esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Uncertainty Estimation for Embedding Vectors
#### 3.3.4 Estimation of Data Uncertainty  
Data uncertainty is described in the mathematical model (Equation (2)) as a distribution \(p(z\,|\,\xi)\) over the embedding space. As is common with probabilistic embeddings, our architecture approximates this distribution as a Gaussian \(p(z\,|\,\xi)\approx\mathcal{N}(\mu,\Sigma)\) by estimating its parameters \(\mu\) and \(\Sigma\). The final data uncertainty estimate \(\Sigma^{(D)}\in\mathbb{R}^{d}\) is obtainedby computing the element-wise mean across the \(K\) versions of the variance vector \({}^{k}\Sigma\) (Equation (13)).  
\[\Sigma_{i}^{(D)} =\mathbb{E}\Big{\{}^{k}\Sigma_{i}\Big{\}}_{k=1}^{K} \tag{13}\] \[\Sigma =\text{diag}\Big{(}\Sigma^{(D)}\Big{)} \tag{14}\]  
The diagonal covariance matrix \(\Sigma\in\mathbb{R}^{d\times d}\) of the multivariate normal distribution is obtained by using \(\Sigma^{(D)}\) as the diagonal of the matrix (Equation (14)).  
The reason for the model's ability to encode data uncertainty into the variance vector \({}^{k}\Sigma\) can be found in the loss function (Section 3.3.7). In essence, the model is encouraged to assign higher uncertainty values to instances where the classification is bad. During training, the model learns to distribute these higher values towards vector positions that are primarily responsible for the inferior classification outcome.
# Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data
## 2 Gait Recognition Framework
### Gait Data Validation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Gaits** & \(\mathbf{q_{1}}\) & \(\mathbf{q_{2}}\) & \(\mathbf{q_{3R}}\) & \(\mathbf{q_{4R}}\) & \(\mathbf{q_{5R}}\) & \(\mathbf{q_{3L}}\) & \(\mathbf{q_{4L}}\) & \(\mathbf{q_{5L}}\) \\ \hline \(N,C1\) & \(-12.12\) & \(12.47\) & \(-67.14\) & \(85.51\) & \(-40.72\) & \(-68.55\) & \(80.28\) & \(-36.012\) \\ \(N,C2\) & \(30.31\) & \(-0.63\) & \(-47.45\) & \(82.77\) & \(31.49\) & \(-6.50\) & \(50.78\) & \(-3.59\) \\ \(N,C3\) & \(24.35\) & \(9.41\) & \(-46.24\) & \(35.27\) & \(61.57\) & \(-13.13\) & \(2.44\) & \(-6.20\) \\ \(N,C4\) & \(-64.40\) & \(34.69\) & \(-81.16\) & \(64.44\) & \(-38.64\) & \(-68.15\) & \(47.96\) & \(-6.58\) \\ \(C1,C2\) & \(43.38\) & \(-15.85\) & \(20.50\) & \(-2.55\) & \(72.63\) & \(72.73\) & \(-33.20\) & \(32.82\) \\ \(C1,C3\) & \(37.29\) & \(-3.70\) & \(21.76\) & \(-46.77\) & \(102.88\) & \(64.96\) & \(-87.62\) & \(30.17\) \\ \(C1,C4\) & \(-53.44\) & \(26.87\) & \(-14.59\) & \(-19.61\) & \(2.09\) & \(0.46\) & \(-36.38\) & \(29.8\) \\ \(C2,C3\) & \(-5.22\) & \(11.99\) & \(0.88\) & \(-51.92\) & \(29.47\) & \(-6.78\) & \(-52.18\) & \(-2.60\) \\ \(C2,C4\) & \(-82.97\) & \(42.18\) & \(-24.77\) & \(-20.03\) & \(-68.71\) & \(-63.06\) & \(-3.05\) & \(-2.98\) \\ \(C3,C4\) & \(-80.65\) & \(32.53\) & \(-30.69\) & \(31.83\) & \(-100.02\) & \(-46.95\) & \(43.71\) & \(-0.38\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical z-values for the eight joint angles of the 5 gaits of the z test. Figure 6: Statistical distribution of the dataset of 5 gait classes for each joint angle. Figure 7: Joint angles of hip flexo-extension (\(q_{3R}\)), knee flexo-extension (\(q_{4R}\)), and ankle dorsi-plantar flexion (\(q_{5R}\)). (**First column**): joint reference angle (black) and real joint angle (blue); (**second column**): joint reference angle (black) and synthetic joint angle (blue) and (**third column**); joint reference angle (black) and average of dataset of this joint angle (blue). Limit between the stance phase and swing phase of the gait cycle (cyan).
