esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Basic Concepts
### PRUDEnce Framework
: _Given an attack, we consider a function \(matching(d,b)\) that determines whether a record \(d\in D\) corresponds to the background knowledge instance \(b\in B_{k}\). We then define a function \(M(D,b)=\{d\text{$\in D$}\mid matching(d,b)=True\}\), whichidentifies all records in \(D\) that match \(b\). The probability of re-identification for an individual \(u\) within the dataset \(D\) is expressed as_  
\[PR_{D}(d=u\mid b)=\frac{1}{|M(D,b)|}\]  
_which represents the chance of linking a record \(d\in D\) to an individual \(u\), given the instance \(b\in B_{k}\)._  
The _compatibility_ is expressed by a function \(matching(d,b)\), which indicates whether or not a record \(d\in D\) matches the instance \(b\). The matching function depends on the background knowledge used during the attack. The PRUDEnce framework characterizes the re-identification risk for an individual as the maximum probability of re-identification among all instances within a background knowledge configuration, as defined in Definition 8 and shown in Step 3 of Figure 1.  
**Definition 8** (Re-identification Risk or Privacy Risk). : _The re-identification risk, or privacy risk, for a specific individual \(u\), associated with a background knowledge configuration \(B_{k}\), is determined as the highest probability of re-identification, expressed as \(Risk(u,D)=\max PR_{D}(d=u\mid b)\), where \(b\in B_{k}\). This risk is bounded by a minimum threshold of \(\frac{|D_{u}|}{|D|}\), which corresponds to a random guess within dataset \(D\), and is \(Risk(u,D)=0\) when \(u\) is not present in \(D\)._  
An individual may face various privacy risks, each corresponding to different configurations of background knowledge used in an attack. Initially, an attack is formulated and customized to use a specific category of background knowledge. Subsequently, a range of background knowledge configurations is examined, denoted as \(\{B_{1},\ldots,B_{m}\}\). For each configuration \(B_{k}\), all instances \(b\) within \(B_{k}\) are analyzed, along with their respective probabilities of re-identification. Finally, the maximum probability of re-identification  
Figure 1: Data flow in the privacy risk assessment within PRUDEnce. across all instances \(b\) within configuration \(B_{k}\) determines the privacy risk for the individual in that specific context.
## 3 An Automated Framework for DSM
### Data Preprocessing  
#### 3.1.1 Collection  
There are two types of data collection: automatic and manual. Automatic acquisition is the process of converting the physical quantities collected with sensors into electrical signals, sending them to the host computer, and then transferring them via TCP to the data-receiving module, where they are stored and analyzed. For structured data, the data-parsing process is not the same for different collectors, as different collectors correspond to different hosts, which have different data formats. The collector calls the corresponding data-parsing interface to obtain the collector number, the collection time, the measured value, and other information.  
The data collected during a manual inspection are manually entered into the datasheet with sensor numbers. The field data and project inspection photos are sent to the database via the GPRS network for further application. The automatic collection and manual collection of monitoring data are linked to the database by setting the collection type of the database table in accordance with different storage methods.
# Modeling of RC Moment Frame Retrofit with Mortar Walls Reinforced with Steel Wire Mesh
## 2 Materials and Methods
### Computational Fiber Models
#### 2.8.1 Materials  
A uniaxial nonlinear model with constant confinement has been considered for the concrete constitutive model, which follows the constitutive relationship proposed by [PERSON] et al. [19] and cyclic rules proposed by [PERSON] and [PERSON] [20]. The confining effects provided by lateral transverse reinforcement are incorporated using rules proposed by [PERSON] et al. [19]. These rules assume a constant confining pressure throughout the stress-strain range [15].  
Five parameters are required to define the concrete constitutive model in SeismoStruct. These parameters are compressive strength (f'c), tensile strength (ft), modulus of elasticity (Ec), strain at peak stress (\(\mathfrak{E}\)c), and specific weight (Y) (see Table 1). The parameters presented in Table 1 are the same values used in calculating the benchmark capacity curves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Column’s Concrete**} & \multicolumn{2}{c}{**Mortar Cover**} \\ \cline{2-5}  & **Value** & **Unit** & **Value** & **Unit** \\ \hline f’c & 20,593.96 & kPa & 13,729.31 & kPa \\ ft & 2059.39 & kPa & 1372.93 & kPa \\ Ec & 19,185,101.16 & kPa & 14,504,230.95 & kPa \\ Ec & 0.0021 & m/m & 0.0019 & m/m \\ Y & 24.00 & kN/m\({}^{3}\) & 24.00 & kN/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete constitutive model parameters used in SeismoStruct.  
Figure 10: Experimental specimen detailing.  
