esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Ultra-High-Dose Rate Electron Beams with ElectronFlash [PERSON]
###### Abstract  
Purpose: The electron linac electronFlash installed at Institut Curie (Orsay, France) is entirely dedicated to FLASH irradiation for radiobiological and pre-clinical studies. The system was designed to deliver an ultra-high-dose rate per pulse (UHDR) (above \(10^{6}\) Gy/s) and a very high average dose rate at different energies and pulse durations. A campaign of tests and measurements was performed to obtain a full reliable characterizations of the electron beam and of the delivered dose, which are necessary to the radiobiological experiments. Methods: A Faraday cup was used to measure the electron charges in a single RF pulse. The percentage depth dose (PDD) and the transverse dose profiles, at the energies of 5 MeV and 7 MeV, were evaluated employing Gatchromic films EBT-XD for two Poly-methylmethacrylate (PMMA) applicators with irradiation sizes of 30 mm and 120 mm, normally used for in vivo and in vitro experiments, respectively. The results were compared with Monte Carlo (MC) simulations. Results: The measurements were performed during a period of a few months in which the experimental set up was adapted and tuned in order to characterize the electron beam parameters and the values of delivered doses before the radiobiological experiments. The measurements showed that the dose parameters, obtained at the energy of 5 MeV and 7 MeV with different applicators, fulfill the FLASH regime, with a maximum value of an average dose rate of 4750 Gy/s, a maximum dose per pulse of 19 Gy and an instantaneous dose rate up to \(4.75\times 10^{6}\) Gy/s. By means of the PMMA applicators, a very good flatness of the dose profiles was obtained at the cost of a reduced total current. The flatness of the large field is reliable and reproducible in radiobiological experiments. The measured PDD and dose profiles are in good agreement with Monte Carlo simulations with more than 95% of the gamma-index under the thresholds of 3 mm/3%. Conclusions: The results show that the system can provide UHDR pulses totally satisfying the FLASH requirements with very good performances in terms of beam profile flatness for any size of the fields. The monitoring of electron beams and the measurement of the dose parameters played an important role in the in vivo and in vitro irradiation experiments performed at the Institut Curie laboratory.  
FLASH-RT; FLASH electron linac; dosimetry +
Footnote †: journal: _Sciences_
## 4 Conclusions  
In this study, we used an acceleration sensor, angular velocity sensor, and geomagnetic sensor to estimate attitude, step count, step length, step direction, and position. As a result of our experiments, we conclude the following:  
* For attitude estimation, our proposed approach is able to cope with disturbance of the geomagnetic sensor and counteract the influence of other devices when estimating the attitude in a real environment.
* For step detection, although some errors may occur at the beginning and the end of walking, the estimation procedure was sufficiently accurate during walking.
* For step length estimation, the error was limited to about \(\pm\)10 cm at the maximum, and the accuracy was about \(\pm\)5 cm under normal conditions.
* For step direction estimation, the error during hand-swinging walking was about \(\pm\)6 degrees, while the error in hand-held walking was estimated with an accuracy of about \(\pm\)2 degrees.
* For position estimation, although it was not possible to estimate the position precisely, it was possible to estimate the position with an accuracy of about 1.5 m for the trajectory evaluated in this study. In addition, the shape of the trajectory could be discriminated sufficiently.
* For the position estimation with disturbances from other devices, although the influence could not be completely eliminated, the error in the estimation results was significantly reduced.  
In conclusion, we found that trajectory estimation is approximately feasible for hand-held walking and hand-swinging walking, and we believe that our results are a sufficient, if not perfect, solution for the problem of unconstrained self-position estimation.  
The following are among of the issues to be addressed in future research:  
* Algorithm for determining the number of steps taken at the beginning and end of a walk.
* Modification of the algorithm for estimating step length during hand-held walking.
* Relationship between acceleration and step direction during step direction estimation and related modification of algorithm.
* Investigation of estimation algorithms for walking methods other than hand-held walking and hand-swinging walking.
* Estimation of errors due to individual differences and adjustment of parameters (experiments with multiple participants).  
