esa-sceva/satcom-chunk-collection huggingface dataset:
# QoS-Aware Flexible Handover Management in Software-Defined Mobile Networks
## 4 Performance Evaluation
### Handover Latency  
The handover latency (HL) in the SDN-based architecture can be defined as the sum of three components: L2 handover latency (\(T_{L2}\)), the address configuration delay (\(T_{AC}\)) and the flow entry update delay after handover (\(T_{FU}\)). In this paper, more specifically, the handover latency is defined as the time between the detachment at the previous AP and the receipt of the first data packet at the new AP after handover. In order to remove the effect of wireless and device characteristics, \(T_{L2}\) and \(T_{AC}\) are assumed to be constant values and are shown as the same parts in the Equations (5) and (6). \(T_{FU}\) is the transmission delay of the signaling messages for handover support before the receipt of the first data packet. In SDN-based PMIP, Class 1 and Class 2, after L2 handover, signaling messages including router solicitation/advertisement, packet-in/packet-out and flow-mod messages as well as the data packet to the MN from the CN through the CS and AS can be transmitted as described in Equation (5). On the other hand, since the flow path is changed before the MN's attachment in advance in SDN-based EPC, Class 3 and Class 4, the data packet can be delivered to the MN right after the signaling messages, including router solicitation/advertisement and packet-in/packet-out messages from the CS via AS, as shown in Equation (6).  
\[\begin{array}{l}\small\begin{array}{l}\small\begin{array}{l}\small{H} {\rm{I}}^{\rm{prop}}_{\rm{class1}}={H}^{\rm{prop}}_{\rm{class2}}=T_{L2}+{T}_{ AC}+{d}_{\rm{MN}-A{\rm{S}}2}{\cdot}(\frac{L_{\rm{F}U}+L_{\rm{F}U}}{L_{\rm{F}U}}+ \beta)+{d}_{\rm{ASI}-Ctrl}{\cdot}(\frac{L_{\rm{F}U}+L_{\rm{F}U}}{L_{\rm{F}U}} +\alpha)+{d}_{\rm{C}t-C{\rm{S}}}{\cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+ \alpha)\\ {+{d}_{\rm{C}t-A{\rm{S}}2}{\cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+\alpha)+{d}_ {\rm{ASI}-A{\rm{N}}}{\cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+\beta)\end{array} \end{array}\end{array}\end{array} \tag{5}\]  
\[\small\begin{array}{l}\small\begin{array}{l}\small\begin{array}{l}\small{H} {\rm{I}}^{\rm{prop}}_{\rm{class3}}={H}^{\rm{prop}}_{\rm{class4}}=T_{L2}+{T}_{ AC}+{d}_{\rm{MN}-A{\rm{S}}2}{\cdot}(\frac{L_{\rm{F}U}+L_{\rm{F}U}}{L_{\rm{F}U}}+ \beta)+{d}_{\rm{ASI}-Ctrl}{\cdot}(\frac{L_{\rm{F}U}+L_{\rm{F}U}}{L_{\rm{F}U}}+ \alpha)+{d}_{\rm{ASI}-A{\rm{N}}}{\cdot}(\frac{L_{\rm{F}U}}{L_{\rm{F}U}}+\beta) \end{array}\end{array}\end{array} \tag{6}\]
## 2 Materials and Methods
### Direct Roxarsone Reduction Experiment in Anaerobic Conditions  
A dissimilatory metal-reducing bacterium, _Shevanella oneidensis_ strain MR-1 and a fermentative Cr(VI)-reducing bacterium, _Cellulomonas_ sp. strain Cellu-2a [21] were used to assess on reduction of the organoarsenic compound, roxarsone in anaerobic conditions. In addition to _S. oneidensis_ MR-1 that has been known to reduce roxarsone as the electron acceptor, _Cellulomonas_ sp. Cellu-2a was chosen to know the potential of roxarsone degradation in the fermentative bacterial growths in comparison to the anaerobic respiration. In a 100 mL serum bottle containing 50 mL modified FW media, roxarsone was added at the final concentration of 0.1 mM from 10 mM stock solution, and 10 mM lactate or 10 mM sucrose was added as a sole carbon source for MR-1 or Cellu-2a, respectively, from 0.1 M stock solutions. The serum bottle was sealed with a butyl rubber stopper, and a mixture of gas (90% N\({}_{2}\):10% CO\({}_{2}\)) was purged to the serum bottle. The cell inoculants were prepared by incubating the seed cultures (MR-1 or Cellu-2a) in tryptic soy broth (TSB) at 30 \({}^{\circ}\)C for 16 h aerobically and transferring the cells at \(10^{-2}\) (\(v/v\)) into the fresh TSB for another 16 h incubations. The harvested cells were washed by centrifugation (150 rpm for 5 min) and resuspension in FW medium and normalized to OD\({}_{600}\) of 1.5 with FW medium (resulting in 2 \(\times\) 10\({}^{9}\) cells/mL). The cells of strain MR-1 or strain Cellu-2a were inoculated to the experimental bottles at \(10^{-1}\) (\(v/v\)) at the final concentration of 2.0 x 10\({}^{8}\) cells/mL. The experiment was performed in triplicate individual incubations at 30 \({}^{\circ}\)C for both strains. Abiotic control without the addition of any microorganism was also performed for both bacterial strains.
