esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Optimisation Meta-Heuristics for the Roughness Estimation Problem in River Flow Analysis
## 4 The Employed Optimisation Methods
### Differential Evolution  
The Differential Evolution (DE), originally proposed in [35], is a peculiar meta-heuristic with a simple algorithmic structure, similar to the one of swarm intelligence algorithms, and unusual use of variation operators from the evolutionary algorithms field [36]. Indeed, in DE, \(N\) individuals \(\mathbf{x}_{1},\ldots,\mathbf{x}_{N}\in\mathbb{R}^{d}\) are evolved by means of a 'differential mutation' operator, which generate the'mutant' solution. The latter is then crossed over with the currently processed individual to generate offspring. This newly generated solution competes with its parent and the winner will enter the new population only at the end of a whole generation cycle (i.e., after having produced \(N\) offspring solutions).  
Several mutation operators are available in the literature, and two main crossover operators are usually employed. The combination of such operators give rise to several DE variants. By using common DE jargon [36; 37], the DE algorithm employed in this study is fully described with the notation _DE/current-to-best/1/bin_. This means that the mutation operator will produce a mutant vector \(\mathbf{v}_{i}\) from the generic \(i\)th solution \(\mathbf{x}_{i}\) (\(i=1,\ldots,N\)) by following the popular _current-to-best_ scheme reported below  
\[\mathbf{v}_{i}=x_{i}+F\cdot(\mathbf{x}_{\text{best}}-\mathbf{x}_{i})+F\cdot( \mathbf{x}_{\tau_{1}}-\mathbf{x}_{\tau_{2}}),\]  
where \(\mathbf{x}_{\text{best}}\) is the best individual in the current population \(F\in(0,2]\) is the scale factor, \(\mathbf{x}_{\tau_{1}}\) and \(\mathbf{x}_{\tau_{2}}\) are two distinct and randomly chosen individuals from the population (\(\mathbf{x}_{i}\) excluded).  
Subsequently, the crossover operator will generate a target vector \(\mathbf{y}_{i}\) by recombining \(\mathbf{x}_{i}\) and \(\mathbf{v}_{i}\) according to the _binomial_ crossover described below:  
\[y_{i,j}=\left\{\begin{array}{ll}\mathbf{v}_{i,j}&\text{if }r_{j}<CR\text{ or }j=j\\ \mathbf{x}_{i,j}&\text{otherwise,}\end{array}\right.\]  
where \(j\) indicates the \(j\)th component of a solution (i.e., \(j=1,\ldots,d\)), \(CR\in[0,1]\) is the crossover rate, \(r_{j}\) is a randomly distributed number in \([0,1]\) and \(j\) is a random integer indexin \(\{1,\ldots,d\}\) ensuring that at least one component is inherited from the mutant vector. Further details on this optimisation paradigm are available at [36; 38; 39; 40; 41; 42; 43].  
Due to the computational complexity of the problem addressed in this study, it is not possible to set a high computational budget for the optimisation process. Hence, we initialise the population of our DE algorithm by using the Scrambled Hammersley procedure [44]. This produces a low discrepancy sample of vectors, thus allowing for a better covering of the search domain since the beginning of the optimisation process. To run the algorithms, we set \(N=30\), \(F=0.8\) and we randomly draw a \(CR\) value in \([0,1]\) any time the crossover operator is called.
## 2 Description of the Analysed Problem
### Shear Strength Reduction Technique  
The Shear Strength Reduction Technique (SSR) is a highly popular and efficient numerical method for stability estimation, which aims to evaluate the location of the slip surface and define the factor of safety (FS). The SSR technique, introduced by [PERSON] et al. [22], entails progressively reducing the soil strength parameters until failure occurs. Many researchers have applied this technique effectively in slope stability analysis [23; 24; 25]. Furthermore, the stability analysis of underground openings with complex geology and geometry [26; 27], and of large-scale open pit structures [28; 29; 30; 31], has also been effectively carried out using the SSR technique. Here, the SSR technique was applied utilising tools such as RS2 v. 11.0 [32], RS3 v. 4.0 [33], FLAC2D v. 7.0 [34], and FLAC3D v. 7.0 [35] codes to evaluate the stability of excavation trenches with temporary support of bentonite slurry (\(\gamma_{\text{s}}=10.5\div 12.0\) kN/m\({}^{3}\)).  
