esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### MEC System and Fed-Batch Fermentation  
The Chemistry Department of Michigan State University (East Lansing, MI, USA) provided the MEC system used in this study, which contained anode and cathode compartments kept separate through the use of a cation-selective membrane (Nafion, NY, USA). Fine carbon felt was used for both anodes and cathodes, which were 70 mm \(\times\) 50 mm \(\times\) 5 mm, with 10.6 cm\({}^{2}\)/g total surface area. A 1 mm, 1.0 \(\Omega\)/cm platinum wire (Sigma-Aldrich, St. Louis, MO, USA) was used to connect the anode and cathode. A power supply (PMC 1000/DC, AMETEK, Santiago, MN, USA) was used to provide potential to this MEC system. The volume in both the anode and cathode compartments was 280 mL. Inoculations before experimental fermentation were controlled at a 5% ratio in the cathode compartment. _A. succinogenes_ cells and catholyte were added into the cathode compartment, with CO\({}_{2}\) added at a rate of 25 mL/min. The catholyte was composed of (per L): glucose, 50 g; NaHPO\({}_{4}\cdot\)12H\({}_{2}\)O, 31.5 g; NaHCO\({}_{3}\), 10.0 g; NaHPO\({}_{4}\cdot\)2H\({}_{2}\)O, 8.5 g; and YE, 5.0 g. The anolyte was composed of (per L) 5.8 g NaCl, 15.6 g NaHPO\({}_{4}\cdot\)2H\({}_{2}\)O, and 35.8 g NaHPO\({}_{4}\cdot\)12H\({}_{2}\)O. In addition, the catholyte was supplemented with 0.0288 g/L NR (determined by experiment) that had been subjected to 0.22 nm syringe filtration, and the anolyte was supplemented with 0.02% dithiothreitol that had been subjected to 0.22 nm syringe filtration. Prior to sterilization, 10 M NaOH was used to adjust the catholyte pH to 7.0. Once the fermentation reaction began, 1% alkaline magnesium carbonate was added to the catholyte in order to maintain pH balance. As a reference electrode, an Ag/AgCl electrode was used. The potential between the reference electrode and the cathode electrode was defined as a constant potential. No potential was supplied to the control non-electric MEC system, in which the open circuit voltage was monitored.  
During fed-batch MEC, the catholyte initially contained the following (per L): NaHPO\({}_{4}\cdot\)12H\({}_{2}\)O, 31.5 g; NaHCO\({}_{3}\), 10.0 g; NaH\({}_{2}\)PO\({}_{4}\cdot\)2H\({}_{2}\)O, 8.5 g; YE, 15.0 g; and NR, 0.0288 g. The initial glucose concentration was 50 g/L, and when the glucose concentration fell to \(<\)5 g/L, 50 g/L glucose was added, while alkaline magnesium carbonate served to maintain pH for the 48 h fermentation duration.
# An Enhanced Message Distribution Mechanism for
## 3 Motivation
### Impact between Apps  
When the SDN controller has a large number of registered apps, although the time complexity of a single app is low enough, the overall processing latency will increase due to various reasons, such as thread switching or event triggering. We now present empirical data to quantify the impact of Apps' quantity on SDN controllers. The environment of the simulation experiment is two virtual machines that are running on a desktop computerwith a 3.6 Ghz quad core and 16 GB of memory, one of which runs the controller and the other runs bench. We choose representative Ryu and ONOS as test controllers and study three Apps for contrast.  
1. simple_switch: an app for simple layer 2 switch, which aims to route the packet. This app is preset in the Ryu project;
2. Hedera: the traffic-engineering SDN app. We rewrite this app using Python for adapting Ryu controller;
3. Reactive Forwarding: A native ONOS app for routing just like simple_switch; and,
4. redundancy: a blank program, which ends after executing 10 times plus-one calculations.  
In Figure 2, _simple_ and _Hedera_, respectively, represent the simple_switch app and dHedera app running by Ryu controller, and only one app is running at one time; \(RF\) represents the Reactive Forwarding app running alone by ONOS; and, \(sth.+red\) means that the given app and the redundancy app are running at the same time.  
