esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Second Messengers in Cyanobacteria
### Calcium, Ca\({}^{2+}\)
#### 2.1.3 Responses to Temperature Stress and Other Stresses Are Mediated by Ca\({}^{2+}\)  
Calcium is also an important second messenger for cells to regulate responses to stresses, such as temperature shock, osmotic stress, and light-to-dark transitions [20, 22, 43]. The mechanosensitive ion channel protein MscL that is found in the plasma membrane of _Synechocystis_ is involved in Ca\({}^{2+}\) homeostasis regulation [44]. In _Anabaena_, heat shock at 44 \({}^{\circ}\)C resulted in an induction of intracellular Ca\({}^{2+}\) levels that had a higher amplitude when Ca\({}^{2+}\) was present in the external medium; similarly, a cold shock at 10 \({}^{\circ}\)C induced an increased magnitude of intracellular Ca\({}^{2+}\) accumulation in cells [20]. The use of inhibitors or pharmacological agents indicated that the source of Ca\({}^{2+}\) in the heat shock-induced elevation of cellular Ca\({}^{2+}\) levels is both from Ca\({}^{2+}\) in the external medium and internal stores, whereas the cold-shock induced elevation of Ca\({}^{2+}\) levels results primarily from import of Ca\({}^{2+}\) from the external medium [20].  
Calcium is also induced by other stresses, including salinity, osmotic stress, light-to-dark transitions, and UV irradiation [22, 43, 45]. Intracellular levels of Ca\({}^{2+}\) increased when _Anabaena_ was exposed to salt or osmotic stresses. The source of increased intracellular Ca\({}^{2+}\) levels was external as inhibiting calcium channels or the use of Ca\({}^{2+}\)-free medium eliminated this response [22]. Detecting changes in external light availability are critical for photosynthetic organisms, such as cyanobacteria. An increase of internal Ca\({}^{2+}\) levels was observed in heterocysts in _Anabaena_ when cells were exposed to UV irradiation [45]. A Ca\({}^{2+}\) transient also occurred during a light-to-dark transition in _Anabaena_[43]. The observed elevation of intracellular Ca\({}^{2+}\) levels was not associated with a specific photoreceptor, but more likely occurs in response to changes in the redox state of components of the photosynthetic electron transport chain [43]. Biochemical assays indicated that the source of the Ca\({}^{2+}\) was external during the response to UV irradiation and light-to-dark transitions [43, 45]. Together, the rapid Ca\({}^{2+}\) transients that occur in cyanobacteria under temperature, osmotic, nitrogen and light stresses provide evidence that Ca\({}^{2+}\) signaling is involved in early responses to these environmental stimuli.
## 4 Experimental setup
### Error Measurement Metrics  
Finding a meaningful objective metric is always a challenge in evaluating the performance of F0 detectors. In fact, one metric may possibly be suitable for a few pitches but not convenient for all. The reason for this may be related to some factors which are influenced by the speed, complexity, or accuracy of the pitch algorithms. Speaker types and environmental conditions should also be taken into account when choosing these metrics. In this article, we try to adopt a series of distinct measurements in accordance with [63; 64] to assess the accuracy of the adContF0 estimation. The results were averaged over the utterances for each speaker. The following three evaluation metrics were used:  
1. Gross Pitch Errors: GPE is the proportion of frames considered voiced \(N_{v}\) by both estimated and referenced F0 for which the relative pitch error \(e(n)\) is higher than a certain threshold (usually set to 20% for speech). The \(e(n)\) can be calculated as: \[e(n)=\frac{F0_{n,refined}}{F0_{n,referenced}}-1,n=1,\ldots,\ N_{v}\] (25) where \(n\) is the frame index. If \(\left|e(n)\right|>0.2\), we classified the frame as a gross error \(N_{GE}\). Thus, GPE can be defined as \[GPE=\frac{N_{GE}}{N_{v}}*100\%\] (26)
2. Mean Fine Pitch Errors: Fine pitch error refers to all pitch errors that are not classified as GPE. In other words, MFPE can be derived from Equation (25) when \(\left|e(n)\right|<0.2\) \[MFPE=\frac{1}{N_{FE}}\sum_{n=1}^{N_{FE}}\left(F0_{n,\ refined}-F0_{n,\ referend}\right)\] (27) where \(N_{FE}\) is the number of remaining voiced frames that do not have gross error (\(N_{v}-N_{GE}\)).
