esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model  
### Network Model  
We consider static wireless multi-hop networks with the following properties:  
* Random access to the shared medium where each node transmits independently of all other nodes, based on its transmission probability. In this way, no coordination among nodes is required. For flow originators, transmission probability denotes the rate at which they inject packets into the network (flow rate). For the relay nodes, transmission probability is fixed to a specific value, and no control is assumed.
* Time is slotted and each packet transmission requires one time slot.
* Flows among different pairs of source and destination nodes carry unicast traffic of same-sized packets.
* All nodes are equipped with multi-user detectors, thus they can successfully decode packets from more than one transmitter at the same slot [13].
* We assume that all nodes are half-duplex and thus, cannot transmit and receive simultaneously.
* We also assume that all nodes always have packets available for transmission.
* As far as routing is concerned, multiple disjoint paths are assumed to be available by the routing protocol, one for each flow. Moreover, source routing is assumed, ensuring that packets of the same flow are routed to the destination along the same path. Apart from that, for each node its position, transmission probability, or flow rate along with an indication of whether it is a flow originator are assumed known to all other nodes. This information can be periodically propagated throughout the network through a link-state routing protocol.
## 2 Materials and Methods
### Algorithmic Approach (Figure 1)
#### 2.2.5 Evaluation  
Affective computing researchers usually decrease model complexity by grouping the output PAD scores (nine categories in the present study) into two or three categories. The present research tested different levels of complexity. First, all ML models were tested with the cross-validation method described above to find the most accurate at identifying pleasure, arousal, and dominance scores across nine categories (the SAM self-reported integer options). A three-category version was also evaluated by separating the groups into low (1-3), neutral (4-6), and high (7-9) scores. Finally, a two-category version of each ML model that split the data into low (1-5) and high (5-9) scores was evaluated.  
In addition to reducing the categories of scores in the arousal and pleasure dimensions, many researchers do not model dominance, the third dimension, further decreasing the complexity of models and improving the results. However, removal of the dominance axis is inappropriate [15], so the dominance component was included for all models in this study.  
The DEAP dataset consists of two sub-studies: an online dataset in which participants self-reported both PAD scores and an emotional word (from a list of 16), and an experimental study in which participants' physiological signals were collected and they self-reported only PAD scores. Once the PAD score is obtained using the ML model, the results can be mapped onto words that represent emotions. The current approach maps the complete PAD score and links those PAD scores to six words that represent emotions. These six words were chosen as a result of a mapping evaluation among datasets that included both PAD scores and words that represent emotions [15].  
Using the physiological signals collected from typically developing individuals (the DEAP dataset with PAD scores from auditory stimuli), and from persons with SMCIs (the BDAT dataset with IAPS PAD scores), the accuracy of identified emotions was evaluated.
###### Abstract  
Restricted production of fungal secondary metabolites hinders the ability to conduct comprehensive research and development of novel biopesticides. Okaramine B from _Penicillium_ demonstrates remarkable insecticidal efficacy; however, its biosynthetic yield is low, and its regulatory mechanism remains unknown. The present study found that the yield difference was influenced by fermentation modes in okaramine-producing strains and performed genomic and comparative transcriptome analysis of _P. daleae_ strain NBP-49626, which exhibits significant features. The NBP-49626 genome is 37.4 Mb, and it encodes 10,131 protein-encoding genes. Up to 5097 differentially expressed genes (DEGs) were identified during the submerged and semi-solid fermentation processes. The _oka_ gene cluster, lacking regulatory and transport genes, displayed distinct transcriptional patterns in response to the fermentation modes and yield of Okaramine B. Although transcription trends of most known global regulatory genes are inconsistent with those of _oka_, this study identified five potential regulatory genes, including two novel Zn(II)2 Cys6 transcription factors, Reg2 and Reg19. A significant correlation was also observed between tryptophan metabolism and Okaramine B yields. In addition, several transporter genes were identified as DEGs. These results were confirmed using real-time quantitative PCR. This study provides comprehensive information regarding the regulatory mechanism of Okaramine B biosynthesis in _Penicillium_ and is critical to the further yield improvement for the development of insecticides.  
