esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A comprehensive multiparameter model for the UWOC channel has been established by integrating the Beer-Lambert law and Monte Carlo phase screens into the split-step propagation framework, so the effects of absorption, scattering, and turbulence underwater can be evaluated simultaneously. In the modeling process, the subharmonic method and the strict sampling constraint method have been applied in combination to improve the modeling accuracy to 96.19% in line with the theoretical statistics.  
The underwater optical channel is impacted by the attenuation caused by absorption and scattering effects, and the fluctuation caused by the underwater turbulence. The light scintillation becomes severer with the decrease of the dissipation rate of the turbulence kinetic energy, the increase of the temperature gradient of water, and the increase of turbulence contributions from salinity than temperature. The extension of transmission distance will aggravate both the attenuation and the fluctuation of light.  
Furthermore, the BER performance is simulated for the UWOC system and it can be effectively compensated by increasing the transmitting light power when the turbulence strength is within a certain range. The power compensation method will become invalid when the turbulence is excessively strong. The power penalty of a 50 m UWOC channel is around 5.8 dBm from pure seawater to ocean water and around 1.0 dBm from weak turbulence to medium turbulence, with the BER threshold of \(10^{-6}\). The proposed model can help in analyzing the effects of real underwater channels and evaluating the performance of the UWOC systems.  
Conceptualization, D.D. and S.G.; Investigation, R.C. and M.Z.; Methodology, R.C.; Project administration, D.D.; Resources, M.Z. and Y.S.; Software, R.C.; Supervision, S.G.; Validation, M.Z.; Visualization, R.C.; Writing--original draft, R.C.; Writing--review & editing, Y.S. and S.G. All authors will be informed about each step of manuscript processing including submission, revision reminder, etc. via emails from our system or assigned Assistant Editor. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61875172), the Zhejiang Provincial Natural Science Foundation of China (LD19F050001), the Fundamental Research Funds for the Central Universities (2020 XZZX005-07), and the National Key Research and Development Program of China (2019 YFB2205202).  
Not applicable.  
The author acknowledges the valuable comments of the reviewers.  
The authors declare no conflict of interest.  
Figure 13: BER of the UWOC system under different absorption, scattering, and turbulence conditions.
# Path Planning Strategy for Vehicle Navigation Based on User Habits
## 2 Materials and Methods  
### Mining and Clustering of Users' Driving Habits  
#### 2.1.1 Establishment of Driving Style Space  
Driving style space should be analyzed before driver style clustering to ensure the rationality and reliability of the results of the LFCM. A driver can choose from a wide selection of varied details, generally, including time-saving [1; 2; 3], economic [26; 27] and comfort [4; 9]. For ease of discussion, we record the set of three indicators above as a style space. For most users, the style features are not limited to a single element of the style space, but a coexistence of multiple coupling indexes, whose index weight varies in each individual.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 4 A Prototype for a Remote ECG Service
### The Sensor: Amplifier and Filter  
The prototype uses the popular _Olimex EKG-EMG_ sensor board [25] (OLIMEX Ltd., Plovdiv, Bulgaria): its size is \(6\times 8\times 3\) cm, it is compatible with the Arduino pinning, and costs around 30$. There are significantly less expensive boards which are not Arduino compatible: for our prototype, such compatibility has the advantage of electrical, mechanical stability. The board is usually sold with ECG pads and wrist bands. It integrates a 3 rd order filter at 40 Hz and two high-pass filters to remove high frequencies and baseline drift.  
A single board acquires a non-diagnostic 3-leads ECG through a standard 3-poles jack. Board pinning allows stacking up to six of them for a diagnostic 12-leads ECG but this outside our scope.