3. Standard Deviation of the Fine Pitch Errors: STD is firstly stated in [64] as a measure of the accuracy of the F0 detector during voiced intervals, then slightly modified in [63]. For better analysis, STD can be calculated as \[STD=\sqrt{\frac{1}{N_{FE}}\sum_{n=1}^{N_{FE}}\left(F0_{n,refined}-F0_{n,referenced}\right)^{2}-MFPE^{2}}\] (28)  
Even if other error analyses are possible, like unvoiced error (UVE), pitch tracking error (which is the mean of VE and UVE), and F0 frame error (based on all voiced/unvoiced frames), it was felt that these metrics were not suitable to algorithms that deal with continuous F0, as there are no unvoiced frames. Therefore, the above metrics are good for checking the performance strengths and weaknesses of each method.
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 3 Results and Discussion
### Power Consumption in Wakelock Mode vs. Audio Recording Mode  
The Android platform relies on several techniques to reduce overall mobile device power consumption. However, these techniques may cause undesirable side effects such as slowing down data processing or disabling certain hardware peripherals. To avoid these undesirable effects, the Android OS has a special software mechanism called _wakelock_, which prevents the device from going into a power-saving mode [27]. In fact, misuse of the wakelock mechanism is often regarded as the most common reason for the abnormal battery drain caused by mobile applications [28].  
We used the same methodology described in Section 2 to measure the power consumption of our test devices in partial wakelock mode (CPU is active, but the screen and the keyboard are turned off [27]), and compared it with background audio recording power consumption. The measurement results for a few selected devices are illustrated in Figure 5. As one can see, the audio recording mode consumes up to 2\(\times\)-6\(\times\) times more power than the partial wakelock mode.  
Figure 5: Average power consumption with confidence intervals for a subset of the tested Android smartphones (wakelock mode vs. audio recording and digital signal processing modes).
## 3 Results and Discussion
### Wind Field Characteristics in the Streamwise Direction
#### 3.1.1 Speed-Up  
Figure 6 shows the profiles of the mean wind speed-up in the streamwise direction measured on the along-wind center line of the hill (\(y=0\)). The most remarkable features of the streamwise mean speed are the increase in the wind speed near the surface on the hill crest, the slight deceleration at the foot of upstream side of the hill, and the flow separation behind the hill. Whether it is in the wind speed-up or deceleration area, the near-surface wind speed is greatly affected by the hill. As the height increases, the wind speed is gradually weakened due to the decreasing influence of the hill. The wind speed-up phenomenon near the hill crest is more significant than that at other positions. The greatest speed-up occurs on the hill crest and its value is 1.34. In the deceleration zone of the upstream side of the hill, the most obvious deceleration effect occurs at \(x/L=-0.75\), where the minimum value of the speed-up is 0.69. At \(x/L=0.75\) and \(x/L=1.00\), due to the generation of the flow separation, the wind speed profiles at these positions are significantly different from the profiles in the undisturbed ABL.  
Figure 7 shows the profiles of the streamwise mean wind speed-up measured on the crosswind center line of the hill (\(x=0\)). Compared to the case of \(y=0\), the flow has different speed-up effects, for the positions along the crosswind direction. Similarly, the closer the location is to the hill crest, the more significant the speed-up effect of the flow can be found. At the same position, as the height increases, the speed-up effect decreases. Compared to the wind speed-up found on the along-wind center line of the hill (\(y=0\)), the wind speed-up observed on the crosswind center line (\(x=0\)) is more significant.  
Figure 6: The streamwise mean wind speed-up \(S_{u}\) on the along-wind center line of the hill (\(y=0\)).  
According to the data acquired at the measuring points at different heights, contour maps of the speed-up are achieved and shown in Figure 8. The maps are for the heights above the hill surface \(Z_{i}\) of 30 m, 60 m, 90 m, 120 m, and 150 m. It can be seen that the speed-up in the area near the hill crest is the most significant for each subplot. It reaches its maximum value at the height of \(Z_{i}\) = 30 m. The area in which the values of the speed-up are greater than 1.1 becomes smaller with increasing \(Z_{i}\). However, at the foot of downstream and upstream side of the hill, the speed-up increases as the height increases. The wind deceleration effect appears on the lower halves of the upstream side and the downstream side of the hill. Relatively, the lower half of the downstream side of the hill is more significant. However, as the \(Z_{i}\) increases, the deceleration effect weakens.  
