esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The present study proposes a back propagation artificial neural network (BPANN) to provide improved precision for predicting the material removal rate (MRR) in ultrasonic machining. The BPANN benefits from the advantage of artificial neural networks (ANNs) in dealing with complex input-output relationships without explicit mathematical functions. In our previous study, a conventional linear regression model and improved nonlinear regression model were established for modelling the MRR in ultrasonic machining to reflect the influence of machining parameters on process response. In the present work, we quantitatively compare the prediction precision obtained by the previously proposed regression models and the presently proposed BPANN model. The results of detailed analyses indicate that the BPANN model provided the highest prediction precision of the three models considered. The present work makes a positive contribution to expanding the applications of ANNs and can be considered as a guide for modelling complex problems of general machining.  
artificial neural network; regression techniques; modelling precision; material removal rate; ultrasonic machining 1
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 2 Basic Principles of Sound Mixing and Speech Separation
### Speech Separation Using ICA  
After dimensionality reduction by using PCA, any ICA algorithm for instantaneous mixtures can be employed at each frequency bin independently, such as the information maximization approach [16] combined with natural gradient [17], FastICA [18], or JADE [19]. In this paper, the information maximization approach combined with natural gradient is used.  
Firstly, the ICA iteration is expressed as  
\[W_{k+1}(f)=\eta\big{[}\mathrm{I}-\big{\langle}\phi(Y(f,t))Y^{\mathrm{H}}(f,t) \big{\rangle}_{r}\big{]}W_{k}(f)+W_{k}(f) \tag{11}\]  
where \(W\) is the iterative separation matrix, \(\eta\) is the step size, \(k\) is the iteration index, \(Y(f,t)=W_{k}(f)\times Z(f,t)\) is the separation signal obtained by the \(k\)th iteration matrix, \(<\gamma_{r}\) denotes the time averaging, and \(\phi(\cdot)\) denotes a nonlinear function defined as  
\[\phi(Y(f,t))=\tanh(\mathrm{Re}(Y(f,t)))+\mathrm{j}\tanh(\mathrm{Im}(Y(f,t))) \tag{12}\]  
where \(\mathrm{Re}(\cdot)\) and \(\mathrm{Im}(\cdot)\) denote the real and imaginary parts of their arguments, respectively.  
Then, the determinant of an expression of nonlinear correlation determines the end of the iteration, which is expressed as  
\[cor=\det\!\left(I-\big{\langle}\phi(Y(f,t))Y^{\mathrm{H}}(f,t)\big{\rangle}_{r }\right) \tag{13}\]  
where \(\det(\cdot)\) denotes the determinant.  
When the nonlinear correlation of the signal is below the threshold or the iteration step exceeds the set threshold, the iteration of ICA stops. Furthermore, because of the independently iteration of ICA at each frequency bin, the scaling ambiguity and permutation ambiguity should be solved. As modeled in Section 2.1, the center microphone is defined as the reference point, so the timeline of the center microphone is used as a benchmark. Here, the \(W^{+}(f)\) is defined as the Moore-Penrose pseudoinverse of \(W(f)\times V(f)\). Then, the scaling ambiguity is therefore decided by  
\[W(f)=diag[W^{+}_{\mathrm{r1}}(f)]\cdot W(f) \tag{14}\]  
where \(W_{r1}{}^{+}(f)\)denotes the first row of \(W^{+}(f)\).  
There are many methods to solve the permutation problem of FDICA [12], such as the directivity of separation matrix and the correlation of separation signal. Besides, independent vector analysis (IVA) could also solve this permutation problem, but it requires too much time to converge. In this paper, the permutation problem is solved by setting the initial value of \(W(f)\), which will be discussed in the next section.
# Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation
## 2 Related Work
### POI Recommendation  
The development of POI recommendation can be attributed to the continuous innovation of data fusion methods, from geographical location information to users' social relations to the spatiotemporal patterns of cities; more and more key elements have been discovered by researchers as new features of recommendation algorithms [10].  