## 4 Testing of the Solution  
The implementation works carried out for the proposed middleware solution have been tested to ensure its applicability. There have been three different environments where testing activities have been taken and a final collection of tests in the demonstrator used for the SWARMs project. To begin with, the middleware components have been tested locally in a single computer. Secondly, the middleware proposal was assessed with the software components running in two separated laptops that were communicating with each other in a network. Later, the software subsystems that have been developed were included in hardware to perform accurate testing activities. Finally, the software components of the semantic middleware architecture were tested in a deployment with an actual acoustic and wireless network communicating a CCS with an AUV.
# Automatic Era Identification in Classical Arabic Poetry
###### Abstract  
The authenticity of classical Arabic poetry has long been challenged by claims that some part of the pre-Islamic poetic heritage should not be attributed to this era. According to these assertions, some of this legacy was produced after the advent of Islam and ascribed, for different reasons, to pre-Islamic poets. As pre-Islamic poets were illiterate, medieval Arabic literature devotes relied on Bedouin oral transmission when writing down and collecting the poems about two centuries later. This process left the identity of the real poets who composed these poems and the period in which they worked unresolved. In this work, we seek to answer the questions of how and to what extent we can identify the period in which classical Arabic poetry was composed, where we exploit modern-day automatic text processing techniques for this aim. We consider a dataset of Arabic poetry collected from the diwas ('collections of poems') of thirteen Arabic poets that corresponds to two main eras: the pre- 'Abbasid era (covering the period between the 6 th and the 8 th centuries CE) and the 'Abbasid era (starting in the year 750 CE). Some poems in each diwan are considered 'original'; i.e., poems that are attributed to a certain poet with high confidence. The diwas also include, however, an additional section of poems that are attributed to a poet with reservations, meaning that these poems might have been composed by another poet and/or in another period. We trained a set of machine learning algorithms (classifiers) in order to explore the potential of machine learning techniques to automatically identify the period in which a poem had been written. In the training phase, we represent each poem using various types of features (characteristics) designed to capture lexical, topical, and stylistic aspects of this poetry. By training and assessing automatic models of period prediction using the 'original' poetry, we obtained highly encouraging results, measuring between 0.73-0.90 in terms of F1 for the various periods. Moreover, we observe that the stylistic features, which pertain to elements that characterize Arabic poetry, as well as the other feature types, are all indicative of the period in which the poem had been written. We applied the resulting prediction models to poems for which the authorship period is under dispute ('attributed') and got interesting results, suggesting that some of the poems may belong to different eras--an issue to be further examined by Arabic poetry researchers. The resulting prediction models may be applied to poems for which the authorship period is under dispute. We demonstrate this research direction, presenting some interesting anecdotal results.  
era identification of arabic poetry; authorship attribution of arabic poetry; classical arabic poetry classification; natural language processing
# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 5 Materials and Methods
### Transfering to Relation Extraction  
The OIE and RE tasks are both subclasses of information extraction, making the two tasks similar in semantics. The dataset used in the RE task for training, testing, and validation is Semeval-2010 Task 8 [50]. The nine predefined relations in the dataset are shown in Table 6. The training set consists of 8000 sentences, however, for a fair comparison we trained our neural network on the same number of relation tuples available in the OIE training set; thus, 2906 tuples were randomly selected from the training set. Similarly, the same experiments were compared against each other when transferring from the OIE news domain to RE:  
Transductive transfer learning: transferring knowledge learnt from the OIE news domain to the RE news domain.  
