esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 4 Results and Discussion
### Theoretical Impact of Buoy Movements on the FLS Horizontal Velocity Estimates  
The buoy motion influences the wind LIDAR velocity measurement in several ways. We separate three distinct effects; the effect of motion on the cone geometry and on the perceived relative velocity; the effect of subsampling due to the temporal averaging of several pulses in the same beam direction, and; the interaction effect between the different beam measurements.  
In order to understand the influence of the various degrees of freedom of motion on the LIDAR measurement, we consider the case of a purely horizontal non-turbulent wind (\(w=0\)). As the directions of the WindCube v2(r) measurement beams are symmetrical in the horizontal plane, roll and pitch have a similar effect on the beams having the same relative position to their rotation axis. Therefore, for the sake of simplicity, we combine these two motions and decompose the rotational motions into two effects: Tilt and yaw. The motions are represented by zero-centred sinusoids, defined by an amplitude \(a\) and period \(P\), of the form:  
\[motion(a,P)=a.\sin\bigl{(}\tfrac{2\pi}{P}\bigr{)} \tag{23}\]  
For a gradual understanding, we first study the reaction of a single LIDAR beam measurement to a period of tilt and yaw. In a second step, we study the effect of the temporality of the measurements, including the effect of subsampling and the effect of interaction between the different beam directions. Based on the study of the order of magnitude of the IMU measurements, we consider the amplitudes: \(a_{tilt}=\bigl{(}1^{\circ},2^{\circ},4^{\circ},10^{\circ}\bigr{)}\), \(a_{yaw}=(5^{\circ},10^{\circ},20^{\circ},40^{\circ})\) and \(a_{heave}=(0.1,0.2,0.5,1)\) m/s.
## 3 Results
### Accuracy and Uncertainty Analysis
#### 3.1.3 Uncertainty of Conversion from Radiance to Gray Scale Level  
This conversion process integrates the complete process of converting spectral radiation to gray levels. It takes into account the whole optical path, the camera sensors, and the associated uncertainties. The relative change in the measured temperature due to the change in the sensitivity coefficients \(K_{\lambda_{1},\lambda_{2}}\) is:  
\[U_{K_{\lambda_{1},\lambda_{2}}}=\Delta K_{\lambda_{1},\lambda_{2}}\bigg{|} \frac{1}{FK_{\lambda_{1},\lambda_{2}}}\bigg{|}, \tag{12}\]  
From the calibration fitting curve, it can be seen that \(\Delta K_{\lambda_{1},\lambda_{2}}\) takes the maximum value of 0.01. The uncertainty caused by the noise and nonlinearity of the camera is related to the gray value G and the offset b, so we can obtain:  
\[U_{N_{G_{1},G_{2},b_{1},b_{2}}}=\Delta N_{G_{1},G_{2},b_{1},b_{2}}\bigg{|} \frac{1}{FN_{G_{1},G_{2},b_{1},b_{2}}}\bigg{|}, \tag{13}\]  
\(\Delta N_{G_{1},G_{2},b_{1},b_{2}}\) is the maximum offset at each temperature.  
Synthetic Standard Uncertainty \(U\) is:  
\[U=\sqrt{{U_{\varepsilon_{R}}}^{2}+{U_{\lambda_{1},\lambda_{2}}}^{2}+{U_{K_{ \lambda_{1},\lambda_{2}}}}^{2}+{U_{N_{G_{1},G_{2},b_{1},b_{2}}}}^{2}}, \tag{14}\]  
Based on the above formula, we have obtained uncertain parameters at different temperatures, as shown in Table 4:Therefore, we can conclude that in the range of 600 \({}^{\circ}\)C-1000 \({}^{\circ}\)C, the standard uncertainty distribution range of the two-color thermometry system is 2.52%-3.43%.  
Consequently, the maximum temperature measurement error of our two-color pyrometry temperature measurement system is 2.31%, and the maximum temperature measurement uncertainty is 3.43%. In the future, the wavelength interval selection and the black body furnace performance can be further optimized to reduce the uncertainty of temperature measurement.