Okaramine B; yield improvement; _Penicillium daleae_; complete genome; transcriptome; novel regulators; tryptophan metabolism +
Footnote †: journal: Molecular Sciences
###### Abstract  
Spring phenology (start of growing season, SOS) in boreal forests plays a crucial role in the global carbon cycle. At present, more and more researchers are using solar-induced chlorophyll fluorescence (SIF) to evaluate the land surface phenology of boreal forests, but few studies have utilized the primary SIF directly detected by satellites (e.g., GOME-2 SIF) to estimate phenology, and most SIF datasets used are high-resolution products (e.g., GOSIF and CSIF) constructed by models with vegetation indices (VIs) and meteorological data. Thus, the difference and consistency between them in detecting the seasonal dynamics of boreal forests remain unclear. In this study, a comparison of spring phenology from GOME-2 SIF, GOSIF, EVI2 (MCD12Q2), and FLUX tower sites, PEP725 phenology observation sites, was conducted. Compared with GOSIF and EVI2, the primary GOME-2 SIF indicated a slightly earlier spring phenology onset date (about 5 days earlier on average) in boreal forests, at a regional scale; however, SOSs and SOS-climate relationships from GOME-2 SIF, GOSIF, and EVI2 showed significant correlations with the ground observations at a site scale. Regarding the absolute values of spring phenology onset date, GOME-2 SIF and FLUX-GPP had an average difference of 8 days, while GOSIF and EVI2 differed from FLUX-GPP by 16 days and 12 days, respectively. GOME-2 SIF and PEP725 had an average difference of 38 days, while GOSIF and EVI2 differed from PEP725 by 24 days and 23 days, respectively. This demonstrated the complementary roles of the three remote sensing datasets when studying spring phenology and its relationship with climate in boreal forests, enriching the available remote sensing data sources for phenological research.  
Spring phenology; boreal forests; solar-induced chlorophyll fluorescence; vegetation index; FLUX; PEP725
## 2 Materials and Methods
The LTs for the American stations are: SALU (UT \(-\) 3) and POVE (UT \(-\) 4). The LTs for the African stations correspond to CPVG (UT \(-\) 1.5) and ADIS (UT + 3). The LTs for the Asian stations are equal to HUTB (UT + 5.30) and PPPC (UT + 8). In order to investigate the seasonal variation in the GPS-TEC, GIM and IRI-2016 models, the data were grouped into four seasons, namely: March equinox (February, March, and April), June solstice (May, June, and July), September equinox (August, September, and October) and December solstice (November, December, and January) by following the classifications of [PERSON] et al. (2019) and [PERSON] (2020). The statistical analysis of GPS-TEC with model predictions of the GIM and IRI-2016 models using the root mean square error (\(RMSE\)) was considered. \[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(S_{i}-O_{i})^{2}} \tag{3}\]  
where \(O_{i}\) represents observation values (GPS-TEC), \(S_{i}\) represents predicted model values (GIM and IRI-2016), and \(n\) is the number of points considered in the error determination. In the present research, \(n=24\). The \(RMSE\) is can be used to measure accuracy by comparing the prediction errors of different models.
## 3 Guiding Technologies
### Hyperconvergence  
Hyperconverged infrastructure (HCI) represents a significant advancement in the evolution of data centers due to its efficiency, simplicity, and flexibility. Since 2016, the business sector has widely adopted hyperconvergence solutions, driving the recent popularity of HCI [34], especially with IT vendors such as VMware [36] and Nutanix [35]. Using industry-standard hardware, HCI combines various internal resources, such as storage, computing, and networking, into a shared resource pool, providing a modern and integrated approach to IT resource management [41].  
Although storage has been the main driver of hyperconvergence, a hyperconverged architecture is typically characterized by the integration of computing, storage, and networking into a single unified system [45]. From a hardware perspective, this implies the combination of computing (CPU and memory) and storage (hard drives and SSDs) into a single server. From a software perspective, this means that all system components are managed, at a minimum, from a common interface [33].  
In this sense, virtualization is the essential pillar that enables the creation of hyperconverged infrastructure. Without virtualization, both for computing and storage,the hyperconvergence model would not be viable. Virtualization abstracts the physical resources from the consumer, allowing each server in the cluster to have a determined amount of directly connected storage while still sharing data over a network, typically based on IP over 1 or 10 GbE, depending on the cost and speed requirements [33].  
