esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
###### Abstract  
To improve the standard point positioning (SPP) accuracy of integrated BDS (BeiDou Navigation Satellite System)/GPS (Global Positioning System) at the receiver end, a novel approach based on Long Short-Term Memory (LSTM) error correction recurrent neural network is proposed and implemented to reduce the error caused by multiple sources. On the basis of the weighted least square (WLS) method and Kalman filter, the proposed LSTM-based algorithms, named WLS-LSTM and Kalman-LSTM error correction methods, are used to predict the positioning error of the next epoch, and the prediction result is used to correct the next epoch error. Based on the measured data, the results of the weighted least square method, the Kalman filter method and the LSTM error correction method were compared and analyzed. The dynamic test was also conducted, and the experimental results in dynamic scenarios were analyzed. From the experimental results, the three-dimensional point positioning error of Kalman-LSTM error correction method is 1.038 m, while the error of weighted least square method, Kalman filter and WLS-LSTM error correction method are 3.498, 3.406 and 1.782 m, respectively. The positioning error is 3.7399 m and the corrected positioning error is 0.7493 m in a dynamic scene. The results show that the LSTM-based error correction method can improve the standard point positioning accuracy of integrated BDS/GPS significantly.  
deep learning; positioning; error correction +
Footnote †: journal: Sensors
## 4 Experimental Results
### Ablation Study
If the model can be further improved to achieve compatible performance with less training data, it will become more practical for real-time applications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**\# of Training Samples** & **FC** & **GAP** \\
**per Character** & & \\ \hline
300 & 41.59\% & 51.40\% \\
600 & 87.74\% & **90.62\%** \\
900 & 85.56\% & **95.71\%** \\
1500 & 91.20\% & **95.35\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of GoogLeNet models using fully connected (FC) and global average pooling (GAP). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**\# of Classes** **(Characters)**} & \multicolumn{3}{c}{**GoogLeNet-FC**} & \multicolumn{3}{c}{**GoogLeNet-GAP**} \\ \cline{2-7}  & **\# of Parameters** & **Training Time** & **Recognition Time** & **\# of Parameters** & **Training Time** & **Recognition Time** \\
**(M)** & **(s/per epoch)** & **(s/per char)** & **(M)** & **(s/per epoch)** & **(s/per char)** \\ \hline
294 & 11.802 & 181.8 & 0.002582 & 11.716 & 180 & 0.002521 \\
1812 & 16.557 & 1077.6 & 0.002603 & **13.271** & **1030.6** & **0.002569** \\
4945 & 40.941 & 4824.5 & 0.002598 & **16.483** & **4014** & **0.002588** \\
13070 & 195.649 & 25855 & 0.003418 & **24.811** & **23,443.5** & **0.002622** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of parameters size and computational complexity of FC and GAP in GoogLeNet model.
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 3 Results
### Sensitivity Study  
In the concluding part of this section, we will examine variations in the model's anomaly detection performance under different input sequence lengths, considering two key aspects.  
Firstly, as discussed in Section 2 regarding missing data imputation, we only impute missing data when the gap between sequences has fewer than six standard time intervals. However, when processing radar sea-current data, we face a particularly severe problem: significant data are missing, especially in the peripheral regions covered by the radar (as depicted in Figure 1). Even after applying missing value imputation, the data inevitably get fragmented into numerous time series segments. Due to the Bi-LSTM model requiring a certain amount of data both before and after the data point being predicted, the data at the beginning and end of each segment are undetectable. Consequently, as the input sequence length increases, the amount of data genuinely available for detection diminishes. This results in certain anomalous data being undetected, leading to the 'escape' phenomenon, as illustrated in Figure 10a,b.  
In the second aspect, we compared the performance metrics of anomaly detection for the model under different input sequence lengths. To account for shorter input sequence lengths, we introduced anomalies only at the points under examination, as opposed to the method described in Section 3.1, where anomalies were added to both the points under examination and their neighboring points. The specific results are shown in Figure 10c.  
