esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Wi-Fi Fingerprint Database Construction Method Based on Environmental Feature Awareness
## 4 Results
### Training Practices  
In the previous section, we established 168 sampling points across six rooms within an area of approximately 240 m\({}^{2}\). After organizing the data, we obtained a dataset consisting of 13 feature columns and 28 label columns, totaling 43,150 data points. The dataset was then split into a training set and a test set in an 8:2 ratio. We set the batch size of the data loader to 32 and conducted 1000 iterations of training with a learning rate of 0.001.  
By plotting the loss curves, we can visualize the model's learning efficiency throughout the training process, allowing us to detect any overfitting or underfitting and adjust the model accordingly. Additionally, by comparing the model's predictions with the actual values, we can intuitively assess the model's accuracy, identify potential deviations, and recognize patterns in the predictions. This analysis provides a valuable empirical basis for further model tuning.  
The comparison between the actual and predicted values from the training results is illustrated in Figure 5. The graph reveals a close alignment between the predicted value curve (orange) and the actual value curve (blue), with only minor deviations at certain peak points. This close overlap suggests that the model accurately captures the data trends and effectively approximates the true output values. The high degree of agreement between the curves indicates that the model possesses strong predictive ability and high reliability.  
The specific loss values per hundred cycles for the training and test sets are depicted in Figure 6, illustrating how the model's loss changes throughout the training process. The figure shows a rapid decline in loss values during the initial stages of training, followed by a gradual leveling off. This pattern suggests that the model quickly learns key patterns and features in the data within the first few cycles, resulting in a significant reduction in loss. Over time, the model continues to optimize, and the decreasing trend of the training loss eventually stabilizes, indicating good convergence.  
Figure 5: Comparison of actual and predicted values.  
As shown in the figure, the training loss and test loss curves are very close to each other and both stabilize at a low level. This suggests that the model performs similarly on both the training and test sets, with no significant signs of overfitting. If the test loss were significantly higher than the training loss, it could indicate overfitting, where the model performs well on the training set but poorly on the test set.  
The near-zero value of the loss curve at the end of training indicates minimal error and strong predictive accuracy. Overall, the loss curve suggests that the model has successfully learned the important patterns in the data and demonstrates a good generalization ability.
## 2 Modifications for Hoek-Brown Yield Criterion and the Pseudo-Dynamic Method on Seismicity Analysis
### Generalized Tangent Technique  
Another challenge is that the principal stresses \(\sigma_{1}-\sigma_{3}\) mode of the H-B criterion presented in Equation (1) cannot be directly enforced in the computations of the rate of energy dissipation along the failure surfaces delineated by the assumed kinematically admissible mechanism. A solution to this problem was to incorporate the H-B criterion into the stability analysis of rock engineering structures using the generalized tangent technique ([PERSON] and [PERSON], 2010; [PERSON], 2010). The nonlinear characteristic of the H-B yield criterion is linearized in the technique, i.e., the outer tangent of the H-B criterion envelope is used instead of the envelope curve itself, which overestimates the rock strength but leads to the solution of the upper limit for the ultimate load. The envelope curve of the nonlinear H-B yield criterion and its outer tangent are illustrated in Figure 1, and the outer tangent can be expressed as the following equation.  
\[\tau=c_{t}+\sigma_{n}\tan\varphi_{t} \tag{5}\]  
in which \(c_{t}\) is the cut-off distance of the tangential line on the vertical axis, and \(\varphi_{t}\) denotes the inclination of the tangent line about the horizontal axis. It can be observed that the above equation closely resembles the form of the classical Mohr-Coulomb (M-C) criterion expression. Thus, the parameters \(c_{t}\) and \(\varphi_{t}\) can be visualized as the cohesive force and the internal friction angle for a particular material, respectively, while the cohesive force \(c_{t}\) can be written as a function of the internal friction angle \(\varphi_{t}\)\[\frac{c_{t}}{\sigma_{c}}=\frac{\cos\varphi_{t}}{2}\left[\frac{mn(1-\sin\varphi_{t})} {2\sin\varphi_{t}}\right]^{\frac{n}{1-n}}-\frac{\tan\varphi_{t}}{m}\left(1+ \frac{\sin\varphi_{t}}{n}\right)\left[\frac{mn(1-\sin\varphi_{t})}{2\sin\varphi _{t}}\right]^{\frac{1}{1-n}}+\frac{s}{m}\tan\varphi_{t} \tag{6}\]  
As can be seen in Figure 1, the failure strength corresponding to the tangential line is more than or equivalent to the actual H-B criterion strength when the positive stress \(\sigma_{n}\) is determined [23]. Consequently, the solution obtained by the generalized tangent technique is the upper bound of the solution by the nonlinear H-B criterion.