Hyperconverged infrastructure can be divided into two main components: the distributed infrastructure plane and the distributed management plane. The distributed infrastructure plane consists of a cluster of nodes that provide storage, virtualization, and networking services for applications hosted in VMs or containers. The management plane, on the other hand, is located at the top level of the stack and is responsible for the global and unified management of the resources provided by the distributed infrastructure plane [35].  
The management plane handles functions such as provisioning, replication, backups, and snapshots based on management policies. To achieve this, it consumes the underlying physical resources and makes them available to the workload. This abstraction allows VMs to access data stored locally or on other nodes in the cluster with the same efficiency, without needing to know the details of the underlying resources.
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
## 4 Software Implementation
### Initialization Stage  
In the initialization stage, nodes need to prepare for data collection, and there are two main things they should complete during this stage: one is route discovery and the other is time synchronization. Route discovery is very important as it is the precondition of data collection. In our monitoring system, we implement a hierarchy routing protocol similar to LEACH protocol [29]. The difference is that there is no cluster heads selection phase because the sink nodes and cluster nodes in our system are existing cluster heads. First, the sink node is assigned a level 0 and broadcasts a route discovery message containing its identity and level. All of the cluster nodes and sensor nodes receiving this message set the sink node as their parent and assign themselves a level 1. Then, the cluster nodes in level 1 broadcast a new a route discovery message, and this process continues until all nodes in the network assign a level. Nodes that already have a parent will ignore later broadcast messages. In this way, a routing tree rooted at the sink node is established and all the sensor nodes send data directly to the sink node or through cluster nodes in the network. Time synchronization is one of the fundamental issues in WSNs. All nodes in the monitoring network sharing a common timescale makes the measured data meaningful. Some scheduling protocols like time division multiple access (TDMA) require time synchronization so that transmissions do not interfere. In addition, an accurate time is essential for efficient duty cycling and helps save energy. The required synchronization accuracy is not high in our monitoring system as the typical sampling period is 30 min. In addition, the algorithm we achieved in our system is a simplified flooding time synchronization protocol (FTSP) [30]. The synchronization process is conducted between neighbor levels of the spanning-tree established in route discovery phase. Each cluster head broadcasts a message time-stamped at the media access control (MAC) layer, and child nodes of that cluster head record the corresponding time from their respective local clocks at message reception. Only clocks offset between them are estimated, and, once synchronized, the network can keep the synchronization error in the second range during the whole monitoring period.
## 2 Materials and Methods
### RT-qPCR Validation  
To verify the microarray results, 10 DEGs were screened via RT-qPCR (Table 1). The primers were designed using Primer3 software, version 0.4.0 [50], and eukaryotic translation elongation factor 1 alpha (_eefta_) was used as a reference gene. Sequences of the primers were analyzed for hairpin structure and self- and hetero-dimers in Vector NTI Express software (v1.1.1). Same biological RNA samples were used for RT-qPCR analysis, including uninfected fish (n = 3), moribund (n = 4) and survivors (n = 3). Annealing temperature was optimized for each primer pair. PCR reactions were performed using SensiFASTTM SYBR No-ROX One-Step Kit (Bioline, Memphis, TN, USA). The PCR Master Mix included 400 nM concentration of forward and reverse primers and 10 ng per reaction of RNA sample. Reactions were performed in Eco Real-Time PCR System (Illumina, Inc., San Diego, CA, USA). The standard cycling conditions were as follows: reverse transcription at 45 \({}^{\circ}\)C for 10 min and 95 \({}^{\circ}\)C for 2 min for polymerase activation, followed by 45 cycles of denaturation, annealing and extension. Melting curve analysis was conducted to confirm that primers did not form primer dimers and non-specific amplification product did not appear. Gene levels were calculated using the \(2^{-\Delta\Delta\text{CT}}\) method [51].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Microarray SPOT\_ID** & **Gene** & **Primers (5\({}^{\circ}\)-3\({}^{\circ}\))** & **Product Size (bp)** \\ \hline \multirow{2}{*}{Omy\#S27585481} & \multirow{2}{*}{Serum amyloid a1 (san1)} & F: GGAAGCTGCGTAGTGGTTCAC & \multirow{2}{*}{100} \\  & R: TGTACTCCTCGTTATCC & & \\ \hline \multirow{2}{*}{Omy\#S26387020} & \multirow{2}{*}{Cathelicidin antimicrobial peptide (_camp_)} & F: GTATGAAGACATCATCACAG & \multirow{2}{*}{110} \\  & R: CATCCTCTGATATCAAGTC & & \\ \hline \multirow{2}{*}{Omy\#S15341081} & \multirow{2}{*}{Prostaglandin-endoperoxide synthase 2 (_ptgs2_)} & F: TCAACAACTCCTGGTTCAC & \multirow{2}{*}{99} \\  & R: GAGGCAGGTCCGCTCCAC & & \\ \hline \multirow{2}{*}{Omy\#S34308694} & \multirow{2}{*}{Steap4 metalloreductase (_step4_)} & F: CAAACGCTTCCTTCTC & \multirow{2}{*}{108} \\  & R: GCATCCACAACAACCAG & & \\ \hline \multirow{2}{*}{Omy\#S15301030} & \multirow{2}{*}{Matrix metallopeptidase 13 (_mmp13_)} & F: GGA
## 6 Experimental Results and Evaluation
### Tuning Internal Parameters of Selected Machine Learning Algorithms  
We performed hyper-parameter tuning to enable better classification. We evaluated the effect of several parameters to have optimal results. The test ratio was always set to 20% during all the tests. Furthermore, we discussed the classification metrics in the following sections.  
