esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to use Distance Measuring Equipment (DME) properly, the impact of intra-system and inter-system electromagnetic interference must be analyzed firstly. However, the error of interference analysis using present methods based on pulse overlap is large when there are more aircraft. The aim of this article is to study a method of analyzing interference on DME whether the number of aircraft is small or not. According to the flow chart of DME signal, we studied the limitations of present methods; then constructed a model of analyzing the collision between duration of desired signal and dead time of receiver based on M/M/1/0 queueing system. Combing this model with other methods, we present a analytic model of analyzing intra-system and inter-system interference on DME. Using this analytic model, we analyzed reply efficiency (RE) and capacity of DME under intra-system and Joint Tactical Information Distribution System (JTIDS) interference. The result shows that the calculation for the probability of overlap between DME dead time and subsequent signals using queueing model agrees well with simulation. Consequently, the analytic model is more accurate than using a single method to analyze interference on DME.  
DME; JTIDS; intra-system interference; inter-system interference; queueing theory; reply efficiency
# TMD Damping for Structures with Uncertain Modal Parameters
## 2 Modelling
### Considered TMD Mass Ratios  
The design parameter of the TMD is its mass ratio, whose equation is shown as follows:  
\[\mu=\frac{m_{2}}{m_{1,nom}} \tag{6}\]  
which relates the oscillating TMD mass \(m_{2}\) to the nominal modal mass \(m_{1,nom}\) that is used for TMD design and--in general--is different from the actual modal mass \(m_{1}\). Commonly adopted TMD mass ratios depend on the types of primary structure and excitation; typical values are shown as follows:  
* Footbridges, TMD against pedestrian excitation: 2% \(\leq\mu\leq\) 8%;
* Stadia, floors of sport halls, TMD against jumping and dancing: 2% \(\leq\mu\leq\) 6%;
* Tall buildings, TMD against wind-induced accelerations: 0.5% \(\leq\mu\leq\) 2%.  
To cover the full range of reasonable TMD mass ratios, this study is therefore performed for the following mass ratios:  
\[values(\mu)=\left[0.5\%,\,1\%,\,4\%,\,10\%\right] \tag{7}\]  
where \(\mu=10\%\) covers special TMD applications with very big TMD mass ratios.
## 2 Literature Analysis of Forest Penetration of L-Band SAR Signal
### Trend in Annual Publications  
Based on the Citexs comprehensive literature database, this paper adopted the bibliometrics method to carry out mining of the literature big database by selecting year, country, institution, author, journal, and so on. Moreover, analyzing and visualizing the general trend and distribution in this field was conducted. Using L band SAR and forest penetration as keywords, we identified 0 papers from January 1978 to April 2024 in the CNKI database and 58 papers from January 1978 to April 2024 in the PubMed database. The average annual number of the papers published was three, and the annual numbers of the published papers are shown in Figure 1.  
Figure 1 shows that the publications related to the use of L-band SAR data in the field of forest canopy penetration first appeared in 1999, indicating a relatively late start. From 1999 to 2011, there is only one SCI paper published each year, which indicates the stage of slow development. From 2012 to 2023, the annual number of the published papers increased to more than two, reaching the peak of eight in 2019. The fastest growth happened from 2012 to 2019, indicating that the research in this field was in a rising stage of rapid development, and then a decreased trend was found.
## 3 Results and Discussion:
### Statistical Analyses
#### 3.2.2 Tukey's Method  
Tukey's method is a graphical tool based on a boxplot that is used to show continuous univariate data. It was used to show the compressive strength of the LWC samples. The method is based on the following steps [30]:  
1. Definition of the Inter Quartile Range (_IQR_). _IQR_ is the distance between the lower (Q1) and the upper (Q3) quartiles.