# Transfer Incremental Learning Using Data Augmentation
## 4 Experiments
### Benchmark Protocol  
We propose an incremental learning scenario in which we have streaming data containing new classes or examples. We test and compare Budget Restricted Incremental Learning (BRIL), Nearest Neighbour search (NN), Nearest Class Mean classifier (NCM), Learn++, incremental Classifier and Representation Learning (iCaRL), and finally the proposed method (TILDA). Learn++ uses Classification And Regression Trees (CART) as weak classifiers.  
We evaluate the different methods using CIFAR10, CIFAR100 and ImageNet ILSVRC 2012 [29]. We also use 50 ImageNet classes which have not been used to train the CNN (denoted ImageNet50), and which contains 900/100 training/test images per class. All methods take the same feature vectors extracted from Inception V3 [6] as input and use the whole dataset for training, unless explicitly mentioned. This requires to modify iCaRL method by replacing its CNN with a fully connected network. In the following, and for the iCaRL method, we use a MultiLayer Perceptron (MLP) with one hidden layer containing 1024 neurons, and output layer containing \(C\) neurons, where \(C\) is the number of classes.  
The non-incremental learning methods (NI) used are denoted by TMLP and TSVM. TMLP uses transfer learning to compute feature extractors of input data through Inception V3, and then trains a MLP over feature vectors, using the hyperparameters previously described for iCaRL. TSVM method uses Inception V3 to get feature vectors as well, and uses them to train an SVM using Radial Basis Function kernel.  
Data augmentation used in TILDA generates a horizontal flip of the original image, and shifts the pixels of the image by one pixel at a time (to the left, right, top, bottom, and on the four diagonals). Thus we generate \(R=10\) images (eight generated by shifting pixels on the image, one generated by horizontal flip and the original one).
# Evaluating the Real-World NOx Emission from a China VI Heavy-Duty Diesel Vehicle
## 2 Experiments and Materials
### Boundary Conditions for Data Evaluation  
The current PEMS procedure for heavy-duty vehicles is defined by a series of boundary conditions that prescribes the amount of data to be taken into consideration for the final analysis including the effectiveness of the test and pass-fail of the pollutants' emissions.  
The main boundary conditions for a valid test are as follows:  
* Ambient temperature: between \(-7\) and \(38~{}^{\circ}\)C.
* Altitude: not more than 2400 m.
* Test route: as abovementioned in \"2.3 Test Route\" (including trip share and vehicle speed, etc.).
* Cold start: at the beginning of the PEMS test, the engine coolant temperature shall not exceed \(30~{}^{\circ}\)C unless the ambient temperature is higher than \(30~{}^{\circ}\)C, in this case, the engine coolant temperature shall not be \(2~{}^{\circ}\)C higher than the ambient temperature. The data used for emissions evaluation is recorded after the engine coolant temperature has reached \(70~{}^{\circ}\)C for the first time or after the coolant temperature is stabilized within \(\pm 2~{}^{\circ}\)C over a period of 5 min (whichever comes first but not later than 20 min after engine starts).
* Payload: 10 to 100% of the maximum vehicle payload.
* Cumulative work: 4-7 times the amount of work performed over the WHTC applicable to the engine used by the tested vehicle.
* Selection of valid windows: the valid windows are the windows whose average power exceeds the power threshold of 20% of the maximum engine power. The percentage of valid windows shall be equal or greater than 50%. If the percentage of valid windows is less than 50%, the data evaluation shall be repeated using lower power thresholds. The power threshold shall be reduced in steps of 1% until the percentage of valid windows is equal to or greater than 50%.
* Power Threshold: In any case, the power threshold shall not be lower than 10%, otherwise, the test shall be void.
* The pass-fail conditions for a valid test are as follows:
* The 90 th cumulative percentile of the valid windows emissions shall be less than the limit required in the regulation (for China VI, NOx limit: 0.690 g/kWh; CO limit: 6 g/kWh; PN limit: 1.2 \(\times\) 10\({}^{12}\) #/kWh);
* NOx concentration is required to be less than or equivalent to 500 ppm for 95% of valid data points.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 7 Applications of Wearables in Sports
### Swing Sports  
Swing sport is generally defined as a category that includes sports such as golf, tennis, hockey or baseball. These sports require a person or team to swing and control the ball or racket in a specific way. WT uses it to analyze the movements of players in swing sports and optimize their performance. Some selected swing studies are summarized in Table 3.  
