esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 6 Synchronization by Conflict Resolution
### Structural Properties of the Conflict Graph  
We call a conflict between two commands _structural_ if the out types of the commands are different, and a _content conflict_ otherwise. An example for a content conflict is when both replicas replace the same directory with different file contents. The best resolution of a content conflict cannot necessarily be achieved by the above winner/loser paradigm, as it uses filesystem-level information only. Fortunately, content conflicts are represented in the conflict graph as isolated edges. It means that all of these conflicts must (and can) be resolved independently of all the other conflicts. We deem it good practice to resolve all content conflicts first.  
If \((\sigma,\tau)\) is a conflicting pair and \(\sigma^{\prime}\in A\) is on a node which is above the node of \(\sigma\), then \((\sigma^{\prime},\tau)\) is also a conflicting pair. Proposition 14 implies the same when \(\sigma\ll\sigma^{\prime}\). Consequently all elements of a _constructor_ cluster (see Section 4.2) of \(A\) are in conflict with exactly the same elements of \(B\), and so the whole constructor cluster can be replaced by a single graph node, thus reducing the graph size. Figure 5 shows the typical structure of conflict graphs. Elements of the destructor chain \(\sigma_{5}\ll\sigma_{4}\ll\cdots\ll\sigma_{1}\) are connected to more and more conflicting commands (this is so as the corresponding nodes go upwards). Choosing \(\tau_{5}\) as a winner automatically resolves all conflicts by wiping out all \(\sigma_{i}\).  
Automatic conflict-based synchronizers typically give precedence to constructors in conflicts between a constructor and a destructor command [10; 16], arguing that it is easier to remove some content again than recreating it. While this is good guidance most of the time, one has to be aware that in the current framework, replacing a directory node with some file content is a _destructor_. Confronted with the conflicting constructor command that creates a file under the same directory, marking this latter command as the winner is not self-evident.
## 3 HJ-1B images simulation
### A brief introduction to MODTRAN  
MODTRAN (MODerate spectral resolution atmospheric TRANsmittance and radiance code) was developed by the U.S. AFRL/VSBT (Air Force Research Lab, Space Vehicles Directorate) in collaboration with Spectral Sciences, Inc. MODTRAN code calculates atmospheric transmittance and radiance for frequencies from 0 to 50,000 cm-1 at moderate spectral resolution, primarily 2 cm-1 (20 cm-1 in the UV) [31]. The original development of MODTRAN was driven by a need for higher spectral resolution and greater accuracy than that provided by the LOWTRAN series of band model algorithms. Except for its molecular band model parameterization, MODTRAN adopts all the (now fully obsolete) LOWTRAN 7 capabilities, including spherical refractive geometry, solar and lunar source functions, scattering (Rayleigh, Mie, single and multiple), and default profiles (gases, aerosols, clouds, fogs and rain). MODTRAN is useful both in sensor signal simulation and atmospheric correction [31; 32]. The latest version, MODTRAN 4.0, was used in this research for the land surface temperature and emissivity retrieval and HJ-1B image simulation. Six standard atmospheric conditions were assumed in the simulation. The key variables of these six conditions are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model atmosphere** & \(T_{n}\) **(K)** & **W (g/cm2)** \\ \hline Tropical & 299.7 & 4.11 \\ Mid-Latitude Summer & 294.2 & 2.92 \\ Mid-Latitude Winter & 272.2 & 0.85 \\ Sub-Arctic Summer & 287.2 & 2.08 \\ Sub-Arctic Winter & 257.2 & 0.42 \\
1976 US Standard & 288.3 & 1.42 \\ \hline \end{tabular}
\end{table}
Table 3: Air temperatures at the first boundary and the total water vapor contents of the six standard model atmospheres prescribed in MODTRAN [33].
