esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 2 Materials and Methods
### Feature Extraction Layer
Common acquisition functions include expected improvement (EI), probability of improvement (POI), probability improvement (PI), and upper confidence bound (UCB), among others. In this study, to reduce time and hardware costs, we chose the upper confidence bound (UCB) as the acquisition function. The UCB algorithm directly compares the maximum value of the confidence interval, featuring simplicity and efficiency. Its calculation formula is as follows:  
\[UCB(i)=u(i)+\beta^{1/2}\sigma(i) \tag{6}\]  
In the equation: u(i) represents the mean function; \(\sigma\)(i) represents the covariance function; and \(\beta\) is a hyperparameter that controls the balance between exploration and exploitation. The UCB acquisition function selects the next sampling point based on a trade-off between the mean function (exploitation) and the covariance function (exploration). By adjusting the value of \(\beta\), we can control the exploration-exploitation balance according to the specific problem requirements. A higher value of \(\beta\) will prioritize exploration, while a lower value will prioritize exploitation. Select the F1-score as the performance metric for the Gaussian regression process, the RBF kernel [31] function as the kernel function of the Gaussian process, and set the hyperparameters of the kernel function. Then, specify a noise item variance to consider the observation errors in the target function. Randomly select several groups of parameters from the search space and train an entity recognition model using these parameters to obtain the corresponding target function values. Use these parameters and their corresponding function values as the training dataset to learn the hyperparameters of the kernel function and the noise item variance. Then, for a given model parameter, use the trained Gaussian process model to predict the mean and variance of the target function based on the prediction results, and select the most promising parameter combination for evaluation until the predefined stopping condition is reached.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 2 Synchronization Issues in LoRa
### Insights on Strategies Used to Synchronize LoRa Signals
#### 2.3.1 Structure of the Synchronization Signal  
The signal transmitted by LoRa node starts with a preamble composed of \(N_{p}\) up-chirps, which are exploited to detect the presence of a LoRa packet and to perform the time and frequency synchronization. \(N_{sw}=2\) special modulated symbols known as synchronization word \"sync word\" are used to verify the accurate synchronization of the received frames (it is used also as a network identifier). The synchronization sequence end by two and a quarter down-chirps, known as the SFD, which help the time and frequency synchronizations [19].
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
## 4 Numeric Examples  
In this section, we provide a series of examples to evaluate the performance of CM DFRC waveform designed by manifold-ADMM. In simulations, the uniform linear array (ULA) incorporates \(N=10\) elements with a half-wavelength interelement interval, and each transmitting pulse has \(L=20\) samples. The range of angles is \((-90^{\circ},90^{\circ})\) with spacing interval \(1^{\circ}\), and the weight for the \(k\)-th angle has \(w_{k}=1,k=1,2,\ldots,K\). For convenience, we choose the unit-power QPSK alphabet as the constellation for communication users, i.e., the power of each entry in the symbol matrix \(\mathbf{S}\) is 1. Firstly, we will discuss the radar waveform design and analysis for beampattern matching. Next, we will design the DFRC waveform for communication performance. Finally, we will analyze the ambiguity function characteristic of the DRFC waveform.
## 3 Kinematic Analysis
### Kinematic Model of Prototype  
The establishment of the coordinate system provides a spatial concept for the position of the prototype, and the next step is to model the kinematics of the prototype to represent the changes in displacement, angular velocity, etc., of the prototype at any given moment by establishing a mathematical model of the physical laws of motion. Before modeling the kinematics, the following assumptions are made:  
1. The center of mass of the MSES virtual prototype is located at the center point of the vehicle, the driving surface is a hard surface, \(O_{1}\), and the shear force between the track and the ground is related to the shear displacement;
2. There is no relative sliding of the tracks against the drive wheels and no deformation of the tracks;
3. There is no lateral sliding of the prototype during steering;
4. When the prototype is steered, the coefficient of steering resistance is the same as the coefficient of resistance for straight-line driving;
5. The ground pressure of the prototype to the ground is linearly distributed.  
Based on the above assumptions, the motion of the prototype in space is decomposed into the motion on the planar coordinate system XOY, as shown in Figure 9b, where \(v_{L}\) and \(v_{R}\) are the linear velocities of the left and right tracks, respectively, which are positive when they are in the same direction as the prototype's center-of-mass velocity \(v_{c}\) and negative when they are in the opposite direction, and \(\beta\) is the angle of \(v_{c}\) with the positive direction of the \(X\)-axis of the coordinate system.  
