esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 2 Acoustic Emission Testing Methods
### Acoustic Emission Detection
An event is a group of hits received by two or more channels from a single source, which is located. The ASTM E610-89 [32] standard defines the standard terminology relating to the quantities and phenomena involved in the study of acoustic emissions. An AE event is characterized by the following:  
* Amplitude: refers to the largest value present in the waveform of the signal and is linked to the type of source that produced it, the material, and its state of stress. It is generally measured in decibels, on a scale ranging from 0 to 100. * Duration: it is the time elapsed between the first overcoming of the threshold and the last overcoming. The relationship between duration and amplitude gives information on the shape of the signal. Figure 3: Typical AE signal: we can identify a very rapid rising edge followed by an exponentially decreasing trend also due to the response of the transducer, and by stationary or almost stationary signals, without decays. * Counts: number of times the amplitude of the signal exceeds a predetermined threshold. A single Hit can provide only a few counts or hundreds of counts depending on the intensity and shape of the signal. * Counts rate: number of counts per unit of time. * Counts to peak: number of counts before the maximum amplitude included. * Signal energy: total elastic energy released in each event. * Rise time: time interval between the first crossing of the threshold and the reaching of the maximum amplitude. * Damping time: time between reaching the peak and the end of the last threshold crossing. * Dead time: time after which, if the threshold is not exceeded, the event is considered to have ended. In addition to measuring the characteristics of individual signals, the instruments generally measure the time in which they are identified and the environmental variables that can cause the activity. In AE techniques, the response of the materials under test can be observed throughout the entire history of stress, without any damage to the specimen. In addition, this monitoring can be carried out with fewer sensors than other nondestructive techniques. The sensors are fixed in a single position on the surface of the specimen, for the duration of the test, as a point-by-point monitoring is not necessary: it is not even required to monitor both sides of the object. This methodology can only detect the formation of new cracks and progression of existing cracks or friction processes, phenomena resulting from the application of external loads or due to internal mechanical or thermal loads. AE-based methods can be implemented under normal operating conditions or during a slight increase in the load: this makes them particularly suitable for carrying out tests on structures under real load conditions to identify a possible failure.
# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
## 5 Discussion and Comparative Analysis
### Impact of Database Nature on Emotion Recognition Performance  
The nature of emotional databases--acted, natural, and induced--strongly affects within-database performance and generalization in speaker-independent scenarios. Acted databases, like INTER1 SP, EM-SpDB, and MESD, often show high accuracy in layer-wise cross-validation due to expressive recordings. However, the LOSO protocol significantly lowers performance, as seen in MESD, where accuracy drops from 95.38% to 53.12%. This highlights the challenge of generalizing exaggerated emotions to unseen speakers.  
Natural (MEACorpus) and induced (EmoWisconsin) databases show lower layer-wise scores but maintain more stable performance under LOSO. MEACorpus retains around 70-75% F1-score in LOSO despite reaching 92% in the layer-wise model, suggesting that genuine expressions generalize better. In contrast, EmoWisconsin struggles with LOSO, usually staying below a 60% F1-score, indicating that elicited emotions are more variable  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & **Metric** & **Result** & **PTM (Layer)** \\ \hline EM-SpDB & Accuracy & 79.66\% & TRILksson \\  & F1-Score & 78.48\% & TRILksson \\  & UAR & 73.24\% & W2V2-L-XLSR53-ES (6) \\ \hline EmoFilm\({}_{ES}\) & Accuracy & 83.68\% & W2V2-L-XLSR53-ES (6) \\  & F1-Score & 83.86\% & W2V2-XLSR-ES (11) \\  & UAR & 72.68\% & W2V2-XLSR-ES (11) \\ \hline INTER1 SP & Accuracy & 70.04\% & W2V2-L-XLSR53-ES (6) \\  & F1-Score & 67.44\% & W2V2-L-XLSR53-ES (6) \\  & UAR & 65.99\% & W2V2-L-XLSR53-ES (6) \\ \hline EmoWisconsin & Accuracy & 54.85\% & W2V2-L-XLSR53-ES (6) \\  & F1-Score & 50.75\% & W2V2-XLSR-ES (11) \\  & UAR & 34.71\% & W2V2-XLSR-ES (11) \\ \hline MEACorpus & Accuracy & 75.85\% & TRILksson \\  & F1-Score & 71.64\% & TRILksson \\  & UAR & 59.47\% & WavLM-L (6) \\ \hline MESD & Accuracy & 53.12\% & W2V2-XLSR-ES (11) \\  & F1-Score & 50.59\% & W2V2-L-XLSR53-ES (6) \\  & UAR & 53.09\% & W2V2-XLSR-ES (11) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of best results of LOSO validation by database and metric.  
and speaker specific. These patterns confirm that acted data achieve high peak accuracy that drops in speaker-independent contexts, while natural or induced data have lower peaks but steadier generalization.  
