esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Microalgae Cultivation in Medium Supplemented with Organic Carbon  
Experiments with _Chlorella_ cultivation were conducted in a multi-tube photobioreactor (Photon Systems Instruments: PSI, Drasov, Czech Republic), (Multi-cultivator MC 1000-OD), with light irradiance set at 0 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\) (heterotrophic growth) or 75 \(\mu\)E m\({}^{-2}\) s\({}^{-1}\) (mixotrophic growth) and temperature at 25 \({}^{\circ}\)C. The medium used for growth was TRIS-minimal medium (TMP), with a composition of: Tris (2.42 g/L), NH\({}_{4}\)Cl (0.4 g/L), MgSO\({}_{4}\cdot\)7H\({}_{2}\)O (0.1 g/L), CaCl\({}_{2}\cdot\)2H\({}_{2}\)O (50 mg/L), K\({}_{2}\)HPO\({}_{4}\) (93.5 mg/L), KH\({}_{2}\)PO\({}_{4}\) (63 mg/L), Na\({}_{2}\)EDTA (50 mg/L), H\({}_{3}\)BO\({}_{3}\) (11.4 mg/L), ZnSO\({}_{4}\cdot\)7H\({}_{2}\)O (22 mg/L), MnCl\({}_{2}\cdot\)4H\({}_{2}\)O (5.06 mg/L), FeSO\({}_{4}\cdot\)7H\({}_{2}\)O (4.9 mg/L), CoCl\({}_{2}\cdot\)6H\({}_{2}\)O (1.61 mg/L), CuSO\({}_{4}\cdot\)5H\({}_{2}\)O(1.57 mg/L) and (NH4)6 Mo7O24\(\cdot\)4H2O (1.1 mg/L). _Chlorella_ was cultivated in TMP (pH = 7) medium supplemented with glucose (TGP), glucose-containing 10% neutralized (pH = 7) enzymatic hydrolysate (TGP-Enz10) or glucose with 10% neutralized (pH = 7) citrate buffer (TGP-Cit10). In all profiles, glucose concentration was set at 1 g/L. Additionally, the effect of different loadings (0.5%, 1%, 2%, 5%, 10%, 15%, 20%) of neutralized (pH = 7) citrate buffer (100% = -0.05 M) on _Chlorella_ growth was evaluated. Enzymatic hydrolysate and citrate buffer were neutralized to pH = 7 with concentrated NaOH solution. A list of abbreviations is included in the end of the text.
## 4 Performance Modeling of the IASF-IoT Framework  
The integration of robust security measures in IoT devices often raises concerns about their impact on device performance, energy consumption, and overall system efficiency. To address these concerns and explore the potential viability of the IASF-IoT framework, a theoretical performance model has been developed. This model assesses the framework's projected impact on critical operational parameters of IoT devices, aiming to predict whether the enhanced security might compromise the devices' primary functions or user experience.
###### Abstract  
In this study, we present an innovative ion-beam doping technique for the controlled modification of the near-surface region of multi-walled carbon nanotubes (MWCNTs) aimed at creating pyridinic and pyrrolic nitrogen defects in their walls. This method involves the irradiation of MWCNTs with nitrogen ions using a high-dose ion implanter, resulting in the incorporation of nitrogen atoms into the nanotube structure. The structural and chemical changes induced by the ion-beam treatment were thoroughly characterized. Scanning electron microscopy (SEM) analysis revealed subtle changes in nanotube morphology, while X-ray diffraction (XRD) measurements exhibited altered peak intensities and a shift in the (002) reflection peak, indicating structural modifications, which correlates with transmission electron microscopy (TEM) data. X-ray photoelectron spectroscopy (XPS) analysis confirmed the successful embedding of nitrogen, mainly in pyridinic and pyrrolic configurations, as evidenced by the presence of corresponding lines in the N1s spectrum. Our findings demonstrate the feasibility of precisely engineering nitrogen defects in MWCNTs using the ion-beam doping technique. This approach is expected to be promising for the use of carbon nanotubes surface-functionalized with nitrogen atoms in the development of new devices for electronics, electrochemistry, catalysis, etc.  
