esa-sceva/satcom-chunk-collection huggingface dataset:
# Legal, Economic and Cyber Security Framework Considerations for Drone Usage
## 6 Discussions
### Main Considerations on Correlation between Cyber-Attack Prevention Measures and Liability  
Even if there are certain general legal rules around the safety of devices/products (including some specific for drones), these are not specific enough given the current threat landscape and the high impact of any incident affecting drones. Furthermore, they do not provide a continuous analysis of cyber risks and risk-addressing mechanisms. [33] This should be applicable for any applications or amendments introduced into the drone software/hardware by users.  
Thus, a first step in creating trust in drone usage for users is having a process in place for prevention.  
In addition, from a technical perspective, safety measures can be contemplated in order to ensure safe landing of the drone in case of a cyber-attack (e.g., by creating a separate read only area for safety source code), especially in case of a distributed denial of services [34].  
Furthermore, in terms of navigation of drones, practical training, especially for actions in the case of cyber-attacks and preventive cyber-security measures, can be contemplated to reduce cyber-risk and costs associated with damages resulting from cyber-attacks.  
In addition, specific liability legal provisions should be implemented for specific situations, including the above, in order to clarify the entities that should be responsible for certain parts of the drone functionalities, security and maintenance. This ensures that the relevant entities are aware of the steps they should take and responsibility they have, giving users the confidence that any damages can be easily addressed and resolved.  
The above measures increase the complexity of actions to be taken by all stakeholders (from the hardware producers, software producers, distributors, authorities, users) involved in the drone usage process. Furthermore, these include additional costs and time for most of the stakeholders. When establishing the level of details and frequency of these measures, the costs associated with them and the time impact on the drone usage have to be considered. Nevertheless, the benefits of preventing damages and litigation have to be placed into balance. In this respect, a cost-benefit analysis may be the foundation for the impact assessment of the above preventive measures [35].  
In cases where the attackers are state players or drones are used for state-targeted cyber-attacks, additional legislation comes into play in terms of liability and can also impact the civil liability aspects. These aspects are not the subject of this article, but it is worth noting that, in certain cases, the attribution of a cyber-attack can have an impact on the establishment of responsible entity for incurred economic damages.
# Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator
## 2 Dynamic Model and New Cross-Coupled Control Law
### Dynamic Model of Robotic Manipulator  
The dynamic model of an n-DOF serial planar robotic manipulator with revolute joints can be expressed as [20]:  
\[M\left(q\right)\ddot{q}\left(t\right)+C\left(q,\dot{q}\right)\dot{q}\left(t \right)+G\left(q\right)+F\left(\dot{q}\right)=\tau\left(t\right) \tag{1}\]  
where \(q\left(t\right),\ \dot{q}\left(t\right),\) and \(\ddot{q}\left(t\right)\) are \(n\times 1\) joint position, velocity and acceleration vectors, defined as functions of time; \(M\left(q\right)\) is the symmetric positive-definite \(n\times n\) inertia matrix; \(C\left(q,\dot{q}\right)\) is an \(n\times n\) matrix of the Coriolis and centrifugal forces; \(G\left(q\right)\) is an \(n\times 1\) vector of gravity terms; \(F\left(\dot{q}\right)\) is an \(n\times 1\) vector of friction forces, and \(\tau\left(t\right)\) is an \(n\times 1\) vector of joint torques/forces.  
In this paper, some properties of a rigid nonlinear robotic manipulator [20] associated with Equation (1) is used in the stability analysis and are described as follows.  
1. The inertia matrix \(M\left(q\right)\) is symmetric positive definite.  
* The matrix \(\frac{1}{2}\dot{M}\left(q\right)-C\left(q,\dot{q}\right)\) is a skew symmetric matrix, i.e., \[\left\{\begin{array}{l}x^{T}\left(\dot{M}\left(q\right)-2C\left(q,\dot{q} \right)\right)x=0\\ \dot{M}\left(q\right)=C\left(q,\dot{q}\right)+C^{T}\left(q,\dot{q}\right)\end{array}\right.\]
* The matrix \(C\left(q,\dot{q}\right)\) satisfies \[\left\{\begin{array}{l}C\left(q,x\right)y=C\left(q,y\right)x\\ C\left(q,x+z\right)y=C(q,x)y+C(q,z)y\end{array}\right.\] where \(x,y,\) and \(z\) are velocity vectors.
* \(M\left(q\right)\), \(C\left(q,\dot{q}_{d}\right)\), \(G\left(q\right)\), and \(F\left(\dot{q}\right)\) are bounded.
