esa-sceva/satcom-chunk-collection huggingface dataset:
# A PEKS-Based NDN Strategy for Name Privacy
## 6 Discussion
Name Lookup (NL): One of the advantages of same ciphertexts in PrivICN is that string matching can be executed for encrypted names to lookup tables. By virtue of that, their work has a faster name lookup property. On the other hand, the application of the PEKS scheme to encrypt NDN name has slower name lookup as presented in Section 5. It is obvious that PEKS-based NDN strategy becomes slower when there are several nodes between producers and consumers. (b) Fully supported cache ability on all routers in NDN network (C): As shown in Figure 12, when the cache hit ratio increases, the download time decreases which means that PEKS-based NDN also provides one of the good features of NDN, i.e., in-network caching. However, ANDaNA uses encrypted encapsulation of original content, using two symmetric keys generated by the consumer for two Anatomizing Routers (AR). The ARs encrypt whole packets with the use of these keys. This fact causes the caching mechanism cannot be fully used. Future Work: In this paper, we do not discuss about content protection and signature. To have privacy-aware content retrieval, these two features should be included. To protect content by using the same scheme is one of our future works. 1. Signature: Whenever a consumer generates PEKS ciphertexts \(C_{1j}\), they become different. The producer needs to sign the data packet before sending out, which is composed of name and data content. If a consumer generates PEKS ciphertext \(C_{2j}\) of the same plaintext name again and creates another interest packets, it differs from the previously created interest packet. Moreover, when there is a cache hit in CS of a router, it needs to reply the data packet which is stored in the CS. The router should reply the data packet with the new name \(C_{2}\) which means the router needs to change the data packet by replacing the name \(C_{1j}\) into the new name \(C_{2j}\) and to generate a new signature. Therefore, adding such kind of feature to PEKS_based NDN is also one of our future work. 2. Packet overhead and table size: To reduce the downside of applying PEKS scheme to NDN, we input multiple name components to PEKS scheme to reduce packet overhead and table size. It seems that inputting the whole name to the PEKS scheme is better, but we showed that this is always not an optimal choice. There are several number of name component inputs to PEKS scheme which have lower average download time comparing to inputting the whole name as shown in Section 5. Note that the simulation result is valid only under the same situation we simulate and we are not claiming any concrete method to reduce the packet overhead and table size. It still shows that there is a room to reduce these values by appropriately selecting the number of name components fed into the PEKS scheme. To determine the optimal number of component inputs to the PEKS scheme without affecting the other operation such test process executions are not yet decided. Therefore, reducing the packet overhead and table size without affecting other operations in PEKS-based NDN is also one of our future works.
# Data Enhancement for Plant Disease Classification Using Generated Lesions
###### Abstract  
Deep learning has recently shown promising results in plant lesion recognition. However, a deep learning network requires a large amount of data for training, but because some plant lesion data is difficult to obtain and very similar in structure, we must generate complete plant lesion leaf images to augment the dataset. To solve this problem, this paper proposes a method to generate complete and scarce plant lesion leaf images to improve the recognition accuracy of the classification network. The advantages of our study include: (i) proposing a binary generator network to solve the problem of how a generative adversarial network (GAN) generates a lesion image with a specific shape and (ii) using the edge-smoothing and image pyramid algorithm to solve the problem that occurs when synthesizing a complete lesion leaf image where the synthetic edge pixels are different and the network output size is fixed but the real lesion size is random. Compared with the recognition accuracy of human experts and AlexNet, it was shown that our method can effectively expand the plant lesion dataset and improve the recognition accuracy of a classification network.  
generative adversarial network; classification network; scarce data; plant lesion; data enhancement +
Footnote †: journal: Applied Sciences
# CROpland Mapping Using Earth Observation Derived Phenological Metrics +
## 5 Conclusions  
The study demonstrated the EO big data analytics' capacity to provide thematic products to support agricultural land management and fulfill the users' requirements. The phenological metrics estimated from high-resolution imagery sensed by the Copernicus S2 satellite constellation, combined with a thematic reference dataset related to crop types, together with the use of advanced computational analytic techniques (the RF algorithm), allowed crop type mapping in heterogeneous, small, and fragmented agricultural systems. The calibrated NDVI and LAI supervised machine learning models showed similar performance, with the LAI model yielding better results.  
