esa-sceva/satcom-chunk-collection huggingface dataset:
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 4 Environment of Simulation
### Performance Evaluation Metrics  
This section explains the evaluation criteria used to estimate the performance of MANET in the presence of assailants.  
* End-to-End Delay (E2 ED): The E2 ED refers to the delay experienced by a packet that has been issued by the legal node and which is to be shared with neighboring nodes. The E2 ED is a metric related to the quality of service (QoS) of the network, as shown in the following equation: \[\text{E2 ED}=T_{R}-T_{G}\] (1) where \(T_{R}\) is the packet reception time, and \(T_{G}\) is the packet generation time. Hence, E2 ED is the difference of \(T_{G}\) and \(T_{R}\).
* Packet Loss Ratio (PLR): The PLR indicates the amount of data that is lost due to MITM nodes, as expressed by the following equation: \[\text{PLR}=\frac{M_{L}}{M_{T}}\] (2)  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Network Simulator & OMNeT++ 4.3 \\ MANET Simulator & INET Framework 2.1.0 \\ NETwork Attacks & NETA v1-1.0 \\ No. of Nodes & 100 \\ No. of Harmful Node & 1, 3, 5, 7 \\ Simulation Area & 3.0 km \(\cdot\) 3.0 km \\ Simulation Time & 1000 s \\ Node Mobility & Stationary \\ Source Application & UDPBasicApp \\ Destination Application & UDPSink \\ Packet Size & 512 B \\ Send Interval & 0.5 s + uniform (\(-\)0.001 s, 0.001 s) \\ MAC Standard & 802.11 g \\ Number of Repetitions & 4 \\ Bit Rate & 54 Mbps \\ Routing Protocol & AODVUU \\ AODVUU Type & Link Layer Feedback \\ Transmitter Power & 2.0 mW \\ Sensitivity & \(-\)85 dBm \\ Thermal Noise & \(-\)110 dBm \\ Carrier Frequency & 2.4 GHz \\ Path Loss Alpha & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Details.  
Figure 5: Snapshot of OMNeT++.  
where \(M_{T}\) is the overall number of messages received, and \(M_{L}\) is the total number of messages lost. Therefore, PLR is the difference of \(M_{T}\) and \(M_{L}\). More specifically, the total number of messages \(M_{T}\) includes messages received at both the legal node \(M_{R}\) and the harmful node \(M_{L}\), as shown in the following equation:  
\[M_{L}+M_{R}=M_{T} \tag{3}\]  
* Amount of delayed messages: This indicator displays the number of data that the rogue node has delayed.
* Amount of dropped messages: The dropping of messages from legal nodes is the statistic that is defined for MITM. The number of messages discarded by network attackers is indicated by this measure.
# Parsing Unranked Tree Languages, Folded Once +
## 4 Unfolding Folded Trees  
Since the input trees we are working with are unordered (see Definition 3), we extend the behaviour of the \(Z\)-automaton \(A\) to the unordered case.  
**Definition 4** (Unordered runs).: _A run of a \(Z\)-automaton \(A=(\Sigma,Q,R,F)\) on an unranked, unordered tree \(t=(V,E,src,tar,lab,r)\) is a mapping \(\rho:nodes(t)\to Q\) such that for each node \(v\), the states assigned to the children of the node \(v\), when viewed as a multiset, belongs to \(\text{park}(\rho(v),\text{lab}(v))\). For a tree fragment \(s\), a partial run is a run, except that the condition on the children does not apply to the nodes in \(nodes(s)_{x}\)._  
We can now define the _signature_ of a tree fragment in terms of which partial runs it can realize. The intuition is that, for each \(x\)-labelled node and possible assignment of a state to it, we have to find a set of trees to attach that can evaluate to states in the appropriate Parikh image.  
Figure 3: To parse a folded graph (**top right**), we first decompose it into a number of tree fragments attaching to the merged node (**bottom row**), and then search for a way of reassembling the fragments into a tree in the folded tree language (**top left**). The single node arising from merging is indicated with a bold outline.  
