esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Speaker Tracking
### Speaker Segmentation  
As shown in Figure 3, first an energy-based Speech Activity Detection (SAD) is performed on the audio. Then, the speech parts are segmented into small segments of \(d\) seconds with an overlap of \((d-\Delta)\) seconds, where \(\Delta\) is the shift. This is referred to as _initial segmentation_ in the segmentation part of Figure 3. The segments generated in this step are reffered to as _small segments_. The shift \(\Delta\) defines the resolution of speaker change detection. Then, Mel-Frequency Cepstral Coefficients (MFCC) features are extracted for every _small segment_. In order to detect speaker change points, the Divergence Shape distance is computed between every adjacent _small segments_ and is thresholded. We compute the Divergence Shape distance as in References [34; 36], using the following simplified expression:  
\[D=\frac{1}{2}tr[(C_{i}-C_{j})(C_{j}^{-1}-C_{i}^{-1})] \tag{3}\]  
where \(tr\) is the trace function that sums the diagonal elements of a matrix, \(C_{i}\) is the covariance of the features from _small segment_\(S_{i}\) and \(C_{j}\) is the covariance of the features from _small segment_\(S_{i}\). A speaker change point is marked if the distance at that point is greater than the distances at the two neighboring points (one before and one after) and a threshold at that point. For example, a speaker change point at _small segment_\(S_{i}\) occurs if:  
Figure 3: Architecture of Speaker Tracking using RBM vector Extraction.  
\[D(i,i+1)>D(i,i+2) \tag{4}\]  
and  
\[D(i,i+1)>D(i-1,i) \tag{5}\]  
and  
\[D(i,i+1)>Threshold_{i} \tag{6}\]  
where \(D(i,i+1)\), \(D(i,i+2)\) and \(D(i-1,i)\) are the Divergence Shape distances of _small segment_\(S_{i}\) to \(S_{i+1}\), \(S_{i}\) to \(S_{i+2}\) and \(S_{i-1}\) to \(S_{i}\), respectively. \(Threshold_{i}\) is an adaptive threshold which is computed for every _small segment_ and is defined in [34] as:  
\[Threshold_{i}=\frac{\alpha}{N}\sum_{n=0}^{N}D(i-n-1,i-n) \tag{7}\]  
where \(\alpha\) is a scaling factor and needs to be tuned experimentally. We have evaluated the segmentation with different values of \(\alpha\) which we will discuss in Section 6. In Equation (7), \(N\) is the number of previous distances used for predicting the threshold. Once we detect the speaker change points by using this method, we segment the audio on these points. The segments generated will be used in the next step, that is, speaker identification. It is worth noting that we did not perform any refining algorithm for the speaker change points. Rather, we fixed the value of \(\alpha\) so as to minimize the Miss Detection error in order not to miss a speaker change. This is because a False Alarm error can possibly be corrected in the speaker identification stage but a Miss Detection error cannot be corrected.
## 5 Conclusions  
In this paper, three concrete shear walls with coal gangue replacement rates of 0%, 50%, and 100% were subjected to lateral cyclic loading to observe the crack development in the three specimens under lateral cyclic loading and study the influence of different coal gangue replacement rates on the seismic performance of the specimens. The following conclusions are drawn:  
1. The crack development of shear wall specimen RFSW-1 with a coal gangue replacement rate of 0% was dispersed and uniform, and the crack width was small. The shear wall specimens CGSW-2 and CGSW-3 with coal gangue replacement rates of 50% and 100%, respectively, had denser crack development and larger crack widths.
2. The stress performance and failure morphology of the three specimens were very similar, and the characteristics of the hysteretic and backbone curves were roughly the same. The failure modes of the three specimens were all typical bending-shear failure.
3. By comparing the bearing capacity, deformation capacity, strength degradation, stiffness degradation, and energy dissipation capacity of the specimens, the bearing capacity, deformation capacity, strength degradation, and energy dissipation capacity of the specimens were negatively correlated with the replacement rate of coal gangue, but the difference was small. However, the stiffness degradation of the specimen is positively correlated with the replacement rate of coal gangue.
