esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
###### Abstract  
To solve the problem of data accuracy degradation of vehicle GNSS/INS integrated navigation systems when the GNSS signal is unavailable or there is a GNSS outage, this paper improves the existing GNSS/INS integration methodology for land vehicle navigation based on the AI method. First, a GNSS/INS integration methodology for land vehicle navigation based on position update architecture (PUA) using LightGBM regression for predicting the position of a vehicle during a GNSS outage is presented. It uses LightGBM to model the relationship between INS data and vehicle position changes. On-board INS and GNSS data are collected when the GNSS signal is available and are used to train the PUA-LightGBM model; in the event of a GNSS outage, INS data are used as the input to the PUA-LightGBM to predict the change in vehicle position. Second, a vehicle navigation data acquisition system was designed for model validation. This included a self-developed GNSS/INS integrated navigation system and a Novatel pwrpak7-e1 GNSS/INS integrated navigation system for data acquisition on six road segments. Finally, the collected data were used for machine learning training of the PUA-LightGBM model and the existing PUA-RandomForest model. As a result, the PUA-LightGBM predicts the vehicle position with less error in the event of a GNSS outage and takes less time to train. It was also demonstrated that by allowing the model to be dynamically trained or updated while the vehicle is moving the PUA-LightGBM could adapt perfectly to the predictions of vehicle position changes in different complex road segments.  
Keywords:LightGBM; integrated navigation algorithm; global navigation satellite system; inertial navigation system; GNSS outage +
Footnote †: journal: Applied Sciences
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 3 Experiments
### Evaluation Metrics  
All models and figures are generated and executed on a desktop computer with an AMD Ryzen7 3800X 8-core processor CPU and an NVIDIA GeForce RTX 2060 SUPER, using the Tensorflow2.3 framework. The Hangzhou Metro dataset mentioned above is divided into a training set, validation set, and test set according to the ratio of 7:1:2. For different time granularities (10, 15, 30 min), we employed 18, 12, and 6 time steps, respectively. The Mean Squared Error (MSE) is used as the loss function with a learning rate of 0.001, the dropout is set to 0.3, and the training epoch is set as 390. More details about the hyperparameters of our model are given in Table 3.  
In this study, four evaluation indicators are adopted to assess the performance of prediction models, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Weighted Mean Absolute Percentage Error (WMAPE).  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{11}\]  
\[WMAPE=\frac{\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert}{\sum_{i=1}^{n}y_{i}} \tag{12}\]  
where \(y_{i}\) is the true value and \(\hat{y}_{i}\) is the predicted value of the \(i\) test sample, and \(n\) is the number of all predicted values.
## 4 Comparison of the AE Source Positioning Error Obtained by the Experimental and Simulation Studies  
The comparison between the average location error of AE source determined by experimental and computational approaches was graphically represented in Figure 11. The dashed line presents the interpolation of finite element modeling results by the least-squares method fitting with a third-degree polynomial.  
It can be inferred from Figure 11 that the maximum level of the location error was registered in the coordinates \(X\) = 100 and 700 mm. Application of FEM analysis allowed to  
Figure 11: Comparison of the average location error for experimental and computational methods.  
Figure 10: Simulation results of AE source coordinate determination. \(\blacksquare\)—AE source location; \(\blacksquare\)—AE sensors location; \(N_{I}\)—the number of AE events.  
estimate with high accuracy the level of the average location error and at the same time avoid carrying out a large number of preliminary tests. The high precision of the calculation was confirmed by the correlation coefficient \(R=0.92\).  
To minimize the positioning error, it is necessary to take into account not only the recording time of acoustic signals but also fluctuations in the wave velocity of AE signals. This approach can help to reduce the influence of the threshold algorithm on the values of \(\Delta t\). For the numerical calculation of \(V_{g}\) fluctuations, statistical models of multiple linear regression and machine learning methods can be used.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 4 DPTracer Logger
### Interval Proof
#### 4.3.3 Selective Commitment Verification  
The commitment of any target log entries can be recomputed using an interval proof. A node selects a version among the targets and replaces the branches leading to the newer-version log entries with \(\varnothing\)-nodes. Thus, the node can compare the recomputed and received commitments to ensure that both are derived from identical records. To verify the commitment for a specific version \(v_{t}\) among the targets, the node can adjust the interval proof as follows:  
1. **Prune newer branches**: replace any branches leading to versions higher than \(v_{t}\) with the empty marker hash \(h_{\varnothing}\) (for \(\varnothing\)-nodes).
2. **Recompute root hash**: recalculate the root hash using the pruned tree.
3. **Compare commitments**: verify that the recomputed root hash matches the known commitment \(C_{v_{t}}\).
# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
###### Abstract
Motivated by the significance of the phase spectrum in other speech-related tasks and its impact on human speech perception, we propose to explore and evaluate the performance of the phase spectrum in DC AMR speech signal detection. To the best of our knowledge, this study represents the first investigation into the effect of phase information on DC audio detection. In addition to proposing the use of phase features for DC AMR speech detection, we examine the influence of phase information on both narrowband AMR (AMR-NB) and wideband AMR (AMR-WB)-coded signals, comparing their performance with the traditional magnitude spectrum (spectrogram). Given that prior  
Figure 1: Cases of DC signal generation: (**a**) Creating a manipulated/tampered signal. (**b**) Generating a fake-quality signal. (**c**) Audio watermarking/steganogprahy. research has primarily addressed the detection of DC AMR-NB speech signals, comparing the results of DC AMR-NB and AMR-WB speech detection will be crucial for future studies on DC speech detection. In summary, our main contributions are as follows:  
* We propose utilizing the phase spectrum as a feature for detecting DC speech signals. As far as we know, the performance of phase features in DC audio detection remains unexplored. Therefore, this study represents the first investigation into the effect of phase information on this task. We examine the impact of three different representations of the phase spectrum on DC speech signal detection. * We assess the performance of the proposed phase features on both AMR-NB and AMR-WB-coded speech signals. Given that existing studies have focused solely on AMR-NB-coded signals, understanding the performance of the AMR-WB codec in DC audio detection and comparing it with the AMR-NB codec will be valuable for the audio forensics community. * We introduce the use of LSTM networks for DC AMR speech detection, marking the first instance in the literature. Both spectrogram and phase spectra can be viewed as time-series data, with the former capturing spectral energy variations over time and the latter representing phase variation over time. Leveraging LSTM networks, known for their effectiveness with time-series data, we propose their application for DC AMR speech detection for the first time in the literature.
## 10 Conclusions  
The long-term performance consistency of wind profiling lidars used for offshore wind energy application has proven excellent. The devices operated offshore from around six months to more than two years. The so-called \"NORSEWInD standard\", where part of the criteria is that the slope of the linear regression should be within 0.98 and 1.01 and the linear correlation coefficient (R\({}^{2}\)) should be \(>\)0.98 for the wind speed range 4-16 m\(\cdot\)s\({}^{-1}\), was used for the pre-deployment validation at Hovsore comparing wind profiling lidar data to observation from a tall meteorological mast at 60, 80, 100 and 116 m. Five lidars passed the standard, two failed slightly whereas one device failed on several criteria. The post-deployment validation of four lidars showed excellent performance. The maintenance offshore was sparse but despite this and the harsh environment, the system availability was on average 95% out of a total of 127 months. The data availability was on average 89%. The system and data availability will have to be improved to obtain bankable offshore wind resource data. This is work for the future and most likely will be reached with a combination of improved devices and improved installation, operation and maintenance offshore.  
The flow distortion on the offshore platforms was estimated to be insignificant for the lidar wind profile observations at hub height. Both CFD modeling and wind tunnel experiments with sub-scale models indicated this. The deployment of wind profiling lidars on large offshore structures appears suitable when the aim is to observe hub height winds at around 100 m AMSL. In contrast, the lidar wind data observed on the coast needed correction for the influence of the terrain as estimated by the flow model in WAsP Engineering and comparing the results to the lidar observations.  
We were able to estimate the vertical wind shear distributions, based on the shear exponent of the power law, at several NORSEWInD wind lidar nodes and found a very broad range of values, peaking very close to zero, which contrasts with the commonly used constant value offshore of 0.2. This broad range of values is partly due to variation of the vertical wind shear with height, surface roughness (and thus sea state), and atmospheric stability, and partly to the atmosphere dynamics, which is not accounted for in many wind prediction models.
## 1 Introduction
### Active Distribution Network Management  
Active distribution network management is seen as the key to a cost-effective integration of DER into distribution network planning and operation. This is in direct contrast to the current \"fit-and-forget\" approach that is still followed by most DSOs. Distribution networks were typically designed using deterministic studies or standard design rules, considering critical situations so that the networks could operate with a minimum amount of control, that is, eventual control problems were solved at the planning stage. Table 1 presents a summary of the experience gained by several DSOs worldwide and their rules of thumb for DG connection.  
The practice of passive operation can limit the capacity of DG that can be connected to an existing system [1]. In contrast, active management techniques enable the distribution system operator to maximize the use of the existing network by taking full advantage of the network's controllable DER in an integrated manner. The emerging electric grid is envisaged to allow for increased flexibility, scalability, and resiliency in handling the inherent variability of some renewable resources without compromising power quality [5]. In the recent past, quick but highly conservative methods such as the 15% rule followed by a large number of DSOs (see Table 1), were used as a way to limit DG integration. These methods do not take into account the location and production/consumption profiles of these resources, leading to a low hosting capacity, while not necessarily ensuring the system's security and efficient operation. They also fail to reflect that some networks have higher hosting capacity, limiting the deployment of DG. As the DER penetration levels grow, other measures need to be taken [6].  