## 3 Literature Review  
Figure 1 illustrates the key steps in the traditional approach to construction monitoring and planning. In this conventional approach, construction drawings are used to create a Building Information Modeling (BIM) model, which is then utilized for project planning and tracking of progress [7]. In this technique, drone images are captured from multiple sites and point clouds (from 3D scanning of the construction site). A 3D model is built using surveying photography techniques. This may be referred to as the \"drone model\". It is compared with the BIM model at various construction stages to monitor construction progress. The study emphasizes the importance of virtual and augmented reality in remotely monitoring construction processes using smart tools, including the Internet of Things and various virtual platforms. This ensures compliance with laws and design plans throughout the project lifecycle [8]. Understanding these challenges and effectively addressing them through modern technological tools play a crucial role in the success of construction projects, contributing to sustainable and safe project outcomes. This research sheds light on the practical application of virtual and augmented reality technologies in monitoring construction processes, emphasizing their role in ensuring compliance and enhancing project efficiency.  
The study discusses the use of drones in various stages of infrastructure development and maintenance related to construction projects, highlighting their efficiency in surveying, designing, building, and inspecting wide-ranging structures. The study focuses on the applications and potential limitations of using drones in these contexts.  
Figure 1: Stages of monitoring the development of the construction project.
## 3 Materials and Methods
### Deep Learning-Based Visual Detection and Tracking of Target Ship
#### 3.1.2 Target Ship Tracking  
Traditional motion-based tracking algorithms often use Kalman filters to predict the motion of the target. However, when the target exhibits nonlinear motion or encounters occlusion, long-term linear estimates may become highly inaccurate, leading to tracking failures. To address this problem, an Observation-Centric SORT (OC-SORT) [47] is used as the main tracking algorithm in this paper. Ship targets are tracked in real-time independently within the left and right images.  
OC-SORT introduces three modules, namely Observation-Centric Online Smoothing (OOS), Observation-Centric Momentum (OCM), and Observation-Centric Recovery (OCR) to cope with the errors caused by target occlusion and nonlinear motion. Among them, Observation-Centric Re-Update (ORU) is proposed to reduce the cumulative error when an object is untracked and re-associated after a certain period of time, e.g., when the last observation before untracking is denoted as \(z_{t_{1}}\), the observation that triggered the re-association is denoted as \(z_{t_{2}}\), and the virtual trajectory \(T_{virtual}\) is denoted as  
\[\hat{z}=T_{virtual}(z_{t_{1}},z_{t_{2}},t),t_{1}<t<t_{2} \tag{8}\]  
When the lost trajectory is re-associated along the virtual trajectory, OOS alternates between the prediction and update stages, backchecking the parameters of the Kalman filter back and forth, to prevent the accumulation of errors due to occlusion or missed detections. When constructing the association cost matrix, most multi-object tracking methods typically rely on two key points: motion features and appearance features. However, OC-SORT introduces OCM into the association cost during the tracking process, by incorporating the directional consistency of the trajectories into the association cost matrix. The association cost matrix can be represented as follows:  
\[C(\hat{X},Z)=C_{\text{IoU}}(\hat{X},Z)+\lambda C_{\text{\tiny{P}}}(\mathcal{ Z},Z) \tag{9}\]  
where \((\hat{X},Z)\) is the set of estimated states and observations for the tracked object, \(\lambda\) is the weight factor, \(\mathcal{Z}\) includes the observed trajectories of all existing tracks, \(C_{\text{IoU}}\) calculates the Intersection over Union (IOU), and \(C_{\text{\tiny{P}}}\) calculates the consistency of the orientation of the trajectory and the orientation of the historical and new observations of the trajectory.  
Finally, after completing the association stage based on the predictions from the Kalman filter, OC-SORT applies OCR techniques to attempt to correlate the observations of mismatched trajectories with mismatched observations. This strategy helps reduce the generation of new trajectories, thereby enhancing the overall accuracy of multi-object tracking.