The numerical models are constructed using a quarter block of soil background structure (Figure 2) with the following dimensions:  
* In 2D (FLAC2D v. 7.0 and RS2 v. 11.0): \(X\times Z\) = 20 m \(\times\) 20 m;
* In 3D (FLAC3D v. 7.0 and RS3 v. 4.0): \(X\times Y\times Z\) = 10 m \(\times\) 10 m (up to 34 m) \(\times\) 20 m. The trench model is also represented by a quarter block with the following dimensions:
* In 2D: width \(\times\) depth = 1 m (or 2 m) \(\times\) 10 m;
* In (3D): width \(\times\) length \(\times\) depth = 1 m (or 2 m) \(\times\) 6 m \(\times\) 10 m. The analysed trench length ranges from 6 m to 54 m.  
The trench model was initially built with vertical effective stresses set according to the Mohr-Coulomb material and pore pressures in a static state. Subsequently, the model stresses were reset to prepare for the next step, which involved excavating the trench under bentonite slurry with a density of 10.5-12.0 kN/m\({}^{3}\).  
The excavation process involved removing elements/nodes of soil layers until the desired excavation depth reached 10 m, while simultaneously applying stress distribution to represent the fluid pressure of the bentonite slurry (Figure 3a,b). It is important to note that the stress distribution of the bentonite slurry was applied along the sides and bottom of the trench to match the unit weight of the bentonite slurry multiplied by the depth of the slurry. This stress distribution of the bentonite slurry was carried out concurrently with the trench excavation, corresponding to each excavation depth. This approach aligns with the site's construction conditions.  
Figure 1: Trench excavation stage with temporary support from bentonite slurry.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 6 Measurements  
Measurements were carried out on a custom platform developed around software defined radios (National Instruments USRPs) and virtual instrumentation (LabVIEW 2018). The Philips Fortimo LED 3000 44W/840 was used as a transmitting element. Three previously mentioned adaptive algorithms--LMS, NLMS, and QR-RLS--were used during the measurements. The monitored parameters were E\({}_{\text{b}}\)/N\({}_{0}\), MER, EVM, and BER. The block diagram of the whole platform can be seen in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adaptive Algorithm** & **QAM** & **Filter Length** & **Regularization Factor** & **Forgetting Factor** \\ \hline LMS & 4 to 64 & 33 & 0.009 & – \\ NLMS & 4 to 16 & 33 & 0.09 & – \\ NLMS & 32 to 64 & 33 & 0.3 & – \\ QR-RLS & 4 to 64 & 34 & - & 0.999 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equalizer settings.  
Figure 10: Block diagram of the tested platform.  
Figure 9: Dependence of EVM on E\({}_{\text{b}}\)/N\({}_{0}\) during simulations.
# Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
## 3 H-Transformer  
The proposed hybrid system is composed of three modules: the linear module (\(M_{L}\)); the nonlinear module (\(M_{N}\)); and the combination module. The linear module \(M_{L}\) employs the SARIMA model to generate the time series forecasting. The residual series \(E_{t}\) is generated from the difference between \(Z_{t}\) and \(\hat{L}_{t}\).  
The statistical analysis [28] of the datasets using the ACF indicated that every time series used in the experiments present seasonal behavior [28]. So, the SARIMA was selected because this linear model is the most suitable when the seasonal component is observed in the data [28].  
The nonlinear module \(M_{N}\) employs a Transformer neural network [24] for residual series modeling. The Transformer is composed of multiple encoder and decoder stacks. The encoder stack has two sublayers: a multi-head attention block and a fully connected feed-forward network. The decoder stacks contain the same sublayers as the encoder, plus another multi-head attention block over the output of the encoder stack. Between all sublayers, layer normalization is also implemented to facilitate the residual connections.  
The Transformer receives as input data the residual series \(E_{train}\), the time series \(Z_{train}\), and exogenous data related to the time series under analysis. In this phase, the Transformer is trained, receiving as input time lags of the \(Z_{train}\) and \(exo_{train}\) to forecast the future values of the \(E_{train}\). This mapping is performed to overcome the challenging task of residual modeling once this phase can degenerate the linear forecast as reported in the literature [23; 29]. So, using this new mapping, the nonlinear model can learn the relationship of the \(Z_{train}\) and \(E_{train}\), and consequently, the bias of the linear model. So, this work supposes that the Transformer is able to correct the forecast, mainly where the linear model is less accurate. The Transformer model parameters and the number of time lags are optimized using BHO (Bayesian Hyperparameter Optimization) [30]. The final output is obtained from the combination step that joins the linear prediction of the time series and the nonlinear prediction of the residual series to generate more accurate forecasting. Table 1 shows each variable of the proposed hybrid system, its source, and its respective description.  
