esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 2 Method
### SRGAN Generator Architecture  
The comprehensive architecture of a _super-resolution generative adversarial network_ (SRGAN), proposed by [PERSON] et al. [43] to address the problem of super-resolution, essentially consists of two separate networks: a generator network and a discriminator network. The underlying concept involves training the generator model to effectively deceive the discriminator model, which in turn becomes proficiently trained to distinguish between super-resolved and authentic images. Through this approach, the generator model can produce solutions that more closely resemble high-resolution reality, whereas the discriminator model progressively becomes more refined in distinguishing these images.  
To address the issue of blind denoising, the generator architecture can be used alone, keeping its design unchanged but with adjustments to the appropriate cost function. Thus, for this study, only the generator portion of the network is implemented. This component incorporates residual blocks, whose main function is to retain the information originating from the previous layers. This arrangement enables the network to adaptively select the most relevant features--a crucial factor for the overall performance of the system.  
As illustrated in Figure 4, the generator section of the SRGAN architecture consists of a series of residual blocks with similar configurations; specifically, 12 blocks were adopted for this study. Each block is composed of three convolutional layers equipped with 3 \(\times\) 3 filters and 64 feature maps, which are followed by batch normalization layers and an activation function called parametric ReLU (PreLU). Following these blocks, the architecture is finalized by two subpixel convolutional layers and an additional convolutional layer adapted for use with 1 \(\times\) 1 dimension filters.
## 4 Payload Design  
The mechanical design, from the inception of Phase A of the mission in Figure 2, was already represented in detail since the sensors and sensitive elements of the payload had already been identified.  
After completion of Phase A some design improvements are under analysis while waiting for the start of Phase B, a review is reported in Figure 3. Redout sensors (photomultiplier tubes and Avalanche photodiode) were already selected, thus this analysis coincided with an in-depth examination of the individual mechanical components, the interfaces among these components, and their integration with the overall platform. The payload consist of:  
* Two identical aluminium interfaces with the platform;
* An aluminium alloy top, bottom lid and 4x panels with a passive tungsten film clamped;
* The collimator may that contain 4 collimators and filters for scatterer and 4 collimators and filters for absorber;
* 32x absorber made from GAGG scintillator hosted on APD board;
* In the same mechanical and electrical framework of the scintillator may are located 64x scatterer made from plastic scintillator and leaned on the Detector frame;
* A set of 4x ribs contributing to the mechanical connection of the parts.  
Figure 2: Phase A design.
## 2 Related Works
### Music Generation  
**Music Composition**. Music composition is a challenging job for both composers and algorithms. Research works on music generation firstly focused on music generation itself, then more studies paid attention to controllable music generation. However, very few studies have been conducted to combine music models with visual models. DeepBach [1] is a deep neural network model with good composition effect which focuses on producing music in the style of Bach. MetaComposer [2] creates harmonious, pleasant and interesting compositions by using a participant-based evaluation to combine the component of its framework effectively. Different from the previous work, MuseGan [3] proposes three multitrack music generation models which provide a method for generating music with the user's control. Since then, more studies have considered controllable music generation. DeepJ [4] is an end-to-end music generative model that is capable of composing music conditioned on a specific mixture of composer styles. DeepChair [5] is a system that can automatically generate a four-part chorus from a given piece of music. However, none of those jobs combine music generation with the given videos.  
**Music Generation From Videos.** A pioneering work of video background music generation was put forward by [13]. The algorithm realized the audio generation of music performance videos collected in the laboratory. A similar tool, Audeo [14], generated background music for an input pianist video. Another work, Foley-Music [15], was proposed to generate music for a given body poses video, which achieved outstanding results in silent performance video tasks. However, the above research work is mainly about generating music for performance videos. Compared to video background music, the music of these tasks is fixed to some extent, as it can be speculated by the human pose, and its style and instrument type are also relatively fixed. In these kinds of work, it is almost impossible to complete the music for scenery videos or videos without performers. As far as we know, the only model that generates background music for general purpose videos is Controllable Music Transformer(CMT) [6]. Although the rhythm of generated music matches the video well, it is not as melodic as the training data. In this paper, we propose a model for background music generation for the given video and we improve the rhythm of the music.
