esa-sceva/satcom-chunk-collection huggingface dataset:
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 2 Recursive Channel Model
### NLoS Propagation Model  
This recursive model computes the NLoS components by dividing the walls into smaller areas or cells \(\epsilon_{i}\), assuming a Lambertian scattering in each cell. Each of these smaller areas plays the role of an elemental receiver (\(e_{i}^{r}\)) and an elemental source (\(\epsilon_{i}^{t}\)). It is crucial that these smaller areas be sufficiently small to ensure optimal model accuracy, although this refinement increases computational time. This method serves as an approximation of the actual propagation process, which involves an infinite number of rays traversing the space. One of these smaller areas is designated the LED source and another is designated as the photodetector, while the remaining segments represent reflective elements on each of the six walls. Figure 3 identifies the areas corresponding to the light source, photodetector, and reflective elements in yellow, blue, and gray, respectively. The channel impulse response for the reflection \(k\) can be calculated as  
\[h_{n}^{(k)}(t;S,R)\approx P_{T,n}(\phi)\sum_{i=1}^{N_{c}}h_{n}^{(0)}(t;S, \epsilon_{i}^{r})*h_{n}^{(k-1)}(t;\epsilon_{i}^{t},R) \tag{8}\]  
Figure 3: Geometric notation for the indoor channel model: \(\mathbf{tx}\) and \(\mathbf{rx}\) represent the RGB LED and the three photodetectors, respectively, \(\mathbf{d}\) represents the distance between the corresponding two points, \(\phi\) denotes an output angle of a light ray at a specific point, and \(\theta\) denotes the input angle of a light ray at that specific point.  
where \(N_{c}\) is the number of cells and \(*\) is the convolution operation. The convolution can be computed as a scalar multiplication, as \(h_{n}^{(0)}\) and \(h_{n}^{(k-1)}\) are impulse functions. The term \(h_{n}^{(0)}(t;S,\epsilon_{i}^{\prime})\) can be replaced for the LED model as follows:  
\[h_{n}^{k}(t;S,R,\lambda)=P_{T,n}(\phi)\sum_{j=1}^{N_{c}}\rho_{j,n}\frac{(m+1)} {2\pi d_{Sj}^{2}}cos^{m}(\phi)cos(\theta_{0})g(\theta)\cdot rect\left(\frac{2 \theta}{\pi}\right)h_{NLOS}^{k-1}\left(t-\frac{d_{Sj}}{c},\epsilon_{j},R \right)\Delta A \tag{9}\]  
In accordance with [19], this definition can be expressed as follows:  
\[h_{n}^{(k)}(t,\lambda)=P_{T,n}(\phi)\sum_{i=1}^{N_{c}}[L_{1}L_{2}...L_{k+1}] \cdot rect\left(\frac{\theta_{k+1}}{FOV}\right)\cdot\delta\left(t-\frac{d_{1 }+d_{2}+\cdots+d_{k+1}}{c}\right) \tag{10}\]  
\[L_{1}=\frac{\Delta A(m+1)cos^{m}(\phi_{1})cos(\theta_{1})}{2\pi d _{1}^{2}} \tag{11}\] \[L_{2}=\frac{\rho_{j,n}\Delta Acos(\phi_{2})cos(\theta_{2})}{\pi d _{2}^{2}},\cdots\] (12) \[L_{k+1}=\frac{\rho_{j,n}A_{R}cos(\phi_{k+1})cos(\theta_{k+1})}{ \pi d_{k+1}^{2}} \tag{13}\]  
where \(\rho_{j}\) represents the reflectance factor at wavelength \(n\) of cell \(j\) (\(\epsilon_{i}\)) and its \(\Delta A\) value. With \(N_{c}\) as the total number of cells and \(k\) reflections, the number of elementary computations performed to calculate the CIR is equal to \(N_{c}^{k}\).
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Prototype Readiness and SNO Integration  
The CMS is prototype-ready and implemented in an integrated architecture. The framework alongside this architecture will be tested in a CubeSat mission in 2025 to validate its implementation and value in a real space mission. The expected timeline for initiating these validation tests is between Q4-2025 and Q1-2026. The details of the mission and the designed tests are published in [92]. Additionally, the CMS will be further validated in the 6 GStarLab CubeSat mission, scheduled to begin in 2026 [93]. The CMS interface with SNOs is essential for every satellite mission. For those missions, this interface is based on the satellite's OM, which serves as the common language between the CMS and the operator. This directly tackles all the identified technology maturation and deployment challenges, providing a real implementation of an integrated operations framework ready for space testing in a satellite mission.