In their study on high school male baseball players, [PERSON] et al. compared marker-based motion capture with the motusBASEBALL sensor to analyze baseball throws [120]. Wearing compression sleeves with the IMU positioned on the medial elbow, they performed five fast throws. For each throw, they recorded and compared arm slot, arm speed, shoulder rotation, and elbow varus torque using the commercially available motusTHROW application. They reported that the IMU was reliable for measuring arm speed (RMSE range: 10.5-57.9 rpm; percentage difference: 1.2-6.2%) and shoulder rotation (RMSE range: 0.8-3.3; percentage difference: 0.5-1.6%). The study also highlighted the potential of IMU technology for performance monitoring, rehabilitation, and injury prevention in pitchers. [PERSON] et al. validated wearable IMU technology with measurements corresponding to motion capture techniques [79]. Ten healthy collegiate or professional baseball pitchers performed five to seven fastballs followed by five to seven off-speed pitches (slider or curveball). Reflective markers and the MotusBASEBALL sensor were attached to the athletes. They reported significant correlation coefficients (r values) of 0.98 (arm slot), 0.75 (shoulder rotation), and 0.67 (arm stress) between the measurements corresponding to motion capture techniques. [PERSON] et al. used a Shimmer3 sensor and ZeppGolf to analyze left knee biomechanics and differences among golfers of different skill levels [121]. The inertial sensor system was attached to the left glove of 18 right-handed golfers divided into three groups based on handicap score (professional, intermediate, and beginner). The accelerations recorded by the sensor in the five shots with the highest scores in ZeppGolf were analyzed, and it was found that professional players had the highest acceleration values. [PERSON] et al. evaluated the concurrent validity of upper and lower extremity kinematic variables (joint angles of shoulder, elbow, wrist, separation angle, hip, knee, and ankle) obtained from an IMU system (Xsens MVN system) compared to an optical motion capture system (OS (Qualisys AB)) during a tennis forehand drive [122]. The study included 18 experienced (13 males and 5 females) and 11 intermediate male tennis players, and three repetitions of the forehand drive acceleration phase were assessed. The comparison between the systems during a forehand drive showed excellent coefficients of multiple correlation for most variables, except for the shoulder in the anteroposterior plane and the elbow in the axial plane.
# Sensors for Sustainable Smart Cities: A Review
## 3 Results
### Communication Technologies  
Some of the most common communication technologies involve 3G, 4G LTE, and 5G Wi-Fi network connections [109; 114]. In general, communication infrastructures can be classified as local area networks (LAN), wide area networks (WAN), and global area networks (GAN) [116]. LAN sensors can communicate with gateways through different protocols, such as: long range (LoRa) [107], Bluetooth [126], and low power radio [41]. Furthermore, new technologies have arisen in recent years, such as ZigBee [110] and narrowband internet of things (NB-IoT) [72]. On the other hand, gateways in WAN connections send the information to the cloud through SigFox, long range wide area network (LoRaWAN) [112], while GAN encompasses more complex technologies and all the cellular networks GSM-GPRS [121; 148]. Other protocols include IP and API, TCP, Dash7 and MQTT [17], along with standards such as IEEE802.15.4, IEEE802.11.x, and IEEE1451 [135].  
However, as the number of network nodes increase, interference problems can take place, particularly in very dynamic environments, such as health and mobility [149]. For instance, inter-body network problems depend on the number of sensors per network, as well as the body networks in a location, traffic, physical mobility of each body, and the location of nodes on the body [150]. In order to attenuate this problem, active and passive schemes have been proposed, which allow a high throughput and packet delivery ratio, as along with both a low average end-to-end delay and low average energy consumption for a single and multi-WBSNs [151; 152]. Similarly, sharing data through connected vehicles can occur through incorporated or third-party systems [62], but accuracy and availability widely depend on the interface used [153]. As the number of users increases, the safety, access, and efficiency can be affected. Here, the vehicle itself is considered an integrated sensor platform [154], since it can work as a data sender, receiver, and router within the V2V or V2I communications used in ITS [19].
