esa-sceva/satcom-chunk-collection huggingface dataset:
# Denoising Directional Room Impulse Responses with Spatially Anisotropic Late Reverberation Tails
## 3 Plane Wave Decomposition and Spatial Analysis
### Frequency Dependence of the PWD  
It should be noted that in the case of a sound field captured by an SMA and encoded to HOA, the SH order-dependent SMA mode strength correcting filters introduce a frequency dependence into the PWD's directivity. Indeed, the theory presented by [PERSON] and [PERSON] [13] (and more recently refined by [PERSON] and [PERSON] [25] and [PERSON] [26], among others) makes use of an increasingly lower cutoff frequency by order so as to avoid the known \"infinite bass boost\" problem upon inversion of the SMA's so-called mode strength or holographic function [27]; as a result, lower frequencies are rendered almost entirely omnidirectional, and directivity increases with frequency until all orders are equally present, before decreasing again with the emergence of strong sidelobes beyond the order-dependent spatial aliasing limit [17].  
Figure 2: Covariance matrices in the spherical harmonic domain (top, **(a,b)**) and PWD spatial domain (bottom, **(c,d)**) for a fully isotropic field (left, **(a,c)**) and an anisotropic field, synthesized using a cardioid directivity pattern with a 60-dB dynamic range (right, **(b,d)**).
## 4 Related Research on Service Discovery Protocols
### Directory-Less Architecture
#### 4.2.3 Service Discovery in Proactive MANET  
Most of the service discovery architectures designed for proactively-routed MANETs rely on OLSR as a routing protocol. Due to its proactive nature, the OLSR protocol always has the routes to the nodes in the network immediately available when a node needs them. Integrating service discovery with this kind of protocol lets a node discover a service almost immediately. The main protocols defined so far are described below: the Mercury [47] service discovery protocol describes service information using Bloom filters. The protocol is fully distributed, and it utilizes intelligent local caching with service handover support. It introduces a new message called Mercury service discovery (MSD). This message contains a field to indicate whether the message is an advertisement or a query request, so that the nodes that compound the network can advertise, as well as discover services. [PERSON] _et al._[48] proposed a service discovery scheme that discovers Session Initiation Protocol (SIP) servers in an _ad hoc_ network. They define a new message type called service location extension (SLE) that is added to the OLSR routing protocol. SIP servers can periodically advertise their location using the SLE messages. Clients can also use SLE messages to request the location of a SIP server. [PERSON] _et al._[49] presented a distributed protocol for dynamic node IP assignment in MANETs. In this protocol, each node is responsible for managing a range of addresses. When a new node wants to begin participating in the network, one of the nodes within the network gives half of its address range to the new node. In the case of any adjacent node not having free addresses, but free addresses do exist, a request to a network node that has a free addresses is done. When a node leaves the network, it does not inform the server of its departure. [PERSON] _et al._[50] present a service discovery mechanism that integrates service discovery messages with OLSR TC messages.
## 2 Methodology
### Uncertain Gravity Response Analysis Based on CIM
As the Chebyshev polynomial is orthogonal on \([-1,1]\) with a weight function \(1/\sqrt{1-x^{2}}\), the coefficient can calculated by:  
\[a_{i_{1},i_{2},\cdots,i_{n}}=\left(\frac{2}{\pi}\right)^{n}\int_{-1}^{1}\cdots \int_{-1}^{1}\frac{f(\textbf{x})\textbf{C}_{i_{1},\cdots,i_{n}}\left(\textbf{x }\right)}{\sqrt{1-x^{2}_{1}}\cdots\sqrt{1-x^{2}_{n}}}dx_{1}\cdots dx_{n} \tag{19}\]  
Although Equation (19) can be carried out by Gaussian Chebyshev integration, the coefficients should be obtained one by one. To overcome integration, a simpler procedure is proposed here. To obtain the coefficient vector of Chebyshev polynomial expansion, a number of samples should be generated within the interval vector. In CIM, the zeros of the Chebyshev polynomial are selected as sample points. In a one-dimensional uncertain problem, the samples can be calculated as:  
\[\alpha_{i}=\left[\cos\ \left(\frac{1}{2^{r}}\pi\right)\ \ \cos\left(\frac{3}{2^{r}}\pi \right)\ \ \cdots\ \ \cos\left(\frac{2r-1}{2^{r}}\pi\right)\ \right] \tag{20}\]  
The sample space for an \(n\)-dimensional uncertain gravity response analysis includes the tensor product of the zeros of the Chebyshev polynomial in each dimension, which can be expressed as \(\alpha=\alpha_{1}\times\alpha_{2}\times\cdots\times\alpha_{n}\). By collecting the response results and their Chebyshev polynomial expansions together, linear equations for obtaining the coefficient vector of Chebyshev polynomial expansion can be expressed as:  