We used six classification algorithms in our case study. We have used Naive Bayes, support vector machines (SVM), k-nearest neighbors (KNN), logistic regression, decision tree, and random forest. We have applied SVM with four kernels: linear, poly, RBF, and  
Figure 6: Effect of BOW size in classification for the selected machine learning algorithms.  
Figure 7: Effect of BOW size in classification (using SVM-linear kernel).  
sigmoid kernels. By applying different kernels of SVM to the same data, we investigated the differences between SVM kernels. These algorithms are widely used in classification problems. However, depending on the dataset used, one of the given algorithms might perform better. Therefore, instead of selecting some of the algorithms, we applied all of the given algorithms to our dataset. Finally, we selected the best classifier for our dataset. For tuning the ML algorithms, we checked each ML algorithm and its corresponding hyperparameters in detail. For the Naive Bayes, we did not have any relevant hyperparameter. For SVM, as stated before, we adopted four different kernels. For the k-nearest neighbor algorithm, we optimized the K value to be used, namely, the number of neighbors used in calculations. For the logistic regression algorithm, we optimized the solver and iteration number. Finally, for the random forest algorithm, we optimized the number of estimators and criteria.
## 5 Methodology
### Data Analysis  
For the analysis, the feedback from the questionnaire was tabulated according to sections. Meanwhile, the recorded audio was transcribed word-for-word and reviewed three times for clarity and understanding purposes. The prepared data were then analysed using three methods, namely, (i) descriptive statistical analysis, (ii) inferential statistical analysis, and (iii) content analysis.  
The descriptive statistical and inferential statistical analysis methods were used to analyse feedback for the quantitative data. The statistical analysis was performed using the software SPSS 26.0 and Microsoft Excel 2021. The data were checked for normality distribution using the value of skewness and the detrended normal Q-Q plot. The results of the normality test indicated that the data were approximately normally distributed. Hence, the relevant sample t-test to determine the significance of pre-post test values and mean value compared to the general population was appropriate to be conducted.  
Meanwhile, content analysis was used to analyse responses received from the qualitative data. The analysis was carried out by using ATLAS.ti version 9.
