esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
When analyzing the results relating to fatigue, a statistically significant reduction was also indicated. Immediately after leaving the water, the level of fatigue was reduced by 0.70 points (post hoc Tukey: \(p\) = 0.021; post hoc Durbin-Conover: \(p\) = 0.008); also, for measurement 3, a reduction was identified by 0.15 points in relation to measurement 2 (nonsignificant) and by 0.86 points in relation to measurement 1 (phase 1 vs. phase 3: post hoc Tukey: \(p\) < 0.001; post hoc Durbin-Conover: \(p\) < 0.001). As for confusion, no statistically significant changes were found (\(p>0.05\)); there was only a trend towards differences (\(0.10>p>0.05\)), pointing at a potential reduction in confusion intensity (phase 1 vs. phase 2: post hoc Tukey: \(p=0.339\); post hoc Durbin-Conover: \(p=0.050\); phase 1 vs. phase 3: post hoc Tukey: \(p=0.061\); post hoc Durbin-Conover: \(p=0.032\)). Figure 2: Mood change dynamics in the group of winter swimmers and the control group (phase 1 vs. phase 3). With reference to the results concerning the concentration of the hormones studied (Table 3, Figure 4), the GLM analysis indicated a trend indicative of a potential time \(\times\) group interaction in the case of noradrenaline. The control group showed a slight (statistically nonsignificant) increase in the results (by 42 pmol/L), while the winter swimming group demonstrated a statistically significant (post hoc Tukey: \(p=0.049\); Wilcoxon: \(p=0.044\); r\({}_{\text{rb}}=0.574\)) decrease in the blood concentrations of the hormone (by 369 pmol/L). More  
Figure 3: Dynamics of changes in emotional states examined in phase 1, phase 2, and phase 3.  
over, time \(\times\) group interactions were reported for adrenaline. Similarly, as with noradrenaline, a negligible, nonsignificant change (\(p>0.05\)) was noted in the control group (by \(-6.25\) pmol/L), with a significant reduction (post hoc [PERSON]: \(p=0.045\); [PERSON]: \(p=0.047\); \(\mathrm{r_{rb}}=0.592\)) in adrenaline levels in the winter swimming group (by 55.1 pmol/L). However, one should observe that in the winter swimming group, despite this reduction, the adrenaline concentration was higher than in the control group. As for the results regarding serotonin, time \(\times\) group interactions were reported. Serotonin concentration significantly increased (post hoc [PERSON]: \(p<0.001\); [PERSON]: \(p<0.001\); \(\mathrm{r_{rb}}=-0.971\)) in the control group (by 55.1 ng/mL) and decreased (but not significantly; \(p>0.05\)) in the winter swimming group (by 10 ng/mL). In the case of cortisol, a statistically significant time \(\times\) group interaction was also found. Cortisol concentration significantly increased (post hoc Tukey: \(p=0.019\); Wilcoxon: \(p=0.050\); \(\mathrm{r_{rb}}=-0.583\)) in the winter swimming group (by three units) and slightly (insignificantly; \(p>0.05\)) decreased in the control group (by 0.9 unit). Dopamine levels were the same in both groups at each measurement (\(p>0.05\)).
## 3 Mobility Support Scheme Based on Decision Tree-Based Pattern Classification and Regression  
In this section, we propose a mobility support scheme that identifies changes in the location of devices with mobility in advance and exploits this information for network operation. The proposed scheme consists of two phases: (1) mobility pattern classification based on a decision tree and (2) mobility path prediction based on regression. Through this proposed scheme, frequent topology changes in industrial wireless sensor networks could be efficiently managed. Through the decision tree, the mobility trajectory is identified as being closer to one of the following patterns: linear, non-linear, or random. Based on  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Strengths** & **Limitations** \\ \hline [PERSON] et al. (2013) [16] & Improves neighbor detection speed and reliability in dynamic environments & Energy efficiency is limited in highly dynamic scenarios \\ \hline [PERSON] et al. (2017) [18] & Reduces latency and improves reliability in mobile networks & Scalability and energy consumption in larger networks are not fully addressed \\ \hline [PERSON] et al. (2020) [19] & Enhances packet delivery ratio and resource efficiency & Focuses primarily on linear patterns; does not address non-linear or random mobility patterns \\ \hline [PERSON] et al. (2023) [20] & Improves resource utilization and network performance in dynamic environments & Applicability to large-scale industrial networks with mobility is not explored \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related works.  
the classification results, a customized prediction model (e.g., linear regression, polynomial regression) is applied to predict future locations or paths. The proposed scheme could enhance network performance by minimizing packet loss and managing resources efficiently.