2. Definition of inner fences following Equations (3) and (4):  
\[\text{Lower limit (inner)}:\ Q1-1.5\ IQR \tag{3}\]  
\[\text{Upper limit (inner)}:\ Q3+1.5\ IQR \tag{4}\]  
Although there are also outer fences, which are higher than the inner ones, the values outside of the inner fences may be considered as possible outliers. In this work, limits to identify outliers were established in the limits of inner fences. Table 6 shows the values obtained as inner fence limits using this method for all the samples. Figure 8 shows a boxplot of all the samples, showing low variability in most of the samples. [PERSON]'s method shows that there are no outliers in the sample because all the results are inside the inner fence limits.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Mix** & **Lower Limit** & **Upper Limit** \\ \hline LWC\_control & 20.56 & 38.86 \\ Zn-S-0.5 & 15.04 & 24.46 \\ Zn-S-1.0 & 9.16 & 31.39 \\ Zn-S-1.5 & 12.64 & 19.03 \\ Zn-S-2.0 & 12.39 & 15.46 \\ Zn-A-0.5 & 17.53 & 26.89 \\ Zn-A-1.0 & 17.48 & 28.21 \\ Zn-A-1.5 & 11.04 & 33.92 \\ Zn-A-2.0 & 12.87 & 22.15 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Turkey’s method results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Mix** & **Specimen 1** & **Specimen 2** & **Specimen 3** & **Specimen 4** & **Specimen 5** & **Specimen 6** \\ \hline LWC\_control & 0.85 & 1.39 & 1.10 & 1.01 & 0.25 & 0.12 \\ Zn-S-0.5 & 1.02 & 1.26 & 0.23 & 1.45 & 0.37 & 0.22 \\ Zn-S-1.0 & 1.36 & 0.58 & 1.03 & 0.95 & 0.03 & 0.90 \\ Zn-S-1.5 & 0.79 & 0.39 &
# Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks +
## 1 Introduction
Different RNN-based localization models are trained and tested using different learning rates and samples on the publicly available LoRaWAN dataset for Antwerp in Belgium and outperforms localization systems in other related work with the minimum mean localization error of 0.29 m. The main contributions of this paper are as follows:  
* Developing a new LoRaWAN-based localization model using RNN for large, dense urban scenarios. * Training and testing the different RNN-based localization systems with various learning rates. * Training and testing the different RNN-based localization models with different data samples in Antwerp dataset publicly available and used in many research papers. * Critically analyzing the results with other popular methods applied to the same dataset. The organization of this paper is as follows: Section 2 gives an overview of LoRa and LoRaWAN; Section 3 summarizes the related work; Section 4 describes the methodology used; Section 5 discusses the obtained results and performance analysis; finally, conclusions are given in Section 6.
## 5 Numerical Results
### Reinforced Concrete Column
#### 5.2.1 Experiment Details  
A numerical method was applied to the column that was investigated in experimental studies by [PERSON] and [PERSON] (2007), which they denoted as IB. The column was simply-sliding and supported in the longitudinal direction on both ends. Longitudinal forces were applied at the ends with a constant eccentricity \(e\,=\,50\,\mathrm{mm}\), which is shown in Figure 11a. Analysis of columns loaded by longitudinal forces with other eccentricity values, such as \(e\,=\,15\,\mathrm{mm}\) and \(e\,=\,65\,\mathrm{mm}\), were presented in the paper (Sarararararararararararararararararararararararar  
The column was made of concrete with a compressive strength of \(f_{c}^{\prime}\,=\,58.0\,\)MPa. The concrete strength was determined on the basis of research that was conducted by [PERSON] and [PERSON] [29]. They tested the cylindrical specimens and the results showed strengths of \(f_{c}^{\prime}\,=\,58.0\,\pm\,2.7\,\)MPa. Because \(f_{c}^{\prime}\,>\,50\,\)MPa, the specificity of high-strength concrete was considered according to the relationships developed by [PERSON] et al. [38], when modeling the material behavior.  
The following material properties were used for concrete. The compressive strength was \(f_{c0}\,=\,44.78\,\)MPa, which was determined according to the strength-reducing criterion for the high-strength concrete proposed by [PERSON] et al. [38]. The tensile strength was \(f_{f0}\,=\,3.78\,\)MPa, which was determined by the relationship \(f_{f0}\,=\,0.3(f_{c0})^{\frac{2}{3}}\) according to Reference [34], where \(f_{c0}\) is expressed in MPa. The deformation modulus was \(E_{c0}\,=\,32.18\,\)GPa determined on the basis of Reference [38]. The strain limits values \(\varepsilon_{fc}\,=\,0.002\) and \(\varepsilon_{uc}\,=\,0.00665\) were taken from Reference [31] and [PERSON]'s ratio \(\upsilon_{c}\,=\,0.2\) was taken from Reference [34].  