**Definition 5** (Signature).: _Let \(A=(\Sigma,Q,R,F)\) be a \(Z\)-automaton with \(m=|Q|\), and let \(t\) be a tree fragment with \(k=|nodes(t)_{x}|\). The signature of \(t\) with respect to \(A\), denoted by \(\text{sig}(t)\), is a set of tuples of the form \((q,S)\), where \(q\in Q\) and \(S\) is a multiset of elements from \(Q\), defined as follows. Let \(v_{1},\ldots,v_{k}\) be the nodes in \(nodes(t)_{x}\). Then, \((q,S)\) belongs to the signature iff there is a partial run \(\rho\) on \(t\) and a partitioning of \(S\) into \(S_{1},\ldots,S_{k}\), such that \(\rho(\text{prior}(t))=q\) and, for each \(i\), \(S_{i}\in\text{parikh}(\rho(v_{i}),\langle\sigma,\alpha\rangle)\)._  
The intuition of the above definition is that \((q,S)\) is in the signature of \(t\) if \(t\) can \"accept\" \(|S|\) tree fragments whose priors have been assigned the states in \(S\) and then \"deliver\" a state \(q\) at its prior.  
Given the input set \(\{T_{1},\ldots,T_{n}\}\), we only need to consider a polynomial number of signatures. Since there are \(n\) input trees, we only consider signatures where \(|S|\leq n\). The number of such signatures is bounded from above by \(mn^{m}\). In other words, the number of possible signatures for all input trees is polynomial. The signature for each input fragment can be computed in polynomial time using a CYK-like dynamic programming algorithm.  
Given a set of tree fragments, we compute their signatures, and then reassemble them as in Figure 3, leading to the final theorem.  
**Theorem 1**.: _The non-uniform membership problem for tree languages folded once under an order-cancelling semantics is decidable in polynomial time._  
Before proving Theorem 1 in Section 4.2, we recall the definition of vector addition systems and prove some properties of these that will turn out to be useful.
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 3 Results and Discussion
### The Activities of Enzymes  
The activities of enzymes are the basis for evaluating the quality of honey [85]. The enzymatic composition of honey includes glucosidases, \(\alpha\) and \(\beta\) amylases, \(\alpha\) and \(\beta\) glucosidases, as well as proteases. Honey types differ from each other in the composition and quantity of enzymes, as their content is completely dependent on both the nectar collection period and the physiological age of the bee colony.  
\(\alpha\) and \(\beta\) amylases, or diastases, are the enzymes that occur in relatively large quantities in honey, and their content depends on their botanical and geographical origin. [85]. A diastase catalyzes the breakdown of starch into maltose [33; 34; 41].  
Diastases are sensitive to heating, and their activity decreases at high temperatures. Therefore, its value is used as a marker of age and an indicator of high-temperature treatment in storage conditions [33].  
Diastase activity is a honey quality parameter used to determine if the honey has been extensively heated during processing [84; 86; 87]. According to the Honey Quality and International Regulatory Standards, the diastase activity must not be less than or equal to 8 [61].  
Active enzymes are very sensitive to high temperatures and will lose their activity when they exceed a certain temperature [33; 34].  
In all 18 samples of the Jara honey samples, the characteristic diastase activity was much higher than 8, ranging from 9.56 to 27.0 (Table 7).
## 2 Motivation and Background  
Privacy is the ability of an individual or group to decide which information about themselves should not be disclosed or which information would be revealed to whom. Therefore, a privacy framework have to use methods which can be easily adapted to different requirements of applications and users.  
The wide usage of WSNs in monitoring applications makes people's life easy but they may cause violation of privacy. Untrusted parties can access the collected information by eavesdropping, physically capturing the sensors, or bypassing authentication to remotely access sensors or central databases [1]. Data encryption, key distribution and authentication mechanisms can limit the effects caused by these types of threats. Applicability of these mechanisms are not straightforward in WSNs, due to limitations like physical capturing possibility of cryptographic credentials or limited battery usage. Many studies proposed various methods in order to solve these security problems under the limitations of WSNs [2, 3].  