multi-walled carbon nanotubes (MWCNTs); modification; nitrogen ion beam irradiation; pyrrolic and pyridinic nitrogen inclusions; scanning electron microscopy (SEM); transmission electron microscopy (TEM); X-ray diffraction (XRD); X-ray photoelectron spectroscopy +
Footnote †: journal:  
Article
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
## 4 Discussion
### Using Diodes to Add SiPMs
This means that, at low data rates, the required irradiance is inversely proportional to the number of SiPMs; however, the important comparison is the data rate that can be supported at a particular irradiance. At irradiances of approximately 4 mWm\({}^{-2}\) an array of 25 C10010 SiPMs is expected to support 2.8 Gbps, whilst an array of 100 C10010 SiPMs would support 3.5 Gbps. These results show that a rapid rise in the count rate penalty at high data rates limits the performance improvements that can be achieved by increasing the number of SiPMs acting in parallel.
###### Abstract  
Ankle foot orthoses (AFOs) are medical devices prescribed to support the foot and ankle of drop-foot patients. Passive-dynamic AFOs (PD-AFOs) are an effective solution for less severe cases. While off-the-shelf PD-AFOs are rather inexpensive, they provide poor anatomical fit and do not account for the required patient-specific biomechanical support. Three-dimensional (3D) scanning and manufacturing technologies allow manufacturing PD-AFOs customized for the patient's anatomy and functional needs. This paper aimed to report the overall procedure for designing and manufacturing a novel, fiberglass-reinforced polyamide, custom PD-AFO. The feasibility of the proposed procedure was tested in a case study. The methodology can be divided into the following steps: (i) foot and leg scanning, (ii) 3D design, and (iii) additive manufacturing via selective laser sintering. A custom PD-AFO was designed and manufactured for a 67-year-old male drop-foot patient following paraparesis in severe discarthrosis after spine stabilization surgery. AFO mechanical properties were measured via an ad hoc setup based on a servohydraulic testing machine. The functional outcome was assessed via gait analysis in three conditions: shod (no AFO), wearing an off-the-shelf PD-AFO, and wearing the patient-specific PD-AFO. As expected, wearing the PD-AFO resulted in increased ankle dorsiflexion in the swing phase with respect to the shod condition. Sagittal rotations of the hip, knee, and ankle joints were similar across PD-AFO conditions, but the custom PD-AFO resulted in faster walking speed with respect to the off-the-shelf (walking speed: 0.91 m/s versus 0.85 m/s). Additionally, the patient scored the custom PD-AFO as more comfortable (VAS score: 9.7 vs. 7.3). While the present analysis should be extended to a larger cohort of drop-foot patients, the novel PD-AFO seems to offer a valid, custom solution for drop-foot patients not satisfied with standard orthotics.  
ankle foot orthoses (AFO); dynamic AFO; composite material; polyamide; fiberglass; custom; mechanical properties; drop-foot; functional evaluation; gait analysis +
Footnote †: journal: Applied Sciences
# Experimental Validation of a Guaranteed Nonlinear Model Predictive Control
## 3 Guaranteed Controller Design via Validated Simulation  
In this section, a validated Nonlinear Model Predictive Control (NMPC) is developed to stabilize the inverted pendulum in the upright position considering the system's constraints and uncertainties. Firstly, we give a general overview of the validated numerical integration methods in Section 3.1. Secondly, we present the validated NMPC in Section 3.2.