The proposed hybrid system is separated into training and testing steps. Each step is further described in the following sections.
###### Abstract  
The accurate detection and mapping of subsurface utilities are critical for ensuring safety and efficiency in excavation and construction projects. Among various technologies, Ground-Penetrating Radar (GPR) has been widely used for locating underground infrastructure due to its non-destructive nature and ability to detect both metallic and non-metallic materials. However, many GPR systems struggle to meet the practical depth requirements in real-world conditions, especially when identifying non-metallic facilities such as PVC and PE pipes. In South Korea, the legal regulations require underground utility locators to meet specific accuracy standards, including a minimum detectable depth of 3 m. These regulations also mandate periodic performance testing of surveying equipment at authorized inspection centers. Despite this, most GPR systems tested at the official performance evaluation site at Sungkyunkwan University demonstrated limited effectiveness, with an average detection range of only 1.5 m. This study evaluates the performance of a handheld All Materials Locator (AML) device developed by SubSurface Instruments, Inc., (De Pere, WI, USA) which uses ultra-high radio frequencies to detect subsurface density variations. Experimental results from both the certified test facility and field conditions indicate that the AML meets South Korea's legal requirements for minimum depth and accuracy, by successfully identifying a wide range of subsurface utilities including non-metallic materials. The findings suggest that the AML is a viable alternative to conventional GPR systems for utility detection in regulated environments.  
underground utility detection; ground-penetrating radar (GPR); all materials locator (AML); surveying instrument performance testing; subsurface facility mapping Article
# A Review of Research on Secure Aggregation for Federated Learning
## 4 Aggregation Techniques for Federated Learning
### Fundamental Aggregation Algorithms
FedDF is designed to address model heterogeneity and data heterogeneity with a certain level of robustness. The FedDF distillation algorithm is as follows:  
\[x_{t,j}=x_{t,j-1}-\eta\frac{\partial KL\Big{(}\sigma\Big{(}\frac{1}{|S_{t}|}\sum_ {k\in S_{t}}f(\hat{x}_{t}^{k},d)\Big{)},\sigma(f(x_{t,j-1},d))\Big{)}}{\partial x _{t,j-1}}. \tag{18}\]  
where \(KL\) represents the Kullback-Leibler divergence, \(\sigma\) is the softmax function, \(\eta\) is the step size, and \(f(\hat{x}_{t}^{k},d)\) denotes the output of the \(k\)-th client model in the \(t\)-th round at the \(d\)-th batch.
## 5 Pseudo-Dynamic Test Results and Analysis
### Analysis of the Load-Displacement and Displacement-Time History Results  
Figure 14 shows the seismic response load-displacement curves for the unreinforced specimen (PD-FR) under the seismic wave of 200 cm/s\({}^{2}\) and the CCSS-reinforced specimen under 200, 300, and 400 cm/s\({}^{2}\). In addition, Figure 15 shows the seismic response displacement-time history curves for the specimens. In Table 5, among the pseudo-dynamic test results for the specimens, the strength and displacement for the input ground motion, which are key factors for seismic safety assessment, were calculated and listed.  
According to the results shown in Figures 14 and 15, and Table 5, compared to the unreinforced specimen (PD-FR) that showed shear failure under the Hachinoe (EW) 200 cm/s\({}^{2}\) seismic wave, the maximum strength of the CCSS-reinforced specimen was approximately 1.79 times higher under the seismic wave of 200 cm/s\({}^{2}\), 2.6 times higher under 300 cm/s\({}^{2}\), and 3.17 times higher under 400 cm/s\({}^{2}\). These results are also well reflected in Figures 10 and 13, which show the test results of the final step. The displacement for the seismic waves was suppressed by 0.16 times under 200 cm/s\({}^{2}\), 0.29 times under 300 cm/s\({}^{2}\), and 0.5 times under 400 cm/s\({}^{2}\).  
