esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 2 System Model
### SINR Realized at Time of Transmission  
The measured SINR \(\gamma_{0}\) is used in link adaptation to choose an applicable transmission rate. For this, the measurement result is fed back by the receiver to the transmitter. There are delays in feedback, and in processing at the measuring end, and the transmitter. Accordingly, when the channel is used for a data transmission, both the propagation channel carrying the wanted signal component, and the channels of the interferers have changed. In addition, the precoders used in the interfering base stations may be changed according to their scheduling decisions. Accordingly, the SINR experienced by the realized transmission differs from \(\gamma_{0}\).  
The received signal for a data transmission at a transmission instance which is delayed \(\tau\) seconds from the measurement is  
\[y_{\tau}=\mathbf{h}_{\tau}\mathbf{v}_{\tau}x+\sum_{j=1}^{J}\mathbf{h}_{\tau,j} \mathbf{v}_{\tau,j}x_{j}+n \tag{3}\]  
where \(\mathbf{h}_{\tau}\) and \(\mathbf{h}_{\tau,j}\) are the realized channel coefficients for the desired transmission and the interferers between the intended transmitters and the receiver, respectively, at time \(\tau\). The corresponding transmitted symbols are denoted by \(x\) and \(x_{j}\). The precoders \(\mathbf{v}_{\tau,j}\) applied on the interferers may be the same as the ones during the measurement, or they may be changed according to scheduling decisions. Therefore, the precoder \(\mathbf{v}_{\tau}\) may either be \(\mathbf{v}_{0}\), or a precoder selected on the basis of scheduling decisions. We assume that the precoder of the wanted signal transmission remains the same as the precoder used at SINR estimation.  
We assume that \(\mathbf{h}_{\tau}\) is a random vector drawn from the same distribution as \(\mathbf{h}_{0}\). These two channels are correlated for short delays \(\tau\). The effect of channel dispersion due to Doppler shift can be modeled by [20]  
\[\mathbf{h}_{\tau}=\rho\mathbf{h}_{0}+\sqrt{1-\rho^{2}}\tilde{\mathbf{h}}. \tag{4}\]  
The estimation error \(\tilde{\mathbf{h}}\) is an independent sample drawn from the same complex channel distribution as \(\mathbf{h}_{0}\) and \(\mathbf{h}_{\tau}\). Assuming [PERSON] model, the normalized auto-correlation coefficient of a Rayleigh faded channel with constant velocity motion is \(\rho=J_{0}\big{(}2\pi f_{D,max}\tau\big{)}\)[18]. The maximum Doppler shift is \(f_{D,max}=2\frac{v}{c}f\), and \(v\), \(c\) and \(f\) are the velocity of the user, velocity of light, and the carrier frequency, respectively. Here, \(J_{0}(\cdot)\) is a zeroth-order Bessel function of the first kind, and delay \(\tau\) is measured in seconds. We remark that effects of imperfect channel estimation lead to a correlation model of the same form as Equation (4), see [21]. In that case, the correlation \(\rho\) would depend, not on \(\tau\), but on SINR, and the channel estimation algorithm. Accordingly, our model extends to imperfect channel estimation as well, with an appropriate interpretation of \(\rho\).
## 3 Results and Discussion
### Tribological Properties
Examination of the ceramic counter body used to test the uncoated sample showed the presence of W (12.86 wt%) and Co, which is evidence of adhesive wear (Figure 19g). Figure 20 shows the wear scar area of Al\({}_{2}\)O\({}_{3}\) counterparts for different coatings and the uncoated specimen in terms of sliding speed. For the coated samples, the wear scar area became larger as the sliding speed increased from 50 to 100 mm/s and then got smaller in size at the speed of 250 mm/s. However, the wear scar area on the balls against the uncoated substrates increased in size with increasing sliding speed and it was found to be proportional to the wear rate. Figure 19: SEM micrographs of worn alumina balls at sliding speed of 50 mm/s against (**a**) CrN/CrCN/DLC, (**b**) CrN/AlTiN, (**c**) CrN/CrCN, (**d**) CrCN/TiSiCN, and (**e**) uncoated WC-Co substrate, EDS analysis of the balls used for: (**f**) CrCN/TiSiCN coating and (**g**) uncoated substrate. Figure 20: Wear scar area on the ceramic ball as a function of sliding speed. The results of the characterization performed on the different coatings in terms of wear resistance and COF are summarized in Table 6. As a result of its higher wear resistance and lower COF compared to the other coatings studied, CrN/CrCN showed the best results, and it was selected for deposition on the tips of commercial circular saws to perform the industrial test at the sawmill.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 6 Discussion
### Impact of Personalized Grid Structure on System Performance  
Tables 5 and 6 indicate that the disadvantage of this algorithm is the high communication cost of the PAG when using Algorithm 3 to calculate the task broadcast area \(GR_{PAG}\). However, Figure 4 and Table 7 demonstrate that the PAG algorithm achieves enhanced privacy protection results and a higher task completion rate, even at the expense of increased communication cost. Therefore, the reduction in salary cost can make up for the increase in broadcast communication cost.