\[\left[ \begin{array}{cccc}C_{0,0,\cdots,0}(\alpha_{0,0,\cdots,0})&C_{1,0, \cdots,0}(\alpha_{1,0,\cdots,0})&\cdots&C_{r,r,\cdots,r}(\alpha_{r,r,r^{ \prime},\cdots,r})\\ C_{0,0,\cdots,0}(\alpha_{1,0,\cdots,0})&C_{1,0,\cdots,0}(\alpha_{1,0,\cdots,0} )&\cdots&C_{r,r,\cdots,r}(\alpha_{1,0,\cdots,0})\\ \vdots&\vdots&\ddots&\vdots\\ C_{0,0,\cdots,0}(\alpha_{r,r^{\prime},\cdots,r})&C_{1,0,\cdots,0}(\alpha_{r,r, \cdots,r})&\cdots&C_{r,r,\cdots,r}(\alpha_{r,r^{\prime},\cdots,r})\\ \end{array} \right]\left[ \begin{array}{c}a_{0,0,-0}\\ a_{1,0,-0}\\ \vdots\\ a_{r,r,-r}\\ \end{array} \right]=\left\{ \begin{array}{c}y_{0,0,\cdots,0}\\ y_{1,0,\cdots,0}\\ \vdots\\ y_{r,r^{\prime},-r}\\ \end{array} \right\} \tag{21}\]  
As can be seen from the above analysis, the coefficients in CIM can be solved at the same time in this paper without calculating integration. Once the coefficient vector is obtained, the Chebyshev polynomial expansion for evaluating the uncertain gravity response can be determined. The Chebyshev polynomial expansion can give a faster prediction of gravity response than the analysis based on FEM, which can be validated in the following analysis.
###### Abstract  
This article describes a new alternative approach to satellite constellation deployment by incorporating momentum exchange tethers (METs). Traditional methods of deploying satellite constellations have limitations, typically involving costly propulsion systems and extended dispersion times. METs offer a novel solution by efficiently transferring momentum between tethered objects, reducing the need for onboard propellants and streamlining the deployment process. This article discusses orbit design and maneuvers for different mission architectures using asymmetrical and symmetrical tether release techniques to deploy satellites into designated orbits. In addition, a short walkthrough of designing one possible constellation is given, showing how quickly a MET-deployed constellation can be established in low Earth orbit (LEO). This work contributes to ongoing research investigating the applicability of METs in satellite constellation deployments, which could potentially be a new opportunity for MET technology to start seeing routine usage in the space environment, and also enable new constellation architectures that have not yet been realized.  
satellite constellation; momentum exchange tether; satellite deployment; orbit design; mission design; small satellite +
Footnote †: journal: _Aerospace_
## 1 Introduction
Hard rock mines, particularly gold and platinum mine stopes, are very harsh environments. Mineral excavation in hard rock slopes is characterized by the usage of explosives. Due to the narrow and short nature of slopes and their rough, rocky surfaces, the wireless signals undergo excessive amounts of signal reflections and scatterings. For home usage, Wi-Fi signals can pass through layers of brick or blocks; however, in underground mines, wireless signals cannot pass through the thick layers of dense rock; thus, the signals are contained within a stope, which significantly increases the possibility of destructive interference alongside excessive multipath propagation. Wireless transmissions in confined areas tend to experience more multipath propagation as compared to spacious areas. In confined areas, the reflected signals have a much greater potential to interact with each other, and there is also the increased possibility of causing distortions, such as fading, ghosting, or signal dropout. For these reasons, slopes are seen as a difficult environment for wireless communications. Stopes are very small in size, and it is not feasible to install excess amounts of cabling in this environment as it would pose many health and safety risks. Cabling should be limited to high-powered equipment that are critical to the operation of the mine. A wireless solution is considered the most viable option for such an environment. Coded Orthogonal Frequency Division Multiplexing (COFDM) has been identified as a promising technology for addressing the harsh conditions of underground hard rock slopes. This technology is well known for its role in the transformation of the terrestrial broadcasting domain [3, 4]. However, the prohibitive cost associated with COFDM equipment limits its widespread adoption in the mining industry. In [5], Wi-Fi6 was shown to offer similar signal robustness as COFDM, However, the experiments were performed in a non-ideal office corridor. This work expands on [5] by performing the Wi-Fi6 experiments in a test slope. Additional experiments were also performed to give a more comprehensive analysis of Wi-Fi6 signal performance in a stope. The signal quality evaluations were performed on an Asus RT-AX53U using the open-source OpenWrt OS with open-source mesh routing protocols, namely B.A.T.M.A.N.-adv [6] and HWMP [7]. The industry standard for low-powered wireless communications was identified as IEEE 802.15.4 [8]. OpenThread was selected as the IEEE 802.15.4 variant. Wi-Fi6 was identified as the potential candidate for stope communications due to its potential for reliable high-bandwidth communications and power efficiency. This work compared the power consumption of Wi-Fi6 vs. OpenThread by using an identical setup. The nRF/002 dk was used alongside Zephyr OS, since it supported both Wi-Fi6 and OpenThread. By using the same boards, same OS, same transmission frequencies, and same input voltage, we were able to achieve the accurate power consumption comparisons of the protocols.
