esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 4 A Connectivity Analysis Based on the CNN-BiLSTM-AM Model  
The structure of the proposed method is depicted in Figure 7. The CNN module, which functions as the feature extractor, comprises a four-layer architecture. A linear rectified activation function (ReLU) is applied after each layer to enhance its ability to model non-linear relationships. Additionally, an average pooling layer is incorporated to reduce the number of parameters and mitigate the risk of overfitting. Specifically, the module can automatically capture and abstract implicit features that influence the ship-shore connectivity--for instance, the movement direction, speed, distance, signal-to-noise ratio, and other key factors, as well as the latent feature patterns among these critical variables. Consequently, the CNN is capable of extracting effective high-level features from complex input data, which are subsequently passed as input to the BiLSTM module.  
Given the constantly changing conditions of the maritime environment and the movement of ships, these factors present considerable difficulties for network connectivity analyses. To address this, the proposed approach leverages historical time-series data for  
Figure 5: AM unit structure.  
Figure 6: The overall workflow of CNN-BiLSTM-AM.  
the analysis. By leveraging the memory mechanism and long-sequence learning capability of BiLSTM networks, the model further explores the dependencies and dynamic change patterns along the temporal dimension and computes the connectivity probability over the time series.  
Subsequently, an AM is introduced to distinguish the significance of the time-related information extracted from the BiLSTM hidden layers. This weighted aggregation process enables the model to uncover deeper temporal features within the response data. Finally, a Dropout layer is applied as a regularization technique to prevent overfitting. By randomly deactivating certain neurons (including both hidden and visible layers) during training, Dropout facilitates a more robust learning environment for the multi-layer perceptron. It accelerates training, reduces the computational demands, and shortens the training time. The final fully connected layer is responsible for generating the output results.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
## 3 Proposed Positioning Framework
### Sensor Fusion  
Because the frequency of the camera was higher than that of LIDAR, LIDAR locked phase when detecting pulses from the GPS and searched for the closest image in time. A point in the LIDAR frame at time \(t\) was denoted as \(p_{i}^{t,l}=[x_{i}^{t,l},y_{i}^{t,l},z_{i}^{t,l}]^{T}\), and it was transformed to the camera frame using the following:  
\[\begin{array}{lcl}p_{i}^{t,c}&=&R_{i}^{c}&0&p_{i}^{t,l}\\ 1&=&t_{i}^{c}&1&1\end{array}\, \tag{1}\]where \(p_{i}^{t,c}=[x_{i}^{t,c},y_{i}^{t,c},z_{i}^{t,c}]^{T}\) is the point in the camera frame at time \(t\), and the extrinsic transformation matrix from the LIDAR frame to the camera frame consisted of a rotation matrix \(R_{i}^{c}\in SO(3)\) and a translation vector \(t_{i}^{c}\in\mathbb{R}^{3}\).  
Because the FOV of the LIDAR was smaller than that of the camera in the horizontal and vertical directions, all LIDAR points that could be projected into the image were considered according to the expression,  
\[uv_{i}^{t}=K_{c}p_{i}^{t,c}, \tag{2}\]  
where \(uv_{i}^{t}=[u_{i}^{t},v_{i}^{t},1]^{T}\) is the image coordinate, \(K_{c}=\begin{array}{ccc}4&0&k_{y}&v_{0}\end{array}\), which is determined by the focal distance \([k_{x},k_{y}]\) and skew \(\beta\), and the central point coordinate of the image is the intrinsic parameter matrix of the camera.  
