esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 30 cm Depth in Bangladesh
#### 5.2.2 Three-Day Lead-Time Forecast  
In parallel with the one-day lead-time forecast at a 30 cm depth, we assess the forecast performance of five tree-based models within a three-day lead-time framework. In order to avoid the repetition of similar tables, we only report the main accuracy results on the testing datasets. The DT model achieved the lowest average RMSE of 1.389 \({}^{\circ}\)C and the highest average R value of 0.946 under the combination of the third input scenario and a five-day time window. In the case of RF forecasts, the eighth scenario consistently performed better than other input scenarios across all time windows. The corresponding average RMSE ranged from 1.287 \({}^{\circ}\)C to 1.40 \({}^{\circ}\)C, the average R value ranged from 0.944 to 0.954, and the use of a five-day time window produced the best forecast results. The GBT showed improved performance with larger time windows, achieving the lowest average RMSE (1.261 \({}^{\circ}\)C) and the highest average R value (0.955) when using the eighth input scenario and a five-day time window. The hybrid DT-GBT model also performed well with the eighth input scenario and a five-day time window. The hybrid RF-GBTs exhibited the strongest performance, particularly achieving the lowest average RMSE of 1.260 \({}^{\circ}\)C and the highest average R value of 0.956 under a five-day time window and using all meteorological factors. Figure 5: Comparison of observed and optimal forecasted ST10 at four stations.
## 3 Hybrid Multi-Agent Reinforcement Learning (Hybrid MARL)
### Single-Agent Reinforcement Learning Framework  
Traffic signal control can be formulated as a Markov Decision Process (MDP), where each intersection functions as an autonomous agent that learns an optimal control policy through interaction with its environment. The MDP is defined as a tuple (\(S\), \(A\), \(P\), \(R\), \(\gamma\)), where  
* \(S\) (State Space) represents the current traffic conditions, including queue lengths at each lane, average vehicle speeds, and the current traffic light phase.
* \(A\) (Action Space) defines the possible traffic signal phase changes at an intersection. Each agent selects an action from a discrete set of phase transition options.
* \(P\) (State Transition Probability) specifies the probability of transitioning from one state to another, given a selected action.  
* \(R\) (Reward Function) evaluates the effectiveness of an action based on metrics such as total vehicle waiting time, throughput, and fairness in traffic signal allocation.
* \(\gamma\) (Discount Factor) determines the importance of future rewards relative to immediate rewards.  
Figure 1: Spatio-temporal feature extraction framework integrating CNNs, LSTMs, and GATs to model dynamic traffic patterns.  
We formulate the traffic signal control problem as a Markov Decision Process (MDP), defined by the tuple \((S,\ A,\ P,\ R,\ \gamma)\), where  
\(S\): the state space (e.g., queue lengths, current phase, average speeds), \(A\): the action space (e.g., possible phase changes), \(P\): the transition probability function, \(R\): the reward function, and \(\gamma\): the discount factor.  
To guide agent behavior, we propose a composite reward function that balances three objectives: minimizing waiting time, maximizing throughput, and ensuring fairness. The overall reward \(R_{t}\) at time \(t\) is given by  
\[R_{t}=\alpha\cdot R_{\{wait\}(t)}+\ \beta\cdot R_{\{throughput\}(t)}+\ \gamma\cdot R_{\{ fairness\}(t)}\]  
where \(R_{\{wait\}(t)}=\ -\ \sum\limits_{\{i=1\}_{i=1}^{\lfloor n\rfloor}\alpha}\): total waiting time across all approaches, \(R_{\{throughput\}(t)}=\ \sum\limits_{\{i=1\}_{i=1}^{\lfloor n\rfloor}\alpha}\): number of vehicles that have passed through, \(R_{\{fairness\}(t)}=\ -\ \backslash\{ret\{Var\}\big{(}\beta_{i(t)}\big{)}\): variance of green time among phases.  
The coefficients \(\alpha\) = 0.5, \(\beta\) = 0.3, and \(\gamma\) = 0.2 are tuned via grid search on the validation set to achieve optimal trade-offs between the three objectives.  