## 5 Experiments and Discussion
### Dataset  
Following the problem definition in Section 4.1, we chose a real-world dataset that meets the description to perform verification experiments. In order to ensure the reliability of our experimental results, we used a dataset from ProgrammableWeb [37] and divided the data into two parts: Web APIs and Mashup application tasks. In the dataset, Web APIs are single-API services which contain APIName, category, description text, port number, etc., while Mashups are a web application tasks composed of multiple Web APIs, which include MashupName, category, MemberAPIs, description text, etc. The dataset consists of 12,919 Web APIs and 6206 Mashups. This real dataset conforms to the problem definition for the recommendation scenario of industrial software service components. According to the statistics, only 940 of all API components are called by the task, or 7.28% = 940/12,919. Even though the majority of the APIs are not called, there are a total of 9297 task-API interactions. The dataset's attributes are presented in Table 2:
## 3 Results
### Transitions in Physiological States of STEC O157:H7 on Romaine Lettuce During Cold Storage
#### 3.3.2 Transition of STEC O157:H7 Cells to the Injured State During Cold Storage  
The median value of injured cells on day 0 was 66.4% (95% CI = 31.6%, 91.1%) while that after 5 d of cold storage was 72.7% (95% CI = 41.9%, 96.9%). Whereas the median value of injured cells on lettuce harvested at 9 \({}^{\circ}\)C was 62.5% (95% CI = 26.8%, 94.6%), that on lettuce harvested at 17 \({}^{\circ}\)C was 71.6% (95% CI = 27.9%, 95.8%); see Figure 5B. The median values of injured cells for strains from the Central Coast, Salinas and Yuma outbreaks were 52.3% (95% CI = 21.0%, 78.7%), 78.6% (95% CI = 32.7%, 97.1%) and 65.1% (95% CI = 28.4%, 86.9%), respectively. There were weak correlations (r \(\leq\) 0.1) between injured cells and cold storage times at both 9 and 17 \({}^{\circ}\)C temperatures.  
Regression methods were used to assess the combined effect of harvest temperature, strain and cold storage days on the percentage of injured cells. Different combinations of factors were incorporated into regression models, and the best fitting combinations were selected using the corrected Akaike Information Criterion (AICc). The selected model inputs were evaluated for their effect on the injured cells. Strain significantly (\(p\) < 0.001) affected the transition to injured cells during cold storage (Figure 7). In the Salinas outbreak strain, significantly (\(p\) < 0.05) more cells transitioned into injured cells than in the Central Coast strain.  
Figure 6: Regression tree analysis of the combined effect of harvest temperature, strain and cold storage period on the log reduction of culturable cells over cold storage period. The criteria used in splitting the trees are indicated for each decision node and the relative probability of each decision indicated as a %.  
The response variable, injured cells, was transformed into the interval (0, 1) and the factors of strain, harvest temperature and cold storage days evaluated using beta regression. A confidence interval of beta coefficient > 0 or <0 denote significant (\(p\) < 0.05) effect, whereas a confidence interval including 0 is not significant (\(p\) > 0.05). Strain was transformed into a dummy variable with the Central Coast 2018 outbreak strain as a baseline category.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Designing an Energy-Conscious Algorithm
#### 5.1.7 Signal Format Conversion  
Some researchers have converted signal formats to reduce complex arithmetic operations in their approaches. This conversion allows them to leverage well-established, efficient models from other domains, which can extract features more effectively. For instance, conversion of an ECG signal to a binary image enabled the authors of [101] to use simple addition and shift operations in their TNN-based cardiac arrhythmia detector. This resulted in a reduction in energy per operation of three times. Moreover, this simplification reduced the bit sizes of the weights, thereby simplifying the memory controller. This not only increased the overall system speed but also reduced latency. The authors of [67] also used the same approach to convert ECG signals into binary images in their ECG classifier on a wearable edge device to exploit the benefits of the bCNN for resource and power optimization.  