Inductive transfer learning: a small percentage of the RE corpus is fed into the neural framework along OIE news data to train the neural network.  
Traditional learning: both training and testing on the RE news domain.  
In all the above-mentioned experiments in both tasks, we used bio-medical OIE and RE, a development set containing 946 tuples composed of the same structure as the source task, for validation purposes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source Task** & **Target** & **Word** & **Hyper Parameters** & & & \\
**(Train)** & **Task (Test)** & **Embeddings** & **(Batches–Epochs)** & & **Results (R–P–F)** & \\ \hline \multirow{4}{*}{OIE (news)} & & GloVe & 100 & 5 & 58.2\% & 54.1\% & 56.1\% \\  & OIE (news) & BERT & 100 & 5 & 64.3\% & 58.2\% & 61.1\% \\  & **XLNet** & **100** & **20** & **68.1\%** & **62.2\%** & **65.0\%** \\ \cline{1-1}  & & XLM-RoBERTa & 100 & 5 & 65.4\% & 58.1\% & 61.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the ROIE model using different word embeddings. Both training and testing were done on the OIE WikiNews dataset. Recall (R), precision (P), and F-measure (F) were used as evaluation metrics.
# On the Use of Data Envelopment Analysis for Multi-Criteria Decision Analysis
###### Abstract  
Data envelopment analysis (DEA) has been proposed as a means of assessing alternative management options when there are multiple criteria with multiple indicators each. While the method has been widely applied, the implications of _how_ the method is applied on the resultant management alternative ranking have not been previously considered. We consider the impact on option ranking of ignoring an implicit hierarchical structure when there are different numbers of indicators associated with potential higher-order objectives. We also consider the implications of the use of radial or slacks-based approaches on option ranking with and without a hierarchical structure. We use an artificial data set as well as data from a previous study to assess the implications of the approach adopted, with the aim to provide guidance for future applications of DEA for multi-criteria decision making. We find substantial benefits in applying a hierarchical approach in the evaluation of the management alternatives. We also find that slacks-based approaches are better able to differentiate between management alternatives given multiple objectives and indicators.  
data envelopment analysis; multi-criteria decision analysis; radial model; slacks-based model; objective hierarchy
## 3 System Model  
In this section, we describe the system model considered in this study and present the conventional LEO satellite constellation and ISL interface. In the LEO constellation, MHR is defined to find the shortest path. The hop-by-hop routing scheme determines the shortest path with the lowest communication overhead. When a packet encounters a link failure, the flooding method is utilized to share the link-state information. Thus, to minimize overhead during flooding, we define the communication overhead required for flooding.
## 5 Conclusions  
We demonstrate the architecture of the proposed model-driven DL-NPE for joint channel estimation and symbol detection in a VLC system. The simulation results show that the overall channel impairment of the IM/DD channel can be effectively compensate and the distorted symbols are demodulated to the bit stream efficiently, which show the unique benefits of the proposed scheme in feature learning of channel characteristics and the constellation de-mapping relationship. Moreover, it can still work effectively as the VLC system is complicated by serious distortions and interference, which proves the robustness and generalization ability of the proposed scheme. Nonetheless, it is quite important to collect the accurate and effective training data as much as possible and to select the appropriate well-trained DL-NPE from the perspective of balancing the learning and generalization abilities since the online deployment in practical scenario may be different from that in the training stage. In addition, the training accuracy also depends on the weight initialization of the network. A more rigorous analysis to optimize the proposed model for better performance is left for future work.  
Figure 10: BER performance of the proposed scheme under clipping conditions.  
**Author Contributions:** Conceptualization, P.M. and W.Y.; methodology, P.M. and Y.Y.; software, P.M. and W.Y.; validation, W.Y. and H.P.; writing, P.M.; writing--review and editing, P.M. and H.P.; grammar check, H.P.; funding acquisition, P.M. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China (NSFC) under grants 61801257 and 61761019, by the Shandong Provincial Natural Science Foundation under grant ZR2019 BF001, and by the China Postdoctoral Science Foundation under grant 2019M652322. The APC was funded by NSFC, grant 61801257.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available from the first author upon request.  