The response latency of simple analog layer 2 switch app on ONOS is approximately 80 us lower than that on Ryu. In addition, when the number of switches increases, the performance of ONOS does not decreases obviously. We observe that, just adding one blank registration app still causes Ryu's response latency to rise up by 31.4% (from 97.028 us to 127.52 us when there are 32 switches). ONOS's situation is slightly better, but it has also increased by 20.5% on average. It is noted that Figure 2 is just a small test to verify our conjecture; greater data comparison is seen in Section 5.1.
# Blockchain and IoT Convergence--A Systematic Survey on Technologies, Protocols and Security
## 4 Internet of Things
### IoT Weaknesses  
#### 4.1.1 Cloud Infrastructure  
To date, the technology behind IoT systems, simple by its very nature, has led to complex protocols with conflicting configurations. Almost all of today's Internet of Things ecosystems are based on centralized systems. Centralized clouds and network equipment involved in these architectures are exponentially expensive as the number of devices increases. In this centralized model, IoT devices are authenticated, identified and communicate their data in real time or semi-real time mode with the cloud. In a highly connected smart-city scenario, where private homes, offices, streets with their traffic lights, transportation and pedestrians produce a mass of data every second, the cloud infrastructure need to scale, leading to a price increase. As the environments become smarter, the higher the cost of this type of infrastructure will be. IoT devices are subject to various types of vulnerabilities and often if a device connected to the cloud infrastructure is breached, the whole infrastructure is at risk. Below is a list of the main security issues affecting the cloud infrastructure related to IoT devices:  
1. Wrapping attack: This attack occurs by duplicating the user credentials during the log in process, and the SOAP2 messages that are exchanged during the connection setup between the web browser and the server are modified by the attackers [16].
2. Eavesdropping: under the term eavesdropping fall the techniques used to intercept communications that occur within a channel established between two authorized users [16].
3. Flooding attack/DOS attack: The goal of a DOS attack is to consume all the available resources of a server to make the system unresponsive to legitimate traffic [16].
4. Data Stealing problem. This type of attack involves hacking the data and security of cloud systems by stealing system access credentials.
5. Man-in-the-Middle Attack (MITM): in this case the attacker succeeds in gaining access to the communication channel between two legitimate users, being able to both intercept and modify the information without making anyone aware of it [16].
6. Reflection Attack: This type of attack is perpetrated in challenge-response type systems that use the same communication protocol in both directions. The idea behind this type of attack is to trick the victim by asking him for a solution (response) to his own challenge [16].
7. Replay Attack: The replay attack is a form of cyberattack that targets computer networks in order to take possession of an authentication credential communicated from one host to another, and then propose it again by simulating the identity of the issuer. Usually the action is carried out by an attacker who interposes himself between the two communicating sides or from a spoofed IP [16].
8. Brute force/Dictionary attack: in a brute force attack, a series of attempts are made to guess the credentials of a certain system, based on information generated through specific dictionaries or by specific rules.
# A Female Multi-Body Head-and-Neck Model and Validation for Rear Impact Simulations
## 2 Volunteer Test  
Three healthy 35 th percentile (in terms of height and mass) female volunteers were subjected to rear impacts in the JARI tests [29]. The average stature and mass were 160.6 cm and 50 kg, respectively, and the average age was 22.5 years. Figure 1 provides an illustration of the inclined sled apparatus used in these tests. A rigid seat that had no head restraint was used, and the bottom of the sled had an angle of 10 deg from the horizontal. The seatback angle was 20 deg from the vertical.  
Figure 1: Volunteer test set-up.  
To simulate rear-end impacts, the sled was released from the top of the inclined rails, and a hydraulic damper at the bottom was used for deceleration. The delta-V (change in velocity) and the peak acceleration of the sled were 10 km/h and 37 m/s\({}^{2}\), respectively, as a result of the impact. The sled impact velocity was 7.9 km/h. The set-up was constructed to simulate rear-end collisions at low speeds with the resulting crash pulse illustrated in Figure 2[29].  
During the experiments, volunteers assumed a normal seating position on the rigid seat, as shown in Figure 1. Two biaxial accelerometers were secured to the head rig in order to record the acceleration of the head center of gravity (C.G.). To find the T1 accelerations, another biaxial accelerometer was adhered to the skin surface over the T1 vertebra. Target markers were attached to the upper sternum, iliac crest, auditory canal, and skin surface over the T1, and a high-speed video camera tracked the motion of the volunteers.  
