esa-sceva/satcom-chunk-collection huggingface dataset:
# Objectively Scoring the Human-Likeness of Artificial Driver Models
## 6 Conclusions  
In the present work, a new method was introduced to objectively measure the degree of human-likeness of artificial driver models. Driving behavior is characterized by various parameters, which are then compared to the behavior of humans in real traffic. Using statistical analysis in the context of a quality function, a final human-likeness score can be computed for each trajectory. Since behavior in urban scenarios is influenced by various factors and assumed to be conditional, the situational context for each trajectory in real and artificial data is characterized by automatically computed context parameters that aim to distinguish between different situations that may occur in urban traffic. Thus, a subset of GT data showing human behavior under similar conditions can be used to compare behavioral parameters.  
Since there is no clear definition of human-likeness, we investigated the ability of the proposed method to reflect the subjective ratings of humans when assessing driving behavior in a survey inspired by the Turing test. Results of the survey showed a significant correlation between the scores calculated by the proposed metric and the scores assigned by participants. In addition, the survey provided interesting insights into which parameters contribute most to the distinction between artificial and human driving behavior. These findings were used to parameterize the quality function and provided valuable insights into specific weaknesses of the used driver models.  
Evaluation of large datasets has shown that the proposed metric has the potential to evaluate models or sub-modules in a wide range of situations, which is crucial for developing reliable solutions for urban traffic. The case study exemplified how the proposed metric can be used for detailed model evaluation and targeted model improvement. The modular structure of the metric allows models to be evaluated according to application-specific requirements. In a driving simulation, for example, the priority is more on human-like behavior than on rule compliance and safety. In contrast, when evaluating a trajectory planner for AVs in real traffic, the focus might be more on non-critical behavior, accepting a lower level of aggressiveness during interactions. By weighting and narrowing the thresholds for individual parameters, the method can be used for a broad range of applications.
## 5 Conclusions  
1. In this study, a henhouse environment monitoring system based on IoT was developed according to the environment monitoring and management requirements of henhouses. Reliable monitoring of henhouse temperature, humidity, CO\({}_{2}\) concentration and NH\({}_{3}\) concentration was realized. Compared with traditional henhouse environment monitoring systems, the system had such advantages as low packet loss rate, high reliability and easiness in extension. In addition, henhouse environment information was released via the Web and corresponding authorized users can remotely check related information via a browser, which met the requirements of the practical production.
2. Under the condition that the system costs were not raised, data fitting was used for acquisition data compensation and correction based on the features of the sensors. Consequently, the acquisition accuracy of the system was raised.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{Temperature (\({}^{\circ}\)C)}} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Self-decision**} & \multirow{2}{*}{**Interpolation**} & \multirow{2}{*}{**Fitting**} & \multicolumn{1}{c}{**Regression**} \\  & & & & & & **analysis** \\ \hline \multirow{2}{*}{Temperature (\({}^{\circ}\)C)} & Maximum absolute error & 0.48 & 0.65 & 3.10 & 0.74 \\  & RMSE & 0.1698 & 0.1846 & 1.2728 & 0.2447 \\ \hline \multirow{2}{*}{Humidity (\%RH)} & Maximum absolute error & 3.84 & 4.21 & 5.12 & 5.32 \\  & RMSE & 3.0859 & 3.5910 & 5.1881 & 3.6851 \\ \hline CO\({}_{2}\) concentration & Maximum absolute error & 232 & 237 & 238 & 437 \\ (ppm) & RMSE & 77.00 & 77.84 & 77.89 & 153.11 \\ \hline NH\({}_{3}\) concentration & Maximum absolute error & 0.30 & 0.30 & 0.35 & - \\ (ppm) & RMSE & 0.094 & 0.102 & 0.118 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of estimation results.  
Figure 10: Comparison of the experimental results and the estimated concentration of CO\({}_{2}\) of Node A.  