## 3 Malfunction Probability Models for Communication Delays and Bit Errors
### Refuse-Operation Probability Model for Bit Errors  
When communication conditions deteriorate, the bit error rate \(w\) increases. If the bit error rate exceeds the threshold \(w_{\text{max}}\), the relay protection would refuse to operate [14]. This section will analyze the refuse-operation probability considering communication errors.  
Assuming that each bit error probability is independent, and the average bit error rate is \(w_{1}\), then the distribution of bit error can be represented by a binomial distribution. Because the average bit error rate \(w_{1}\) is usually very small, the distribution of bit error can be represented by the Poisson distribution. Thus the probability density of bit error rate during a period of time (for example 1 second) can be expressed as:  
\[p(x=k)=\frac{(m_{\text{${W_{1}}$}})^{k}e^{-m_{\text{${m_{1}}$}}}}{k!} \tag{17}\]  
where \(k\) is the number of bit error; \(n\) is the total bit number transmitted during the period of time.  
Rewriting Equation (17) as the form that takes bit error rate as variable:  
\[p(w)=\frac{(m_{\text{${W_{1}}$}})^{mw}e^{-m_{\text{${m_{1}}$}}}}{(mw)!} \tag{18}\]  
The probability density of the bit error rate is shown in Figure 8. The orange area represents the probability of bit error rate exceeding the threshold \(w_{\text{max}}\) in the case of the expected value of the bit error rate being \(w_{2}\).  
Refuse-operation would occur if the measured bit error rate \(w\) is larger than \(w_{\text{max}}\) and a fault happens within the protection area. Therefore, the refuse-operation probability can be expressed as:  
\[P_{rb}(w)=1-\sum_{mw=0}^{mw_{\text{max}}}\frac{(m_{\text{${W_{1}}$}})^{mw}e^{- m_{\text{${m_{1}}$}}}}{(mw)!} \tag{19}\]  
Figure 9 gives the refuse-operation probability of relay protection caused by bit errors.  
Figure 8: The probability density function for bit error rate.
## 4 Outbound-Type Bundle Protocol
### Section between the Sending Node and a Relay Node  
The message sending sequence between the sending node and a relay node is shown in Figure 11. The algorithm for determining whether a bundle can reach its destination area is described below:  
1. After the sending node receives a \"hello message\" from a relay node, it sends a \"forwarding-bundle request\", which contains information about the destination area of the bundle, to the relay node in order to get permission to send the bundle.
2. When the relay node receives the request, it compare the information about the destination area in the request with information it owns about the covered areas of other traveling nodes and sends a \"forwarding-bundle response\", which includes information about whether the bundle can reach its destination area.
3. The sending node that has received the response sends a copy of the bundle to the relay node if the response says that the bundle can reach its destination area.
4. The relay node that has received the bundle sends an ACK (acknowledgement) message to the sending node to acknowledge that it has received the bundle. The sending node never again sends a bundle that has the same bundle identifier as that contained in the ACK message.  
The structures of the messages exchanged above are shown in Figure 12. Node ID is a globally unique node identifier, and since the DTN is an _ad hoc_ network, each node must have a unique identifier.  
Figure 11: Procedure for sending a bundle from the sending node to a relay node. Note: the bold arrow shows that a bundle copy is sent.  
Figure 10: Bundle relay sections between the sending node and the receiving node.  
Bundle ID is a unique bundle identifier. Since a considerable time can pass for a bundle to reach the receiver in a DTN, bundles can stay in the network for an extremely long time. To avoid confusion of bundles, we assign a unique identifier to each bundle.
# Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics
## 2 Motion Equations of an FFSR
### Calculation of the Location of Feature Points Based on Configuration Parameters  
In Section 2.1, basic equations of motion in position form only give the relationship among the centroid position of each rigid body, the position of the base, and the configuration parameter. Therefore, this section will give a method for calculating the base centroid position according to the configuration parameter. Then, the centroid position of each rigid body can be calculated.  
The linear momentum conservation of FPSRs is a holonomic constraint. It can be integrated into a positional form. The equation of this integral form is shown in Equation (18). It means that the centroid position of the FPSR, multiplied by the total mass of the system, is equal to the composition of the centroid position of each rigid body multiplied by its mass.  
\[M\mathbf{r}_{g}\ =\ m_{0}\mathbf{r}_{0}+\sum_{i\ =\ 1}^{n}\left(m_{i}\mathbf{r}_{i}\right) \tag{18}\]  
Bring Equation (2) into the above equation and merge the terms related to the position of the base centroid; we can get  
\[M\mathbf{r}_{g}\ =\ m_{0}\mathbf{r}_{0}+\sum_{i\ =\ 1}^{n}\left(m_{i}\mathbf{r}_{i}\right)\ =\ M\mathbf{r}_{0}+\sum_{i\ =\ 1}^{n}\left[m_{i}\left(\mathbf{b}_{0}+\sum_{j\ =\ 1}^{i-1}\left(\mathbf{a}_{j}+\mathbf{b}_{j}\right)+\mathbf{a}_{i}\right)\right] \tag{19}\]  
where \(\mathbf{r}_{0i}\ =\ \mathbf{b}_{0}+\sum\limits_{j\ =\ 1}^{i-1}\left(\mathbf{a}_{j}+\mathbf{b}_{j} \right)+\mathbf{a}_{i}\) represents the position vector of the centroid of each link relative to the base centroid. Therefore, the above formula can be written as  
\[M\mathbf{r}_{g}\ =\ M\mathbf{r}_{0}+\sum_{i\ =\ 1}^{n}\left(m_{i}\mathbf{r}_{0i}\right) \tag{20}\]
## 2 Materials and Methods
### Wavelength Selection  
In order to reduce co-linearity and redundancy in hyperspectral data, to simplify the complexity of computation, to improve the efficiency of rottenness detection, and to meet the inspection speed required by industry, wavelength selection is a critical and important step. The successive projections algorithm (SPA), a novel variable selection algorithm, is designed to solve the co-linearity problem by selecting variables with minimal redundancy [30]. In brief, there are three steps in SPA. First, the algorithm builds candidate subsets of variables with minimum co-linearity as the result of projection operations applied to the columns of the spectral matrix available for the training data. Second, from the results of the multi-linear regression (MLR) model assessed by cross-validation or by applying a separate validation set, the algorithm evaluates candidate subsets of variables on the basis of the value of the root mean square error (RMSE). Finally, a variable elimination procedure is carried out to remove uninformative variables without significant loss of predictive power [31; 32]. SPA has been successfully applied in hyperspectral image analysis for multivariate calibration in food safety and quality evaluation [33; 34]. SPA was performed using Matlab software (Version 7.9, The Mathworks Inc., Natick, MA, USA, 2009) in this work. Then a series of wavelengths was formed according to the importance for discriminating black-hearted radishes. After wavelengths selection by SPA, [PERSON]'s linear correlation was applied to further simplify the wavelengths. If the Pearson correlation coefficients were greater than 0.99, one of the wavelengths was selected and used for the next step. Usually, the group of adjacent wavelengths had high coefficients. In such a case, the wavelength of the most importance was selected. [PERSON]'s linear correlation analysis was executed in IBM (International Business Machine) SPSS (Statistical Product and Service Solutions) Statistics (Version 20; IBM Corporation, Armonk, NY, USA, 2011).