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 3 Performance Analysis
### Convergence Time Constant
From Equations (32) and (37), and because the positive value of \(\left\|\mathbf{w}^{o}(n,\phi)\right\|_{2}\), the magnitude of \(\left\|\widetilde{\mathbf{w}}(n,\phi)\right\|_{2}\) is expressed as  
\[\left\|\widetilde{\mathbf{w}}(n,\phi)\right\|_{2}=\left[1-\mu\lambda_{\min}( n)\right]^{n}\left\|\mathbf{Q}^{T}(n)\right\|_{2}\left[\frac{\sqrt{C^{2}+D^{2}-2 CD \cos\phi}}{\left|X^{\prime}\right|}+\left\|\mathbf{w}(0,\phi)\right\|_{2}\right] \tag{38}\]It can be seen that when the system is converged, the magnitude satisfies  
\[\left[1-\mu\lambda_{\min}(n)\right]^{n}M(n,\phi)<\varepsilon_{0} \tag{39}\]  
where the \(\varepsilon_{0}\) is a very small constant, and the \(M(n,\phi)\) is defined as  
\[M(n,\phi)=\left\|\mathbf{Q}^{T}(n)\right\|_{2}\left[\frac{\sqrt{C^{2}+D^{2}-2 CD \cos\phi}}{|X^{\prime}|}+\left\|\mathbf{w}(0,\phi)\right\|_{2}\right] \tag{40}\]  
Therefore, the number of iterations which satisfied convergence is given by  
\[n_{0}(\phi)=\log_{1-\mu\lambda_{\min}(n)}[\varepsilon_{0}/M(n,\phi)] \tag{41}\]  
Since the iterations is a positive integer, the number of iterations can be written as  
\[N(\phi)=[n_{0}(\phi)]+1 \tag{42}\]  
where \([n_{0}(\phi)]\) indicates that \(n_{0}(\phi)\) is rounded down. From the above expressions, the following conclusions can be drawn:  
1. For stationary signals, according to Equations (38)-(42), when the sequence \(\left\|\widetilde{\mathbf{w}}(N(\phi),\phi)\right\|_{2}<\varepsilon_{0}\) is satisfied, then \(\mathbf{w}(n,\phi)\approx\mathbf{w}^{o}(n,\phi),n=N(\phi)+1,N(\phi)+2,\cdots\). Additionally, \(N(\phi)\) indicates the iteration number of the weight vector \(\mathbf{w}(n,\phi)\) from \(\mathbf{w}(0,\phi)\) to \(\mathbf{w}^{o}(n,\phi)\), which corresponds to the convergence of the MSE from \(\xi(0,\phi)\) to \(\xi_{\min}\). The iteration number \(N(\phi)\) is related to the phase error \(\phi\). Based on Equation (37), the relationship is as follows: when \(\phi:0\rightarrow\pi\), \(N(\phi)\) increases progressively, and when \(\phi:\pi\to 2\pi\), \(N(\phi)\) decreases progressively. Thus, the iteration number \(N(\phi)\) reaches a peak when \(\phi=\pi\). Consequently, in real applications, there can be a large convergence time constant when the disturbance and command signals are out of phase. 2. According to Equations (14) and (37), the phase error \(\phi\) has an impact of \(\left\|\mathbf{w}^{o}(n,\phi)\right\|_{2}>\left\|\mathbf{w}^{o}(n,0)\right\|_ {2}\). Combining this with Equation (38), it can be concluded that the phase error increases the norm of vector \(\left\|\widetilde{\mathbf{w}}(n,\phi)\right\|_{2}\); in other words, the phase error \(\phi\) may increase the convergence time constant by resulting in a \(\mathbf{w}^{o}(n,\phi)\) more distant from \(\mathbf{w}(0,\phi)\). From Equations (37)-(42), it can be seen that, if the value of \(\left\|\widetilde{\mathbf{w}}(n,\phi)\right\|_{2}\) is large, the iteration number \(N(\phi)\) is also large, which implies a large convergence time constant.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 6 Open Issues  
The AI/ML-empowered and learning-driven wireless networks will bring unique decision-making capabilities and real-time predictions towards revolutionizing 5G and beyond 5G networks. In this direction, the latest advancements in the AI/ML techniques have opened up new opportunities for the UAV-based systems and have led to the possibility of realizing highly autonomous UAV operations while enhancing the performance, safeguarding the security, and mitigating human faults under complex and dynamic scenarios. Nevertheless, there exist open research issues that require attention and should be addressed. The range of future research directions can be summarized as follows.
# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
## 3 SREM Model
### Design of SREM
Furthermore, unlike SRCM, where UAVs move continuously along a curve, SREM constructs mobility paths by randomly selecting points on the ellipse and connecting them via straight-line segments. This approach provides the following advantages:  
* Similarity to real-world navigation: UAVs typically follow straight-line paths using waypoint-based navigation, which aligns with the proposed structure. * Spatial control with flexible routing: Elliptical boundaries constrain movement, while diverse waypoint combinations enable adaptable paths. * Simplified analysis: Straight-line movement simplifies distance calculation, relay assessment, and path optimization. Therefore, SREM functions as a high-precision mobility model that is well-suited to both real-world UAV operations and simulation frameworks, enhancing the overall reliability and applicability of FANET simulations. Table 2 presents a comparative summary of the SRCM model and the proposed SREM model, highlighting the key differences discussed above.