## 2 Exhaled Breath (EB) Analysis
### EB Analysis
#### 2.2.2.1 Proton Transfer Reaction Mass Spectrometry (PTR-MS)  
PTR-MS use in breath research is relatively recent, but very promising because it can deliver results in a real-time online analysis, with high sensitivities for VOCs detection and quantification (up to the pptv range). This analytical performance compares in terms of sensitivity (but not selectivity) with the one obtained by GC-MS (or is even greater, as shown by [PERSON] _et al._[30]). PTR-MS uses H\({}_{3}\)O\({}^{+}\) ions for proton-transfer reactions with many common VOCs, while having little to no reaction with the highly abundant atmospheric gases (N\({}_{2}\)CO\({}_{2}\) and H\({}_{2}\)O) that compose more than 99% of EB (reviewed in [14, 73, 133]). There are, however, a few disadvantages to point to PTR-MS, namely the number of compounds that can be simultaneously analysed and the limit of detection. According to [PERSON] _et al._[144], in an online breath analysis, a minimum of 3 Hz of sampling frequency is necessary in order to resolve individual breath phases. Thus, this trade-off between the number of measured _m/z_, and the signal-to-noise ratio (S/N), limits the number of compounds that can be simultaneously monitored and the respective limit of detection (LOD) that can be obtained. On the other hand, as the PTR-MS detection relies on the atomic mass of compounds and the resolution of quadrupole MS instruments is limited, it is impossible to identify compounds with the same molecular weight [144, 145]. To overcome this sensitivity disadvantage, a TOF-MS was associated to the PTR, _viz._ the PTR-TOF-MS. In this technique, similarly to GC-TOF-MS, the ions are accelerated to a regular energy by an electric field. Then the ions travel a defined distance without acceleration. The m/z will determine the time of flight of the compound. Thus, the mass spectrum can be obtained by measuring a single shot (a fraction of a second) or adding more shots (although this last option increases the S/N (reviewed in [144]). This methodological improvement allows a three order of magnitude increment in PTR-TOF-MS and consequently it is now possible to separate distinct chemical compounds with the same molecular weight using this real-time approach ([146] reviewed in [144]). Nevertheless, as already mentioned, since pre-concentration is not possible, very low abundant VOCs can hardly be detected using this approach. Moreover, it is a far more expensive technique than GC-MS [144, 32].
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 3 Area of Research
### Design Considerations in Modular Construction
#### 3.2.1 Material  
The primary factor involved in the mix design of concrete used in modules is the demoulding strength, which determines when a module can be safely removed from the mould. This is crucial in modular construction because factory-based manufacturing relies on rapid casting cycles, where modules are typically demoulded within 12 to 24 h to maintain production efficiency [1]. The typical demoulding strength of concrete required and the strength of concrete at 28 days is given in Table 2. It is seen that the demoulding strength is generally 50% of that of the required 28 days of strength. Rapid hardening cement, steam curing, and electrical heating are used to accelerate the early age strength of concrete during the manufacture of concrete modules. Self-Compacting Concrete (SCC) has been increasingly used in modular construction as it provides better finishes to walls and reduces the use of vibrators.