The following material properties were used for the reinforcing steel. These material properties were taken from Reference [29]. The yield stress in compression and tension was \(f_{y}\,=\,430.0\,\)MPa, the compressive and tensile strength (perfect plasticity) was \(f_{t}\,=\,430.0\,\)MPa, the deformation modulus was \(E_{s}\,=\,200.0\,\)GPa, the strain limit was \(\varepsilon_{t}\,=\,0.15\), and [PERSON]'s ratio was \(\upsilon_{s}\,=\,0.3\).
# A New Model of Quasigeoid for the Baltic Sea Area
## 5 Calculation of Quasigeoid Anomalies at Selected Tide Gauge Stations  
With the three versions of quasigeoid counted for the Baltic region, quasigeoid separations were interpolated at three tide gauge stations in Wladyslawowo, Rozewie, and Leba. The interpolation results are given in Table 7 and Figure 11.  
Comparisons have shown that quasigeoid anomalies at the tide gauge stations calculated from different geopotential models differ from 1 to 2 cm (Figure 11). The results based on the global satellite-only gravity field models GOCE and GOCO up to degree and order 300 have significantly increased the usability of satellite-only gravity field models for applications like height reference frame unification. This is of special value for areas with a lack of high-quality terrestrial gravity data.  
The results of this work were also compared with the results obtained in the publication [49], p. 83, and we found that our results are 5 cm smaller at all tide gauge stations.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Name**} & \multicolumn{3}{c}{\(\Phi\)} & \multicolumn{3}{c}{\(\Lambda\)} & \multirow{2}{*}{**GOCO**} & \multirow{2}{*}{**GOCE**} & \multirow{2}{*}{**EIGEN**} & \multirow{2}{*}{**Mean**} \\ \cline{3-3} \cline{5-12}  & & \({}^{\circ}\) & \({}^{\prime}\) & \({}^{\prime\prime}\) & \({}^{\circ}\) & \({}^{\prime\prime}\) & \({}^{\prime\prime}\) & \({}^{\prime\prime}\) & **GOCO** & **GOCE** & **EIGEN** & **Mean** \\ \hline
1 & Wladyslawowo & 54 & 47 & 48.324 & 18 & 25 & 7.493 & 28.824 & 28.830 & 28.838 & 28.831 \\
2 & Rozewie & 54 & 49 & 46.1 & 18 & 20 & 7.9 & 28.968 & 28.966 & 28.993 & 28.976 \\
3 & Leba & 54 & 45 & 13.3 & 17 & 32 & 5.6 & 30.712 & 30.714 & 30.726 & 30.717 \\ \hline \end{tabular}
\end{table}
Table 7: Computed quasigeoid anomalies at tide gauge stations.  
Figure 10: Spatial distribution of differences (GOCE-EIGEN) in meters.
## 2 Extended Pick Failure Probability Assessment Approach
### A Typical Cutting Cycle
As the failure probability analysis approach of body failures is the same as that of tip failures and it is assumed that the tip failures are independent of body failures, the following analysis mainly focuses on the pick tip failure probability estimation. In addition, as the failure probability of a pick tip over a cutting cycle consisting of three stages is a combination of its failure probability over these three stages, the pick tip failure probability over each individual stage will be estimated initially.