However, a major limitation of single-agent reinforcement learning [25] in traffic control is its inability to capture interactions between adjacent intersections. Since traffic flow at one intersection directly affects the conditions at neighboring intersections [26], independent learning approaches often lead to inefficient, locally optimized policies [27]. Figure 2 illustrates the basic structure of the single-agent reinforcement learning model, where each intersection operates independently without considering the global traffic state.
# Refinement and Computation Method for Line/Body Topological Relationships
## 4 Experimental Validation  
To validate the effectiveness of the proposed model and algorithm in this study, we developed a prototype system for computing the refined topological relationships between simple lines and bodies by using the Nef polyhedron model [35; 36; 37], C++ language, QT framework, and the Computational Geometry Algorithms Library (CGAL). We conducted experiments by using real pedestrian walkway lines (including \"Indoor\" and \"Outdoor\" attributes) and building body data from Hong Kong for validation [38], which include 925 building bodies and 4409 pedestrian walkway lines (see Figure 6). For body data, open data include the three-dimensional coordinates of the building's base, the height of the building, and various attribute data such as the type of building, name, construction date, and area. For line data, the most important attribute information includes the three-dimensional coordinates and position (\"Indoor\" or \"Outdoor\") of the line data. This also encompasses attributes such as name and length.  
The computation results are depicted in Figure 6, where blue represents buildings, green represents pedestrian pathways, pink denotes unit intersections, and purple indicates selected unit intersections. In Figure 6b, the column labeled \"NefA\" represents the involved buildings, \"NefB\" represents the pedestrian lines, \"Order\" indicates the sequence of basic intersections, and \"Type\" denotes the value of basic intersection types shown in Figure 4. For example, rows number \"945\" and \"946\" have the same NefA (A260999) and NefB (B12107), but different order \"1\" and \"2\", which indicates that there are two intersections between A260999 and B12107, and the type of value \"1, 2, 0, in, out\" corresponds to Figure 4s (same as \"1, 2, 0, out, in\"). In total, there are 1519 unit intersections between pedestrian pathways and buildings, encompassing 20 types. After calculation, there are 227 lines that have 1 of the 23 basic types, but the attribute is labeled as \"outdoor\", which  
Figure 6: Three-dimensional representation of the experimental area, with blue representing bodies, green representing lines and pink representing unit intersections: (**a**) experimental data; (**b**) experimental results; (**c**) zoomed-in view of the region illustrated in Figure 1.  
means that the topological relationship type of these 227 lines of data conflicts with the attribute data. Detailed information is provided in Table 1.
## 3 M2M Information and Services
### Sensor Web Enablement
#### Scenario 1--Sensors without Embedded IP Stack  
**1.1** Multiple sensor nodes connect to a SWE Gateway. Sensor networks that utilize various network topologies and low level communication protocols may be connected to the SWE architecture in this manner. The Gateway layers the sensor network data with Internet and Web protocols and relays it to a SES server. Initially the gateway calls the RegisterPublisher operation to add sensors to the SES. The request includes a SensorML which describes the sensor and its capabilities.  
**1.2** Sensor data is published to the SES via the Gateway. Notify operation, which contains sensor data in the O&M standard, is used.  
**1.3** A SOS server and a user application subscribe to sensor data using the Subscribe operation. The requests include the topic and the filtering specifications, as well as the information about the endpoint to which the filtered sensor data should be delivered. The step would probably be preceded by GetCapabilities and DescribeSensor operations which allow the consumers to collect information about the available sensor data that is being brokered by the SES. Note that while the SOS acts as a consumer in this interaction it is in fact a broker of sensor data. In SWE any service, application or process may act as a consumer as long as it provides an endpoint where notifications can be delivered to. Thus, even chaining of information brokers is possible.  
**1.4** SES forwards the sensor data that conforms to the subscription filters; to the WNS by invoking the DoNotification operation. The request includes the id of the delivery endpoint account.  
**1.5** WNS delivers the sensor data to the intended consumers. It must be noted that to receive notifications consumers have to be registered to the WNS beforehand. In this scenario the SOS consumer receives the data via HTTP POST while the user application receives it via XMPP.