By transforming complex representations into simpler ones, this method also reduces memory requirements, computational load, and power consumption, which are critical factors for WMDs. However, this approach introduces the overhead of initial data processing for conversion and post-processing, which may degrade system latency. On the contrary, the authors of [102] used a simple slope-based detection (SBD) algorithm instead of complex machine learning for seizure detection. To save power, they computed the data at shorter intervals instead of at the sampling period and determined the running sum of the squared data values before calculating the slope.  
In conclusion, researchers have employed various optimization techniques to enhance the efficiency of complex algorithms in WMDs. These optimizations address the challenges posed by the increasing complexity of AI-integrated algorithms, ensuring that AI models are compressed and optimized to operate effectively within the resource, memory, and power constraints of WMDs.  
The benefits and constraints associated with these optimization techniques are summarized in Table 3, along with their descriptions.
## 3 Literature Review
### IoT
#### 3.3.6 Addressing Authentication Issues in Supply Chain with Zero Trust
Zero trust architecture with PEP, PDP, PIP, PA and PE have been applied by authors to address malicious access requests, authentication issues and the zero trust principle of \"authentication before authorisation\" [47; 48; 49; 50; 51; 52; 53]. * Authorisation issues related to IoT devices have been studied by researchers, and many have applied zero trust architecture to address the issues [55; 56; 58]. * Network segmentation to address lateral movement is one of the zero trust principles. Authors have used blockchain to achieve segmentation [12; 59; 60; 61; 62; 63]. Network segmentation and credential management have been achieved without blockchain by [61]. Network security to support zero trust principles has been achieved with network slicing by [62]. * Issues with authentication and authorisation in IoT environments leading to implicit trust has been studied by authors who have included mutual authentication and zero trust to address the identified issues [64]. * Implicit trust is a byproduct of perimeter-based security, where an identity, when authenticated at the perimeter, is not verified when an access request is made. Researchers have studied this problem and provided proposals/solutions using zero trust principles [65; 66; 67; 69; 70; 36]. Access control issues and addressing them with zero trust has been studied by [71]. Access control issues have been addressed using quantum safe keys by [72]. * Issues in supply chain leading to compromises of intellectual property (IP) and competitive edge has been researched by [73; 74; 75]. * The authors of [76] studied the applicability of zero trust to power grids to address cyber-attacks and concluded that zero trust does not address prevalent issues. * Researchers have studied technology frameworks to facilitate zero trust and used zero trust to address the changing threat landscape [77; 78]. * Researchers have studied network topology to address authentication and authorisation issues in IoT by including network segmentation to limit lateral movement [80; 81; 82; 83]. Regarding the zero trust five step implementation process, authors and researchers have generally not considered Steps 1, 2, 4, and 5. One author has considered Steps 1 and 2 [77]. A few authors have considered Step 4 [62] and Step 5 [61], but these considerations were more from a perspective of including policies and continuous monitoring in their work than from a perspective of considering the adoption of the five-step implementation process.
## 2 Materials and Methods
### Aroma Extract Dilution Analysis with GC-O/GC-MS
#### 2.3.4 Conditions of GC-MS/GC-O  
GC-MS and GC-O were performed employing an Agilent 7890B GC equipped with an Agilent 5977A MS or a sniffing port (ODP3 C200; Gerstel, Mulheim an der Ruhr, Germany). All the samples were analyzed on a DB-WAX capillary column (60 m \(\times\) 250 \(\upmu\)m i.d., 0.25 \(\upmu\)m film thickness; Agilent Technologies Inc, Santa Clara, CA, USA). Helium (99.999%) was used as carrier gas at a constant flow rate of 1.5 mL/min. The injector temperature was 250 \({}^{\circ}\)C in splitless mode. The initial temperature of the oven was 40 \({}^{\circ}\)C, then raised to 50 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, and held for 5 min; then raised to 80 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, and held for 5 min; finally raised to 230 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and held for 10 min. The flow split ratio at the end of the column was 1:1 to the MS held at (250 \({}^{\circ}\)C) and sniffing-port (230 \({}^{\circ}\)C). MS (the electron ionization mode) was in full scan mode with an acquisition range of \(m/z\) 30-350 fragments and ionization energy of 70 eV. The ion source temperature was 230 \({}^{\circ}\)C.
