esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Microgravity in Human Health  
The first evidence for the presence of pathophysiological alterations associated with microgravity was published in 1975, with the observation that 15 out of 29 of the Apollo mission's astronauts displayed increased viral or bacterial infections [1]. This observation, later confirmed by the analysis of blood samples of nine astronauts who participated in the Skylab missions [2], was finally explained by the studies of Dr. [PERSON]'s group, who demonstrated that Space flight induces lymphocyte immunosuppression [3, 4, 5]. Since then, many other investigations, conducted both through simulated and authentic microgravity, have demonstrated that exposure to Space conditions can trigger a plethora of physiological alterations, spanning from hemodynamic and cardiovascular complications [6], neuro-vestibular and circadian rhythm-related alterations, to loss of muscle tone and bone tissue [7, 8]. These findings clearly demonstrated that understanding the molecular mechanisms behind these alterations, also in view of finding effective countermeasures to protect astronauts' health [9, 10], represented an essential goal of the forthcoming prolonged Space missions, such as colonization of Mars.  
Over the last 20 years, we have focused our attention on the role of lipid signalling on human immune response and bone remodelling under authentic microgravity conditions, taking advantage of four different missions. The first one took place in the year 2000 during the 28 th parabolic flight campaign of the European Space Agency (ESA), with the goal of assessing the role of weightlessness on the catalytic constants of purified lipoxygenase-1 (LOX-1). Then, we performed the ROALD (Role Of Apoptosis in Lymphocyte Depression), RESLEM (Role of the Endocannabinoid System in human Lymphocytes Exposed to Microgravity), and SERiSM (Role of the Endocannabinoid System in Reprogramming Human Pluripotent Stem Cells under Microgravity) projects, which were all conducted on board the International Space Station (ISS), again to interrogate the possible impact of microgravity on in vivo lipid signalling. Against this background, here we sought to gather current knowledge on endogenous lipid signals in immune response and bone remodelling in Space, with a focus on the two most studied families of bioactive lipids: eicosanoids and endocannabinoids. Key studies performed on this topic by us and others are summarized in Figure 1, along with the microgravity conditions under which they were performed.
## 2 Materials and Methods
### Description of Test Machines, Fatigue Tests Setup, and Specim Geometry
#### 2.2.1 Test Machine for Rim Section Fatigue Tests  
The fatigue tests on rim section specimens were conducted on a customized test machine made of the following components:  
* The hydraulic actuator, with a maximum load capacity of 100 kN;
* The Hydraulic Power Unit (HPU), allowing a maximum oil flow of 62.5 L/min at 207 bar pressure;
* The Controller, which allows a real-time closed-loop control of the system in load or displacement mode.  
They are manufactured by MTS Systems Corporation (Eden Prairie, MN, USA). A simple machine frame gives the possibility of testing different kinds of components, after a proper customization of the fixture for specimens clamping. In this case, the specimens were clamped at both sides, in a horizontal configuration, which is the same configuration of the rim in contact with the ground. The test machine and the setup for the fatigue testing of rim sections are shown in Figure 5.  
The specimens were tested in tension-tension bending fatigue. This was necessary to keep the maximum stress in the outer surface of the radius of interest, which is the most critical point of the rim in service conditions. In fact, in the case of fully reversed bending, fatigue cracks would have developed from non-significant areas because of the curved geometry of the specimens. Moreover, this load condition is more similar to the real service load condition of rims, where tensile stresses mainly originate from the superposition of a constant mean load, due to the tyre pressure, to additional variable loads due to the vehicle maneuvers [8,14].  
Figure 4: Most stressed areas of the rim that were subjected to service load conditions.  
The fatigue tests were conducted in load control mode at 4 Hz frequency, with load ratio R = F\({}_{\text{min}}\)/F\({}_{\text{max}}\) = 0.1 and a sinusoidal load function. These parameters were chosen to optimize the test machine behavior from the point of view of dynamics and oil temperature control. An example of the load function is shown in Figure 6. An additional strain gauge was applied on each specimen, in the area of maximum stress, to acquire the actual strain value, \(\varepsilon\), during the fatigue test. The strain gauges used were Micro Measurements (Wendell, NC, USA) WK-06-125 BT-350, with a gauge factor of 2.01. A change in the behavior of the strain range, \(\Delta\varepsilon=\varepsilon_{max}-\varepsilon_{min}\), from steady to continuously increasing values, was considered the signal of fatigue crack development, as will be discussed in Section 3.1. In the absence of this trend, tests were conducted up to 2 \(\times\) 10\({}^{6}\) cycles, considered as a limit to runout. At the end of the test, each specimen was submitted to magnetic particle inspection to confirm the fatigue crack development.