\[\left\{\begin{array}{l}x=\frac{v_{R}+v_{L}}{2}\cdot v_{c}\cdot\cos\beta\\ y=\frac{v_{R}+v_{L}}{2}\cdot v_{c}\sin\beta\\ \beta=\frac{v_{R}-v_{L}}{B}\end{array}\right. \tag{24}\]  
It can be seen that the prototype realizes its traveling path by controlling the speeds of the left and right tracks. When \(v_{R}=v_{L}\), the prototype travels in a straight line; when \(v_{R}>v_{L}\), the prototype travels to the right; when \(v_{R}<v_{L}\), the prototype travels to the left; and when \(v_{R}=-v_{L}\), the prototype performs a turn in the same place.  
Integrating Equation (24), the coordinates of the center of mass position during planar motion of the prototype are obtained:  
\[\left\{\begin{array}{l}x=\int\limits_{0}^{t}\dot{x}dt=\frac{1}{2}\int \limits_{0}^{t}(v_{R}+v_{L})v_{c}\cdot\cos\beta(t)dt\\ y=\int\limits_{0}^{t}\dot{y}dt=\frac{1}{2}\int\limits_{0}^{t}(v_{R}+v_{L})v_{c }\cdot\sin\beta(t)dt\\ \beta=\int\limits_{0}^{t}\dot{y}dt=\frac{1}{B}\int\limits_{0}^{t}(v_{R}-v_{L})dt \end{array}\right. \tag{25}\]
## 2 Materials and Methods
### XUV Optimization Studies
#### 2.2.1 Laser Beam Diameter and Focus Position  
For the investigations on the laser aperture and focus position, we installed a chevron-type multichannel plate (MCP) detector in front of the spectral imaging XUV CCD camera. The 1\" in diameter MCP detector had a 1 mm in width entry slit and was installed on a linear motorized stage, so that the whole spectrum supported by the grating is recorded during one travel of the stage (\(\sim\)5 min duration) with an adequate spectral resolution \(\Delta\lambda/\lambda\sim\) 0.02.  
In Figure 2, some indicative results from this part of the study are presented. In Figure 2a, the dependence of the recorded XUV spectrum on the laser beam diameter and corresponding energies is shown. Here, the laser beam was truncated by an iris prior to its focusing, thus affecting the focal area and, therefore, the HHG. It can be clearly seen that an iris diameter of 7 mm provides the most intense harmonics for the spectral region of interest. This is a known effect due to geometrical phase-matching conditions [37]. Thus, we adopted this condition, which corresponds to an energy of 1 mJ, for all of our later studies. Even though these data were obtained for Ar gas at 60 mTorr, similar qualitative behavior was observed for other pressures and gases.  
In Figure 2b-e, we present the HHG spectra obtained after varying the laser focus position with respect to the exit pinhole of the semi-infinite gas cell for various Ar gas pressures. For this, the focusing lens was mounted on a linear micrometric stage variable along the laser beam propagation axis. The location of the laser focus in combination with the gas pressure is a quite sensitive combination of parameters, as it substantially affects the phase matching conditions, and thus the efficiency of HHG. It is evident that, by focusing the laser beam after the exit pinhole and for relatively high pressures, an increase of the HHG efficiency was achieved for the spectral region of interest here. Based on our overall detailed study, it was decided that the best location for the laser focus was 1 mm after the pinhole, and this condition was adopted for all of our later studies.
## 2 Materials and Methods
### Methylene Blue Adsorption Experiments  
20 mg of the resulting materials and 20 mL of a 1 ppm methylene blue (MB) solution were placed in a 50 mL conical tube. The pH was adjusted to 7 \(\pm\) 0.002 in an Orion StarA2110 potentiometer using 0.1 M NaOH or 0.1 M HNO\({}_{3}\). Samples were incubatedat 25 \({}^{\circ}\)C, and the pH was measured over periods of 12 hours. Once the equilibrium was reached (48 h), the remaining MB concentration was determined by UV-Vis spectrometry using a wavelength of 664 nm.  