**Acknowledgments:** The authors express their gratitude to the editors and the anonymous reviewers for their insightful suggestions and general assistance.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Specifically, when the constraint of suspicious data rate is given, we formulate an optimization problem to find the most efficient jamming power allocation at \(UAV_{L}\) to maximize the eavesdropping rate, which is polynomially solvable. Moreover, we propose a selection policy to facilitate the simultaneous eavesdropping and jamming for \(UAV_{L}\) on the flight, which also derives the optimal jamming power by using linear programming. In particular, the proposed policy allocates the jamming power over the fading channel according to the limited jamming power constraint, as well as the position of \(UAV_{L}\). The impacts of fading states on the performance of our policy are analyzed by applying the proposed policy to four common fading models, i.e., Rayleigh, Ricean, Weibull, and Nakagami. In our paper, we considered the topology between the legitimate UAV and two suspicious UAVs is a semi-circle with a diameter \(D\). We mainly consider an optimal policy strategy for the legitimate UAV to obtain a good performance on monitoring. From the analysis, it is clear that the distance between UAVs is the key to the problem. Thus, considering UAVs' distance is much more meaningful compared to the trajectory design in our model. In fact, the change of trajectories causes the change of distances between legitimate UAV and suspicious UAV, so we can apply our results in various trajectories. The main contributions of this work can be summarized as follows:  
1. Traditional works focused on achieving secure UAV-ground (U2G) communications in the presence of terrestrial eavesdroppers/jammers, while in our paper, we considered UAV-UAV (U2U) communications in the air, so we formulated suspicious UAVs' distance model, which considered the dynamic mobility of suspicious UAVs in sequence time slots;
2. Traditional works usually consider one case for eavesdropping and jamming, while in our paper, we proposed four cases of eavesdropping and jamming over fading channels, and then formulated an optimization problem to find the most efficient jamming power allocation at \(UAV_{L}\) to maximize the eavesdropping rate;
3. Traditional works focus on improving power consumptions or data receive rate respectively, while in our paper, we proposed a selection policy to facilitate the simultaneous eavesdropping and jamming for \(UAV_{L}\) on the flight, which allocated the jamming power over the fading channel according to the limited jamming power constraint as well as the position of \(UAV_{L}\). The rest of the paper is organized as follows: Section 2 introduces related works on security techniques in UAV networks. In Section 3, we design the system model on legitimate eavesdropping and jamming. Section 4 proposes the problem formulation and selection policy, as well as the complexity and feasible solution analysis. Simulation results are shown in Section 5, followed by a conclusion in Section 6.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.3 Fault Detection  
Fault detection systems are very important to achieving ultra-reliable low latency communication (URLLC). For example, mission-critical industrial automation applications is a type of application that demands stringent timing and reliability guarantees for data collection, transmission, and processing [28]. Identifying faults is crucial to ensure low latency (since damaged equipment may increase the time transmission) and reliable communication (since point of failure may reduce the overall network performance). However, due to the device heterogeneity of 5G networks, identifying faults is a complex task that requires sophisticated techniques in order to automate such task.  
In this systematic review, we found two papers that addressed fault detection in 5G scenarios using deep learning models.  
In [29], a deep-learning-based schema was proposed to detect and locate antenna faults in mmWave systems. Firstly, the scheme detects the faults (using a simple neural network with a low cost), and then it locates where the fault occurred. Since the second step is a more complex task due to the high number of antennas present in a mmWave system, a more complex neural network was proposed.  
In [30], [PERSON] et al. covered fronthaul network faults. The model was designed to locate single-link faults in 5G optical fronthaul networks. The proposed model was able to identify faults and false alarms among alarm information considering single-link connections.