3. For the packet loss issue of wireless transmission, a data frame transport protocol for closed henhouse environmental parameters was designed based on loss recovery. Reliability of environmental parameter transport was increased and packet loss rate was decreased. In turn, reliable acquisition of environmental parameters was realized and the practicability and reliability of the system were enhanced.
4. For the issue of inevitable loss of close henhouse environment monitoring data, a self-decision and on-line filling method was proposed to comprehensively utilize the temporal-spatial correlations of node data to estimate the missing data. Compared with a single estimation algorithm, this method has higher estimation accuracy. Multithread programming was used to realize the on-line identification and real-time filling of missing data. Consequently, the integrity of system monitoring data was raised.
# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 3 Methodology  
### Model Architecture  
Figure 1 illustrates the general structure of the proposed SMS-Net model, consisting mainly of two modules--feature extractor and stacked hourglass module. The feature extractor extracts key features from the input image using a feature extraction model. Afterward, 1 \(\times\) 1 convolutions and up-sampling operations are performed, and the generated feature map is inputted into the stacked hourglass module. This approach reduces resource consumption and improves the inference speed using a relatively small input containing key feature information instead of directly processing the initial input image. Various feature extraction models can be used; however, EfficientNet [19], which has a relatively low computational cost, was used for efficient feature extraction in this study.  
The stacked hourglass module is composed of multiple connected hourglass modules. Each hourglass module consists of an hourglass network for effectively extracting low- and high-level features from the input image and a Keypoint Prediction Head that uses the extracted feature maps to accurately predict keypoint locations. Each hourglass network is composed of an encoder and a decoder. To reduce the computational load while preventing reduced pose estimation performance, the encoder uses the shuffle-gated block, and the decoder applies the multi-dilation block; these are the main lightweight techniques proposed in this paper. The Keypoint Prediction Head consists of a shuffle-gated block and 1 \(\times\) 1 convolutions and extracts heatmaps for each keypoint to be predicted from the feature maps extracted by the hourglass network to accurately predict keypoint locations.  
The proposed SMS-Net model reduces the computation required for training and inference by applying lightweight techniques to the hourglass network while improving memory costs. The following section describes the main features of the SMS-Net model, focusing on the lightweight techniques.
## 2 Materials and Methods
### Non-Parametric Signal Analysis  
#### 2.3.1 Time Domain Analysis  
The first step in the study of the electrical signal is to perform a time domain analysis of the signal. Through this analysis, different statistical characteristics of the signal were obtained such as mean, maximum, minimum, peak to peak, variance, and the number of zero crossings. This last one was determined by counting how many times the signal crosses the mean value in its oscillations throughout the measurement time period. The use of these metrics allowed us to obtain a first description of the electrical signal.
## 3 Results
### Cross-Pathology Generalization Testing  
To test the model's ability to generalize beyond its training domain, the trained ML framework was further evaluated on retinal pathologies explicitly excluded from the original training set (age-related macular degeneration, central serous retinopathy, and macular hole), assessing its capacity to detect anatomical disruptions despite lacking prior disease-specific exposure.  
Testing the model on anatomically analogous but etiologically distinct pathologies is justified through four complementary mechanisms. The VGG16 biomarker extractor learns cross-pathology retinal disruption patterns from training classes (CNV/ DME/DRUSEN), enabling detection of shared morphological anomalies like subretinal fluid accumulations in CSR and structural voids in MH. Consistent OCT imaging physics ensures domain invariance through standardized representation of pathophysiological signatures, allowing the model to focus on fundamental tissue properties rather than disease-specific artifacts.  
Figure 5 presents a confusion matrix evaluating the SMOTE-optimized model's performance on other OCT pathologies (DR, MH, CSR) on which it was not originally trained and validated, within a new 4000-image test set. The matrix exhibits strong diagnostic discrimination with 2206 true-normal classifications representing 95.05% specificity and 1997 true-abnormal detections achieving 95.09% sensitivity. Classification errors remain clinically acceptable at 4.95% false-abnormal (115 instances) and 4.91% false-normal (103 cases), indicating balanced performance across both healthy and pathological cohorts. The symmetrical error distribution suggests equivalent clinical risk between over-referral of normal cases and underdetection of disease.  