# Reducing Defense Vulnerabilities in Federated Learning:
###### Abstract
Poisoning attacks can be divided into two sub-branches according to their goal and method. In terms of attack goals, backdoor attacks are targeted attacks because they focus on a specific sub-task, and Byzantine attacks are non-targeted attacks because they can be carried out randomly and at a certain rate. This paper specifically addresses poisoning attacks in federated learning, which represent one of the most significant security threats to this distributed paradigm. Unlike other security concerns such as gradient leakage or model inversion that primarily compromise privacy, poisoning attacks directly undermine model utility and performance. These attacks occur in two primary forms: data poisoning, where malicious users intentionally mislabel their training data (e.g., through label flipping), and model poisoning, where attackers manipulate model updates before sending them to the server (e.g., through gradient sign flipping or adding targeted noise). The distributed nature of FL makes these attacks particularly challenging to detect, as the server cannot directly inspect client data or training  
Figure 1: Federated learning attack types. processes. Our work focuses on developing robust defenses against both attack types, even when malicious participants constitute a substantial portion of the network. This paper has three important research contributions:  
* A new algorithm called NC-FLD is proposed that detects malicious users and removes them from the training cycle of FL. The FL training cycle has been made resistant to data poisoning and model poisoning attacks. By examining the scenarios in which the rate of malicious user participation is up to 40% in the network, it has been proven that the success of the model is maintained with the proposed defense algorithm. * It has been proven that dynamically selecting the most effective layers and neurons while training with the NC-FLD algorithm significantly contributes to the defense. The performance of our proposed security approach in various attack scenarios on three different datasets has been demonstrated quantitatively with the latest literature studies. * The implementation of a dynamic neuron selection strategy has demonstrated enhanced robustness in FL defense mechanisms across diverse data distributions, encompassing both independent and identically distributed (IID) and non-IID scenarios, while simultaneously mitigating the impacts of data and model poisoning attacks. The distribution characteristics of datasets significantly influence training performance. Consequently, models trained on non-IID datasets may be falsely classified as malicious, potentially leading to false positive identifications in attack detection protocols. This paper is structured as follows: Section 2 introduces a comprehensive literature review, Section 3 includes the details of the proposed method, Section 4 includes attack scenarios and the results of the NC-FLD method based on extensive experiments, and finally, Section 5 includes the discussion section.
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
###### Abstract
In a word, the proposed method for MIMO communication is more robust to the complicated underwater acoustic channel, and the proposed MIMO receiver provides an available scheme for the design of high-speed underwater acoustic communication systems. The contributions of this paper are as follows:  
1. We propose a novel receiver for underwater acoustic MIMO communication. The proposed receiver consists of channel estimation with CoI cancellation and channel equalization with CoI cancellation. 2. In Ref. [29], only two transmitters were considered for underwater acoustic MIMO channel estimation. Without loss of generality, in this paper, we extend the number of transmitters to more than two. Additionally, we analyze the performance of the proposed channel estimation method. 3. We compare the difference between the proposed CE-DFE receiver and the traditional DA-DFE receiver in terms of analysis and experimental results. The results demonstrate that the proposed receiver achieves better communication performance, and the proposed receiver is not sensitive to parameters; therefore it provides an alternative to the design of a robust underwater acoustic MIMO MODEM. The remainder of this paper is organized as follows. In Section 2, the framework for underwater acoustic MIMO system with CoI cancellation, including channel estimation and channel equalization, is expressed. In Section 3, the results and analysis are described. Finally, some conclusions are drawn in Section 4. The following notations are used in this paper. Bold upper-case and lower-case letters denote matrices and column vectors, respectively. Superscripts \((\cdot)^{T}\), \((\cdot)^{H}\) denote transpose and Hermitian transpose. Notation \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Methods** & **Limitations** \\ \hline frequency domain equalization [6; 7; 8] & In [6; 8], CP or ZP are needed to prevent inter-block interference, thus degrading the bandwidth efficiency. In [7], if the reconstructed CoI is not correct, the error will propagate to the next iteration. \\ DA-DFE in [9; 10; 11; 12] & Sensitive to parameters, and need long sequences to achieve convergence. \\ turbo and / or MMSE in [13; 14; 15] & Several iterations increase the computational complexity of turbo equalization, and the computation complexity of MMSE is exponential compared to the number of transmitters \\ CE-DFE in [16] & The channel estimation does not perform interference cancellation; the equalizers’ filter contains much noise. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The limitations of the existing methods. \(\prod_{i=1}^{M}\mathbf{P}_{i}\) denotes multiplication from \(\mathbf{P}_{1}\) to \(\mathbf{P}_{M}\). Notation \(\|\cdot\|_{2}\) denotes \(l_{2}\) norm. Notation \(\mathbb{E}(\cdot)\) denotes the expectation. Notation \(\mathbf{I}\) is the identity matrix, \(\mathbf{A}(\cdot;i)\) denotes a vector from \(i\)-th column of matrix \(\mathbf{A}\).