4. Although the shear wall specimens CGSW-2 and CGSW-3 with coal gangue replacement rates of 50% and 100%, respectively, were worse than the shear wall specimen RFSW-1 with a coal gangue replacement rate of 0% in bearing capacity and deformation capacity, the difference is not obvious, and the requirements of seismic performance can be met. Through finite element simulation, it can also be seen that coal gangue concrete shear walls have the best seismic performance at the axial pressure ratio between 0.2 and 0.3. In addition, shear walls made using coal gangue can reduce the weight of the wall, save cost and reduce waste. Therefore, it is feasible to make shear walls using coal gangue concrete instead of conventional concrete.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 6 Implementation and Tests with Real Hardware
### Detailed Update Procedure  
The memory map of the internal flash memory is shown in Figure 23. The bootloader, stored at the beginning, handles the boot of the device. It is also responsible for receiving, checking, and writing the updated firmware into the flash memory. Its size is 10 KB approximately.  
There is a 4 KB section of shared space between the bootloader and the application's firmware, allowing for communication between them. It is used for booting purposes, with three modes: (a) _application booting_, the normal one; (b) _FUOTA booting_, after an FUOTA process; and (c) _serial-update booting_, after an update via serial port, which is only used for updating the firmware of the LoRa PCB of the gateway.  
During the FUOTA process, the application firmware handles the saving of the new firmware into the external memory; later, it checks the correctness of the SHA256 hash. If correct, it communicates to the bootloader that a new firmware has been stored by using the shared section; finally, it resets the device. After the reset, the bootloader, running in the _FUOTA booting_ mode, checks the firmware stored on the external memory, and, if it is correct, it overwrites the internal flash memory (also with SHA256 verification); finally, the bootloader is launched again, this time in the _application booting_ mode.
## 4 Electrochemical and Bio-Electrochemical Applications
### Capacitive Deionization (CDI)  
Capacitive deionization (CDI), an emerging electro-sorption technology for desalination and ion removal, is also being explored for utilizing SDB as an electrode. Owing to its porous carbon matrix and intrinsic salts, SDB can function similarly to activated carbon electrodes. The extensive surface area of SDB creates numerous sites for ion adsorption, while its electrical conductivity ensures efficient charge transfer during the CDI process. [PERSON] et al. [60] used aerobic granular sludge as the precursor for preparing hierarchically porous biochar. The specific capacitance is enhanced by 12% compared to commercial activated carbon. [PERSON] et al. [61] developed an iron-enriched SDB from dyeing industry sludge, which exhibited a remarkable salt adsorption capacity of 83.0 mg/g under batch mode CDI conditions with 1000 mg/L of NaCl. The presence of iron species enhanced the electrode's capacitive behavior and salt adsorption capacity, likely due to faradaic (pseudo-capacitive) reactions. Furthermore, the electrode demonstrated excellent cycling stability, maintaining both salt adsorption capacity and charge efficiency over 200 cycles during the desalination/regeneration CDI process. Recent studies have shown that SDB-based CDI electrodes can achieve high salt removal capacities and regeneration efficiencies, offering a promising approach for sustainable water treatment. Ongoing research is optimizing electrical conductivity (by increasing the carbonization temperature or adding conductive additives) and the wettability of sludge char electrodes to further boost CDI salt uptake.
## 2 Governing Equation and Calculation Setting  
### Multiphase Flow Model  
The VOF model simulates flow by solving the continuity equation, momentum equation, energy equation, and volume fraction equation of the two phases. Given the limited pressure variation (0.3 MPa) and maximum fluid velocity (30 m/s) within the apparatus, compressibility-induced temperature changes are negligible. Therefore, the isothermal assumption is considered a reasonable approximation for the flow conditions in this study, and the influence of temperature on the entrainment rate is not accounted for. Additionally, due to the significantly high liquid Weber number (the ratio of inertial to surface tension forces) and the need for convergence in the calculations, surface tension effects are neglected.  