# Experimental Seaborne Passive Radar
## 4 Results of Moving Target Detection  
A group of objects took part as cooperative targets in the APART-GAS campaign, such as civil and military aircraft, boats, and navy ships. In the following experiments, the integration time was set to 262 ms to facilitate the detection of fast-moving targets, unless noted otherwise. Target positions shown in maps have been derived from GPS logs and a separate ADS-B radio receiver.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 6 Detection and Mitigation
### Mitigation Mechanisms
#### 6.2.2 Network-Level Blocking and Packet Analysis  
Within technical mitigation for botnet propagation, the use of network-level blocking is one of the most cited strategies. Many detection papers focus on network-level detection, which can be used by intrusion detection systems to block and contain botnets. In [202], an autonomous system (AS) is used to mitigate botnet threats. The AS stores a list of hosts' IP addresses and a threshold per host based on classification. Categories can be \"Blacklist\", \"Whitelist\", \"Suspected Attacker\" and \"Possible Victim\". AS are connected synchronously via the Ethereum blockchain. The threshold is monitored by every AS and refreshed after 20 s. Another way to ensure packets blocking is the software-defined networking approach [84; 203]. The main purpose is to analyse the incoming packets rate at defined IoT switches to separate legitimate from malicious communication. Legitimate traffic is accepted, while malicious ones are blocked. Many mitigation strategies use a locally installed agent on host machines to block detected botnet traffic, informing the user of the infected nature of their machine [201]. Blocking can also be performed at the edge of the service provider but would face high implementation costs and requires some coordination across ISPs [204]. At the network level, removal of malware can be performed by agents installed locally or by the use of a continuous communication protocol with a master device. This validates of the integrity of local hosts and allows administrators to perform removal of botnet-enabling malware from hosts, either automatically or manually [5; 176; 205].  
In [136], the authors propose a self-adaptive system for mitigation. In corporate area networks for instance, resilience can be ensured by using scenario-driven adaptive reconfiguration of networks. Scenarios are assessed and based on cluster analysis coming from previous botnet attacks. Moreover, the described system can apply more advanced actions such as reducing requests timeouts, decreasing allowed HTTP request size and blocking source hostname and IP addresses. In [98], a neural network-based system called BoNeSSy will notify the administrator if a threat is found and apply appropriate security actions such as blocking IP addresses or putting the system or suspicious network segment under surveillance. Many papers describe the detection of botnets using Machine Learning clustering via statistical behaviour correlation, but some of them are lacking of specific countermeasures description. Some characteristics can be countered by packets or IP addresses blocking.
# Phase Alignment of an Array Optical Telescope System Using Balanced Detection
## 4 Results and Discussion
The power size had a tendency to be stable following the phase difference correction, and its value was stable at the power value without the phase difference. Figure 13 depicts the BER before and after phase difference correction. Because the BER varied widely, we used the exponential power for statistical analysis, and we could see that the BER values were larger before the phase difference correction, and the BER decreased after the phase difference correction, and their powers were all less than \(-9\), indicating that the BER was better than \(10^{-9}\), ensuring normal communication. The comparison of the Q parameter before and after the phase difference correction is shown in Figure 14. When the phase difference was zero, the value of the Q parameter was greater. The Q parameter decreases gradually as the phase difference value increased, eventually reaching 0 rad when the phase difference reached 1.57 rad or \(-1.57\) rad. However, the figure shows that the Q parameters were all increased after the phase difference correction of this scheme, indicating that the communication quality was improved. Figure 12: Power values before and after phase compensation. Figure 13: BER before and after phase compensation.
## 2 Train Ride Comfort Measurement System
### Data Processing
#### 2.2.2 Equivalent Continuous A-Weighting  
In the field of measuring noise, simply measuring decibels is no longer enough to describe the feeling that is closer to what the human ear hears. In order to reflect the high sensitivity of the human auditory system around the frequency of 3 kHz and the low sensitivity at the frequency of 60 Hz, the usual processing method is to use the A sound level weighting curve to weight the signals of different frequencies. Its formula is:  
\[A(f)=20 lg\left[\frac{f_{4}^{2}f^{4}}{\left(f^{2}+f_{1}^{2}\right)\left(f^{2}+f_ {2}^{2}\right)^{\frac{1}{2}}\left(f^{2}+f_{3}^{2}\right)^{\frac{1}{2}}\left(f^ {2}+f_{4}^{2}\right)}\right]-A_{1000}, \tag{1}\]  
where \(f\) is the frequency of the input signal; \(A_{1000}=-2.000\) dB is a normalization constant expressed in decibels; \(f_{1}=20.6\) Hz; \(f_{2}=107.7\) Hz; \(f_{3}=737.9\) Hz; and \(f_{4}=12194.0\) Hz.  
