esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis  
In this section, the exact closed-form expression of the system connection outage probability (COP), secrecy outage probability, and strictly positive secrecy capacity (SPSC) are derived. In addition, to gain more insight about the derived expression, the asymptotic expression of the concerned system is obtained.
## 3 Results and Discussion
### Basic Chemical Composition of Raw Materials and Bread  
CP enriched the bread in terms of mineral, TDF, and fat content. In particular, TDF content increased almost threefold, from 3.56 (C) to 9.27% (CP6), whereas fat and ash content changed from 1.81 (C) to 2.56% (CP6) and from 0.928 (C) to 0.974% (CP6), respectively (Table 5). Conversely, increasing the percentage of CP in the bread tends to decrease the protein and carbohydrate content. However, the differences in protein content between the samples were small, and the addition of up to 3% CP was not statistically influential on this component. The correlation analysis revealed significant linear relations (\(p<0.05\)) between pomace content in bread and ash, TDF, fat, protein, and carbohydrate content (r = 0.998; 0.972; 0.998, \(-\)0.975 and \(-\)0.998, respectively). Bread enriched with chokeberry pomace can be a valuable source of fiber and minerals. CP is an especially rich source of such macroelements (K, Ca, Mg and Na) and microelements (B, Cr, Cu, Fe, Mn, Ni, Se, Sr and Zn) [27]. Moreover, CP is a also abundant in insoluble dietary fiber, which makes up 76% of its total fiber content. It also contains about 20% of high molecular weight soluble fraction and 4% low molecular weight soluble fiber fraction [45].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **L*** & **a*** & **b*** & **\(\Delta\)E** \\ \hline C & 72.00 \(\pm\) 0.38 e & 1.92 \(\pm\) 0.19 a & 16.83 \(\pm\) 0.53 f & - \\ CP1 & 59.67 \(\pm\) 0.86 d & 5.39 \(\pm\) 0.27 b & 8.63 \(\pm\) 0.12 e & 15.22 \(\pm\) 0.76 a \\ CP2 & 51.12 \(\pm\) 1.11 c & 6.84 \(\pm\) 0.04 c & 6.42 \(\pm\) 0.26 d & 23.85 \(\pm\) 0.87 b \\ CP3 & 44.31 \(\pm\) 1.13 b & 9.59 \(\pm\) 0.24 d & 4.12 \(\pm\) 0.27 c & 31.43 \(\pm\) 1.06 c \\ CP4 & 42.03 \(\pm\) 1.63 b & 10.56 \(\pm\) 0.10 e & 2.31 \(\pm\) 0.59 b & 34.41 \(\pm\) 1.66 d \\ CP5 & 38.81 \(\pm\) 0.75 a & 11.77 \(\pm\) 0.09 f & 1.47 \(\pm\) 0.62 ab & 37.88 \(\pm\) 0.41 e \\ CP6 & 37.53 \(\pm\) 0.29 a & 12.74 \(\pm\) 0.22 g & 0.97 \(\pm\) 0.01 a & 36.84 \(\pm\) 0.24 d \\ \hline \hline \end{tabular} C—control bread, CP1, CP2, CP3, CP4, CP5, CP6—broad enriched with 1, 2, 3, 4, 5, and 6% of chokeberry powder, respectively; the values designated by the different letters a*-g are significantly different (\(p<0.05\)).  
\end{table}
Table 4: Results of colorimetric measurements of control and enriched bread samples.
## 5 Conclusion  
In this paper, an approach for allocating human-robot functions dynamically in LSO missions using the hierarchical reinforcement learning algorithm has been proposed. First, functions that quantify an agent's ability in unstructured environments, including task, environment, and distance fitness, are designed. Next, an HRL algorithm is designed to accurately compute the most optimal human-robot function allocation scheme in real-time, which consists of two layers that take into account the mobility of the agents and the unpredictability of the lunar surface. Finally, we validate the effectiveness of the proposed approach by comparing different algorithms with the same or different number of agents. The main conclusions of the paper are summarized below.  
1. Compared to RL, DRL, and HDRL algorithms, the proposed method improves the task allocation efficiency by approximately 98.24%, 98.78%, and 71.79%, respectively, for the same number of agents;
2. When the number of agents is varied, the proposed method improves the task allocation efficiency by about 90.49%, 94.89% and 88.26% compared to RL, DRL and HDRL algorithms, respectively. It can demonstrate the better robustness of the proposed approach when the number of agents varies.  