The multiphase flow volume fraction equation, namely, the continuity equation, can track the interface between the phases, which is expressed as follows:  
\[\frac{1}{\rho_{n}}\bigg{[}\frac{\partial}{\partial t}\big{(}v_{n}\rho_{n}\big{)} +\
abla\Big{(}v_{n}\rho_{n}\overset{\rightarrow}{u}\Big{)}\bigg{]}=S_{vn}+ \sum_{n=1}^{a}\left(M_{mm}-M_{nm}\right) \tag{1}\]  
where \(\rho_{n}\) is the density of the \(n\)th phase fluid, kg/m\({}^{3}\); \(\overset{\rightarrow}{u}\) is the fluid flow velocity, m/s; \(M_{nm}\) is the mass transfer from the \(n\)th phase to the \(m\)th phase; \(M_{mm}\) is the mass transfer from the \(m\)th phase to the \(n\)th phase; \(S_{m}\) is the source term, and the default value is 0.  
The volume fraction of the first phase will not be determined by the volume fraction equation. Instead, it will be calculated based on the following constraints:  
\[\sum_{n=1}^{a}\alpha_{n}=1 \tag{2}\]  
\(\alpha_{n}\) is the volume fraction of n phase.  
The essence of the momentum equation is to satisfy [PERSON]'s second law. The VOF model for multiphase flow shares a momentum field and solves a single momentum equation in the entire computational domain, instead of obtaining it by summing the momentum equations of all phases. The resulting velocity field is common to all phases and can be expressed as follows:  
\[\frac{\partial}{\partial t}\Big{(}\rho\overset{\rightarrow}{u}\Big{)}+ \
abla\cdot\Big{[}\rho\Big{(}\overset{\rightarrow}{u}\Big{)}^{2}\Big{]}= \bigg{[}\mu\bigg{(}\
abla\overset{\rightarrow}{u}+\
abla\overset{ \rightarrow}{u}^{T}\bigg{)}\bigg{]}-\
abla p+\rho\overset{\rightarrow}{g}+ \overset{\rightarrow}{F} \tag{3}\]  
where \(\overset{\rightarrow}{F}\) is the volume force, N; \(\rho\) is the average density of fluid volume fraction, kg/m\({}^{3}\); \(\mu\) is the average viscosity of the fluid volume fraction, Pa\(\cdot\)s.
# A 1D-2D Coupled Lattice Boltzmann Model for Shallow Water Flows in Large Scale River-Lake Systems
## 2 1D and 2D Shallow Water Submodels and Their Coupling Strategy  
### 1D Shallow Water Submodel  
The 1D shallow-water equations can be expressed as  
\[\frac{\partial h}{\partial t}+\frac{\partial(hu)}{\partial x}=0 \tag{1}\]  
\[\frac{\partial hu}{\partial t}+\frac{\partial}{\partial x}(hu^{2}+\frac{gh^{2 }}{2})=gh(\frac{\partial z}{\partial x}-S_{f}) \tag{2}\]  
where \(h\) is the water depth, \(u\) is the velocity, \(x\) is the spatial coordinate, \(t\) is the temporal coordinate, \(g\) is the gravitational acceleration, \(z\) is the water surface elevation. \(S_{f}=n^{2}u^{2}R^{-4/3}\) is the frictional loss, in which \(n\) is the Manning roughness coefficient, and \(R\) is the hydraulic radius.  
The LB method has been used to numerically solve the 1D flows governed by the Equations (1) and (2). The D1Q3 model geometry has been chosen, in which \(\{e_{2},e_{0},e_{1}\}=c\cdot\{-1,0,1\}\), and \(c=\Delta x/\Delta t\). The common LB equation [10] with Bhatnagar-Gross-Krook (BGK) collision operator can be written as  
\[f_{\alpha}(\mathbf{x}+\mathbf{e}_{\alpha}\Delta t,t+\Delta t)-f_{\alpha}(\mathbf{x},t)=- \frac{1}{\tau}\Big{[}f_{\alpha}(\mathbf{x},t)-f_{\alpha}^{eq}(\mathbf{x},t)\Big{]}+F_ {\alpha},(\alpha=0,1,2) \tag{3}\]  
where the left side terms of Equation (3) denote the propagation process, and the right side terms of Equation (3) denote the collision process. \(\mathbf{x}\) is the lattice node coordinate, \(\mathbf{e}_{\alpha}\) is the discrete particle velocity, \(f_{\alpha}(\mathbf{x},t)\) is the discrete distribution function along the \(\alpha th\) particle velocity direction, \(f_{\alpha}^{eq}(\mathbf{x},t)\) is its corresponding equilibrium, \(\tau\) is the relaxation time, and \(F_{\alpha}\) is the external forcing term.  