In an overall assessment, longer input sequences lead to the presence of more undetectable data. As the amount of detectable data diminishes, some anomalous data can slip through the detection process, thereby undermining the overall quality control effectiveness. Conversely, overly short input sequence lengths make it challenging for the model to address consecutive anomalies, resulting in suboptimal quality control. Considering these factors, we chose an input sequence length of 7 to strike a balance.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 6 Blockchain Potential in V2X Networks
### Integrating Blockchain with Mec and 5G Technology in V2X Networks
#### 6.3.2 Integration of Blockchain with Mec in V2X  
One way to tackle the above issue is to use blockchain integrated with edge computing in vehicular networks without using cellular networks. There are several key enabling technologies, such as mobile edge computing, SDN, Network Function Virtualization (NFV), and D2D communication, which are developed to promote next-generation vehicular communications. The edge computing, such as MEC, Fog, or cloudlets, have emerged as a promising technology to empower vehicular networks and blockchain. In vehicular networks, the blockchain can be used in geo-distributed edge networks to compute heavy computational tasks. The MEC reduces the delay and background congestion by computation offloading [74; 75] and distributed content caching [76; 81]. By integrating edge-computing and blockchain together with new and emerging vehicular technologies discussed in Section 3, efficient performance, security and privacy can be maintained in the absence of 5G networks and some of them are discussed in Table 8. The authors in [82] proposed a permissioned blockchain between the different elements involved in handling the data obtained from the vehicles for forensics applications. They used decentralized blockchain using Proof of Concept (PoC) consensus mechanism. They used private cloud for data storage to reduce storage space and lower the processing overheads, whereas the authors in [83] applied Vehicular Cloud Computing (VCC) based on permissioned blockchain using Proof of Driving (PoD) consensus to store intelligent vehicle data for privacy. The authors in [84; 85] proposed computation offloading and content caching in wireless blockchain networks integrated with MEC. The authors in [85] used Deep Reinforcement Learning (DRL) and permissioned blockchain in vehicular network edge computing. The vehicles and RSU are used as edge servers for intelligent and secure content caching using Proof of Utility (PoU) consensus and DRL mechanism. Similarly,the authors in [86] proposed a reliable Fog computing framework, where RSUs are employed to offload tasks to neighboring Fog vehicular nodes based on their reputation scores held at a semi-private decentralized blockchain. They used Proof of Elapsed Time (PoET) consensus mechanism for leader selection and a voting mechanism among the consortium members for block validation.
## 5 Discussion, Evaluation and Comparison of the Results of Analytical and Experimental Models
### Structural Behavior Criteria and Parameters  
The experimental specimen has a similar behavior as expected by the numerical model. Vertical supports of the Wall 1 rotated from their base and bended, simultaneously with the standing beams transversally loaded. The deformation pattern expected for the upper anchor profile, and the behavior pattern of wall 2 and 3, which was the transversal support of wall 1, was also observed. The state of the simple support on the base reproduces perfectly the building process, as the only restriction to the spin of the vertical steel profiles is this torsional stiffness of the anchor profile of the base (shown on Figure 25) and this stiffness is nearly minimal on opened sections. Nevertheless, the perpendicular walls collaborate in the increasing of the stiffness of the module.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & **Detail** & **Stiffness** \\  & & **(kg/cm)** \\ \hline Experimental specimen & Average of the stiffness values obtained on the & 461 \\  & Higher elasticity unit specified by the manufacturer & 692 \\  & E = 40,816 kg/cm\({}^{2}\) & 578 \\ Numerical specimen & Lower elasticity unit specified by the manufacturer & 637 \\  & E = 38,622 kg/cm\({}^{2}\) & 678 \\  & Average elasticity unit specified by the manufacturer & 638 \\  & E = 38,622 kg/cm\({}^{2}\) & 678 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the stiffness obtained.  
Figure 25: Diagram of the deformation experimented by the specimen.  
On the other hand, the rising permanent deformations appearance due to the increasing loads and displacement were remarkable. The origin of these permanent deformations was on the local buckling of some of the components of the transversal section of the thin sheet profiles and the separations that would appear between the steel profiles and the fibrocement sheets as the applied loads would be increased.  
Finally, compared to the registered information of the strain gauges, that gave no evidence of damage of strange behavior, we could confirm that the experimental model behaved effectively in a similar way as predicted by the numeric model and was observed on the experimental specimen.  