The early POI recommendation algorithm uses the user's check-in frequency to make recommendations [11]. [PERSON] et al. [12] used regularized matrix factorization to provide personalized recommendations to users in social networks. However, the raw data values recommended by POI have a very wide range. In addition, the sparsity of the user check-in matrix is much larger than that of the item-rating matrix. For example, the sparsity of the Netflix dataset is 99%, while the sparsity of Gowalla is \(2.08\times 10^{-4}\). These situations facilitate POI recommendations for data fusion [13]. In the data fusion method, the earliest introduction of POI recommendation is the geographic location information. For POI recommendation systems, the first law of geography is shown as follows: users are more interested in POIs that are geographically closer to them, and they will have higher interest in POIs near the POIs they are interested in. [PERSON] et al. [14] proposed an adaptive POI recommendation method combining users' activities and spatial features, which can operate adaptively according to users' activities. [PERSON] et al. [15] proposed the Geo-ALM model, which utilizes adversarial learning with geographic information to fuse geographic features and generative adversarial networks. In LBSNs, some POI recommendation works utilized social relationship networks to improve recommendation quality [7; 16]. [PERSON] et al. [17] formed a three-layer model of LBSN with multi-label, social, and geographic influences and finally integrated multi-dimensional information into a matrix factorization framework. [PERSON] et al. [18] proposed a contextual graph attention model, and [PERSON] et al. [19] used a graph-enhanced spatial-temporal network for POI recommendation.  
However, research on POI recommendations is still in its preliminary stage. Although the existing work takes into account the influence of space, social interaction, time, and other factors on the recommendation effect of points of interest, the recommendation target tends to fit the historical check-in behaviors of individual users who are unable to learn the travel rules of people in the city and finally obtains a recommendation result that does not conform to common sense.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 5 Algorithm Development: GNSS/Vision Integration
### Tightly-Coupled GNSS/Vision
It is defined as:  
\[\mathbf{v}_{k}=\mathbf{z}_{k}-\mathbf{H}_{k}\mathbf{\hat{x}}_{k}^{-} \tag{34}\]  
The innovation is interpreted as the new information brought from the measurements and is used in the context of statistical testing and reliability [63] to prevent measurement errors from corrupting the solution.
# ARMONIA: A Unified Access and Metro Network Architecture
###### Abstract  
We present a self-configured and unified access and metro network architecture, named ARMONIA. The ARMONIA network monitors its status, and dynamically (re-)optimizes its configuration. ARMONIA leverages software defined networking (SDN) and network functions virtualization (NFV) technologies. These technologies enable the access and metro convergence and the joint and efficient control of the optical and the IP equipment used in these different network segments. Network monitoring information is collected and analyzed utilizing machine learning and big data analytics methods. Dynamic algorithms then decide how to adapt and dynamically optimize the unified network. The ARMONIA network enables unprecedented resource efficiency and provides advanced virtualization services, reducing the capital expenditures (CAPEX) and operating expenses (OPEX) and lowering the barriers for the introduction of new services. We demonstrate the benefits of the ARMONIA network in the context of dynamic resource provisioning of network slices. We observe significant spectrum and equipment savings when compared to static overprovisioning.  