## 5 Results
### Simulation Environment  
The simulations were conducted by using C++. We consider an underwater wireless sensor network (UWSN) with the BPSK modulation [49] and Rayleigh fading channel [47]. We set the simulation parameters according to the commercial LinkQuest underwater acoustic modems [51]; specifically, we set the transmitter powers in the range of 1-40 W [52]. For UWSN communications, we set the frequency and packet size as 10 KHz [53] and 3 KB [54], respectively.  
The output measures are (1) SNR; (2) BER; and (3) delivery ratio. As mentioned in Section 4, the proposed channel models can produce the SNR, BER, and delivery ratio sequentially. First, the channel models calculate the four main elements of the SNR (i.e., the source level, transmission loss, noise level, and directivity index), and thus produce the SNR. Then, after choosing the appropriate models (i.e., the BPSK modulation and Rayleigh fading channel) according to the underwater characteristics, the channel models produce the BER. Finally, considering the practical packet size, the channel models produce the delivery ratio (i.e., the reliability). To validate the whole channel model, we evaluate each part of the models as follows.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 3 Security Protections in IoT Environments
### Evolving Threats in the IoT
#### 3.1.1 Denial of Service Attacks (DoS)  
A Denial of Service (DoS) attack (see Figure 5) is a cyber attack that makes a website or service unavailable by overwhelming the system with an enormous amount of requests from multiple sources [36]. A Distributed Denial of Service (DDoS) attack [37] is a special type of DoS attacks that utilizes a network of compromised devices, called a botnet, to send attack requests. Such swift DoS and DDoS attacks are difficult to defend against and therefore, their consequences are highly destructive. In DDoS attacks, web servers are flooded with a large number of requests for replies to the message, consuming network bandwidth or system resources, resulting in overloaded networks or systems where normal services stop providing [38]. DDoS manifests itself in two main forms, namely flow-of-traffic attacks [39] and resource-exhaustion attacks [40].  
* _Flow-of-Traffic Attacks._ Under this type of attacks, the network bandwidth is blocked by a large number of malicious packets that overwhelm legitimate traffic. These attacks aim to exhaust the network's capacity to handle data, effectively denying service to legitimate users.
* _Resource-Exhaustion Attacks._ The focal point of these attacks is the server itself, which receives a multitude of attack packets that deplete memory or CPU resources. Such attacks aim to prevent servers from processing legitimate requests, leading to a denial of service.  
Figure 5: Denial of service attack in IoT systems.
# A Study on the Amplitude Comparison Monopulse Algorithm
## 5 Performance Analysis  
Any noise \(n\), including in the measurement signal, results in estimation error, and this error takes exactly the same linear form as Equation (24) in the case of the full measurement-based algorithm:  
\[\delta\hat{\rho}_{error}^  
For the case of sum-difference measurement-based algorithms, similar analysis is possible to predict its performance. With the same assumption for the measurement noise, the expected covariance of the estimates for the least-square solution takes on a simple form:  
\[\begin{array}{ll}C_{\theta}^{sd}&=E\bigg{[}\delta\theta_{error}^{sd}\big{(} \delta\theta_{error}^{sd}\big{)}^{T}\bigg{]}\\ &=E\bigg{[}\big{(}H_{sd}^{T}H_{sd}\big{)}^{-1}H_{sd}^{T}Bnn^{T}B^{T}H_{sd} \Big{(}H_{sd}^{T}H_{sd}\Big{)}^{-1}\bigg{]}\\ &=4\sigma^{2}\Big{(}H_{sd}^{T}H_{sd}\Big{)}^{-1}\end{array} \tag{45}\]  
In this case, matrix \(\big{(}H_{sd}^{T}H_{sd}\big{)}^{-1}\) is used to quantify how the level of measurement noise can be related to the expected level of errors in the target location estimate.  