**Author Contributions:** Conceptualization, Y.I. and N.K.; methodology, Y.I. and N.K.; software, Y.I.; validation, Y.I. and N.K.; formal analysis, Y.I. and N.K.; investigation, Y.I. and N.K.; resources, Y.I.; data curation, Y.I.; writing--original draft preparation, Y.I; writing--review and editing, Y.I. and N.K.; visualization, Y.I.; supervision, N.K.; project administration, Y.I. and N.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: 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.
## 2 Materials and Methods
### Ship-Based GNSS Meteorology System Architecture  
The system on board each ship was equipped with a commercial geodetic station for GNSS signal detection and with a weather station. A 3D choke ring GNSS antenna able to significantly mitigate signal multipath effects was connected with a scientific receiver with a capacity of more than 500 receiving channels. The antennas were installed in a high ship position in order to be as free as possible from obstacles along the signal paths, making artificial signal reflections negligible for non-horizontal (or non-sub-horizontal) paths. The receivers were installed in environments protected from atmospheric agents. These GNSS stations were able to simultaneously detect signals from all global satellites constellations (i.e., Galileo, GPS, GLONASS, BeiDou).  
Surface meteorological observations were collected using an automatic system comprising a weather station and an attitude heading reference system (AHRS). The latter used an inertial measurement unit (IMU) consisting of micro-electro-mechanical system (MEMS) inertial sensors to measure the angular rate, the acceleration, and Earth's magnetic field to derive an estimate of the ship's attitude. The heading and speed of the ship were then used at any time to transform relative (to the ship) wind vectors to absolute wind vectors, i.e., relative to the Earth's reference system. The set of atmospheric measurements was completed by collection of surface pressure, temperature, humidity and precipitation rate (both rain and hail) data. The processing and control system acquired all the measurements collected from the GNSS receiver and the weather station, undertook some preliminary processing and sent the data to the ground center through a mobile communication 4G system, consisting of a router and an external omnidirectional antenna. Both weather data (in a simple text format) and GNSS receiver data (in standard Receiver INdependent EXchange: RINEX format) were aggregated hourly and transmitted in real-time, or quasi real-time, according to the spatial coverage of the 4G cellular network connectivity. A schematic diagram of the system architecture is shown in Figure 1.
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
## 7 Evaluation
### QoS Monitoring Scenario  
Here, we have tested the capability of QoS monitoring over FD-SDWMN. Figure 9 depicts a simple scenario of application of video streaming over two local domain's architecture, where A.H1 is the server that streams a video (size = 400 MB and length = 10 min) to the client B.H2. Initially, QoS properties of the switches' ports were set as follows: 5 Mbit for bandwidth and 2500 us for the delay. During the video streaming, QoS properties (bandwidth, delay, jitter and packet loss) were changing due to the consumption of resources. The properties gathered by monitoring agent at controller A proves that the aggregated mechanism can also effectively monitor QoS changing over LLDP. Figure 10 shows fluctuations curves (QoS over LLDP frontend GUI snapshots) of the QoS properties changes. The Figure 10a-d present the real-time statistics of QoS properties bandwidth, delay, jitter and packet loss, respectively, of interface B.S4-eth2 as instance (i.e., the second Ethernet interface eth2 of router s4 in the local domain B) monitoring from the controller B, then the controller A controller aggregates this information in its aggregated-DB, and the monitoring process continues for all switch interfaces of the domain. Duration of 15 s was used as the default interval of LLDP.  