Table 3 evaluates the trained model's performance and cross-pathology testing results, emphasizing clinical utility and diagnostic reliability. The clinical utility value is calculated using Equation (17), which prioritizes metrics like NPV and F1 score to align with clinical decision-making priorities. The metrics show improvements through nine key metrics, demonstrating enhanced generalizability despite testing on etiologically distinct pathologies. The model achieves a 1.8% relative accuracy gain (0.9420 to 0.9507) alongside a 3.55% absolute increase in clinical utility (0.9163 to 0.9488), underscoring its robust adaptation to unseen conditions. Specificity improves from 0.9460 to 0.9519 while maintaining recall above 0.95, confirming preserved sensitivity to novel pathological features. The composite score rises to 0.9656 through optimized weighting of discriminative metrics,  
reflecting enhanced equilibrium between diagnostic precision and clinical decision priorities. This multidimensional validation approach confirms the model's capacity to navigate n-dimensional biomarker manifolds rather than memorizing class-specific features.
## 4 DD Toposphere Delay and Ionosphere Delay Analysis  
For medium baselines, the DD troposphere delay and ionosphere delay cannot be ignored. In order to analyze their influence on ambiguity resolution, it is necessary to study their magnitude. The satellite data, with ambiguity fixed, from six pairs of baselines are used for analyzing the DD troposphere delay and ionosphere delay residuals.
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 3 Proposed Method
### Structure of MBDQN
Nevertheless, the choice of backbone structure is beyond the scope of this paper. For simplicity, MBDQN only employs a relatively shallow network for investigation. The detailed network structure of the MBDQN model is presented in Table 3. The height and width of the input to the RP-Branch are both \(N\). In addition, the trick of expanding the feature map is applied at the third convolutional layer (Conv3 in Table 3). Batch normalization [21] and dropout are also employed in MBDQN to alleviate internal covariate shift and overfitting problems. Once training is completed, the de-BN operation is performed to improve the inference speed of the model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Block** & **Module** & **Module Structure** & **Tensor Size** \\ \hline \multirow{8}{*}{RP-Branch} & Conv1 & _Conv(K = 3)-BN-ReLU-MaxPool_ & _(1,N,N)_\(\rightarrow\) (32,N/2,N/2)_ \\ \cline{2-4}  & Conv2 & _Conv(K = 1,3,1)-BN-ReLU, & _(32,N/2,N/2)_\(\rightarrow\) (64,N/4,N/4)_ \\ \cline{2-4}  & Conv3 & _Conv(K = 1,3,1)-BN-ReLU, & _(64,N/4,N/4)_\(\rightarrow\) (128,N/4,N/4)_ \\ \cline{2-4}  & GAP & _Global Average Pooling_ & _(128,N/4,N/4)_\(\rightarrow\) (128,1)_ \\ \hline \multirow{4}{*}{TL-Branch} & FC1 & _FC(128)-BN-Dropout(0.5)-FC(256)_ & _(3 * \(M\),1)_\(\rightarrow\) (256,1)_ \\ \cline{2-4}  & FC2 & _FC(256)-BN-Dropout(0.5)-FC(128)_ & _(256,1)_\(\rightarrow\) (128,1)_ \\ \hline \multirow{4}{*}{Value-Branch} & Concat & _Concat_(GAP, _FC2_) & _[(128,1),(128,1)]_\(\rightarrow\) (256,1)_ \\ \cline{2-4}  & FC3 & _FC(256)-BN-Dropout(0.5)-FC(128)_ & _(256,1)_\(\rightarrow\) (128,1)_ \\ \cline{1-1} \cline{2-4}  & FC4 & _FC(128)-BN-Dropout(0.5)-FC(128)_ & _(256,1)_\(\rightarrow\) (128,1)_ \\ \cline{1-1} \cline{2-4}  & Q-value & _[FC(2),FC(M)]_ & _(128,1)_\(\rightarrow\) _[(2,1),(M,1)]_ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detailed network of Multi-Branch DQN.