The equilibrium distribution functions can be expressed as  
\[f_{0}^{eq}=h-\frac{gh^{2}}{2c^{2}}-\frac{hu^{2}}{c^{2}},\ f_{1}^{eq}=\frac{gh^ {2}}{4c^{2}}+\frac{hu}{2c}+\frac{hu^{2}}{2c^{2}},\ f_{2}^{eq}=\frac{gh^{2}}{4c^ {2}}-\frac{hu}{2c}+\frac{hu^{2}}{2c^{2}} \tag{4}\]  
Water depth and velocity of the fluid are defined as  
\[h=\sum_{\alpha}f_{\alpha},hu=\sum_{\alpha}\mathbf{e}_{\alpha}f_{\alpha} \tag{5}\]  
The Chapman-Enskog expansion [10] can be adopted to recover the physics of the 1D shallow-water Equations (1) and (2). The viscosity and relaxation time must satisfy the following relation.  
\[\
u=c^{2}(\tau-\frac{1}{2})\Delta t \tag{6}\]  
The treatment of external forcing term proposed by [PERSON] [10] has been adopted, and \(F_{\alpha}\) can be written as  
\[F_{0}=0,\ F_{1}=\frac{gh}{2}(\frac{\partial z}{\partial x}-S_{f}),\ F_{2}=- \frac{gh}{2}(\frac{\partial z}{\partial x}-S_{f}) \tag{7}\]  
As for boundary treatment, the unknown distribution functions at the inlet and outlet boundary can be defined as  
\[\text{Inlet boundary}:\ f_{1}=h-f_{0}-f_{2},orf_{1}=\frac{(1+u)f_{2}+uf_{0}}{ 1-u} \tag{8}\]  
\[\text{Outlet boundary}:\ f_{2}=h-f_{0}-f_{1},orf_{2}=\frac{(1-u)f_{1}+uf_{0}}{ 1+u} \tag{9}\]
# Spoken Language Identification System Using Convolutional Recurrent Neural Network
## 5 Experiments
### Performance Evaluation  
Two options are presented in this article for evaluating the system's performance. The first is the evaluation adopted on the sequence level, and the second is the evaluation based on the file level. Each speech file contained different numbers of sequences. Therefore, to compute the complete speech file accuracy, as in [51], the accuracy of each sequence was calculated. Choosing the right metrics is crucial for accurately measuring the performance of trained architectures using a testing corpus. It is always desirable to ensure the exactness of the system by computing various metrics. This article used four performance metrics: accuracy (A), precision (P), recall (R), and F-measure (F1) [26] to determine the effectiveness of the CRNN architecture for the spoken LID problem. For all metrics, TP represents all true positives, TN denotes all true negatives, FP shows all false positives, and FN represents all false negatives [32].
# The Odyssey of Entropy: Cryptography
## 3 Entropy and Encryption
### Application  
Cryptosystems and cryptographic schemes designed, implemented and evaluated using entropy, were used in a variety of platforms, environments, applications and computing paradigms, among which we can mention fog-based IoTs [208]. In fog-based IoT, a nearby light-weight middleware is used to bridge the gap between IoT devices and the far-away cloud. This helps provide the required support and communication between devices, sensors, receptors on one side and the servers on the other side. Even entropy-as-a-service was proposed by some researchers [209]. However, among these environments, we will discuss blockchain and cryptocurrencies in more detail because of their importance in the modern world.  