# On Flexural Performance of Girder-To-Girder Wet Joint for Lightweight Steel-UHPC Composite Bridge
## 4 Experimental Test of the Second Generation T-Shaped Joint
### Discussion  
#### 4.3.1 Specim Stiffness and Deflection  
(1) Section stiffness analysis  
Figure 13 displays the theoretical curvatures of sections I, II, and III (sections shown in Figure 12). The theoretical curvatures used the calculation method of ideal elasticity. It is seen that the stiffness at the joint interface was greatly reduced, and for the cracked section (Section III, ignored the tensile strength of UHPC), the theoretical value can better fit with the test result. While for the other sections, the theoretical value shows great agreement with the test value.  
Figure 13: Moment-curvature curves of critical sections.  
Analysis of specimen overall deflection  
In Figure 14, the deflections of seven measurement points (the gauge 1-7 in Figure 12) were plotted with every 200 kN and compared to the theoretical value calculated by Section III. The comparative result showed excellent agreement before 815.2 kN. When the applied load reached 1033.9 kN, the theoretical value (1033.9 kN--theoretical-1) calculated by using Section III was a greatly underestimated experimental result; alternatively, the curve of 1033.9 kN--theoretical-2 calculated by Section IV (Figure 12, the section of I-shaped steel girder) was in good agreement with the final deflection. This indicates that when the specimen broke, the steel bars in the UHPC panel yielded, and the applied load was completely borne by the I-shaped steel girder.  
Consequently, when the specimen was subjected to the negative bending moment, the cracked section ignoring the UHPC tensile strength could be used to predict the theoretical deflection in the serviceability limit states. In some sense, the section of I-shaped steel girder appeared to be more reasonable for predicting the final deflection in the ultimate limit state.
###### Abstract
In this way, an output indistinguishable from reality can be obtained, unlike with CNNs, which tend to minimize the Euclidean distance between the predicted and ground-truth pixels, leading to the production of blurry results [14]. Conditional generative adversarial networks (cGANs), an extension of the GAN model that enables the model to be conditioned with external information, have also been studied [14; 15]. cGANs are suitable for image-to-image translation tasks where an input image is conditioned to generate a corresponding output image. When a cGAN is combined with a U-Net architecture, a mapping relationship between the geometry shape and flow field can be established and good prediction results with large-scale test sets can be obtained [8]. In this study, we developed an airfoil flow field and aerodynamic performance prediction model that uses deep-learning technology instead of CFD simulation. Among the various deep-learning models, the pix2 pix method [14] for image-to-image transformation and the deep neural network (DNN) method were selected. The pix2 pix method, a universal solution to the image-to-image translation problem that utilizes cGANs [14], was implemented to predict the airfoil flow field. In addition, the DNN method was implemented to predict the airfoil aerodynamic performance coefficient. A dataset obtained using an in-house CFD code with a genetic algorithm was used to train the pix2 pix and DNN models.
# The Influence of Electrode Impedance on the Parameters of Visual Evoked Potentials
## 4 Discussion
However, in the case of EEG data, the multiple use of electrodes is acceptable due to several parameters considered in the final report [29]. In EEG, the frequency value of individual brain waves refers to selected ranges, i.e., alpha (8-13) Hz, beta (14-30) Hz, theta (4-7.5) Hz, and delta (0.1-3.5) Hz, whereas for VEP, a single value of latency is analyzed. Moreover, for EEG examination, except for amplitude and frequency values, the degree of brain activity symmetry and the degree of brain wave synchrony (i.e., alpha, beta, and theta) or reactivity (e.g., response to eye opening and closing) also have diagnostic significance. In addition, according to the research presented in [30] reusable electrodes, even after cleaning, pose a risk of patient cross-contamination owing to the bacterial species present on these electrodes and wires. The solution may be to use waterproof electrodes designed for long-term monitoring (LTM) that can cleaned by autoclaving. However, the problem of electrode impedance changes after each usage would still remain. Alternatives to the reusable cup electrodes commonly used in clinical practice may be the solutions proposed in other studies [18-25]. Currently, this author is working on a portable device that checks the degree of degradation of the reusable electrode coatings. The proposed device could be used before recording brain potentials.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
### Treatment Planning in Orthognathic Surgery
#### 1.1.2 Virtual Surgical Planning  
The introduction of 3D imaging and computer-aided design (CAD) has led to the development of computer-assisted 3D virtual surgical planning (VSP) in OGS, through which a detailed representation of the craniofacial anatomy and enhanced analysis of surgical planning became available. Three-dimensional VSP has been shown to improve the predictability of surgical outcomes [7, 8, 9, 10], and it certainly is becoming popular within the field [11].  