## 3 Incremental Nonlinear Dynamic Inversion Control Law Design
### Overall Control Structure  
#### 3.5.1 Outer-Loop Control  
The control structure of the system is divided into two main components: outer-loop control and inner-loop control. Specifically, the lateral acceleration \(a_{y}\) and the bank angle \(\phi\) are selected as the outer-loop control variables. To achieve attitude decoupling control,  
Figure 7: Roll mode dynamic simulation results with and without PCH: **(a)** Roll angular rate comparison and **(b)** Virtual control commands comparison.  
the time-scale separation principle is employed [38]. The control loop is divided into a fast-speed inner angular rate control and a low-speed outer attitude control loop. The output of the outer loop comprises the roll and yaw angular rate control commands.  
In order to achieve lateral acceleration control, the closed-loop dynamics of the yaw angular rate control is modeled as a first-order lag system. This can be approximated using the formula \(a_{y}=V^{-}_{Y_{\phi}}\beta\), as depicted in Figure 8b. Consequently, the closed-loop dynamics of \(a_{y}\) can be expressed as  
\[\frac{a_{y}(s)}{a_{yc}(s)}=\frac{K_{ay}}{s^{2}+K_{r(r)}s+K_{ay}} \tag{34}\]  
where \(K_{r(r)}\) is the control bandwidth of the yaw control loop. In addition, the damping ratio of the second-order system is selected as 0.8, and the \(a_{y}\) controller gain is calculated as  
\[K_{ay}=\frac{K_{r(r)}}{1.6^{2}} \tag{35}\]  
As depicted in Figure 8a, nonlinear dynamic inversion (NDI) control is used for the bank angle control. The virtual control \(\
u_{\phi}\) is defined as  
\[\
u_{\phi}=K_{\phi}(\phi_{c}-\phi) \tag{36}\]  
where \(K_{\phi}\) is the control bandwidth of the bank angle. The integrated navigation module, which is commonly installed in aircrafts, provides readily available and accurate information on both the angular rate and attitude. This makes it possible to effectively cancel these factors during NDI design. Therefore, the roll angular rate command \(p_{c}\) can be derived as  
\[p_{c}=\
u_{\phi}-\tan\theta(q\sin\phi+r\cos\phi) \tag{37}\]
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 3 Methodology
### Case Study Context  
Our analysis is based on the DroneResponse case study presented by [PERSON] et al. [34]. The DroneResponse Project is a real-world initiative aimed at developing a robust system for managing sUASs operating in shared airspace. The project focuses on enabling drones to autonomously compute and maintain safe minimum separation distances while accounting for dynamic environmental factors. Unlike traditional centralized airspace management systems that enforce fixed, large separation distances, DroneResponse seeks to implement a more adaptive and real-time approach where each drone is responsible for its own separation calculations. The project integrates multiple technologies, including PX4 autopilot firmware, MAVROS communication protocols, and Jetson Xavier NX onboard computing, to facilitate autonomous flight operations. Drone's exchange real-time status messages via an MQTT-based mesh radio network, which allows them to assess environmental conditions such as GPS accuracy, wind speed, and braking distance.  
Each drone periodically broadcasts its state and receives updates from its neighbors. These updates help estimate external conditions such as GPS accuracy, wind forces, and braking capacity, which are critical for real-time collision avoidance. The system's success depends on correctly identifying and validating key environmental assumptions, such as geolocation precision, communication latency, and the impact of wind on drone movement. These factors directly influence how drones determine their safe separation distances. In the case study, the authors break down the high-level requirement, i.e., \"_When in flight, drone **d** shall continually compute the minimum separation distance with each neighboring drone **d** within its region of interest,_\" into several derived requirements and systematically validate the related environmental assumptions using simulations, field data, and empirical models to improve the system's reliability and safety.