The intrinsic parameter matrix and the extrinsic transformation matrix were calibrated based on a fixed chessboard plane, which shared the same FOV as the LIDAR and camera. The calibration process is shown in Figure 2. The difference of normals (DoN) [35] feature description was applied to detect chessboard planes in the LIDAR frame. Meanwhile, the internal parameter matrix \(K_{c}\) was estimated using a flexible camera calibration toolbox [36], which reconstructed the position and plane of the chessboard corner. The chessboard plane at the LIDAR and camera frames was indicated by their normal vector \(\theta\) and the distance to the sensor optical center \(d\), respectively, as shown in the following equation,  
\[\begin{array}{l}\alpha_{t}^{l}:\theta^{t,l}p_{i}^{t,l}+d^{t,l}=0,\\ \alpha_{t}^{l}:\theta^{t,l}p_{i}^{t,c}+d^{t,l}=0.\end{array} \tag{3}\]  
The collection of the chessboard in \(n\) FOVs was obtained as follows:  
\[\begin{array}{l}\text{A}^{l}=\begin{array}{c}\text{A}^{l}=\begin{array}{c} \text{A}^{l}_{1},\text{A}^{l}_{2},\dots,\text{A}^{l}_{n}\\ \text{A}^{c}=\begin{array}{c}\text{A}^{c}_{1},\text{A}^{c}_{2},\dots,\text{A  
Equation (5) is a nonlinear optimization, which can be solved using the Levenberg-Marquardt algorithm [37]. The last image in Figure 2 shows the color point cloud fused from the LIDAR and camera calibration results in one FOV, where the corresponding relationship between the fitted points and the real scene indicate accurate alignment.
## 2 Materials and Methods
### Soil and Leaf Sampling and Analysis  
Soil samples from each orchard were collected at the end of November at a 0-40 cm depth. A 5 cm diameter auger was used, and soil samples were taken after clearing the soil surface from any plant biomass. Three composite soil samples were collected per orchard and per cultivar.  
In mid-July (fruits were already harvested, and new shoots had completed their spring growth), approximately 20 healthy, fully expanded leaves were collected per tree (60 leaves per plot). Leaves were randomly sampled around the canopy of each tree, approximately 1-2 m above ground, from the middle part of non-bearing shoots. Leaves sampled from the three trees of each plot were considered as one sample. The leaves were placed in paper bags and transferred to the laboratory, where they were firstly washed with running tap water and later thrice with deionized water. After drying in an oven at 70 \({}^{\circ}\)C until constant weight, the leaves were ground into a fine powder (<1.0 mm) with a centrifugal mill.  
Soil and leaf samples were then analyzed following the procedures described by [PERSON] et al. [12]. Briefly, the soil samples were firstly air-dried and ground to pass 2 mm sieve. The particle size was determined by the hydrometer method, waiting a period of 2 h for the clay concentration estimation [13]. Electrical conductivity (EC) and soil pH were measured in a water-soil suspension (1:1 v-w) [14]. Organic matter was determined according to Walkley-Black wet digestion method [15]. The exchangeable cations as well as the cation exchange capacity (CEC) were determined using the ammonium acetate extraction method [16]. Plant available P was determined according to [PERSON] et al. [17]. For the determination of available metals 10 g of soil sample were extracted by shaking for 2 h with 20 mL 0.005 M diethylenetriamine-pentaacetic acid (DTPA), previously adjusted to pH = 7.3 [18].  
For the leaf mineral analysis, the leaves were carefully washed with running tap water and then thrice with deionized water in order to determine the concentrations of nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), iron (Fe), zinc (Zn), manganese (Mn), copper (Cu) and boron (B). Leaf samples were then dried at 65 \({}^{\circ}\)C until constant weight, milled into a fine powder (<1 mm), and dry-ashed in a furnace at 500 \({}^{\circ}\)C for 5 h. The ash produced was treated with 5% \(v/v\) HCl. For the determination of P concentration, the vanado-molybdo-phosphate yellow color method was used, while azomethin-H was used for B determination. Potassium, Ca, Mg, Fe, Mn, Zn, and Cu concentration was determined by atomic absorption spectrometry (Varian SpectrAA, 240 FS). Nitrogen concentration was measured by the indophenol-blue method in the wet digest [19; 20]
# Development and Biomechanical Evaluation of a Back Exoskeleton to Support Lifting Tasks
## 4 Results
### User Feedback  
Overall, the participants indicated that the exoskeleton was straightforward to operate and offered a high level of comfort during use. They noted that soft textile components at the user-system interfaces effectively minimized the risk of pressure points, allowing for a non-restrictive fit throughout the movement.  