## 2 Related Work  
### Process Discovery Algorithms  
Various process discovery algorithms have been proposed in the last decade to discover accurate process models from inputted event logs. The alpha miner [19] was the very first discovery algorithm that aims to automatically discover Petri nets based on clean and noise-free event logs. Later research papers extended the original alpha miner to discover invisible tasks [3] and non-free-choice behaviours [20]. However, the alpha algorithm suffers from poor quality in real-life event logs. The Heuristic Miner [6] was proposed to handle noisy event logs by calculating the relative frequency of each dependency from event logs and removing the dependency from the directly follows graph based on a predefined threshold. The Inductive Miner [21] adopts a divide-and-conquer approach to recursively filter out infrequent relations from discovered process trees and can discover sound process models. The BPMN Miner [22] can also handle noisy event logs by employing approximate dependency discovery techniques, which enables the algorithm to detect and remove infrequent relations even if not all instances are matched in event logs. The newly proposed split miner [7] utilises the breadth-first forward exploration to search for the best incoming and outgoing edges for each node on the directly-follows graph while maintaining its connectivity. The algorithm preserves the most frequent incoming and outgoing relations for each activity and filters out the rest. There are also other types of algorithms for discovering process models, such as the genetic algorithm [23] and declarative algorithms using the association rule mining [4].  
To summarise, efforts have been made to propose algorithms that can accurately discover process models from event logs. However, most process discovery algorithms require high-quality event logs as input. The performance of existing process discovery algorithms can be affected if data-quality issues are presented in event logs, such as the redundancy of activity labels.
## 2 Seawater Channel Modelling Elements
### Scattering Functions  
The accuracy of the MCS numerical simulation techniques is profoundly reliant on the characteristics of angular scattering of marine particles in a seawater propagation link. To describe the accumulative impact of the density of marine particles across the communication link on the travelling photons, we need to use mathematical instruments that capture the limits of the potential light-matter interaction. These analytical tools are the VSF and SPF. Such tools allow us to compute the convoluted impact of the seawater channel on the propagated photons and eventually conclude the spatial and temporal profile of the received optical intensity \(I_{r}(t)\). A considerable research effort, e.g., [48; 66; 71; 72], exists to formulate these scattering indicators with different application limits and accuracy. The following subsections review, derive, compare, and visualise these tools and their computational limits.
# Characterizing the Mechanical Performance of a Bare-Metal Stent with an Auxetic Cell Geometry
## Materials and Methods
### Mechanical Testings
#### 2.2.1 Three-Point Bending  
As shown in Figure 3a, three samples were tested under three-point bending conditions with a 12 mm span between 4 mm-diameter supporting pins and a 10 mm-diameter loading pin located in the middle of the sample., Force-displacement values, linear, nonlinear behaviors, flexibility on the linear region were plotted and evaluated graphically by applying a cross-head speed of 3 mm/min and force-displacement values saved for every 0.02 s. The test recorded on video and deformed shape and points on force-displacement graphs coincided for proper mechanical evaluation. The test was terminated when an almost complete fold was observed around 6.8 mm.  
Figure 3: Mechanical testing set-up to perform (**a**) three-point bending; (**b**) longitudinal compression; (**c**) lateral compression; (**d**) torsion and (**e**) radial expansion.  
Figure 2: (**a**) Unit-cell structure for re-entrant geometry; (**b**) repeating unit-cell pattern femtosecond micromachined laser-cut stainless-steel metal plate; and (**c**) fabricated laser-welded auxetic stent.