Restricting the details of information gathered by WSN is required for limiting the privacy risks originated from untrusted parties. On the other side, privacy requirements of individuals also force the designers of WSNs to carry out some restrictions for data shared with trusted parties. These trusted parties are actually sinks in WSNs. There are mainly two reasons for these types of requirements: First, the trusted party can be captured physically by un-trusted parties. Especially in applications where attackers have high motivation or where major vulnerabilities exist, including lack of physical security, data shared with trusted parties have to obey some privacy criteria. Enemy tracking, health monitoring, or wild-life monitoring applications are examples for these types of applications. The second reason is that individuals generally want to hide detail information of their personal lives directly from the trusted party. For example, in applications where health monitoring is performed outside the hospital, individuals do not want their exact location and time information to be sent to hospital particularly in non-urgent times. However, they may want to weaken their privacy requirements in urgent times in order to get appropriate medical helps. If there are many sinks in WSN, requirements of individuals may change for each sink as well. Revisiting health monitoring applications, information about individuals may be sent to the family members as well as to hospital. In these applications, individuals may be willing to share more detail information with their family members but not necessarily with the hospital.
## Conflicts of Interest: The authors declare no conflict of interest. Abbreviations  
* ADS Active Distribution Systems ADN Active Distribution Network AI Artificial Intelligence AMPC Adaptive Model Predictive Control DER Distributed Energy Resources DG Distributed generation DNO Distribution Network Operator DMS Distribution Management System DR Demand Response DSM Demand-Side Management DSR Demand-Side Resources DoS Denial of Service EV Electric Vehicle GPS Global Positioning System GAS Genetic Algorithms FDI Fault Data Injection FIA Fault Injection Attacks HMIs Human Machine Interface IADs Integrated Anomaly Detection System IEDs Intelligent Electronic Devices IoE Internet of Energy IoT Internet of Things ICT Information Communication Technologies LP Linear Programming LTC Load Tap Charging LV Low Voltage MPC Model Predictive Control MV Medium Voltage MILP Mixed-Integer Linear Programming MITM Man-in-the-middle OLTC On Load Tap Changes PDC Phasor Data Concentrator PMUs Phasor Measurement Units PSO Particle Swarm Algorithm SAU Sub Automation Unit SAS Substation Automation System SDG Smart Distribution Grid SDN Software Defined Network SG Smart Grid SES Smart Energy Solution SM Smart Metering SMI Smart Metering Infrastructure SMV Sample Measure Values VPP Virtual Power Plant VVC Volt/VAR Control WAN Wide Area Network
# The Role of Data-Driven Methodologies in Weather Index Insurance
## 2 Objectives  
Given that the application of data-driven methodologies for weather index insurance products, including machine learning frameworks in low-income countries, represents an important field of research in agricultural development [5], we have the following three goals.  
First, a unique database from satellite images and flood records (the proxy for crop yield loss) from the department of Antioquia will be built.  
Second, we want to compare a linear and interpretable model with a highly non-linear black-box model. Therefore, we will establish whether a machine learning model, such as the one recently used in the literature to model weather indices and loss [24], can be employed in a productive region in Colombia to establish if there is a relationship between weather indices and the observable losses due to floods. We used a logistic regression model as a reference point to evaluate the performance of a more complex model, ANN. The results from these machine learning models could serve to elucidate if the weather indices can better explain the losses in a particular agricultural region where sufficient crop yield data is not available.  
Finally, using both the artificial neural network (ANN) and logistic regression models, we hope to assess the related costs for a hypothetical policyholder and an insurance provider. This could be used to derive fairer insurance policies before their implementation.  
The main contributions are two-fold. First, we propose a novel way to implement an index insurance methodology by adapting a proxy variable to asses the crop losses in a country where no sufficient crop production data is available. Second, in addition to performance metrics, a utility measure is used to evaluate the performance of the ANN in relation to the base model, which is logistic regression.  
Results obtained in this stage may be useful for the design of reliable indices for index insurance instruments with lower basis risk levels in low-income countries where agricultural and catastrophic data records are difficult to obtain. In this study, we use flood data records as a proxy of agricultural losses and satellite images for weather indices. We believe that the use of non-linear data-driven methodologies promotes the construction of fairer weather index insurance models in low-income countries.