The amount of MB adsorbed (\(C_{e}\)) and the adsorption capacity (\(q_{e}\)) of the dye were calculated by mass balance and Equation (1), respectively, to obtain the adsorption isotherms. Finally, the isotherms were fitted to the [PERSON] and [PERSON] models using the equations (Equations (2) and (3)).  
\[q_{e}=\frac{(C_{o}-C_{e})V}{m} \tag{1}\]  
\[q_{e}=\frac{q_{max}k_{L}C_{e}}{1+k_{L}C_{e}} \tag{2}\]  
\[q_{e}=k_{F}C_{e}^{1/n} \tag{3}\]  
\(q_{e}\) = Adsorption capacity at equilibrium, mg\(\cdot\)g\({}^{-1}\)  
\(C_{o}\) = Initial concentration of the solution, mg\(\cdot\)L\({}^{-1}\)  
\(C_{e}\) = Equilibrium concentration of the solution, mg\(\cdot\)L\({}^{-1}\)  
\(q_{max}\) = Maximal adsorption capacity, mg\(\cdot\)g\({}^{-1}\)  
\(k_{L}\) = Adsorption energy, L\(\cdot\)mg\({}^{-1}\)  
\(k_{F}\) = Adsorption capacity index, mg\(\cdot\)g\({}^{-1}\) (L\(\cdot\)mg\({}^{-1}\))\({}^{1/n}\)  
\(n\) = Ratio of adsorption intensity
###### Abstract  
Visibility is a major factor affecting the safety and efficiency of aviation operations. As instrumental visibility measurements are the basis for calculating runway visual range, measurement accuracy is crucial. In this study, we collected instrumental visibility data (V\({}_{\rm I}\)) from two sets of Vaisala transmissions (referred to as LT\({}_{\rm OS}\) and LT\({}_{\rm 23}\)) and one set of forward-scatter meters (referred to as FD) installed at Xiamen Airport. We also considered manually observed visibility (V\({}_{\rm M}\)) and relative humidity (RH) records of automatic weather stations for 2015 to 2020. Taking the V\({}_{\rm M}\) data as the benchmark, we comprehensively evaluated the difference between the V\({}_{\rm I}\) of each instrument and V\({}_{\rm M}\) as well as the influence of RH on such deviations based on the deviation (\(\Delta\)V), relative deviation (\(\Delta\)V\({}_{\rm Mdi}\)), root mean square error (RMSE), relative-RMSE, mean value, and correlation analysis method. Our results showed that: (1) among the three sets of visibility meters, the V\({}_{\rm I}\) values of LT\({}_{\rm OS}\) were the closest to the V\({}_{\rm M}\) values under different V\({}_{\rm M}\) levels and at high RH. Deviations in the FD measurements were greater than those of the two LT sets under low-visibility conditions that significantly affect aviation operations (V\({}_{\rm M}\) < 800 m). (2) In general, the V\({}_{\rm I}\) values were lower than the V\({}_{\rm M}\) values, and the larger the V\({}_{\rm M}\) value, the greater the deviation. (3) Extremely large \(\Delta\)V and \(\Delta\)V\({}_{\rm Mdi}\) values appeared in spring when the visibility level was rapidly increasing or decreasing. (4) The FD results were greatly affected by RH, with higher proportions of large \(\Delta\)V and large \(\Delta\)V\({}_{\rm Mdi}\) data than the two LT sets under high-humidity conditions. Therefore, the Vaisala transmissions outperformed the forward-scatter meter at the airports along the southeastern coast of China and those in high-humidity environments.  
visibility; deviation; transmissometer; forward-scatter meter; manual observation 29 June 2023  
16 August 2023  
19 August 2023  
22 August 2023
## 3 System Architecture, Software Services and Deployment
### Fog Computing-Based Data Processing Hierarchy
They are able to support services for (i) analysing incoming packet rates, as well as the signal quality from the installation's meters, (ii) key management for storing and accessing the meter's decryption keys, (iii), advanced analytics over the obtained datasets, (iv) local storage and (v) syncing data with the central cloud infrastructure. Tergo nodes support additional communication protocols such as MQTT [100], which allow direct links with the available LoRa gateways. The top-most layer of the prototype incorporates the Tethys Cloud Services, which are designed to collect datasets from the whole infrastructure and provide APIs together with the necessary communication interfaces for accessing the aggregated data over the Internet. The cloud services reside in a layer that corresponds to the backend or Cloud Services Layer (CSL) of fog computing as presented in Figure 4 and are accessible via common IP interfaces, supported by both cellular and landline networking infrastructure. In addition, Tethys Cloud Services also integrate sophisticated authentication mechanisms for mitigating malicious attacks, which could potentially lead to sensitive data loss.