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
###### Abstract  
The interferometric Global Navigation Satellite System Reflectometry (_i_GNSS-R) exploits the full spectrum of the transmitted GNSS signal to improve the ranging performance for sea surface height applications. The Inter-Modulation (IM) component of the GNSS signals is an additional component that keeps the power envelope of the composite signals constant. This extra component has been neglected in previous studies on _i_GNSS-R, in both modelling and instrumentation. This letter takes the GPS L1 signal as an example to analyse the impact of the IM component on _i_GNSS-R ocean altimetry, including signal-to-noise ratio, the altimetric sensitivity and the final altimetric precision. Analytical results show that previous estimates of the final altimetric precision were underestimated by a factor of 1.5\(\sim\)1.7 due to the negligence of the IM component, which should be taken into account in proper design of the future spaceborne _i_GNSS-R altimetry missions.  
Global Navigation Satellite System Reflectometry (GNSS-R); ocean altimetry; auto-correlation function (ACF); passive reflectometry and interferometry system (PARIS) +
Footnote †: journal: Remote Sensing
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 2 Market Segmentation  
The market segments of rooftop PV and utility scale (The Solar Energy Industry Association defines a solar PV plant with more than 1 MWp as \"utility scale\") were globally reasonably balanced in 2023. Approximately 40 to 45% of new capacity was installed on rooftops [6]. The range is mainly due to the uncertainty in the Direct Current (DC) to Alternating Current (AC) ratio of utility scale installations in China.  
In the European Union, utility-scale PV plants were more dominant with a 65% share of the market, followed by rooftop installations (approx. 30%). Building-integrated photovoltaics, agrivoltaics, and floating photovoltaics accounted for only 5% all together [11]. The 2024 International Technology Roadmap for Photovoltaics reports a projected decrease in utility-scale and rooftop applications (55% and 25%, respectively) in favor of the building-integrated photovoltaics (5% share), floating photovoltaics (5% share), and agrivoltaics (10% share) that will account for the remaining 20% in the EU market in 2034 [11].
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 4 Bromelain Enzymatic Activity Measurement
Although more expensive than the previously described substrates, Z-Arg-Arg-p-nitroaniline (pNA), Bz-Phe-Val-Arg-pNA, pGlu-Phe-Leu-p-nitroanilide and N-Cbz-L-Gln-p-nitrophenyl ester are widely used as they allow continuous measurements of enzymatic activity to be performed, with free pNA being detected at 400-410 nm [15; 69; 70; 71]. The peculiar chemical structure of these synthetic compounds also facilitates the easy detection of bromelain sources, since the enzymes derived from the various plant parts have shown preferential hydrolytic activity towards these substrate types [15]. In particular, the Z-Arg-Arg substrate is specific for stem bromelain, whereas fruit bromelain and ananain have shown more preferential activity towards the Bz-Phe-Val-Arg substrate [15; 72; 73].
# Effects of Synaptic Pruning on Phase Synchronization in Chimera States of Neural Network
## 5 Conclusions and Outlook  
This research investigates the effect of pruning on a ring-shape-FHN-system with chimera states, which explains how synaptic pruning affects the spatial distribution of synchronized and desynchronized regions in neural networks on a theoretically and numerically sound basis. The approach of the present research is not seen in the current literature. The pruning process is conducted by modifying the coupling in a small region of the network and making the pruned neurons synchronize. The impact of the number of pruned neurons on the performance of the system is also investigated. For a system with 300 neurons, pruning 5 neurons is sufficient to control the spatial position of the coherent domain precisely and fast. Moreover, the mechanism of this method is illustrated. The pruned neurons accelerate their neighbors so that the center of the incoherent structure is attracted.  