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 3 Related Work
### Shallow Learning Methods  
Among unsupervised domain adaptation methods based on shallow structure, the transfer component analysis (TCA) model [13] is a typical model that attempts to minimize the distance between source and target domains in a new feature space using the maximum mean discrepancy (MMD). [PERSON] et al. proposed an unsupervised domain adaptation method based on an extreme learning machine network to retain the space information of the target domain [36], which seeks to transfer the source domain for better matching of the data distribution in the target domain by reducing the MMD distance. [PERSON] et al. proposed an unsupervised domain adaptation model for multi-view data [37]; the features extracted from one view of the data are considered privileged information from another view. [PERSON] et al. proposed combination of domain-adversarial learning and self-training with the intention of combining the strengths of both methods [38]. The pseudo-label prediction and the confusion matrix were learned using self-training and using an adversarial approach, respectively. [PERSON] et al. proposed a symmetric and positive-definite matrix network for domain adaptation (daSPDnet) [39]. Inspired by Riemannian manifold methods, daSPDnet aims to enable EEG emotion recognition by overcoming the variability in the physiological responses of subjects.  
Some effort has already been invested in applying unsupervised transfer methods to heterogeneous domains. For example, [PERSON] et al. proposed a heterogeneous unsupervised domain adaptation model [40], which introduced an \(n\)-dimensional metric of fuzzy geometry to compute the similarity between different vectors. Based on the results, the fuzzy equivalence relations were explored and the cross-domain clustering categories were captured. [PERSON] et al. proposed an optimal matrix transport method for heterogeneous domain adaptation [41], which introduced the entropic Gromov-Wasserstein discrepancy for learning an optimal transport matrix. [PERSON] et al. proposed a distance metric learning method for heterogeneous domain adaptation [42]. This method used existing models to learn the knowledge fragments in the source domain, which can reduce domain divergence.  
However, unsupervised domain adaptation methods based on shallow structure have two main drawbacks. The first is utilization of labeled data information, since a small amount of labeled data can significantly improve domain adaptation performance. The second drawback is the capacity for feature learning. How to learn more transferable representations to alleviate domain discrepancy represents a major challenge.
## 3 Research Approach
### NRTR Model  
The NRTR (non-autoregressive transformer recognizer) model presents a series of positive aspects that significantly distinguish it. Firstly, unlike RNN- and CNN-based methods, NRTR completely eliminates recurrences and convolutions. This allows the problems of computation parallelization and vanishing/exploding gradient that are encountered with RNNs to be avoided, and the increasing complexity that occurs with CNNs when trying to learn dependencies between distant positions. Secondly, NRTR is based exclusively on the self-attention mechanism. This allows global dependencies between different input and output positions to be established at once, rather than one by one, as in RNNs, and reduces the entire operation to a constant number, unlike CNNs. Furthermore, thanks to the use of self-attention, NRTR allows for greater computation parallelization, leading to superior performance. NRTR also introduces a new mode transformation block that effectively converts 2D images into 1D sequences. This pre-processing step before the encoder allows to handle images with large variation of scales/aspect ratios and backgrounds. From a performance point of view, NRTR achieves state-of-the-art or highly competitive results on various benchmarks, both in lexicon-free and lexicon-based cases, without any rectification module. In addition, NRTR has an eight-times higher training speed than the best existing recognizer. Finally, the model can be trained with more computation parallelization and less complexity. The mode transformation block, combined with the encoder, allows more discriminative features to be extracted. These aspects make NRTR an extremely effective and efficient text recognition model [21; 22].
# A Single-Bit Incremental Second-Order Delta-Sigma Modulator with Coarse-Fine Input Buffer
## 3 Results and Discussion  
A chip die photograph of the circuit is shown in Figure 13. The circuit was fabricated using the TSMC 180 nm process. The supply voltage operates at 1.8 V, and the supply current is 1.4 mA. The active area of the circuit is 1.06 mm\({}^{2}\).  
Figure 12: Schematic of the two-stage comparator.  
Figure 13: Die photograph of the proposed circuit.  
Figure 14 shows the output transient measurement results for the buffer according to the sinusoidal input. The activation timings of the coarse buffer and fine buffer are shown in Figure 15. The coarse-fine buffer can drive the input switching load of the DSM.  
To measure the slew rate of the input buffer, the step input with the magnitude of 200 mV, from 800 mV to 1 V, is applied. The slew rate was 2.13 V/ us for the voltage arrival times from 10% to 90% of signal amplitude.  
Figure 16 shows the input-referred noise measurement results for the coarse and fine buffers. To measure the buffer noise, the output of the buffer was amplified using the low-noise voltage amplifier SR560 (Stanford Research Systems, Sunnyvale, CA, USA). The input-referred noise floor of the coarse buffer was measured to be 36 nV/rtHz 100 Hz, and that of the fine buffer was measured to be 17 nV/rtHz. The coarse and fine buffers used currents of approximately 225 and 270 uA, respectively. As shown in Figures 16 and 17, it is possible to achieve a fast settling time and low noise characteristics simultaneously through coarse and fine buffers.  