# 5G NPN Performance Evaluation for I4.0 Environments
## 1 Introduction
This proposal forms a prototype architecture combining different technologies and access technologies that can benefit I4.0, with 5G as the key enabler. Another key aspect of this work is the integration of a TSN [15; 16; 17] as a functional component in the I4.0 environment, through the 5G network slicing paradigm. TSNs, as a specification, define strict time and priority requirements, conforming to the demands of the factory floor. 5G slicing, on the other hand, offers a multipoint, fast-path communication channel and, using the appropriate configuration on Radio Access Network (RAN) provision, the transmission of TSN-aware communication. Within the framework of this paper, a set of experimental results investigating the 5G network slicing capacity for bandwidth (downlink, uplink) and latency are presented, in order to ensure current system standards in terms of TSN facilitation. The experiments were performed in a real-world 5G NPN factory simulation network. Finally, AI can play a pivotal role in the seamless integration of these heterogeneous fields, being able to improve and optimize specific vertical domains in the manufacturing industry [18]. In the framework of Zero-Defect Manufacturing (ZDM), AI can be used to minimize error defects and detect challenging production anomalies, in order to optimize high-cost part manufacturing [19]. In the field of energy, production material efficiency and reducing CO\({}_{2}\) emissions are crucial factors. The use of AI can help to drastically reduce a production line's CO\({}_{2}\) emissions, as well as rendering it as environmentally circular as possible. Finally, for cybersecurity, it is crucial to assure protection of every industrial asset [20]. The use of data processing acceleration techniques (e.g., in-network computing and programmable ASICs) and novel communication paradigms (e.g., OPC UA) allows us to address cybersecurity at different levels, providing the basis for a secure I4.0 [21]. The remainder of this paper is organized as follows: Section 2 presents various domains in which I4.0 and 5G can be combined, and describes how this can be mutually beneficial. Section 3 presents an I4.0 architectural proposal based on the combination of different technologies, and details how the different aspects of each are leveraged. Section 4 describes a set of preliminary experimental tests considering 5G network slicing on a prototype 5G NPN factory floor, and outlines the advantages of 5G. Finally, Section 5 concludes the manuscript, and provides future directions for related research.
## 4 Evaluation Methodology
### Evaluation Method
#### 4.3.2 F1-Measure  
The F1-measure is an improved method for evaluating the performance of simple precision, or detection rate, including data classification, document classification, and classification detection. The true positive (TP), true negative (TN), false positive (FP), and false negative (FN) conditions used to obtain precision and recall values have some conceptual differences. When precision and recall values have been obtained, their harmonic mean is calculated by making the weight of each value the same [4]. If this value is high, the performance of the classifier is considered to be high. In this evaluation, the positive class is set to normal for performance evaluation. \(P\) is precision, and \(R\) is recall, which can be calculated using Formula (2a) with the F1-measure shown in Formula (2b).  
* _TP_: Normal data correctly classified as normal.
* _TN_: Anomalous data correctly classified as anomalous.
* _FP_: Normal data classified as anomalous.
* _FN_: Anomalous data classified as normal.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Attack Type** & **Description** \\ \hline DOS & Denial-of-service, e.g., SYN flood \\ R2L & Unauthorized access from a remote machine, e.g., password guessing \\ U2R & Unauthorized access to local superuser (root) privileges, e.g., various \\  & buffer overflow attacks \\ Probing & Surveillance and other probing, e.g., port scanning \\ \hline \hline \end{tabular}
\end{table}
Table 1: The attack type of KDDcup99 data set.  
\[\mathbf{P}=\frac{\mathbf{TP}}{\mathbf{TP}+\mathbf{FP}}\mathbf{,}\mathbf{R}=\frac{\mathbf{TP}}{\mathbf{TP}+\mathbf{ FN}} \tag{2a}\] \[\mathbf{F1}-\mathbf{measure}=\frac{2\times\mathbf{P}\times\mathbf{R}}{\mathbf{P}+\mathbf{R}} \tag{2b}\]  
A receiver operating characteristic (ROC) curve is a measurement method used in signal processing feature analysis and has been widely used as a performance evaluation method for binary classification problems, such as pattern recognition, data search, and classification [41]. The graph shown in Figure 5 can be obtained by plotting the true positive rate against the false positive rate. Performance can be regarded as high when the inflection point in the valley of the graph approaches 0 on the \(x\)-axis and 1 on the \(y\)-axis.  