The possibility of using the inherent unpredictability of blockchains and especially Bitcoin as a source of randomness was investigated by [PERSON] and [PERSON] [210]. They demonstrated that random numbers generated by this method are malleable in the sense that an adversary will be able to manipulate them, even with limited computational power and financial budget. Some indicators for evaluating public blockchains were introduced by [PERSON] and [PERSON] [211]. They used entropy for ranking the indicators. [PERSON] et al. [212] argued that while decentralization is a critical selling point of most public blockchain platforms, most research works on this property fail to quantify it and perform calculations on it. They presented a method based on entropy for quantification of the degree of decentralization in blockchains.
## 3 Results
### Comparison of Biomass and Productivity with Other Satellite Products  
In our study, we found that differences in forest attributes depend on whether the forest was in an edge or core area. We observed a biomass of 172 Mg ha\({}^{-1}\) in the edge area, whereas it was 235 Mg ha\({}^{-1}\) in the core area (Table 1). This result is confirmed when the Santoro biomass map [32] is also divided into forest edge and core areas (Figure 8a,b). This analysis shows a clear agreement with our biomass estimations, as well as confirming the differences between the forest edge and core (Santoro: mean for forests at edge 186 Mg ha\({}^{-1}\); Santoro: mean for core forests 248 Mg ha\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Highly Fragmented (CV)** & **Moderate Fragmented (CV)** & **Low Fragmented (CV)** \\ \hline
**Mean aboveground biomass** & 171 \(\pm\) 42 & 210 \(\pm\) 38 & 238 \(\pm\) 37 \\
**[Mg odm ha\({}^{-1}\)]** & (24\%) & (18\%) & (16\%) \\ \hline
**Mean net primary productivity** & 5.2 \(\pm\) 0.4 & 4.9 \(\pm\) 0.3 & 4.6 \(\pm\) 0.3 \\
**[Mg C ha\({}^{-1}\) a\({}^{-1}\)]** & (7.6\%) & (6.7\%) & (7.2\%) \\ \hline
**Mean gross primary productivity** & 22 \(\pm\) 3 & 23 \(\pm\) 2 & 24 \(\pm\) 2 \\
**[Mg C ha\({}^{-1}\) a\({}^{-1}\)]** & (14\%) & (10\%) & (8\%) \\ \hline
**Mean leaf area index** & 3.8 \(\pm\) 0.6 & 4.3 \(\pm\) 0.5 & 4.6 \(\pm\) 0.5 \\  & (16.3\%) & (12.5\%) & (11.2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean values and standard deviations, as well as the coefficient of variation (CV) averaged within a 10 \(\times\) 10 km\({}^{2}\) section for the four forest attributes considered: aboveground biomass, net primary productivity, gross primary productivity and leaf area index. Each fragment was assigned to a fragmentation landscape type based on the amount of edge area it contained: low-fragmented, moderate-fragmented and highly fragmented. CV is the ratio of the standard deviation to the mean.  
A similar comparison was made between the NPP values from MODIS. In general, for low and medium NPP values there is a good agreement between the NPP values estimated by MODIS and the method of this study (Figure 8c,d). Our NPP estimates tend to be slightly higher, which is mainly due to the fact that our workflow can detect structural changes in the forest, and thus also forest degradation. MODIS generally tends to produce lower NPP estimates, which has already been discussed in other studies [15; 34].  
In our study, we found that NPP is 5.4 Mg C ha\({}^{-1}\) a\({}^{-1}\) in the edge area and slightly lower at 4.7 C ha\({}^{-1}\) a\({}^{-1}\) in the forest core area (Table 1). Although MODIS provides overall lower NPP estimates, our observed trend is also confirmed by the MODIS measurements: the NPP from MODIS is 4.5 Mg C ha\({}^{-1}\) a\({}^{-1}\) for forest edges and 3.8 Mg C ha\({}^{-1}\) a\({}^{-1}\) for core forests.