The results also indicate that the number of speakers alone is not the only variable impacting performance. Databases with few speakers, like INTER1 SP (2) and MESD (3), perform well in the layer-wise model but decline in LOSO. EM-SpDB (50 speakers) shows consistent results (88.28% layer-wise, 79.66% LOSO), suggesting that diversity improves generalization. Intermediate datasets, like EmoWisconsin (27 speakers), vary more, with a 58.92% FI-score in the layer-wise model, dropping to 50.75% in LOSO. Thus, while speaker count matters, database nature and emotion variability are more critical for generalization.
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 3 Methodology
### Anomaly Detection  
We can leverage the trained Autoencoder models to compress newly observed time series data (encoding) and remap them into their original state (decoding). The remapped data can then be compared to the original input to calculate the difference as a remapping error. Here, we use the Mean Absolute Error (MAE) to calculate the remapping error for each sample. The MAE is given by Equation (1) for \(R\) multivariate time series features:  
\[\text{MAE}=\frac{1}{R}\sum_{i=1}^{R}\int\Big{|}\Big{(}X_{r}(s)-\widehat{X_{r} (s)}\Big{)}\Big{|}dt. \tag{1}\]In the second step, we want to identify each time step of the evaluation data as normal or an anomaly based on the remap error and a threshold. We hold out part of the model training data to validate the remap error range under a normal data setting and establish an anomaly threshold used in testing. In the proposed approach, the anomaly threshold is calculated based on the \(k\)th percentile of the error range. In our experiment, the 99 th percentile is set as the cutoff, and the top 1% of the validation errors is set as the anomaly threshold. Using this threshold, we can identify the anomalies at each time step \(t\).  
Recently, it has become more common to use an adjustment strategy for partially detected anomaly sequences, as mentioned in [50; 55; 56]. The adjustment strategy works as a post-processing step that considers all time points in a certain successive abnormal segment as defects if the anomaly detection method successfully detects at least one time point of this segment. This strategy is based on the observation that an abnormal time point will cause an alert, which then draws attention to the whole segment. Therefore, in real-world applications, all anomalies in this abnormal segment would be correctly detected. An example of label adjustment is shown in Figure 3. Of the 10 observed time steps in this example, 5 consecutive time steps are anomalies. Initially, only two anomalous time steps are detected, but with the adjustment strategy, all five anomalous time steps are considered to be correctly identified.
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 4 Proposed Scheme
### Fair Power Adjustment (FPA)  
After the FBA component is completed, the power applied for the sub-channels selected in Equation (11) is further adjusted on the basis of the multi-level water-filling concept, with the aim being to better meet the requirements of the constraints in the considered optimization problem. For convenience, the term \(p_{i,j}^{*}(s)\) is defined as the optimal power allocation for sub-channel-\(j\) of CC-\(i\) during round \(s\), and can be expressed as:  
\[p_{i,j}^{*}(s)=\left[\sigma_{k}(s)-\frac{1}{\widetilde{\gamma}_{k}(i,j,s)} \right]^{+},\ k\in\Re_{\text{w\_sel}}(s) \tag{13}\]  
Notice that in Equation (13), \(\Re_{\text{w\_sel}}(s)\) indicates the set of active users whose requests have been selected based on Equation (11), \(\sigma_{k}(s)\) is the water level for user-\(k\) in \(\Re_{\text{w\_sel}}(s)\) during round \(s\) of the water-filling problem, \(\widetilde{\gamma}_{k}(i,j,s)\) indicates the estimated channel gain-to-noise ratio between user-\(k\) and sub-channel-\(j\) of CC-\(i\) during round \(s\), and \([x]^{+}=\max(0,x)\). Lastly, with regard to those requests from the users in \(\Re_{\text{w\_sel}}(s)\), if their allocated power levels are less than those necessary to satisfy their minimum required data rates, they are forced to continuously wait in the scheduling queue.
