esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Data Analysis  
In total, there are 3118 half-hourly samples of good quality data obtained under different weather conditions. All variables were expressed with mean values, except for PAR and GPP which were total values either in half-hourly or daily time units. The effects of main bioclimatic factors (PAR, vapor pressure deficit (VPD), CI, Ta and SM) on PRI and LUE variations and their relationship were assessed for the whole growing season and individual months using linear and logarithmic functions (y = a \(\times\) x + b, y = a \(\times\) ln(x) + b; x and y denote bioclimatic factors and PRI or LUE, respectively). Statistical analyses were performed using MATLAB and Microsoft Excel software.
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 3 Methodology  
### Overview  
The architecture of the RGB-D semantic segmentation model DBCAN consists of an encoder and a decoder, as illustrated in Figure 1. Specifically, the encoder is built upon the DFormer [10], which is a powerful and efficient approach designed to effectively integrate semantic and depth features. In the decoder, we employ the lightweight Hamburger head [11] to aggregate the multi-scale features from the last three stages of the encoder. These aggregated features serve as preliminary prediction maps, which are then utilized as input for our decoding process.  
**Encoder:** In the feature encoding stage, two parallel backbone networks with similar structures are utilized to extract RGB and depth features. These features are denoted as \(f_{i}\) and \(f_{di}\), respectively, where \(i\in\{0,1,2,3,4\}\) (except for \(i=0\), which represents the output of the stem layer, and the remaining \(i\) represents the output features of each block). To simplify notation, we use a single symbol to represent multiple symbols. For example, \(f_{(d)0}\) represents \(f_{0}\) and \(f_{d0}\), while \(f_{0-1}\) represents \(f_{0}\) and \(f_{1}\).  
**Decoder:** The encoder output features \(f_{i}\) and \(f_{di}\) (\(i\in\{2,3,4\}\)) are employed in the decoder. Firstly, \(f_{3-4}\) and \(f_{d3-44}\) are upsampled separately to match the size of \(f_{(d)2}\), resulting in maps \(f_{3^{\prime}-4^{\prime}}\) and \(f_{d3^{\prime}-4^{\prime}}\). Then, we obtain \(f_{r1}\) (\(f_{r1}=Concat(\{f_{2},f_{3^{\prime}},f_{4^{\prime}}\})\)) and \(f_{s1}\) (\(f_{s1}=Concat([f_{d2},f_{d3^{\prime}},f_{d4^{\prime}}])\)), where \(Concat()\) represents the concatenation of features along the channels. The Hamburger module [11] aggregates the features of \(f_{71}\) and outputs \(f_{h}\). The Object-Region Generated Module aggregates the features of \(f_{h}\) and produces the deeply-supervised result \(f_{org}\)[22]. Subsequently, the input features \(f_{r1}\) and \(f_{s1}\) are passed through the parallel Feature-Region Relation Module, resulting in \(f_{r2-r3}\) and \(f_{s2-s3}\). Finally, \(f_{r4}\) (\(f_{r4}=Concat([f_{r2},f_{r3}])\)) and \(f_{s4}\) (\(f_{s4}=Concat([f_{s2},f_{s3}])\)) are input into the Spatial-Semantic Feature Fusion Module to obtain \(f_{last}\).
## 5 Simulation Analysis
### Zigbee
The ratio between the total number of data packets received by the sink and the total number of data packets transmitted by all end nodes can be determined. The data traffic is sent on average of 3500 bits/s from each node for the simulation period of 1600 s, i.e., around 42,000 bits/s send by all nodes in the network. Now the data traffic received by the coordinator is an average of 38,000 bits/s. The PDR value is nearly 0.90. In other words, the ZigBee coordinator receives 90% of data packets received correctly. Packet delivery ratio is expressed in Equation (4) as: \[\text{PDR}=\frac{DP_{R}}{DP_{TN1+DP_{TN2+-----DP_{TNn}}}}\] (4) where \(DP_{R}\) is no of data packets received, \(DP_{TNn}\) is no of data packets sent by 'N' nodes. * Retransmission Attempts The overall amount of retransmission tries made by all 802.15.4 MACs in the network until a packet is successfully sent or the attempt limit is reached. The retransmission attempt is 0.5 packets for the simulation period of 1600 s. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Network Layer Parameters**} \\ \hline
**Parameter** & **Value** \\ \hline ZigBee end device ZigBee router ZigBee coordinator Packet size Packet interval time & 1024 bytes Constant (1.0) \\ \hline \multicolumn{2}{c}{**Physical Layer Parameters**} \\ \hline
**Parameter** & **Value** \\ \hline Transmission band Transmission Power Receiver Sensitivity & 2.4 GHz Receiver Sensitivity & 0.05W \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter of Zigbee configuration. Figure 12: Simulation environment of Zigbee network in OPNET modeler. 3. Throughput  
Throughput refers to the rate at which data is successfully transmitted from a source node to a destination node. Here highest throughput is reached 50 kbps at starting time of simulation and it shows minimum level throughput is 40 kbps at 250 s. The average throughput carried is 45 kbps. 4. Queue Size and Queuing Delay  
The total number of packets outstanding in the MAC queue is represented by the queue size. This is the number of requests in a queue that are waiting to be executed. The average queuing size is displayed in the Figure 12(a) is 0.5 packets which are considered very less. The queuing delay is the time it takes for packets from the network layer to reach the MAC queue and be executed. It is the most significant contributor to overall network latency. Figure 12(b) indicates that the queuing delay was about 0.03 s at the start of the simulation cycle, but it was significantly reduced to 0.01 s at the 200-s simulation time. Furthermore, the delay has been reduced to almost zero and has remained constant until the end.
