esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Acquisition and Preprocessing
### Data Acquisition  
This research uses images of seedling-stage maize plants for the segmentation dataset because of the low vegetation coverage and the ease of identifying the leaf boundary. With the further growth of maize, the images often contain 100% leaf coverage with no visible soil, which makes it difficult to separate the leaves from the background. To capture the canopy structure of the maize seedlings, a UAV-based image acquisition system was used to photograph the entire experimental area. This system consisted of two main parts: A flight mechanism (DJI-S1000, DJI Company, Shenzhen, China) and an RGB camera. The photos were taken from approximately 40 m above the canopy, looking vertically downward. A total of 83 photographs were obtained, with a lateral overlap of 50% and a longitudinal overlap of 70%. For this paper, we collected data in overcast and breezeless conditions to avoid shadows and target sloshing. The parameters of the digital camera used to acquire the images are given in Table 1. The color images were recorded in jpeg format and downloaded to desktop computers for subsequent processing.  
Figure 1: Description of the study area and design of the data acquisition based on an unmanned aerial vehicle (UAV) platform. (**A**) Schematic diagram of experimental area and selected plots, (**B**) Location of sampling points and ground control points.
## 2 Materials and Methods
### Information Sources  
The electronic search was performed in June 2021 and updated in May 2022, on PubMed ([[http://www.ncbi.nlm.nih.gov/sites/pubmed](http://www.ncbi.nlm.nih.gov/sites/pubmed)]([http://www.ncbi.nlm.nih.gov/sites/pubmed](http://www.ncbi.nlm.nih.gov/sites/pubmed)), accessed on 21 June 2021); Scopus ([[http://www.scopus.com](http://www.scopus.com)]([http://www.scopus.com](http://www.scopus.com)), accessed on 21 June 2021, accessed through Advanced Search, Enter query string); Embase ([[https://www.embase.com](https://www.embase.com)]([https://www.embase.com](https://www.embase.com)), accessed on 22 June 2021), accessed through Elsevier ([[https://www.elsevier.com](https://www.elsevier.com)]([https://www.elsevier.com](https://www.elsevier.com)), accessed on 22 June 2021); Web of Science, WOS ([[https://www.webofknowledge.com](https://www.webofknowledge.com)]([https://www.webofknowledge.com](https://www.webofknowledge.com)), accessed on 21 June 2021), accessed through the Clarivate Analytics ([[https://clarivate.com](https://clarivate.com)]([https://clarivate.com](https://clarivate.com)), accessed on 22 June 2021); Cochrane Library ([[https://www.cochranelibrary.com](https://www.cochranelibrary.com)]([https://www.cochranelibrary.com](https://www.cochranelibrary.com)), accessed on 22 June 2021); and ILLACS database was consulted in Virtual Health Library, VHL ([[https://bvsalud.org](https://bvsalud.org)]([https://bvsalud.org](https://bvsalud.org)), accessed on 22 June 2021). Grey literature was consulted through OpenGrey (www.opengrey.eu, accessed on 6 August 2021) and Google Scholar ([[https://scholar.google.com](https://scholar.google.com)]([https://scholar.google.com](https://scholar.google.com)), accessed on 6 August 2021.). Database alerts were set to retrieve new publications. Experts in the field were identified at Expert Scape ([[https://www.expertscape.com/](https://www.expertscape.com/)]([https://www.expertscape.com/](https://www.expertscape.com/)), accessed on 4 January 2022) and contacted by the \"List Experts\" for ongoing studies or unpublished results regarding the focused question, using up to five email contact attempts until January 2022.
## 2 Results and Discussion
### Nephroprotective Effect of APTM
#### 2.4.5 Effect of APTM on Urinary Total Protein and Albumin Levels  
In all test groups, the effect of the APTM extract on total urine protein and albumin levels was investigated, and the results are shown in Figure 5. When rats were given GM, urine concentrations of total proteins and albumin were significantly higher (\(p<0.01\) and \(p<0.001\), respectively) than in the NCG. These results are in agreement with those found by several researchers [8; 17]. Increased urinary protein levels in GM-treated rats have been confirmed in other relevant studies [17; 29]. However, compared to GGM (80 mg/kg), the APTM extract at dosages of 200 and 400 mg/kg significantly reduced the urine rate of total protein and albumin in GM-intoxicated rats. The bioactive components in the aqueous extract of APTM may be responsible for the increasing effect on total plasma proteins and albumin. This finding implies that kidney cells are generally protected by these natural substances.  