# Mechanical and Physical Characterization of Papercrete as New Eco-Friendly Construction Material
## 3 Results
### Mechanical Tests  
Mechanical strength test results, performed on the mortar specimens with a universal testing machine (model Dual Column Instron 3369) [24], were satisfactory and within the usual values present for blocks fixing, as well as ceiling and wall coating mortars. A typical specimen failure observed after a flexural test on pulp specimen is reported in Figure 3, while the results obtained with flexural and compressive tests are numerically reported in Table 2 and Figure 4. The values reported are the average values calculated for each triplet of prepared specimens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Specimens**} \\ \hline  & **Reference** & \multicolumn{3}{c}{**Volumetric Percentage**} \\ \hline
**Component** & & **2\%** & **5\%** & **10\%** \\ \hline CEMENT [kg] & 1.5 & 1.47 & 1.425 & 1.35 \\ SAND [kg] & 4.5 & 4.5 & 4.5 & 4.5 \\ PAPER/PULP [kg] & - & 0.03 & 0.075 & 0.15 \\ WATER [kg] & 0.75 & 0.75 & 0.75 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components proportioning for obtaining the specimens to be tested.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Flexural Strength [MPa]** & **Compressive Strength [MPa]** \\ \hline REF & 5.42 & 22.56 \\ PAPER2 & 4.67 & 13.56 \\ PAPER5 & 4.38 & 11.74 \\ PAPER10 & 3.17 & 6.92 \\ PULP2 & 4.87 & 14.11 \\ PULP5 & 5.32 & 11.27 \\ PULP10 & 3.48 & 10.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average strengths of tested triplets.  
Figure 2: Some of the prepared specimens.  
As one may observe, flexural and compressive strength reduce if waste paper/pulp volumetric ratio increases, except for specimen PUL5 flexural strength. As for the flexural strength, for the same volumetric ratio, pulp papercrete specimen provides higher strength than the waste paper one. On the contrary, as far as the compressive strength is concerned, a negligible difference is observed between the two recycled paper types except for the 10% of volumetric ratio. In this latter case, the compressive strength of the papercrete with pulp is higher than the one with waste paper.
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 4 The Taxonomy of Malware Analysis and Detection Approaches
### Malware Analysis Approaches and Data Types
#### 4.1.2 Dynamic Analysis  
Several researchers performed a dynamic analysis approach to collect various data types to differentiate between malware and benign files by running the executable files in isolated environments, virtual machines (VM) or emulators to monitor the executable file behavior during the run-time and then collect the desired dynamic data [43]. Various kinds of data have been collected utilizing a dynamic analysis approach. Malicious activities can be dynamically represented using both executable file behavior and by retaining memory images during run-time. The executable files' behaviors are identified through collecting the invoked API calls [44; 45; 46; 47; 48], machine activities [49; 50; 47], file-related data [51; 52; 53], and registry and network data [45; 54]. Opcode-based memory image can be taken to represent the malicious activities dynamically [15]. Even though obfuscated malware cannot hide how it behaves when dynamically analyzed, dynamic analysis is unable to satisfy all malicious conditions in order to explore all execution paths.
## 1 Introduction
[21] proposed a compound control strategy that combines velocity compensation with ADRC, which enhanced the anti-jamming ability and improved the control precision of the system. [PERSON] et al. [22] improved the three parts of ADRC, and the gray wolf algorithm was used to set the parameters, which met the precision requirements and tracking performance of a continuous rotary motor electro-hydraulic servo system under unknown strong nonlinear and uncertain strong disturbance factors. [PERSON] et al. [23] integrated linear ESO and a neuro-adaptive controller for the precise position control of the system. SMC is a widely used nonlinear control method that has received growing attention due to its advantages of a fast response, strong robustness to disturbances and unmodeled dynamics, and simple physical implementation [24; 25; 26; 27]. The application of SMC in electro-hydraulic servo systems is also increasing. [PERSON] et al. [28] proposed an innovative approach to the adaptive fuzzy sliding mode control method, to adapt the sliding mode control parameters by introducing the fuzzy self-tuning mechanism; the chattering problem in classical sliding mode control was reduced, and the convergence speed of the system was improved. [PERSON] et al. [29] proposed integral sliding mode control and applied it to the electro-hydraulic servo loading system of a heavy vehicle steering test board; the pressure control performance of the system was improved. [PERSON] et al. [30] proposed a control strategy combining feedback linearization theory and sliding mode variable structure theory, which effectively improved the pressure output precision and dynamic response characteristics of the direct-drive volume control electro-hydraulic servo system. Based on the existing problems of the load-generating devices in pavement mechanical properties testing, a loading scheme using an electro-hydraulic servo loading system is proposed, which was applied to a pavement mechanical properties test device. Simulinksimulations and tests were used to verify the effectiveness of the loading scheme. A fast terminal sliding mode control strategy with ESO is also proposed, which further improved the control performance of the electro-hydraulic servo loading system.