access and metro optical networks; SDN; NFV; joint optimization; network slicing Article
## 3 Results and Discussion
### Effect of Surface Material and Nutrient Composition on Biofilm Formation and eGFP Expression
#### 3.2.2 eGFP Expression  
The fluorometric analysis of the biofilm cells (Figure 4g-i) showed that the highest eGFP production was obtained for biofilms grown in TB medium on PVC material on day 1 (207 fg-cell\({}^{-1}\); \(p<0.05\); Figure 4h), followed by those formed in the same culture media on SST (169 fg-cell\({}^{-1}\); \(p<0.05\); Figure 4g) and SIL (130 fg-cell\({}^{-1}\); \(p<0.05\); Figure 4i). After day 1, eGFP production under these conditions decreased to values close to 100 fg-cell\({}^{-1}\). Regardless of the material, eGFP production in biofilms formed in LB was constant throughout the assay, achieving on average 74 fg-cell\({}^{-1}\). For biofilms developed in the M9 ZB medium, the eGFP production was only detected from day 5 onward and remained constant up to day 9 (64 fg-cell\({}^{-1}\)). Additionally, the eGFP production was slightly lower in biofilms developed in the M9 ZB medium than in the LB medium. This finding aligns with a previous study that evaluated the effects of nutrient media on _E. coli_ biofilm development and eGFP production, which reported that LB medium enhanced protein production compared to M9 ZB medium [24]. LB is a complex medium that contains yeast extract and tryptone (Table 1), which are known to provide biosynthetic precursors, such as vitamins and amino acids, for protein expression in _E. coli_[69; 70]. In addition, yeast extract can reduce cellular stress during the synthesis of recombinant proteins [71]. The higher quantities of these two components in TB may also explain the higher eGFP production in biofilms developed in this medium. On the other hand, the lower eGFP expression in the M9 ZB medium could be attributed to the presence of glucose (Table 1), which may repress the _lac_ promoter [24; 72]. In fact, in the plasmid system used in this study, the eGFP transcription is regulated by the lacUV5 promoter [4].  
In summary, the maximum eGFP production obtained for each tested culture medium was 207 fg-cell\({}^{-1}\) for TB, 99 fg-cell\({}^{-1}\) for LB, and 69 fg-cell\({}^{-1}\) for M9 ZB. These results are encouraging, as there was an increase in specific eGFP production under the studied operational conditions compared to our previous studies using another biofilm platform: 207 vs. 22 fg-cell\({}^{-1}\) for TB [58], 99 vs. 16 fg-cell\({}^{-1}\) for LB, and 69 vs. 2 fg-cell\({}^{-1}\) for M9 ZB [24]. Therefore, they provide important insights for further assays and the potential scale-up of recombinant protein production.
## 1 Introduction
analyzed rear-end collision data from 23 U.S. states during 2009 and 2016, revealing that vehicles equipped with AEB had a significantly higher likelihood of collisions when turning compared to moving straight [35]. [PERSON] et al. introduced a target recognition model designed to acquire road curvature and to calculate the relative lateral distances, addressing the issue of incorrect recognition in the AEB systems on curved roads [36]. [PERSON] et al. proposed a curved road collision prevention warning system based on V2X technology [37]. [PERSON] et al. introduced a method to improve the performance of AEB systems for curves through curvilinear coordinate conversion, which was used to reflect the geometric information of roads for the navigation of an autonomous vehicle [38]. As for the research on lateral control, [PERSON] et al. developed a model predictive control method using road geometry as constraints to allow all parts of the bus to remain within the lane during travel [39]. [PERSON] et al. developed a model-based system to activate the evasive maneuver assist function, which could help improve the driver's steering and braking operation in a coordinated way [40]. In fact, the operation of the AEB system may cause the vehicle to move laterally out of the lane, especially when traveling on a curved path, posing potential risks of collisions with vehicles in adjacent lanes or roadside barriers [41]. In summary, there are few studies that consider both AEB and LKA simultaneously in curved road situations for intelligent vehicles. Based on this point, this paper proposes an integrated longitudinal and lateral control strategy of emergency collision avoidance for intelligent vehicles under curved road conditions, curbing the lateral offset of the vehicle during emergency braking. The lateral control is enhanced by incorporating the LKA system into the AEB system. This integration aims to suppress the lateral offset during emergency braking in curved road scenarios. To verify the effectiveness of the proposed collision avoidance strategy, a vehicle dynamics model is established using CarSim software 2020 version. A typical curved road scenario is constructed within PreScan software 2019 version, and the control algorithm is formulated using Matlab/Simulink R2022b version for joint simulation. Three different control systems are subjected to comparative testing: 1 AEB control system alone; 2 independent control system of AEB and LKA; and 3 integrated control system of AEB and LKA. The subsequent sections of this paper are outlined as follows: Section 2 introduces control objectives, examines the challenges encountered during emergency braking on curved paths, and offers potential solutions. Section 3 delivers a comprehensive elucidation of the control strategies for the AEB system, the LKA system, and three distinct control systems. Section 4 carries out a comparative simulation analysis of the three control systems across various curved radii and presents the outcomes of this comparative study. Finally, in Section 5, a summary of the entire content is provided, along with an assessment of its limitations.
# Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
## 5 Limitations and Future Work  
The results and findings presented in this work are primarily limited by the fact that only a single data source (FSN) and single data consumer (CIV) were studied. Also, the 5G SA network utilized for this work was proprietary, and as such technical details regarding the core network are unavailable. It should be noted that latency and throughput measurements were analysed separately and that no real data streams were analysed. In addition, the cellular sidelink was not included as part of this measurement campaign as it is not representative of what is currently available in cellular-capable vehicles. Regardless, these results and findings can be used to advise MNOs and automotive manufacturers on which V2X communications applications can be implemented now and on the challenges that must be addressed to support all V2X communications applications in the future.  
In the future, the current dataset can be further bolstered by the inclusion of an increased number of data sources and consumers, where a more realistic pseudo-FSN with actual sensors is used. Thus, a deeper analysis of more specific factors in different working conditions such as static obstructions, environmental factors, and vehicle telemetries will be carried out to isolate the parameters that have the greatest effect on the KPIs studied in this work. Additionally, the use of cloud computing or the MEC paradigm [39] as alternate locations for the computation resources may be of value to provide insights into the cost and network performance trade-offs associated with moving the data away from the locality of interest. Lastly, when available, a re-creation of the measurements conducted can be repeated using the cellular sidelink to more accurately represent the whole capabilities of cellular technology as a V2X communications system.
# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 9 Multidimensional Evaluation
### Average Delivery Probability Throulout the Network
#### 9.1.2 Grey-Hole Attack  
The graph shows that across all four protocols, grey hole nodes had far less of an effect on the network versus black holes. This would be expected given that grey holes only dropped half of the messages rather than all of them. Interestingly, the results of the two types of attack were very similar up to around 25%. From here the black holes decreased the delivery probability significantly whereas for grey holes, the graph indicates that the messages were more likely to be delivered. This was unexpected as the more grey holes, the more the network should be disrupted (more messages dropped). One reason may be that as more grey hole nodes were introduced, there was potential for fewer messages to be generated (a grey hole could just drop its own message instantly).  
Maxprop and Spray and Wait performed slightly better with grey holes rather than black which was as expected. PRoPHET appeared to be the other way around, for black holes the highest delivery probability was around 0.27 and for grey it was around 0.25. This was an unexpected result, possibly due to the algorithm trying to reduce the number of copies made. If the grey holes managed to drop the messages before they were replicated, there was a much smaller chance of them being delivered.  
Although Maxprop lent itself to being the algorithm of choice to maximise delivery probability, the trend lines indicated that PRoPHET was more consistent. This showed that the complex PRoPHET algorithm was more resilient to both attacks but provided a much lower delivery probability.
## 3 Results and Discussion  
### Study of 3D Printing Quality  
The results of the morphological characterization for comparing the two alternative designs for housing the UV light guide are shown in Figure 5.  
Figure 5: Morphological characterization as a function of the number of printed layers and the printed device used for housing the UV light guide. (**a**) Cross-section of the printed ribbons, (**b**) average height, (**c**) average width and (**d**) cross-sectional area.  