###### Abstract  
Background: Energy-dense diets have been implicated as a driving force in the global obesity crisis. Sucracge derived from sugar came (_Sacacarum efficiarum_) is a carbohydrate source at the centre of this discussion. However, sugar cane is a complex plant containing a wide variety of phytochemicals that may have anti-obesity properties. The objective of this study was to assess if polyphenols extracted from sugar cane were capable of mitigating the progression of diet-induced obesity. Methods: Forty-five male, six-week-old C57 BL/6J mice were divided into groups of 15 and fed a high-fat, high-carbohydrate diet supplemented with 0%, 2% or 4% polyphenol-rich sugarcane extract (PRSE) for twelve weeks. Body weight, food intake, water intake and faecal content were measured in addition to dual energy x-ray absorptiometry (DEXA) of the mice. Gene expression was also assessed for a range of key metabolic pathways in both blood and tissue samples in order to determine PRSE's potential mechanisms of action. Data was analysed using ANOVA and post-hoc statistical methods. Results: Mice fed 4% PRSE were found to have a significantly lower overall bodyweight and adipose tissue accumulation compared to control (0%). This finding was supported by a reduced plasma leptin concentration and an increased excretion of carbohydrates. Uregulated gene transcriptions of adiponectin, PPARy, PPARa, UCP2 and fatty acid synthase mRNAs were also observed. Conclusions: These results indicate that reduced carbohydrate absorption is the primary mechanism leading to the reduction of body weight in mice fed a high-fat, high-carbohydrate diet. This is predominately supported by the detection of increased carbohydrate concentration in the faeces of mice that lost weight. Other potential mechanisms, such as feed intake and energy expenditure, did not show significant differences between groups and are less likely to be involved.  
polyphenols; sugarcane; obesity +
Footnote †: journal: _Sci._
# A QoS Classifier Based on Machine Learning for Next-Generation Optical Communication
## 3 ML Procedures
### Random Forest  
An ensemble learning technique for classification, regression, and other problems is the random forest (RF), often referred to as random choice forest, which works by creating a huge number of decision trees during training. The class that the majority of trees select is the output of the random forest for classification problems. The issue of DTs that overfit their training set is addressed by RF. So, RF typically performs better than DT [17].  
Deeply developed trees, in particular, have a tendency to acquire highly irregular patterns: they overfit their training sets, resulting in low bias but large variation [18]. Random forests are a method of minimizing variation by averaging numerous deep decision trees trained on various regions of the same training set. This comes at the cost of a modest increase in bias and some loss of interpretability, but it significantly improves the final model's performance.  
The training algorithm for RF, adopts the technique of bagging. Assume a training set \(X=x_{1},\ldots,x_{n}\) with outcome \(Y=y_{1},\ldots,y_{n}\) bagging repetitively (\(N\) times) chooses a random sample with the replacement of the training set and fits trees [19]:  
For \(b=1,\ldots,N\):  
* Bootstrap samples with n training examples from \(X\), \(Y\); call these \(X_{b}\), \(Y_{b}\).
* Training a model tree \(f_{b}\) on \(X_{b}\), \(Y_{b}\).  