Despite QoS monitoring over LLDP adding extra bytes to customize the optional TLVs of LLDP packets for QoS properties collecting, it has no significant difference in network traffic compared to that caused by original packets such as those used for topology discovery. To evaluate this increase of network traffic, we compared between pure LLDP packets (used for topology discovery) and customized LLDP packets (used for QoS monitoring). Both scenarios run separately during video streaming in the topology stated in Figure 9. Network traffic was captured using Wireshark, to measure the total network traffic and only LLDP packets (packet filter set to \"LLDP\") for 15 min. The evaluation
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Cooperative Games
#### 3.1.5 Comparison of the Protocols Based on the Technical Strengths and Weakness  
The technical strengths and weaknesses of the protocols discussed in Section 3.1 are comparatively summarized in Table 4 for readers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Protocol** & \multicolumn{2}{c}{**Technical Strengths**} & \multicolumn{2}{c}{**Technical Weaknesses**} \\ \hline CH-C-TEEM 1 [31] & \(\bullet\) & Cooperation among CHs leads to energy efficiency. & \(\bullet\) & Routing metrics like packet delivery ratio, end-to-end delay are not considered. \\ \(\bullet\) & Consideration of network security via elimination of the malicious nodes. & \(\bullet\) & Comparison with more of the existing state-of-the-art routing techniques in terms of important routing metrics. \\ \hline RCFR 2 [35] & \(\bullet\) & Reliable data delivery and route maintenance. & \(\bullet\) & No consideration for node failure scenario. \\  & \(\bullet\) & Integration with other protocols for better performance is possible. & \(\bullet\) & No consideration for the downstream links \\ \hline Assignment game & \(\bullet\) & Extremely energy-efficient and can perform well when a lot of devices & \(\bullet\) & No consideration for the downstream links \\ approach [34] & \(\bullet\) & Avoidance of selfishness among the nodes and building cooperation for better routing to save energy. & \(\bullet\) & Not suitable for distributed architecture. \\ \hline \hline \end{tabular}  
* Cluster Head Cooperative Trustworthy Energy-Efficient multiinput multioutput; 2 Reliable Coalition Formation Routing; 3 Probabilistic Routing Based on Game Theory.  
\end{table}
Table 4: Comparison of routing protocols based on cooperative games in terms of technical strengths and weaknesses.
# Tracking Mobile Sinks via Analysis of Movement Angle Changes in WSNs
###### Abstract  
Existing methods for tracking mobile sinks in Wireless Sensor Networks (WSNs) often incur considerable energy consumption and overhead. To address this issue, we propose a Detour-Aware Mobile Sink Tracking (DAMST) method via analysis of movement angle changes of mobile sinks, for collecting data in a low-overhead and energy efficient way. In the proposed method, while a mobile sink passes through a region, it appoints a specific node as a region agent to collect information of the whole region, and records nodes near or on its trajectory as footprints. If it needs information from the region agent in a future time it will construct an energy efficient path from the region agent to itself by calculating its own movement angles according to the footprints, as well as getting rid of detours by analyzing these movement angles. Finally, the performance of the tracking method is evaluated systematically under different trajectory patterns and footprint appointment intervals. The simulation results consolidate that DAMST has advantages in reducing energy consumption and data overhead.  
wireless sensor networks; tracking; mobile sinks; trajectory; movement angles +
Footnote †: journal: Sensors
# An IP Multimedia Subsystem Services Proxy Gateway Based on a JAVA Dynamic Module System
## Abbreviations  
Abbreviations are arranged in alphabetical order.  
3 GPP The 3 rd Generation Partnership Project AS Application Server CSCF Call Session Control Function DLNA Digital Living Network Alliance HSS Home Subscriber Server I-CSCF Interrogating-Call Session Control Function ICT Information and Communication Technology IMS IP Multimedia Subsystem IoT Internet on Things IMPI IMS Private User Identity IMPU IMS Public User Identity JMF Java Media Framework JADE Java Agent DEvelopment NGN Next Generation Network OSGI Open Service Gateway Initiative P2P Peer-to-Peer P-CSCF Proxy-Call Session Control Function RTT Round-Trip Time RTP Real-Time Transport Protocol REST Representational State Transfer SIM Subscriber Identity Module SIP Session Initiation Protocol S-CSCF Serving-Call Session Control Function VolP Voice over IP USIM Universal Subscriber Identity Module UI User Interface UE User Equipment XML Extensible Markup Language