In future work, the path planning algorithm will be integrated based on the parameter optimization of the task assignment method, with the aim of enhancing the adaptability and efficiency of the human-robot function allocation during the lunar mission.  
Y.F.: Conceptualization, Formal analysis, Investigation, Methodology, Project administration. W.G.: Conceptualization, Investigation, Writing--original draft, Writing--review and editing. H.W.: Investigation, Methodology. Writing--review and editing. S.X.: Investigation, Writing--review and editing. C.W.: Conceptualization, Methodology, Investigation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Aerospace Medical Research Fund [No: HYZHXM03009] on the research of Adaptive Human-Robot Collaboration Method based on Cognitive Engineering, and by National Laboratory of Human Factors Engineering Stable Support Fund [No: GJSD22004] on the research of Key Technology and System Implementation of Human-Robot Collaboration in Planet Exploration.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare that they have no competing interests.
## 2 Materials and Methods
### Test 1: Dry-Land Salom  
The dry-land slalom simulation was performed in a car park during a holiday period where multipath interference was considered low. The environment was devoid of reflective surfaces or buildings exceeding a height of 20 m. Pilot work conducted prior to the simulation confirmed excellent signal reception at the venue, with an average of 25 \(\pm\) 1 satellites, 95% CI 24.6-24.7. The horizontal accuracy was found to have high precision, with a horizontal route mean square (Hrms) of 0.10 \(\pm\) 0.00 m, (95% CI 0.099-0.102). Additionally, the stability of the positioning solution was demonstrated by a Positional Dilution of Precision (PDOP) value of 1.12 \(\pm\) 0.04 (95% CI 1.12-1.13) over a 25 s duration of slalom.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Base Station Distance** & \multicolumn{2}{c}{**Sat No. Used**} & \multicolumn{2}{c}{**Hrms**} & \multicolumn{2}{c}{**PDOP**} & \multicolumn{2}{c}{**HDOP**} \\ \cline{2-9}
**(km)** & **Static** & **Moving** & **Static** & **Moving** & **Static** & **Moving** & **Static** & **Moving** \\ \hline
18 & 17 \(\pm\) 0 & 17 \(\pm\) 0 & 0.09 \(\pm\) 0.02 & 0.19 \(\pm\) 0.03 & 1.43 \(\pm\) 0.07 & 1.50 \(\pm\) 0.00 & 0.80 \(\pm\) 0.00 & 0.76 \(\pm\) 0.05 \\ \hline
50 & 25 \(\pm\) 1 & 23 \(\pm\) 1 & 0.11 \(\pm\) 0.00 & 0.20 \(\pm\) 0.02 & 1.07 From a stationary start, at a predetermined position marked on the surface as the start, and when the device had met the predetermined Hrms threshold of less than 30 cm, the participant followed one of two painted lines on a black asphalt surface configured to replicate a typical slalom trajectory dictated by a set course of gates (Figure 1A). Slalom poles were replaced by two surface markers set 1.2 m apart as per International Canoe Federation Slalom Rule 27 for the spacing of gate poles. Each trial finished when the participant reached the finish marker and remained stationary until data logging ceased. The total length of each trajectory as per the tape measure method of measurement was 47.4 and 57.9 m for the short and long trajectories, respectively. The participant completed each trajectory on foot (\(n=25\)), where time, longitude, latitude, speed (m\(\cdot\)s\({}^{-1}\)), direction of travel (\({}^{\circ}\)), compass reading, horizontal accuracy (Hrms), PDOP, Horizontal Dilution of Precision (HDOP), and the number of satellites being used were recorded at a frequency of 20 Hz.