The sled coordinate system, which is attached to the sled, is denoted by \(\mathcal{F}_{0}\) as shown in Figure 1 and its \(x_{0}\) axis is parallel to the ground (i.e., horizontal). The head anatomical coordinate system is denoted by \(\mathcal{F}_{1}\). The head anatomical coordinate system was attached to the head with its origin at the head center of gravity, whose \(x_{1}\) axis is positive towards the front, being parallel to the Frankfort line, while its \(z_{1}\) axis is positive in the downward direction. The T1 accelerometer coordinate system is denoted by \(\mathcal{F}_{2}\) whose \(z_{2}\) axis is on the skin lying on the spinous process of T1. The T1 anatomical coordinate system (denoted by \(\mathcal{F}_{3}\) in Figure 1) was located such that its origin was at the center of T1, and the center was defined between the T1 and sternum skin markers. In the T1 anatomical coordinate system, the \(x_{3}\) axis signifies forward; the \(z_{3}\) axis was defined along a line passing through the occipital condyles (O.C.) and oriented downward in the initial condition of the volunteer before the impact, as shown in Figure 1. The coordinate systems \(\mathcal{F}_{2}\) and \(\mathcal{F}_{3}\) move almost together with T1.
# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 2 Materials and Methods
### Case Study on Sentinel-2
#### 2.2.5 Description of the Generated Dataset  
A set of simulated images has been generated to train several classifiers to discriminate between seawater and patches of debris. In total, 1110 images describing various scenes have been generated, with 15 different materials, be it plastic (e.g., bags or bottles) or not (e.g., wood). For each material, 74 images have been generated by changing the shape, the size, the rotation and the fraction of coverage. For instance, the smallest circular shape is 1 m radius and the biggest one is 75 m radius; the fraction of coverage goes from 40% to 90%. The lower bound was chosen by calculating the ratio between, on the one hand, the plastic response from one of the images of PLP, and on the other hand, a simulated plastic with 100% coverage.  
The final dataset is composed of the shuffled collection of the pixels of all the images. One pixel is represented by a vector made of the 13 reflectance values of the S2 spectral bands, and a Boolean that specifies if the pixel contains debris or not. Based on the material type, we created 3 classes. Class 0 is for seawater pixels, class 1 is for pixels containing plastic, and class 2 for pixels containing wood. In total, the dataset is composed of 166,619 pixels: 150,000 pixels of seawater, 13,291 pixels of plastic and 3328 pixels of wood. It is then a rather unbalanced dataset, similar to the S2 data.
## 2 Ensemble Neural Network and Signal Graph-Based SEI  
### Signal Graph and Improved Graph Convolution  
#### 2.1.1 Signal Graph  
An emitter signal can be input into a neural network directly or after being converted into another form. In this paper, the raw signal without any processing is called a signal sequence, which has two channels (i.e., components I and Q of the emitter signal) and a length of \(N\). A signal sequence is data in Euclidean space; its elements are arranged as per the relations in the time sequence, and the geometric position relationships between the elements are specific. In order to represent the relationship between the nodes of a signal sequence, adjacent edges were added between the nodes to constitute a signal graph and obtain the signal sequence transformed from a Euclidean space to a non-Euclidean space. Then, a non-Euclidean feature of the signal can be extracted from the signal graph by graph convolution.  
Figure 1 shows a signal sequence with a length of 10. The signal graph is constructed by adding adjacent edges between the nodes. After that, the nodes in the signal graph do not have the positional relationship in the signal sequence anymore but only the connection relationship.
## 4 Software
### Components  
Apache Flume offers multiple components: Source, Channel, Sink, Interceptors, Channel Selectors, and Sink Processors [21].  
The first of the three base components to be defined is Source, responsible for data input. It is necessary to expose that it has a major limitation, only extracting and supporting unstructured data (events) [19; 21].  
The Channel component is the intermediate component that handles data storage in the transition from the Source to the Sink. The operation performed resembles a queue [19; 21].  
To conclude the base components, the Sink component is the component responsible for sending data to the destination. Usually, the destination is the HDFS, but other options, like HBase, Hive, and Elastic Search, are supported, as well [19; 21].  
Regarding additional components, the Interceptors are used to modify or review the events transmitted between the source and the channel [22].  