On Figure 26, two charts are presented, corresponding to the strain gauges that provided information about the experiment and registered values of strain comparing with the experiment step. Figure 26a presents the charts of strain of the strain gauges identified as 1 WV2, 1 WV9 and 1 WV16. Figure 26b presents the ones which are identified as 1 EV9 and 1 EV16 (and the one identified as 1 EV2, presented evidence of damage).  
According to the location of the strain gauges shown on Figures 11-13 and the behavior expected, the precision was that the first group registering compressions, and the ones from the second group located on the east face registered tensions. Expansions and reductions are consistent on the bending moment that would have been generated on the vertical direction on these zones.
## 4 Simulations
In other words, if parameters where stable, frequency was lower. By means of some of the collected values, it was possible to tune each parameter of the simulator, including, for example, compressor efficiency and regulation, fan real absorption curve, coil transmittance, and enthalpy correction. Further particulars will be described in a dedicated paper. Figure 12: Water production: comparison between real and simulated data; average error < 2%; maximum error < 5%. In these environmental conditions, the integrated system treats 9244 m\({}^{3}\)/h of air, which is emitted from the machine at T\({}_{\text{out}}\) = 19.14 \({}^{\circ}\)C and RH\({}_{\text{out}}\) = 77%, producing 1642 dm\({}^{3}\)/day of water and providing 90 kW of heating power, sufficient to heat domestic water to 50 \({}^{\circ}\)C.  
From the monthly occupancy data provided by hotel owners, as a percentage of the maximum occupancy of 308 guests average occupancy throughout the year can be calculated to be equal to 52.16% (Figure 17). Taking into account the averages of occupancy, temperature and relative humidity, annual savings can be calculated with the same procedure applied previously to the testing month. Calculations, about to the annual balance, provide the total energy and economic savings obtained with the integrated system (Table 9). A cost of about USD 7600 can be attributed to consumables and maintenance; thus, the net saving is about USD 80,000 a year. A rough estimation of the payback time, considering costs of the integrated system's major items purchased in Europe, associated manpower, taxes and duties, and a reasonable profit margin, provides a value of about 2-2.5 years. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Daily Savings** & **Avoided Costs** & **Daily Savings** \\ \hline Bottled water & 160.5 dm\({}^{3}\) & 7 pesos/dm\({}^{3}\) & 1123.5 pesos \\ Kitchen water & 1481.5 dm\({}^{3}\) & 1.4 pesos/dm\({}^{3}\) & 2074.1 pesos \\ LPG & 166.4 dm\({}^{3}\) & 7.47 pesos/dm\({}^{3}\) & 1243.0 pesos \\ Electricity saving & 494.4 kWh & 0.8243 pesos/kWh & 407.5 pesos \\ \hline  & Total & & 4848.1 pesos \\ \hline  & **Daily energy consumption** & **Energy cost** & **Daily cost** \\ \hline Electricity consumption & 0.5 kWh/L & 0.8243 pesos/kWh & \(-\)676.75 pesos \\ \hline Results & & & \\ \hline  & Daily saving & & 4171.35 pesos \\  & Annual saving & & 1,522,542 pesos \\  & & Exchange rate & \\  & Annual saving & 17.3735 pesos/USD & 87,635.9 USD \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Water, energy, and economic savings. Figure 17: Occupancy percentage of each month and mean annual value.
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 4 Findings and Discussion
### Obtained Results Discussion
#### 4.4.2 Network Slicing  
In [72; 73], full isolation between slices is reported, with 512 users divided into 16 slices. The lowest priority slices experience a maximum delay of 2.5 ms, whereas the rest of the traffic shows less than 1 ms of delay. When applied to an industrial smart grid use case (i.e., uRLLC) [74], a round trip time (RTT) of 10.02 ms is obtained, with the authors reporting high reliability and only 0.3% packet loss when the system is under the highest level of stress. In [76; 77], where another industrial application is presented, experiments show delays of 15 ms for digital twin app data, 30 ms for telemetry packets, and 50 ms for remote support, all of which use virtual queues with priority queuing. The study in [79] presents an e-health video transmission use case in which slices with a high QoS level have an average delay of less than 0.05 ms. Moreover, in [52], the authors include the committed information rate (CIR) ratio as an adjustment parameter in the hardware switches for the throughput of each supported vertical for three IoT scenarios. For instance, mMTC applications require a CIR of 100% to achieve a 100% throughput percentage without any packet loss. The study in [85] demonstrates that the proposed prototype can support up to 16,000 slices, while [80] reports that creating slices takes 400 ms, which is faster than slicing implementations that do not use programmable data planes. The network slicing framework in [86] shows an average delay seen at the end-host of 69.8 ms. The solution presented in [84] demonstrates that slices experiencing high traffic loads, defined as four or more flows within a slice, share the remaining bandwidth with a maximum difference of 4%.Furthermore, the bandwidth allocated to each flow within a slice varies only slightly, ranging from 0.54% to 8.23% of the total slice bandwidth.  