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 3 Bounded Number of Data Replication in Message Passing
### Algorithm  
Here, to improve the performance of data dissemination, we propose an efficient data replication algorithm EDDA that can make the network system enter an \(\epsilon\)-balanced status in a short time, which is presented as algorithm 1.  
In the proposed data replication algorithm, a message \(M\) is carried by one specific node. We need to distribute the message to the nodes in a given area. The total number of messages \(M\) that can be replicated is controlled by a parameter \(n\). Each node \(i\) receives a corresponding parameter \(n_{i}\geq 1\) to control the number of copies for which \(M\) can be replicated when it receives message \(M\), where \(n_{i}\) indicates the number of copies that can be distributed by node \(i\). After a certain number of stages, those \(n_{1},\cdots,n_{m}\) will enter into a balanced status (see Definition 3). The complexity to enter into a balanced status is measured by the number of stages that are counted in algorithm 1. To give an overview, EDDA schedules with the following three steps.  
1. First, EDDA constructs the graph and initializes the value of every vertex of graph \(G\). In the initialization procedure (see algorithm 2), the vehicle node that carries the message will be assigned a value of \(n\). All other nodes will be assigned a value of zero, which means they do not have the message.
2. Second, select independent edges from \(G\) so different pairs of nodes can communicate with each other in parallel. After the selection, replace the values of nodes with their new values by taking the average of the current values. Each stage should update the values of the nodes in the graph one time. Then, go to the next stage, and stop the average operations until the system is \(\epsilon\)-balanced.
3. Third, EDDA outputs graph \(G^{\prime}\), with the final values of all nodes updated. If new nodes enter into the network and break the balance, the procedures will be executed again to achieve network balance.  
The pseudocode of EDDA is described as follows, the initialization procedure is presented separately as algorithm 2.
# First Evaluation of Topography on GNSS-R: An Empirical Study Based on a Digital Elevation Model
## 2 Theoretical Background
Further details on the incoherent scattering over rough surfaces can be found in Reference [22]. The impact of vegetation should be also considered to establish a model of the incoherent scattered field. A comprehensive method can be found in References [23; 24]. Over regions without rough topography, DDMs can be expressed as follows [25]:  
\[\left\langle\left|Y_{\mathrm{r}}(\tau,\mathrm{f})\right|^{2}\right\rangle= \left|\chi(\tau,\mathrm{f})\right|^{2}\ast\ast\left|\sigma^{0}(\tau,\mathrm{f })\right|^{2}, \tag{5}\]  
where \(\ast\ast\) is the 2-D convolution in both domains, delay and Doppler. The WAF can be defined as follows [17]:  
\[\left|\chi(\tau,\mathrm{f})\right|=\Lambda(\tau)\mathrm{S}(\mathrm{f}), \tag{6}\]  
where \(\Lambda\) is the autocorrelation function of the pseudo-random noise (PRN) codes and S is the system impulse response in the frequency domain. Regions with rough topography are characterized with a root mean square error (RMSE) of the surface height variation higher than the length of the GNSS code under study. The effects of rough topography in the DDMs can be understood as follows:  
\[\left\langle\left|\mathrm{Y}_{\mathrm{r,topo}}(\tau,\mathrm{f})\right|^{2} \right\rangle=\left\langle\left|\mathrm{Y}_{\mathrm{r}}(\tau,\mathrm{f})\right| ^{2}\right\rangle*\ast\xi(\tau,\mathrm{f}), \tag{7}\]  
where the function \(\xi\) represents the mean density of scattering points as a function of the delay, and the Doppler shift.