esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Study on the Damping Effect of a Lateral Damping Buffer under a Debris Flow Load
## 3 Experiment Validation
### Responses in Frequency Domain  
Figure 6 shows the responses of the uncontrolled structure and the structure with buffer in the frequency domain under the loading cases of grade 4 and grade 6.  
From the figure, it can be seen that:  
1. The Fourier spectrum of the acceleration time history curve of the uncontrolled structure has two obvious peak values in the vicinity of 3.6 Hz and 14 Hz respectively, which correspond to the measured natural frequencies. However, the Fourier spectrum of the structure with buffer has just one peak value in the vicinity of 8 Hz, since the attachment of the buffer has strengthened the integrity of the structure.
2. As for the amplitude, the response of the structure with a buffer is clearly reduced. That is, the vibration of the structure is under control and the lateral damping buffer has good effects.
3. The Fourier spectral lines show the distribution of the vibration power of the primary structure in the frequency domain. The area under the Fourier spectral line of the structure with the buffer is smaller than that of the uncontrolled one, which shows the lateral damping buffer can greatly decrease the vibration energy of structures.
4. Compared the response under fourth loading grade and sixth loading grade, the latter is evidently smaller than the former, indicating that with increasing impact, the buffering and vibration controlling effects of the device will increase.
## 2 Proposed Method
The detailed process of the Mamdani and Sugeno inference engine is illustrated in Figure 3. In Equations (2) and (3) we define two rules using two inputs (the full rule base is described in Figure 8). In these equations, RAM and storage are fuzzy values. Two inputs are fuzzified by applying an intersection on the crisp input values using a membership function. We used the \"and\" operator for combining two fuzzified inputs to obtain the rule strength. The Mamdani and Sugeno inference engine uses a membership function for each rule and then, according to the condition of the rule, reaches a conclusion. In the last step, the defuzzification module converts the resulting fuzzy output, that is, the Sugeno FIS outputs crisp value and Mamdani FIS outputs linguistic value on CPU utilization. Here the scalar value of the fuzzy system is fuzzified, the rules are applied, the training model is built, and each rule generates fuzzy outputs and converts them to a scalar quantity. We used the centroid defuzzification method for the Mamdani FIS and the weighted average defuzzification method for the Sugeno FIS as shown in Figure 3.  
Figure 2: Gaussian membership function. The linguistic variables like low, normal and high are the criteria for evaluating the CPU, RAM, and the storage represented in the linguistic descriptors as the value ranges. For example, the variable CPU has a range of values from 0 to 40, which is considered \"Low\", from 20 to 80 is considered \"Normal\", and from 60 to 100 is considered \"High\". \"Low\", \"Normal\", and \"High\" are linguistic descriptors for the value sets for all variables of CPU, RAM, and storage. As in principles of the fuzzy theory, sets of values overlap, and thus a value may partially belong to the set and have a membership degree of \(0\leq\mu\leq 1\), which can be any point between zero and one, where \(\mu\) represents the degree of membership [21]. Consequently, the value belongs to several sets, and the total membership is added to one. The linguistic descriptors \"Low\" and \"Normal\" are two fuzzy sets of CPU variables that can overlap, so a CPU of 20 can be mostly \"Low\" with \(\mu=0.7\) and somewhat \"Normal\" with \(\mu=0.3\). Thus, the fuzzy system converts the crisp input values into fuzzy inputs through a fuzzification block, which sets the degree of belonging to fuzzy sets for previously defined variables. The fuzzy system then uses a rule base developed by a human expert to predict a fuzzy inference of the phenomenon under investigation, as a result of which the fuzzy system has a defuzzification block that converts the fuzzy output to a crisp value. In this work, we built a fuzzy inference system with two crisp input variables and one output as shown in Figure 6.
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 2 Preliminaries
### Communication in ADASs  
Through the study of over ten different commercial ADAS devices, we found that, unlike traditional IoT devices, open-source components are less commonly used in ADASs. Most ADAS programs choose to use commercial SDKs or to develop them entirely independently. During port scanning or analyses of the devices, we found that, apart from a few debugging ports (such as SSH and telnet), most open ports are related to the business and are listened to by the related business programs. Figures 1 and 2 show two commercial ADAS devices' opening ports and corresponding listening programs.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Devices** & **Operating System and Kernel Version** & **Libc Version** \\ \hline ADAS-1 & QNX Neutrino 6.6.0 SP1 & & ldqnx-arm.le.so.2.1.0 \\ ADAS-2 & Linux cid 4.1.27-PLK \#1 SMP PREEMPT x86\_64 GNU/Linux & libc-2.22 \\ ADAS-3 & Linux (none) 4.14.0-xilinx \#52 SMP PREEMPT armv7l & libc-2.18 \\  & GNU/Linux & & \\ ADAS-4 & Linux (none) 4.9.0-xilinx-svn403 \#1 SMP PREEMPT armv7l & libc-2.26 \\  & GNU/Linux & & \\ ADAS-5 & Linux Ambarella 4.14.139 \#1 SMP PREEMPT aarch64 Flexible & libc-2.28 \\  & Linux & & \\ \hline \end{tabular}
\end{table}
Table 1: Version information of the operating system and libc.  