## 3 Indoor Positioning Systems
### Systems Based on Radio Frequency
#### 3.1.1 Range-Based
Traditionally, the log-distance trajectory loss model is used, where the attenuation (in dB) is equivalent to the logarithm of the distance covered [53], that is:  
\[RSSI=P_{1m}-10\alpha\log_{10}d-\gamma \tag{2}\]  
where \(d\) is the distance between the receiver and the transmitter, \(P_{1m}\) is a reference power value measured in dBm at a distance of 1.00 m from the transmitter, and \(a\) is the exponent of the loss of path \(\gamma~{}\sim N\left(0,~{}\sigma_{\gamma}^{2}\right)\) that models the effects produced by the shadow. Note that to obtain the model parameters, that is, \(P_{1m}\) and \(\alpha\), it is necessary to perform a calibration. The calibration process consists of collecting the RSSI at predefined positions, with known distances to the anchor nodes, which is usually done using regression methods. Once calibrated, the distance is estimated according to the path loss model using the maximum likelihood estimator (MLE), which for the case of the distance estimate is [46]:  
\[d=10^{\frac{RSSI-P_{1m}}{10\alpha}} \tag{3}\]  
As in the case of time-based location algorithms, the user's position is estimated by combining distance information from various anchor nodes using a lateration method [45]. RSSI-based methods are attractive because of their simplicity, as RSSI measures are supported natively by most transceivers. Unfortunately, the variability of the Wi-Fi channel, together with the signal attenuation caused by building structures such as walls, or other elements such as the human body itself, introduces errors into the distance estimation and makes RSSI-based location algorithms less accurate than time- or angle-based algorithms [46]. An example of an RSSI-based localization system can be found in the work of [PERSON] et al., where the authors use correlations between RSSI samples in nearby locations to keep the user on a predefined path, adapting the value read to the propagation changes between areas of the same building [45]. [PERSON] et al. presented another example of using WSN, applying a cooperative method to locate nodes in an indoor environment [46]. Figure 3: Method of trilateration between nodes S1, S2, S3. In several studies, the authors use multiple receivers to improve the accuracy of the model, which, in its raw state, varies in RSSI measurements and provides approximate location estimates for the user [42]. Two of the approaches to indicate the efficiency in recovering a pre-defined location or path are the Cramer-Rao limit calculation in Gaussian and log-normal models [57]. RSSI-based systems can be easily scaled for large areas and multiple users due to the simplicity of their signal measurements. A feature that facilitates the application of the model based on RSSI is that measurements can be obtained without the need to be part of the network. Besides, the cost of the system's infrastructure is minimal if existing Wi-Fi networks are used. Unfortunately, the accuracy provided by RSSI-based systems is low and is negatively affected by the distance between the transmitter and the receiver.
# Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model
## 4 Simulation and Results
### Simulation Setup  
Because the neural network based on LSTM is one of the common schemes for passenger flow prediction, we take the LSTM network without migration learning as the control group. In this experiment, we compare the results of using transfer learning and not using transfer learning to prove the effectiveness of the experimental design.  
For this model, we build it through the following steps:  
1. All data sets were randomly divided into a training set and test set, accounting for 70% and 30% respectively;
2. The normal passenger flow training set data are used as input to train the network, and the normal passenger flow test set is used to verify the network performance to obtain the normal passenger flow prediction network \(K\);
3. Freeze the LSTM part of network \(K\), use the training set of emergency passenger flow, train the network again based on network \(K\), and then use the test set of emergency passenger flow to verify the network performance, and get the prediction network \(K^{\prime}\)The trigger uses Selu and Adam optimizer. Considering that there are 17 samples in a whole day, the learning step is set to 34, and other parameters are adjusted according to experience and learning effect. The above-mentioned weighted average percentage error function was used for model evaluation.
## 4 Capability on Obstacle Avoidance
### Comparison Front Wheel Steering (FWS) and Rear Wheel Steering (RWS)
#### 4.1.1 Understanding of Vehicle Posture on Avoidance Behavior  
Although there is no difference between FWS and RWS in Equation (6), which describes vehicle condition on steady circle, RWS vehicles show a clearly inward posture (rear end pushed out), as shown in Figure 17.  