The Channel Selectors are useful for identifying which channel to select for data transmission when there are multiple channels. This component is typified according to the type of channel. The Default Channel Selectors, often known as Replicating Channel Selectors, are responsible for replicating all events on each of Flume's channels. Multiplexing Channel Selectors use the address provided in the event header to decide the channel to which the event should be sent [21].  
Finally, the Sink Processors are responsible for calling a specific selector from a selected group of sinks. To do this, fail-over paths are created for the sinks. These components are commonly used for load balancing events on multiple collectors [21; 22].  
Figure 1 illustrates the architecture that is obtained when all of these components are in use.  
Figure 1: Flume architecture. Adapted from Reference [21].
# Cytoprotective-Antioxidant Effect of _Brunfelsia grandiflora_ Extract on Neuron-like Cells
## 3 Results
### Molecular Assay
#### 3.5.2 Biomarkers of Oxidative Stress  
In Figure 5, an induction effect of the expression of the \"master regulator\" NF\(\kappa\)B caused by t-BOOH was determined to be greater than 3.5-fold (Figure 5A); this effect was significantly reduced with concentrations of 100 and 200 ug/mL of _B. grandiflora_. On the other hand, the antioxidant biomarkers SOD (Figure 5B) and NRF2 (Figure 5C) were significantly reduced by t-BOOH, and the molecular expression of these biomarkers was fully recovered at the highest concentration of _B. grandiflora_ extract.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **(A)** \\ \hline \hline  & **GR Levels** \\ \hline \hline  & **mU GR/mg Prot. \(\pm\) SD** \\ \hline Control & 3.94 \({}^{\text{a}}\)\(\pm\) 0.29 \\
50 ug/mL BG & 4.57 \({}^{\text{b}}\)\(\pm\) 0.53 \\
100 ug/mL BG & 4.92 \({}^{\text{b}}\)\(\pm\) 0.67 \\
200 ug/mL BG & 5.27 \({}^{\text{b}}\)\(\pm\) 0.26 \\ \hline \hline  & **(B)** \\ \hline  & **mU GR/mg Prot. (Mean \(\pm\) SD)** \\ \hline  & **Cotereatment** & **Pretreatment** \\ \hline Control & 3.94 \({}^{\text{a}}\)\(\pm\) 0.29 & 3.94 \({}^{\text{a}}\)\(\pm\) 0.29 \\ t-BOOH & 13.69 \({}^{\text{b}}\)\(\pm\) 0.78 & 7.71 \({}^{\text{b}}\)\(\pm\) 1.02 \\
50 ug/mL BG + t-BOOH & 9.35 \({}^{\text{c}}\)\(\pm\) 0.49 & 5.06 \({}^{\text{c}}\)\(\pm\) 0.31 \\
100 ug/mL BG + t-BOOH & 7.00 \({}^{\text{d}}\)\(\pm\) 0.30 & 3.72 \({}^{\text{a}}\)\(\pm\) 0.10 \\
200 ug/mL BG + t-BOOH & 5.25 \({}^{\text{e}}\)\(\pm\) 0.44 & 4.79 \({}^{\text{a}}\)\(\pm\) 0.42 \\ \hline \hline \end{tabular} Data are from 3 replicates. Superscripts (a, b, c, d, e) indicate statistically significant (\(p<0.05\)) differences between groups.  
\end{table}
Table 4: (**A**) Direct effect of different concentrations of _B. grandiflora_ extract on GR levels in SH-SYSY cells after 22 h treatment period. (**B**) Effect of _Brunfelsia grandiflora_ extract (co- and pretreatment) on GR concentration (mU GR/mg protein) in SHSY-SY cells.  
Figure 4: Effects of _B. grandiflora_ extract (50–200 \(\upmu\)M) on cell death-related mRNA levels (**A**) BAX, (**B**) BNIP3, and (**C**) APAF1 in SH-SYSY cells after co- (green bars) and pretreatment (blue bars). mRNA levels were normalized with respect to control (mean \(\pm\) SEM) of three replicates. Different letters (a, b, c, d) show significance (_p_ < 0.05) between groups. \(\uparrow\) represents increase (%) compared with control and \(\downarrow\) represents decrease (%) compared with t-BOOH.