The A-weighted frequency weighting coefficient \(\alpha_{A}(f)\) can be obtained from the following formula:  
\[\alpha(f)=\frac{f_{4}^{2}f^{4}}{\left(f^{2}+f_{1}^{2}\right)\left(f^{2}+f_{2} ^{2}\right)^{\frac{1}{2}}\left(f^{2}+f_{3}^{2}\right)^{\frac{1}{2}}\left(f^{2 }+f_{4}^{2}\right)}, \tag{2}\]  
The A-weighting coefficient in the frequency domain can be obtained as:  
\[X_{A}(k)=\alpha_{A}(f_{k})X(k), \tag{3}\]  
For discontinuous and unstable noises such as urban vehicle traffic noise and train running noise, a single A-weighted sound level cannot be accurately reflected. Therefore, this system introduces the equivalent continuous A sound level, which uses the mean of the noise energy to evaluate the impact of noise on people according to the time-average method.  
According to [PERSON]'s theorem, the total energy of the same signal in the time domain and the total energy in the frequency domain are always the same, that is:  
\[\sum\
olimits_{n=0}^{N-1}\left|x(n)\right|^{2}=\frac{1}{N}\sum\
olimits_{k= 0}^{N-1}\left|X(k)\right|^{2}, \tag{4}\]  
where \(N\) is the number of sampling points; \(x(n)\) is the discrete-time domain signal obtained by sampling the signal; and \(X(k)\) is the Fourier transform corresponding to \(x(n)\).  
By using Equation (4), the average energy of the A-weighted signal can be obtained, and the corrected form is:  
\[\overset{\wedge}{P}=\sqrt{\frac{1}{N}\sum_{n=0}^{N-1}\left|x(n)\right|^{2}}= \sqrt{\frac{1}{N^{2}}\sum_{k=1}^{N-1}\left|X_{A}(k)\right|^{2}}\, \tag{5}\]  
Then, according to the sound level calculation formula, the A-weighted sound level can be obtained as:  
\[L_{A}=20\ lg\left(\frac{\overset{\wedge}{P}}{2\times 10^{-5}}\right)+2, \tag{6}\]
# Assessing Landsat Images Availability and Its Effects on Phenological Metrics
## 1 Introduction
Section 2 describes the materials and the methods to map Landsat imagery availability and the modelling of the effect of valid observations scarcity on phenological metrics estimation. Section 3 presents (i) the Spatio-temporal patterns of image availability at the national level and for an area of the Brazilian Northeast semi-arid and, (ii) the uncertainty of phenological metrics related with the different patterns of availability in the semi-arid area. We discuss the impacts of remotely-sensed observation scarcity on land studies in Section 4. Finally, Section 5 presents our conclusions.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 2 Wellsite Diagnostic Method  
### Well Testing and Well Logging Methods  
The well testing method [16; 17; 18; 19] can obtain essential parameters [20; 21; 22] such as the epidermal coefficient, plugging ratio, additional pressure drops, formation permeability after damage, and depth, which characterize the degree of formation damage. The epidermal coefficient is interlinked with the other parameters and is considered the most classical parameter in characterizing the degree of formation damage. However, the epidermal coefficient derived by the well testing method reflects not only the real formation damage characteristics but also offers a comprehensive representation of various links and multiple factors. It is the sum of the real damage epidermal coefficient and the proposed epidermal coefficient. It comprises the well slope epidermal coefficient, formation shape epidermal  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types of Formation Damage** & **Causes** & **Classification** \\ \hline \multirow{4}{*}{Physical Damage} & Mechanical deformation of & Microparticle migration, Solid phase intrusion, \\  & formation pores, invasion of & Hydration swelling, \\  & drilling mud solids, and fines & Water blocking, \\  & migration during fluid flow. & Stress sensitive damage, \\  & & Sand production \\ \hline \multirow{4}{*}{Chemical Damage} & Reactions between injected fluids & Inorganic Scale, \\  & and formation minerals, scale & Organic Scale, \\  & deposition from incompatible & Wettability alteration, \\  & water mixing, and pH alteration. & Bacterial damage \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common types of formation damage and the corresponding causes.  