Figure 4: Effect of APTM on creatinine clearance in GM-intoxicated rats. Data are expressed as means \(\pm\) SEM, (n = 6). \({}^{\#\#}p<0.001\) related to NCG. \({}^{*}p<0.05\), \({}^{**}p<0.01\) compared to GGM (80 mg/kg, b.w).  
Figure 5: Effect of APTM on total urinary protein (**a**) and albumin (**b**) in GM-poisoned rats. Data are given as means \(\pm\) SEM, (n = 6). \({}^{\#\#}p<0.001\), \({}^{\#\#}p<0.01\) related to NCG: \({}^{*}p<0.05\), \({}^{**}p<0.01\) compared to GGM (80 mg/kg, b.w). Ns: not significant.
## 2 Methodology  
### Problem Definition  
We firstly give the following definitions used in this paper.  
**Definition 1** (Temporal knowledge graphs).: _A temporal knowledge graph (TKG) is represented as a set of chronological knowledge graphs with discrete time stamps, \(\{G_{1},G_{2},\cdots,G_{T}\}\), where each graph at time \(t\) is \(G_{t}=(V,R,E_{t}),t\in[1,T]\). Here, \(V\) is the set of entities, \(R\) is the set of relation types, and \(E_{t}\) is the set of edges. Each edge represents a fact which includes two entities linked by a relation type. Therefore, \(E_{t}\subset\{(s,r,o)_{t}|s\in V,o\in V,r\in R\}\), where the triple \((s,r,o)_{t}\) stands for an event or fact that the subject entity \(s\) has the relationship \(r\) with object entity \(o\) at time \(t\)._  
**Definition 2** (Entity prediction task).: _Given the query \((s,r,?,t)\), the entity prediction task is to model the conditional probability distributions of all object entities under the subjects \(s\) when relation \(r\) is given and historical graphs in a fixed length of observation windows \(m\), \(\{G_{(t-m+1)},\cdots,G_{t-1}\}\), are also given. The conditional probability distribution is represented as function \(f_{1}\) in Formula (1). Meanwhile, we add a sub-query \((s,?,t)\) to constrain the reasoning process. The sub-query is to model the conditional probability distribution of all relation types when \(s\) and historical graphs are given. This probability distribution is represented as function \(f_{2}\) in Formula (2). Therefore, our task is to find appropriate trainable functions \(f_{1}\) to fit the conditional probability distribution of entities on TKGs. The formulations are shown as:_  
\[p(o|s,r,t)=f_{1}(s,r,G_{t-1:t-m+1}) \tag{1}\] \[p(r|s,t)=f_{2}(s,G_{t-1:t-m+1}) \tag{2}\]  
Figure 1: An example of entity prediction task on temporal knowledge graphs.  
**Definition 3** (Neighbor set of an entity).: _Given a snapshot of the TKG at time \(t\), the entity \(s\) with its neighbor entities and linked relations types make a sub-graph \(Sub^{t}(s)\). In this sub-graph, all nodes from the neighbor entity set of \(s\), which is denoted as \(N_{e}^{t}(s)\). Its linked relations constitute the neighbor relation set of \(s\), denoted as \(N_{t}^{t}(s)\)._
###### Abstract  
The overlap of visual items in data visualization techniques is a known problem aggravated by data volume and available visual space issues. Several methods have been applied to mitigate occlusion in data visualizations, such as random jitter, transparency, layout reconfiguration, focus+context techniques, etc. This paper aims to present a comparative study of the reading of visual variables values with partial overlap. The study focuses on categorical data representations varying the percentage limits of partial overlap and the number of distinct values for each visual variable: hue, lightness, saturation, shape, text, orientation, and texture. A computational application generated random scenarios for a unique visual pattern target to perform location tasks. Each scenario involved presentation of the visual items in a grid layout with 160 elements (\(10\times 16\)), each visual variable had from three to five distinct values encoded, and the partial overlap percentages applied, represented by a gray square in the center of each grid element, were 0% (control), 50%, 60%, and 70%. Similar to the preliminary tests, the tests conducted in this study involved 48 participants organized into four groups, with 126 tasks per participant, and the application captured the response and time for each task performed. The results analysis indicated that the hue, lightness, and shape visual variables were robust to high percentages of occlusion and gradual increase in encoded visual values. The text visual variable showed promising results for accuracy, and the resolution time was a little higher than for the last visual variables mentioned. In contrast, the texture visual variable presented lower accuracy to high levels of occlusion and more different visual encoding values. Finally, the orientation and saturation visual variables exhibited the highest error and worst perfomance rates during the tests.  