Considering that FHN systems widely exist in many neuroscience and engineering fields, the conclusion of this research can find many potential applications. One example is that in the multi-link network with bio-inspired motor neuron dynamical unit [24; 27], pruning can be used to easily generate rhythmic (coherent) and chaotic (incoherent) dynamics. Rhythmic patterns can be adopted in locomotion control, and chaotic dynamics are useful when searching reflexes are triggered only in a subpart of the network.  
As shown in the explanation of the phenomenon, the neurons will be accelerated by coupling with the pruned neuron when there is a phase difference. Therefore, it is natural to think about what would happen if the phase velocity of the neurons is also modified in the pruned region, especially if it is the same as the phase velocity in the coherent domain.  
The mechanism of pruning found in the research shows a type of symmetry breaking in a coupled identical oscillator system, which illustrates the possibility of complicated collective behavior based on simple individual behavior. This differentiation caused by symmetry breaking may explain how neurons in a network are divided into groups with different functions.  
[PERSON] conducted the conceptual study, formal analysis and investigation. Both the authors contributed to method development, paper review and writing. [PERSON] supervised the research. All authors have read and agreed to the published version of the manuscript.  
This research was funded bythe Natural Sciences and Engineering Research Council of Canada (NSERC), Mathematics of Information Technology and Complex Systems (Mitacs), and Wuhan Polytechnic University grant number WGZ202000011.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Detailed Design and Implementation
### The Monitoring Windows Application
#### 4.3.1 Configuring Real-Time Events  
To receive real-time refresh signals, we created a connection to the Notification Hub server and configured the reception of the automatic refresh signal, as in Figures 11 and 12.  
Figure 11: Block diagram for creating connection to Real-Time Server Events.  
Figure 10: Windows client architecture [42].  
In order to calculate the Connection Establishment Time, Retry Interval, Message Handling, and Notification Update we have used the formulas below:  
Connection Establishment Time:  
\[T_{connect}=T_{init}+T_{auth}+T_{SignalR} \tag{1}\]  
where \(T_{connect}\) = total time to establish the connection.  
\(T_{init}\) = initialization time of the SignalR and Notification Hub setup.  
\(T_{auth}\) = time to authenticate with the Notification Hub.  
\(T_{SignalR}\) = time to open the SignalR connection.  
Retry Interval:  
\[T_{retry}=T_{init}\times 2^{n} \tag{2}\]  
where \(T_{retry}\) = time between retries.  
\(T_{init}\) = initial retry time (e.g., 5 s).  
\(T_{auth}\) = number of failed attempts (exponential backoff).  
Message Handling:  
\[M_{received}=\{msg_{1},\ msg_{2},\ \ldots,\ msg_{n}\} \tag{3}\]  
where \(M_{received}\) = a set of messages received from the server or Notification Hub.  
Each message \(msg_{i}\) is processed based on predefined event handlers.  
Notification Update:  
\[N_{update}=M_{push}+M_{realtime} \tag{4}\]  
where \(N_{update}\) = notification updates shown to the user.  
\(M_{push}\) = messages from the Notification Hub (push notifications).  
\(M_{realtime}\) = real-time messages received from the SignalR hub.
# Chemometric Classification of Feta Cheese Authenticity via ATR-FTIR Spectroscopy
## 2 Materials and Methods
### Classification Models
#### 2.5.2 Partial Least Squares Discriminant Analysis  
The same train/test split (70/30) was applied, using stratified sampling to preserve class proportions. On the training set, partial least squares discriminant analysis (PLS-DA) was performed with up to ten latent components. Model performance was evaluated by ten-fold cross-validation, repeated ten times, using balanced error rate (BER) as the selection criterion. The optimal number of components minimizing BER was chosen for the final PLS-DA model. Scores on the first two latent variables were plotted, and sample classification was assessed via maximum-distance prediction. Receiver operating characteristic (ROC) curves and confusion matrices quantified AUC, sensitivity, specificity, and accuracy in both the training and test sets. To interpret the results, variable importance in projection (VIP) scores were computed for the selected model, smoothed with a moving average, and merged with mean absorbance to identify key spectral regions, which were highlighted in a VIP spectrum plot. The process was repeated for the raw spectra and the first and second derivative spectra.  
\begin{table}
\begin{tabular}{c c} \hline
**Signal Representation** & **Equation** \\ \hline Raw absorbance & - \\ First derivative & \(\text{d1}(\text{v})=(\text{A}(\text{v})-\text{A}(\text{v}-\Delta\text{v}))/\Delta \text{v}\) \\ Second derivative & \(\text{d2}(\text{v})=(\text{A}(\text{v}+\Delta\text{v})-2\cdot\text{A}(\text{v })+\text{A}(\text{v}-\Delta\text{v}))/(\Delta\text{v})^{2}\) \\ \hline \end{tabular} where \(\text{A}(\text{v})\) is the absorbance at wavenumber \(\text{v}\) and \(\Delta\text{v}\) is the spacing to the adjacent data point.  