Figure 8: The wind speed-up \(S_{u}\) at different heights.  
Figure 7: The streamwise mean wind speed-up \(S_{u}\) on the crosswind center line of the hill (\(x\)= 0).
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
###### Abstract  
Highly deceptive deepfake technologies have caused much controversy, e.g., artificial intelligence-based software can automatically generate nude photos and deepfake images of anyone. This brings considerable threats to both individuals and society. In addition to video and image forgery, audio forgery poses many hazards but lacks sufficient attention. Furthermore, existing works have only focused on voice spoof detection, neglecting the identification of spoof algorithms. It is of great value to recognize the algorithm for synthesizing spoofing voices in traceability. This study presents a system combining voice spoof detection and algorithm recognition. In contrast, the generalizability of the spoof detection model is discussed from the perspective of embedding space and decision boundaries to face the voice spoofing attacks generated by spoof algorithms that are not available in the training set. This study presents a method for voice spoof algorithms recognition based on incremental learning, taking into account data flow scenarios where new spoof algorithms keep appearing in reality. Our experimental results on the LA dataset of ASVspoof show that our system can improve the generalization of spoof detection and identify new voice spoof algorithms without catastrophic forgetting.  
generalizability; voice spoof detection; incremental learning; spoof algorithm recognition +
Footnote †: journal: Applied sciences
## 2 Enhancing Food Production through RS Applications in Agriculture
### Modelling and Forecasting Future Crop Yields
In Syria, sparsely available, inaccurate or non-existent ground agriculture data, due to regional instability, has limited the capacity of the country to monitor and increase agricultural productivity. However, this information can be replaced through RS techniques. With the advantage of available RS data in the conflict zone of Syria, summer crop yield was predicted with a significant correlation (\(p<0.05\)) between the predicted and ground crop data [57]. The method was also shown to be advantageous for forecasting crop yield during conflict years where reported data are questionable. Similarly, [PERSON] and [PERSON] [17] indicated that RS can provide a credible prediction of agricultural yield in the absence of statistics. They utilised MODIS and Landsat imagery to predict wheat and barley in ISIS-controlled areas in both Syria and Iraq for the years 2014-2015 and irrigated summer crop yield in Northeast Syria [17]. Therefore, RS-derived information can be critical to monitoring agricultural productivity and assessing food security where traditional data collection might not be viable due to natural or anthropogenic crises. A leading and successful system which has provided crucial data on food insecurity is the Famine Early Warning System Network (FEWS NET). This system integrates remote sensing and ground observation data to undertake crop assessment in areas where crop production is a concern. It is a lead provider of early warning and analysis on food insecurity which has provided evidence-based analysis for around 34 countries. Figure 2 illustrates the general methodology for forecasting crop yield using RS data. To understand in more depth the application of RS in yield forecasting, a search for relevant peer-reviewed research articles was conducted in Scopus. This was achieved by using several specific search terms within Scopus; for example, \"crop yield prediction\" or \"yield estimation\" or \"yield prediction\" and \"remote sensing\" or \"Earth observation\" and \"arid and semiarid regions\". In addition, abstract revision and title screening were performed to pre-select the most relevant papers in which RS data were used for this purpose in ASA areas. In total, 50 research articles were selected as summarised in Table 2.
## 5 Verification and Discussion
### Discussion about the Consolidation Behavior
Inverse trapezoidal pattern (\(\zeta<0\)) delivers the fastest dissipation of excess pore-water pressure as well as the largest relative variation of \(U_{\text{P}}\) within each period; trapezoidal pattern (\(\zeta>0\)) brings about the slowest dissipation of excess pore-water pressure as well as the smallest relative variation of \(U_{\text{P}}\) within each period; and under rectangular pattern (\(\zeta=0\)), the dissipation of excess pore-water pressure and the relative variation of \(U_{\text{P}}\) fall in between those of two aforementioned patterns. During the consolidation process, the differences between three \(U_{\text{P}}\)-curves initially go up to a maximum and then gradually decrease as the time increases. Figure 9b shows that higher period (i.e, lower frequency) delivers a larger relative variation of \(U_{\text{P}}\) within each period, but the period has no influence on the stable value of \(U_{\text{P}}\). Figure 10 illustrates the influence of rheological parameters on consolidation behavior under sinusoidal loading. The conditions that \(\kappa\), \(\vartheta\) and \(T_{\lambda 1}\) individually varying from 0.1 to 10 are taken for the investigations. As observed in Figure 10a, higher modulus ratio results in faster dissipation of excess pore-water pressure in the early stage of consolidation process, and the influence of modulus ratio on the change of \(U_{\text{P}}\) gradually reduces with the increase in time. Figure 10b demonstrates that higher viscosity coefficient ratio delivers slower dissipation of excess pore-water pressure as well as smaller ultimate consolidation degree. This influence characteristic is the same as the result for the case of ramp loading. Figure 10c shows that higher \(T_{\lambda 1}\) brings about faster dissipation of excess pore-water pressure in the beginning of consolidation process, and then the difference of \(U_{\text{P}}\)-curves gradually decreases with the increase in time.