## 3 Numerical Simulation and Comparison of the Results
The modified Birkhoff theory, however, does not clearly address the issue in the derivation generating a slight change in the jet projection direction [9]. The slug velocity of the modified Birkhoff theory and the simulation results are compared as well in Figure 8.  
The slug velocity of the modified Birkhoff theory shows somewhat higher values in the entire range of the M/Cs than that of the simulation results. However the differences ranging from 0.04 to 0.15 km/s and \(\pm\)0.08 km/s as an average difference are believed to be reasonable. The original apex angle of LSCs is another important factor that controls the jet and slug velocities. According to the Birkhoff theory, a large apex angle creates slower jet velocity but slightly faster slug  
Figure 8: Modified Birkhoff and numerical simulation comparison. velocity, and the same tendency can be applied to the modified Birkhoff theory as well. Results comparing dependencies of the jet and slug velocities on the original apex angles follow (Table 3). The jet velocity difference between the modified Birkhoff and numerical simulation results are quite similar in the range of higher original apex angles. The differences range from 0.07 up to 0.98 km/s with the lower original apex angle exhibiting the most difference in jet velocity, and high accuracy for higher original apex angles. The reason for this difference in the range of lower original apex angle is not clearly known at this point, but it probably represents the fundamental problem of steady-state assumption that we made in the derivation. The nonlinearity of the numerical simulation produces faster jetting at smaller apex angles which cannot be described by the steady-state equation. This falls into the same regime with the comparison in Figure 7.  
In summary, the comparison between the two approaches including the analytical calculation of the modified Birkhoff theory and numerical simulation results in terms of jet and slug velocity showed somewhat favorable results in a limited range depending on the original apex angle, M/C ratio and other configuration properties.
## 5 Simulations
Figure 5(a)-d summarize the average reliability, average redundancy of packet reception, average number of paths, and average number of path changes. The average reliability of the method employing three fixed paths was the highest, reaching up to 0.969, and also achieved the highest redundancy of 1.063 in packet reception. The average reliability of the single-path method was 0.776, which was the lowest and less than the target threshold, although it achieved a minimum redundancy of \(-\)0.224 in packet reception. The average reliability of the proposed method with an offset of 0 was 0.903, which was close to the target threshold. This method achieved a relatively low redundancy of 0.478 in packet reception, but it achieved the highest number of path changes (18) among the evaluated methods, as shown in Figure 5(d). The average number of paths in the multipath connection was 2.05, as shown in Figure 5(c). The average reliability of the proposed method with an offset of 0.03 was 0.908, which was higher than the target threshold. This method achieved an average redundancy of packet reception of 0.751, as shown in Figure 5(b). The average number of paths in the multipath connection was 2.5, as shown in Figure 5(b). The number of path adjustments in this scenario for the proposed method was 11, as shown in Figure 5(c). Compared with the proposed method employing the target reliability as the threshold for decreasing the number of paths, there were fewer changes in the number of paths. This is because the offset used only allowed for decreasing the number of paths when the reliability was higher than the sum of the target reliability and the offset. Furthermore, the Appendix A examines the impact of the offset in the reliability threshold on reliability, path Number, and redundancy.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 6 Biofilm Formation Enhances Antibiotic Resistance
### The Roles of eDNA and Horizontal Gene Transfer  
eDNA may also affect antibiotic resistance by altering the surrounding environment. The eDNA is an anionic macromolecule that can chelate cations such as magnesium ions, causing a decrease in the effective concentration of Mg\({}^{2+}\) in the atmosphere [97]. eDNA may also physically interact with antimicrobials and try to impound the agent from the sequence targets. It also facilitates the horizontal gene transfer of resistance genes within the competent cells in the biofilm [97]. However, the direct mechanisms of eDNA on antibiotic resistance need to be considered in _H. pylori_ for further understanding.  