## 6 Ethical and Privacy Issues
### Accountability in Decision Making  
As chatbots become more autonomous, determining accountability for decisions made by these systems, especially erroneous ones that could lead to harm, becomes challenging. This is particularly critical in fields like healthcare, where decisions have significant consequences, as evidenced by the concerns raised in the evaluation of ChatGPT's ability to predict drug interactions [1].  
Recommendations  
The following recommendations are proposed to address accountability in decision-making by AI chatbots:  
* Establish clear guidelines on the accountability of chatbot actions, particularly in cases where they operate semi-autonomously. For instance, some red flags were raised in the analysis of ChatGPT's performance in medical licensing examinations, as reported in [54].
* Ensure that there is always a human in the loop in critical decision-making processes, especially in sensitive, mission-critical applications and legal scenarios, to supervise and override chatbot decisions when necessary.
* Implement robust monitoring and auditing systems to track chatbot decisions and identify potential errors or biases, such as the efforts reported in [55].  
Addressing these ethical and privacy issues is crucial for the responsible development and deployment of AI chatbots. By implementing robust data protection measures,ensuring unbiased and fair interactions, maintaining transparency in user consent, and establishing clear accountability guidelines, we can harness the potential of these technologies while mitigating the risks associated with their use. Furthermore, considering the technical limitations and application challenges, such as data security, cultural adaptability, and language-specific performance, is essential for the effective and ethical integration of AI chatbots across various domains.
## 3 Numerical Results  
### Displacement Distribution Characteristics of the Backfill Roof  
This section analyzes the displacement distribution characteristics of the backfill roof with the basket weave drift layout in stable and unstable states. The numerical results presented are based on the case with a drift size of 3.5 m \(\times\) 3.0 m.  
Figure 4 illustrates the vertical displacement distribution of the backfill roof at L2 and L3 (monitoring planes located at \(z=6\) m and 3 m, respectively) after the secondary drifts at L3 are excavated. The cohesion of the backfill roof was assigned a value of 425 kPa, under which no plastic failure occurs. The vertical displacements vary smoothly across different locations of the roof and exhibit a downward-concave distribution pattern. In Figure 4a, certain areas of the roof at L3 exhibit higher vertical displacement, which leads to a columnar distribution pattern. The maximum displacement of 2.9 cm takes place near the intersection of the secondary drifts of L3 and L2. In Figure 4b, the vertical displacement of the roof at L2 shows a strip-like distribution parallel to the \(y\)-axis, with a maximum value of 5.4 cm located in the central drift of L2. These are similar to previous observations that the vertical displacement of the backfill roof exhibits a wave-like distribution along the direction of drift width [25; 27]. However, they did not give further explanations of the findings.  
These two displacement distribution patterns arise from the superposition of vertical displacements of the roofs at L2 and L3 with the basket weave drift layout. As shown in Figure 2b, the drifts at L2 are parallel to the \(y\)-axis and the vertical displacement of the roof exhibits a strip-like distribution parallel to the \(y\)-axis. Similarly, the drifts at L3 are oriented to the \(x\)-axis, and the vertical displacement of the roof should exhibit a strip-like distribution parallel to the \(x\)-axis. However, due to the superposition of vertical displacements of the roof at L2, the final distribution pattern appears columnar. This indicates that the basket weave drift layout can disperse deformations of the roof exposed in a single void drift and transform the vertical displacement distribution from a strip-like pattern to a columnar pattern.  
Figure 5 presents the vertical displacement distribution of the yielded backfill roof with cohesion of 200 kPa. It is evident that the roof displacement still exhibits a columnar distribution; however, the displacements in the failure zones are significantly larger than those in adjacent areas, with a maximum displacement of 55 cm. This suggests that, for the basket weave drift layout, the superposition of vertical displacements makes the roof near the intersection of secondary drifts at adjacent layers more susceptible to deformation and failure. In other words, the roof stability for the basket weave drift layout approaches that of an intact roof, which is consistent with the findings reported by [PERSON] et al. [15].
# Sensitivity Analysis of P-Wave Polarization Direction and Velocity Gradient Inversion
###### Abstract
Therefore, it is essential to investigate the reasons for the poor performance, as well as to develop methods for improving the inversion results. Currently, there is no paper that specifically investigates the sensitivity of P-wave polarization direction data to relative velocity gradients. However, this analysis is crucial for advancing seismic inversion techniques and enhancing the accuracy of seismic imaging and interpretation. We will delve into the sensitivity of P-wave polarization direction data to relative velocity gradients. This paper analyzes the sensitivity of P-wave polarization direction data to the relative velocity gradient and performs the inversion of the relative velocity gradient based on this sensitivity characteristic. Analysis shows that the sensitivity of polarization direction is related to the spatial position of the model parameter. The further the parameter position is from sensor, the lower the sensitivity is. Based on this sensitivity characteristic, we introduced an adjustment factor into the correction scheme for relative velocity gradients to balance the correction amplitude for the relative velocity gradient values at different locations, thereby improving the inversion accuracy. Through numerical experiments, the accuracy of our analysis and the effectiveness of the proposed method are validated.