Therefore, in this example, C shows the best performance, and A shows the worst performance. To obtain numerical values for analysis rather than visual analysis of the graph, the area under the ROC curve (AUC) is calculated from the ROC curve. ROC calculations were performed and graphs were drawn using the ROCR package of R [42].
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 3 Communication
### Coding Experiment
#### 3.1.3 Validation Test  
The operator interface on the host computer was utilized to input and configure the encoded information. After the acquisition process was finished, the encoded characters \"T\", \"J\", and \"U\" were decoded on the acquisition side, employing the described algorithm and sliding window FFT. The decoding outcomes are depicted in Figure 9.  
The experimental results demonstrate the favorable performance and feasibility of the TARF communication system utilizing frequencies of 146 Hz and 243 Hz. The amplitudes of the surface waves generated by acoustic signals of different frequencies exhibit significant variations, allowing for easy observation and distinction. This indicates that, by selecting an appropriate carrier frequency, efficient signal transmission can be achieved in the underwater environment, thereby providing an effective solution for water-air cross-medium communication.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 6 Evaluation and Discussion of the Results
### Scheduling Comparison with the Optimal  
In this subsection, we compare the pure algorithmic performance of the scheduling approaches, without considering network requirements such as sync packets. We compare the performance between two heuristics, _Light_ and _Global_, and the optimum solution as it is computed by solving the optimisation problem defined in Section 5. As we can observe from Figure 3(a), both heuristics achieve identical performance to the optimum in terms of data collection time for all node populations. Moreover, this performance is achieved with a very small computation time which does not exceed 1.7 ms for _Light_ and 9.9 ms for _Global_ (see Figure 3(b)). On the contrary, due to the increased range of binary values of some parameters, such as the number of slots, the solver may require multiple hours to find the optimal solution.  
We repeat the comparison using variable data sizes randomly assigned to the nodes in the range 200 bytes to 18 kB. This performance evaluation is depicted in Figure 3(c),d.  
Figure 4: Comparison with the optimal for a scenario with fixed data size (upper) and variable data size (lower).  
The results reveal that for the tested scenarios the two heuristics perform only up to 3% worse compared to the optimal solution. As it was expected _Global_ achieves a slightly better performance than _Light_ in terms of data collection time. The gap occurs as _Light_ is unable to assign consecutive node transmissions to different--and eventually more efficient--frames, since it schedules only the first transmission. Finally, similarly to the fixed data size scenario, the transmission schedules are calculated in a very short time.
# Secure Internal Data Markets
## 3 Security Issues of Internal Data Markets
### GDPR Responsibility  
In a global data market, all responsibilities regarding regulations, such as the General Data Protection Regulation [32], can be pushed to the respective users, i.e., data owners are responsible for the data that they provide to not be in violation of any of these regulations. The data market only provides the brokering and, sometimes, the transfer of (encrypted) data. Internal markets, on the other hand, have to take care of these regulations: as internal markets, they are typically owned by the same institution that holds the data, thus making it difficult to shift responsibilities to external entities. Furthermore, as internal markets are typically not the focus of data providers inside the company but rather a side issue, and thus, dedicated privacy experts are not available at the owner's side, they will expect the market to take on these issues.
## 1 Introduction
[22] proposed an adaptive controller where the desired region is time varying and the region tracking control problem was solved instead of the region-reaching control problem. However, autonomous underwater vehicles (AUVs) consistently converge on the boundary of the region, which will cause AUV chattering when disturbances exist. Nonlinear \(H\infty\) and region-based control schemes in [23] are used to control an underwater vehicle to track a moving region. However, as long as the AUVs are inside the desired region, the control input is turned off; when the disturbances pull the vehicles out, the control input is turned on, which will cause input chattering. In [24], an adaptive region boundary-based concept was presented for an AUV, in which the controller was designed to allow the convergence of the vehicle to the boundary or a motionless region surface regardless of its initial position. In [25], the authors proposed a region tracking controller that guarantees an AUV to move within a region considering input delay. In the present study, we propose a non-strict trajectory tracking scheme for a fully actuated USV to track a region trajectory. Based on a traditional RC method, a regression matrix is used to handle parameter uncertainties. Moreover, a switching gain control term based on the exponential function is proposed to make the USV's trajectory converge into the desired moving region rather than the boundary of the moving region, thereby reducing system's chattering, which is good for energy saving. The USV can follow a straight-line region trajectory, as well as a curved region trajectory, under the proposed controller. The traditional RC is used for a comparative study to illustrate further the performance of the proposed non-strict trajectory tracking control (NTTC). The remainder of the paper is organized as follows. Section 2 presents the kinematics and dynamics model of the USV. Section 3 shows the proposed control strategy. In Section 4, simulation results are presented. Concluding remarks are given in Section 5.