The system can be applied and removed independently by the user in less than three minutes without the need for external assistance. The adjustable design of the hip belt and supporting structure was particularly appreciated, resembling a backpack configuration with the use of straps, buckles, and Velcro closures. This self-adjustment feature ensured that the exoskeleton could be tailored to different body shapes and sizes without any complications. Additionally, the lightweight design was not perceived as a burden, and joint flexibility ensured unrestricted movement, including walking, lateral bending, and forward bending.  
Figure 9: (**a**) Mean activity of m. semitendinosus during the lifting task with (red) and without (blue) exoskeleton and (**b**) paired-samples _t_-test statistic SPM [t]; * critical t-value.  
Regarding usability, the participants praised the simple and intuitive operation of the system, noting that only an On/Off switch was required to start the assistive function. However, during lifting tasks, some users felt that the dynamic response of the assistive force was slightly delayed when performing rapid movements. A few test participants suggested that a more adaptive or responsive control system could further enhance the experience, particularly when handling rapid or repetitive lifting sequences.  
Overall, the participants described the experience of working with the exoskeleton as \"natural\" and supportive, emphasizing that it felt like an extension of their own body rather than an external device. Many expressed interest in using the exoskeleton in real-world applications, particularly in occupations involving repetitive lifting tasks, as they experienced a notable reduction in lower back strain.
# Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation
## 3 Results and Discussion
### Factors Influencing Steel Passivation  
#### 3.3.1 Simulated Pore Solution System  
Variation in the water-cement ratio, clinker constitution, hydration time, temperature or other factors may cause the ion composition to change greatly. Thus, it would not be suitable to adopt an SPS of a certain composition to simulate the pore solution of CSA. A SPS system covering the chemical composition of most cases is required. Considering the values listed in Table 5, as well as the information from literature review, the variation ranges of the main anions were set as follows: SO\({}_{4}\)\({}^{2-}\) (0-0.05 M), OH\({}^{-}\) (0.001-0.1 M), and Al(OH)\({}_{4}\)\({}^{-}\) at (0-0.2 M). In this study, Al(OH)\({}_{4}\)\({}^{-}\) was produced by AlO\({}_{2}\)\({}^{-}\). Given that the hydrolysis of Al(OH)\({}_{4}\)\({}^{-}\) will release OH\({}^{-}\), the maximum dosage was limited to control the increase of pH within 0.1. For convenience, both Na\({}^{+}\) and K\({}^{+}\) were represented by Na\({}^{+}\). Hence, the SPSs were prepared with NaOH, NaAlO\({}_{2}\), and Na\({}_{2}\)SO\({}_{4}\) following the compositions shown in Table 2. All the solutions were measured using a pH meter to ensure that the pH deviations of the solutions with the same NaOH concentration were kept within 0.1.
## 3 Machine Learning and Fault Diagnosis
### Supervised Learning  
Supervised learning algorithm is a type of algorithm designed to learn how to classify data based on certain input parameters. The algorithm is trained on a set of data accompanied by the desired outputs or labels. This helps the algorithm to identify patterns and correlations in the data, which can be used to predict or classify new data. The goal of supervised learning is to accurately predict the correct output or label for future observations based on what it has learned from the training data [5]. Supervised learning problems (see Figure 8) can be described mathematically in the following way: we have a set of \(n\) samples \(\left\{\overrightarrow{x_{i}}\right\}_{i\in\{1,2,\ldots,n\}}\) in a universe \(\Omega\) and their respective labels \(\left\{y_{i}\right\}_{i\in\{1,2,\ldots,n\}}\) in a universe \(\mathcal{P}\), we define a function \(\varphi\colon\Omega\to\mathcal{P}\) (which is fixed and unknown) that takes the sample as input and produces the labels \(y_{i}=\varphi\left(\overrightarrow{x_{i}}\right)+\omega_{i}\) as output, where \(\omega_{i}\) is some random noise involved which can affect the accuracy of the labels generated from the samples. Thus, the data used makes it possible to determine a function \(f\colon\Omega\to\mathcal{P}\) such that  
Figure 6: Representation of an open-circuit fault on an inverter arm.  