After training, predictions for unknown samples \(x^{\prime}\) is done by having the average predictions from all the individual trees on \(x^{\prime}\):  
\[\hat{f}=\frac{1}{B}\sum_{b=1}^{N}f_{b}\big{(}x^{\prime}\big{)}. \tag{6}\]
## 4 Results and Discussion  
### Compression Characteristics  
Due to the self-weight of the grain load, the vertical pressure distribution in the grain bin is uneven and the degree of compression of the grain is different [24]. According to the mass-volume relationship, the experimentally calculated average porosity was 0.43. Compaction changes the pore structure distribution of the grain pile, which affects the pressure drop characteristics through the soybean packed bed. Vertical pressure causes the soybean packed bed to compact, increasing the resistance to airflow through the grain bulk. The compression and compression change rate of soybean bulk at 50, 150, and 250 kPa vertical pressures are shown in Figure 4. The vertical pressure had a significant effect on the deformation of the soybean bulk. After the application of vertical pressure, the grain had a notable transient deformation, and the change rate of deformation decreased with increasing time. The amount of transient deformation increased with the vertical pressure. The compression was 97.6%, 98.2%, and 98.8% of the total compression after two hours of loading at 50, 150, and 250 kPa, respectively, which indicates that the total compression of the grains occurred mainly within two hours after loading. After 4 h, the compression change rate was reduced to less than 0.2%. The compressions under 50, 150, and 250 kPa pressures were 4.29 mm, 7.18 mm, and 8.98 mm, respectively.
## 3 The PID-CTOPC Approach
### Stability Analysis  
In this subsection, stability of the closed-loop system under the derived optimal predictive controller (15) is analyzed, which is given in Theorem 2.  
**Theorem 2**.: _Consider a class of SISO nonlinear affine system (1) satisfying (A1)-(A3) with system dimension \(n\geq 2\). For the derived optimal predictive controller (15), there must exist a predictive period \(t_{M}>0\), PID gains\(K_{p}\), \(K_{i}\), and \(K_{d}\) such that the closed-loop system is stable._  
**Proof of Theorem 2.** In the derived controller (15), the gain matrix \(K_{M}\) can be expanded as \(K_{M}=[K_{0},K_{1},\ldots,K_{n-1}]\). Then the expanded formulation of the term \(K_{M}H_{0}\) can be given by:  
\[\begin{array}{c}K_{M}H_{0}=K_{0}K_{i}\int_{0}^{t}e_{1}dt_{0}+\big{(}K_{0}K_{ p}+K_{1}K_{i}\big{)}e_{1}\\ \qquad\qquad+\sum\limits_{j=1}^{n-2}\big{(}K_{j}K_{p}+K_{j+1}K_{i}+K_{j-1}K_{ d}\big{)}e_{1}^{[j]}+\big{(}K_{n-1}K_{p}+K_{n-2}K_{d}\big{)}e_{1}^{[n-1]} \end{array} \tag{23}\]  
Recall in system (1) that \(y^{[n]}=\dot{x}_{n}=f(x)+g(x)\cdot u\), then bringing controller (15) into the formulation \(y^{[n]}\) by considering Formula (23) yields:  
\[\begin{array}{c}0=e_{1}^{[n]}\ +\ \frac{(K_{n-1}+1)K_{p}+K_{n-2}K_{d}}{K_{ d}}e_{1}^{[n-1]}\ +\ \frac{K_{n-2}K_{p}\ +\ (K_{n-1}+1)K_{i}+K_{n-3}K_{d}}{K_{d}}e_{1}^{[n-2]}\\ \qquad+\ \frac{1}{K_{d}}\sum\limits_{j=1}^{n-3}\big{(}K_{j}K_{p}\ +\ K_{j+1}K_{i}\ +\ K_{j-1}K_{d}\big{)}e_{1}^{[j]}\ +\ \frac{K_{0}K_{p}+K_{1}K_{i}}{K_{d}}e_{1}\ +\ \frac{K_{0}K_{i}}{K_{d}}f_{0}^{t}e_{1}dt_{0}\end{array} \tag{24}\]  
Differentiating both sides of Formula (24) with respect to time yields:  