# Microbially Induced Desaturation and Carbonate Precipitation through Denitrification: A Review
## 4 Potential Applications of Denitrification-Based MICP Biotechnology
### MICP-Driven Applications
#### 4.1.5 Heavy Metal and Metalloid Immobilization  
MICP revealed as a great potential for the immobilization of metals and metalloids as well as their recovery from wastes, thus protecting human health and the entire natural environment. Several microorganisms, such as fungi and ureolytic bacteria, with the carbonate precipitation abilities were reported for the removal of various heavy metals and metalloids [12; 70; 71; 72]. In immobilization of heavy metals and metalloids, the adequate divalent metals (e.g., Cu\({}^{2+}\), Cd\({}^{2+}\), Co\({}^{2+}\), Zn\({}^{2+}\) and Pb\({}^{2+}\)) are integrated into the calcium carbonate lattice during a competitive co-precipitation mechanism given in (Equation (9)) and thus removed from the environment.  
\[\text{M}^{2+}+\text{CO}_{3}^{2-}\ \rightarrow\ \text{MCO}_{3} \tag{9}\]  
where, M\({}^{2+}\) represents a divalent metal ion.  
The removal percentages could reach up to 100%, confirming the applicability of MICP based metal removal and recovery [13; 70; 72; 73]. As discussed above, denitrification was proven as an effective and environment friendly metabolic pathway for a wide range of MICP applications [24; 25; 30], which paves the way for further investigation of denitrification based MICP for metal and metalloid immobilization, particularly in anoxic environments.
## 1 Introduction
Since ternary compositions do not allow for the precise determination of the role of their polyesters in the processes occurring during the sorption of metals from aqueous media, a study of the hydrolysis, biodegradation, and sorption capacity of dual-film compositions, namely PLA-chitosan and PHB-chitosan with respect to iron ions was carried out in [24]. It was shown that the PLA-chitosan compositions have greater stability in acidic aqueous media, increased sorption capacity with respect to iron ions, and, at the same time, are more easily degraded under exposure to soil after end of their service life, i.e., they show better characteristics for their use as the sorbents of iron ions. This may be connected to the difference in polarity and crystallinity of polyesters, and thus, with the various interactions between the ester groups of polyesters and amino groups of chitosan. It should be noted that materials based on polyesters and chitosan could be used not only as metal sorbents, but also for food packaging. The basic role of food packing nowadays is to preserve and protect food products from the aggressive action of the environment, including UV radiation, which falls within 100-400 nm of the electromagnetic spectrum. Generally, the UV-range is divided into sub-regions, namely UV-A (315-400 nm), UV-B (280-315 nm), and UV-C (100-280) nm. The most rigid UV-C range is intensely absorbed by ozone, oxygen, and carbon dioxide as sunlight passes through the Earth's atmosphere. As a result, people and the environment are exposed to higher levels of UV-B radiation, and it is UV-B rays that have the most adverse effects on human health, animals, marine organisms, and plant life. UV radiation may also destroy films for food packing, resulting, for example, in lipid oxidation, color and odor changes in products, and vitamin degradation [25]. On the other hand, UV has been used in the food industry as a sterilizer and antifungal agent germicide [26]. The purpose of the present work was a comparative study of the thermal stability and the thermophysical transitions in the binary polyester-chitosan compositions as well as in their compositions containing sorbed iron ions. In studying of the influence of UV radiation on the structural changes of PHB and PLA, special attention was paid to the analysis and interpretation of their infrared spectra. The obtained results make it possible to predict the behavior of such compositions under negative environmental conditions when they are used as sorbents or packing materials.
## 4 Experiment Setup
### Hyperparameter Tuning  
In pursuit of optimizing the performance and robustness of our ANN model, a systematic hyperparameter tuning process was conducted. Table 4 summarizes the hyperparameters and the selected values based on experimental evaluation. Below, we explain the role and selection rationale for each parameter.  
* Number of Hidden Layers: Determines the depth of the neural network. We tested configurations ranging from shallow (1 layer) to deeper networks (up to 5 layers). A 2-layer model offered a good balance between learning complexity and computational efficiency.
* Activation Function: Controls the nonlinear transformations applied at each neuron. We compared sigmoid, tanh, and ReLU. ReLU was selected for its faster convergence and ability to mitigate vanishing gradient issues in deep networks.
* Learning Rate: Dictates how quickly the model updates its weights during training. We tested values of 0.1, 0.01, and 0.001. A learning rate of 0.001 provided stable convergence without overshooting minima.