Three-dimensional VSP starts with the acquisition of intra-oral optical light scans of the dental arches and a cone beam computed tomography (CBCT) scan of the facial bones. A voxel size of 1.0 mm or less is normally required for the CBCT scan, and the acquired CBCT data are extracted in the Digital Imaging and Communications in Medicine (DICOM) format for alignment and fusion with the dental arch scans [12]. This process creates a virtual patient model that can be segmented and osteotomised for simulation of the planned surgical movements. The maxilla-first approach is most commonly practised as it has been shown to be more predictable than the mandible-first approach unless there is an anti-clockwise rotation of the maxilla and mandible [13].  
A virtual OSS is then created by either inserting a virtual object between the upper and lower teeth and making an indentation of the occlusal surfaces of the upper and lower teeth or virtual filling in of the void between the upper and lower dental models in the new position. The final design of the virtual OSS is then extracted in Standard Tessellation Language (STL) file format and fabricated using a 3D printer [14]. The OSSs fabricated in this manner have been shown to be highly accurate [15, 16, 17].
###### Abstract  
In this article, both the optimization of a backhaul network and the indirect estimation of demographic behaviors from spectral analysis are proposed. Electromagnetic spectrum in 5 GHz frequency band was scanned, and graphs generation associated with the spectral map were obtained using AirView Spectrum Analyzer software incorporated into Ubiquitin devices; these devices were installed in Ovejas and Chalain municipalities as part of a rural backhaul network located in Sucre, Colombia to bridge the digital divide in rural areas. In addition to telecommunications system working frequency optimization between Almagra and La Ceiba nodes, which was configured at 5790 MHz--spectral analysis is presented as a non-traditional information source of demographic behavior, an indirect measure of population data from rural and difficult access areas. Initially, power intensity levels measured in Almagra and La Ceiba nodes based on the spectral map are presented as a tool to continuously measure the demographic behavior relating telecommunications equipment use to population density.  
backhaul networks; rural connectivity; urban rural gaps; spectral analysis; population density
# Optimization of Processing Parameters to Increase Thermal Conductivity of Rice Straw Fiber Film
## 3 Results and Discussion
### Response Surface Analysis
#### 3.2.3 Effects on Thermal Conductivity  
The regression model showed the linear relationship between the thermal conductivity and processing parameters (Equation (4)). Figure 4 shows the detailed effect of those processing parameters on the thermal conductivity. Thermal conductivity was positively correlated with basis weight (Figure 3(a)), charcoal addition ratio (Figure 3(c)), wet strength agent content (Figure 3(d)), and beating degree (Figure 3(e)), but negatively correlated with neutral sizing agent content (Figure 3(b)).  
Figure 3: Effects of five variables on wet tensile strength(**a**) Basis weight and wet strength agent content (**b**) Basis weight and beating degree (**c**) Neutral sizing agent content and beating degree (**d**) Charcoal addition ratio and wet strength agent content.  
It was found that the charcoal addition ratio was the most influential factor on thermal conductivity (Figure 4c). The highest thermal conductivity was 0.0733 W/m\(\cdot\)K, which was achieved at the highest charcoal addition ratio (20%). Through the direct contact and tunnel effect, thermal conductivity was enhanced between the charcoal particles and fibers, thus improving the thermal conductivity of the whole film. Because of the existence of the fiber and charcoal particles, there were two types of thermal conductivities in the BMF. The first type is remote thermal conductivity formed by the mutual bonding of the fibers with a contact network path. The second type is short-range thermal conductivity formed by charcoal particles with the function of a bridging effect between the fibers [49; 50]. Therefore, the thermal conductivity of the BMF was improved.
## 1 Introduction
### Related Work  
V2V communications rely on the IEEE 802.11p radio access technology [15]. This technology has been specifically designed for the vehicular environment and adopted by the European Telecommunication Standards Institute (ETSI) for supporting the exchange of cooperative awareness messages (CAMs) [2] and decentralized environmental notification messages (DENMs) [16]. On the one hand, CAMs are regularly transmitted by vehicles to provide information about their movement status. The information included in CAMs not only helps vehicles maintain connectivity with their neighbors, but also supports high-level road safety applications. On the other hand, DENMs are generated when a potential risk is detected in order to notify surrounding vehicles about the particular situation. DENMs provide support to event-driven safety applications. Since this work focuses on both the physical layer of V2V communications and safety applications, the default specifications of the IEEE 802.11p standard are adopted, which are a carrier frequency of 5.9 GHz, a transmission rate of 6 Mbps, and a control channel (CCH) of 10 MHz [15].  
