esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 2 Simulation Environment
### Ground-Truth Dynamics
A standard cannonball model, which assumes that the satellite is a homogeneous sphere of given radius [24], is used to express the magnitude of the SRP acceleration, as shown in Equation (4):  
\[a_{SRP}=\frac{S_{\sun}}{c}\frac{(1\mathrm{AU})^{2}}{d_{\sun}^{2}}c_{R}\frac{A_{ \sun}}{m} \tag{4}\]  
where \(S_{\sun}=1367\) W m\({}^{-2}\) is the Sun mean flux at 1 AU, \(c=299\),792,458 m s\({}^{-1}\) is the speed of light, \(d_{\sun}\) is the current Sun-spacecraft distance, \(c_{R}\) is the reflectivity, and \(A_{\sun}\) is the cross-sectional area of the satellite exposed to the radiation. Although a traditional _box-wing_ model provides a more accurate representation of the SRP acceleration [25], by modelling the spacecraft as a sphere, the cannonball model allows one to decouple the user trajectory from the geometry and actual orientation of the spacecraft. Additionally, we assume that throughout the landing mission, the spacecraft is in constant Sun illumination.
## 3 Ambient Parameter Monitoring and Sensor Technology
### Shock and Vibration  
During transport and distribution, products are loaded and unloaded several times, and may experience shock or vibration. Fresh fruit and vegetables are easily damaged during those incidents. This is relevant for quality and shelf life because mechanical damage or injuries strongly promote quality loss and spoilage. For instance, [PERSON] et al. [57] measured significantly higher respiration rates during storage for damaged apricots than for non-damaged ones. For carrots, different processing steps from mechanical harvesting to packing can cause a huge amount of loss through damage [25]. The severity of the damage also plays an important role for quality retention and shelf life, as [PERSON] et al. [58] show for different spinach types. Hence, using accelerometers (motion sensors) for detecting shock or vibration can be useful for very sensitive products such as berries, but also for many other commodities. Most suitable accelerometers for IoT devices are based on microelectrochemical systems (MEMS), because they are small, inexpensive and accurate [59; 60]. These devices are widely available and can be used for detecting shock or vibration throughout the chain by simple attachment to a pallet [60]. Table 1 gives an overview of the presented ambient parameters and the corresponding sensor technology suitable for an IoT application. In addition, fresh fruit and vegetables are listed for which monitoring of the respective ambient parameter could bring value by preventing product losses through unexpected deterioration. The examples for commercially available sensors contain ready-to-use solutions including battery, communication modules, etc. For ethylene, only sensing units without inbuilt battery or connectivity were found. Depending on the intended application it can also be favorable to develop a customized sensor from single modules. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ambient Parameter** & **Sensor Technology Suitable** & **Solutions Commercially** & **Examples** & **Relevant Fruit and Vegetables** & **References** \\ \hline  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline  & & & & & & \\  & & & & & & \\ \hline  & & & & & & \\ \multirow{4}{*}{Relative Humidity} & \multirow{4}{*}{Sensitive Myrometer} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Yes} & \multirow{4}{*}{Sentius \#RSIox by LairdTM} & \multirow{4}{*}{} & \multirow{4}{*}{} \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline  & & & & & \\  & & & & & \\ \hline  & & & & & \\ \multirow{4}{*}{Ethylene} & \multirow{4}{*}{Chemissistors} & \multirow{4}{*}{Cennicapostors} & No & \multirow{4}{*}{Sensing units}
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Diffuse Optical Tomography (TD-DOT)