Figure 18 shows the sampling clock and bitstream output results as functions of the sinusoidal input. The frequency of the input sine wave was applied at 100 Hz, 1.2 V\({}_{\text{pp}}\), and 900 mV common mode voltage. The sampling clock was set to 125 kHz, and the OSR was set to 512 during the design process. It was observed that the bitstream output changed according to high and low sine waves. The result of the pulse density modulation (PDM) FFT analysis based on the bitstream output result is shown in Figure 19. Analysis of the PDM signal was performed using an APX500 (Audio Precision, 5750 SW Arctic Dr, Beaverton, OR 97005, NA). The passband frequency was determined from the Nyquist frequency of the sampling frequency and oversampling rate, which was 122.07 Hz. Performance indicators such as ENOB, SINAD, and the spurious free dynamic range of the modulator were calculated based on the above, with the average noise floor at \(-\)113 dB.  
Figure 14: Transient measurement result of coarse-fine input buffer.  
Figure 15: Enable timing signal measurement of coarse and fine buffers.  
Based on this, we calculated the RMS value of the noise up to the passband, wherein the signal's amplitude was \(-10\) dB. The calculated SINAD and ENOB were 80.87 dB and 13.14 bits, respectively.  
Figure 16: Noise measurement result of coarse and fine buffers.  
Figure 17: Transient measurement results of square input signal and output signal of buffer.  
Figure 18: Sinusoidal signal of buffer output and bitstream output.
## 4 Analysis of Position-Holding Strategy of Constellation Stations
### Relative Station Position-Keeping Strategy of Constellation  
In this section, a relative phase control strategy based on a dynamic reference satellite is designed based on the method of referring to a dynamic reference star. The specific process is as follows. Any satellite in the constellation is dynamically selected as the reference star, and then the sum of the phase drifts of other satellites in the constellation and the reference satellite is calculated. The satellite with the minimum cumulative value of the phase angle drift and the minimum number of satellites to be controlled is selected as the control reference, and the relative station position maintenance of the constellation is carried out. The control flow chart of the satellite relative control strategy is shown in Figure 3.  
The control process of the relative phase-maintenance strategy based on a dynamic reference satellite is as follows:  
1. All the satellites in the constellation are utilized as dynamic reference stars, and the phase angle drift \(\Delta\lambda_{m,k}=\Delta\lambda_{k}-\Delta\lambda_{m}-\Delta\hat{\lambda}\) of the other satellites relative to the reference satellite is computed. Here, \(m=1,2,\ldots n\), \(k=1,2,\ldots n\), and \(\Delta\hat{\lambda}\) represent the designated nominal value of the phase difference between satellite \(k\) and satellite \(m\).
2. The orbit extrapolation of a satellite in the constellation is carried out by a high-precision orbit forecaster to calculate the satellite phase deviation \(\Delta\lambda\). With the change rate of the phase deviation \(\Delta\dot{\lambda}\), the phase angle summation \(\Delta\lambda_{m}\) (\(\Delta\lambda_{m}=\sum_{k=1}^{n}\Delta\lambda_{m,k}\)) is selected. In addition, the number of phase maintenance satellites \(i\) and the smallest satellite \(k\) (\(k=1,2,\ldots n\)) should be used as the reference satellite.
3. Based on the reference satellite \(k\), orbit control is implemented for satellite \(i\) to adjust its semi-major axis (raising or lowering) to alter the phase drift direction between the satellites and maintain the phase angle of the constellation.  
Figure 3: Flow chart of satellite relative control.  
According to the perturbation variational equation of the relative phase angle, the cumulative semi-major axis control quantity can be deduced as  
\[\Delta a_{i}=\frac{4a_{k}\Delta\lambda_{0}}{\left(3n+7\Delta\lambda\right)\Delta t }+\Delta a_{0}, \tag{15}\]  
where \(\Delta\lambda_{0}\) is the maximum allowable drift of the phase difference, \(\Delta t\) is the phase drift time, and \(\Delta a_{0}\) is the absolute value of the semi-major axis deviation between the reference satellite and the control satellite.