real-time performance and efficiency of FAS algorithms in smart devices require attention. A review can analyze the performance of algorithms across different devices, helping researchers understand how to maintain detection accuracy while reducing computational costs and ensuring real-time operation. Based on previous research, this article comprehensively introduces the types of face presentation attacks and detection methods based on DL. Compared to previous reviews, this survey is unique in the following three aspects:  
1. This article comprehensively surveys face presentation attacks and detection methods based on DL, including face anti-spoofing on smartphones. 2. This survey covers 229 multiple articles, categorizing them into smaller sub-topics, and provides a comprehensive discussion. 3. This survey reveals the current challenges and future research trends about face anti-spoofing and provides a reference basis for research in face authentication. The rest of this survey is organized as follows. Section 2 reviews face presentation attacks designed to deceive a FRS. Next, we introduce detection methods against face
## 5 System Description
### Main Building Blocks  
In the proposed system architecture, three main roles are introduced: the user client, the virtual Identity Provider (vIdP), and the Relying Party (RP).  
* User Client: The user client module serves to transmit the username and password to the IdP to initiate the distributed authentication process. It oversees distributed token and p-ABC mechanisms through two modules: the client logic module, which handles token recomposition and presentation while delegating credential management to the credential manager module, the second one, which manages p-ABC credentials, reassembles and stores them, and generates access tokens according to access policies for use with relying party services.
* Virtual Identity Provider (vIdP): The virtual identity provider of AURORA MINDS serves as the cornerstone of its blockchain-based architecture. This innovative approach transforms traditional federated identity management by decentralizing the identity provider into multiple interconnected entities, known as virtual identity providers. This design effectively eliminates the single point of failure that often plagues conventional systems. Furthermore, it enhances user privacy by preventing any single identity provider from tracking users through their interactions with various service providers. This decentralized framework not only increases security but also fosters a more user-centric identity management experience. By protecting user identities before data is shared or processed, AURORA MINDS reduces the risk of privacy breaches.  
Each of these vIdPs enables authentication, credential issuance, and token management. The process of authentication handles distributed password verification by interacting with authentication modules across vIdPs. The issuance manages distributed issuance via tokens or p-ABC credentials, specifically:  
1. P-ABC Credential Management: Each vIdP generates a credential share. When combined, these shares form a complete, reusable credential for accessing protected services.
2. Token Management: Similar to credentials, vIdPs generate token shares that, when combined, create a one-time use presentation token tied to an access policy.
* Relying Party (Web application): The relying party is the service provider that requests some kind of authentication from the user to grant him access to the service and deposits its trust in the AURORA MINDS vIdP for this authentication. Its main purpose is to ensure that the user has permission to use the service or resources. The user must agree to the terms of the access policy and consent to the disclosure of the required attributes or proofs (out-of-band of the Verification System). The Relying Party assumes also the role of the verifier, confirming the signed access form presented by the user and their compliance with the access policy, using methods like OAuth, SAML, or p-ABC tokens.  
The overall architecture is depicted in Figure 2.