If we assume \(\theta_{az}^{2}+\theta_{el}^{2}\approx 0\), then covariance \(C_{\theta}^{sd}\) is approximately computed by  
\[C_{\theta}^{sd}\approx\frac{\theta_{\mathit{3 dB}}^{4}}{32\ \left(\log 2\right)^{2} \theta_{s}^{2}\ \big{(}g(\theta_{s})\big{)}^{2}A^{2}}\ \sigma^{2}\ I_{2} \tag{46}\]  
This is the same covariance equation to that of the full measurement-based algorithm, which means that the sum-difference measurement-based algorithm has the same nominal performance as the full measurement-based algorithm.  
Formal noise analysis on the conventional algorithm of Equations (15) and (16) is extremely complex, and no useful information comes from it. Instead, Equation (44) or (46) can be utilized to quantify the level of estimation error, even for the conventional algorithm, because the conventional algorithm is a special case of the proposed iterated least-squares-based algorithm with the first iterative step.  
It is worth noting that the covariance prediction Equations (44) and (46) do not depend on the measurement itself. Thus, if monopulse radar parameters are known beforehand, these equations can be used to predict the performance of the monopulse algorithms off-line before the algorithms are actually performed. The covariance prediction equation quantitatively states estimation accuracy in terms of major parameters of amplitude comparison monopulse radar.  
The estimates from the monopulse algorithm, together with covariance predictions, can be passed as measurements to a nonlinear filter such as an extended Kalman filter, which processes them further to provide filtered estimates of the direction angle and angular rate of the unknown target.
# A Novel Tool for Biodiversity Studies: Earthworm Classification via NGS and Neural Networks
## 4 Conclusions  
The application of image analysis and machine learning allowed for the objective, non-destructive, and inexpensive distinguishing of earthworms belonging to _Eisenia fetida_, _Dendrobena_ ssp., and _Lumbricus terrestris_. Innovative models were developed based on selected image texture parameters from earthworm images in individual color channels R, G, B, L, a, b, X, Y, Z, U, V, and S using various machine learning algorithms. The classification accuracy reached 100%. The models providing completely correct classifications were developed using Ensemble, Logistic, and Narrow Neural Network. Future research can focus on the application of deep learning for distinguishing different species and genera of earthworms using image texture parameters. The observed trends confirm that texture features are not only mathematically distinct but also biologically meaningful, reflecting progressive morphological changes during earthworm development.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Template (Species; GenBank** & **Template Length** & **Number of** & **Query Identity** & **\(p\) Value** \\
**Accession Number; Gene)** & **Template Length** & **Sequence Reads** & **Query Identity** & **\(p\) Value** \\ \hline _Eisenia fetida_; A7874481.1; 285 rDNA & 2031 & 110 & 94.23 & \(1.0\times 10^{-26}\) \\ \hline _Eisenia fetida_; AF212166.1; 285 rDNA & 3633 & 68 & 94.59 & \(1.0\times 10^{-26}\) \\ \hline _Eisenia fetida_; EF534709.1; 185 rDNA & & & & \\ partial seq., ITSI, 5.8S, ITS2, and 28S & 2972 & 88 & 95.09 & \(1.0\times 10^{-26}\) \\ partial seq. & & & & \\ \hline _Hexuplex trunculus_; AM920318; 5S & 258 & 7 & 89.29 & \(1.0\times 10^{-26}\) \\ rRNA and intergenic sequence & & & & \\ \hline _Urechis unicinctus_; X00998.1; 5S rRNA & 120 & 6 & 94.68 & \(1.0\times 10^{-14}\) \\ \hline \end{tabular}
\end{table}
Table 3: Sequence read assignment to species.  