## 2 Design of the Plasmonic Sensor
The reflected light inside the prism, in the glass-MgF\({}_{2}\) interface, is injected into another optical fiber and is conducted to a detector. It is constantly in contact with an electronic system to process the signal in real-time. The angle of incidence with respect to the normal in the MgF\({}_{2}\) is \(\theta\). When the light strikes the hemispherical prism-MgF\({}_{2}\) interface at an angle \(\theta\) greater than the critical angle, an evanescent wave is generated and is propagated along with the hemispherical prism-MgF\({}_{2}\) interface. This evanescent wave is able to excite two SPPs depending on the thickness of the layers. The energy of the incident light is then transferred to the surface plasmons, resulting in a sharp dip in the intensity of the light reflected [14]. The theoretical modeling of SPR reflectance was carried out using the transfer-matrix method to solve the Fresnel equations for the multi-layer stack [21,22] with a WinSpall software package. WINSPALL is a free PC-based piece of software that computes the reflectivity of optical multilayer systems. It is based on the Fresnel equations and the matrix formalism. It is used to analyze surface plasmon experiments. The sensors that we propose are based on intensity interrogation and therefore have the advantage of not having moving parts, so there is no mechanical wear and tear, and they will have a long useful life. MgF\({}_{2}\) is very transparent to visible wavelengths (our sensor) and is an anti-reflective compound so it will increase the light intensity in the back metal layer (Ag or Al). Another advantage of this compound is that it is very easily deposited on the glass, which is why the manufacture of the sensor is cheaper. The last layer of Al or Ag is easy to deposit so the central sensor block is easy to manufacture. The optical fibers are glued with index equalizing epoxy resin (n\({}_{\text{ep}}\) = 1.44) to reduce the inevitable Fresnel reflections as much as possible. The optical fibers are glued at an angle with the normal to the base of the prism equal to that calculated for each sensor. Being a hemispherical prism, the light enters perpendicular to its face, and we do not have to take into account [PERSON]'s law as in the case of prisms with flat faces.
## 3 Results and Discussion
### Hardened State Properties
#### 3.4.1 Compressive Strength
The negative value of the enthalpy (\(-\)415.6 kJ) indicates that the reaction is exothermic, which indicates that the high temperatures favour the speed of the product's formation. Gibbs' free energy, which also has a negative value (\(\Delta\)G = \(-\)437.1 kJ), indicates that the reaction is spontaneous so that hydrogenation does not require energy supply to occur. \[\text{Al}+\text{OH}^{-}+3\text{H}_{2}\text{O}\to 1.5\text{H}_{2}+\text{Al}( \text{OH})_{4}^{-}\Delta\text{H}=-415.6\text{ kJ};\Delta\text{G}:-437.1\text{ kJ} \tag{5}\]  
Naturally, mixes with higher MIBA content have lower cement content, diluting the latter component and decreasing the amount of hydration products, which also leads to a decline in performance. Still, while MIBA presents some pozzolanicity thereby increasing strength over time with the ensuing reaction between the amorphous phases and Ca(OH)\({}_{2}\) \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & \multicolumn{3}{c}{**7 Days**} & \multicolumn{3}{c}{**28 Days**} & \multicolumn{3}{c}{**91 Days**} \\ \cline{2-11}  & \(\mathbf{f_{cm,cube}}\) & \(\mathbf{\sigma_{fem,cube}}\) & \(\mathbf{f_{cm,cube}}\) & \(\mathbf{\sigma_{fem}}\) & \(\mathbf{\sigma_{fem}}\) & \(\mathbf{\mathbf{f_{cm,cube}}}\) & \(\mathbf{\sigma_{fem,cube}}\) & \(\mathbf{\mathbf{f_{cm}}}\) & \(\mathbf{\sigma_{fem}}\) \\ \hline RC & 50.3 & 5.3 & 57.6 & 4.2 & 55.4 & 12.5 & 80.1 & 2.3 & 65.6 & 12.4 \\ C10 FA20 BA & 27.6 & 3.6 & 34.0 & 2.7 & 14.4 & 1.4 & 39.6 & 0.5 & 16.7 & 1.4 \\ C20 FA10 BA & 23.0 & 2.1 & 31.7 & 1.7 & 24.6 & 1.7 & 39.3 & 0.8 & 26.3 & 2.5 \\ C20 FA40 BA & 11.2 & 1.6 & 17.5 & 0.8 & 6.2 & 0.1 & 20.6 & 2.3 & 8.8 & 0.7 \\ C40 FA20 BA & 12.4 & 1.0 & 19.7 & 0.6 & 8.6 & 0.7 & 23.1 & 3.0 & 10 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Compressive strength (MPa) cylindrical and cubic specimens. Figure 5: Expansion of specimens inside the moulds. the reaction with Al is spontaneous and corresponds to the first reaction to develop, the detrimental effect of which overlapped the positive one from pozzolanic reactions. Another possible factor potentiating the compressive strength loss could have been due to the high content of Fe\({}_{2}\)O\({}_{3}\). [PERSON] and [PERSON] [52] stated in their study that amounts greater than 2.7% of Fe\({}_{2}\)O\({}_{3}\) led to a compressive strength loss due to decreased pozzolanic activity. Hematite reacts with the Ca(OH)\({}_{2}\) producing iron ettrinite, Ca\({}_{3}\)Fe\({}_{2}\)(OH)\({}_{12}\)[53], which is characterized by a negligible binding ability [54].