# From Ethnographic Research to Big Data Analytics--A Case of Maritime Energy-Efficiency Optimization
## 4 Applying Big Data Analytics for Maritime Energy Efficiency
### Data Sources and Preprocessing
#### 4.1.1 Data Assimilation  
Weather data was acquired once for all journeys, before the machine model was trained, in order to speed up the training process. Historical weather forecasts data were obtained from forecast.io (An online service which aggregates multiple weather models--see forecast.io/raw) and another data set was provided by the German Meteorological Office (DWD) (Deutscher Wetterdienstsee [[https://www.dwd.de/DE/Home/home_node.html](https://www.dwd.de/DE/Home/home_node.html)]([https://www.dwd.de/DE/Home/home_node.html](https://www.dwd.de/DE/Home/home_node.html))) Data points included in the sets are shown in Table 1. Weather forecasts describe values for rectangular grid cells at defined time intervals. For the DWD data, grid cells span 0.05\({}^{\circ}\) in south-west and 0.1\({}^{\circ}\) in east-west (equivalent to 5.57 \(\times\) 5.90 km/5.57 \(\times\) 6.54 km) and are generated for 3, 6, 9, and 12 h in one model run. Model runs were performed every 12 h. Interpolation of time and location was used to retrieve weather information from the DWD data set.  
Figure 3: Data pipeline to provide an overview in data pre-processing.  
Ships and traffic control use the broadcasted information from an automatic identification system (AIS) to monitor other vessels' navigational intentions. The AIS data provided by SSPA (A Swedish company which provides maritime solutions--see [[https://www.sspa.se](https://www.sspa.se)]([https://www.sspa.se](https://www.sspa.se))) covers information about ships up to 60 km from the Swedish coastline. This provides information on the direction of travel for each journey, checking whether the vessel was located in a 10 km radius of the pier of Gothenburg at journey start time.  
Unfortunately, the AIS data provided cannot be used to reliably track a vessel during a complete voyage, it is necessary to get location approximation by using speed. All average speed (km/hour) samples over all 10-min intervals of the current journey (starting at t\({}_{0}\)), divided by 6 results in traveled distance relative to journey start (see Equation (1)). Taking the voyage from Gothenburg to Kiel as an example, the position can be approximated using a linear interpolation via relative distance travelled. The track used consists of 83 waypoints that span a total length of 437.43 km.  
\[\text{dist}(\text{t})=\sum\
olimits_{i=t0}^{t}\frac{speed(i)*10}{60} \tag{1}\]
# Securely Computing the Manhattan Distance under the Malicious Model and Its Applications
## 3 Preparatory Knowledge  
### Paillier Encryption Algorithm  
[PERSON] proposed a probabilistic encryption algorithm [33] to solve the problem of composite residue classes. It has additive homomorphism.  
Preparation: Select two large prime numbers \(p\) and \(q\), satisfying \(\gcd((pq,(p-1)(q-1))\)\(=1\), calculate \(N=pq\), \(\lambda(N)=lcm(p-1,q-1)\), \(S_{N}=\left\{\mu<N^{2}|\mu\equiv 1\text{mod}N\right\}\), and defining function \(L(\mu)=\frac{\mu-1}{N}(\forall\mu\in S_{N})\). A random number \(g\in Z_{N^{2}}^{*}\) is selected, with \((N,g)\) as the public key, and \(\lambda\) as the private key.  
Encryption: A random number \(r<N\) is selected to perform the encryption operation \(c=E(m)=g^{m}r^{N}\text{mod}N^{2}\).  
Decryption: The private key \(\lambda\) is used for decryption to obtain the ciphertext: \(m=\frac{L(c^{2}\text{mod}N^{2})}{L(g^{3}\text{mod}N^{2})}\text{mod}N\).  
Addition homomorphism: \(E(m_{1})E(m_{2})=g^{m_{1}}r^{N}\text{mod}N^{2}\cdot g^{m_{2}}r^{N}\text{mod}N^ {2}=g^{m_{1}+m_{2}}r^{N}\text{mod}N^{2}\)\(=E(m_{1}+m_{2})\).