## 4 Experiment and Results
### Binary Classification
#### 4.1.1 Model Training and Selection
To develop an optimal DNN model for our binary classification task, we utilized Keras with TensorFlow, leveraging the high-level API of Keras for building and training neural networks while benefiting from TensorFlow's powerful backend capabilities [44]. The DNN architecture consisted of three hidden layers with 64, 32, and 16 neurons, respectively, each followed by a dropout layer to prevent overfitting [45]. Rectified Linear Unit (ReLU) [46; 47] activation functions were used in the hidden layers, while a sigmoid [46] activation function was applied in the output layer to facilitate binary classification. We compiled the model using the Adaptive moment estimation (Adam) [48] optimizer with binary cross-entropy loss function [49]. After identifying the best hyperparameters through tuning, we retrained the model using the optimal settings. The training was conducted over ten epochs (the number of training iterations) with a validation split to monitor performance.
## 4 PCI Prediction Model Based on the ISTD-DisNet Outputs  
### PCI and Distress Pixel Density Ratio  
(1) PCI assessment  
Currently, road maintenance departments often refer to the internationally recognized PCI to develop maintenance programs. In the pavement condition evaluation task based on manual visual discrimination, Equation (10) is usually used to calculate the comprehensive pavement damage rate (DR) of each evaluation unit (100 m or 1000 m) of the route, and Equation (11) is used to calculate the PCI [6; 49].  
\[DR=100\times\sum\limits_{i=1}^{i_{0}}\frac{w_{i}A_{i}}{A} \tag{10}\]  
\[PCI=100-a_{0}\times DR^{a_{1}} \tag{11}\]  
where \(DR\) is the comprehensive damage rate of the pavement, which is the sum of the area of the various distress types and the percentage (%) of the pavement survey area; \(PCI\in[0,100]\); \(i\) is the type of distress; \(i_{0}\) is the total number of types of distress; \(A_{i}\) is the area damaged by pavement distress in category \(i\) (m\({}^{2}\)); \(A\) is the area of the surveyed pavement (the product of the surveyed length and width of the pavement, m\({}^{2}\)); and \(w_{i}\) is the weighting of the pavement damage of the pavement distress in category \(i\).  
(2) Calculation of the distress \(PDR\)  
The manual visual discrimination process has the problems of a long detection period, high cost, and subjectivity [4]. Therefore, in the following, we describe how we calculated the distress \(PDR\) of each evaluation unit based on the output of the ISTD-DisNet model. We then describe how we investigated the correlation between the \(PDR\) and PCI and finally developed a new model for calculating the PCI. It should be noted that, due to the large differences in the background and distress type appearance of asphalt and cement concrete pavements, we analyzed and constructed the PCI calculation model for the two pavement types separately. If we assume that the examined line contains \(n\) 100 m evaluation units, then the optimal weighted model of ISTD-DisNet was used to extract all the distressed pixels in the image for each evaluation unit, and the \(PDR\) of the distressed pixels in that evaluation unit was calculated according to Equation (12):  
\[PDR_{i}=\frac{\sum\limits_{j=1}^{50}NP_{j}}{A_{j}\times 50} \tag{12}\]  
Figure 10: The qualitative experimental results of the generalization analysis. The red box is the focus area.  
where \(i\in[1,n]\); \(NP_{j}\) is the total number of distressed pixels of image \(j\) in evaluation unit \(i\); and \(A_{j}\) is the total number of pixels of image \(j\). The ortho-corrected image resolution is \(A_{j}=3517\times 2193\), which corresponds to an actual size of 3.2 m \(\times\) 2.0 m.
# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 4 Experimental Results  
### Influence of Threshold on AE Entropy  
The elastic wave in the metal plate as a result of PLB was acquired by six channels (refer to Figure 5 for the experimental setup). Each channel captured the elastic wave as a waveform with a different threshold than the others. The AE entropy and the traditional AE features for the same waveform acquired with different thresholds are shown in Figure 12. To understand the influence of threshold, a polynomial trend-line with an order of 4 has been added to all the graphs in Figure 12. It is evident from the graphs (Figure 12a,d) of this figure that as the acquisition threshold increases, traditional features (such as rise-time, duration, count and energy) extracted from the same waveform decrease. This is because the traditional feature extraction process is heavily dependent on the acquisition threshold. It is also obvious from Figure 12e,f that the peak amplitude and AE entropy do not show any variance with the acquisition threshold and are independent of it.  