## 2 Materials and Methods
### Statistical Analysis
**Wilcoxon Signed-Rank Test:** The Wilcoxon Signed-Rank test, originally proposed by [30], is a nonparametric analysis method widely used in data analysis to deal with unique sample problems. In the context of paired data, this procedure has been used to evaluate the hypothesis that the probability distributions of both samples are identical. Such an evaluation is performed by analyzing the statistics derived from the within-group differences, with the primary objective of determining whether these differences are due to a distribution with a median of zero. For the Wilcoxon Signed-Rank Test, the null hypothesis represents if the set of differences observed between pairs has a probability distribution with the center at zero [31]. To carry out the test, we initially proceeded to calculate the absolute values of the differences \(|d_{i}|\). Subsequently, these absolute values are sorted in ascending order, excluding zero values. The Wilcoxon test methodology involves the sum of the ranks corresponding to positive differences, denoted as (\(T_{+}\)), as well as the sum of the ranks assigned to negative differences, represented by (\(T_{-}\)). Thus, for a set of \(n\) differences, the following relationship is established between both sums:  
\[T_{-}=\left\{\frac{[n(n+1)]}{2}\right\}-T_{+} \tag{5}\]  
To evaluate the null hypothesis, a rejection region was defined for the test statistic called \(T_{+}\). The rejection region was determined using the exact distribution of the null hypothesis of \(T_{+}\). The standard normal distribution can be adopted for large samples as an approximation for hypothesis verification. For these circumstances, a two-tailed rejection region is established for the null hypothesis based on the statistic \(T_{+}\) or \(T_{-}\) as:  
\[Z_{+} =\frac{\left\{\frac{T_{+}-[n(n+1)]}{4}\right\}}{\left\{\frac{[n(n+ 1)(2n+1)]}{24}\right\}^{1/2}}>Z_{1-a/2} \tag{6}\] \[Z_{-} =\frac{\left\{\frac{T_{-}-[n(n+1)]}{4}\right\}}{\left\{\frac{[n(n+ 1)(2n+1)]}{24}\right\}^{1/2}}>Z_{1-a/2} \tag{7}\]  
Finally, a one-tailed test is performed analogously to the comparison made for \(Z_{1-a}\).
## 5 Communication Protocol Design
### Evaluation  
The primary goal of the protocol evaluation was to characterize the PHY layer performance in a real-world scenario. For that, the PHY layer protocol was implemented using Marmote SDR nodes and an USRP N210/GNU Radio base station. The configured nodes were deployed in an office environment, and the packet reception ratio was registered under varying traffic load, using a different number of nodes and different spreading factors.