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 4 Our Proposed Method
### Stacked Sparse Autoencoder
Taking the added sparsity penalty term in the sparse autoencoder into consideration, \(\delta^{l}\) can be calculated in (6):  
\[\delta^{l}=\left(\left(\sum\limits_{r=1}^{s}W_{r}^{l}\delta^{(l+1)}\right)+ \beta(-\frac{p}{\hat{p}_{i}}+\frac{1-p}{1-\hat{p}_{i}})\right)f^{\prime}(z_{i }^{l}) \tag{6}\]  
where \(f^{\prime}(z_{i}^{l})=W^{l}x_{i}+b^{l}\). The output of the sparse autoencoder is represented as \(W_{2}(W_{1}x_{i}+b_{1})+b_{2}\). Due to space limitation, more details for (4)-(6) are provided in Appendix A. To utilize the labeled information in the source domain to alleviate domain discrepancy, we follow the approach used in [2]; the softmax encoder weight regularization is introduced into the stacked sparse autoencoder. The objective function is described in (7):  
\[J=J_{1}(x,\hat{x})+\alpha J_{2}(\zeta,\theta)+\beta J_{3}(W_{1},W_{2},b_{1},b_ {2}) \tag{7}\]  
where \(\alpha\) and \(\beta\) are the trade-off parameters, which aim to balance the effectiveness of each item in (7). The first term \(J_{1}(x,\hat{x})\) in (7) is the reconstruction error, which can be defined as (8):  
\[J_{1}(x,\hat{x})=\sum\limits_{i=1}^{n}\left\|x^{(i)}-\hat{x}^{(i)}\right\|^{2} \tag{8}\]  
The second term \(J_{2}(\zeta,\theta)\) in (7) is the cost function of the softmax encoder weight regularization, which can be formalized as (9):  
\[J_{2}(\zeta,\theta)=-\frac{1}{n}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{c}1 \{y_{i}^{(s)}=j\}\log\frac{e^{\theta_{j}^{T}\xi_{i}^{(s)}}}{\sum\limits_{l=1} ^{c}e^{\theta_{l}^{T}\xi_{i}^{(s)}}} \tag{9}\]  
where \(\theta_{j}^{T}\) denotes the \(j\)-th row of \(W_{2}\), and \(y_{i}^{(s)}\) denotes the label \(x_{i}\) in the source domain. The last term \(J_{3}(W_{1},W_{2})\) in (7) is the total weight regularization, which can be written as (10):  
\[J_{3}(W_{1},W_{2})=\left\|W_{1}\right\|^{2}+\left\|W_{2}\right\|^{2}+\left\| b_{1}\right\|^{2}+\left\|b_{2}\right\|^{2} \tag{10}\]As the objective function is an unconstrained optimization problem, the minimization of \(J\) with respect to \(W_{1}\), \(W_{2}\), \(b_{1}\), \(b_{2}\) and \(\theta_{j}\) is adopted using the l-bfgs method, which has been demonstrated to be a more efficient backtracking method [22]. The partial derivatives of \(\theta_{j}\) can be formalized as (11):  
\[\frac{\partial J}{\partial\theta_{j}}=\alpha(-\frac{1}{n}\sum_{i=1}^{n}\sum_{j= 1}^{c}1\{y_{i}=j\}(1-\frac{e^{\theta_{j}^{T}\xi_{i}}}{\sum\limits_{l=1}^{c}e^{ \theta_{l}^{T}\xi_{i}}})\xi_{i}) \tag{11}\]  
The alternate optimization method is adopted to derive the solutions as follows:  
\(W_{j}\gets W_{j}-\eta\frac{\partial J}{\partial W_{j}}\), \(b_{j}\gets b_{j}-\eta\frac{\partial J}{\partial b_{j}}\), \(\theta_{j}\leftarrow\theta_{j}-\eta\frac{\partial J}{\partial\theta_{j}}\) where \(\eta\) is the step length, which determines the speed of convergence.