\end{table}
Table 1: Preprocessing transformation equations for FTIR spectra.
# Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
## 3 Results
### Characterization of Nanocapsules
The pH value at which the nanoparticles do not exhibit any net charge is termed the isoelectric point (IEP). LNC\({}_{\text{SVT-LMWchit}}\) and LNC\({}_{\text{SVT-HMWchit}}\) showed an IEP at pH value 7.1 \(\pm\) 0.2 and 7.2 \(\pm\) 0.5, respectively. The positive zeta potential obtained for all the formulations prepared, and an IEP near the IEP of chitosan (IEP = 6.8) [58] is a good indicator that the chitosan was present at the external interface of the nanocapsules. LNC\({}_{\text{SVT-LMWchit}}\) and LNC\({}_{\text{SVT-HMWchit}}\) were further characterized in terms of their morphologies (Figure 2). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Header**} & \multicolumn{2}{c}{**DLS**} & \multicolumn{2}{c}{**NTA**} & \multicolumn{2}{c}{**Zeta**} \\ \cline{2-7}  & \(\mathbf{Z}\)**-Average** & **PDI** & **Mean (nm)** & \multicolumn{2}{c}{**PND**} & **Potential** & **(mV)** & **pH** \\  & **(nm)** & & & & & & \\ \hline LNC\({}_{\text{LMWchit}}\) & \(166\pm 5\) & \(0.13\pm 0.02\) & \(174\pm 5\) & \(1.3\pm 0.3\times 10^{12}\) & 25.4 \(\pm\) 4.1 & 4.1 \(\pm\) 0.01 \\ LNC\({}_{\text{SVT-LMWchit}}\) & \(168\pm 5\) & \(0.12\pm 0.04\) & \(166\pm 7\) & \(1.2\pm 0.6\times 10^{12}\) & 28.95 \(\pm\) 2.1 & 4.1 \(\pm\) 0.02 \\ LNC\({}_{\text{HMWchit}}\) & \(179\pm 14\) & \(0.13\pm 0.02\) & \(188\pm 7\) & \(1.1\pm 0.4\times 10^{12}\) & 33.6 \(\pm\) 3.9 & 4.1 \(\pm\) 0.03 \\ LNC\({}_{\text{SVT-HMWchit}}\) & \(185\pm 7\) & \(0.16\pm 0.03\) & \(210\pm 10\) & \(6.6\pm 0.2\times 10^{11}\) & 33.8 \(\pm\) 5.5 & 4.4 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physicochemical characterization of the nanocapsules (\(n=\) 3, Mean \(\pm\) Standard Deviation). Figure 2: Transmission electron microscopy (TEM) micrographs (magnification 40,000\(\times\)) of chitosan-coated lipid core nanocapsules: (**A**) LNC\({}_{\text{SVT-LMWchit}}\) and (**B**) LNC\({}_{\text{SVT-HMWchit}}\). Concerning the nanocapsule morphology, TEM images clearly displayed spherical-shaped particles, with a core with low electron density, as expected for lipid nanocapsules. Also, from the results of the particle diameters, the images were in good agreement with the mean nanocapsule sizes determined with DLS, NTA, and LD.