## 3 Methods
### Identification of Threshold for When to Blend  
The incomplete time-series pixels of Landsat are gap-filled with the L-M blended pixels (\(L_{LM}\)). The threshold to indicate when blending is beneficial is identified by quantifying the impact of the fraction of missing data on the prediction accuracy at 25 m. Firstly, we group MODIS and \(L_{LM}\) time series in eight groups, based on the fraction of Landsat missing data (\(<\)10%, 10-20%, 20-30%, 30-40%, 40-50%, 50-60%, 60-70%, and 70-80%). Then, the accuracy of each group is analyzed by calculating the \(R^{2}\) and the \(RMSE\). The performance of C-Crop using MODIS and \(L_{LM}\) is compared by the fraction of missing data in Landsat across time. The threshold can be identified, when the model provides the same \(R^{2}\) and \(RMSE\) using \(L_{LM}\) as with MODIS (Figure S3, Supplementary Materials). This threshold determines when, where, or how much L-M blended data improves crop yield prediction when the fraction of missing data in Landsat is lower than the identified threshold.
###### Abstract  
The main objective of the research described in the article was to determine the following: Is it possible to estimate the magnetic flux linkage in the core of a voltage transformer using analogue or digital methods? Will it be possible to estimate it approximately both in the normal state and in the state of deep core saturation (ferroresonant state)? The research aimed to identify the advantages and disadvantages of both proposed estimation methods. As part of the research described in this paper, a simulation model was developed and executed in the MATLAB/Simulink environment to generate a series of secondary voltage and flux waveforms in voltage transformers. The secondary voltage and flux waveforms were modelled under ground fault conditions, initiating ferroresonance oscillations in the medium-voltage network. To determine the associated flux from the simulated secondary voltages of the voltage transformers, an analogue integration circuit, a voltage analogue input circuit and a numerical integration algorithm with offset elimination were developed and implemented in an STM32 microcontroller. The obtained reference flux waveforms were used to verify the accuracy of the estimation of flux waveforms obtained using the analogue and digital methods. As a result, it was determined that both methods allow for a relatively accurate estimation of the periodic component of the magnetic flux. It also presented how both methods respond to the presence of slowly changing (aperiodic) components. Possible applications were proposed in order to create an innovative criterion for detecting ferroresonance oscillations.  
magnetic flux; voltage transformer; ferroresonance; medium voltage networks; integrating circuit; trapezoidal integration method; STM32 microcontroller; MATLAB/Simulink +
Footnote †: journal: applied sciences  
Article
## 5 Security Analysis of the New Protocol
### Comprehensive Privacy Preservation
#### 5.3.2 User Identity Privacy Preservation  
In CL-PGSDP protocol, we design a log file that is used to record the information of the index of data block and the information of its tag generator, including the hash value \(H_{1}(ID_{i}~{}+~{}\delta)\) and the public key \(Y_{1D_{j}}\) of user \(u_{i}\). During the auditing process, the TPA gets the hash value \(H_{1}(ID_{i}~{}+~{}\delta)\) for the challenged data block \(m_{j}\) from the log file. Because the user identity is randomized by \(\delta\in Z_{q}^{*}\) in Partial-Private-Key-Gen, it is impossible for the TPA to obtain user's real identity. Therefore, the user identity cannot be known by the TPA.
# Configuration Design and Gait Planning of a Six-Bar Tensegrity Robot
## 4 Gait Planning of the Six-Bar Tensegrity Robot
### Single-Step Roll  
The six-bar tensegrity is a symmetrical icosahedron, including 8 closed equilateral triangles and 12 open isosceles triangles. For the convenience of description, an equilateral triangle is called a closed triangle and is represented by the letter C (close); an isosceles triangle is called an open triangle and is represented by the letter O (open).  
According to the rolling principle of the robot, the continuous rolling path of this robot can be regarded as the change between 20 triangles. In this paper, the gait planning of the robot prototype is carried out in a closed triangle landing method. The rolling gait can be divided into three types:  
CO denotes a closed triangle to an open triangle;  
OC denotes an open triangle to a closed triangle;  
OO denotes an open triangle to an open triangle.  
The motion gait planning of the closed triangle landing is shown in Figure 8. The robot can deform itself to offset the center of gravity. When the center of gravity crosses a line to reach another triangle, the robot rolls.  
