esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Proposed System Overview  
To effectively enhance the detection and response capabilities of T2G communication system against cyber intrusions, this study proposes an intelligent security defense framework that integrates intrusion detection, risk scoring, and decision execution. Based on the TON_IoT dataset, the framework incorporates a DOA-BP intrusion detection model, a Bayesian risk scoring module, and a reinforcement learning-based decision-making module to identify potential threats and dynamically generate response strategies for T2G systems. As illustrated in Figure 3, the framework consists of three main stages: data acquisition and preprocessing, intrusion detection, and risk evaluation with corresponding decision execution.  
Stage One: The latest high-quality IoT security dataset, TON_IoT, is selected as the experimental dataset to simulate the communication behavior of transmission devices in the T2G environment of EMU/DMU systems. This stage involves data import, preprocessing, partitioning, and feature construction.  
Stage Two: This study introduces DOA to initialize parameters and optimize the structure of the classical BPNN, resulting in an efficient and stable intrusion detection model that outputs classification labels.  
Stage Three: Since intrusion detection results alone cannot directly guide system behavior, further evaluation of the potential risk caused by the intrusion is required. Risk levels are quantified based on attack types, posterior probabilities, and contextual state features. The system then activates the Reinforcement Learning-Based Decision Module, which dynamically generates optimal response actions (such as blocking, isolation, or logging) according to the evaluation results. This module iteratively improves its policy to achieve adaptive feedback defense.  
Figure 3: Workflow of DOA-BPNN and RL-based T2G protection system.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 4 Discussion
### Challenges  
#### 4.1.1 Data-Related Challenges  
The effectiveness of AIoT applications in solar energy systems is fundamentally dependent on the availability of high-quality, diverse, and real-time datasets for training, validating, and refining AI models. However, ensuring consistent and accurate data collection remains a major obstacle. Environmental variability, sensor degradation, and intermittent or incomplete data streams frequently compromise data reliability. The integration of heterogeneous data sources--ranging from meteorological information and PV performance metrics to IoT sensor outputs--adds complexity, necessitating sophisticated data preprocessing, cleaning, and fusion techniques to ensure consistency and usability. Moreover, data privacy and security are critical concerns, particularly when sensitive operational data are transmitted over cloud-based platforms. This risk is amplified in regions lacking robust cybersecurity infrastructure, increasing vulnerability to data breaches or unauthorized access. These challenges must be addressed to establish trustworthy, scalable, and resilient AIoT frameworks capable of supporting real-time monitoring, predictive analytics, and adaptive control in solar energy systems.
# Combined Optical Fiber Transmission System Based on QNSC and BER-LM
## 2 Integrated Architecture of QNSC and BER-LM
### Transmission and Negotiation Integrated Transformation  
Figure 3a shows the process of encrypted transmission over the optical fiber channel. The key distribution channel transmits the key to the legitimate sender and receiver. The sender encrypts the original data with the key, and the receiver can recover the original data with the key. Figure 3b shows the principle of transmission and negotiation integrated transformation (TNIT). As shown in Formula (2), the negotiation data (d) can be XOR with any bit except the highest bit (k9). The transmitted data (d) can only be XOR with the highest bit (k9) of the key to obtain the encrypted data (E).  
\[E=f(d,\ b,\ key)=(k0,\ k1,\ \ldots,\ k6\oplus b,\ k7,\ k8,\ \ k9\oplus d) \tag{2}\]
## 2 Materials and Methods  
### Instrument Design  
The upper steel plate is the contact plane of the system. Figure 1 depicts a photograph of the GPVS. The dimensions of the system are 11 cm in height and 60 \(\times\) 60 cm\({}^{2}\) on the surface plane. The cores of the GPVS are the underwater high-fidelity audio recorder and pressure sensor. The audio recorder is a waterproof pickup that is enclosed in a metal box attached to the upper plate's bottom. The sonic-sensitive part is closely attached to the inner wall of the metal box so that the audio recorder can only collect and detect the metal plate vibration sound caused by gravel movement. During the experiment, there is no audio signal other than the sound produced by the gravel movement. This substantially overcomes the problem of the gravel-movement-generated sound being drowned out by ambient sound, reducing the burden of post-data processing greatly. A cantilever framework supports the pressure sensor. As illustrated in Figure 1, there are four pressure sensors positioned at the four corners and screwed to the metal plate. The strain gauge, which is mounted within the pressure sensor, is utilized to reflect the pressure generated by the deformation caused by gravel movement. The soft waterproofing is employed to ensure the strain gauge's sensitivity.  