Horizontal gene transfer is another critical aspect of biofilm-associated antibiotic resistance, suggesting an evolutionary strategy to adapt to diverse environments. Vertical gene transfer of chromosomes or plasmids by cell division can be energy expensive; hence, the utility of horizontal gene transfer is more favorable. _H. pylori_ have conjugation tools such as the Type IV secretion system and pilis [98; 99]. This process involves the transfer of genetic material between bacterial cells, allowing for the exchange of antibiotic resistance genes and other beneficial traits. In biofilms, the structure includes vesicles that protect nucleic acids from degradation, enabling the sharing of antibiotic-resistant genes among bacteria [63]. A previous study reported that exposure to clarithromycin increased biofilm formation with a point mutation in locus 2142 or 2143 of 235 rRNA [66].
The major components in pine essential oil are pinene, camphene, sabin, carene, myrcene, terpino-lene, \(\alpha\)-terpineol, limone, carophyllene, bornyl acetate, p-cement, felandren, \(\gamma\)-terpene,germacrene D and spathulenol. Bhalla et al. [78] showed that pine essential oil improves the activity of white blood cells, which are responsible for removing microbes from the body. Terpenoids, the major components of pine essential oil, have proven antimicrobial, antiallergic, antifungal, antiviral, antispasmodic, and anti-inflammatory properties useful in the prevention and treatment of many diseases, including cancer [79; 80; 81]. Several types of Pinus are already well-known sources of antioxidants that are commonly used as dietary supplements (against alcohol-induced liver disease [82] or against lipopolysaccharide-standardized inflammation, hippocampal memory-enhancing activity, and activity for the early management of dyslipidemia), as phytochemical remedies, and in the treatment of chronic inflammation, circulatory problems [83] and sometimes cancer [84]. Koutsaviti A et al. [83] and Xie Q et al. [85] reported that terpene derivatives such as germacrene \(\alpha\),\(\beta\)-caryophyllene, and \(\gamma\)-terpinene exert antioxidant activity. Also, in the study of Zeng WC et al. [86], the essential oil from pine needles demonstrated significant antioxidant activity, especially against superoxide radicals, and hydroxyl radicals. The purpose of this paper was to evaluate the physicochemical parameters for five commercial essential oils (fennel, sage, eucalyptus, thyme, and pine essential oils) and their antimicrobial effect on the ability to grow, multiply and generate monospecific biofilms of four standard microbial strains: two Gram-negative strains (_Escherichia coli_ ATCC 25922, _Pseudomonas aeruginosa_ ATCC 27853), one Gram-positive strain (_Staphylococcus aureus_ ATCC 25923), and one yeast strain (_Candida albicans_ ATCC 10231).
## 3 Background
### Current Challenges with Conventional Authentication Methods
#### 3.5.1 Social Engineering  
In cybersecurity, security guarantees rely on the security of protocols and implementations of specific algorithms, but these technical aspects are not the only factor. A password authentication process might involve a secure protocol and a complex password. Still, if the user is tricked into giving away their password, then even the best protocol and password do not help. Even in authentication, a chain is only as strong as its weakest link, which is usually the human, and therefore, this remains the main target of social engineering. In itself it is not a complex attack, but more of a set of strategies or tools that an attacker can use to gain access or information to restricted data. A common example is email phishing, which is the most widespread type of social engineering [34]. This will be further detailed in Section 3.5.2.  
Generally, social engineering can be defined as \"social disguises [...] and psychological tricks [...] to assist hackers in illegal intrusion of computer systems and networks\". They therefore, aim at manipulating a victim into providing sensitive data or access to critical systems. The 'ease' of using social engineering over trying to crack passwords has made it the most common technique since 2007 [34].  
Common countermeasures against social engineering attacks are enhancing protections such as email filtering and adapting 2 FA authentication for verifying actions. These steps still do not fully prevent social engineering attacks; therefore, another huge countermeasure is increasing focus on educating users. While these things help and security measures improve, so do the attackers trying to intrude, resulting in increasingly better and more sophisticated social engineering attacks [28,34].