For the steel constitutive material, SeismoStruct includes the Menegotto-Pinto model [21]. Table 2 includes the parameters used in SeismoStruct to define the steel constitutive model. These parameters are modulus of elasticity (Es), yield stress (fy), strain hardening parameter (u), initial shape parameter of the transition curve (R0), calibration coefficients of the shape of the transition curve (a1; a2), calibration coefficients of isotropic hardening (a3; a4), fracture/buckling strain (Eult), and the specific weight (Y).
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 2 Materials and Methods
### Modelling
#### 2.2.3 Message Interdependence  
Interdependence is the mutual relationship between two variables. It is when both variables rely on each other. We seek to identify message interdependence from the classification model. It is essential to understand this from a queueing perspective, as one would need to know the order of messages and which, if any, messages are dependent on other messages. For example, if two messages come into the system, where the first message is a parent message, and the subsequent message is a child message, unravelling this information is key for stress testing a system. The service times may depend on the arrival times of the subsequent message or messages. When applying simulation techniques, a deciding factor would be how to handle these parent and child messages and what queueing model would be the best approach, such as a G/G/1 queue model. We attempt to understand if this parent-child relationship exists within the dataset using the classification model.
## 2 Overview of Artificial Intelligence and Machine Learning in Road Transport Systems
### Advance Approaches for Incident Detectors of Road Traffic Accidents Using AI and ML
#### 2.5.2 Incident Detector through Image Processing at Intersections  
This provides a method for incident detection that takes advantage of categorized vehicle interactions. A video-based automated incident detection at intersections (AIDI) system is built around this algorithm. Both in a driving simulator and in actual traffic, the AIDI system's effectiveness was successfully tested. Because the rate of detection depends on the particular method used, only the most efficient methods should be used. These include reports by witnesses calling rescue services on a mobile phone, information from CCTV cameras, incident detection using detection systems (generally using inductive loops or video processing techniques), incident detection using monitoring systems in vehicles (e.g., eCall), or traffic incident detection by specialized patrols supervising individual sections of the road (generally on dual carriageways) [61].  
Intelligent transportation systems must effectively and reliably recognize vehicles from photos and videos. New uses for on-road vehicle identification algorithms have been made possible by the advancement of computer vision methods and the ensuing accessibility of the video image data [53].  
Ref. [62], the usage of an omnidirectional (fisheye) camera for junction surveillance is explained. We offer a methodology for incident detection that includes the following three steps: (1) identify cars in the field of view of the camera (FoV); (2) monitor the cars entering and leaving the field of view; and (3) compare their trajectory with the expected path and look for any anomalies.  
The \"hybrid\" approach uses two distinct models: (1) the rapid video-based vehicle identification System (RVIS), an image processing technique-based tool that attempts to identify the number of vehicle axles and is particularly applicable to light traffic conditions; and (2) VEVID, a semi-automatic system that is particularly applicable to heavy traffic conditions [54].
## 2 Materials and Methods
### Data Analysis  
#### 2.5.1 Impact of Nematode Infestation  
A dataset comprising crop vigor ratings, SCN populations, 24 VI features, five REF, and SY measurements (lbs/plot) was generated for 108 plots and checked to ensure that all the data followed a normal distribution (histogram and Shapiro-Wilk test). In the first analyses, the vigor readings, vigor progression, and SCN populations (females and juveniles) for both susceptible and resistant varieties under different treatments were evaluated and compared using two sample \(t\)-tests. Remotely sensed crop vigor (VI) was then examined for differences between the susceptible and resistant varieties using the two sample \(t\)-test. Next, the Pearson correlations between SCN and VIs as well as SCN and yield were evaluated.
# A LoRaWAN Network Architecture with MQTT2 MULTICAST
## 3 Technical Overview of LoRaWAN
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Rate** & **Spreading Factor** & **Bit Rate (bps)** & **ToA for a 24B Packet (ms)** \\ \hline
0 & 12 & 293 & 1482.75 \\
1 & 11 & 540 & 823.30 \\
2 & 10 & 980 & 411.65 \\
3 & 9 & 1757 & 205.82 \\
4 & 8 & 3125 & 113.15 \\
5 & 7 & 5470 & 61.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data Rate configuration for a 125 kHz bandwidth in the EU868 ISM band. Figure 2: LoRaWAN device classes. Regarding frequency spectrum usage, LoRaWAN uses license-free sub-gigahertz radio frequency bands like 433 MHz, 868 MHz, and 915 MHz depending on the country. In Europe, LoRaWAN networks operates in the EU863-870 ISM band which allows up to eight channels with a 125 kHz bandwidth each, being three of them mandatory (868.1, 868.3, and 868.5 MHz). In order to respect the different regulations for these frequency bands, LoRaWAN devices are limited to a duty-cycle, being 1% in Europe due to ETSI (European Telecommunications Standards Institute) regulations.