## 2 Materials and Methods
Geneland identifies clusters based on an attempt to maximize Hardy-Weinberg and linkage equilibrium and uses MCMC to estimate the number of clusters (K). In addition, Geneland incorporates the spatial location of individuals, which can help to elucidate the influence of geographic spread on, e.g., gene flow. MCMC iterations were set to 1,000,000, uncertainty on the coordinate was set to 500 m, and thinning was set to 100. The uncorrelated model was run 10 times where K was set as minimum 1 and maximum 10 to ensure the correct number of K [48]. When K was determined, the model was run an additional 25 times with a set K to be able to choose the run with the highest log posterior probability of population membership for further analyses. Pixels were calculated with 500,000 iterations and 300 was chosen for the number of pixels. To estimate the gene flow between the four study areas, the population size parameter \(\Theta\) (4\(N_{\rm Ei}\)), was estimated, where \(N_{\rm E}\) is the effective population size and \(\mu\) is the mutation rate per site, as well as the level of historical gene flow (M: migration rate m per generation divided by the mutation rate \(\mu\) per generation) between the red fox populations of Sweden and Norway. This process used MIGRATE 3.0.3 [49; 50] on microsatellite repeats with a Brownian microsatellite mutation model, default settings, and a maximum likelihood (ML)-strategy with mutation rates treated as constant for all loci. MIGRATE estimates migration rates based on coalescent theory (i.e., migration back to the most recent common ancestor). In addition, we estimated contemporary gene flow (m: immigration rate per generation) between the Swedish and Norwegian red foxes using BAYESASS 1.3 [51] on microsatellite repeats. This software calculates migration rates over the last few generations based on the proportion of immigrants (m\({}_{\rm\hat{q}}\) is the fraction of individuals in population i that are immigrants from population j). Since the mutation rates are unknown in the red fox, we used the default settings in MIGRATE and BAYESASS (two independent runs with 100,000 MCMC steps, where the first 1000 steps were burn-in, were considered sufficient to obtain convergence).
## 4 Results
### Assessment of Concerns Related to Satisfaction, Engagement, and Attractiveness  
Figure 3 presents the EG's perception of the application of VR in teaching-learning processes with VR support.  
Figure 3 displays the average ratings given by participants in the experimental group for each question in the evaluation. The highest rating was 6.8, while the lowest was 6.2, indicating that, overall, the group that received VR-supported classes strongly believes that VR positively affects student satisfaction, engagement, and attractiveness in teaching. Additionally, Table 9 presents descriptive statistics for the twelve questions, providing information about the experimental group's perception of VR's impact on teaching and learning processes.  
Figure 3: Satisfaction, engagement, and attractiveness indices of using VR for EG on a scale from 1 to 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Descriptive Statistics**} \\ \hline
**Question** & **Minimum** & **Maximum** & **Average** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Skewness** \\ \hline Q1 & 5 & 7 & 6.53 & 0.615 & \(-\)0.950 \\ \hline Q2 & 5 & 7 & 6.53 & 0.615 & \(-\)0.950 \\ \hline Q3 & 5 & 7 & 6.41 & 0.701 & \(-\)0.786 \\ \hline Q4 & 5 & 7 & 6.79 & 0.479 & \(-\)2.360 \\ \hline Q5 & 4 & 7 & 6.47 & 0.748 & \(-\)1.509 \\ \hline Q6 & 4 & 7 & 6.18 & 0.936 & \(-\)0.843 \\ \hline Q7 & 5 & 7 & 6.35 & 0.774 & \(-\)0.724 \\ \hline Q8 & 5 & 7 & 6.59 & 0.557 & \(-\)0.929 \\ \hline Q9 & 4 & 7 & 6.71 & 0.629 & \(-\)2.795 \\ \hline Q10 & 5 & 7 & 6.74 & 0.511 & \(-\)1.816 \\ \hline Q11 & 5 & 7 & 6.74 & 0.511 & \(-\)1.816 \\ \hline Q12 & 6 & 7 & 6.71 & 0.462 & \(-\)0.946 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Descriptive statistics of responses on EG’s perception of using VR.  
The results displayed in Table 9 are consistent with the summarized averages in Figure 3. None of the questionnaire responses fell below a score of 4 on the Likert scale of 1-7, indicating that, overall, the feedback was positive. Upon analyzing the 408 responses (N = 34 \(\times\) 12 questions), we found only 4 neutral responses (scoring 4), and approximately 93% of respondents selected options on scales 6 and 7. Regarding the blocks (1 to 4 for satisfaction, 5 to 8 for engagement, and 9 to 12 for attractiveness), all satisfaction ratings were at least a 5, with engagement receiving 87% of responses between 6 and 7 and attractiveness garnering 98% of responses between 6 and 7.  
The findings reveal that students express high levels of satisfaction, engagement, and interest in using virtual reality (VR) for educational purposes. Figure 4 illustrates that integrating VR into educational activities enhances content comprehension by offering a more immersive learning experience. Participants noted that VR usage enhances class interactivity, engagement, and dynamism.