## 4 Results and Discussion
### Gain and Efficiency  
The simulated and measured values of the gain and efficiency of proposed antenna elements are depicted in Figure 10. Due to the similar performances and deployment of antenna elements in the layout of a smartphone antenna, the gain and efficiency of a single antenna element are shown in Figure 10. A minimal difference between simulated and measured values of gain and efficiency is observed, which may be due to the tolerance of the coaxial cable with the SMA connector, 50 \(\Omega\) load termination impedance, and the impact of hand soldering. However, the simulated gain and measured gain vary from 4 dBi  
Figure 8: Simulated and measured Co/Cross pol plot of 4-element mobile MIMO antenna at (a) 3.5 GHz and (b) 5.1 GHz.  
Figure 9: Simulated 3D radiation pattern at (a) 3.5 GHz (b) 5.1 GHz.  
to 5 dBi in the desired frequency range. Likewise, the simulated and measured efficiency is well above 85% throughout the desired operating band of interest.
# Design of a Novel Linear Shaped Charge and Factors Influencing its Penetration Performance
## 5 Parametric Influences on Penetration Performance
### Combinations of Small and Large Apex Angles  
The influence of small and large apex angle is further discussed in this part. Figure 14 shows the numerical results of BLSC with 0.6 mm thick liner, COMP B as the explosive and different combinations of small apex angle (2a\({}_{1}\) = 30\({}^{\circ}\), 40\({}^{\circ}\), 50\({}^{\circ}\) and 60\({}^{\circ}\), respectively) and large apex angle (2a\({}_{2}\) = 60\({}^{\circ}\), 80\({}^{\circ}\) and 100\({}^{\circ}\), respectively).  
Figure 14: Penetration depths of different combinations of small and large apex angles.  
Figure 13: Penetration depth of BLSCs with different explosives.  
It shows that, (i) with the small apex angle increasing, the penetration depth shows a trend of increase before a decrease; and (ii) with the large apex angle increasing, the penetration depth shows a trend of increase before decreasing as well. The reason is that when the small apex angle starts increasing, the jet velocity is getting slower but the jet mass is becoming bigger. Therefore, the penetration depth becomes deeper. With the small apex angle further increasing, the jet velocity drops dramatically and the penetration depth decreases. The part liner with large apex angle mainly forms the wing and the slug. Therefore, when the large apex angle starts increasing, the part that forms effective slug penetrating into a steel target speeds up, resulting in a deeper penetration depth. With the large apex angle further increasing, the mass of effective slug decreases, resulting in a smaller penetration depth. It should be noted that to make the charge height H remain constant, the differences of the explosive mass varies from 30.4 g/200 mm to 40.4 g/200 mm and this cannot be ignored.
## 5 Discussion
### Low Reproducibility, Reliability and Explainability  
Artificial Intelligence (AI) has made significant progress in the last decade. Open-source deep learning frameworks and public implementations of state-of-the-art methods make the deep learning model able to be easily accessed by the community. If newly designed deep neural network architectures, loss functions and pretrained model weights for COVID-19 diagnosis can be conveniently utilized, then the technical barrier can be lowered and the research cycle can be accelerated. Unfortunately, among our covered 179 studies, only 48 of them provide an official implementation (i.e., 26%). This poses difficulties when researchers came to this field and tried to establish some baselines for comparison.
* _Recommendation: If possible, upload clean codes accompanying the posted papers. Prepare easy-to-follow documents that describe how to re-implement the proposed method._ If no official code is released, researchers can also reimplement the proposed methods according to the paper. Unfortunately, a proportion of papers have the problem of missing important implementation details, such as a detailed data preprocessing procedure, neural network architectures, hyperparameter settings, the learning rate, batchsize, number of total epochs, etc. Without these details, other researchers would face a great difficulty in reimplementing and performing a fair comparison. Moreover, readers and reviewers cannot identify the potential mistakes without these details.