information visualization; visual variables; evaluation; occlusion; overlap; visual perception +
Footnote †: journal: Applied Sciences
# Energy-Efficient Opportunistic Routing Algorithm for Post-Disaster Mine Internet of Things Networks
## 5 Performance Evaluation
### Performance for Varying Communication Ranges
Moreover, DEOR makes each node have multiple forwarders through a recovery mechanism for void routing, which means that when a sender encounters a void node, it can quickly find recovery relay nodes, which in turn improves the packet delivery rate. However, ORR, OBRN, and ECSOR only consider the energy and load factors to make routing decisions. When encountering void nodes, a large number of control packets need to be sent to update network information to calculate a new forwarder set, which leads to a higher probability of packet loss; see Formula (24). As a result, the DEOR algorithm outperforms the other three algorithms in terms of PDR. Figure 12 illustrates the influence of the communication range on the network lifetime. As can be seen in Figure 12, the network lifetime of all four algorithms increases as the communication range \(r\) becomes larger. Compared to ORR, OBRN, and ECSOR, DEOR achieves the longest network lifetime, while ORR has the shortest network lifetime. In OBRN and ECSOR, the remaining energy of nodes around the sink is not considered when selecting forwarders. There exists routing nodes nearthe sink participating in excessive packet forwarding, leading to the hotspot problem. Thus, premature depletion of energy by critical routing nodes causes network connection interruption. However, in DEOR, only nodes in the upward path to the sink are eligible to forward packets, which effectively reduces the number of redundant packets and shortens the routing path. Meanwhile, DEOR balances the load of nodes through the transmission distance factor \(\widetilde{d_{i,j}}\) in routing decisions, thereby reducing energy consumption. As a result, DEOR has a longer network lifetime than ORR, OBRN, and ECSOR.
# Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks
## 4 Optimization Framework  
In this section, we elaborate the proposed optimization framework in detail. We first explain the decision variables used in the model and constraints imposed on them in Section 4.1, and then add more constraints to model the OFDMA and MU-MIMO operations in Section 4.2. In Section 4.3, we discuss the complexity of the model and a method of reducing the model size, and we describe the extended model for the joint MU-MIMO and OFDMA case in Section 4.4.
## 5 Results
### Result of the Spatial Durbin Model for Hypothesis 2
#### 5.3.2 LM Test  
According to Table 8, both LM-ERR and LM-LAG tests passed the significance test at the 10% level, indicating that the lag term and residual sequence of dependent variables are spatially autocorrelated. The results show that the Robust LM-ERR passed the significance test at 10% level, while the Robust LM-LAG failed the significance test at the 10% level.  
Considering the above test results, we adopted Spatial Durbin Model (3) to study the impact of transportation integration on industrial agglomeration in urban agglomerations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year** & **Moran Index** & **Year** & **Moran Index** \\ \hline
2011 & 0.1939 ** & 2016 & 0.2444 ** \\
2012 & 0.2016 ** & 2017 & 0.2507 *** \\
2013 & 0.2088 ** & 2018 & 0.2470 ** \\
2014 & 0.2151 ** & 2019 & 0.2542 *** \\
2015 & 0.2333 ** & 2020 & 0.2559 *** \\ \hline \end{tabular} Note: the significance of ** and *** are 5%, and 1%, respectively.  
\end{table}
Table 7: Spatial autocorrelation test of industrial location entropy under the time spatial weight matrix.
# A Survey of Planning and Learning in Games
## Planning and Learning in Games
### World Models  
The work in [73] explores the implementation of generative neural network models of game environments (e.g., OpenAI Gym), referred to as world models. These world models can be trained quickly via unsupervised learning, using observations collected from the actual game environment, in order to learn a compressed representation of the spatial and temporal aspects of that environment. The agent can then use this world model during its learning process. A distinctive feature of this model, however, is that the agent can be entirely trained using only the model and without ever interacting with the environment as if dreaming or hallucinating via the model. The policy trained this way (i.e., by dreaming) can then be transferred to the real environment in order to solve the specified task.  