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 4 Discussion
### Mixed Tree Species Forests Peculiarities
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & & & \multicolumn{3}{c}{**SOS RMSE**} & & & \multicolumn{3}{c}{**EOS RMSE**} \\ \hline  & **BP*** & **FS** & **Qs.** & **FE** & **Ts.** & **PT** & **BP** & **FS** & **Qs.** & **FE** & **Ts.** & **PT** \\ \hline
**MODIS GPP** & 8.98 & 7.89 & 9.07 & 11.1 & 7.71 & 14 & 14.6 & 9.88 & 12.1 & 14 & 16.7 & 9.07 \\
**MODIS LAI** & 19.4 & 26.4 & 35.7 & 24.6 & 22.4 & 29.9 & 20 & 23.5 & 23.2 & 23.8 & 17.8 & 42.6 \\
**MODIS EVI** & 18.1 & 15.7 & 19.1 & 14.6 & 19 & 14.9 & 16.1 & 12.6 & 14.4 & 16.6 & 19.1 & 9.21 \\
**MODIS NDVI** & 20.7 & 16.1 & 16 & 19.7 & 22.8 & 19.2 & 20.9 & 13 & 14.8 & 18.2 & 19.1 & 11.4 \\
**Landsat 7 EVI** & 26.3 & 26.9 & 28.4 & 19.7 & 19.4 & 22.9 & 25 & 23.7 & 21.5 & 17.5 & 21.4 & 18.9 \\
**Landsat 7 NDVI** & 35.2 & 30 & 36 & 27.3 & 26.1 & 30.6 & 25.7 & 33.4 & 27.4 & 33.5 & 27.5 & 30 \\ \hline \end{tabular}  
* BP, FS, Qs., FE, Ts. and PT are the different tree species, i.e., _Betula pendula, Fagus sylvatica, Quercus_ sp., _Fnximus excision_, _Tilia_ sp. and _Populus tremula_, respectively.  
\end{table} 
Table 1: RMSE of estimated SOS and EOS during a leave-one-out cross-validation, using different vegetation indices.
## 1 Introduction
Although the colorimetric method is straight forward, it offers only pure qualitative information. On the other side, chromatography based methods have been also used for quantifying PZA and POA in biological fluids [29; 30; 31]. Nevertheless, for routine high throughput analysis chromatographic methods are unpractical and cost intensive. As an alternative analytical tool Raman spectroscopy-based methods are very promising approaches. Raman spectroscopy is suitable for the fingerprint specific detection of low molecular weight substances, such as PZA and POA. It provides a specific and unique spectroscopic pattern. Combining Raman spectroscopy with plasmonic nanoparticles (NPs) the surface-enhanced Raman spectroscopy (SERS) enables us to detect very low concentrations of target analytes. Thus, SERS is well known to offer sensitive and specific information on the molecular level [32; 33]. With the advent of miniaturized, low cost Raman setups and the development of in situ synthesis protocols, SERS is considered to have a high potential for clinical applications [34; 35]. The combination of SERSwith microfluidics offers an attractive method (lab-on-a-chip SERS, LoC-SERS). The application of a microfluidic flow-through device allows for a high throughput measurement protocol employing small sample volumes. Thus, a large data set can be achieved in a short time frame. Furthermore, the microfluidic environment ensures a very stable and easy to reproduce measurement conditions. The majority of the antecedent investigation of PZA and its metabolite POA applying Raman spectroscopy focused on the vibrational assignment of the theoretical determined spectral positions of the Raman bands of these two molecules [36; 37; 38]. However, only few efforts were made so far to investigate PZA or POA by means of SERS [39; 40; 41]. Within this study, we aim for the molecular specific and sensitive detection of the target molecules PZA and POA employing SERS as an analytical tool. To the best of our knowledge, this is the first study focusing on the simultaneous determination of PZA and its metabolite POA within a droplet-based lab-on-a-chip SERS (LoC-SERS) platform using in situ synthesized silver NPs. Thus, in this proof of principle study the detection and discrimination of the two molecules in one approach is addressed and the potential of the ratiometric detection scheme is evaluated for the determination of PZA resistance in MTB isolates.