\[\begin{array}{c}0=e_{1}^{[n+1]}+\underbrace{\frac{(K_{n-1}+1)K_{p}+K_{n-2}K _{d}}{K_{d}}}_{\beta_{n}}e_{1}^{[n]}+\underbrace{\frac{K_{n-2}K_{p}+(K_{n-1}+1 )K_{i}+K_{n-3}K_{d}}{K_{d}}}_{\beta_{n-1}}e_{1}^{[n-1]}\\ \qquad+\sum\limits_{j=2}^{n-2}\left(\underbrace{\frac{K_{j}K_{p}+K_{j+1}K_{i}+ K_{j-1}K_{d}}{K_{d}}}_{\beta_{j}}\right)e_{1}^{[j]}+\underbrace{\frac{K_{0}K_{p}+K_{1}K_{i}} {K_{d}}}_{\beta_{1}}e_{1}+\underbrace{\frac{K_{0}K_{i}}{K_{d}}}_{\beta_{0}}e_{1} \end{array} \tag{25}\]Poles of the system (25) can be calculated according to the following characteristic equation:  
\[e_{1}^{[n+1]}+\beta_{n}e_{1}^{[n]}+\beta_{n-1}e_{1}^{[n-1]}+\sum_{j=2}^{n-2}\beta_ {i}e_{1}^{[j]}+\beta_{1}\dot{e}_{1}+\beta_{0}e_{1}=0 \tag{26}\]  
Notice that the parameters \(K_{p}\), \(K_{i}\), \(K_{d}\), and \(K_{j}\), \(j=0,\dots,\ n-1\) are all constants such that roots of the polynomial function (26) can be computed for different PID gains and predictive periods. Thus, appropriate values of \(K_{p}\), \(K_{i}\), \(K_{d}\), and \(K_{j}\) can make the system (25) stable. Then, proof of this theorem is fulfilled. \(\square\)
## 5 Results
### Network Results
As mentioned previously, five infrastructure nodes were installed in the home to form a network which could communicate remotely to a mobile node, employing the routing protocol our team developed. Figure 14 illustrates the network configuration employed in our proposed network architecture, which employs two Cluster Head devices (ID16 and ID 64), two End Devices (ID 24 and ID 40), a mobile node (ID 32) and a SINK (ID 8). Figure 13: (a) Security Test 1; (b) Security Test 2. Once the network is formed, communication between the mobile node and the infrastructure can begin and data can be sent. In this test, a person representing a mobile node moved freely throughout the home to determine the number of hops and RSSI. To do this, data packets were generated and routed to the SINK, as shown in Table 5.  
A total of 50 events (40 different falls and 10 tachycardia events) were simulated under test conditions using healthy subjects. Each even generated packets that possessed a sequence number and an event identifier. Adding these values to the DATA packets allowed us to identifying the totality number of packets that were generated and received. From this data set, it was possible to determine the percentage of lost packets and the number of packets corresponding to each event. Specific information is provided by Table 6.  
According to the information provided by Table 6, of the 109 DATA packets received, 97 were attributable to fall events, 10 to heart rate events and only two packets were lost, which represents a 1.83% rate of error. \begin{table}
\begin{tabular}{c c c c c} \hline
**Total** & **Fall DATA** & **Heart Rate DATA** & \multirow{2}{*}{**Lost Packets**} & **Percent of DATA** \\
**Packets** & **Packets** & **Packets** & **Lost Packets** \\ \hline
109 & 97 & 10 & 2 & 1.83\% \\ \hline \end{tabular}
\end{table} 
Table 6: Received DATA Packets. Figure 14: Created network. \begin{table}
\begin{tabular}{c c c c} \hline
**ID Node** & **Trunk Destination** & **Hops** & **Link RSSI** \\ \hline
8 & D/A & D/A & D/A \\
16 & 64 & 2 & \(-\)64 dBm \\
24 & 16 & 3 & \(-\)57 dBm \\
32 & Dynamic & Dynamic & D/A \\
40 & 16 & 3 & \(-\)47 dBm \\
64 & 8 & 1 & \(-\)53 dBm \\ \hline \end{tabular}
\end{table} 
Table 5: Scenario routing table.
# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
## 3 Proposed Method
### Data Preprocessing  
Before abnormal traffic detection, raw network traffic data needs to be preprocessed to extract useful feature information. Packet length, as a key feature of network traffic, can effectively reflect the size and transmission of packets, which is closely related to network load and transmission efficiency. Specifically, packet length not only reveals the size of data transmission; the arrival frequency of packets can reflect the active degree of the network, with high-frequency packet arrivals indicating more frequent data transmission in the network, while low-frequency packet arrivals may indicate that the network transmission is more sparse; and lastly, a sudden change in packet length is often a sign of unexpected behavior in the network, such as DDoS attacks and port scans and port scanning, etc. Anomalous traffic usually leads to significant changes in packet lengths.  
In this paper, the packet length sequence is chosen as the network traffic feature, and the specific steps are to extract the network traffic data from the PCAP file, group the packets according to the five-tuple, and extract the packets belonging to the same five-tuple to form multiple packet sequences. Each packet sequence contains a certain number of packets, and the length of the packet sequences is adjusted according to the actual demand and the characteristics of the dataset. The data preprocessing flowchart is shown in Figure 3. We tested packet sequences of different lengths. We found that when the sequence length was set to no more than 750, the model demonstrated the best performance in capturing the key information of network traffic dynamic behavior and detecting abnormal traffic. If the sequence length is too short, the model cannot obtain sufficient traffic information, which will affect the detection accuracy of abnormal traffic. On the other hand, when the sequence length is too long, the model will introduce excessive redundant information. Therefore, we choose the length of the sequence to be no more than 750, which ensures that  
Figure 2: Framework diagram of the proposed method. The framework focuses on malicious traffic (red) and normal traffic (blue), which are preprocessed, features are extracted by TCN model, and finally, classification of normal and malicious traffic is achieved by Softmax.  
the sequence contains enough information and does not lead to the sequence being too long and increase the computational complexity.
# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
###### Abstract  
Clustering is an efficient approach in a wireless sensor network (WSN) to reduce the energy consumption of nodes and to extend the lifetime of the network. Unfortunately, this approach requires that all cluster heads (CHs) transmit their data to the base station (BS), which gives rise to the long distance communications problem, and in multi-hop routing, the CHs near the BS have to forward data from other nodes that lead those CHs to die prematurely, creating the hot zones problem. Unequal clustering has been proposed to solve these problems. Most of the current algorithms elect CH only by considering their competition radius, leading to unevenly distributed cluster heads. Furthermore, global distances values are needed when calculating the competition radius, which is a tedious task in large networks. To face these problems, we propose a flooding tree uneven clustering protocol (FTUC) suited for large networks. Based on the construction of a tree type sub-network to calculate the minimum and maximum distances values of the network, we then apply the unequal cluster theory. We also introduce referenced position circles to evenly elect cluster heads. Therefore, cluster heads are elected depending on the node's residual energy and their distance to a referenced circle. FTUC builds the best inter-cluster communications route by evaluating a cluster head cost function to find the best next hop to the BS. The simulation results show that the FTUC algorithm decreases the energy consumption of the nodes and balances the global energy consumption effectively, thus extending the lifetime of the network.  
wireless sensor network (WSN); flooding tree; uneven clustering; unequal cluster; network +
Footnote †: journal: Sensors
# Compression of Vehicle Trajectories with a Variational Autoencoder
## 4 Methodology
### Latent Space Visualization  
In Figure 7, an interpolation is visualized in the latent representation space. The series begins with a right lane change and ends with a left lane change. The three internal states result from decoding the weighted average of the two extreme states' context vectors. Continuous transition is observed in terms of the distance traveled longitudinally and laterally and in the trajectory shape. The continuous transition between two completely different trajectories suggests that the regularization properly organizes the latent space. The decoded trajectories from the internal states are not uncertain as expected without regularization but eligible ones, thus the decoder can be used for content generation. The VAE described in Section 4.3 learns meaningful mapping and coding.  
The t-distributed Stochastic Neighbor Embedding (t-SNE) is a widespread technique for the latent space visualization [39]. After 120 data trajectories are encoded by the VAE into a 3 to 10-dimensional vector space, this procedure is applied to visualize the data in three dimensions.  