As previously mentioned, one of the technical impacts resulting from the connection of large amounts of DG to distribution networks is related to the voltage rise effect. This fact, in addition to the fact that microgenerators are typically required to operate at unity power factor, results in voltage fluctuations at the equipment's terminals that are directly proportional to the active power injected in the grid. Since it is a requirement of the distribution utility to supply its customers at a voltage level within specified limits, and of certain quality with minimal fluctuation, the development of voltage control techniques is crucial in order to increase the LV network's maximum allowable DG connection capacity. Basically, two main control strategies can be found in the available technical literature--local distributed voltage control and centralized voltage control [7,8].
## 2 Materials and Methods
### Fault Mitigation Mechanisms  
In addition to the TMR applied to the reconfigurable partitions, the configuration memory is protected against SEU using the scruber IP provided by Xilinx for their FPGAs. This IP, called Soft-Error Mitigation (SEM) IP, is integrated in the design and controlled by the rest of the logic. A wrapper has been added around the SEM IP in order to access important information from the processor AXI4 interface. This controller allows the Cortex-R5 to send commands to the IP for state changes or fault injection, and to parse monitoring reports generated by the IP during its activity. Information from these reports is used to feed several counters, counting the number of injected faults, how much has been corrected or how many are uncorrectable. It also indicates the current SEM state.  
The detected faults can be categorized, or classified, as correctable or uncorrectable, and as essential or non-essential (Figure 3). The later information is generated during the bitstream generation phase by the Xilinx tools in the form of an ASCII file containing a one  
Figure 2: Payload Board Prototype.  
or a zero character for each bit in the design, indicating if it is essential or not. It represents several million bits for the UltraScale+ MPSoC family devices. This information is requested by the SEM each time a fault has been detected. A module has been added to the controller to provide the correct information to the SEM regarding the frame where the fault has been detected (Figure 4). If the bit is essential, the SEM intends to repair it; otherwise the fault will be discarded and the scrubber will continue to scan the configuration memory.  
In the case of an uncorrectable error, a custom mitigation procedure has been setup: a backup memory containing the original frames is used to dynamically restore the faulty frame using the DPR. Frames content is extracted using the RapidWright utility [16] and stored in the DDR4 memory at boot time (Frames DataBase).  
The EBD info file generated by the Vivado tools has a major drawback: it is highly memory-consuming (several MBytes) and so it must be stored in a memory distant from the SEM controller. In order to leverage this issue, an optional hardware module can be added (Hw Fetch IP). For each frame, instead of storing the information of each bit contained in the frame, we store a bit indicating whether the frame contains an essential bit or not. This allows storing the EBD information in several BRAM blocks, simplifying the content of this file. With most of the essential bits being concentrated in about 15% of the frames, the overhead to check a frame--even if the faulty bit is not essential--is compensated by the improvement of the read memory latency when performing a fetch of EBD information for the SEM Controller.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 3 AI Applications in Software Engineering
### Requirements Analysis  
#### 3.1.1 Natural Language Processing (NLP) for Requirements Gathering  
Requirements gathering constitutes a critical phase in the software development lifecycle, wherein stakeholders' needs are translated into precise technical specifications. Traditionally, this process has been predominantly manual, labor-intensive, and susceptible to misinterpretations and omissions. AI, particularly natural language processing (NLP), presents significant advancements in automating and enhancing requirements elicitation [44].  
NLP algorithms can process vast amounts of unstructured data from stakeholders, including emails, meeting transcripts, and user feedback. By leveraging sophisticated language models, AI systems can extract meaningful requirements efficiently, discern underlying functional and non-functional requirements, and detect ambiguities or inconsistencies. Furthermore, these systems can proactively suggest clarifications, thereby mitigating the risk of misunderstandings that could propagate costly errors in later stages of development.  
Recent advancements in NLP, such as transformer-based models like BERT and GPT, have enhanced the capability of AI systems to comprehend and process human language with higher accuracy [45; 46]. These models enable the extraction of nuanced requirements from complex and diverse stakeholder communications, improving the fidelity of requirements documentation.