The GPVS uses coupling pressure and acoustic measuring technology to achieve remote, real-time monitoring of gravel movement. The system is set up on the surface of the riverbed. The pressure and audio signals are sent to the PC's storage via wires. The gravel movement is determined by analyzing the collected data. The GPVS offers the  
Figure 1: Schematical diagram of GPVS. (**a**) Location of pressure sensor; (**b**) arrangement of elements of the system; and (**c**) cover of GPVS.  
advantages of function even when buried, easy recovery, and numerous configurations along the river length. This allows for repeated usage of the system and simple maintenance, as well as good monitoring of the space distribution of the gravel movement.  
The features of the system are that, when the gravel movement is weak, the sole pressure method has limited accuracy, and the gravel movement is mostly monitored by recording the auditory signals generated by the collision of the gravel movement. When the gravel movement is strong and the audio signal analysis is impractical due to the enormous bedload particles, the pressure system is utilized to detect the gravel movement by monitoring the pressure fluctuation caused by the gravel movement.
# Microstructural, Electrochemical and In Vitro Analysis of Mg-0.5 Ca-xGd Biodegradable Alloys
## 2 Experimental Details
### Cytocompatibility Analysis
#### 2.3.3 Cell Viability  
The alloys' cytocompatibility was evaluated by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) colorimetric assay [35], similar to in the literature [37; 38], which allows quantification of the live cells' metabolic activity. The cell viability study was done through direct co-incubation of the cells with the studied Mg alloy samples (i.e., continuous cell exposition to an 100% extract) but avoiding cell mechanical injury as a consequence of the Mg alloys disintegrating by dangling the permeable supports, containing the metal samples, into the 24-wellplates. Briefly, after established incubation periods (i.e., 1, 3 and 5 days), the permeable supports were removed from the wells and the cells were treated with a MTT dye solution (for 3 h at 37 \({}^{\circ}\)C) in order to facilitate intracellular formazan crystal formation. Subsequently, the solubilization of the insoluble dark-blue formazan product was performed with isopropyl alcohol by continuous agitation for 15 min using an Environmental Shaker-Incubator (type ES-20, Biosan, Riga, Latvia). The characteristic absorbance of each well was read at a wavelength of 570 nm using a microplate reader (Tecan Sunrise, with Magellan V.7.1 data processing software). The cell viability data for the studied Mg alloys were expressed as a percentage of the cell viability data related to the controllwells by means of the formula: cellular viability = 100 \(\times\) (alloy wells' optical density--empty wells' optical density)/(control wells' optical density--empty wells' optical density). The one-way ANOVA test was used for statistical analysis of cell viability data; [PERSON]'s method was used for comparing the obtained results and statistically significant differences were accepted at \(p\) < 0.05.
# Multi-Objective Optimization for Outer Rotor Low-Speed Permanent Magnet Motor
###### Abstract  
For outer rotor low-speed permanent magnet motors (LSPMMs), in the optimization design process, not only is the electromagnetic performance optimized, but the influence of the thermal load and copper loss on the temperature in the electromagnetic parameters should also be considered. However, these optimized designs that comprehensively consider electromagnetic performance and temperature characteristics are rare in the existing literature. Therefore, this paper focuses on a multi-objective optimization process considering efficiency, thermal load and copper loss. Firstly, according to the initial design, the influence of the design parameters on the efficiency and thermal load is analyzed, and the range of optimization parameters is determined. Next, the response surface is built, the response-surface analysis of electromagnetic performance is performed, and the parameter sensitivity is calculated. Then, the multi-objective optimization design is carried out by comprehensively considering the electromagnetic performance and temperature. Finally, compared with the initial scheme, the optimized scheme improves the efficiency and reduces the temperature, and the optimized scheme is verified by experimentation on a 22 kW, 56 rpm LSPMM prototype, which can provide a reference for the multi-objective optimization of LSPMMs by comprehensively considering the electromagnetic performance and temperature.  