# An Improved Confounding Effect Model for Software Defect Prediction
## 4 Experiments and Results
### Experiments for the Proposed Prediction Model
#### 4.3.3. Presents of Prediction Result
Compared with the best baseline model, the F1 score of HSR-LR increases 1% in CM, 1.1% in JMI, 0.3% in KC1, 3.9% in MC2, 2.9% in KC3 and 3.4% in KC5, with an average of 2.2%. 2. HSR-LR performs better than LR, which verifies that the confounding effects mentioned in this paper affect the LR prediction ability. With the help of HSR, we can quantify the confounding effect. Under controlling the confounding effect, LR significantly improves. 3. Tables 5-10 show that HSR-LR and LR have similar precision rates, and each has wins and losses but not much difference. However, HSR-LR has better recall performance than that of LR. These points are why HSR-LR has better F1 values; that is to say, controlling the confounder can effectively increase the recall rate of LR, thereby increasing the F1 value of the LR model. 4. HSR-LR performs better than CM-LR, indicating that our proposed confounding effect model is more suitable for software defect prediction content than the traditional confounding effect model. CM-LR performs similarly to IR, indicating that the existing confounding removal methods are unsuitable for software defect prediction, which further illustrates the necessity of this paper. Based on the proposed model, HSR-LR could help solve this issue. 5. Compared to SVM and NN, our model also achieves better performance. Both SVM and NN are commonly used classifiers in the field of software defect prediction. On six projects, our model outperforms SVM by an average of 3.7% and outperforms NN by an average of 2.5% under F1 scores.
# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 1 Introduction
As a result, they operate seamlessly and do not require cryptographic keys or network IDs [10]. To counter these threats, numerous preventive strategies have been proposed to fortify the security of the network layer within WSNs [10; 11; 12]. These strategies encompass a variety of techniques, including authentication, intrusion-detection systems, and secure routing protocols. Authentication mechanisms ensure that nodes interact solely with authenticated and trusted counterparts, thereby preventing unauthorized nodes from connecting to the network. Intrusion-detection systems monitor network activity, identifying anomalous behavior patterns to promptly detect attacks and initiate appropriate countermeasures. Figure 1: Routing attacks in a wireless sensor network. Critical to prevention, secure routing protocols focus on establishing secure and efficient pathways for data transmission. Protocols like low-energy adaptive clustering hierarchy (LEACH) and ad hoc on-demand distance vector (AODV) incorporate security features to safeguard against a multitude of attacks. This safeguarding ensures data confidentiality, integrity, and authenticity throughout the routing process, enhancing the overall security of the network layer in WSNs. In contrast to these traditional strategies, ANN-based techniques provide the best solution for intrusion detection at the network layer in WSNs. In contrast to traditional methods, ANN-based solutions offer a superior approach to detecting intrusions within WSNs at the network layer. Among these solutions, options like the feed-forward ANN technique and the convolutional neural network (CNN) model stand out. After evaluating various ANN-based models, we will select the most effective one for the detection of routing attack in WSNs.
## 3 Results and Discussion
### Raman Spectra of Reference Biological Macromolecules
#### 3.3.4 Bacterial Sodium Hyaluronate  
Hyaluronic acid (HA) is the only non-sulfated glycosaminoglycan (GAG) that plays a prominent role in many physiological and pathological processes in connective tissues [39]. Some microorganisms, such as _Streptococcus_, produce HA as a capsular polysaccharide, whichprotect cells [40]. Sodium hyaluronate (sodium salt of HA) has acetamide (\(N\)-Ac or N-COCH\({}_{3}\)) and carboxylate (COO\({}^{-}\)) groups originating from \(N\)-acetyl-\(\beta\)-D-glucosamine (GlcNAcAc) and \(\beta\)-D-glucuronic acid (GlcNAc) units, respectively. The Raman spectra of this polysaccharide measured using laboratory and portable devices (Figure 4d) show similar patterns. Table S1 summarizes band assignments based on the literature [41; 42]. The most intense band of the symmetric bending vibration of CH\({}_{3}\) is located at 1374 cm\({}^{-1}\) for DXR SmartRaman, 1376 cm\({}^{-1}\) for i-Raman Plus, and 1371 cm\({}^{-1}\) for Ahura FirstDefender. The spectrum measured using DXR SmartRaman also has three well-resolved bands at 1328, 1410, and 1456 cm\({}^{-1}\), attributed mainly to amide III, symmetric stretch of COO\({}^{-}\), and CH\({}_{2}\) and CH\({}_{3}\) scissoring vibrations, respectively [41; 42]. In the spectra recorded using portable instruments, some of these bands are present as shoulders. Similarly, the DXR SmartRaman spectrum shows three well-resolved bands of C-O-C, C-O, and C-C stretching and C-OH bending vibrations at 1048, 1097, and 1125 cm\({}^{-1}\)[41; 42]. For Ahura FirstDefender, the former band is non-resolved. Two bands of the DXR SmartRaman spectrum at 896 and 947 cm\({}^{-1}\) attributed to the C-O-C symmetric stretching vibration of the glycosidic bonds and amide V vibrations, respectively [41; 42], are well resolved in all spectra, while in the case of Ahura FirstDefender, the former band was at slightly higher frequencies, i.e., at 900 cm\({}^{-1}\).  