## 1 Introduction
#### Innovations  
By leveraging the latest advancements in the current state of the art, this work fills the gaps in the integration from scratch of autonomous multi-UAV systems for collaborative actions. More specifically, the main added value of this work lies in the following points:  
* The field of autonomous swarms is still in its infancy and, as a result, there is a lack of research that consistently provides a formalism of the constraints related to the collaborative actions of multi-UAV systems. In this context, our work has added significant value by filling the gap concerning a well-defined mathematical formulation of collaborative actions of multi-UAV systems in a large distributed environment.
* The study of an autonomous and cooperative drone swarm infrastructure is an incredibly complex field, and there are still very few scientific studies that fully explore the intricacies of how it operates. Due to this lack, we conduct an extensive investigation of multi-UAV infrastructures, which results in a scientific work providing valuable knowledge to answer the question of how to choose the right communication architecture as well as the most suitable routing protocol. This will save a lot of time and avoid erroneous conceptual studies.
* Despite the importance of understanding the collaborative paradigm of autonomous multi-UAV systems for target detection in distributed environments, there is a lack of clear visibility of the challenges and future directions in developing effective collaborative swarm. As added value, we have highlighted the most important challenges and clarified the open perspectives for research in this area.
## 5 Simulation Results and Discussion  
In order to get the total communication energy consumption, the average energy per bit required for a given BER \(P_{b}\), \(\overline{E}_{b}\) needs to be determined. In this approach, the value of \(\overline{E}_{b}\) is found by using a numerical search. Ten thousand randomly generated channel samples are taken and averaged to find the desired bit error rate at each transmission distance. The value of the constellation size is kept fixed. For the long haul communication, SISO is taken as a special case of MIMO structure. A list of system parameters used in our simulation is shown in Table 1 where the power consumption values of various circuit blocks are quoted from [4].
## 2 Architectural Framework of IBSN and Its Components  
The building components of the IBSN, such as the sensors for measuring physiological data, the medical devices for delivering medical therapies, the implantable radio, and the microprocessor are commercially available. Functionality of these components is trivial, however the environment and the requirements of IBSN make the functionality of these devices non-trivial. For a closed-loop operation, it is important that the components of the IBSN are integrated in an optimized architecture to ensure reliability, energy-efficiency, and QoS. The architectural framework of IBSN will define:  
* Sensing strategies
* Actuation strategies
* Power scavenging and energy-efficiency strategies
* Data handling strategies
* Communication strategies  
Figure 1: Common architecture of a sensor node with WuR.  
The common architecture of the closed-loop operation is shown in Figure 2. In the following sections, we briefly explain each component of the architectural framework.