low-speed permanent magnet motor; outer rotor; sensitivity analysis; multi-objective optimization; efficiency; temperature +
Footnote †: journal: Applied
# Integrated Photonic Neural Networks for Equalizing Optical Communication Signals: A Review
## 2 Fundamentals of Photonic Neural Networks Working as Equalizers
The output \(y(t)\) from photonic reservoir computing can then be described by [88]  
\[y(t)=W_{out}u(t), \tag{2}\]  
where \(W_{out}\) is the weights in the output layer, and \(u(t)\) is defined by [88]  
\[u(t)=f\left(W_{in}x(t)+W_{r}(t-1)+W_{fb}y(t-1)+b\right), \tag{3}\]  
where \(x(t)\) is the input signal, \(b\) is a constant, \(t\) is the time instant, and \(W_{in}\), \(W_{r}\), and \(W_{fb}\) are matrices with the parameters of the input layer, the reservoir, and the feedback connections, respectively. Specifically, Figure 3 shows the architecture of an integrated photonic reservoir computing for equalizing optical communication signals [73]. Initially, a direct bit detection process is converted into a pattern recognition problem, and the detected patterns are employed to train a linear classifier. This photonic reservoir computing uses a delay loop divided into intervals that define the virtual nodes of a neural network. The input patterns are then multiplied by a random mask and subsequently enter the reservoir [73]. In the sequence, the virtual node values are combined using optimal weights, which are deter  
Figure 1: Sketch of the integrated photonic circuit that works as a perceptron. Reproduced without modification from [PERSON]. [PERSON] et al. [76], © 2022, licensed under a Creative Commons Attribution International License 4.0 (CC BY 4.0). mined using ridge regression. Finally, the prediction output is determined by a sum of the weighted reservoir's responses [73].
## 2 Experiments and Results
### Picosecond Amplifier System at 1 kHz
#### 2.1.3 End-Pumped Single-Pass Amplifiers  
A 1.5 mJ, 1064.4 nm, 1 kHz pulse with high contrast and good beam quality was obtained by the regenerative amplifier as described in the previous section. Higher pulse energies are required for many applications [4, 5, 6]. As previously reported [12], multiple stages of side-pumped amplifiers were used to obtain 80 mJ picosecond laser pulses. However, due to the inhomogeneity in the directions of the side-pumped modules, the beam quality (\(M^{2}>4\)) was not good enough for some applications. To obtain a high pulse energy picosecond laser with good beam quality, we implemented a hybrid power amplifier consisting of four stages of end-pumped single-pass Nd:YVO4 amplifiers and a side-pumped Nd:YAG amplifier as shown in Figure 7. In end-pumped amplifiers, 0.3 at. % Nd:YVO\({}_{4}\) crystals with a dimension of \(4\times 4\times 10\) mm\({}^{3}\) were used as gain media and also wrapped with indium foil and mounted on water-cooled copper heat sinks at 14 \({}^{\circ}\)C. The fiber-coupled quasi-CW laser diodes with an 800 \(\upmu\)m core diameter centered at 808 nm (DILAS, Mainz-Hechtsheim, Germany) were used as pump sources, and each of them produced a 120 \(\upmu\)s pulse at 1 kHz and focused into the crystal by an imaging system with a magnification of two. For time synchronization of the whole system, a homemade delay generator provided trigger signals at the repetition rate of 1 kHz to drive the Pockels cells and quasi-CW pumps.  
Figure 5: Pulse building process in the regenerative amplifier.  
Figure 6: The beam quality of the output pulses from the regenerative amplifier.  