The supervised machine learning model, applied to a wider spatial extent, could contribute to the measurement and assessment of sustainability foreseen by the European Green Deal strategy, in terms of sustainable agricultural practices and environmental monitoring, and climate change mitigation and adaptation, in accordance with the stakeholders' requirements.  
Conceptualization, F.F. and D.S.; methodology, F.F.; formal analysis, F.F.; investigation, D.S.; writing--original draft preparation, D.S. and F.F.; writing--review and editing S.M. and A.T.; supervision, A.T. All authors have read and agreed to the published version of the manuscript.  
The research was funded by Italian Space Agency (ASI) in the framework of the agreement between ASI and the Italian Institute for Environmental Protection and Research (ISPRA) on \"Air Quality\" (Agreement number F82F17000000005).  
Not applicable.
###### Abstract  
The population of space debris in near-Earth space is continuously growing and it represents a serious problem for active satellites and spacecraft. A performant ground-based and space-based network of sensors is necessary for space surveillance and consequently to prevent new collisions and monitoring atmospheric reentry of these objects. This paper illustrates the possible role of the Italian ground-based novel bi-static radar sensor, named BIRALET, for space monitoring and resident space objects tracking. The main characteristics of the receiver system, the Sardinia Radio Telescope with its P-band mono-beam receiver, are described in detail. Then, a preliminary analysis of the performance of the sensor is presented, and the results of numerical simulations are shown, providing a general overview on both observation capabilities and orbit determination accuracy achievable with the Sardinia Radio Telescope.  
Keywords: radar sensors; space surveillance and tracking; orbit determination +
Footnote †: journal: Accepted: 28 September 2019; Published: 30 September 2019
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 4 Proposed Method
### Airlight Estimation  
In [4], airlight estimation entails selecting the brightest top 1000 pixels, a method pivotal for distinguishing foreground from background in hazy images based on brightness. However, applying this technique to inherently low-light images can lead to excessive darkening of the images, thereby compromising the retention of original features and risking the loss of image details.  
To resolve this challenge, we leverage the brightest and the darkest 1% of pixels to estimate airlight. The process can be outlined as follows: First, calculate the average brightness of \(I(x)\), denoted as \(\bar{B}\), and normalize it to the range of \([0,1]\) through division by 255. Secondly, determine the number of pixels, denoted as \(S\), that corresponds to 1% of the total number of pixels in the image. Lastly, estimate the number of dark pixels as follows:  
\[S_{d}=\lfloor S\times(0.1\times(1-\bar{B}))\rfloor \tag{5}\]  
where \(S_{d}\) is the dark pixel count, and the bright pixel count is obtained by subtracting \(S_{d}\) from \(S\). We select the darkest \(I_{d}\) pixels with \(S_{d}\) and the brightest \(I_{b}\) pixels through \(S_{b}\) from the dark channel image.  
The estimated airlight \(AR\) can be determined from the following:  
\[AR=\frac{\sum_{i\in I_{d}\cup I_{b}}\mathbf{P}_{i}}{S} \tag{6}\]  
where \(\mathbf{P}_{i}\) represents the color of pixel \(i\), and \(I_{d}\) and \(I_{b}\) are the indices of the darkest and brightest pixels, respectively.
# Latency-Bounded Target Set Selection in Signed Networks
## 5 The \(1\)-Ustss Problem  
The \(1\)-USTSS problem is trivially Set Cover-hard: actually, a dominating set of a graph \(G=(V,E)\) is also a \(1\)-target set for the signed graph \(G^{\prime}=(V,A^{+}\cup A^{-})\) where \(A^{+}=\{(u,v),(v,u):\{u,v\}\in E\}\) and \(A^{-}=\emptyset\). Hence, \(1\)-USTSS is non-approximable within \(c\log|V|\) for some \(c>0\).  
In the remainder of this section, we study the approximability of \(1\)-USTSS.  