At a high level, the agent's architecture proposed is composed of three main components, namely, the variational autoencoder (VAE), \(V\); the MDN-RNN (mixture density network combined with a RNN) model, \(M\); and the controller, \(C\). \(V\) learns an abstract (and compressed) representation of each of the observed frames (i.e., raw images) in the form of a (compressed) latent vector \(z\). \(M\) serves as a predictive model of the future \(z\) vectors that \(V\) is expected to produce. The controller \(C\) is responsible for choosing the actions \(a_{t}\) that the agent performs at each time step \(t\), based on the concatenation of the latent vector \(z\) and \(M\)'s hidden state \(h_{t}\). After each action \(a_{t}\), \(M\) updates its own hidden state using \(h_{t}\) and \(a_{t}\) to derive \(h_{t+1}\); see Figure 10. Finally, the parameters of C are optimized via an EA, more specifically the Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).  
Embed to Control (E2C) [247], a method for model learning and control in non-linear dynamical systems, proposes a somewhat similar approach. Similarly to world models, E2C consists of a deep generative model belonging to the family of autoencoders that can be trained in an unsupervised way, using raw pixel images. The model is derived from a control formulation in latent space, holds a belief over viable trajectories in sensory space and supports long-term prediction of image sequences or trajectories. Overall, the objective of E2C is to simplify the problem of locally optimal control in high-dimensional non-linear systems by identifying a low-dimensional latent space where this optimal control can be performed more easily.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 4 Resistance Parameter Analysis
### Analysis of Body Resistance Parameters \(k_{rb}\)  
The body resistance parameter \(k_{rb}\) mainly represents the negative impact of factors related to the speed of quadcopter motion (such as friction caused by relative air motion) on the acceleration performance of unmanned aerial vehicles. Figure 5 shows the optimal state trajectory \(s^{*}(t)\) corresponding to different body resistance parameters when the rotor resistance is approximately zero \(k_{rp}=0.01\). The resistance component \(f_{rb}\) of the aircraft body is inversely proportional to the velocity trajectory by \(k_{rb}\) times, while the thrust component \(f_{t}\) changes with \(f_{rb}\) to counteract its effects, ultimately resulting in complex effects on \(f_{t}\), changing the timing of its extreme and zero points.  
By comprehensively analyzing the impact of two types of resistance on thrust, the optimal thrust trajectory presents a way to counteract the influence of resistance, allowing the optimal state trajectory to operate in a manner close to the optimal state trajectory without resistance.
## 2 Theoretical Derivation
### Based on the Revised Crack Growth Formula  
For the life prediction of metal materials, the fracture analysis method is mostly used. The fracture analysis method uses the Paris formula to calculate the crack growth rate to obtain the life cycle of the container failure. The traditional Paris formula [17] is presented as Equation (4):  
\[\frac{da}{dN}=C(\Delta K)^{m} \tag{4}\]  
where \(C\) and \(m\) are the material constants, \(\Delta K\) is the increment of the stress intensity factor, \(da\) is the crack growth, \(dN\) is the number of cycles, and \(da/dN\) is fatigue crack growth rate. In the category of elastic fracture mechanics, the following relationship between the stress intensity factor increment \(\Delta K\) and the strain energy release rate \(\Delta G\) is:  
\[\Delta G=\frac{\Delta K^{2}}{E^{*}} \tag{5}\]  
where  
\[E^{*}=\left\{\begin{array}{ll}E_{r}&\quad\text{\it Plane stress}\\ \frac{E}{1-\mu^{2}},&\quad\text{\it Plane strain}\end{array}\right. \tag{6}\]By combining (2) and (5), the modified \(\Delta G^{*}\) can be obtained:  
\[\Delta G^{*}=\frac{\left(\Delta K^{*}\right)^{2}}{E^{*}}=\frac{\Delta(K\cdot F)^{ 2}}{E^{*}} \tag{7}\]  
Considering that the stress intensity factor correction factor \(F\) is a constant coefficient related to the initial state of the crack, the Formula (7) can be rewritten as:  
\[\Delta G^{*}=\frac{(\Delta K)^{2}\cdot F^{2}}{E^{*}} \tag{8}\]  
Based on the low-cycle fatigue crack growth criterion of components under subcritical cyclic loading, the G-form strain energy expression of the Paris formula is presented as Formula (9):  
\[\frac{da}{dN}=c_{3}\Delta G^{c_{4}} \tag{9}\]  
By combining (8) and (9), the modified fatigue crack growth rate is presented as Formula (10):  
\[\frac{da}{dN}=c_{3}\Bigg{(}\frac{(\Delta K\cdot F)^{2}}{E^{*}}\Bigg{)}^{c_{4}} \tag{10}\]  
where \(c_{3}\) and \(c_{4}\) are the material constants. From this, the relationship between the parameters \(c_{3},\ c_{4}\) and the modified Paris formula parameters \(C\), \(m\) can be calculated as:  
\[c_{3}=C\Bigg{(}\frac{E^{*}}{F^{2}}\Bigg{)}^{c_{4}} \tag{11}\]  
\[c_{4}=\frac{m}{2} \tag{12}\]
## 4 Experimental Results  
In this section, we revisit the central problem addressed in this work: the accurate attribution of attacks in multi-adversary environments, where each alert sequence must be correctly associated with the responsible attacker. As highlighted by the exploratory t-SNE analysis presented in Section 3, the sequence representations exhibited significant overlap and complexity, suggesting that the decision boundaries between attackers were non-trivial. This challenge was further exacerbated by severe class imbalance and the presence of multiple attackers with partially overlapping behavioral patterns.  