Figure 6: The 10-dimensional latent space visualized by t-SNE embedding in three dimensions. Every dot represents a trajectory, and the colors encode the maneuver classes.
## 2 Advances and Achievements in Seed Biotechnologies in the Decade 2014-2024
### Seed Quality Improvement Through Biotechnological Innovations  
The key factor in consistent agricultural output is the healthy and high-value traits in seeds, comprising an encapsulated form of gene pools in plants. In times of climate fluctuations, it is imperative to generate seeds that can withstand environmental changes and produce crops with key socio-economic traits [63]. The traditional approaches to seed quality improvement were based on seed treatment and selective breeding methods, demonstrating some limitations [64]. Selective crop breeding is an effective yet labor-intensive and time-consuming process, and seed treatment approaches may not sufficiently address the growing number of pathogens and chemical procedures that adversely impact the environment. With the advent of advanced biotechnological approaches, the \"next era\" has just begun--progress in synthetic biology, omics technologies, marker-assisted selection, and genome editing [19; 41] has opened new avenues in seed development and quality improvement. Research efforts into seed quality improvement have multiple benefits: good quality seeds enhance crop yields via early growth seed vigor, better abiotic/biotic stress tolerance, and crop genetic integrity [41], shaping self-resilient and sustainable agriculture. Genetic engineering has revolutionized agriculture and facilitated the introduction of traits that are not achievable via traditional breeding methods [65; 66; 67]. Among the emerging techniques, marker-assisted selection (MAS) and genome selection define great potential for seed improvement [68]. In crop production, seed traits, namely shape, mass, size, number, composition, and color, are important and can be measured through digital image technologies. A computer program, Smart Grain ([[https://www.kazusa.orjp/kazusalab/smartgrain.html](https://www.kazusa.orjp/kazusalab/smartgrain.html)]([https://www.kazusa.orjp/kazusalab/smartgrain.html](https://www.kazusa.orjp/kazusalab/smartgrain.html)) accessed on 27 October 2024), records seed images (measures seed traits) and advanced measurements of seed size and shape [69] and can be effectively used for multiple crops. Another rapid method, GrainScan, scans seed images to measure grain color and size with high accuracy [70]. Other advanced techniques, including spectroscopy analysis and Hyperspectral image-based analysis, facilitate seed trait phenotyping on large levels. In the present decade, high-throughput seed phenotyping assays (3D and 2D imaging platforms, GERMINATOR, and Machine vision platform) have been developed to measure seed imbibition and germination in crops [34; 71; 72]. Substantial progress in statistical modeling and bioinformatics has enabled assembly and study of large data sets to identify key candidates in breeding programs [73; 74; 75].
# Anti-Jamming Low-Latency Channel Hopping Protocol for Cognitive Radio Networks
## 1 Introduction
The simulation results confirm that, in comparison to the practical solution, OLAA, proposed recently, the suggested method improves network throughput and TTR by as much as 33% and 30%, respectively. On average, ALL achieves better performance than OLAA, with a 25% increase in network throughput and a 20% improvement in TTR. It should be emphasized that anti-jamming techniques are important and have broad applications in fields involving wireless communication [31], such as military communications, satellite communications, and cyber-physical systems. For example, in cyber-physical power systems, where physical and digital components are integrated to optimize power generation and distribution, dependable and secure communication is vital [32]. Hence, anti-jamming approaches could be relevant in this field to ensure communication channel integrity and availability. The rest of this paper is organized as follows. In Section 2, we provide a review of the current solutions for the rendezvous problem. Section 3 introduces the preliminary and system models, while Section 4 describes the proposed solution. In Section 5, we present the simulation results, and in Section 6, we provide the conclusions and discuss future work. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **with Pre-Shared Secrets** & **without Pre-Shared Secrets** \\ \hline Asymmetric & - & [22; 23; 24] \\ \hline Symmetric & [25; 26] & [19; 20; 21; 27; 28; 29; 30], ours \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing Anti-jamming Solution Classification.