#### 5.3.1 General Concept of TD-DOT
The Levenberg-Marquardt algorithm, which is a standard method in the Newton-type minimization process, leads to the following inversion matrix equation for the updates at the \(k\)-th iteration, \(\delta\mathbf{x}^{(k)}\)[69; 166],  
\[\mathbf{W}^{(k)}\delta\mathbf{x}^{(k)}=\Delta\boldsymbol{\Gamma}^{(k)}= \boldsymbol{\Gamma}_{cal}^{(k)}-\boldsymbol{\Gamma}_{mes}\, \tag{50}\]  
where \(\mathbf{W}\) is the sensitivity (weight or Jacobian) matrix of the featured TD data to the changes in the optical properties, \(\delta\mathbf{x}\). If the time-gated intensity is chosen for the featured TD data, \(\mathbf{W}\) will be given by equations similar to Equations (17) and (18). Then the optical properties are updated by use of the Tikhonv regularization as follows,  
\[\delta\mathbf{x}^{(k)}=\left(\mathbf{W}^{(k)\,T}\mathbf{W}^{(k)}+\lambda \mathbf{I}\right)^{-1}\!\mathbf{W}^{(k)\,T}\Delta\boldsymbol{\Gamma}^{(k)}, \tag{51}\]  
\[\mathbf{x}^{(k+1)}=\mathbf{x}^{(k)}+\delta\mathbf{x}^{(k)}. \tag{52}\]  
Some other various schemes have been employed for image reconstruction, and please refer to References [4; 167; 168] for examples. [PERSON] and [PERSON] evaluated qualities of the reconstructed images with varying featured TD data such as the CW intensity \(M_{DC(0,\infty)}\) = \(E\), first temporal moment \(M_{1}\) = \(<\)\(t\)\(>\), 3 rd central moment \(c_{3}\), normalized Laplace transform \(M_{ML}\)(0,0.001)/\(E\), normalized Mellin-Laplace transform set \(M_{ML}\)(1,0.01)/\(E\) + \(M_{ML}\)(3,0.001)/\(E\), and combination of \(c_{3}\) + \(M_{ML}\)(0,0.001)/\(E\)[169]. Here,  
Figure 15: Standard process of MOBIR for TD-DOT. \(E^{-1}\int_{0}^{\infty}\left(t-<t>\right)^{n}\Gamma(t)dt\). Among these six featured TD data, the combination of \(c_{3}+M_{ML}(0,0.001)/E\) provided the best reconstruction results. [PERSON] et al. proved that using full TOF distributions provides better images over the featured TD data, although the computation cost was as high as two orders of magnitude of that using featured TD data such as \(E\) (CW), \(<\)\(t\)\(>\), \(<\)\(t\)\(>\) + \(c_{2}\), and \(<\)\(t\)\(>\) + \(c_{2}\) + \(c_{3}\) even for 2D reconstruction [170]. Figure 16 illustrates that the \(\mu_{a}\) image reconstructed using the full TOF distributions shows qualities better than those using the featured TD data of \(E\) and \(<\)\(t\)\(>\).
## 2 Materials and Methods  
### Experimental Setup  
The simulations included in the study were conducted using the TOol for PArticle Simulations Monte Carlo (TOPAS MC). We prepared an experimental setup utilizing a water phantom. We constructed a water phantom of 50 \(\times\) 50 \(\times\) 50 cm\({}^{3}\) in the simulations. At a depth of \(d_{\text{box}}\) = 5 cm, the water phantom houses a metal box of 5 \(\times\) 5 \(\times\) 2 cm\({}^{3}\), which is the most frequently utilized material in implant orthopedics. The source-to-surface distance (SSD) is 100 cm, and the scoring grid dimensions are 20 \(\times\) 20 \(\times\) 50 cm\({}^{3}\), with bins defined as 40 \(\times\) 40 \(\times\) 500 cm\({}^{3}\). We specified a radiation field size of 10 \(\times\) 10 cm\({}^{2}\), as it is the most often utilized field size for irradiating prostate tumors in patients with a metallic femoral joint replacement. The dose is administered at the isocenter at a depth of 6 cm within a phantom using various energies. The area measures 10 \(\times\) 10 cm\({}^{2}\) using SSD technology. The isocenter is located at the center of the metal box (5 \(\times\) 5 \(\times\) 2 cm\({}^{3}\)). The materials utilized for the metal box are those most employed in orthopedic replacements, specifically cobalt-chromium-molybdenum (Co-Cr-Mo) alloy, titanium alloy, and stainless steel [19]. The properties of the various metallic materials utilized in our simulations are presented in the table depicted in Figure 1, showing the experimental water phantom setup.  