# Semi-Global Stereo Matching Algorithm Based on Multi-Scale Information Fusion
###### Abstract  
Semi-global matching (SGM) has been widely used in binocular vision. In spite of its good efficiency, SGM still has difficulties in dealing with low-texture regions. In this paper, an SGM algorithm based on multi-scale information fusion (MSIF), named SGM-MSIF, is proposed by combining multi-path cost aggregation and cross-scale cost aggregation (CSCA). Firstly, the stereo pairs at different scales are obtained by Gaussian pyramid down-sampling. The initial matching cost volumes at different scales are computed by combining census transform and color information. Then, the multi-path cost aggregation in SGM is introduced into the cost aggregation at each scale and the aggregated cost volumes are fused by CSCA. Thirdly, the disparity map is optimized by internal left-right consistency check and median filter. Finally, experiments are conducted on Middlebury datasets to evaluate the proposed algorithm. Experimental results show that the average error matching rate (EMR) of the proposed SGM-MSIF algorithm reduced by 1.96% compared with SGM. Compared with classical cross-scale stereo matching algorithm, the average EMR of SGM-MSIF algorithm reduced by 0.92%, while the processing efficiency increased by 58.7%. In terms of overall performance, the proposed algorithm outperforms the classic SGM and CSCA algorithms. It can achieve high matching accuracy and high processing efficiency for binocular vision applications, especially for those with low-texture regions.  
binocular vision; stereo matching; multi-scale information fusion; multi-path cost aggregation; left-right consistency check
# Lightweight Path Recovery in IPv6 Internet-of-Things Systems
## 2 Related Work
[23] presents Atlas, a system that facilitates the automated capture of IPv6 network topology information, and encountered some network phenomena in their experiments such as varied router responsiveness and unstable routing. These existing efforts have the potential to benefit from the output of path recovery for more accurate results and better performance. Meanwhile, the topological dynamics resulting from these aforementioned phenomena and the large number of devices that IPv6 itself represents access to make existing path recovery methods face performance bottlenecks. Unlike previous path recovery strategies, our work records the association information of nodes on similar paths with less overhead and adjusts the observation window size with node activity, which makes it possible to cope well with larger and more complex network situations.
## 4 Properties and Applications of Synthesized Polymers with AAO Templates
### Energy, Sensing and Electronic Applications
Since the pioneering works by [PERSON] [108; 157; 158], a number of studies, including the electrical characterization of \(\pi\)-conjugated polymer nanowires, have been reported. In general, an increase of the electrical conductivity has been observed for nanowires, resulting  
Figure 13: (a) Side-view schematic of the asymmetric device, where gray represents the coaxial MnO\({}_{2}\)/PEDOT nanowire cathode and blue represents the PEDOT nanowire anode (b) Picture of the same demo device [150]. Copyright 2012. Reproduced with permission from the publisher: RSC. from a preferential alignment of polymer chains or crystals within the nanowires. Moreover, it seems that the larger the spatial confinement imposed, the larger their conductivity. For example, [PERSON] et al. found the electrical conductivity of electrochemically synthesized PEDOT nanowires had strong size dependency, with the conductivity increasing as the nanowire diameter decreased [159]. From the estimated doping levels (\(\sim\)5%) and conductivity data (\(\sim\)100 S/cm), they claimed that the charge carrier mobility of the nanowire with the smallest diameter amounted to 2.0 cm\({}^{2}\)/V s, which is four orders of magnitude larger than that of the bulk PEDOT material (4.0 \(\times\) 10\({}^{-4}\) cm\({}^{2}\)/Vs). Along the same lines, highly conductive (PEDOT) nanotubes, exhibiting conductivity values of 2000 S/cm (for a single nanotube as measured by conductive scanning probe microscopy) were synthesized by vapor deposition polymerization by [PERSON] et al. [112]. For that, they first deposited an oxidant layer on the pores by wetting the pore walls with 10% _w/w_ solution of FeCl\({}_{3}\cdot\)6H\({}_{2}\)O in THF. Then, the functionalized AAO templates were exposed to an EDOT monomer vapor for about 10 min in a vapor deposition chamber under ambient conditions. Polyaniline nanowires with diameters ranging from 20 to 100 nm were chemically synthesized by [PERSON] et al. within AAO membranes [160]. They analyzed their electrical properties as well as their field emission properties, for which they observed a low threshold electric field (5-6 V/\(\upmu\)m) and maximum emission current density of \(\sim\)5 mA/cm\({}^{2}\), which suggest the applicability of this material for the development of field emission displays. [PERSON] et al. produced semiconducting nanotubes by thermal polymerization of the monomer conjugated VB-FNPD ([123]). They characterized the I-V response of single nanotubes and calculated conductivity values of 3.2-5.6 \(\times\) 10\({}^{-6}\) S cm\({}^{-1}\). Lastly, [PERSON] et al. used BDS to probe charge transport of the polymerized ionic liquid 1-butyl-3-vinylimidazolium bis-(trifluormethanesulfonyl)imide poly([bvim][NTf2]) confined within AAO nanopores [29]. They first studied how the polymerization process proceeded in the confined medium and interestingly, they also observed that nanoconfined polymeric ionic liquid had a higher electrical conductivity than same material obtained by bulk polymerization.