coefficient, open formation imperfection epidermal coefficient, non-Darcy flow epidermal coefficient, shot hole epidermal coefficient, and others. These epidermal coefficients must be decomposed to get the true damage epidermal coefficients [5]. When decomposed, the true damage epidermal coefficients would not exactly reflect the responsible types of damage, much less the percentage of each damage type.  
\[S_{d}=\bigg{(}\frac{h}{h_{\text{P}}}\bigg{)}\Big{(}S_{t}-S_{pp}-S_{\theta}-S_{f} -S_{G}\Big{)}-S_{P} \tag{1}\]  
where \(h\) is the well depth, m; \(h_{\text{P}}\) is the shot hole thickness, m; \(S_{d}\) is the formation damage skin factor; \(S_{t}\) is the total skin factor; \(S_{pp}\) is the partial shot hole skin factor; \(S_{\theta}\) is the well slope skin factor; \(S_{f}\) is the flow rate skin factor; \(S_{G}\) is the gravel fill skin factor; and \(S_{P}\) is the shot hole skin factor.  
The evaluation of formation damage using well logging can determine the damaged zone and can calculate the depth of the formation damage caused by external fluids. However, this method can only provide an approximation of the damage depth and does not yield a pattern of permeability change for the formation around the well [23; 24].
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 4 Simulation Model  
We conducted a performance comparison among the methods described in Section 3 through simulations. We used OMNET++ Network Simulator version 4.4.1. [[https://omnetpp.org/](https://omnetpp.org/)]([https://omnetpp.org/](https://omnetpp.org/)) (accessed on 21 January 2022). As far as the modeling of the WirelessHART protocol stack is concerned, we started with NIST modules [32], [[https://github.com/usnistgov/tesim_omnetpp](https://github.com/usnistgov/tesim_omnetpp)]([https://github.com/usnistgov/tesim_omnetpp](https://github.com/usnistgov/tesim_omnetpp)) (accessed on 21 February 2022) which we customized according to our needs. In this section, we describe the main peculiarities of the models we developed for the presented methods. As a preliminary consideration, we point out that all our simulations consider networks with a square grid topology (Figure 1), where the CNC is always the node with coordinates \((0,0)\) in the top left vertex. We did not reproduce node failures nor battery discharge; hence, all nodes correctly work for the whole simulation duration.
# A Low-Loss Circuit with High-Pass Low-Pass Broadband Flat Negative Group Delay Characteristics
## 3 Circuit Simulation with High-Pass and Low-Pass Negative Group Delay  
Based on the analysis of the circuit parameters, and considering the factors of the average negative group delay bandwidth and insertion loss, the parameters of the high-pass circuit are determined as in Table 14, and those of the low-pass circuit are as shown in Table 15. The circuit was simulated using the ADS software and Murata simulation library. The simulation results are shown in Tables 14 and 15 and Figures 27 and 28, and they are consistent with the theoretical results, indicating the feasibility of the experiment. We take inductor \(L_{3}\) as an example to analyze the effects of component tolerance on the circuit. We select inductors with different tolerances and the same inductance value in the Murata component library for simulation, and the simulation results are shown in Figure 29. The results obtained using inductors with different tolerances are essentially the same, which indicates that the component tolerances have a small effect on the circuit. As shown in Table 16, we achieve the characteristics of a larger negative group delay bandwidth, flatter  
Figure 26: \(S_{21}\) results for different \(L_{3}\) values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(L_{2}\) (nH) & \(\tau\) (ns) & \(FBW\) (MHz) & **IL (dB)** \\ \hline
0.3 & \(-0.168\) & 85.5 & \(-1.938\) \\
1.3 & \(-0.156\) & 85.5 & \(-1.938\) \\
2.3 & \(-0.144\) & 85.5 & \(-1.938\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Circuit results with different \(L_{3}\) values.  