**Author Contributions:** Conceptualization, T.M. and E.R.; methodology, T.M. and E.R.; formal analysis, A.S.; investigation, A.L. and A.S.; writing--original draft preparation, T.M., E.R. and A.Z.; writing--review and editing, A.Z.; supervision, T.M. and E.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The study was carried out within the framework of the research project no. ZOR/4/2022-2.4.22, financed by the Ministry of Education and Science, as part of the research subsidy granted to the National Institute of Horticultural Research.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Reliability Assessment of Steel-Lined and Prestressed FRC Slabs against Projectile Impact
###### Abstract  
A simulation-based probabilistic method is proposed for assessing the reliability of steel-lined and prestressed fiber-reinforced concrete (PFRC) slabs against the impact loads. The impact testing of several prestressed and non-prestressed FRC slabs of 800 \(\times\) 800 \(\times\) 100 mm in size was carried out. The experimental program involved projectile impact testing of prestressed and non-prestressed FRC slabs using a gas gun. Three parameters were varied for testing of slabs under the projectile impact, viz., a quantity of steel fibers in the concrete, steel lining on the backside of the slab, and the prestressing level. The probability-based reliability analysis of all the tested specimens was then performed to highlight the influence of steel lining, steel fibers, and prestress in enhancing the safety of RC (reinforced concrete) slabs against projectile impacts. Projectile impact velocity exceeding the slab's ballistic limit was assumed to lead to the failure of the PFRC slab, i.e., the perforation failure of the slab. Study results indicate that when no steel fiber was present, slab reliability was low. Adding fibers to slabs increases slabs' reliability significantly. Specimens with the highest steel fiber content (1.2%) showed the greatest increase in reliability. The steel lining on the back face makes the slab's reliability almost doubled in comparison with those without lining. Additionally, steel lining makes the PFRC slabs as reliable as desired. It was further noticed that the prestressing helps in enhancing the slab safety against projectile impacts. Even a minimal amount of prestressing makes a noticeable improvement in the reliability of PFRC slabs.  
Keywords:RC slabs; reliability; steel fibers; projectile impact; steel lining; concrete; prestressing +
Footnote †: journal:
## 4 Performance Analysis
### Analysis of Experimental Results
In particular, SA-DQN-DE, after integrating deep learning, exhibits enhanced capabilities in addressing complex scenarios with coexisting rough and fine peaks. From the above analysis, it can be seen that the TCDE algorithm can be applied to simple and complex multimodal optimization problems, including those with gentle and steep connections between multiple peaks and coexisting rough-fine peaks. To further highlight this characteristic of the TCDE algorithm, F7 (2D Vincent) and F13 (2D Composition Function 3), as two test functions with complex characteristics, were selected for detailed analysis.
# Two-Party Quantum Private Comparison Protocol Based on Rotational Encryption
## 3 The Proposed Two-Party QPC Protocol
After confirming that TP has received \(SE_{A}\) (\(SE_{B}\)), [PERSON] ([PERSON]) will publish the measure basis (\(Z\)-basis used for measurement if the state is \(|0\rangle\) or \(|1\rangle\) and \(X\)-basis used if the state is \(|+\rangle\) or \(|-\rangle\)) and the positions of \(D_{A}(D_{B})\). TP performs the quantum measurements on \(D_{A}(D_{B})\) to obtain the measurement results, which are sent to [PERSON] ([PERSON]) who can determine the presence of an eavesdropper (often referred to as \"[PERSON]\") by calculating the error rate. If it exceeds a pre-agreed threshold, they conclude that the communication has been compromised and restart the protocol from Step 2. If the error rate is acceptable, [PERSON] ([PERSON]) announces \(K_{A}\) (\(K_{B}\)) to TP. **Step 7**. TP discards all decoy photons in \(SE_{A}\) (\(SE_{B}\)) to obtain \(SE_{A}\) (\(SE_{B}\)) and decrypts \(SE_{A}\) (\(SE_{B}\)) with \(K_{A}\) (\(K_{B}\)) to recover \(S_{A}\) (\(S_{B}\)). That is, each single-photon state in \(SE_{A}\) (\(SE_{B}\)) is rotated by an angle with \(a_{i}\) (\(b_{i}\)) in the opposite direction. Then, TP measures \(S_{A}\) (\(S_{B}\)) with Z-basis to obtain the measurement results. If all measurement results of \(S_{A}\) and \(S_{B}\) are identical, then \(X=Y\); otherwise, \(X\
eq Y\).
## 3 Results and Discussion
### Method Validation
#### 3.2.2 Intra-Day Precision (Repeatability) and Intermediate Precision (Inter-Day Precision)  
Repeatable and precise peak areas of the three analytes were obtained, where intra-day peak areas for metformin, glipizide and gliclazide were 2,524,883 \(\pm\) 2430.12, 1,497,714.17 \(\pm\) 3171.46 and 1,225,869.50 \(\pm\) 2266.39, respectively.  
Inter-day peak areas of the three analytes also showed high precision and repeatability of the analytical method where % RSD never exceeded 0.006% over 3 consecutive days in accordance with ICH [42] (% RSD < 2). Intra and inter-day precision are presented in Table 4.