In radiotherapy, the SSD parameter is essential to achieving optimal dose distribution and to minimizing the dose burden on healthy tissues. The most used SSD for electrons and photons is 100 cm, which balances beam penetration and homogeneous coverage of the target area. For electrons, an SSD of 100 cm provides a uniform distribution over the surface due to the limited depth penetration of the electrons. For VHEE, where the energy is greater than 50 MeV, the SSD is often increased to 150 cm, allowing irradiation of deeper tumors with better homogeneity. A more comprehensive range of SSDs is used for proton-specific Bragg peaks, depending on the technology (fixed scattered beam, pencil beam scanning). Commonly, SSDs of 150 to 250 cm are used for protons. Our simulations aimed to compare the effect of the metal box on the dose distribufor different energies. We chose the same SSD, namely 100 cm for all energy. This should provide consistent conditions for a clear comparison of the influence of the metal box on the dose distribution.
# Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone
## 2 Methods
### GPS/Galileo PPP Algorithm  
Using GNSS carrier phase observation and pseudorange observation, considering the receiver and satellite instrumental delays, the observation equations can be expressed as [19; 20]:  
\[P_{r,j}^{s,Q}=\rho_{r}^{s,Q}+c(dt_{r}-dt^{s,Q}+d_{r,j}^{s,Q}-d_{j}^{s,Q})+T_{r }^{s,Q}+\mu_{j}^{Q}.I_{r,1}^{s,Q}+\varepsilon_{r,j}^{s,Q} \tag{1}\]  
\[\phi_{r,j}^{s,Q}=\rho_{r}^{s,Q}+c(dt_{r}-dt^{s,Q})+\delta_{r,j}^{s,Q}-\delta_{ j}^{s,Q}+T_{r}^{s,Q}+\lambda_{j}^{s,Q}N_{r,j}^{s,Q}-\mu_{j}^{Q}.I_{r,1}^{s,Q}+ \varepsilon_{r,j}^{s,Q} \tag{2}\]  
where \(P_{r,j}^{s,Q}\) and \(\phi_{r,j}^{s,Q}\) are code and phase observations, respectively; superscripts \(s\) and \(Q\) are PRN (pseudo-random noise) and satellite system, respectively; subscripts \(r\) and \(j\) (\(j=1,2,5\)) are receiver ID and carrier frequency band, respectively; \(\dot{\rho}_{r}^{s,Q}\) is the geometric distance between satellite \(s\) and station \(r\); \(c\) is the speed of light in vacuum; \(dt_{r}\) and \(dt^{s,Q}\) are receiver and satellite clock offsets, respectively; \(d_{r,j}^{s,Q}\) and \(d_{j}^{s,Q}\) are the receiver and satellite instrumental delays of code in seconds, respectively; \(\delta_{r,j}^{s,Q}\) and \(\delta_{j}^{s,Q}\) are frequency-dependent carrier phase instrumental delays in meters, respectively; \(T_{r}^{s,Q}\) is the zenith tropospheric delay; \(\lambda_{j}^{s,Q}\) is the signal wavelength at frequency \(f_{j}\); \(N_{r,j}^{s,Q}\) is the integer ambiguity; \(\mu_{j}^{Q}.I_{r,j}^{s,Q}\) is a frequency-dependent ionospheric delay term, \(\mu_{j}^{Q}\) is the frequency-dependent factor, \(I_{r,j}^{s,Q}\) is the line-of-sight(LOS) ionospheric delay on the frequency \(f_{j}\); \(\varepsilon_{r,j}^{s,Q}\) and \(\varepsilon_{r,j}^{s,Q}\) are measurement noise, multipath error and the sum of other unmodeled errors for pseudorange and carrier phase observation, respectively. In addition, other error terms in the GNSS observation equations, such as satellite and receiver antenna phase center correction, relativistic effect, tidal load deformation (solid tide, pole tide and ocean tide), sagnac effect, and satellite antenna phase wind-up correction, have been corrected by the models in advance. For the convenience of description, the DCB (Differential Code Bias) and the constants \(\alpha_{i,j}\), \(\beta_{i,j}\) are defined as [21]:  
\[DCB^{s,Q}\big{(}s_{i},s_{j}\big{)}=d_{i}^{s,Q}-d_{j}^{s,Q} \tag{3}\]  