## 2 Geological Setting  
There are huge reserves of marble, limestone, dolostone, and granite in Pakistan, with the reserves of marble and onyx topping 300 billion metric tons, according to the Trade Development Authority of Pakistan [33]. The Government of KPK, in collaboration with Pakistan Stone Development Company (PASDEC), drew up a prefeasibility report in 2010 on marble and granite quarrying and processing in the FATA (Mohamand Agency) and Chitral (Figure 1) [34]. The huge reserves of Gahirat Marble in Chitral display colors varying from white to light grey (Figure 2a-d). [PERSON] et al. [35] described the Gahirat Marble deposits in Chitral, which have a total potential of around 1000 million metric tons.  
Figure 1: Geological and sample location map of the Chitral area.  
Figure 2: Exposed Unit of Gahirat Marble (**a**) showing gray color Gahirat Marble near the Chinar area. (**b**) The line showing contact between Chitral slates and Gahirat Marble near Gahirat Gol area. (**c**) Type locality of Gahirat Marble showing white color near Gahirat village. (**d**) Showing intrusive contact of Kesu-Buni zone grandoiorite with Gahirat Marble.  
The Chitral district mostly lies in northwest Pakistan, on the southern side of the Karakoram Block (Figure 1) [36]. The rocks exposed in the Karakoram Block are situated between the Indus Zangpo Suture Zone on the northern side and the Indus Suture Zone on the southern side [37]. In the north of the Indus Suture Zone, the Chitral area is divided into two geological zones on the northern and southern sides of the Reshun Fault.  
The northern zone comprises Chitral Slates (Permanin to Cretaceous), Koghozi Greenschist (Jurassic), Kesu-Buni Pluton medium-grained granodiorite, Gahirat Marble (Cretaceous), Krinj Limestone (Cretaceous), Reshun Formation (Late Cretaceous), and Karakoram Block [38]. Paleozoic and Mesozoic sedimentary and volcanic rocks that have undergone regional metamorphism characterize the rocks in the Chitral area [39]. The metamorphosed rocks are mainly greenschist to amphibolite facies. The low-grade metamorphic rocks are slate, chlorite mica schist, green stone, marble, and graphitic schist. The high-grade metamorphic rocks are garnet-biotite schist and biotite-staurolite schist. The age of metamorphic rocks is from the Ordovician to lower Cretaceous [40; 41; 42]. [PERSON] [43] described Gahirat Marble as a thick complex of white to ash gray, massive, and coarse crystalline marble. [PERSON] [44] described this formation as a light grey marble containing massive and coarse crystalline grains. Gahirat Marble primarily contains calcite along with accessory minerals, including muscovite, phlogopite, chlorite, and opaque minerals.
## 2 Literature Review
### Soil Type--Plasticity Index (PI)  
Soils are classified as cohesionless if the amount of sand and gravel exceeds 50% by weight or cohesive if the amount of silt- and clay-sized mineral exceeds 50% [28]. The engineering properties of granular soil are often established by applying confining pressure and looseness or denseness, as indicated by relation of the current void ratio to the lowest and highest value of this parameter [28]. The basic mechanical parameter of granular soil is the internal friction angle \(\varphi\). The engineering properties of cohesive soil are characterized by stiffness and strength and by relating the current water content and past consolidation history to the compositional characterization provided by the plasticity index (_PI_) [28]. Thanks to the amount of silt- and clay-sized minerals (which correlates with _PI_), cohesive soil, besides internal friction angle \(\varphi\), has another basic mechanical parameter, which is cohesion \(c\).  
_PI_ is one of the most important physical parameters of cohesive soil, which distinguishes it from granular soils. Plasticity index affects both mechanical and dynamical parameters. _PI_ has such influence on damping characteristic that, as the _PI_ increases, the influence of the effective pressure on the damping ratio decreases. In soil with _PI_ > 30%, effective stress influence on the discussed parameter can be omitted [9].  
Damping ratio curves against shear strain for soils with different plasticity index are presented in Figure 3a [16; 29]. It can be seen that damping ratio values decrease with _PI_ increases. The curve for _PI_ = 0 indicates noncohesive soil. Generally, sandy soils have a higher value of damping ratio than cohesive soil. Moreover, with increases in the plasticity index, the range of linear response of soil  
Figure 2: The effect of isotropic effective stress on (**a**) minimal damping ratio (\(D_{MIN}\)) [22] and (**b**) damping ratio (\(D\)) curves [25].  
for loading is increased [30]. The same phenomenon can be observed for nonlinear elastic zone [30], as presented in Figure 3b. Furthermore, in Figure 3a, no influence of the overconsolidation ratio (OCR) on the damping ratio curves is confirmed.