Figure 7: Illustration of the Machine Learning hierarchy.  
for any pair \((\overrightarrow{x},\,y)\in\Omega\times\mathcal{P},f\left(\overrightarrow{x} \right)\approx\ y\). The universe on which these data are defined is typically a set of possible inputs for the algorithm \(\Omega=\mathbb{R}^{N}\). As a result, the whole \(\left\{\left(\overrightarrow{x}_{i},y_{i}\right)\right\}_{i\in\{1,2,..,n\}}\) makes up the training data set. Figure 8 provides a demonstration of how a learning algorithm operates in a supervised learning environment, which differs from unsupervised learning.
## 4 Discussion
The ear and eye endpoints were not affected in response to a WIFI signal (2400 MHz) and 1800 MHz at 1 mW/cm\({}^{2}\) exposure for 21 days of pregnancy [35] but incisor eruption was delayed in response to 3500 MHz exposure at a wbSAR of 0.07 W/kg [24]. However, premature eye opening was shown in rat new-borns by [PERSON] et al. [36] in both males and females when exposed to tributylin, abiocide used in the industry, with an increase in serotonin and dopamine levels at PND 57 in males. Neurotransmitter alterations were also observed in males in the F1 to F3 generation when only ancestors were exposed to tributylin (F0). After pharmacological treatment with methamphetamine in pregnant rats (inducing increased dopamine released in the synaptic cleft), a delay in eye opening, ear detachment, as well as incisor was observed in the offspring [37]. The physical development effects found in our study were also shown when toxicants impacted serotonin. [PERSON] [38] reported a delay in eye opening in mice treated with mercuric chloride from GD 1 to PND 15 with decreased levels of both serotonin and dopamine observed in the forebrain (from PND 7 to PND 36). The neurotoxic effect is shown to be induced by increased oxidative stress and the downregulation of Na+/K(+)-ATPase [39]. Interestingly, the same animals showed decreased body weight. Premature eye opening was also reported in pups injected with fluvoxamine (a selective serotonin reuptake inhibitor) from PND 1 to PND 14 with reduced weight from PND 1 to PND 16 [40]. The association between RF-EMF exposure and increased oxidative stress is one of the most-studied effects following exposure to further elucidate the mechanisms of action [41]. In rats, increased oxidative stress has been shown following 850 MHz or 900 MHz exposure in the testis [42; 43], and in the brain [44]. The increased oxidative stress in the brain has also been associated with changes in serotonin levels [45], which may be of interest to further investigate this pathway after RF-EMF exposure. Our exposure for the male pups in the OcM group resulted in earlier ear detachment and eye opening with reduced body weight. These data may suggest a metabolic disturbance with higher energy expenditure. One possible mechanism for lower weight gain may be the activation of brown adipose tissue, increased energy consumption, and non-shivering thermogenesis [46]. It would be worth following the rats over a longer life period to assess adult body weight and possible food intake with behavior or learning alterations. General public exposure is much weaker than the ICNIRP exposure limits evaluated in the present study [47]. Here, our goal was not to mimic environmental exposure but to question the regimentary threshold set to prevent the impact of RF-EMF on health.
# A Data-Driven Situational Awareness System for Enhanced Air Cargo Operations Emergency Control
## 3 ULD Remote Monitoring System
### Monitoring the Structural Health of the ULD  
The piezoelectric elements (\(\times\)3) provide an analog voltage. This is measured every 20 ms in 12-bit resolution into an exponential average of 80% for each element by the ULD microprocessor, except when reporting the data. When the ULD is powered on, a calibration procedure is executed and a calibration reference is created for each element, assuming the conditions cause relaxed values. After every measurement, the current value is set as the calibration difference and compared to the threshold for an alarm or not.