\[DCB_{r}^{Q}\big{(}s_{i},s_{j}\big{)}=d_{r,j}^{s,Q}-d_{r,j}^{s,Q} \tag{4}\]\[\alpha_{i,j}=\frac{f_{i}^{2}}{f_{i}^{2}-f_{j}^{2}} \tag{5}\]  
\[\beta_{i,j}=\frac{f_{j}^{2}}{f_{i}^{2}-f_{j}^{2}} \tag{6}\]  
where subscripts \(i\)\((i=1,2,5)\) is carrier frequency band of observations; \(s_{i}\) and \(s_{j}\) are signals on frequency \(i\) and \(j\), respectively; \(DCB^{g,Q}\big{(}s_{i},s_{j}\big{)}\) and \(DCB^{g}_{r}\big{(}s_{i},s_{j}\big{)}\) are satellite and receiver DCBs between signals \(s_{i}\) and \(s_{j}\), respectively.
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 3 Burden of Diabetic Foot Ulcers  
As of 2021, there are approximately 537 million cases of diabetes mellitus reported globally [21]. Annually, between 9.1 and 26.1 million new cases of diabetic foot ulcers (DFUs) are reported, with an occurrence rate of 19-34% [13], and an annual incidence rate of 1.9-4.0% [22; 23]. The cost of treating DFUs varies significantly by country. In the UK, it averages $7539 per patient, in India about $1960 per patient, and in the US, it totals between $3.93 and $10.9 billion per year [24]. This highlights the global threat that DFUs pose to patient safety and the economic burden they impose.  
Infection is a major complication, affecting 50-60% of DFU cases [13], which often progresses to amputation due to necrosis. The likelihood of developing a DFU in a hyper-glycemic patient range from 15-25%, with an amputation incidence between 25-50% [25].  
Diabetic foot ulcers (DFUs) and diabetes mellitus most commonly affect individuals around 45 years of age, particularly males. Patients over 50 are at a greater risk of developing DFUs [26]. Additionally, male patients generally show higher susceptibility and those with lower educational levels are also at an increased risk [26]. Understanding the  
Figure 1: Role of Biofilms in the development of Diabetic Foot Infections.  
pathophysiology and progression of the disease is crucial for managing and preventing DFUs. On searching the published reports on the incidence of biofilms in DFUs globally in the last 10 years, we found that a significant majority of currently available literature mentioned the presence of monomicrobial or polymicrobial biofilms in DFU patients.  
A breakdown of 88 articles found on PubMed using specific parameters, (last 10 years, biofilms in DFUs) are summarized showing the number of articles that mentioned whether biofilms were present or not. The 66 articles that did report the occurrence of biofilms in were further separated by whether the biofilm composition was discussed. The 45 articles that provided information on biofilm makeup are further separated based on whether they provided information on the genetic makeup or specific species within the biofilms.  
As shown in Table 1, within those 88 articles, 75% talked about biofilms in general. Of those 66 articles, 68% talked about the genetic makeup of the biofilms that were studied. Of those 45 articles, 93% of the articles mentioned _Staphylococcus_, _Pseudomonas aeruginosa_ as the bulk of the biofilm formation that have antibiotic resistance. Biofilms are present on every wound that appears in the human body. Understanding the genetic make-up of wounds and ulcers in addition to the role biofilms play on DFUs can dramatically lessen recovery time, prevent DFUs leading to amputation, and improve prognosis. By seeing a common pattern of microorganisms, we can better generalize treatment in cases where microbiology reports are not yet concluded. In addition, knowing that most biofilms in DFUs are made up by specific microorganisms, we can better target biofilms using a narrower spectrum of antibiotics.