## 3 Results  
### Coastal Effects in the German Bight  
Figure 4 shows the distribution of wind directions for the period 2017-2020 at the location of the cluster N-4 for wind speed at 10 m height from DWD. The wind rose indicates a clear predominance of west/southwestly wind directions [51; 52; 53]. Previous studies showed a correlation between atmospheric stability and wind direction in the North Sea [10; 51]. Based on FINO1 data [48], [PERSON] et al. [51] found that stable atmospheres are coupled with Southwest wind directions. Warmer air originating from land in southwestly direction is advected eastward over the colder sea inducing stable stratification over the North Sea.  
A clear seasonal variability is observed for the air-sea temperature difference and thus the thermal stability (Figure 5a-h). A climatology of the SST from DWD over the period 2017-2020 shows that the coolest temperature occurs during winter time in December-January-February (DJF) (see Figure 5a). The temperatures at the eastern and southern coast are cooler than the ones in the deeper sea because of the shallow depth near the shore. The same observation is made for the air temperature in winter (not shown), where the air is colder in general. Because of the shallow depth at the coast, the water cools faster there than in the deeper sea. This leads to weaker thermal stratification of the boundary layer further offshore than closer to land (see Figure 5b). In spring, the air starts heating up and the sea surface also gets warmed (Figure 5c). The thermal stability increases over the entire sea (Figure 5d). Relatively small differences in the thermal stability between the shallow and deep areas can be seen. The air temperature reaches its maximum in summer June-July-August (JJA), with highest values in the coastal regions (Figure 5e). As the sea surface temperatures are also at their highest in this season near the coast, the thermal stability ultimately decreases (Figure 5f). Higher instability is found in the shallow areas than in the deeper water, because the smaller water volume heats up faster. The transition from summer to autumn is characterised by a decrease of the air temperature. The sea surface temperature also decreases because of the air-sea interaction (Figure 5g). However, due to the higher heat capacity of the water, the sea surface is still warmer than the air, which causes unstable stratification with higher value in the sea (Figure 5h).  
In the following, we introduce two case studies of the coastal effects in Sections 3.2 and 3.3 to give a first impression of wind speed gradients derived from the SAR data, before a respective statistical analysis is presented in the subsequent sections. The first case on 6 April 2018 demonstrates a shape of horizontal wind speed gradients that is mostly frequently observed, whereas the second case on 25 August 2019 refers to an unusual decrease in the wind speed.
## 3 Modeling and Simulations of the Assembly Error Effect on the Stability Accuracy
### Integrating Simulation of the Overall System Stability Accuracy  
By integrating all the above modules, the overall system stability accuracy is modeled in Simulink, which is shown in Figure 14. The original system base angular velocity \(\overset{\rightarrow}{\omega}_{ib}^{p}=\left[\omega_{ibx},\omega_{iby}, \omega_{ibz}\right]^{T}\) is set as the input. All the modules of the kinematics, components, and assembly errors are imbedded in the middle. The final index of stability accuracy \(\triangle\theta\) is set as the output by referring to Equation (1) and Equation (2).  
Figure 14: Overall simulation modeling of the system stability accuracy.  
Figure 13: Simulation module of the signals and assembly errors of the gyroscopes.