## 4 Discussion
### Weibull Analysis  
Statistical evaluation of the breakdown strength values obtained for solid insulation materials produced with a 3D printer was performed with the Weibull distribution, as recommended in IEC-62539. Weibull parameters were determined with the White method [42]. The expression of the Weibull cumulative probability function is presented in Equation (1).  
\[F(V)=1-e^{(\frac{V}{\pi})^{\beta}} \tag{1}\]  
Here, \(V\) represents the breakdown strength to be calculated as probability [kV], \(\alpha\) represents the scale parameter [kV] that provides the equality of \(F=63.2\%\), and \(\beta\) represents the shape parameter, which is the slope of the Weibull curve.  
In these expressions, the \(\alpha\) parameter is the Weibull scale parameter. It represents the characteristic breakdown strength of the dataset obtained from the relevant electrode arrangement and voltage rise rate of the insulating materials. \(\beta\) is the shape parameter and is the slope of the regression curve obtained with the experimental data. It represents the width of the value range that the breakdown strength values will take. Here, statistical distribution analysis was performed for cylindrical insulating material specimens. The data obtained as a result of Weibull distribution analysis are given in Table 4.  
When the data in Table 4 are examined, it is observed that the material with the highest characteristic breakdown strength is ABS. The characteristic breakdown strength of ABS was determined as 31.36 kV/mm. PC/ABS came in second with a breakdown strength value of 30.78 kV/mm and PETG came in third. The breakdown strength of the PETG material was 27.92 kV/mm. When we ranked the materials from best to worst in terms of breakdown strength, it turned out to be ABS--PC/ABS--PETG--PLA--ASA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Weibull Parameter** & **PLA** & **ABS** & **ASA** & **PETG** & **PC/ABS** \\ \hline \(\alpha\) & 23.92 & 31.36 & 21.05 & 27.92 & 30.78 \\ \hline \(\beta\) & 27.35 & 13.68 & 14.83 & 42.27 & 25.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Qidi Tech Q1 Pro 3D printer technical specifications.  
Figure 8 shows the statistical Weibull distribution results for the breakdown strength of materials produced with 3D printing.  
When the results in Figure 8 are examined in detail, it is seen that the breakdown strength of the ASA material will be 22 kV/mm with a 95% probability. This situation is determined as 24 kV/mm for the PLA material, 28 kV/mm for the PETG material, 32 kV/mm for the PC/ABS material, and 34 kV/mm for the ABS material.
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 5 Case Study and Discussion
### Case Description
#### 5.1.4 Case 4  
In this case, cooperative multi-objective optimization combines the inner-loop economic dispatch model and the outer-loop reactive power optimization model. The ATSET of EV between RMG and OBMG is considered. By optimizing the active power output of RES, EVs, and DC/AC converters, the total daily operating cost of MMGS is reduced. By optimizing the reactive power output of DC/AC converters, the loss of the distribution network is reduced, and the total economic cost of MMGS is reduced synergistically.
# Tracking of Moving Targets Through Asynchronous Measures
## 3 Benchmark Tracking Algorithms
### Particle Filter  
The PF algorithm is implemented as described in the sequel:  
1. First, we generate 1000 particles, according to the initial 3D position estimate and covariance matrix.  
At each step, each particle is propagated according to the following motion model \[\begin{cases}\mathbf{a}^{\prime}=\mathbf{a}+\sigma_{a}\mathbf{n}\\ \mathbf{v}^{\prime}=\mathbf{v}+\mathbf{a}T\\ \mathbf{x}^{\prime}=\mathbf{x}+\mathbf{v}T+\frac{1}{2}\mathbf{a}T^{2}\end{cases}\] (12) where \(\mathbf{a}\), \(\mathbf{v}\) clearly denote the acceleration and velocity vectors, and \(\mathbf{n}\) a 3D white Gaussian noise with unit variance and zero mean.
3. After measuring the ranges towards the anchors, for each particle the corresponding weight is computed as \[w=\exp\bigg{[}-\frac{1}{2}\sum_{k=1}^{N_{A}}\bigg{(}\frac{\big{|}\mathbf{x}- \mathbf{x}_{k}\big{|}-\rho_{k}}{\sigma_{\rho_{k}}}\bigg{)}^{2}\bigg{]}\] (13) where \(\mathbf{x}_{k}\) is the 3D position of the \(k^{th}\) anchor, \(\rho_{k}\) is the corresponding range and \(\sigma_{\rho_{k}}\) is the RMSE of the range measurement.
4. The distribution of particles is then resampled according to the computed weights.
5. The procedure is iterated at each step, in order to track the target over time.