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 5 Computational Results
### Performance Comparison with Other Surrogate Robustness Measures
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{t_{c}\theta}\)} & \multicolumn{2}{c}{\(\mathbf{RM1}\)} & \multicolumn{2}{c}{\(\mathbf{RM2}\)} & \multicolumn{2}{c}{\(\mathbf{RM3}\)} & \multicolumn{2}{c}{\(\mathbf{SR_{AC}}\)} \\ \cline{2-9}  & \(\mathbf{R^{2}}\) & _Sig._ & \(\mathbf{R^{2}}\) & _Sig._ & \(\mathbf{R^{2}}\) & _Sig._ & \(\mathbf{R^{2}}\) & _Sig._ \\ \hline
20,0.5 & 0.495 & \textless{}0.001 & 0.414 & 0.002 & 0.512 & \textless{}0.001 & 0.992 & \textless{}0.001 \\
20,1.0 & 0.388 & 0.003 & 0.376 & 0.003 & 0.403 & 0.002 & 0.996 & \textless{}0.001 \\
20,1.5 & 0.285 & 0.013 & 0.312 & 0.008 & 0.296 & 0.011 & 0.996 & \textless{}0.001 \\
40,0.5 & 0.369 & 0.003 & 0.303 & 0.010 & 0.397 & 0.002 & 0.994 & \textless{}0.001 \\
40,1.0 & 0.229 & 0.028 & 0.223 & 0.030 & 0.243 & 0.023 & 0.997 & \textless{}0.001 \\
40,1.5 & 0.232 & 0.027 & 0.222 & 0.031 & 0.246 & 0.022 & 0.999 & \textless{}0.001 \\
60,0.5 & 0.334 & 0.006 & 0.262 & 0.018 & 0.352 & 0.005 & 0.997 & \textless{}0.001 \\
60,1.0 & 0.147 & 0.086 & 0.150 & 0.082 & 0.156 & 0.076 & 0.997 & \textless{}0.001 \\
60,1.5 & 0.125 & 0.116 & 0.143 & 0.091 & 0.135 & 0.102 & 0.998 & \textless{}0.001 \\
80,0.5 & 0.420 & 0.001 & 0.349 & 0.005 & 0.432 & 0.001 & 0.996 & \textless{}0.001 \\
80,1.0 & 0.360 & 0.004 & 0.291 & 0.012 & 0.378 & 0.003 & 0.997 & \textless{}0.001 \\
80,1.5 & 0.192 & 0.047 & 0.185 & 0.051 & 0.202 & 0.041 & 0.997 & \textless{}0.001 \\ \hline \hline \end{tabular} _Sig._ is the significance level for linear model ANOVA.  
\end{table} 
Table 3: Solution robustness correlation analysis. As shown in Table 4, the significance values of \(QR_{AC}\) and other measures are all maintained at a tiny value. All \(R^{2}\) values of \(QR_{AC}\) are almost close to 1.0 and larger than that of the others. This indicates that \(QR_{AC}\) can approximate the schedule robustness more efficiently than the other measures. Figure 8b shows that the \(RM2\) also has minimum linear correlation with \(QR_{\text{MC}}\). The \(R^{2}\) values of \(RM2\) and \(RM3\) are in the range of \([0.6,0.85]\) and fluctuate with the change of failure parameters. This indicates that \(RM2\) and \(RM3\) can partially approximate the quality robustness of schedule. To summarize, the three surrogate robustness measures can only perform well in the case of a very high level of RMB, while \(M_{AC}\) can adapt to various levels of RMB conditions.
## 6 Results and Discussion
According to the previous results, the relative errors of the energy consumption (average and maximum) of the C2-T60, C3-T50, and C4-T50 configurations were quite low; however, this can be misleading because, by implementing one or none of the regularization techniques, these configurations cannot reliably generalize over the test-sets, as they exhibit overfitting. For the C1-T50 configuration, although the relative errors are not so low, the model could generalize better to the data since the implemented regularization techniques allowed capturing of the temporal variations.