# Reduction Potential of Gaseous Emissions in European Ports Using Cold Ironing
## 12 Conclusions  
The extensive application of cold ironing is a promising solution to reduce emissions, improve the air quality in European ports, and limit GHG production, depending on electricity production processes.  
Indeed, significant savings are achievable in emissions of SO\({}_{x}\), NO\({}_{x}\), PM and GHG. Nevertheless, various accompanying measures are recommended, such as investments in reliable power infrastructure, development of clear and consistent regulations, implementation of financial incentives for ship owners, and more intensive collaboration among stakeholders.  
The research carried out also highlighted some limits, such as the following:  
* A still restricted sampling group of ports with cold ironing in operation, partially limiting the generalizability of findings and feedback on implementation hurdles, resistance to adoption, etc.,
* A certain lack of data on emissions and energy demands, often considered sensible and not disclosable by the concerned stakeholders.  
Beyond these limits, the following set of further primary research developments finalized to remove remaining technical, socio-economical, and normative uncertainties and hurdles have been identified:  
* Enlargement of the sampling groups of ports in Europe and worldwide,
* Comparison of effectiveness and integration with other initiatives, such as electric or hydrogen-powered cargo handling equipment,
* Addressing concerns and educating stakeholders,
* Prediction of long-term impacts,
* Prediction of effects on local communities, such as health and well-being, including noise and vibration reduction,Assessment of regulations for large implementation in EU ports, such as identification of legal or policy obstacles,  
Evaluation of potential impacts of weather conditions.  
The distribution of the contributions and the corresponding authorship of the three authors in the various sections of the paper is equal. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 First Application: AE Events in a Simulated Fracture Process
### Results
We noticed the sharp decrease in the \(b\)-value as the AE activity increased, which agreed with other works, such as [39] and the seismic records of the Aquila region in Italy, as described in [58]. Figure 6 depicts the AE signals from the virtual sensors \(S_{1}\) and \(S_{2}\) captured during the test. Two events were highlighted: one at the beginning of the damage process and the very last event before the rupture. This information was complemented by the number of broken bars during the time interval of each event: (0.504, 0.512) for event 1 and (0.979, 0.987) for event 2. We observed that event 1 was associated with the rupture of only 1 bar, whereas several broken bars appeared for event 2. The link between event 2 and the space-time distribution of the broken bars is shown in Figures 7 and 8. Figure 5: Correlation of \(b\)-value variations with the AE amplitude distribution during the simulated fracture process. Figure 6: AE signals during the simulated damage process, with the bars broken throughout each highlighted interval. to the traveling of the elastic wave from the perturbation source to the virtual sensor. We noted the differences in the magnitudes due to the number of broken bars: only 1 for event 1 and about 1000 for event 2. We also observed that the signal from \(S_{1}\) suggested that event 2 could be regarded as 2 events separated by a brief period of silence. Figure 8 depicts the spatial distribution of energy increments for event 2, between the 0.980 and 0.986 instants of normalized time. We noted the high amount of elastic energy at the event's end, indicating its tendency towards continued propagation during the process. Moreover, two primary sources were identified in the event's buildup, reinforcing the concept that it could be treated as two separate events: one produced by the blue and light-blue broken bars and another from the yellow to the red broken bars. In Figure 9, the simulated damage process is illustrated through increments of kinetic and dissipated energies, i.e., \(\Delta E_{i}=E_{i}^{\,t+dt}-E_{i}^{\,t}\), where \(dt\) is the integration time-step, and \(E_{i}^{\,t+dt}\) and \(E_{i}^{\,t}\) are the energy values in 2 successive steps, with a sub-index indicating whether they were dissipated or kinetic energy. We noted that the peaks of the incremental kinetic energy coincided with the AE events indicated by the virtual sensors, which was expected because the AE events were directly related to the kinetic energy, i.e., the \"energy that makes noise\", according to [59]. Furthermore, as shown in Figure 9, the energy had dissipated in two ways: smooth or spasmodic. The latter was associated with avalanches of the dissipated energy that also contributed to the local kinetic energy increments. A thorough discussion of the correlation between the energy increments and the acoustic emission signals was found in [60].
# Hierarchical Parallel Evaluation of a Hamming Code
###### Abstract  
The Hamming code is a well-known error correction code and can correct a single error in an input vector of size \(n\) bits by adding \(\log n\) parity checks. A new parallel implementation of the code is presented, using a hierarchical structure of \(n\) processors in \(\log n\) layers. All the processors perform similar simple tasks, and need only a few bytes of internal memory.  
error correcting codes; Hamming code; parallel algorithms +
Footnote †: journal:
# Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication
## 1 Introduction
### Research Status
#### 1.1.2 Control Methods  
In recent years, there have been some studies on PZT FSM control methods. In [29], the author adopts the feedback control method of a double-proportional-integral-derivative (PID) closed loop, which reduces the tracking error by 46% compared to single-PID control when using a tracking signal with a frequency of 30 Hz. However, since this method improves hysteresis nonlinearity through the internal closed loop, it also reduces the response speed and bandwidth of the system. In [30], the author adopts the Smith pre-estimation method combined with the PID control method. When compared with traditional PID control, the system's error suppression bandwidth improves by 26%. However, since hysteretic nonlinear disturbance was not considered in this paper, the dynamic performance of the system could be further enhanced. In [31], the author proposes a robust adaptive control method that treats hysteretic nonlinearity as an external disturbance. This control method exhibits strong robustness in the presence of uncertain system parameters. However, it may not be well suited for the fine tracking control system, which has rapidly changing input signals.  