The problem of shadowing in V2V-intersection scenarios has been usually addressed by using relay nodes [8], such as RSUs, in order to improve the connectivity in critical areas. For instance, References [11; 12] focused on covering as much area as possible by deploying a minimum number of RSUs because of the low market penetration of V2V-enabled vehicles and the deployment cost of RSUs. However, the delay permitted for road safety applications was not considered by these works. On the contrary, in [13; 14], the RSU placement problem was addressed as an optimization problem in order to minimize the delay, but even so, the values achieved did not satisfy the requirements of road safety applications. In addition, RSUs are not adaptable to changes in their surroundings. This lack of adaptability significantly affects the performance of systems based on RSUs. In this context, the use of beamforming may lower the delay for road safety applications and increase the adaptability to the surroundings.  
The problem of using RSUs has been noted, and some investigations have focused on optimizing beam shapes for urban V2V communications. For instance, Reference [17] presented preliminary results on optimizing values for a \(4\times 4\) antenna array for urban V2V communications using GA, and it was shown to outperform the baseline isotropic antenna. Similarly, in [18], the values of a \(4\times 4\) antenna array were optimized by using particle swarm optimization (PSO) and interpolating the results for unknown positions. This method has also been shown to outperform an isotropic antenna.
## 4 Investigations Results  
Using the analysis method described in Section 2, the computations of both the circuits shown in Figure 3 were carried out for all the considered transistor cooling systems. The calculations adopted the diode model embedded in SPICE, and the values of its parameters are as follows: IS = 1 pA, N = 1, CJO = 30 pF. Due to the significant differences in the values of thermal resistance for individual cooling systems, the range of the calculations was limited to the operating conditions, where the excess of the junction temperature of the transistor over the ambient temperature at the steady state does not exceed 200 K. The values of switching frequency \(f\) were also limited to the range from 1 to 400 kHz.  
In Section 4.1 the comparison of the results performed with the use of the new method and a selected method given in the literature are presented. Next, in Section 4.2 the influence of properties of the used cooling system and the operation range of the tested networks are presented.
# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 4 Analysis of Experimental Results  
### Comparative Analysis of Different Algorithms  
To assess the performance of the enhanced YOLOv8n model (VIEM-YOLOv8n) in sugarcane target detection tasks and to compare it with other mainstream object detection algorithms, this study selected several algorithms for experimental comparison, including SSD, Faster R-CNN, and the YOLO series. These comparative experiments aimed to analyze the performance of each algorithm and further validate the performance advantages of the improved model. The experimental results are shown in Table 1. Based on the experimental data in Table 1, the Faster-RCNN framework exhibited limited adaptability for this dataset due to its high parameter complexity and the lowest F1 score, suggesting that this two-stage object detection algorithm is not well-suited for this particular dataset. In contrast, although the SSD model has slightly fewer parameters than Faster-RCNN, its F1 score improved to 81%, indicating that single-stage object detection architectures are more adaptive for this type of dataset. However, the parameter count of the SSD model is still large, making it unsuitable for deployment on mobile devices, and its accuracy does not meet the requirements for high-precision detection.  
The YOLOv3 model has the largest number of parameters, which may lead to slower inference speeds and increased storage demands. Despite displaying the highest accuracy, it does not perform ideally for this dataset. YOLOv7-tiny, a lightweight model introduced in the YOLO series over the past two years, achieves higher accuracy while maintaining a low parameter count and computational cost, yet its effectiveness is not pronounced on this dataset. The v5n version of the model has 71% fewer parameters than YOLOv7-tiny but shows a 4.5% decrease in mAP, indicating limited recognition capabilities for this dataset. From the table, it can be seen that VIEM-YOLOv8n outperforms the original YOLOv8n in multiple key metrics: its [EMAIL_ADDRESS] increased by 5.1 percentage points (from 90.9% to 96.0%), and its [EMAIL_ADDRESS]:0.95 improved by 6.4 percentage points (from 65.1% to 71.5%), while the recall rate significantly increased to 93.3%. Although the parameter count slightly increased (from 3.0 M to 3.93 M), this change is justified by the model's high precision and efficiency.  
The experimental results demonstrate that the optimizations made to the YOLOv8n model, including the substitution with a VanillaNet backbone, integration of the Inception-NeXt deep convolutional architecture, introduction of the ECA attention mechanism in the C2F feature fusion module, and the adoption of the MPDIOU loss function, led to a slight increase in model parameters but significantly improved its mAP (mean Average Precision) by 5.1%. This confirms that the VIEM-YOLOv8n model substantially enhances recognition performance on the sugarcane dataset, making it more suitable for precise target detection.