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
## 5 Results
### Fixed Decision Threshold
For this reason, \(BA\) consistently surpasses \(F1\) and serves as a fairer average metric, providing a more realistic quantification of the mean performance. Furthermore, as observed in the previous results, notable improvements in both \(F1\) and \(BA\) can be observed as the distance threshold increases from 1500 km to 2500 km. Figure 10: Probability distribution of \(F1\) and \(BA\) for all samples in both synthetic and Celestrak test sets across three different distance thresholds. Vertical dashed lines indicate mean performances. (**a**) Synthetic data, \(T\) = 1500 km, decision threshold of 0.75. (**b**) Synthetic data, \(T\) = 2000 km, decision threshold of 0.5. (**c**) Synthetic data, \(T\) = 2500 km, decision threshold of 0.25. (**d**) Celestrak data, \(T\) = 1500 km, decision threshold of 0.75. (**e**) Celestrak data, \(T\) = 2000 km, decision threshold of 0.5. (**f**) Celestrak data, \(T\) = 2500 km, decision threshold of 0.25. Finally, Table 2 summarizes the results with the mean values for each distance threshold, dataset, and metric. Increasing the distance threshold from 1500 km to 2500 km makes the F1 score improve by around 20%. However, as the encounter duration does not evolve linearly with \(T\), \(F1\) does not increase linearly, and a major improvement is observed between 1500 km and 2000 km. Similarly, the dissimilarity between \(BA\) and \(F1\) decreases non-linearly with \(T\), reducing from 18% with \(T\) = 1500 km to 9% with \(T\) = 2500 km. Due to the reduced number of true communication opportunities in the ground truth, the model tends to predict low values; thus, \(R\) is usually lower than \(P\). Overall, the model can anticipate two days of communication opportunities with a mean accuracy between 80% and 93%.
# Seventy-Five Years since the Point-Contact Transistor: Germanium Revisited
## 5 Open Issues and Perspectives for the Future  
Here, we will aim to raise a number of open issues and perspectives that will need to be considered so as to consider the defect processes in Ge as a fairly complete field.  
Considering oxygen and carbon defects, what is missing from the literature at the present time is an understanding of how these defects could attract vacancies in a competitive manner to form CV pairs and VO\({}_{\rm i}\) (A-centre), in this was reducing the available vacancies for dopant atoms. This is particularly important for donor atoms because vacancies are their vehicles for diffusion and may cause deactivation via the formation of larger clusters. The A-centre in Ge and its impact is far less understood in Ge as compared to Si although they have a similar configuration [52; 108; 109; 110; 111; 112; 113].  
Strain induces changes to the electronic properties of microelectronic materials [40; 114; 115; 116; 117; 118]. For example, a significant disadvantage for the implementation of Ge in optoelectronic applications is its indirect bandgap (0.67 eV), which can be engineered to a direct bandgap of 2.91 eV upon the application of significant compressive strain [118]. As was recently demonstrated, strained few-atomic-layer (effectively two-dimensional) Ge can exhibit blue photoluminescence at room temperature as well as quantum confinement effects [118]. With respect to nanoelectronic devices, strain can be used to enhance carrier mabilities, as was demonstrated by the paradigm of strained Si, SiGe and Ge channels for MOSFETs (refer to [119] and the references therein).  
Another issue that needs to be determined is the impact of surface and interfaces on the conductivity and diffusion properties of nanoelectronic devices. It should be stressed that in nanoelectronic systems where the characteristic dimensions are a few nanometers, there is a significant percentage of the atoms at the interfaces and surface or at regions in close vicinity to these. As has been determined in previous studies (for example [120; 121; 122; 123] and the references therein), mainly for oxide interfaces, there will be profound deviations from the bulk-like defects and electronic properties. Therefore, these need to be investigated for Ge interfaces using both advanced simulation techniques and thermodynamic methodologies such as the cB\(\Omega\) model [124; 125; 126; 127; 128; 129; 130; 131; 132; 133]. The absolute limit, of course, for advanced simulation work on surfaces/interfaces is germene, the two-dimensional form of Ge that is analogous to graphene [134; 135; 136; 137] where all the atoms are at the surface or interface, and the use of machine learning or related techniques [138; 139; 140; 141; 142]. Finally, it should be considered that Ge is an important material for quantum technologies, as it may be employed in devices aiming to encode, process and transmit quantum information [143].