An image of vehicle posture on left direction avoidance is shown on Figure 18. The rear vehicle end moves to right at first in RWS, and this causes a delay in the lateral displacement \(LD\) of RWS vehicles.  
\[\rho\;=\;\left(1-\frac{m}{2l^{2}}\frac{l_{f}K_{f}-l_{r}K_{r}}{K_{f}K_{r}}v^{2} \right)\frac{l}{\delta} \tag{6}\]  
\(\rho\): turning radius  
\(m\): vehicle mass  
\(l\): wheel base  
\(l_{f}\): front wheel base  
\(K_{f}\): front cornering power  
\(l_{r}\): rear wheel base  
\(v\): velocity  
\(\delta\): tire steer angle  
Figure 17: Vehicle postures on steady-state cornering.
# A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
## 2 Methods
### Detection of Adversarial Samples
#### 2.4.3 SafetyNet  
[PERSON] et al. [47] proposed an adversarial sample detection architecture called SafetyNet which uses RBF-SVM as the adversarial detector with features extracted from the outputs of later activation layers. The hypothesis of the method is that adversarial samples produce different patterns of activation in late stage than those produced by normal samples. There are two different kinds of features used in [47]: raw features extracted directly from activation denoted as _DeepF_ and discrete features obtained by quantizing activation as discrete levels denoted as _DiscF_. _DiscF_ forces the attacker to solve a hard discrete optimization problem [47]. Both _DeepF_ and _DiscF_ are considered in our study.
## 3 Results and Discussion
### Microbiological Profile of Table Olive and Brine Together  
Yeasts were dominant over lactic acid bacteria during most of the fermentation period (Figure 2). Changes in viable numbers of yeasts (between 6 and 7 log CFU/mL) were indeed not very significant throughout the process. On the other hand, a significant increase in the number of LAB (from 3 to 7 log CFU/mL) throughout 7 months was observed, with a plateau thereafter. As expected, such increase in viable LAB runs along with pH decrease (Pearson, r = 0.999 and \(p\) = 0.000). In other words, brine starts with a richer population of yeasts but ends with a microbial population of yeasts and LAB similar in numbers--with pH and salt playing a major role in facilitating the survival of pH- and salt-resistant strains of LAB. The microbial framework depends on olive cultivar, crop management, fermentation style, and technology processing type [4]; the complex olive/brine matrix can, in turn, affect the formation of the microbial consortium responsible for fermentation, and thus influence the characteristics of the final product. Therefore, it is hard to compare microbial evolution in different types of table olives. For instance, yeasts are the main microorganisms involved in the fermentation of _Arbequina_ table olives, but lactic acid bacteria become important when such olives undergo maturation prior to packaging--thus unfolding a trend quite similar to our results; on the other hand, the dominant microflora from _Picual_ table olives are LAB, although yeasts can also be detected to significant numbers [6].  
The large variability in LAB counts was also anticipated in view of the differences among protocols followed by the various producers. For instance, no counts were obtained for producers D, E, and F--this fact is certainly related to the too frequent changes in water (Table 1). For producers B and C, the counts of LAB were rather unstable and could not be perceived any more beyond 3 months.  
Figure 1: Changes in (**a**) salt, (**b**) pH, and (**c**) total tittatable acidity in brine during spontaneous fermentation of _Cobranposa_ table olives.  
Additionally, the disappearance of food-borne pathogens is somewhat guaranteed by the antimicrobial activity of some species of yeasts and LAB, further to the previously discussed harsh environmental conditions derived from salt concentration and pH [40]. The antimicrobial action of yeast strains is related to mycocins, or yeast killer toxins; the latter are toxins active against members of the same species, or closely related species--with activities analogous to those of bacteriocins in bacterial species [41].
## 2 Backgrounds
### Performance Evaluation Metrics
#### 2.4.1 F1-Score  
For a classification task, the precision and the recall with respect to a given class \(c\) are defined as \(Precision_{c}=\frac{TP_{c}}{TP_{c}+TP_{c}}\) and \(Recall_{c}=\frac{TP_{c}}{TP_{c}+FN_{c}}\), respectively, where TP, FP, and FN denote true positives, false positives, and false negatives, respectively. The macro-averaged F1-score is the average of the harmonic means of precision and recall of each class, as follows:  
\[F1-macro=\frac{1}{|C|}\sum_{c\in C}\frac{2 Precision_{c}\text{-}Recall_{c}}{ Precision_{c}+Recall_{c}}, \tag{6}\]  
where \(C\) is the set of all classes.