* _Recommendation: authors should provide sufficient technical details of their proposed methods in order to guarantee the reproducibility._ In existing classification-based methods, Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) [217] are adopted by many researchers [40, 50, 87, 95, 96, 98, 110, 113, 114, 115, 117, 120, 137, 139, 140, 155, 158, 160, 169, 218] to output heatmaps for explaining the final result and present an intuitive understanding of which area the model is focusing on. Ideally, these heatmaps can provide radiologists with more useful information and further help them. However, we found that most papers only present the output heatmaps, but provide no analysis about them. We think that one possible reason for this is that they lack radiologist experts to analyze the correctness of the heatmaps. As already illustrated in [9], working as a multidisciplinary team and respect of the opinions of clinicians are important in automated COVID-19 diagnosis research. If this is not conducted, some AI researchers could fail to realize the mistakes that were made by the model according to the heatmaps [10].
* _Recommendation: Work as a multidisciplinary team. Opinions from domain experts are valuable for evaluating the correctness of AI models._
## Materials and Methods
### PDMS Stir Bar Extractions (SBSE and HSSE)  
For each sample, clean glassware, vials and PDMS stir bars (for both HSSE and SBSE techniques) were prepared. An 8.00 g \(\pm\) 0.05 g aliquot of the reconstituted sheep milk was transferred by pipette to a 20 mL glass vial. For headspace analyses (HSSE), a clean loop of metal wire was suspended from the septa and the PDMS stir bar was suspended from the loop of wire above the sample of sheep milk. For SBSE, the clean PDMS stir bar was directly added to the sheep milk in the vial by forceps. The vials were then added to a pre-warmed water bath at 35 \({}^{\circ}\)C with multi-point magnetic stir plate. The sample was stirred constantly for 90 min. Initial experiments explored extraction times between 60 and 180 min (data not included) and temperatures of 20 \({}^{\circ}\)C (room temperature) and 35 \({}^{\circ}\)C according to recommended guidelines from the manufacturer for sample size and extraction times [(39)]. It was found that 90 min at 35 \({}^{\circ}\)C gave the best sensitivity while not posing unnecessary risk of artefact formation from excessive thermal load due to high temperatures or very long times. After 90 min, the PDMS stir bars were gently removed with flat, round tipped forceps to avoid damaging the PDMS phase. The PDMS stir bars were rinsed with 3 \(\times\) 5 mL of deionized water and dried with a lint-free tissue between each rinse, manually removing any sample residues that may have adhered to the stir bar. Extracted and dried PDMS stir bars were placed in clean glass thermal desorption tubes with transport adapters. Samples were desorbed with a programmed temperature ramp from 50 \({}^{\circ}\)C to 240 \({}^{\circ}\)C at 500 \({}^{\circ}\)C/min and held for 5 min. The desorbed sample was cryo-trapped prior to injection in a Gerstel Cooled Injection System (CIS-4) at \(-\)60 \({}^{\circ}\)C (Gerstel GmbH). The CIS-4 transferred the volatiles to the analytical column by heating from 60 \({}^{\circ}\)C to 240 \({}^{\circ}\)C at 12 \({}^{\circ}\)C/s. The total splitless injection time was 0.8 min. Six sample replications were performed for both the HSSE and SBSE techniques.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 3 Methodology
### Dataset  
MSAD [1] datasets containing anomalies and normal instances were collected. The MSAD dataset has anomaly videos, normal testing, and normal training datasets in video form. The dataset also contains annotated csv files that contain frame numbers. The CSV file dataset has a column containing the range of values indicating when anomalies began. The MSAD dataset, along with any new datasets, serves as the input to the system. Here, each dataset represents a different task.  
A collection of 720 video datasets was compiled, categorized as follows: anomaly videos, normal testing videos, and normal training videos. The anomaly video dataset comprises 240 videos, encompassing 11 distinct anomaly types across 13 different scenarios, excluding highway scenes. The normal testing video set includes a total of 120 videos. Finally, the normal training video dataset contains 360 videos covering 14 different scenarios, including highway scenes.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 2 Research Methodology and Proposed System
### Proposed System
#### 2.3.1 Local Access Network  
As mentioned before, this network contained the measurement layer as end-devices and link layer as a gateway. The design of end-devices consists of several sensors that can detect the occurrence of the logging and the movement position of the cutting log. A gyro accelerometer sensor was used to detect the slope of the trunk trees, and a sound sensor was used as the chains saw sound detector. Raspberry Pi 3 was used because LoRa GPS HAT Shield is compatible with Raspberry Pi 3. Besides, LoRa GPS HAT Shield is customized and can be programmed on-site or remotely. Arduino Uno was used to handling the sensors that were applied in the system. Outdoor LoRa Gateway OLG01 is placed as a LoRa Gateway to interface the local access and remote access.  