Our hypothesis was that Transformer-based models, due to their ability to model temporal dependencies and capture complex interactions between entities (such as IPs  
Figure 7: Conceptual diagram of the proposed Transformer-based model for attack attribution in multi-adversary network alert sequences. The architecture integrates input and IP embeddings, sinusoidal positional encoding, a multi-layer Transformer encoder (with multi-head attention, feed-forward, and residual connections), mean pooling for temporal aggregation, and a final classification layer. This design enables the model to capture both contextual and temporal dependencies and robustly attribute attacks to specific adversaries.  
and alert types), are better suited to this task than traditional architectures like MLPs. We posited that their capacity to manage multiple information flows and learn contextual relationships enables them to generalize more effectively, particularly in the presence of minority classes and contaminated data subsets.  
To evaluate this, we first implemented a baseline MLP model to establish a performance reference and then assessed a Transformer-based model using standard multiclass classification metrics (accuracy, macro and weighted F1-score), per-class analysis, training dynamics, and robustness tests. This comparative analysis allowed us to validate whether the modeling advantages of Transformers translate into tangible improvements in attacker attribution performance.
## 4 Results
### Using Artificial Neural Networks to Estimate Remaining Capacity
In experiments, the irreversible capacity fade (\(C_{fade}\)) of the battery is influenced by discharging current (\(i\)), ambient temperature (\(T\)), and cumulative discharging time (\(cdt\)). The typical \(C_{fade}\) algorithm is as follows:  
\[C_{fade}=f(i,T,cdt). \tag{16}\]  
Through Equation (16), the topographical structure of BPNN includes: (1) an input layer: input variables, including (\(T\)), (\(i\)), and (\(cdt\)); (2) one or more hidden layers; (3) an output layer. This model is shown graphically in Figure 7.  
The parameter \(ak\) represents the data input to the model in the Figure 7; \(tg\) represents the model output result, which is the remaining capacity of a battery; and \(w\) and \(b\) are the weight and bias values, respectively. There are three nodes in the input layer. According to Kolmogorov rule [62; 63], the number of neurons was set as seven in the hidden layer. The activation function selected for the model construction was log-sigmoid in last two layers [64], written as  
\[f(n)=\frac{1}{1+e^{-n}}. \tag{17}\]  
The output value (\(tg\)) of each neuron (\(j\)) in the hidden layer was calculated by  
\[tg_{i}=f(\sum\
olimits_{i=1}^{3}ak_{i}w_{i,j}+b_{j}),\ i=1,\ldots,3,\ j=1, \ldots,7, \tag{18}\]  
where \(ak_{i}\) represents the input vector, \(w_{i,j}\) is the weight value connecting the \(i\)th input vector and the \(j\)th neuron, \(b_{j}\) represents bias values, and \(f\) represents log-sigmoid activation function. The model output \(tg\) can be calculated from  
\[tg_{v}=f(\sum\
olimits_{j=1}^{7}g_{j}w_{j,v}+b_{v}),\ v=1, \tag{19}\]  
where \(v\) is the number of neurons in the output layer. In this study, a variant of the gradient descent method called the Levenberg-Marquardt (LM) algorithm was employed to train the constructed model [62; 65]. The LM algorithm integrates together, the gradient descent method and the Newton method, which can solve non-linear least squares problems. When it is used for ANN training, the iteration can continue to proceed quickly while ensuring model training speed and accuracy. Finally, the prediction values of remaining capacity were attained finally through the above modeling.