###### Abstract  
Brain-computer interface (BCI) technology enables humans to interact with computers by collecting and decoding electroencephalogram (EEG) from the brain. For practical BCIs based on EEG, accurate recognition is crucial. However, existing methods often struggle to achieve a balance between accuracy and complexity. To overcome these challenges, we propose 1D convolutional neural networks with bidirectional recurrent attention unit network (1 DCNN-BiRAU) based on a random segment recombination strategy (segment pool, SegPool). It has three main contributions. First, SegPool is proposed to increase training data diversity and reduce the impact of a single splicing method on model performance across different tasks. Second, it employs multiple 1D CNNs, including local and global models, to extract channel information with simplicity and efficiency. Third, BiRAU is introduced to learn temporal information and identify key features in time-series data, using forward-backward networks and an attention gate in the RAU. The experiments show that our model is effective and robust, achieving accuracy of 99.47% and 91.21% in binary classification at the individual and group levels, and 90.90% and 92.18% in four-category classification. Our model demonstrates promising results for recognizing human motor imagery and has the potential to be applied in practical scenarios such as brain-computer interfaces and neurological disorder diagnosis.  
recurrent neural network; segment pool strategy; 1D CNN; EEG; motor imagery recognition +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Feature Selection
#### 2.5.2 Partial Least Squares-Discriminant Analysis  
PLS-DA is an algorithm used for predictive modeling and discriminative variable selection. PLS-DA determines the separation among classes and helps to understand which variables contain class-defining information [50; 51]. The variable important for projection (VIP) was used in this work to determine the feature variables to retain for building the LSTM model. The VIP summarizes the importance of the variables that explain the regressor feature variables that correlate with the class labels [52]. The VIP was normalized, with a VIP greater than 1 indicating that the feature variable is important for the projection.
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## 8 Categories of Network Attacks in WSN-IoT Layers
### Physical Layer Attacks  
(1) Tampering  
A denial of service (DoS) attack occurs when network tampering detaches or changes the existing network. The attacker can replace the actual node with a dummy or malicious node. After demonstrating the physical theft of devices, the attacker may extract sensitive data from the captured devices to launch other attacks in the WSN-IoT framework [78].  
(2) Malicious code injection attack  
Code injection or remote code execution occurs when an attacker utilizes a software input validation error to construct and execute malicious code, known as code injection or remote code execution [79]. The server-side interpreter injects and executes code into the language of the targeted application. Any software that takes invalidated input directly is vulnerable to code injection, and online apps are a common target for hackers [80].  
(3) RF interference jamming  
To perform DoS attacks on Radio Frequency Identification (RFID) tags/sensor nodes, an attacker generates and delivers noise signals over radio frequency [17]/WSN signals [81]. All devices communicate via radio and operate on a wireless signal [17]. If a more powerful signal overshadows such signals, a \"stronger\" signal that drowns out the standard wireless frequency is jamming.  
(4) Fake node injection attack  
Many infected nodes collaborate to create a bogus report and insert it into the network in false node injection attacks. An attacker places a faulty node between them to monitor the data transfer between two valid network nodes. Since they only validate a fixed number of message authentication codes (MACs) held in the files, these attacks are brutal to defend against [82].  
(5) Sleep denial attack  
Denial-of-sleep (DoSL) is a denial-of-service attack that prevents sensor nodes powered by batteries from sleeping. By feeding incorrect inputs to the battery-powered computers, the attacker makes them alert. As a result, their batteries are exhausted, forcing them to shut down and causing network capacity to be disrupted [83].  
(6) Side-channel attack  
A side-channel attack is a computer security attack that relies on knowledge obtained from the execution of a computer system rather than flaws in the algorithm itself. The attacker obtains the encryption keys using timing, control, and fault attacks on the system's computers [57].  
(7) Permanent denial of service attack (PDoS)  
Denial of service through hardware sabotage is known as a permanent denial of service (PDoS) attack. Phishing is a general term for one way of launching a PDoS attack. An attacker damages a computer or hardware during such an attack, making the device or whole machine worthless [84].
## 2 Experimental Study
### Remote Real-Time Monitoring System  
With the development of modern communication technology and the wide application of computer technology, unattended remote monitoring systems have become a research focus in academia and engineering fields in the world [18]. The system enables the analysis of the structural conditions of the structures, remotely, based on the collected data.