## 6 Analysis of Scan Patterns Dimensions: Rules-of-Thumb for Scanner Designs
### FOV of Scanners with a Pair of Rotational Risley Prisms  
#### 6.1.1 Study with Regard to the Prisms Angles  
One of the most common requirement of applications is to have the largest possible FOV. From Figure 9 one can see that the optimal choice of prism orientation in order to reach this maximum FOV is in the (a) configuration in Figure 2, followed by the (d) configuration. In contrast, configurations (b) and (c) produce, using the same pair of (identical) prisms, a much smaller maximum angular deviation. The same inequality  
\[D_{a}^{max}>D_{d}^{max}>D_{b}^{max}>D_{c}^{max},\forall\theta \tag{48}\]  
can be observed from the simulated scan patterns analysis in Figure 8. However, what cannot be entirely obtained from simulations is the limit of the prism angle for which the inequality (48) still holds. Thus, from Figure 9 one can see that this limit angle is \(\pi/8\) rad. This makes the above conclusion valid for optical wedges, which have prism angles smaller than (roughly) \(\pi/18\) rad (the deviation of a single prism is \(D=10^{\circ}\) for \(\theta=18^{\circ}9^{\prime}\) for \(n=1.517\)).  
As \(\theta=\pi/8\) rad is the limit angle for Risley prisms scanners in the (a) and (d) configurations, from Figure 9, if one aims to reach higher maximum deviation angles and therefore a larger FOV, the (b) configuration could be used up to prism angles of \(\theta\approx 0.72\) rad, or the (c) configuration for up to \(\theta\approx 0.96\) rad (both values evaluated for \(n=1.517\)).  
From the point of view of these limit prism angles, one should consider the analysis in Section 5.1, based on the condition to avoid the total reflection on the second diopter of each prism, and therefore Equation (41) for (a), Equation (43) for (b), Equation (45) for (c), and Equation (46) for (d).  
One could also consider a \(k\
eq 1\) case, therefore using different prisms, in order to increase the FOV. Such an approach would give results that are in-between the above cases of (a) to (d) configurations (with identical prisms), in a more complicated analysis that could be subject of a future optimization but may be too complicated to be of interest in practice. Therefore, one may consider a (simpler) scanner with identical prisms without failing in an optimization process from the point of view of the FOV. The maximum angular deviations, namely Equations (2)-(5), as well as the linear ones, namely Equation (6), give the maximum FOV of the scan pattern for each configuration. From Figure 4 this radius is identical for different values of \(M\) and from Figure 5 it increases with \(k\), in agreement with Equation (5) for configuration (d).  
As concluded in the previous section, one must also point out that, from Figure 9, configurations (a) and (d) have the strongest non-linearities of the deviation versus prism angle function, while configurations (b) and (c) have the lowest. One could use this conclusion to build scanners with prism angles close to the values in Table 2, for example (for a specific \(n\)), to maximize the angular deviation and therefore the FOV.
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Meaning** \\ \hline \(\beta\) & Sliding window \\ \(\gamma\) & Shift number from sliding window \\ \(a\) & Element produced by the Wavelet-named approximations \\ \(d\) & Element produced by the Wavelet-named details \\ \(k\) & Represents the plane based on the _information cube_ to be processed \\ \(\mu\) & Type of mother Wavelet \\ \(\zeta\) & Level of applied Wavelet \\ \(\alpha\) & Vector from _information cube_ bounded by the sliding window of \(\beta\) and \(\gamma\) \\ \(Am\) & Approximation matrix \\ \(Dm\) & Detail matrix \\ \(S_{1}\) & Input columns from _Pooling_ process \\ \(S_{2}\) & Input rows from _Pooling_ process \\ \(PS_{1}\) & _Pooling_ window size in terms of the columns \\ \(PS_{2}\) & _Pooling_ division named shifting between Pooling windows \\ \(\rho\) & Number of neurons from output layer \\ \(\Omega\) & Weight Matrix \\ \(\Delta_{1,1}^{\mathrm{x}}\) & Training Matrix \\ \(\alpha\) & Learning rate \\ \(\eta^{\star}\xi\) & Number of neurons \(\rho\) \\ nc & Number of features to be considered \\ \(dif_{i,j}\) & Euclidean distance from \(\omega_{i,j,1}\) and \(\psi_{1,1}^{\mathrm{x}}\) \\ \(T_{x,y}\) & Minimum from \(\Lambda\) \\ \(\phi\) & Approximation of neighboring neurons \\ H & Proximity matrix according to differences within the map \\ k & Number of planes of interest for training \\ RedSOME & Function generated to process training from _Self-Organizing Map_ \\ RedSOMC & Function generated to process characteristics from _Self-Organizing Map_ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: This table shows the variables taken into account in the algorithm’s experiment.