## 4 Time-Delay Chaotic Circuit
Figure 10: Time-delay Lorenz chaotic circuit. Figure 9: Time-delay waveform of sine signal (0.8 ms/div, 1 V/div). Since no voltage is applied to the capacitor before integration, the initial value of its terminal voltage is zero, which is negligible. The output gains \(G_{1},G_{2}\) of the analog multiplier in the \(y(t),z(t)\) variable channels are set to 0.1. According to the values of the system parameters, the resistance values are obtained as \(R_{1}\), \(R_{2}\), \(R_{6}\), \(R_{7}\), \(R_{14}\), \(R_{16}\), \(R_{21}\) = 10 k\(\Omega\), \(R_{5}\), \(R_{10}\), \(R_{15}\), \(R_{17}\), \(R_{22}\) = 100 k\(\Omega\), \(R_{11}\), \(R_{13}\) = 1 k\(\Omega\), \(R_{4}\) = 35k\(\Omega\), \(R_{8}\) = 15 k\(\Omega\), \(R_{9}\) = 8.3 k\(\Omega\), \(R_{20}\) = 20 k\(\Omega\), and the integrator capacitance \(C_{1},C_{2},C_{3},C_{4}\) are 1 uF. Based on the nodal voltage method to analyze the structure shown in Figure 10 and substituting the parameters into Equation (28), it can be verified that the mathematical model of the circuit is the same as the system (9). \[\left\{ \begin{array}{l}\dot{V}_{x}=-\frac{R_{4}R_{7}}{R_{16}R_{6}C_{1}}V_{x} +\frac{R_{4}}{R_{26}R_{5}C_{1}}V_{y}+\frac{R_{4}}{R_{36}R_{5}C_{1}}V_{x}(\tau) \\ \dot{V}_{y}=\frac{R_{16}}{R_{8}R_{7}C_{2}}V_{x}+\frac{R_{16}}{R_{8}R_{7}C_{2}}V _{y}-\frac{R_{16}R_{7}}{R_{6}R_{11}R_{7}C_{2}}V_{y}V_{x}+\frac{R_{16}}{R_{10}R _{11}V_{2}}V_{w}\\ \dot{V}_{z}=-\frac{R_{16}R_{9}}{R_{12}R_{18}R_{5}}V_{z}+\frac{G_{26}R_{44}}{R_ {13}R_{15}C_{3}}V_{x}V_{y}\\ \dot{V}_{w}=-\frac{R_{17}R_{7}}{R_{6}R_{20}R_{22}C_{4}}V_{x}\end{array} \right. \tag{28}\]  
We reduce the integral capacitances \(C_{1},C_{2},C_{3},C_{4}\) to 1 nF to adjust the capacitance multiplier, and thus can avoid interference from high-frequency signals, which means increasing the output signal frequency by a factor of 1000 while maintaining the original system properties, changing only the time series of the signal. The experiment was conducted in Multisim, and the results were presented in Figure 11. The circuit simulation of the time-lagged Lorenz system is consistent with the numerical analysis of the system (9), which proves the effectiveness of the method for high-dimensional time-delay chaotic systems and the feasibility of time-delay Lorenz circuits and lays an experimental foundation for the study of time-delay chaos in secure communication and practical engineering applications.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 9 Tackling LoRaWAN Challenges and SWOT Analysis
### Power Usage Issues  
Low power usage by end nodes is one of the main characteristics that LoRaWAN offers. The power usage is decreased by letting the end node sleep most of the time. However, in congested networks, this is not the case, as end nodes will end up retransmitting many packets or will be locking touplink traffic in receive windows. However, in the current LoRaWAN standard [11], there are some improvements that can be done to even further decrease the power consumption.  
As noticed in [98], much of the node power is used during receive windows when they lock to uplink frames. This can be avoided by using two different SYNC words at the end of programmable part of preamble for uplink and downlink. The end node will stop listening for a packet in any of the receive windows if the SYNC word is of an uplink packet. Thus, end node will be on receiving mode only for the length of programmable part of the preamble that is normally 6-8 symbols long. Another approach is to use totally different preambles for uplink and downlink traffic [98]. This approach will decrease the power usage even further.  
Under heavy traffic conditions and networks that are near to congestion, most of the first receive windows are missed by network, mainly due to duty cycle [40]. This is obvious, keeping in mind the low duty cycle (1%) of sub-band channels that can be used for the first receive window. Thus, another unnecessary power usage happens when node opens the first receive window and does not receive any packet. To avoid this, it is beneficial if the first receive window will use the SF of the uplink communication and high-power high duty cycle channel, while the second receive window can be transmitted using the same SF and channel that uplink communication used. Note, that we do not propose to use higher SF in any of the receive windows assuming symmetric links. If the end node sensitivities are more relaxed compared to the gateway, end node need to inform with the uplink packet gateway for unsymmetrical link. In that case, the end node will open only RW2 and the downlink in RW2 uses SF12. Such a scheme will decrease even further the power usage of end devices during receive windows.