The specific contribution of these individual bands to the Raman spectrum of sodium hyaluronate, like the spectra of the reference compounds described above, explains the differences in signal overlap determined by the proximity, intensity, and width of adjacent bands, which, in turn, depend on the technical parameters of the measuring equipment. The spectra of reference compounds obtained using different devices adequately illustrate that the laboratory instrument can obtain spectra of higher resolutions than both portable instruments, which is an advantage when analyzing spectroscopic markers of tissue constituents. Although portable devices have technical limitations compared to laboratory instruments, they can still be used to obtain high-quality spectra of biological macromolecules, which enable the identification of these marker compounds in biological tissues.
# Online Attentive Kernel-Based Off-Policy Temporal Difference Learning
## 2 Background and Preliminaries
### On-Policy and Off-Policy Learning  
In RL, policy evaluation and updates are crucial processes through which the agent can learn and improve its strategy. Based on the data source and updating method used for the policy, RL methods can be classified into two categories: on-policy and off-policy.  
In on-policy methods, the agent uses the data generated by the current policy to evaluate and update itself. That is, during the policy evaluation and update process, the agent only utilizes samples generated according to the current policy, \(\pi\). The update rule of SARSA [6] is as follows:  
\[Q(s_{t},a_{t})\gets Q(s_{t},a_{t})+\alpha[r_{t}+\gamma Q(s_{t+1},a_{t+1}) -Q(s_{t},a_{t})]. \tag{3}\]  
On-policy methods have the advantage that their update process is directly related to the current policy, allowing for a more accurate reflection of the current policy's performance. However, on-policy methods also have limitations, such as difficulties balancing exploration and exploitation, often leading to local optima. Additionally, on-policy methods typically require more samples to achieve effective learning, especially in high-dimensional state spaces.  
Off-policy methods allow the agent to learn from data generated by other policies, even if these data are not generated by the current policy \(\pi\). A typical off-policy method is Q-learning [32], which excels in continuously updating and optimizing the action-value function \(Q\) through interactions with the environment. Specifically, the update rule of Q-learning is as follows:  
\[Q(s_{t},a_{t})\gets Q(s_{t},a_{t})+\alpha[r_{t}+\gamma\max_{a^{\prime}\in A }Q(s_{t+1},a^{\prime})-Q(s_{t},a_{t})]. \tag{4}\]The advantages of off-policy methods lie in their flexibility and sample efficiency; the agent can improve the current policy by leveraging historical data or data generated by other policies, which makes it particularly suitable for learning in sparse reward environments. This paper investigates online attentive kernel-based off-policy TD learning. Below, we will introduce the online attentive kernel-based approach used for state representation and the TD update rule.
## 2 Materials and Methods
### Rubber Phenological Events and Remote Sensing  
Rubber trees are considered mature and ready for latex harvesting at 5-7 years. At full maturity, rubber trees commence annual wintering behaviour, where leaves are shed during the drier months before being replaced with fresh foliage.  
Prior to defoliation, the normally dark green, mature leaves turn brown-yellow before they fall. This takes approximately three to four weeks [45] and indicates the end of the growing season. In the refoliation period, the new leaves emerge and expand. This is considered the start of the growing season. The changes in canopy density are dramatic and are illustrated in Figure 2. The images were taken one month apart using an unmanned aerial vehicle. At the canopy level, the leaf colour indicates the age or maturity of leaves, and intense defoliation marks the junction between the defoliation and refoliation process. During refoliation, emerging leaves change from a reddish colour to green (Figure 3). Similar phenological stages during rubber leaf development have been previously reported [9]. The whole process of defoliation and refoliation takes approximately six to eight weeks and occurs in the dry season, typically May to September for South Sumatra, Indonesia. Plant metabolism is substantially affected during this period, with latex production decreasing up to 60% from the peak. The phenological stages in the wintering period are strongly influenced by climatic factors [45]. Apart from this period, the tree maintains a full canopy of leaves throughout the year.