First, Figure 5a shows the cross section of the 3D printed ribbons. Here, a significantly higher accuracy and a better distribution of the ribbons printed with the 2-stages printing device compared to the 1-stage printing device was observed when increasing the number of printed layers. In addition to this, the average height, width and cross-sectional area are analyzed in Figure 5b-d, respectively, as a function of the number of printed layers and the printed device used for housing the UV light guide. In this regard, the circuits printed with the 2-stage printing device showed a substantially higher height and a lower width compared to the circuits printed with the 1-stage printing device. The different quality of 3D printed circuits is explained by the different configuration and geometry of the device for housing the UV light guide used during the printing process. When using the 1-stage printing device, the UV light was pointing directly to the printing nozzle during the extrusion process. This may have totally or partially blocked the conductive ink flow due to the photopolymerization of the ink, which may have caused misalignment when extruding the ribbons. Moreover, the partial or total block of the printing nozzle due to the UV light can also cause substantial changes on the ink flow or even flow disruptions, which can be observed as a slight decrease in the cross-sectional area when using the 1-stage printing device. In this regard, the printability when using the 2-stages printing device was better, due to less misalignment that led to higher and narrower printed ribbons. Moreover, the cross-sectional area was not affected by partial or total clogs in the printing nozzle caused by UV light since the photopolymerization process occurred at a different stage than the extrusion process as the UV light was not pointed directly to the printing nozzle. Furthermore, the printing process was more robust and consistent, as the error bars of Figure 5b-d were wider for the 1-stage printing device as a result of higher differences between different measurements. For all these reasons above mentioned, it is highly recommended to select the 2-stage printing device for housing the UV light guide, in spite of the printing time, which is twice the printing time compared to using the 1-stage printing device.
## 6 Implementation and Experiment  
### Experimental Setup  
In accordance with the method proposed in this research, a system prototype was implemented that recognizes lines in image-format P&IDs. The prototype system was implemented in Python 3.7 on Windows 10 OS. A deep neural network for the detection of line signs and flow arrows was implemented using Keras 2.4 and Tensorflow 2.3.0 libraries. For the hardware, a computer with an AMD RYZEN 7 2700X CPU, 64 GB RAM, and two Nvidia GeForce RTX 2080 Ti graphics cards were used.  
The P&IDs used in the experiment are 82 P&IDs remodeled based on the data provided by the 'K' company. The resolution of the diagram was 9933 \(\times\) 7016. For detection of line signs and flow arrows, nine sheets out of the 82 sheets of diagrams are used as a test set for performance evaluation. Of the remaining 73 sheets of diagrams, 90% were used as the training dataset, and 10% were used as the validation dataset. Figure 18 shows the P&IDs included in the test set. Recognition results of lines in the areas indicated as (a)-(e) are shown in Figure 19.  
Figure 17: Line recognition data format.  
Performance in each training epoch is assessed based on the validation dataset. In the learning training, the epoch was set to 50, and the step was set to 2500. After performing the training under the set epoch and step, a sufficient level of convergence was confirmed with the regression loss at 0.118 and the classification loss at 0.0374. Table 1 shows the network parameters set during training with the deep neural network. When line signs and flow arrows are detected through the deep neural network, class, bounding box, and prediction confidence are calculated. Usually, multiple candidates bounding boxes are detected for one object. A greedy non-maximum suppression (NMS) algorithm extracted the bounding box with the highest confidence among the candidate bounding boxes. The greedy NMS algorithm calculates the intersection over union (IOU) between boxes detected as the same object, selects boxes with an IOU threshold above a specific value, leaves only the box with the highest confidence, and deletes the rest. A threshold score of 0.5 and an IOU threshold of 0.5 was applied in this experiment.  
Figure 18: Test P&IDs used for the experiment.  
In the proposed line recognition method, the results of recognizing symbols and texts included in P&ID are used as input. Therefore, using the method in [40], the symbols and texts included in the test P&IDs were recognized prior to the experiment.
## 3 Improved Q-Learning Algorithm for Better Management of Channel Utilization in UWCNs
### Action Space (\(A_{s}\))  
The potential actions that the node may take are included in the action space:  
* Modify transmission energy (\(E\)): Data packets may be sent using varying transmission energy levels.
* Retransmission strategy (\(R\)): Chooses which packets to retransmit and at what time. A combination may be any action \(a\in A\), such as \(a=(E,R)\).