Figure 25: Group delay results for different \(L_{3}\) values.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 4 Proposed System Overview
### System Stakeholders  
Our system consists of four basic stakeholders that operate independently from each other and in accordance with their own incentives and goals. Advertisers are advertising firms that wish to promote products through advertising much like in the OBA system. Advertisers commission the services of a Broker, which is a privately owned company that generates revenue by distributing ads to consumers. The Broker delivers the adverts through a network of distributors that are called Ad-Dealers. Ad-Dealers have a physical presence in publicly accessible areas and are able to deliver adverts upon request by establishing wireless connections with the mobile devices of users. Users represent potential consumers who own smartphone devices and are targeted by advertising companies through the services they use, such as websites and free mobile apps.  
The Broker is responsible for the Ad-Dealers in his/her network and therefore needs to institute some form of trusted relationship by performing background checks and signing legal agreements. Ideal to play the role of the Ad-Dealers are local entities that have the capability to install and manage advert distribution hardware in publicly-accessible locations such as malls, shops and local WiFi hotspots. The Ad-Dealers conduct their own business independently but at the same time share resources and work cooperatively under the administration of the Broker.  
All of the participants operate independently and the system can easily be expanded. The Broker can recruit new Ad-Dealers by authorizing the installation and use of the required infrastructure, while new users only need to download and register the client software on their devices.
## 4 Submitted Systems
### Speech-to-Text Challenge
#### 4.1.2 Closed-Set Condition Systems  
* G6-VICOMETECH-PRHLT [20]. VICOMETECH, Visual Interacion & Communication Technologies, Donostia, Spain and PRHLT, Pattern Recognition and Human Language Technologies Research Center, Universidad Politecnica de Valencia, Spain. G7-VICOMETECH-PRHLT submitted three systems. The primary system was a bidirectional LSTM-HMM based system combined with a three-gram language model for decoding and a nine-gram language model for re-scoring lattices. The training and development sets were aligned and filtered to get nearly 136 h of audio with transcription. The acoustic model was trained with a nearly perfectly aligned partition, which was three-fold augmented through the speed based augmentation technique obtaining a total of 396 h and 33 min of audio. The language models were estimated with the in-domain texts compiled from the RTVE2018 dataset. The first contrastive system was set up using the same configuration of the primary system, but the acoustic model was estimated using the three-fold augmented acoustic data of the perfectly aligned partition given a total of 258 h and 27 min of audio. The same data were used to build the second contrastive system, but it was an E2E recognition system that followed the architecture used for the open-set condition. The language model was a five-gram.
* G7-MLLP-RWTH [21]. MLLP, Machine Learning and Language Processing, Universidad Politecnica de Valencia, Spain and Human Language Technology and Pattern Recognition, RWTH Aachen University, Germany. G7-MLLP-RWTH submitted two systems. The recognition engine was the translectures-UPV toolkit (TLK) decoder [24]. The ASR consisted of a bidirectional LSTM-HMM acoustic model and a combination of both RNNLM and TV-show adapted n-gram language models. The training and development sets were aligned and filtered to get nearly 218 h of audio with transcription. For the primary system, all aligned data from train, dev1, and dev2 partitions, 218 h, were used for acoustic model training. For the contrastive system, only a reliable set of 205 h of the train and dev1 partitions were used for training the acoustic models. The language models were estimated with the in-domain texts compiled from the RTVE2018 dataset with a lexicon of 132 K words.
* G14-SIGMA [22]. Sigma AI, Madrid, Spain. G14-SIGMA submitted only a primary system. The system had the same architecture as the one submitted for the open-set conditions, but only 350 h of manual transcription of the training set were used for training the acoustic models. The language model was trained using the subtitles provided in the RTVE2018 dataset and manual transcriptions.