Figure 14: Comparison of the seismic response strength–displacement curves.  
Consequently, compared to the PD-FR specimen with non-seismic features, the seismic response strength of the CCSS-reinforced specimen increased by 1.9 times under the same seismic wave of 200 cm/s\({}^{2}\). Furthermore, its maximum seismic response displacement was suppressed by 84%, thereby increasing the seismic energy dissipation capability. This verifies the seismic safety of the developed CCSS method.
# Can Urban Environmental Problems Be Accurately Identified? A Complaint Text Mining Method
## 1 Introduction
A careful review of the existing literature shows that there are limited studies on environmental complaint text mining. Text mining is the process of extracting previously unknown, understandable, potential, and practical patterns or knowledge from the collection of text data [2]. It has been actively used in various fields, including biomedical, medicine [15], risk management [16], policy, crime [17], market such as multilingual recommendation system [18], education, and informatic fields. Recently, some scholars have carried out research on complaint text. These studies focused on the following aspects: semantic network analysis and keyword analysis of citizen complaints [19]; use of text mining to determine citizens' policy needs for safety and disaster management [20]; and the utilization of text mining to identify and evaluate the indicators of cultural ecosystem services [21]. Overall, previous studies using text mining analysis focus on civil complaints from various viewpoints to provide assistance to the government in decision-making. However, such studies have several limitations: (1) while previous studies are based on civil complaints, few studies have targeted specific urban environmental issues; (2) some only used a certain method of text mining, such as keyword extraction or the semantic network, to analyze the complaint text; therefore, they lacked the systematic application of text mining. As citizens are direct victims of environmental pollution, the text mining of citizens' complaints will not only help to elucidate their awareness of environmental pollution but also determine more precise countermeasures for the environmental management of smart cities. In this paper, civil environmental complaint records regarding six pollution topics (air, water, noise, waste, electromagnetic radiation, and light) from Guangzhou city are used, and a text mining framework for Chinese environmental pollution complaints is proposed. With this framework, we extract keywords, calculate the complainants' sentiment score, and analyze the characteristics of the semantic network from each class of pollution complaint. These results underline the positive impact of text mining on urban environmental management in both the current and future development of the smart city.
## 5 Case Analysis
### Results Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \(r^{2}\) & \(X^{2}\) & RMSE & MBE & MPE & Skew. & Kurt. & \begin{tabular}{c} **Mean** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline SSA-BPNN & 0.998 & 1.76 & \
\begin{tabular}{c} \(1.25\times \\ 10^{-6}\) \\ \end{tabular} &
\begin{tabular}{c} \(5.83\times \\ 10^{-7}\) \\ \end{tabular} & 0.1 & 1.648 & 4.971 & 0.056 & 1.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The verification of the SSA-BPNN models. Figure 12: The relationship between the gradient and the MSE of the training data. Figure 13: Comparison chart of erosion rate predicted by SSA-BPNN. Through the test of the SSA-BPNN model and error analysis and discussion, it is found that the SSA-BPNN model has high prediction accuracy and fitting degree, which can link the nonlinear relationship between coiled tubing erosion rate and influencing factors well while accurately and quickly predicting coiled tubing erosion rate.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 3 Method and Implementation
### Use by Existing IPv6 Applications
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Software (Version)** & **Testbed Network** & **Global Network** \\ \hline curl (7.76.1) [36] & Yes & No \\ nginx (1.20.0) [37] & Yes & No \\ EdgeOS1  & Yes & Yes \\ tcpdump (4.9.3) [39] & Yes & Yes \\ Suricata (6.0.4) [40] & Yes & No \\ rsync (3.3.0) [41] & No & Yes \\ ping6 (\(\sim\))2 [42] & No & Yes \\ ssh (9.6p1) [43] & Yes & Yes \\ tshark (4.2.5) [44] & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: This is the list of software applications used in the evaluation and which network scenario each was used in. All applications were unmodified, using pre-compiled binaries from the relevant FreeBSD14 repositories. Figure 7: A callgraph showing the modifications to libc, and how they would impact a typical application. The library API is unchanged—the expected calls to getaddrinfo, socket, and connect all shown—and so that existing binary programs can execute without modifictaion. _gethtent, which parses /etc/hosts, was extended to parse ILNP entries and register them with the kernel.are reorder was extended to account for ILNP preference values when ordering the list of results, and to prefer ILNP responses over IPv6. White boxes show functions that are unmodified. Blue boxes show existing functions in libc that were modified. Green boxes show new functions that were added to libc for ILNP.