Finally, in [81; 82], network slicing in the fronthaul section of NG-RAN results in update routing entry latencies of less than 6 \(\upmu\)s, with support for multipoint routing of 80 Gbps.
# A New Model for Cleaning Small Cuttings in Extended-Reach Drilling Based on Dimensional Analysis
## 3 Case Application
### Influence of Drill Pipe Rotation Speed on the Transport of Cutting Particles  
During the drilling of an extended-reach drilling operation, the rotation speed of the drill pipe also affects the cutting transport in the downhole annular flow path. The rotation of the drill pipe drives the drilling fluid to produce vortex flow, and the distribution of cutting particles producing a vortex state can be observed at the entrance. The mechanical scraping and churning action between the drill pipe and the cutting bed makes the drill pipe rotation disruptive to the cutting bed ([PERSON] et al., 2019) [19]. In the study of the effect of drill stem rotation speed on the movement of cutting particles, the drill stem rotation speed was set between 60 r/min and 160 r/min, while the variation law of the thickness of the dimensionless cutting bed was calculated when the diameter of cutting particles was 0.3 mm, 0.4 mm, and 0.5 mm, respectively.  
From Figure 7, it can be seen that the thickness of the dimensionless cutting bed is inversely related to the drill pipe rotation speed. The effect of increasing the drill pipe rotation speed decreases significantly at the same time. When the cutting diameter was 0.3 mm, the thickness of the dimensionless cutting bed decreased by 0.45% when the speed increased from 60 r/min to 80 r/min; when the speed increased from 140 r/min to 160 r/min, the thickness of the dimensionless cutting bed decreased by only 0.21%.
## 3 Research Issues  
### Gap between Sensory Extraction and Citizen Feelings  
Data-extraction methods (such as satellite photographs, global fine-scale information, and multi-sensors) all derive data from sensor networks, and the information extracted from sensor networks is the only data they use (see Figure 1). For example, air-quality sensors only sense the component of air and the subjects in their location. In contrast, citizens are complicated, diverse, and nearly everywhere. The continuous expansion of both the urban environment and the Internet means that numerous activities occur simultaneously within a city. Thus, once a gap occurs between sensory extraction and citizen feelings, problems arise (such as traffic jams, air pollution, global warming, power overloads, and confined living spaces). How to distribute city resources (such as street space or seating on transportation) is extremely important, especially in the post-pandemic era. Ideal urban planning can solve these kinds of problems. To address the information gap, we suggest considering citizen feelings as part of urban analysis.  
Figure 1: The information gap between the sensor network and citizen feelings. The IoT sensor network only senses the information type and where it is. The gap will appear in locations without sensors.
# Effects of DNA Topology on Transcription from rRNA Promoters in _Bacillus subtilis_
## 3 Results  
### The Activity of _rmB_ P1 Decreases during Entry into Stationary Phase  
As the main model rRNA promoter, we selected the _rmB_ P1 promoter as it is one of the best-characterized rRNA promoters in _B. subtilis_ that is regulated by [iNTP], [11; 34; 35; 36]. Furthermore, the dynamic range of the activity of _rmB_ P1 is wide, which facilitated the design and interpretation of the experiments. As the main control promoter, we selected the strong _Pveg_ promoter that forms stable open complexes and is saturated with a relatively low level of its iNTP. This promoter drives transcription of the _veg_ gene that is involved in biofilm formation [37; 38]. Promoter sequences are shown in Figure 1A.  