## 2 Materials and Methods
### OD\({}_{730}\) and Cell Counts Analysis  
The measurement of OD\({}_{730}\) and cell count only applies to _M. aeruginosa_ as it can be unicellular or gather in small colonies, whereas _P. foetida_ exists in long colonies entangled with each other.  
OD measurements are commonly used for unicellular or small colonies of cyanobacteria to measure the cell growth rate rapidly. The cell growth rate can be monitored through changes in the OD value, as shown in Equation (1) [23].  
\[\mu\propto\frac{\log_{10}\mathrm{OD}_{t}-\log_{10}\mathrm{OD}_{0}}{t} \tag{1}\]  
where \(\mu\) is the cell growth rate; \(t\) is days since inoculation; OD\({}_{t}\) is optical density after \(t\) days; OD\({}_{0}\) is optical density when \(t=0\).  
In this study, \(t=30\) days. The optical density was measured at 730 nm and the average OD\({}_{0}\) was 0.012. Thus, Equation (1) can also be written as follows:  
\[\mu\propto\log_{10}\frac{\mathrm{OD}_{730}}{0.012} \tag{2}\]  
where \(\mu\) is the cell growth rate;  
OD\({}_{730}\) is the optical density measured at 730 nm after 30 days of re-culturing.  
The OD\({}_{730}\) and cell counts were measured on each sampling day, and 10 \(\upmu\)L of a well-shaken _M. aeruginosa_ solution was added to a hemocytometer (C-Chip, NanoEnTek, Hwaseong-si, Gyeonggi-do, Republic of Korea) using an automated cell counter (LUNA, Logos Biosystems, Anyang-si, Gyeonggi-do, Republic of Korea) to determine the density of _M. aeruginosa_ cells.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Link Cost Metric
#### 3.3.1 Cost Initialization  
Since GCS-routing is an aerial network routing protocol, we must consider environmental characteristic of aerial networks. Most significantly, LoS between drones is usually guaranteed. Thus, distance between two drones is proportional to the link cost between them. We conducted an experiment to confirm the relationship between drone distance and throughput. One drone hovered at 10 m altitude, while the other drone flew away from the hovering drone at constant 2 m/s. We measured TCP and UDP throughput flows at 4, 8, and 16 Mbps. Data were transmitted through the network interface using IEEE 802.11g. Figure 4 shows that, regardless of the protocol or data rate, throughput decreased proportionally with distance. Data transmissions fluctuated, but were disconnected at almost the same distance, exceeding 80 m.  
Figure 3: Example routing table distribution in GCS-routing.  
We normalized the data into [0, 1] to identify common features,  
\[X(d)^{\prime}=\frac{X(d)-X_{min}}{X_{max}-X_{min}}, \tag{1}\]  
where \(X(d)\) is the throughput at distance \(d\); and \(X_{max}\) and \(X_{min}\) are the maximum and minimum throughput, respectively. We then calculated the average of all datasets to generalize results from various transmission rates, as shown in Figure 5. The fact that mean of normalized data is 0.8 indicates that the best performance of link was 80% of the ideal case. Performance was approximately 50% of the best performance when \(d=60\) m, implying that it takes twice the time to deliver the same number of data. Thus, link cost can be estimated using the reciprocal of the fitted curve in Figure 5. The resulting cost function indicates the amount of time to deliver data through the link at the given distance.  
There was significant difference between theoretical and experimental data rates outdoors, consistent with many previous studies [30; 31; 32]. Thus, it is more appropriate to use the experimental link cost estimation function rather than the theoretical result. Many studies have experimentally established communication standards' data rates, hence GCS-routing used suitable previous results depending on the communication module employed. For example, estimated throughput, \(T_{est}\), at distance \(d\) using IEEE 802.11g can be expressed as shown in Equation (2) according to [33].  
\[T_{est}=-0.00001d^{3}-0.0013d^{2}-0.2454d+25.424. \tag{2}\]
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 5 Future Directions and Challenges
### Challenging Problems  
Certain existing works related to game theory-based protocols even apply two types of games to design the routing protocol. Furthermore, some protocols incorporate a secondary algorithm along with game theory-based models. It is obvious that sensor nodes have limited processing power and memory capacity. Too much complexity in a routing protocol design is not feasible with the core design of sensor nodes. For cooperative games, cooperation overhead always exists. Too many coalition formations may reduce the network lifetime, as the nodes have to exchange the data frequently. For noncooperative games, it is difficult to attain the Nash equilibrium and obtain the optimal strategies. Selfish nodes are often excluded from the routes in other types of games. This may also lead to network isolation. It is a significant challenge to design a routing protocol using game theory that is also simple and can be implemented in energy critical sensors.  