The center of gravity change of the CO-step was calculated through MATLAB software to verify the correctness of the rolling principle. As shown in Figure 9, the empty green circles, red stars, and thin dotted lines represent the node positions, the center of gravity, and the moving direction of the center of gravity, respectively. The red dotted line represents the tumbling edge. The change in the COC-step center of gravity is shown in Figure 10.  
Figure 8: Locomotion gait planning.  
Figure 9: CG trajectories in the CO-step.  
The gait planning of the six-bar tensegrity robot is the path planning of selecting the appropriate rolling gait many times according to the landing method, which finally reaches the target point. In addition, due to the large value of the gravitational moment of the OO-step, the robot rolls with difficulty; thus, the OO-step is not chosen in this paper during the rolling process.
## 2 Overview of VISFFB  
### Mechanical Structure of VISFFB  
The proposed VISFFB performs visual inspection on flexible fluid bags at the end stage of the fluid bag manufacturing process. During the manufacturing process in a production line, a new fluid bag is produced every second and dispatched on the conveyor belt. To provide enough time for the inspection of each bag, the VISFFB loads five bags from the conveyor belt at the same time, and this parallelization allows 5 s of inspection time for each bag. The main rotor, equipped with five grippers on each side of its hexagonal structure, rotates to move the bags to the next stage, where each bag is rotated for visual inspection. For the first 3 s, the system moves the bags to the inspection position while circulating each bag to make the internal fluid move. Once the fluid starts rotating, the bag stops rotating, and for the last 2 s, visual inspection is performed to detect foreign particles in the bag.  
To enhance detection accuracy, two cameras are assigned to each bag, inspecting both the upper and lower parts. The inspection algorithm detects moving objects inside the fluid bags, differentiating between foreign particles and air bubbles. Each moving object is assigned a track ID for tracking, allowing for multiple analyses of objects with the same track ID across sequence images to determine if they are foreign particles.  
This visual inspection process is repeated for each bag at the second visual inspection position of the main rotor. The combined results of these two inspections determine whether the bag is free of foreign particles or defective. After completing the visual inspections, the main rotor moves the bags to the discharge stage, where they are mechanically sorted based on the inspection results. Normal bags are transferred to the discharge conveyor belt, and defective bags are directed to the defective container.  
Figure 2: The proposed system for foreign particle inspection.
# Analysis of Vibration Characteristics of the Grading Belt in Wolfberry Sorting Machines
## 2 Materials and Methods
### Mechanical Modeling of Transverse Vibration of a Flat Belt  
#### 2.3.1 Analysis of Belt Drive Systems  
This transmission mechanism is mainly composed of belts and pulleys, and the size classification of fresh wolfberries is carried out through the gap size of the belts. Figure 4 presents the force analysis of the belt of the fresh wolfberry grading machine. Among them, pulley 1 serves as the driving pulley, while pulleys 2, 3, and 4 act as driven pulleys. In the process of pulley 1 functioning as the driving pulley and moving, pulley 1 conveys power to the entire belt system via friction with the belt. The difference in tension is caused by frictional force, which is related to the torque transmitted by pulley A. According to the tension relationship of classic belt drive, the relationship between the tight side tension F\({}_{1}\) and the slack side tension F\({}_{2}\) can be expressed by the following formula [16; 17]:  
\[\frac{F1}{F2}=e^{\mu\theta} \tag{2}\]  
In the formula:  
\(\mu\) is the coefficient of friction between the belt and the pulley;  
\(\theta\) is the wrap angle of the belt around the driving pulley.  