In fact, feedforward compensation based on a rate-dependent hysteresis model is an effective method for enhancing the performance of PZT FSM. However, according to the current research status, few researchers have examined the impact of this method on the bandwidth of a closed-loop tracking system. In satellite laser communication, factors such as micro-meteorite impacts, Earth and Moon gravity, external temperature changes, and other influences can lead to vibrations in the satellite platform. These vibrations can result in communication link instability and a reduction in communication rates. The OLYMPUS satellite, launched by the European Space Agency, tested the frequency range of satellite vibrations mainly from 1 Hz to 200 Hz, with the disturbance frequency range of greater impact being around 1 to 100 Hz [32]. Thus, in the laser communication fine tracking control system, the suppression bandwidth of system errors stands as a crucial performance index. Improving the error suppression bandwidth has been proven to be an effective method for enhancing the communication rate.
## 5 Discussion  
In this section, we provide an analysis of the proposed approach, described in Figures 3 and 4, to show its robustness and the strength of its security features. We start by describing the key generation approach using only physical layer methods, without having to carry the keys from the Control Center. Then, we discuss how the hybrid symmetric/asymmetric approach ensures data confidentiality. By adopting a hybrid approach combining the advantages of symmetric and asymmetric cryptography, the proposed approach provides data confidentiality in the harsh conditions considered, where the UAV performs multiple stops in remote areas without permanent internet connectivity. Afterwards, we analyze how the proposed approach allows for dealing with physical attacks on the UAV. Lastly, we clarify how the proposed approach allows cipher block chaining, physical layer security, and symmetric and asymmetric encryption techniques, to work harmoniously together to track goods delivery and secure the transaction logs. The purpose is to show the need for each of these techniques to provide the required levels of security in the absence of connectivity.
# High-impedance Fault Detection in DC Microgrid Lines Using Open-Set Recognition
## 4 Case Study
### Fault Detection Algorithm  
The OSR improved performance of the NN, allowing it to be used not only as a classifier but also as an outlier detector (HIFs in this case). Algorithm 1 summarizes the fault detection method. The offline part of the algorithm consists of the dataset creation, the classifier training, and determining the bounds of the samples per class. The bounds can be chosen in any manner that separates outliers. In this work, a fixed bound for both classes is proposed, but other methods can be used instead. The goal of the bounds is to capture the range in which the normal samples for that class occur. In the case shown in Figure 9, the bound for both classes could be any number below the lowest scoring sample of each class. Anything under this limit is considered an outlier.  
```
OFFLINE  1. Obtain dataset with normal operating conditions  (steady state, load changes)  2. Transform data from time to frequency domain  using FFT algorithm  3. Train NN-based classifier \(\mathcal{C}\) with OSR suggestions  4. Obtain responses to HIFs  5. Find MLS for normal samples (test set) and HIFs  6. Determine boundaries that enclose normal samples  of each class  ONLINE  1. Take \(N\) consecutive samples of current  measurement and create vector \(\mathbf{X}\)  2. Find \(\mathbf{MLS}\) and assigned class \(k\) from \(\mathcal{C}(\mathbf{X})\)  3. if \(\mathbf{MLS}\) outside boundaries for class \(k\):  HIF detected. else return to step 1.
```  
**Algorithm 1** High-impedance fault detection  
The online part describes how fault detection works. The method requires passing a vector of length \(N\), in this case 500, to the pre-trained classifier. The output of the classifier  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Type of NN** & **Complexity** & \
\begin{tabular}{c} **Detection** \\ **Accuracy** \\ \end{tabular} &
\begin{tabular}{c} **OSR** \\ **Capability** \\ \end{tabular} \\ \hline
[30] & BPNN & Medium & 95.31\% & No \\
[31] & CNN & High & 96.79\% & No \\
[32] & FFNN & Low & 70.09\% & No \\ Proposed & FFNN & Low & **99.99**\% & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the proposed method with existing methods.  
contains logits from which the maximum (MLS) is determined, along with the class that the classifier assigned to the input. The assigned class specifies the bounds against which the MLS is to be compared. If the MLS is outside the bounds, it is declared an outlier, in this case an HIF. If it is not, **X** is updated.