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 4 Experimental and Analysis
### Physical Experiment
#### 4.2.2 Induced-Signal Processing Circuit Test Experiment  
In the induced-signal processing circuit test experiment, the original induction signals from the _SIN+_ and _SIN\(-\)_ pins of the round inductsyn's coarse channel and fine channel were selected and input in a differential manner. After debugging the circuit, the input/output waveforms were recorded using an oscilloscope, as shown in Figures 13 and 14.  
In Figure 13, CH2 is the differential signal at the SIN end of the round inductsyn's coarse channel. According to the previous description, the amplitude of the single-ended signal at the _SIN+_ end of the round inductsyn is approximately 5 mVp-p; hence, the measured differential signal is 10 mVp-p, which aligns with the expected results. After passing through the signal processing circuit for amplification, filtering, and phase shifting, the output waveform is shown in CH1 of Figure 10, with an amplitude of 3.16 Vp-p. The waveform is smooth, exhibits clear sinusoidal characteristics, and has a phase shift of less than 44\({}^{\circ}\) compared to the excitation signal.  
From CH1 in Figure 14, it can be observed that the original induced-signal output from the fine channel is approximately greater than twice that of the coarse channel. After passing through the induced-signal processing circuit, the amplitude reaches 3.08 Vp-p, which aligns with the theoretical calculations and simulation test results. The induced-signal processing circuit meets the design specifications of the circuit.
# Energy-Aware Security Adaptation for Low-Power IoT Applications
## 4 Dynamic Security and Energy Adaptation
### Analytical Evaluation of Controller Behavior
#### 5.1.1 Evaluation Setup  
To test the behavior of the proposed controller, we consider various scenarios combined with different configuration setups. We also assume that each application-layer message requires 51 bytes, and a rate of two messages per run. The preference threshold, the minimum level and the initial level correspond to the variables changed in the initial settings of each test, and Table 4 illustrates the corresponding values.  
The scenarios considered in our implementation and evaluation of the proposed adaptation logic are as follows:  
* A scenario with random attacks, both in what regards the type of attack, the time of its occurrence and its duration;
* Two scenarios with 20% of high risk attacks, namely with 80% and 90% of impact;
* A scenario with 20% of medium impact attacks, namely with 40%;
* A scenario with 20% of attacks with low impact, in this case with 20%.  
With regard to the evaluation study, the controller was implemented in the C language for each approach described in Section 4.2. The goal of our evaluation is to determine its effectiveness in implementing energy-aware security adaptation, as per the goals previously discussed. For this study, we consider the energy consumption impact of the AES and SHA algorithms [18], illustrated in Table 5. As for the battery capacity of the sensor, we consider the usage of an AA LR6 alkaline battery with a capacity of 1.8 Ah and a voltage of 1.5 V, which corresponds to 2.7 Wh of energy generated and 9720 Joules. It is important to note that this setup still does not take into account any LoRa/LoRaWAN configuration regarding energy consumption, only the energy cost of the cryptography process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Preference Threshold** & **Minimum Level** & **Initial Level** \\ \hline
1 & 80\% & 2 & 4 \\ \hline
2 & 20\% & & \\ \hline
3 & 80\% & & \\ \hline
4 & 20\% & & 3 \\ \hline
5 & 80\% & & \\ \hline
6 & 20\% & 1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial settings considered for evaluation.  
Each algorithm was used to test all the considered scenarios, in particular considering the six combinations of initial configurations presented in Table 4, in total accounting for 60 tests. Given the high amount of time required to perform each test and considering the typical discharging curve of an alkaline battery [17], measurements were made only in three battery intervals: from 100% to 95%, 75% to 70% and from 25% to 20%. We therefore consider the behavior of the algorithm in the three distinct and representative phases of the lifetime of a battery. The final value corresponds to an extrapolation of the values obtained in these three intervals. Next, we proceed with a discussion on the results obtained in our evaluation tests.