## 3 Methodology
### Assessment Metrics  
Spatial data precision and accuracy can be quantified using two common measures: RMSE and standard deviation [12; 59]. RMSE is the square root of the average of the set of squared differences between the data set coordinate values and the coordinate values from the location checkpoints. Given the \((x_{data,i};y_{data,i})\) locational coordinates and the \((x_{base,i};y_{base,i})\) base coordinates of n points; RMSE is defined to be Equation (1).  
\[\begin{gathered} RMSE_{x}=\sqrt{\left(RMSE_{x}\right)^{2}+ \left(RMSE_{y  
The following relation Equation (2) expresses the standard deviation:  
\[\sigma=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}} \tag{2}\]
## 3 Results
### Daily Coverage of Mineral Requirements After Consumption of Infant Formulas and Human Milk
#### 3.5.1 Conventional Initial and Follow-On and Specialist Infant Formulas  
The coverage of mineral requirements in initial IFs exceeded the values determined by the established norms of nutrition for the Polish population. The highest values were determined for Fe, where the coverage of the requirement after the consumption of a daily portion of the formula exceeded the established norms for this element by up to 20-25 times in C-A1 and C-B1. For Mn, in C-D1, the values exceeded the standard by up to 18 times the set standards. Among the initial IFs, the whole-day intake of formula C-B1 covered the standard for Mg, Na, and P to the greatest extent. In the case of Na, formulas C-A1, C-B1, and C-D1 exceeded the coverage of the daily requirement for this element by 28, 46, and 42%, while in formula C-C1, the coverage of the requirement for Na was only 89%. Large differences were observed in the coverage of P requirements between C-B1 and C-C1. In the first mentioned formulas, the proportion of P exceeded the daily requirement by almost three times, while in the second, the standard for this macronutrient was exceeded by only 40%. The standard for Zn was exceeded by more than 100% in C-A1, C-B1, and C-D1, while in C-C1, it was exceeded by about 30%.  
Follow-on IFs, intended for infants after six months of age, covered the requirements for selected minerals at a significantly lower rate than initial ones. After consuming the recommended daily portion of each milk, there was an excess of the recommended daily intake for Ca, Zn, Cu, and P (except C-D2), with the highest being for Ca (by up to 126% in C-B2). The daily requirement for Mg was met by about half after consumption of C-A2, C-B2, and C-C2, while it was met by about 30% after consumption of C-D2. Large deviations were noted for Fe, where the requirement coverage after whole-day intake ranged from 27% in C-B2 to 60% in C-A2. The intake of all follow-on IFs covered the daily requirement for Mn (5-6%) and Na (31-41%) to the lowest extent.  
Specialist IFs showed the greatest variations in estimated coverage of selected minerals after consumption of the recommended daily portion. The daily intake of Ca was exceeded by up to four times in S1 and S6. In contrast, Mg coverage ranged from 22% in the S1 formula to 180% in the S3 formula. As in the conventional initial IFs, there was high coverage of Fe requirements, which exceeded standards by up to twenty-four times in S5. Of the specialist IFs, the standout formula in terms of Fe requirement coverage was S4 meeting Fe demand by only 47%. In general, the most deviating estimated requirement coverage values were recorded for Mn.
## 4 Rock Damage Recognition  
### Ultrasonic Velocity Ratio  
The ratio \(V_{P}/V_{S}\), which reflects the damage process, decreased at first and rose later in the experiment (Figure 8). In the original salt rock sample, the P-wave velocity was 1.71 times the S-wave velocity; it decreased to 1.55 at the beginning of testing as the internal structure was compacted with increasing axial stress. When the stress increased, the salt rock was in the linear deformation stage, and \(V_{P}/V_{S}\) began to increase slowly. In the early stage of the stabilized-growth crack stage, the velocity ratio of the salt rock was 1.78; it then increased significantly to 2.04 at the end of this stage. After that, it increased sharply. When the stress level reached a peak, \(V_{P}/V_{S}\) rose to 3.08 and a large number of macroscopic cracks were produced inside the rock; at this point the rock approached failure. Based on three samples, the results show that rock damage can be effectively interpreted by \(V_{P}/V_{S}\); if \(V_{P}/V_{S}\) is less than 2.0, the rock is in a good condition; if \(V_{P}/V_{S}\) is 2.0-2.5, the rock had suffered serious damage; if \(V_{P}/V_{S}\) is over 2.5, the rock is about to fail.