The tension attenuation model based on [PERSON]'s formula offers a theoretical foundation for the tension distribution of belt drives and discloses the relationship between the tension on the tight side and that on the slack side. It can be observed from the figure that the belt tension between pulley 1 and pulley 2 is the minimum, and the greater the possibility of belt vibration.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.6 Reliable Energy-Efficient Cross-Layer Routing Protocol (RE2 CRP)  
In [152] the author proposed a cross layer reliable energy efficient routing protocol (RE2 CRP), to ensure the excellent data packet delivery in UWSNs. The (RE2 CRP) have two different phases: (i) Route change phase, (ii) Routing phase. During the first phase, every communicating node changes its self-routing information, with communicating node level, remaining energy, node ID, and distance. The received signal strength indicator (RSSI) value is used to calculate the distance from the nearby nodes to Tx anchor node. The next hop is chosen for data packet transmission using the routing table data.
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
## 4 Machine Learning  
Machine learning is closely linked to Artificial Intelligence (AI) technology. It trains an algorithm to find regular patterns in a dataset. This training results in a model that can be used to predict or automate things. For IDSs, machine learning can be used to detect either known attacks or unknown attacks if the model has been sufficiently trained.  
As shown in Figure 2, there are three main types of machine learning methods: supervised, unsupervised and semi-supervised machine learning [5]. These methods are discussed further in this section.
## 2 Results
### Inner Membrane Permeation  
To evaluate the effects of the two peptides on the permeability of the inner membrane of the EDL933 strain, a Sytox Green assay was performed. Indeed, this allowed the assessment of the permeabilization of the bacterial cytoplasmic membrane by monitoring the increase in Sytox Green fluorescence upon its binding to intracellular nucleic acids.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strain** & \multicolumn{2}{c}{**Esc(1-18)**} & \multicolumn{2}{c}{**Esc(1-21)**} & \multicolumn{2}{c}{**Kanamycin**} \\ \hline \multirow{2}{*}{**EDL933**} & MIC & 32 & MIC & 4 & MIC & 16 \\ \cline{2-6}  & MBC & 64 & MBC & 8 & MBC & 32 \\ \hline \multirow{2}{*}{**K12**} & MIC & 16 & MIC & 2 & MIC & 16 \\ \cline{2-6}  & MBC & 32 & MBC & 4 & MBC & 32 \\ \hline \hline \end{tabular}  
MIC = Minimal inhibitory concentration, MBC = Minimal bactericidal concentration.  
\end{table}
Table 1: Minimum inhibitory and bactericidal concentrations of Esc(1-18), Esc(1-21), and kanamycin against _E. coli_ strains. The results are expressed in \(\upmu\)M concentration and are derived from three independent experiments.  
Figure 2: Time-kill assay of the _E. coli_ O157:H7 EDL933 strain in the presence of Esc(1-18) (**A**), Esc(1-21) (**B**), and kanamycin (**C**). Concentrations of antimicrobial compounds were 2 \(\times\) and 4 \(\times\) Mic: 64 and 128 \(\upmu\)M for Esc(1-18), 8 and 16 \(\upmu\)M for Esc(1-21), and 32 and 64 \(\upmu\)M for kanamycin. CTR indicates the controls. The dotted line represents a 2-log reduction (99%) of the initial inoculum. The data are the means \(\pm\) standard deviations (SDs) of at least three independent experiments (although they are not visible).  
Sytox Green is a molecule that is impermeable to intact phospholipid bilayers, and the increase in fluorescence intensity is directly correlated to the membrane's damage, which leads to the intracellular influx of the dye, followed by its binding to DNA. As reported in Figure 3, after peptide addition (time = 0), the increase in fluorescence intensity was dose dependent, with a more marked perturbation induced by Esc(1-21). In fact, low concentrations of Esc(1-18) (i.e., 4 and 8 uM) did not provoke any significant fluorescence change with respect to the control cells, while the greatest effect was recorded at the highest concentration of 64 uM within 60 min. This is in line with the membrane-perturbing activity that was previously observed for Esc(1-18) on the reference _E. coli_ strain ATCC 25922 [17]. On the contrary, at the same low concentrations of 4 and 8 uM of Esc(1-21), a fluorescent signal was recorded within 5-10 min, while, at the highest concentration of 64 uM, the membrane-perturbing effect occurred within the first minutes after peptide addition.