## 4 Results and Discussion
These findings suggest that RF, in combination with count vectorization, is a promising approach for analyzing small data sets of Roman Urdu tweets related to protests in order to detect public sentiments. In this research, we also found that multinomial naive Bayes (MNB) and count vectorization performed best on a large data set related to anti-government gatherings in Roman Urdu, obtained from Twitter, for the task of sentiment analysis. The accuracy achieved was 0.75. This is in line with previous studies, such as by [PERSON] and [PERSON] (2018), who found that MNB and count vectorization were effective for classifying tweets related to political protests, achieving an accuracy of 85% [34]. A study on the sentiments of restaurant reviews [35] showed that MNB outperformed other classifiers. The data set consisting of a few thousand applied with different ML techniques in Roman Urdu showed MNB as the best technique to follow [36]. However, the novelty of our technique is that we used different vectorization methods to gain better results. Therefore, on our data set, the results of ML classifiers showed diversity, the highest accuracy was achieved by using MNB with the count vectorization technique while the lowest was with TFIDF. Another study claimed that the MNB outperformed in terms of precision in the experimental results for sentiment analysis for customer reviews on social media as compared to GNB and RF [37]. The studies discussed above were conducted in Roman Urdu, English, and Chinese languages. Our data set was diverse as it was obtained from Twitter in real-time, and it contained mixed expressions of English and Roman Urdu. Roman Urdu does not follow a standardized form, so the public uses a variety of phrase patterns and spellings. The data set was multidimensional, which allowed us to gain a more accurate picture of the public's sentiments toward protests. By utilizing the techniques mentioned above, we were able to provide a more comprehensive view of the public's sentiments about protests and unrest in countries where Urdu is the primary language. Figure 7: Accuracy of the large data set (4000 tweets). Tables 2 and 3 show the macro-averages (average of precision, recall, and F1 score) with TFIDF and count vectorization techniques and ML classifiers for the large data set. The overall results in terms of precision with count vectorization achieved were the best. MNB outperformed all other ML classifiers with a precision of 0.75 while RF and GNB followed up with averages (F1, Recall, Precision) of 0.72 and 0.71, respectively.
## 3 Results  
The individualised L-V relationships obtained from the multiple-point method were strong for the concentric-only (\(r^{2}\) range = 0.889-0.998) and eccentric-concentric (\(r^{2}\) range = 0.889-0.992) PBP variants.  
The ANOVA conducted on the L-V relationship variables did not reveal any significant method \(\times\) PBP variant interaction (F\({}_{(1,22)}\leq 1.2\); \(p\geq 0.281\)). A significant main effect of method (F\({}_{(1,22)}\geq 79.3\); \(p<0.001\)) but not of variant (F\({}_{(1,22)}\leq 2.2\); \(p\geq 0.148\)) was observed for the three L-V relationship variables (Table 1). Specifically, the two-point method provided significantly higher values for the three L-V relationship variables compared with the multiple-point method (\(L_{0}\): 5.5 kg [95% confidence interval (CI) = 4.3 to 6.7 kg], \(p<0.001\), ES = 0.17; \(v_{0}\): 0.05 m\(\cdot\)s\({}^{-1}\) [95% CI = 0.04 to 0.06 m\(\cdot\)s\({}^{-1}\)], \(p<0.001\), ES = 0.20; \(A_{line}\): 8.3 kg\(\cdot\)m\(\cdot\)s\({}^{-1}\) [95% CI = 6.6 to 10.0], \(p<0.001\), ES = 0.43).  
Figure 1: Load–velocity relationship obtained from the data averaged across the subjects modelled by the multiple-point (filled circles and continuous line) and two-point (open circles and dotted line) methods during the concentric-only (upper panel) and eccentric–concentric (lower panel) prone bench pull (PBP) variants. The regression equations and [PERSON]’s multivariate coefficients of determination (\(r^{2}\)) are depicted. \(L_{0}\), load–axis intercept; \(v_{0}\), velocity–axis intercept; \(A_{line}\), area under the load–velocity relationship line.  
Nearly perfect correlations (\(r\) range = 0.972-0.995) were observed between the multiple-point and two-point methods for both PBP variants (Figure 2). Very large to nearly perfect correlations (\(r\) range = 0.798-0.909) were observed between the concentric-only and eccentric-concentric PBP variants for both methods (Figure 3).  
Figure 2: Correlations between the load–velocity relationship variables obtained with the multiple-point and two-point methods during the concentric-only (left panels) and eccentric–concentric (right panels) prone bench pull (PBP) variants. \(L_{0}\), load–axis intercept; \(v_{0}\), velocity–axis intercept; \(A_{line}\), area under the load–velocity relationship line; \(r\), Pearson’s product-moment correlation coefficient.  
Figure 3: Correlations between the load–velocity relationship variables obtained during the concentric-only and eccentric–concentric prone bench pull (PBP) variants from the multiple-point (left panels) and two-point (right panels) methods. \(L_{0}\), load–axis intercept; \(v_{0}\), velocity–axis intercept; \(A_{line}\), area under the load–velocity relationship line; \(r\), [PERSON]’s product-moment correlation coefficient.