The measured power curves and beam images are shown in Figure 8. The 1.5 mJ, 1064.4 nm, 1 kHz picosecond pulses were amplified to be more than 34 mJ after four stages of end-pumped single-pass amplifiers. The total pump energy was about 125 mJ, corresponding to an optical efficiency of 26%.  
Figure 8: Power curves and beam images of each stage. (**a**) First stage; (**b**) second stage; (**c**) third stage; (**d**) fourth stage.  
Figure 7: Diagram of the hybrid amplifier. M1–M3, M5–M7, M9–M11, M13–M15, M17–M18: Plane high-reflection mirrors; M4, M8, M12, M16: Plane high-reflection pump mirrors; S1–S8, S10: Plane-convex lens; S9: Plane-concave lens.
## 4 Concept of Manipulator-Specific Path Planning  
The 'divide and conquer' approach was chosen for the concept of a manipulator-specific path planner. The trade-offs of guaranteeing executability, optimality of robot execution, print result and computational effort can be addressed in this way.  
The newly developed path planner first fragments a slice into exclusively convex polygons whose neighborhood relations are recorded in a graph. For each of the convex polygons, multiple infill patterns with varying orientations can be feasible for covering the respective polygon completely. This results in several possible infill options for each convex area, extending the graph. Infills of a polygon are therefore clustered, which means that graph edges can only be generated between infills of distinct clusters. An adapted Hamiltonian search finds the most cost-effective path through all infill options while making sure that every convex polygon is only visited once. In order to avoid calculating a multitude of exact paths that are not relevant to the final path as well as the corresponding robot movement to generate the costs for all possible infill options, simplified evaluation strategies were developed that allow an efficient and fast cost estimation to complete the graph with edge weights. The resulting meta path can then be planned in detail by the infill algorithms used. Furthermore, continuous connections between neighboring infill options are added whenever possible so that the whole slice area is covered by a precisely defined geometric path. The print path is then prepared for trajectory planning by means of smoothing strategies as well as the completion of the path by the print head orientation (Figure 6).
### Literature Review of AI, Acupuncture and EEG
#### 1.3.1 Neuroimaging and the Neurochemical Model of EA, TEAS and Acupuncture  
The effects of EA, TEAS (and, indeed, acupuncture) are usually explained using neurochemical models, in which different regions of the brain play key roles [17; 18]. In particular, stimulation at low, medium and high frequencies, or low or high amplitudes, may activate different pathways in the spinal cord and brain [18]. In brief, low-frequency stimulation (at 2-4 Hz) activates both large- and small-diameter afferents, and thus, has both segmental and supraspinal effects, with the release of enkephalin and beta-endorphin in the brain (less so in the spinal cord). These central effects may mean that any resulting analgesia has a slow onset and outlasts the stimulation itself. In contrast, high-frequency stimulation (at 50-200 Hz) activates predominantly the large-diameter afferents, so that the effects are segmental (associated with the release of dynorphin in the spinal cord), not supraspinal. Analgesia thus has a rapid onset but does not last long. Stimulation frequencies of 10-20 Hz may activate both mechanisms [19]. Numerous functional magnetic imaging (fMRI) studies have been conducted to explore these connections, but far less research has investigated the effects of acupuncture, EA or TEAS on EEG (315, 69 and 2 studies are currently indexed in PubMed, respectively), despite the advantages of EEG over other forms of neuroimaging, such as fMRI and magnetoencephalography (MEG) in terms of cost, portability and/or temporal resolution and usefulness for frequency analysis.
## 4 Results
### Fracture Development Characteristics  
#### 4.3.1 Macroscopic Fracture Characteristics  
From the core results, all the four lithologies have fracture development, but the development degree is different according to the different lithology. As shown in Figure 5, the macro-fractures of felsic shale have the best development, most of which are structural fractures and dissolution fractures, etc., but most of them are filled by alkaline minerals, and the fracture width is small, with an average width of less than 1 mm. The second is the siltstone interlayers, and the macro-fractures are well-developed, mostly structural fractures, contraction fractures, etc., with an average width of 2.21 mm. Macroscopically, there are many types of fractures such as multi-developed interlayer fractures and structural fractures, and their widths are medium, with an average width of 1.78 mm. The macro-fractures of clay-bearing felsic shale are the least developed, and most of the types are structural fractures and dissolution fractures, etc., which are mostly developed around mineral particles and filled with clay minerals. The fracture width is small, and the average fracture width is less than 1 mm.  