We start by noticing that if a diffusion process of latency \(1\) is required to occur, then the A-reduction described in Section 4.2 yields a weakly positive set of clauses such that all its negative clauses have size \(2\) (that is, they contain exactly one positive literal and one negative literal). In fact, it directly follows from Lemma 3 that \(I_{0}\subseteq V\) is \(1\)-target set for a signed graph \(G=(V,A^{+}\cup A^{-})\) if and only if for any \(u\in V-I_{0}\)  
1. \(N^{+}_{in}(u)\cap I_{0}\
eq\emptyset\), and for any \(v\in N_{in}^{-}(u)\), it holds that \(v\in V-I_{0}\).  
As a consequence, for any \(u\in V\) the set of the negative clauses associated to \(u\) (as defined in Section 4.2) is  
\[Q_{u}=\{(\
eg x_{v}\lor x_{u}):v\in N_{in}^{-}(u)\}. \tag{3}\]  
This proves that the A-reduction shown in Section 4.2 works also as an A-reduction from 1-USTSS to weakly positive minimum ones such that the set of clauses corresponding to a signed graph, besides being weakly positive, has all its negative clauses of size 2. In [44] it is shown that the weakly positive minimum ones problem is \(B\)-approximable when restricted to such instances, where \(B\) is the maximum size of a positive clause in the set.  
The size of the positive clauses in the set \(f_{G}\) described in Section 4.2 is very large: for \(u\in V\), \(|c_{u}|=1+|\tau^{+}(u)|\), since \(c_{u}\) had to state that \(u\) is in \(I_{0}\) or there exists some node in \(I_{0}\) connected to \(u\) by a path (of any length). However, when diffusion processes of latency 1 are to be described, \(f_{G}\) can be slightly modified in order to decrease the size of its positive clauses: more precisely, condition 1 above implies that, for each \(u\in V\), \(c_{u}=x_{u}\lor x_{v_{1}}\vee\cdots\lor x_{v_{j}}\) where \(\{v_{1},\ldots,v_{j}\}=N_{in}^{+}(u)\), that is, \(|c_{u}|=|N_{in}^{+}(u)|+1\).  
Hence, by denoting as \(\Delta^{+}\) the maximum in-degree of a node in \(G^{+}\), that is, \(\Delta^{+}=\max\{|N_{in}^{+}(u)|:u\in V\}\), the next theorem follows from Lemma 4, from definition (3) and from the already cited result in [44].  
Theorem 4.2: _The \(1\)-USTSS problem is \((\Delta^{+}+1)\)-approximable, where \(\Delta^{+}=\max\{|N_{in}^{+}(u)|:u\in V\}\)._  
We remark that the result in Theorem 4.2 only depends on the topology of \(G^{+}\), no matter which is the topology of \(G^{-}\). Throughout the next subsection we shall follow the opposite way, that is, we shall study the complexity of 1-USTSS in relation to the topology of \(G^{-}\), no matter which is the topology of \(G^{+}\).
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G
### SON for IoT  
SON applied in IoT infrastructures is a necessity as well and a key enabler for mMTC (massive Machine-Type Communications). IoT networks must be treated as large distributed systems and in this case, apart from neighbor sensor discovery, path establishment and service recovery, the key point is to apply SON functions for sensor energy management. The authors of [110] introduced a new IoT paradigm called Fog of Things (FoT), including FoT-Devices, FoT-Gateways and FoT-Servers, where IoT services have different profiles (FoT-Profiles) and they are offered in a distributed manner. Moreover, they proposed a platform called SOFTIoT enabling SON capabilities for FoT-Profiles, devices, and gateways.  
Another very interesting work is described in [111], where the authors introduced a resilient, self-organizing middleware for IoT applications called SORIR. The rationale of the system is to decouple resilience from business logic, monitor the applications operation, react to failures, and trigger automatically needed reconfigurations.  
The authors of [112] investigated self-organization for low-power IoT networks through a lightweight distributed learning approach instead of legacy centralized optimized approaches implemented in the IoT devices. The aim was to reduce signaling between IoT nodes, which leads to decreased energy consumption and minimizes possible collisions.  