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
## 4 Experimental Results
### Implemented Controllers  
4.2.1. Outer Loop PD Controller  
The performance of all the other tested schemes was evaluated based on the performance of the outer loop PD controller, which will be denoted hereafter as the PDC (PD controller). The PDC scheme is defined in [24] and was implemented using the gain matrices  
\[K_{P} =\text{diag}\{4.5\;3.0\;7.5\;3.0\},\] \[K_{D} =\text{diag}\{1.5\;1.5\;1.5\;1.5\}.\]
## 4 Experimental Analysis
### Failure Cases  
The proposed model delivers impressive performance in static environments, but it fails to generate very accurate shapes in some scenarios. One key failure case occurs in dynamic scenes, where objects are in motion, or the environment undergoes rapid changes. Since WGA-SWIN is designed for static scenes, its reliance on fixed window-based attention groupings limits its ability to track moving objects or capture temporal changes. As a result, the model may fail to track moving objects accurately and handle interactions between objects across different frames, leading to sub-optimal performance in dynamic environments. Additionally, WGA-SWIN may fail to generate very accurate shapes in some cases of extreme occlusions or highly complex geometries. In scenarios where large portions of an object are hidden in all views and there are objects with intricate geometries or fine details, WGA's local window-based attention may fail to capture the missing information and smaller geometric details in complex objects. We expect to continue optimizing this scheme with learnable grouping techniques in future work to improve our model's robustness and performance in more complex real-world scenarios.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 3 LDPC Decoding Algorithms
### Nmsa  
The operation flow of the NMSA using a layered scheme can be divided into four steps. In the NMSA, we initially define \(y_{j}\) as the received channel information, \(C_{j}\) as the initial prior message, \(Q^{k}_{i,j}\) as prior messages, and \(R^{k}_{i,j}\) as the extrinsic message, where \(i\) is the index of the row of \(\mathbf{H}\), \(j\) is the index of the column of \(\mathbf{H}\), and \(k\) is the index of the decoding iteration. Subsequently, the core decoding processes are stated as follows.  
In the first step, all parameters in the decoder must be initialized. The received channel information \(y_{j}\) is then imported and stored in the memory for subsequent operations. The logarithm expression of the initial prior message \(C_{j}\) for each VN is stated as:  
\[C_{j}~{}=~{}log\Bigg{(}\frac{P\big{(}x_{j}~{}=~{}0|y_{j}\big{)}}{P\big{(}x_{j} ~{}=~{}1|y_{j}\big{)}}\Bigg{)}. \tag{1}\]  
Figure 1: The base matrices of (**a**) rate-11/13 BG 1 and (**b**) rate-5/7 BG 2 in the 5G NR standard. The element \({}^{*}\) is a zero matrix and 0 is an identity matrix. The element of non-zero integer denotes a cyclic-shifted matrix with a shifted value.  
The prior messages \(Q^{0}_{i,j}\) are updated as \(C_{j}\), which is the log likelihood ratio (LLR), and the extrinsic messages \(R^{0}_{i,j}\) are zeros.  
In the second step, the prior message \(Q^{k}_{i,j}\) is updated by subtracting the \(R^{k-1}_{i,j}\) from the \(LLR_{j}\) to recover the decoding capability in the last iteration. The inclusion of the two conditions above in the updates is called a prior message process. The formula for the updated prior message is shown as:  
\[Q^{k}_{i,j}\ =\ LLR_{j}-R^{k-1}_{i,j} \tag{2}\]  
In the third step, the check nodes \(R^{k}_{i,j}\) are updated to find the bits that have the lowest reliability in every layer. The check node process contains the product of the total sign excluding the updated node, the first minimum excluding the updated node, and the scaling factor \(\gamma\). The set of VNs corresponding to CN is denoted as \(N(c)\). The formula for updating the check nodes is expressed as:  
\[R^{k}_{i,j}\ =\ \gamma\cdot\left(\prod_{j^{\prime}\in N(c)\backslash j}sgn \left(Q^{k}_{i,j}\right)\right)\left(\underset{j^{\prime}\in N(c)\backslash j }{min}\left|Q^{k}_{i,j}\right|\right) \tag{3}\]  
In the fourth step, the variable node \(LLR_{j}\) is updated with \(Q^{k}_{i,j}\) added to an extrinsic message \(R^{k}_{i,j}\) to increase the reliability of the correct nodes and correct the bits that have lower reliability and a higher error rate in every layer. The formula for updating the variable nodes is expressed as:  
\[LLR_{j}\ =\ Q^{k}_{i,j}+R^{k}_{i,j} \tag{4}\]  
Subsequently, the NMSA iteratively processes the second to fourth steps until \(k\) reaches the maximum iterations or the early termination scheme is satisfied.