The hardware requirements for the end-device consist of Arduino Uno, Raspberry Pi 3, LoRa GPS HAT Shield, gyro accelerometer sensor, sound sensor, and Outdoor LoRa Gateway OLG01. Meanwhile, the software specification requirement is an Arduino IDE and Raspbian Jessie Stretch 2019. The design of the sensor nodes can be seen in Figure 3. A power bank of 10,050 mAh was also applied as a battery to supply the energy at the sensor node.  
The topology network that connected between sensor nodes and the gateway can be seen in Figure 4. The system is built with more than two similar end-devices connected to the outdoor LoRa Gateway OLG01 using STAR topology network in LoRa configuration. The STAR topology can be developed by adding new sensor nodes or combining another STAR topology network into an existing STAR topology, which becomes a STAR of STAR topology for multi-hop architecture [39].  
Figure 4: The STAR Topology of the Sensor Nodes and Gateway.  
Figure 3: Sensor Node Architecture.  
Sensor nodes supervised and interacted with the real environment and forwarded the data to the network server through the gateways. The distance between sensor nodes and the gateway arranged by the received signal strength indicator (RSSI). LoRa Gateway forwards the uplink radio packets to the LoRa network server, and downlink to perform transmission requests coming from LoRa network server.
## 2 Results
### Identification of Unigenes Related to the Biosynthetic Pathways of Terpenoids and Polysaccharides
These TFs included two ZHD, two ZAT11, two YABBY, fifteen WRKY, one WIP2, two TINY, six TCP, one SUVR4, two STOP, one squamosa promoter-binding-like protein 12, one SNAT2, one SHR2, one SCR, four SCL, one SBH1, one RSM2, two REVEILLE, one RAP2, one RADIALIS, one RAD5a, two PT16, one pTAC14, three PRR, one protein indeterminate domain 2, three PCF2, one OSH1, four ORR, one NPR1, one nodulation-signaling pathway 2 protein, one ATXR7, six NF-YA/B/C, one NATA1, one NAT1, seventen NAC, thirty-four MYB and MYB-related, one multiprotein-bridging factor 1c, three MTERF, three MADS, six AUX/IAA, one LATERAL ROOT PRIMORDIUM 1, three KNAP/T, two KAN, four JAZ, one indeterminate domain 7, six Hsf, one HMG1/2-like protein, two HLS1, three HK, twelve HD-ZIP, one HD1, one HB29, one GT-2, two GRF6, one glycine-rich protein 2, one GLK1, one GIS, one CHD7, six GATA, one GAG2, two FAR1, one ETR2, one ETHYLENE INSENSITIVE 3-like 1 protein, seventeen ERF/EREBP, one E2 FB, four DREB, thirteen DOF, one DIVARICATA, one DDT, one CPRF2, six CONSTANS-LIKE, five CCCH, one lix-loop-helix protein A, eight B3, two AS2, one ARG13, one ARF6b, one APRR5, and fifteen other TFs (Supplementary Table S3). Of these, after stimulation with MeJA, the TFs that were increased were mainly the WRKY, NAC, MYB, HD-ZIP, ERF/EREBP, DOF, bZIP, BTB/POZ, bHLH, and B3 TF families.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 2 Materials and Methods
### GC-MS Analysis of EO and Hy  
To characterize the chemical composition of EO and Hy from exocarp, a Clarus 500 model Perkin Elmer (Waltham, MA, USA) gas chromatograph coupled with a mass spectrometer and equipped with an FID (flame detector ionization) was used. Chromatographic separation was performed using a Varian Factor Four VF-1 capillary column and the gas carrier was He at flow rate of 1.0 mL min\({}^{-1}\) in constant flow mode. The analytical conditions were applied following [22], with some modifications. For MS detection, an electron impact ionization (EI) system was used at 70 eV in scan mode in the range 35-400 \(m/z\). The volatile separated compounds were identified by matching their mass spectra with those stored in the Wiley 2.2 and Nist 02 mass spectra libraries database and by comparison of their linear retention indices (LRIs), relative to C8-C30 n-alkanes analyzed under the same conditions, with those available in the literature. Relative concentrations of individual compounds were expressed as a percentage of the relative peak area to that of the total peak area without the use of an internal standard and any factor correction. All analyses were carried out in triplicate.