## 1 Introduction
Figure 2 compares the S-N curves of injection-moulded and FFF-printed ABS specimens at different  
Figure 1: Schematic illustration of the FFF process. stress levels. In another study by [PERSON] et al. [14], the flexural fatigue life of FFF polycarbonate prints was reported to be notably lower than that of their IM counterparts. However, the quality and mechanical strength of FFF prints can be substantially enhanced by introducing reinforcing fillers to the polymer matrix (composite filament) or optimizing the 3D printing parameters. In the former method, fillers of different shapes and materials are mixed with the polymeric filament to increase its strength. However, it has been shown that the fatigue strength of FFF prints can be enhanced [16; 17] or deteriorated [18] by introducing filler materials, depending on the loading type, material, shape, and content of the added fillers. Therefore, optimizing 3D printing parameters is the key to obtaining the highest potential mechanical strength in FFF parts, including composite prints. [PERSON] et al. [19] fabricated thermoplastic polycarbonate urethane (PCU) samples using injection moulding and FFF printing. They subjected the fabricated samples to monotonic compression and tensile tests, as well as shear and tensile fatigue loads. They concluded that the monotonic and fatigue strength of FFF samples not only matched that of IM parts but also outperformed them in some cases. Choosing suitable printing parameters, such as a high nozzle temperature, was stated as the key to achieving high-quality parts in this work. It was also reported by [PERSON] et al. [20] that high-quality PLA prints with near isotropic fatigue properties are possible by implementing optimum parameters during FFF printing. These results highlight the significance of 3D printing parameters in obtaining durable prints with high fatigue properties. Although the influence of 3D printing parameters on the quasi-static performance of prints has been widely discussed within the literature [21; 22], there are limited accounts of this effect on fatigue performance. Numerous studies are available on the overview of the fatigue data in additively manufactured parts; however, the influence of 3D printing parameters has not been discussed in detail. An overview of fatigue data in additively manufactured parts was provided by [PERSON] et al. [13]. A discussion of FFF technology was included in the paper cited above, but no in-depth analysis of the impact of different 3D printing settings was included. In addition, [PERSON] et al. [23] reviewed the fatigue behaviour of FFF parts from a material standpoint. This study aims to provide a detailed account of the effect of 3D printing parameters on the performance of FFF parts under tensile, compressive, flexural, and rotating bending fatigue. Influential 3D parameters are discussed, and the fatigue testing of polymers is explained. Research gaps are then identified, and suggestions for future works are provided in this review. Figure 2: S-N curve for 3D-printed and injection-moulded ABS specimens (Adapted from S-N curves by [PERSON] et al. [15]. CC BY 3.0).
## 3 Results and Discussion
### R/A-AF Association Analysis
Therefore, the variation trend of the tensile crack and shear crack can be clearly seen from the R/A-AF association diagram, which facilitates the analysis of the damage affected by steel corrosion in concrete from the perspective of the cracking mode. Using R/A-AF association analysis, the type of damage in the RC beams can be identified with reasonable accuracy. Furthermore, R/A and AF values are important parameters used for characterizing the cracking mode for corroded RC beams.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 5 Parameters Analysis on Deformation
### Crown Beam Size and Strength  
In Figure 14, the horizontal deformation curve of the pile at \(x\) = 70 m is shown for varying sizes and material strengths of the crown beam. It can be observed that these parameters have minimal impact on the horizontal deformation of the pile. Based on this analysis, it can be concluded that, within the pile-anchorretaining structure system, increasing the size of the pile is the most effective measure for controlling the deformation of the retaining structure.