The main hypothesis of game theory is the assumption of the nodes being rational in a WSN, where they also seek for their best interest. This behavior of the nodes is hard to guarantee because a WSN is subjected to numerous changes (change of energy level, topology, network structure).  
In cooperative games, players cooperate with each other to increase an overall benefit. The players (nodes) in a game sometimes tend to be selfish and have a tendency to display selfish behavior. Incentive mechanisms are necessary to ensure cooperation, which is a tough task.  
Designing and formulating pay-off functions for a game is crucial and challenging. A bad design may not satisfy the needs and introducing rationality among the players (nodes) can fail drastically.  
The formulation of noncooperative game theory requires the attainment of Nash equilibrium, where no player can benefit without decaying another's pay-off through a unilateral move. This equilibrium point is not always guaranteed to be attained by the players. Attaining this point is one of the biggest challenges in formulating noncooperative game theory in a WSN paradigm.
## 3 Results and Discussion
### Effect of Pressure  
Figure 3 shows the influence of pressure (between 10-40 bar) on the reaction indices. The remaining operating conditions have been fixed as follows: Feed, ternary mixtures of \(\mathrm{H_{2}+CO+CO_{2}}\) with \(\mathrm{CO_{2}/CO molar}\) ratios between 1/3 and 1; temperature, 275 \({}^{\circ}\)C; space time, 10 g\({}_{\mathrm{cat}}\) h (mol\({}_{\mathrm{C}}\))\({}^{-1}\); \(\mathrm{H_{2}/CO_{x}}\) molar ratio, 3. The results indicate that CO\({}_{x}\) conversion increases with pressure (Figure 3a), which is characteristic of the DME synthesis reaction, given that it involves a reduction in mole number. It is observed that, for the CO\({}_{2}\)/CO molar ratio used in the feed (2/3), an increase in pressure from 20 to 40 bar results in a very low improvement of the reaction indices. Hence, DME yield rises from 11.0% to 12.3% (Figure 3b). Moreover, under the operating conditions studied, the conversion of CO\({}_{2}\) is positive (Figure 3c) in the whole pressure range, with a minimum within the 20-30 bar range. Nevertheless, it should be noted that for a CO\({}_{2}\)/CO ratio lower than that plotted in Figure 3 (that is, for CO\({}_{2}\)/CO molar ratios of 1/3), the effect of pressure on the reaction indices is more pronounced (results not shown). In consequence, it can be concluded that an increase in the CO\({}_{2}\) content in the feed attenuates the favorable influence of pressure on the reaction indices.  
The good kinetic performance of the catalyst for all the CO\({}_{2}\)/CO feeds studied (between 1/3 and 1) and under moderate pressures (20-30 bar) should be highlighted, as the results of CO\({}_{x}\) conversion and DME yield are only scarcely lower than those obtained at 40 bar, with positive CO\({}_{2}\) conversion. On the  
Figure 2: Effect of temperature on the conversion of CO\({}_{x}\); (**a**) Product yield; (**b**) CO\({}_{2}\) conversion (or CO\({}_{2}\) yield, for negative values); (**c**) Reaction conditions: Feed, \(\mathrm{H_{2}+CO+CO_{2}}\) with \(\mathrm{H_{2}/CO_{x}}\) molar ratio of 3, and CO\({}_{2}\)/CO molar ratio of 1/3; 30 bar; 10 g\({}_{\mathrm{cat}}\) h (mol\({}_{\mathrm{C}}\))\({}^{-1}\).  
other hand, despite CO\({}_{2}\) conversion is greater at 10 bar, as previously discussed, the other reaction indices are significantly lower under these conditions, in particular the yield of DME. In view of these results, it is concluded that a good balance among CO\({}_{\rm x}\) conversion, DME yield and selectivity and CO\({}_{2}\) conversion is achieved within the 20-30 bar range.  
Figure 3: Effect of pressure on the conversion of CO\({}_{\rm x}\); (**a**) Product yield; (**b**) CO\({}_{2}\) conversion (or CO\({}_{2}\) yield, for negative values); (**c**) Reaction conditions: Feed, H\({}_{2}\) + CO + CO\({}_{2}\) with H\({}_{2}\)/CO\({}_{\rm x}\) molar ratio of 3, and CO\({}_{2}\)/CO molar ratio of 2/3; 275 \({}^{\circ}\)C; 10 \(\rm{g_{ext}}\) h (mol\({}_{\rm c}\))\({}^{-1}\).