Finally, in [113], industrial IoT load balancing through the scope of self-organization was investigated through a proposed load balancing scheme that considers wireless link quality, the congestion of the wireless channel, and the amount of data that need to be sent or received. The outcome is that through the specific scheme, reliability can be significantly improved since the packet drop rate is reduced by up to 85%.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 3 Results and Discussion
### Effectiveness of Preprocessing  
Focusing on the best performing model and its base (M3.3 and M8.2 respectively), the contribution of the preprocessing steps to the data was studied. Particularly, Figure 5 shows the effects of Drug Name Replacement (DNR, Figure 5a,b) and Negative Instance Filtering (NIF, Figure 5c,d) on the \(F1\) metric of each positive class and the overall \(F1_{micro}\) score.  
First, reducing imbalance greatly benefits the _Advice_ and _Int_ classes in both models. Especially in M3.3 (Table 3), applying NIF improved the Recall by 7.6% and 10.9%, respectively, and the Precision by 3.4% and 6.2% respectively. Simultaneously, a relative robustness is demonstrated in the _Effect_ class when restoring the rejected pairs; however, their inclusion appears to favor the _Mechanism_ class. Conclusively, the overall \(F1_{micro}\) is improved in each case through the rejection of trivial negative instances; however, M3.3 maintains a better balance between Recall and Precision than M8.2, where although no class is perceptibly affected, their removal leads to an increase in false positives.  
Figure 4: GIN MLP performance compared to the number of aggregation layers using the baseline BioBERT and no additional MLP units.  
In contrast, the use of \(drugi\) over the original drug names provides a clear performance improvement when generating word embeddings with BERT-based LM. This fact confirms the hypothesis that the type of interaction is determined by their syntactic role within the sentence and its content and not by specific substances' names. This method of replacing drug names, also known as entity blinding or entity masking, supports the generalization of the model. Therefore, through these preprocessing steps, noise is reduced in the feature vectors of the Token nodes.
## 4 Discussion
### Effect of Individualization on the Perceived Realism of a Rowing Simulator (LIAC vs. IND)
#### 4.2.2 Auxiliary Aspects of Rowing Movement  
Overall, the individualization of the \(G_{dl}\), \(\varphi_{bias}\), and \(C_{Bdrug}\) significantly improved the perceived realism of the horizontal forces resulting from blade contact with the water. However, due to previously presented blade modeling simplifications and safety limits, the increment on the rating of vertical contact forces were insignificant.  
Setting \(\varphi_{bias}\) back to \(0^{\circ}\), as preferred by the majority of experts, led to a significant increase in the realism perception of the horizontal forces when sliding the blade (Question 1). Although experts were more consistent with the rating values for the configured model IND, on average the realism was only above average (\(\mu_{IND}^{Q\mathcal{G}}=6.60\)). This result may be explained with the lack of longitudinal curvature in the modeling of the blade surface. In a real rowing case, inserting a feathered blade into the water requires a deliberate attempt to push the oar handle upwards due to the curved design of the blade. However, due to the flat-plate modeling of the oar blade, rowers were still able to insert the feathered blade into the simulated water, which produced slight decelerating drag forces.  
Significant improvement in the realism of the braking forces (in Question 2) may be attributed to the rowers who settled on a lower value for \(G_{dl}\) and/or \(C_{Bdrug}\) during individualization, which reduced the high braking forces encountered in the first part of the study. Nonetheless, the average rating for the realism of braking forces were only above average (\(\mu_{IND}^{Q\mathcal{G}}=6.80\)). The result can be attributed to the rowers, who preferred higher values for the both drag coefficients. Thus, the previously reported issue regarding the excessive braking forces was repeated.  
For the vertical forces acting on the blade due to contact with water surface, individualization of \(G_{dl}\) only resulted in a marginally significant increment (\(\mu_{IND}^{Q\mathcal{G}}=6.30\)). The majority of the rowers, who preferred \(\varphi_{bias}=0^{\circ}\), mentioned that the minor reduction of the vertical forces were more realistic compared to \(\varphi_{bias}=2^{\circ}\). However, for the tapping with feathered blade onto the water surface, previously reported unrealistically high vertical force rendering was stated again in Question 3 and 4. Repetition of the issue was an expected outcome due to the lack of realistic blade modeling and used safety limit of 200 N. in \(B_{z}\)-axis, which were not among the configurable parameters.