The macroscopic fracture density of the cores of four lithology types in well Maye-1 is calculated through core observation. As shown in Figure 6, the macroscopic fracture density of felsic shale is the highest, followed by siltstone interlayers, and the fracture density of clay-bearing felsic shale is the lowest. Therefore, from a macroscopic perspective, the fluidity of felsic shale is the best, while the connectivity of clay-bearing felsic shale is the worst.  
The fracture development of the four lithology types is mainly affected by stratigraphic tectonic movement, in which the proportion of brittle minerals in the dolomitic felsic shale increases due to the increase in the content of carbonate rock, making fractures more likely to occur. In other lithologies, multiple small fractures are connected together, showing large  
Figure 5: The core fractures of each lithology(red line is fractures). (**a**) Felsic shale, 4691.43 m; (**b**) dolomitic felsic shale, 4658.23 m; (**c**) clay-bearing felsic shale, 4587.95 m; and (**d**) siltstone interlayers, 4624.10 m.  
fractures in length and width. The brittle minerals of felsic shale are poor, and its fractures are dominated by a large number of smaller fractures. Because of the poor brittleness of clay minerals, clay-bearing felsic shale will fill cracks, so its fracture development is the worst and the number of cracks is the least. The fracture development of siltstone interlayers is better, which is related to the properties of siltstone interlayers themselves, with a larger grain size and higher brittleness.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 3 Overview of the Discrete Analog Functional Blocks of ArduSiPM
### Optical Test  
We conducted several tests of the Cosmo ArduSiPM using a dedicated optical test bench at INFN-Roma. For this purpose, we employed a basic setup with a fast LED source positioned in front of the SiPM, within a lightproof box equipped with an optical breadboard. To ensure proper alignment between the SiPM and the LED source, the photosensor is mounted on a 3D-printed custom holder (Figure 11). It is worth noting that the SiPM used has a small area, chosen to maximize temporal performance. While it is possible to use SiPMs with larger areas, in such cases, temporal performance is degraded.  
For the test, we chose to use the same type of SiPM usually employed on the standard ArduSiPM [2] application, that is, the MPPC Hamamatsu S13360-1325 CS [3]. This photosensor has an area of only 1.3 \(\times\) 1.3 mm\({}^{2}\) and a pitch of 25 \(\upmu\)m with a fill factor of 47%. The typical dark count at the standard ambient temperature (25 \({}^{\circ}\)C) is only 70 kcps; the low rate of dark count allows us to use a single channel in standard scintillation detectors, reducing the noise to fractions of cps with a threshold of just 3 photoelectrons;  
The light source used in this arrangement is an LED driver Picoquant PDL 800-B equipped with a sub-nano-second pulsed LED head. This source can generate a fast light pulse at 800 ps (FWHM) with a wavelength of 450 nm and a fine adjustable optical power until 2800 \(\upmu\)W peak [21]. It is possible to set the amplitude of every LED pulse to work in the single photoelectron or few photoelectron regimes; this is important to emulate the standard condition of working of SiPM in Cosmo ArduSiPM-based particle detectors. The maximum rate of the LED driver is 80 MHz, which permits the measurement of the maximum acquisition rate of the Cosmo ArduSiPM system in a typical configuration.  
Figure 10: Linear regression of ADC digital output as a function of signal input amplitude. A fast 40 ns signal pulse, with 5 ns of transient time, is used as input to simulate a typical SiPM pulse. The curves with and without the right offset compensation value are shown.  
Given the limited range of selectable frequencies on the PicoQuant LED driver, the repetition rate of the light pulse was controlled using an external TTL pulse generator capable of selecting frequencies ranging from 0 to 40 MHz. To ensure an entirely dark environment, the lightproof box with source and sensor is wrapped in a black nonwoven fabric. The dark count condition was verified before each measurement.