During AE monitoring, multiple elastic waves can be generated in a small timeframe from the activation of many damage sources. As a result, a waveform can contain information regarding many elastic waves (many damage sources) [20]. With a small threshold, traditional AE features are efficient in extracting the collective information in these waveforms. However, a higher threshold can compromise the feature extraction process and can bring difficulty in AE monitoring. Discrete voltage values collectively define the nature of the waveform; hence, its distribution is very crucial.  
Figure 11: Experimental setup for the fatigue test.  
Figure 10: Experimental setup for the tensile test.  
Although the peak amplitude feature is independent of threshold, it does not take into account the collective information from the discrete voltage values. Therefore, it is not efficient in identifying the nature of the waveform. On the other hand, AE entropy is independent of acquisition threshold and its computation takes into account every discrete voltage value in a complete waveform, including the ones that are below the acquisition threshold. As a result, the collective information can be easily interpreted with AE entropy.
# A Unified Database Solution to Process BIM and GIS Data
## 2 Materials and Methods
The search for more optimal solutions is still needed. Data exchange between GIS and BIM systems is becoming more advanced and easier to use, but the level of actual integration of both solutions can be considered as low. Solving the problem by data conversion may be sufficient only for some applications. Therefore, the authors undertook research to find solutions that would allow a greater integration of both systems. Among the many potential options, the possibility of BIM and GIS applications in a joint database was analyzed to an extent not previously present in the literature. However, it should be noted that the idea of using the database environment as an intermediary layer between BIM and specialized information systems appears in various contexts and applications, e.g., in [45]). After analyzing the literature and performing a series of analyses of the available BIM and GIS models and experiments involving data combinations, the concept of BIM and GIS integration was proposed, involving the use of one integrated spatial database but without the need to create a new data schema and only through the appropriate use of existing IFC and CityGML schemas. The proposed integration is released primarily at the technological level. The authors focused on finding a solution that facilitates the operation of BIM and GIS data in a uniform environment. Integration in this context means providing easier than before procedures for accessing BIM data from the GIS software level and vice versa. Moreover, guidelines on how to transform a selected data element from IFC to the corresponding elements in CityGML were developed. The guidelines cover a procedure for geometrically transforming IFC data into a GIS-readable form, including a procedure for changing the local coordinate systems used in BIM-GIS. A full description of these issues, however, is too voluminous and goes beyond the possibility of presentation in one article. To verify the proposed concept, the following procedure was adopted:  
1. [leftmargin=*]
2. Elaboration of a relational spatial database project with a structure based on the IFC and CityGML data models (two schemas implemented in a single database). Creation and testing of automated procedures for importing IFC data into the relational database. Experimental transfer of six IFC models into the created relational database. Evaluation of the adopted procedure. Improvement of the procedure. Checking access to the created database from BIM and GIS software. In the experimental works, BIM models were used, derived from the management systems of the building technical documentation used by the institutions of public administrations and commercial companies in Sweden. The models were selected in such a way as to represent different types of objects and the different levels of complexity of the model.
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 3 Materials and Methods
### Firmware
If isGunshot is True and isPrevGunshot is False, then the program tries to connect with the central server with TCP/IP protocol using socket [58] having a timeout of 5 s. In Python, a socket is similar to a communication endpoint that allows to send and receive data over a TCP/IP network, which is a set of rules for transmitting data between devices on the Internet or a local network. Once the IP address and port number of the target computer are specified, the socket can be used to establish a connection and exchange data. After connecting with the server, the device sends a data string containing the serial number of the device and the current date and time. The program reads the Bluetooth's media access control (MAC) address [52] of the Jetson Nano and it is used as the serial number of the device. Then, isPrevGunshot flag is updated with the isGunshot flag value and the process repeats. Server for Accessing Recorded Sounds: To access and play the recorded gunshot sounds for post-crime analysis, a Hypertext Transfer Protocol (HTTP) server [59] runs in the device at port 8000 having the working directory as the rec_gs folder, where the gunshot sounds are saved. Thus, these files can be accessed and played by the user's smartphone using the local Internet Protocol (IP) of the device and the port number as long as the smartphone and the device are connected to the same Wi-Fi network. The smartphone obtains the local IP of the device when its Wi-Fi is configured.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## 2 Accurate Modeling of SINR in the Presence of RFI
### Verification of the SINR Expressions in the Presence of RFI
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point** & **Integration Time/s** & **Calculated Results/km** & **Evaluated Results/km** & **Range Drift/m** \\ \hline A & 25 & 6.20 & 6.25 & 179 \\ \(50\) & 25.00 & 24.45 & 679 \\ D & 25 & 6.28 & 6.25 & 170 \\ \(50\) & 25.5 & 24.4 & 668 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Length and drift of the interference zone in range-direction for different points and integration time.