## 2 System Reliability Evaluation Methods  
### Traditional System Reliability Evaluation Method  
Bridge structure is composed of multiple components, and the system reliability level is closely related to the reliability of a single component. The reliability level related to a particular limit state is then quantified via the failure probability or reliability index. Failure probability is defined in Equation (1), and the corresponding reliability index can be computed following Equation (2):  
\[p_{fi}=P(Z_{i}\leq 0)=\int_{D_{f}}f_{X}(X)dX, \tag{1}\]  
\[\beta_{i}=\Phi^{-1}(1-p_{fi}), \tag{2}\]  
where \(p_{fi}\), \(\beta_{i}\) and \(Z_{i}\) represent the failure probability, reliability index and the limit state function of the \(i\)th component, respectively. \(f_{X}(X)\) is the joint probability distribution function (PDF). \(D_{f}\) is the failure domain, where \(Z_{i}\leq 0\). \(\Phi^{-1}(\cdot)\) is the inverse of cumulative distribution function of standard normal variate.  
For the series system, the failure probability of the system is defined following Equation (3);  
\[p_{f}=P(\mathop{\cup}\limits_{i=1}^{n}Z_{i}\leq 0)=\int_{\mathop{\cup}\limits _{i=1}^{n}Z_{i}\leq 0}f_{X}(X)dX. \tag{3}\]  
For the parallel system, the failure probability of the system is defined using Equation (4):  
\[p_{f}=P(\mathop{\cap}\limits_{i=1}^{n}Z_{i}\leq 0)=\int_{\mathop{\cap} \limits_{i=1}^{n}Z_{i}\leq 0}f_{X}(X)dX. \tag{4}\]  
However, the PDF is not easy to obtain as the calculation of the integral is very difficult. Therefore, some approximation methods have been proposed to solve this problem, such as the series method and the parallel method [38], which are represented by Equations (5) and (6), respectively:  
\[\mathop{max}\limits_{1\leq i\leq n}p_{fi}\leq p_{f}\leq 1-\prod_{i=1}^{n}\Big{(}1 -p_{fi}\Big{)}, \tag{5}\]  
\[\prod_{i=1}^{n}p_{fi}\leq p_{f}\leq\mathop{min}\limits_{1\leq i\leq n}p_{fi}. \tag{6}\]
## 4 Experiments and Results
### Prediction
#### 4.2.1 Feature Set Ablation  
Recalling that we have in total 35 features and that there are three feature categories: Engagement (E), Behavior (B), and Performance (P) (see Section 3.6.1). We assessed model performance using each feature category (E, B, P) and their combinations (E + B, E + P, B + P, E + B + P).
## 3 Ransomware Dynamic Analysis Studies Utilizing both Machine Learning and Deep Learning
#### 6.1.3 Hybrid Analysis  
Hybrid analysis is an analysis approach which takes the advantages of static and dynamic analysis to produce more accurate analysis result. In hybrid analysis, both static and dynamic features are used [77].  
A mobile based ransomware detection model which utilized the advantages of static and dynamic analysis was presented in [65]. Machine learning classifiers NB, SVM, RF, and AdaBoost and DNN a deep learning algorithm, was used for the detection. The subjected app was checked statistically before submission to the dynamic analysis. If app was found suspicious then it was sent for the dynamic analysis. This system compared the malicious sample with ransomware DNA. By using the dynamic analysis, profile of each family was built.  
A detection approach HELDROID was presented in [72], which performed detection by using the hybrid approach. The strings were analyzed statically after which the dynamic analysis was performed to check C&C traffic. Due to hybrid analysis this approach was able to detect unknown samples as well. Static analysis was used to make deterministic decisions.  
A quantification model to prevent and detect the ransomware for the local drive was proposed in [120]. This model utilized hybrid analysis and social engineering for detection of ransomware. This model calculated the situational frequencies and possibility of registered and unregistered signatures. For social engineering characteristic and frequency analysis was performed to generate prediction.
# Portable Optical Fiber Bragg Grating Sensor for Monitoring Traffic Density
## 3 Experimental Measurement of Transport Vehicles
### Traffic Density Monitoring  
Traffic density monitoring involved two experimental measurements. In the first measurements, a location with lower traffic density in a quiet suburban part of Ostrava was selected in order to verify the basic functionality of the sensor (this location mainly has passenger vehicle traffic). After successfully analyzing the measured data, a second, comprehensive set of measurements was conducted at a location with more frequent traffic in order to detect cars of different types and sizes (at the main entrance to the VSB-TUO premises).  
Figure 4: Measuring scheme of experimental setup.