## 2 Materials and Methods
### Image Acquisition and Calibration  
The Visible/near-infrared (Vis/NIR) hyperspectral imaging system used for the experiments consisted of an Offner structured spectrometer (N Series, Headwall Photonics, Bolton, MA, USA), an EMCCD detector ([PERSON], Luca-R EMCCD, Belfast, United Kingdom), a variable-focal-length lens (Schneider, XENOPLAN, Bad Kreuznach, Germany), a motorized stage (WN500 TA1000H, Beijing, China), two 500 W Tungsten-Halogen lamps positioned at approximately 45\({}^{\circ}\) angles and 35 cm above and lateral to the samples (Photoflex, Watsonville, CA, USA), and a computer with the image acquisition software. The wavelength range of this system was from 350 to 1000 nm with an average wavelength gap of 3.7 nm; the line-scan width contained 1008 pixels, and the pixel resolution of the system was 0.226 \(\times\) 0.226 mm.  
The hyperspectral images were taken in a dark room. The distance of the camera from the target was 40 cm. Exposure time was set as 32 ms, and the speed of the motorized stage was 1.2 cm/s. Prior to imaging, a white reflectance image (\(W\)) was taken for image calibration by acquiring the spectral image of a Spectralon(r) reflectance panel with 100% reflectance values (SRT-75e050, Labsphere, North Sutton, NH, USA), and the dark reference image (\(B\)) was obtained by recording the spectral image when the camera lens was completely covered [23]. During imaging, the Petri dish with its lid removed was placed on a white Teflon(r) (Polytetrafluoroethylene) plate, and then the hyperspectral images were acquired by moving the samples pass by the lens in front of the hyperspectral image camera via the motorized stage. The acquired hyperspectral images (\(I\)) were calibrated by the following calibration model:  
\[R= \frac{I-B}{W-B}\times 100\% \tag{1}\]
# Development and Preliminary Evaluation of a Lower Body Exosuit to Support Ankle Dorsiflexion
## 3 Preliminary Evaluation
### Data Collection and Analysis  
The data were captured using the provided Xsens Analyze software and the ProEMG Software (2.1.3.6, Myon AG, Schwarzenberg, Switzerland, Win10) for the Myon320 system. The electromyography (EMG) data were normalized to maximum voluntary contractions (MVC), rectified and bandpass-filtered (4 th order Butterworth filter; cut-off frequency, 20-200 Hz). While for the EMG device no input for a trigger signal was available, an EMG Sensor was used to capture the trigger output signal of Xsens, which was lowered to 0.5 mV by using a voltage divider. Inside Xsens Analyze, events were marked at heel strikes and toe-offs.  
In Matlab (9.5.0.944444, R2018b, MathWorks Inc., Natick, MA, USA, Win10), the kinematic data were cut from start to stop trigger signal, segmented between heel strikes and time-normalized to 0-100% of gait. For EMG data, the events from Xsens were used to export the muscle activation at swing phase, between toe-off and heel strike. The first and last stride of each trial were removed to analyze the steady state without including accelerating effects at the beginning and end of walking. Therefore, 20 strides for each condition were included to calculate the mean trajectories of the ankle sagittal, frontal and transverse plane, as well as hip and knee sagittal plane angles.  
The design of the exosuit regarding the subjective perception, acceptance and usability was determined following the biomechanical measurements. The participants were encouraged to describe their overall impressions on aspects such as the efficiency of ankle assistance when walking with the exosuit, the local perceived discomfort and the size adjustment when putting the system on.