## 3 Preliminaries
### Cryptographic Primitives
#### 3.4.3 Key Derivation Functions (KDFs)  
A key derivation function (KDF) is used to derive one or more cryptographically secure secret keys from a master secret value, such as a pre-shared key, a password, or a Diffie-Hellman exchanged value, often in conjunction with a salt [8]. Our protocol uses a KDF based on a secure hash function (e.g., PBKDF2 as defined in RFC 2898 [8], using HMAC-SHA-256 as the underlying pseudorandom function) for deriving keys like \(K_{O}\) and \(KS\).  
* \(K_{derived}=\) KDF(MasterKey, Salt, ContextInfo, OutputLength) KDFs are designed to be computationally intensive to slow down brute-force attacks on the input master secret (especially if it is a low-entropy password). They should produce outputs that are pseudorandom and indistinguishable from truly random keys.
## 2 Quasi-Static Experiment
### Test Setup and Loading Program  
A quasi-static test of horizontal cyclic loads under constant axial pressure is conducted. In the experiment, the axial compression ratio is set to 0.2. The axial compression ratio is calculated by Equation (1). Figure 3 shows the test setup of the specimens. The vertical load is applied to the specimens by a 500 kN hydraulic jack, and the horizontal cyclic load is applied by a 500 kN MTS actuator. According to the Code for Seismic Test of Buildings [45], the horizontal load is controlled by displacement. Before the specimen yields, the amplitude of the applied horizontal cyclic displacement is gradually increased, such as 1 mm, 2 mm, 3 mm, 3.5 mm, 4 mm, 4.5 mm, and 5 mm. The specimen yields when the displacement reaches 5 mm. After that, the amplitude of the applied horizontal cyclic displacement is increased by multiples of the yield displacement, such as 5 mm, 10 mm, 15 mm, 20 mm, 25 mm, 30 mm, and 35 mm, until the longitudinal rebars at the bottom edge of the wall are broken, marking the failure of the specimen. The loading protocol is shown in Figure 4.  
During the test, the horizontal force and horizontal displacement at the loading point are measured by a force sensor and displacement sensor in the MTS actuator. The slipping movement of the connector in the test is measured by two displacement transducers, i.e., linear variable differential transformers (LVDTs), placed on both left and right sides of the connector, as shown in Figure 5. In addition,  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen** & **Wall Strength** & **Wall Limb** & **Ground Beam** & **Elastic Modulus** \\
**(MPa)** & **Strength (MPa)** & **Strength (MPa)** & **(10\({}^{4}\) MPa)** \\ \hline RC (cast-in-place reinforced concrete) & 32.7 & 32.7 & 32.7 & 3.07 \\ \hline PC (precast reinforced concrete) & 32.7 & 38.5 & 32.7 & 3.07 \\ \hline \end{tabular}
\end{table}
Table 1: Material properties of concrete.  
Figure 3: Test setup.  
the strains of the connector are measured by strain gauges placed on the bottom side of the bolt holes, and the details are shown in Figure 6.  
\[n=\frac{N}{f_{c}A} \tag{1}\]  
where \(n\) is the axial compression ratio; \(N\) is the vertical axial load; \(f_{c}\) is the design value of the axial compressive strength of concrete; and \(A\) is the concrete compressive area.  
Figure 4: Loading protocol.  
Figure 5: Arrangement of displacement transducers.