## 4 Conclusions  
In this paper, a high gain wideband circularly polarized antenna using the sequential rotation feeding technique was presented. The dual-dipole element was investigated by cascading two dipoles, showing a bandwidth of 58.4% from 1.575-2.875 GHz. The proposed feeding network was created by using three Wilkinson power dividers and three Schiffman phase shifter units to maintain stable circular polarization over a wide bandwidth. The wide bandwidth element and the hybrid feeding network with a good performance helped the proposed antenna achieve a 71.2% bandwidth (\(S_{11}\leq-10\) dB), 63.6% 3 dB AR bandwidth, and a 9.9 dBi peak gain. These results show that the proposed antenna is a good candidate for multiband energy harvesting systems. The antenna is applied as the receiving antenna in a multiband ambient 3G/4G/5G/Wi-Fi energy harvester, the measured RF-DC efficiency is 27% at 1.8 GHz, 26% at 2.1 GHz, 25.5% at 2.45 GHz, and 27.5% at 2.6 GHz under the input power of \(-6\) dBm.  
Data curation, H.Q.N.; Investigation, M.T.L.; Methodology, M.T.L.; Supervision, M.T.L.; Validation, M.T.L.; Visualization, H.Q.N. and M.T.L.; Writing--original draft, H.Q.N.; Writing--review & editing, M.T.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Hanoi University of Science and Technology under projectnumber T2021-SAHEP-007.  
Not applicable.  
All data that support the findings of this study are included within the article.  
The authors declare no conflict of interest.
# GNSS Real-Time ZTD/PWV Retrieval Based on PPP with Broadcast Ephemerides
## 2 Data and Methods
### Technological Route  
The flowchart for retrieving ZTD and PWV data using BE-PPP is shown in Figure 2. The whole process is divided into two parts. The first part is the BE-PPP algorithm for solving the ZTD, which first constructs the observation model based on the pseudorange and carrier observations of GPS and Galileo, corrects the errors, then estimates the parameters to be estimated using Kalman filtering, and finally outputs the ZTD. It is important to note that the use of broadcast ephemeris causes errors, which need to be modeled by a random wandering process as the parameters to be estimated. The second part is the PWV inversion. Due to the absence of real barometric pressure and temperature, the ZWD estimated in the first part is not directly used to invert the PWV, and we use the ZTD. With the barometric pressure and temperature provided by the ERA5 data, we interpolate to the station positions and compute an accurate ZHD value based on the Saastamoinen model, which in turn yields an accurate ZWD. Then, we compute the transformed parameters, and the PWV can be accurately retrieved. RDS and ERA5 are the external checks used to evaluate the accuracy of the PWV data, and the accuracy of the ZTD data is evaluated using the ZPD product of the IGS. Also, this method was compared with the conventional RT-PPP to assess the difference between the two.
# Rethinking Modbus-UDP for Real-Time IIoT Systems
## 3 Protocol Specification
### TID Generation and Check  
The logic of TID generation and check varies depending on the traffic class, defined by the TID Type field, as described in Section 3.3. For unicast transactions, the master that initiates the transaction generates a TID with Type = 01\({}_{2}\) and stores it in the MBAP header of the request. The master keeps using the same TID upon retransmission of the same request to allow the addressed slave to identify duplicates.  
The addressed slave plays no role in TID generation and simply echoes it back when it sends its response. To ensure that transactions are not aborted at the application layer due to packet losses, the Modbus-UDP protocol layer on the slave side replays the same response upon receiving multiple consecutive requests bearing the same TID.  
The master checks the TID it finds in the responses it receives. It discards a response if the TID does not match the one it used for the request. The same mechanism also enables the master to identify and discard duplicate responses when the master detects it received more than one response with the same TID. These checks, like the ones described in Section 3.5, are made simpler and more efficient by relying on the fact that no node will ever generate a legitimate TID whose value is zero. Hence, this special value can be used, for instance, as an \"invalid TID\" marker.  
For broadcast transactions, the master generates a TID with Type = 11\({}_{2}\). Any responding node must also generate a TID of the same Type for its response. No TID checks are performed in this case.