* Batch Size: Specifies the number of training samples used in one iteration of model updates. We tested 16, 32, 64, and 128. A batch size of 32 provided a good trade-off between computational cost and convergence speed.
* Number of Epochs: Refers to the number of times the entire training dataset is passed through the model. We evaluated ranges from 10 to 150. We selected 100 epochs as the model showed consistent convergence without signs of overfitting.
* Optimizer: Defines the algorithm used to adjust model weights. We chose Adam for its adaptive learning rate and proven effectiveness in deep learning tasks.
* Dropout Rate: A regularization parameter that randomly deactivates a fraction of neurons during training to prevent overfitting. We tested rates from 0 to 0.5. A dropout rate of 0 was ultimately selected, as the model did not exhibit overfitting during training.  
Although detailed ablation results are beyond the scope of this paper, these values align with best practices in deep learning [48, 49] and were selected based on grid search and empirical performance.
# Study on Gage Widening Methods for Small-Radius Curves
## 2 Gage Widening Calculation Methods
### Dynamically Free Inscribing  
Dynamic inscribing means that when the bogie passes through the curve, the guiding force between the wheel and rail changes with many factors, including speed, height, axle load, and wheelbase; in addition, the principle of the minimum guiding force is used to determine the geometric position of the bogie on the curve and further calculate the required gage widening value according to the geometric position.  
As shown in Figure 2, mechanically free inscribing of the bogie means that when the bogie passes through a curve, only the rim of the outer wheel of the front axle and the inner surface of the rail experience a lateral guiding force \(N_{i}\), and no lateral force exists between the rim of the other wheels and the rail. The vertical load applied to the top surface of the rail by the front and rear axle wheel treads is \(Q\) (wheel weight). When the bogie rotates around the center of rotation \(O_{i}\), the rotational torque generated by the frictional force \(\mu Q\) between the wheel tread and the top surface of the rail is \(\mu Qr_{iZ}\) and \(\mu Qr_{iH}\), respectively, and \(r_{iZ}\) is the rotation radius of the front axle wheel tread contact point around the center  
Figure 1: Schematic of the geometrically free-inscribing method.  
of rotation \(O_{i}\). Furthermore, \(r_{iH}\) is the rotation radius of the rear axle wheel tread contact point around the center of rotation \(O_{i}\)  
The torque balance equation of the bogie around the center of rotation \(O_{i}\) in the track surface is given by Equation (3):  
\[2\mu Q(r_{iH}+r_{iZ})+P_{i}L/2=N_{i}X_{i} \tag{3}\]  
Here, \(X_{i}\) represents the distance from the instantaneous center of rotation to the front axle, and \(P_{i}\) represents the lateral force acting on the bogie core, which can be calculated using Equations (4)-(6) as follows.  
\[P_{i} =H_{a}-T \tag{4}\] \[H_{a} =Gv_{i}^{2}/gR\] (5) \[T =Gh/s \tag{6}\]  
In the above equations, \(G\) represents the maximum load of the bogie; \(v_{i}\) represents the speed; \(g\) represents the acceleration of gravity; \(R\) represents the curve radius; \(h\) represents the superelevation of the curve; and \(s\) represents the distance between the contact points on the top surface of the rail.  
According to the minimum guiding force \(N_{i}\), the value of \(X_{i}\) at different curve radii, outer rail superelevations, and driving speeds \(v_{i}\) is calculated according to Equations (3)-(6). After the value of \(X_{i}\) is determined (see Equation (7)) according to the geometric relationship shown in Figure 3, the minimum value of the normal rail wheel flange and gage line, \(\delta_{\min}\), which is necessary for calculating the outer wheels of the rear axle of the bogie based on mechanically free inscribing, the gage widening value can be calculated using Equation (8).  
\[\delta_{\min}=f_{1}-f_{2}=\frac{X_{i}^{2}}{2R}-\frac{(L-X_{i})^{2}}{2R}=(2X_{ i}L-L^{2})/2R \tag{7}\]  
\[e=\delta_{\min}-\delta_{0} \tag{8}\]  
Figure 2: Schematic of the dynamically free-inscribing method.