# Sigma Routing Metric for RPL Protocol
## 4 Proposed SIGMA-ETX Metric
### SIGMA-ETX Cdf  
Figure 3 presents the Cumulative Distribution Function (CDF) of SIGMA-ETX for routes with two hop counts based on the 40 nodes simulation. The figure shows that 80% of all two-hop routes in the network have a SIGMA-ETX value of 6000 at most.  
Figure 3: SIGMA-ETX CDF.  
Following the definition of SIGMA-ETX, as the sigma value approaches zero, the homogeneity of the ETX values in the path increases, and thus the probability of long hops decreases. Figure 3 can be interpreted as follows: 40% of the results of the metric have a value of up to 2500, while 100% of the results can reach values of up to 20,000. This confirms the high disposition of the network to create long hops that are more unstable.
## 1 Introduction
### Scope and Contributions of This Paper
In this paper, a simplified, but realistic friction model is employed, namely viscous friction based on the hyperbolic tangent law, to perform the numerical experiments on the performance of the proposed controller. Consequently, some complex nonlinear features related to general friction phenomena, such as the stick-slip effect, the static friction, or, to some extent, the Stribeck curvature, are not captured by the present model. Although there is some loss of information by doing so, nonlinear viscous friction laws are widespread in engineering applications for implementing the analysis of articulated mechanical systems. In fact, in general, specific computational tools should be employed to consider the friction phenomena thoroughly. This mainly reflects using an appropriate numerical solution procedure for solving the equations of motion with discontinuous and fully nonlinear friction effects, at the expense of the computational time required to solve the set of ordinary differential equations. This is one of the main reasons why simplified versions of the friction model are preferred in engineering applications, such as linear and nonlinear friction laws. This is particularly true for the present work since, for each case considered in the paper, the differential equations of the cart-pole system must be solved a hundred times to run the deep reinforcement learning algorithms. Furthermore, in this paper, the effect of friction is mainly taken into account to demonstrate the effectiveness of a given controller designed in the absence of friction and subsequently post-trained to cope with its presence as an additional unmodeled disturbance phenomenon. Therefore, as mentioned before, in general, one should model friction forces considering more complete laws that better capture the phenomenon, but this is not functional or useful for the goals of the present investigation.
## 3 Results
### Agreement of the Circuit Analysis Method and Image Processing for \(\beta\) Ratio Diagnostic  
Two operating points obtained at 5 kHz with the sine voltage generator, corresponding to two different power levels (10 W and 25 W), are selected. Figure 13 presents the obtained results in each column (top-down): the electrical waveforms (voltage--blue, and current--green), the Manley diagram build with these data, the acquired reactor's pictures, the segmented and cropped (area of interest) grayscale image together with the \(\beta\) discharging ratio obtained from the equivalent circuit approach and with the image processing. The last data is associated with the margin error (the difference between the maximum and minimum values) estimated through repeated calculations using three different images acquired in steady state conditions (see below) during three different experiments.  
It should be mentioned that the coverage of the discharge area varies from the time the system is started, with a typical transient shown in Figure 14. Here, the record starts when the power supply is turned ON, with a gas flow already stabilized (see Figure 2). For the presented operating conditions (f = 10 kHz, P = 30 W), one can observe during the  
Figure 13: Application of both diagnostic methods: (**a**) low power level (10 W). (**b**) high power (25 W). Measures are acquired in steady state.  
transient the rise of the discharging surface (and accordingly the \(\beta\) ratio), which definitively stabilizes only after approximatively 120 s. For steady-state process analyses, data should only be acquired after this system time constant.  
The two methods described in the previous section are now applied on a set of measures acquired with operating points covering a large domain (sine voltage power supply). This diversity of considered conditions is aimed at comparing the results of the two methods, whose results are presented on Figure 15: for each operating condition, three measurements are acquired in steady state during different experiments. The displayed value is the average of these three results, and the error bar indicates the maximum and minimum values. One can notice the very good agreement with a difference which remains generally below 10%. In most cases, the \(\beta\) discharging ratio obtained with the electrical circuit approach remains in the error range of the image processing method. The latter is obtained, using several images repeatedly acquired for each operating point; this systematic uncertainty remains below 12%.