# Utilizing Duplicate Announcements for BGP Anomaly Detection
## 4 Result Analysis
### Validation of EMA Autoencoder Performance over LSTM  
In contrast to LSTM, where temporal dependencies are leveraged, the Autoencoder rather conducts a compact representation of normal BGP behavior, making it resilient to unseen traffic anomalies and fluctuations in the routing updates. In contrast to LSTM, which suffers when faced with rare and diverse BGP attack patterns, its reconstruction error mechanism also binds well to the ability to detect the outlier without needing labeled data. Moreover, the autoencoder is less sensitive to noise and generalizes better for varied anomaly situations, consequently reducing false positives and false negatives. The experimental results reinforce this advantage since the approaches can achieve a higher level of precision (higher than L = 96.79% for Moscow Blackout) and recall (higher than L = 98.31% for Route Views) in comparison to LSTM.  
From a practical perspective, due to their lower computational complexity and scalability, Autoencoders are better suited for near real-time BGP Monitoring. They screen out minor routing cycles, something LSTM is vulnerable to in terms of false positives due to regular traffic changes. Furthermore, Autoencoders are more efficient in dealing with high-dimensional BGP data, as they can learn stable structures without needing to learn from sequential dependencies. The ROC curves are presented in Figures 13-17 and show that Autoencoder has a higher AUC overall, implying better performance of anomaly classification. All these factors make Autoencoders the optimal selection for scalable, real-time, and high-accuracy BGP anomaly detection so that the networks will be better protected against cyber threats and network disruptions.
## 4 Discussion  
In the discussion of research results, we need to refer to the fact that, in the existing sources, there is no similar contextual model that simultaneously combines the factors of ergonomic suitability of the cabin for the operator of the mining machine, working conditions of the operator, compliance with safety procedures and rules and the occurrence of various types of downtime in mining machinery (although it is known that the human factor plays an important role in downtimes).  
Due to the fact that the area of operator-machine interoperability has been unexplored, the highest percentage of injuries and adverse events in mining machinery is caused by the human factor, and hence, the collected databases give us the possibility to explore the cause-and-effect relationships in an operator-machine system. In the analysis of the collected data on the ergonomic adjustment of the cabin to the operator, the majority of respondents (87.69%) expressed dissatisfaction with the seat (swivelling capability and armrests not adjustable or non-existent). Additionally, a significant proportion of the surveyed operators pointed to frequent absence from work due to poor working conditions (83.98%) (questions Q1-Q3, construct ES, questions Q7-Q8, construct EE in the SEM model).  
Through the analysis of the collected data on safety and health operators' attitudes, we have discovered that only four groups of questions had significant influence according to SEM modelling. These groups include safety awareness and competencies, safety communication, rules and procedures, safety policy, and cover range of questions Q10-Q21.  
Cause-and-effect relationships were found between the constructs: EO and ES (\(-0.89\)), EO and EC (\(-0.91\)), EO and EE (\(0.67\)), EO and RM2 (\(0.49\)), SHW and SAC (\(0.52\)), SHW and SC (\(0.76\)), SHW and RP (\(0.89\)), SHW and SP (\(0.93\)). Based on these cause-and-effect relationships between constructs, we can notice that the relation between EO and the risk of machine downtimes is more significant than the relation between SHW and the risk of machine downtimes. This conclusion highlights ergonomic adjustments to operators' cabins as an important factor in the prevention of unwanted mining machine downtimes. For easier use of the obtained mathematical model (Equations (1)-(8)), a DSS tool was designed with the inference logic that indicates risk prevention measures. The tool interface is user-friendly, and a list of preventive measures is compiled in a simple and comprehensible way in order to mitigate work risks. On the other hand, the value of the risk related to individual constructs in the multivariable system that follows from the structural equation model is important for the employees in the mining industry, risk assessors, local residents near mining companies, and beyond.