## 2 Materials and Methods
### General Setup  
Each participant completed a single experimental session lasting approximately 30 min. Participants were seated in an armchair with the right hand and arm relaxed on a table, supported by 1-inch thick foam pads. The arm was oriented to have 60 degrees of flexion at the elbow, 15 degrees of shoulder flexion, 0 degrees of shoulder and/adduction, and the forearm was supinuated. Several anthropometric variables were measured: arm circumference (Figure 1: at each gray marker in dermatome C7 in), forearm length (from the lateral epicondyle of the humerus to the radial styloid process while the arm is supinated), and 2-point discrimination distances at the source vibration (Figure 1: red marker); see Appendix A (Table A2). One 10 mm eccentric rotating mass (ERM) motor was used to deliver vibration stimuli (Precision Microdrives Ltd., London, UK, Model # 310-117). These motors have an operational frequency range of approximately 60-250 Hz, coupled to an amplitude range of 0.5-2.4 G [30]. For simplicity, we will refer to vibration intensity throughout this document in terms of frequency because the frequency and amplitude of vibration covary for these ERM motors.  
The vibration motor was powered and controlled using custom drive circuitry that was interfaced with a portable laptop computer running a custom script within MATLAB R2017a computing environment (MathWorks Inc, Natick, Massachusetts, USA). The input voltage to the motor was provided through a Pulse Width Modulation signal. Vibration propagation was measured using an InvenSense MPU-6050 (TDK Corp., San Jose, California, USA) 3-axis accelerometer with 16-bit resolution, a full-scale range set to \(\pm\)2 G, a sampling rate of 1 kHz, and a digital lowpass filter implementing a lowpass cutoff frequency of 260 Hz [31]. The accelerometer was interfaced with the laptop computer using I\({}^{2}\)C communication protocol.
## 3 Data and Methods
### Flash-Flood Potential Assessment
#### 3.3.2 Flash-Flood Conditioning Factors
The modified Fournier index was calculated according to Equation (2):  
\[MFI=\sum_{i=1}^{12}\frac{P_{i}^{2}}{P} \tag{2}\]  
where \(P_{i}\) is the mean monthly precipitation (mm), and \(P\) is the mean annual precipitation (mm). In order to determine the MFI values for the two years considered for the analysis, the monthly precipitation amounts from 1961 to 1989 and 1990 to 2018 were taken into account. According to the information already presented in the manuscript, the precipitation datasets were collected from a total number of 32 meteorological stations located around the study area. In the first step, the MFI values were calculated for each individual meteorological station using Equation (2). In order to represent the spatial variability of MFI values across the study area, the residual Kriging method was used [100]. Thus, the first stage of the application of the residual Kriging method was the exploration of the relationship between the MFI values and the altitude of the meteorological stations. The analysis of this relation was performed through ordinary least squares (OLS) regression [101]. In the applied OLS regression model, the independent variable is the altitude of meteorological stations, while the dependent variable is the MFI value [96]. Finally, the OLS equation, in which the MFI values (\(\Upsilon\)) will vary according to the altitude (\(\mathsf{X}\)), were derived for each of the two periods. These OLS equations were further used in the Map Algebra (Raster Calculator tool) function of ArcGIS 10.5, where the \(\mathsf{X}\) parameter  
Figure 4: The factors considered for the FFPI calculation: (**a**) slope, (**b**) profile curvature, (**c**) hydrological soil group, (**d**) lithology, (**e**) topographic position index, and (**f**) topographic wetness index. was replaced with the DEM and the output of this procedure was the theoretical values of MFI in a raster format. The residual of the OLS regression was also calculated for each point represented by the meteorological stations and then were interpolated at a \(30\times 30\)-m spatial resolution. The final estimated MFI values across the study area were obtained through the addition of the theoretical MFI values in raster format with the rasters resulting from the interpolation of the residual values [102] (Figure 5c,d). Once obtained, the MFI values were divided into four classes, taking into account the existing classification in the international literature [96]. The land uses/land covers for 1989 and 2019 (Figure 7a,b, respectively) were acquired via the classification of the Landsat images (USGS). This last factor represents a major driving force for the surface runoff as it influences different processes, such as evapotranspiration or interception of the pluvial water, as well as producing a specific roughness coefficient. Water runoff will be diminished above the forest vegetation land use/land cover, while on areas having a low roughness coefficient [103], such as pastures, bare rocks, or built-up areas, the runoff will increase.