## 4 MATANA Detailed Architecture
### Probe Connection Types for Low-Frequency Attack Detection  
The hardware Detection Module can be integrated into the target components and share the same communication channels to the software. However, when deploying MATANA on the final system, placing the hardware Detection Module separately from the rest of the system for better modularity, using a reconfigurable fabric, and benefiting from the flexibility on detection algorithms implemented are important to deal with attacks that are constantly evolving. For that purpose, we have to consider that the hardware DetectionModule may be asynchronous and run at a lower frequency than the target components. In that case, the Detection Module's I/O must be properly synchronized. For that purpose, we propose several synchronization approaches for probed signals that aim at reducing the hardware logic complexity while preserving sufficient information transmitted for attack detection.  
The proposed Probe Connection types are shown in Figure 4, with a target component's frequency \(divN\) times higher than the hardware Detection Module's frequency:  
A _Parallel Connection_ collects all signals in \(divN\) adjacent cycles and sends them in parallel to the hardware Detection Module. This connection type maximizes the preservation of the information collected and pinpoints the state of the signals at a specific time, but requires more synchronization logic and internal logic to process it.  
A _Preprocessing Connection_ preprocesses and buffers the probed signal, then sends the processed data to the hardware Detection Module. It provides a balance between the hardware resource utilization and the amount of useful data transmitted, and can reduce analysis effort in the hardware Detection Module. For example, this logic can count some hardware event occurrences or extract the instruction traces, and provide the possibility to implement existing detection solutions based on a hardware event counter or instruction tracing.  
An _Extraction Connection_ is a special and simple case of Preprocessing Connection that picks one interesting signal in a window of \(divN\) samples. It can be used where the signal value rarely changes or where some loss of data is acceptable with respect to the accuracy of the detection. It preserves the time of occurrence of events, which is useful for detecting time-based information leakage in microarchitectural attacks, and this property is not guaranteed by the use of a simple FIFO.  
Let us note that these connection types can be mixed, i.e., different signals from the same probe can use different connection types.
## 5 Performance Evaluation Results and Discussion
### Simulation Model
#### 5.2.4 Evaluation Function Used to Calculate Bid Scores  
In both the existing and proposed methods, the server and mobile hosts use an evaluation function to calculate the score of a bid in the RevAuc procedure. The evaluation function \(E\) is defined as the average of functions \(E_{Q}\), \(E_{T}\), and \(E_{C}\). The evaluation function \(E_{Q}\) evaluates the distance between the POI and the location where a mobile host will obtain data. The evaluation function \(E_{T}\) evaluates the time between an auction end time and the time when the mobile host will obtain data (data acquisition time). The evaluation function \(E_{C}\) evaluates the reward requested by the mobile host:  
\[E=\begin{cases}\left(E_{Q}+E_{T}+E_{C}\right)/3&\left(E_{Q}E_{T}E_{C}>0\right) \\ 0&\left(E_{Q}E_{T}E_{C}=0\right)\end{cases} \tag{1}\]  
Functions \(E_{Q},\ E_{T},\ \text{and}\ E_{c}\) are defined as follows:  
\[E_{Q}(d)=\left(1-d/120\right)\,(d\leq 100\,\text{m}),\ 0\ (d>100\,\text{m}) \tag{2}\]  
where \(d\) is the distance between the POI and the location where the mobile host will obtain data. \(E_{Q}(d)\) is designed so that \(E_{Q}(d)\) decreases linearly as \(d\) increases while \(d\leq 100\) m and \(E_{Q}(d)\) becomes zero if \(d>100\) m, which means that data obtained at a place closer to the POI is more valuable:  
\[E_{T}(t)=\left(1-t/40\right)\,(t\leq 30\,\text{s}),\ 0\ (t>30\,\text{s}), \tag{3}\]  
where \(t\) is data acquisition time (\(t\geq 0\)). \(E_{T}(t)\) is designed so that \(E_{T}(t)\) decreases linearly as \(t\) increases while \(t\leq 30\) s and \(E_{T}(t)\) becomes zero if \(t>30\) s, which means that data obtained sooner is more valuable:  
\[E_{C}(c,\ C_{\text{max}})=\text{max}[(1-c/C_{\text{max}}),\ 0], \tag{4}\]  
where \(c\) is the reward requested by a mobile host \((c>0)\) and \(C_{\text{max}}\) is the maximum reward paid to a mobile host by the server \((C_{\text{max}}>0)\).  
Table 3 shows the parameter values used by the existing method and the method proposed here.  
Figure 16: Example of decision-making by a mobile host on whether to tender a bid based on an evaluation score of a bid.