To monitor promoter activities, we used core promoter-_lacZ_ fusions. The endogenous copy of _Pveg_ initiates transcription with ATP (+1A). Here, we used a +1G variant of _Pveg_ so that both transcripts (from _rmB_ P1-_lacZ_ and _Pveg-lacZ_) were identical, excluding any effects due to, e.g., potentially differential decay of the transcripts. The +1G _Pveg_ promoter variant behaves identically with the +1A variant [11]. Throughout the study, promoter activity was determined by quantitative primer extension (qPE) or reverse transcription followed by quantitative PCR (RT-qPCR).  
We used defined rich MOPS medium to grow the cells and measured (i) relative GTP level ([GTP]) and (ii) relative promoter activity (_rmB_ P1 and _Pveg_) from early exponential phase till approximately two hours into stationary phase by qPE (Figure 1).  
We detected a moderate decrease in [GTP] already during exponential phase (Figure 1B). This moderate decrease was followed by a precipitous decline during the transition between the two phases. This correlated with a sharp spike in the (p)ppGpp level (Supplementary Figure S1). However, early on in the stationary phase, [GTP] even slightly increased and then remained at the same level till the end of the experiment. The activities of both _rmB_ P1 and _Pveg_ decreased during the time course of the experiment--the activity of the former more than of the latter, consistent with the behavior of these promoters as reported in previous studies [10; 11].  
Surprisingly and interestingly, the activity \(rrn\)B P1 decreased even after the relative GTP concentration had been stabilized at a constant level. This strongly suggested that another mechanism, besides rRNA promoter regulation by [GTP], exists in the cell. DNA supercoiling is known to change between growth phases, typically the negative supercoiling from exponential phase becomes more relaxed in stationary phase, as demonstrated for _Escherichia coli_[39] and also _B. subtilis_[40]. Also, we noticed that the activity of _Pveg_ significantly decreased, although the decrease was not as pronounced as that of the ribosomal promoter. As DNA topology is an important factor for gene expression regulation, we decided to address the potential of _B. subtilis_ rRNA promoters to be regulated by the level of supercoiling.
# Robustness Analysis on Graph Neural Networks Model for Event Detection
## 2 Related Work  
### Event Detection  
Early on, the research on ED mainly adopted machine learning methods that through feature engineering use statistic and linguistic features such as N-grams, part of speech, etc. [7; 8; 9; 10]. However, feature engineering greatly determines the overall performance of the ED model, and also has high requirements for human resources and expertise.  
The most prominent advantage of deep learning over machine learning is learning effective features from data through multi-layer neural networks [31]. With the gradual improvement in deep learning theory and technology in recent years, deep learning has proven to be more suitable for NLP tasks [11; 12; 13]; therefore, more and more researchers use deep learning methods to perform ED. Convolutional Neural Networks (CNN) [14] were applied to capture the semantic associations between continuous words through convolution operation; thereby candidate trigger word could aggregate the contextual information. The dynamic multi-pooling strategy [15] was used to model various semantic associations and thus enhanced CNN. [PERSON] et al. considered that general CNNs are poor at fusing discontinuous words, then they proposed the skip-grams method [16] to make up for this deficiency. [PERSON] et al. constructed a Recurrent Neural Networks (RNN) model for the first time [17] to detect multi-word events.  
Recently, more research has focused on how to convert a text sequence into graph-structured data to incorporate rich semantic information, and then GNN was introduced into the NLP tasks. [PERSON] et al. first leveraged Graph Convolutional Networks (GCN) [18] to perform ED through a syntactic dependency tree and achieved remarkable model performance. [PERSON] et al. introduced a self-attention mechanism and highway network structure into GCN [19] to improve the performance of ED. MOGANED utilized multi-order syntactic dependency to aggregate each order word vector representation through the attention mechanism [20], further improving the performance of GCN. [PERSON] et al. proposed the learning of the embedding vectors for the edges of the syntactic dependency graph by the node update module and edge update module [21], and achieved the SOTA effect of GCN. [PERSON] et al. achieved an improvement in the GCN effect by using trigger word filters [22] to reduce the influence of irrelevant noise between adjacent words.