## 3 Conclusions and Future Work
Furthermore, there is growing interest in extending these approaches to new domains. In the case of sentiment analysis applied to tourism, the methodology is expected to be scaled to other tourist destinations, aiming to contribute to feasibility studies in sustainability and strategic planning. In parallel, emerging applications in business process intelligence are explored, an area in which models can add value by interpreting, monitoring, and optimizing business workflows based on linguistic data. From an architectural perspective, future research will consider the use of large language models, whose integration will enable the exploration of higher levels of generalization and inferential capabilities. Although the computational demands of these models have limited their use in previous studies, their progressive incorporation opens a field for comparing the effectiveness of various architectures and better understanding the model's adaptability to different linguistic and semantic contexts. Similarly, the use of ensemble techniques and graph neural networks is proposed to enhance the representation of entities and relationships, particularly in tasks such as link prediction in knowledge graphs and the extraction of complex relationships. Particularly in the field of knowledge graphs, designing better negative sampling strategies and reducing model time complexity by replacing attention mechanisms without sacrificing performance are considered priorities. Contextual information is also planned to be integrated through graph neural networks to improve the quality of inferences and the semantic contextualization of relationships. Moreover, in relation extraction tasks, a shift toward hybrid systems that combine architectures with large language models is anticipated. This integration will allow the injection of prior knowledge about the extracted entities, improving the assignment of attention weights and the identification of complex or implicit relationships. Finally, the design of defense mechanisms against adversarial attacks should be oriented toward solutions that are independent of the type of attack, incorporating them as an integral part of the models from their inception. Ensuring data integrity and the stability of predictions against external manipulation will be key to the trustworthiness and practical utility of systems in areas such as public opinion, mental health, justice, and decision-making. This work was partially supported by Xunta de Galicia grants ED481D 2024/014 and ED481B-2022-093, Spain. The authors declare no conflict of interest.
## 2 Materials and Methods
### Flight Data and BIT Records of a UAV  
#### 2.1.1 Flight Data  
The flight data of the UAV are a collection of time series data points from self-check before takeoff to landing and shutdown. The numerous sensors for devices are carried in the new generation of UAVs, while recording signals as flight data from these sensors frequently, which describe the health status in detail.  
The characteristics of flight data are as follows:  
* Large-scale. A single flight may record tens of thousands of data instances, that provide an ideal foundation for the data-driven fault diagnosis methods, also bringing a lack of computing memory.
* High-dimensional. Attained from the numerous sensors, the actual flight data of a UAV has hundreds of attributes, which may lead to the \"Curse of Dimensionality\".
* Multi-class. As a typical complex system, a UAV has many types of fault modes, requiring effective multi-class classification technology.
* Imbalanced. The amount of flight data in different classes would have such inevitable disparities in the actual operation that the diagnosis model training should pay attention to both the majority and minority classes.
* Noisy. The mission conditions of UAV are too complicated, such as the interruption of transmission or electromagnetic interference caused by temperature, to bring serious noise interference. Null and error values are found abundantly in the flight data; as a result of this heterogeneity, data might be missing and corrupt in processing, duplicating, format conversing and secondary storage.  
Figure 1: Flowchart of the proposed compound fault labeling and diagnosis method based on actual flight data and Built-In-Test (BIT) records of an Unmanned Aerial Vehicle (UAV).
# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 4 APSIA Conceptual Architecture and Building Blocks
### Data types  
Information systems may store and process a huge amount of data. However, the criticality of the data is not always the same. For instance, some processing activities may focus on publicly available data, others on financial data, and other personal or even sensitive data. This variation indicates the need to assign a different criticality levels to the aforementioned data types and treat personal and sensitive data, as data types that can clearly have a higher impact on the fundamental rights and freedoms of the individuals in case of data breaches [52]. To materialize this in the proposed method, the data that an organisation stores/processes, are classified in the following categories, following the classification proposed in [53].  
* Sensitive personal data (medical data, legal documents, etc.)
* Personal data (data which uniquely identify a person, such as IDs, Social Security Number (SSN), personal or marital status, etc.)
* Financial data (data related to financial transactions, accounting entries, etc.)
* Operational data (data generated during the execution of a service, log files, etc.)
* Other data (data that cannot be classified in any of the above categories, and belong to a lower criticality level)  
Thus, each data entry, which is part of a processing activity, falls into one these categories and the privacy scoring algorithm considers the criticality of the processed data. Table 4 provides the mapping between the data type and the corresponding quantitative value. Given the above, this property is reflected to the final score, which will be used by the decision maker in the privacy risk mitigation actions.  
\begin{