Figure 1: Commercial ADAS devices’ port opening and corresponding program listening-1.  
root@memississintnett-anltspu|grepLISTN  
tcp00000.0.1680100.0.0.0.**LISTN 676/mv_app  
tcp0000.0.0.1680200.0.0.**LISTN 651/mv_update  
tcp0000.0.0.168030.0.0.0.**LISTN 676/mv_app  
tcp00000.0.0.168040.0.0.0.**LISTN 676/mv_app  
tcp00000.0.0.2168040.0.0.0.**LISTN 676/mv_app  
tcp00000.0.0.2168040.0.0.**LISTN 676/mv_app  
tcp00000.0.0.210.0.0.**LISTN 633/tcpsvd  
tcp00000.0.0.2210.0.0.**LISTN 635/telnetd  
root@memissint  
**Figure 2.** Commercial ADAS devices' port opening and corresponding program listening-2.  
We captured working communication traffic from multiple commercial ADAS devices and analyzed the traffic. We found that most communication traffic used the raw TCP protocol without encryption rather than other mature application layer protocols (such as HTTP and WebSocket). Figure 3 shows the communication traffic of a commercial ADAS device, and the situation of other commercial devices is similar to that of this device. Based on this, using traffic fields/characteristics to assist the binary static analysis is feasible. We improved the efficiency of BOF vulnerability mining with the assistance of communication traffic.
## 4 Challenges to Developing a Remote Sensing Based EHA System
### Data Availability  
Ecosystem health indicators, such as biochemical properties and invasion species identification, require hyperspectral images, and others including canopy height and canopy morphology may need LiDAR data. The hyperspectral and LiDAR sensors usually are not activated until request. In addition,the imagery acquired has a very small footprint and consequently do not provide a global coverage, and such data are usually costly [150].
## 2 Materials and Methods
### Selection of the Sensitive RISI  
Donate \(s_{1},s_{2},\cdots,s_{m}\) as the sequence of \(a_{1},a_{2},\cdots,a_{m}\) in ascending order, and its corresponding severity levels caused by cotton aphids are represented as \(g_{1},g_{2},\cdots,g_{m}g_{i}\in\{0,1,2,3,4\}\). The RISI indicates the difference between the sample and all of the healthy samples in the training dataset. The lower the disease severity level, the closer to the healthy ones, and the higher rank in the ideal level sequence, while the higher severity level induced a lower rank in the desired sorting sequence. Therefore, donate \(m_{0}\),\(m_{1}\),\(m_{2}\),\(m_{3}\),\(m_{4}\) as the number of samples in the training set at the levels of 0, 1, 2, 3, and 4 respectively, then the ideal disease level sequence of \(s_{1},s_{2},\cdots,s_{m}\) can be formulated as \(0,0,\cdots,0,1,1,\cdots,1,2,2,\cdots,2,3,\cdots,3,4,4,\cdots\), where there are \(m_{0}\) samples with G0, followed by \(m_{1}\) samples with G1, \(m_{2}\) samples with G2, \(m_{3}\) samples with G3, and \(m_{4}\) samples with G4. Then, the consistency \(KI\) between \(g_{1},g_{2},\cdots,g_{m}\) and the ideal disease level sequence is represented as follows:  
\[KI=\frac{\rho_{o}-\rho_{e}}{1-\rho_{e}} \tag{9}\]  
where \(\rho_{o}=\sum\limits_{r=0}^{4}\frac{\Psi_{r}}{m}\rho_{e}=\frac{\sum\limits_{r= 0}^{4}m_{r}^{2}}{m^{2}}\); \(\Psi_{r}\) is the number of samples with grade \(r\) at the same position in both sequences; and \(m\) is the number of samples in the training dataset. According to Equation (9), the KIs of all possible combinations in RDSI, RRSI, RNDSI, RIDSI, RIRSI, and RPSI were calculated.  
After the calculation of the KIs for all RISIs, the sensitive band combinations for each type of RISI were selected with the maximum KI, that is, the severity grade sequences of the RISIs similar to the ideal sequence were taken as the sensitive band combination of RDSI, RRSI, RNDSI, RIDSI, RIRSI, and RPSI, respectively. If the maximum KI was obtained for more than one RISI, the severity ratio between the inter- and intra-grades is defined as:\[\eta=\frac{\sum\limits_{r=1}^{4}\left(\alpha_{r}-\beta_{r-1}\right)}{\sum\limits_{r =0}^{4}\sigma_{r}} \tag{10}\]  
where \(\alpha_{r}\) is minimum RISI with grade \(r(0\leq r\leq 4)\) of sequence \(s_{1},s_{2},\cdots,s_{m}\), which reached the maximum KI, and \(\beta_{r}\),\(\sigma_{r}\) are the maximum and standard deviation of grade r in the sequence, respectively. The numerator of \(\eta\) describes the boundary difference between adjacent grades. The larger the value, the stronger the discrimination. The denominator of \(\eta\) denotes the intra-grade difference, and the smaller the denominator, the stronger the discrimination. Therefore, if there are more than one maximum KI value, the sensitive RISI is selected with the largest \(\eta\) from RDSI, RRSI, RNDSI, RIDSI, RIRSI, and RPSI, respectively.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 4 LoRaWAN v1.1 Security Structure Analysis
### Operational Stage
#### 4.2.2 Key Storage  
The storage of root keys NwkKey and AppKey on the end device and the join server are the most vital parts of the overall security of LoRaWAN. However, LoRaWAN v1.1 does not describe in detail how the key should be stored. It only suggests that the implementation of it can include Secure Elements (SE) and Hardware Security Modules (HSM). Hence, the root keys must be stored in a way that the unauthorized party cannot gain access to it even by the side channel analysis.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 3: Coastal Area Monitoring
#### 3.3.2 Sea Surface Temperature  
The sea surface temperature (SST) is a critical input to study the sea current systems, eddies, jets, and any upwelling in coastal areas, if measured with a sensor accuracy of at least 0.5 K, which is not yet reachable by small satellites (e.g., [157; 158]). In addition to being a tool for monitoring sea phenomena and bacteria activities, SST variation in space and time is used for weather forecasting and for maritime services, as well as for studying the sea-atmosphere interactions [158].  
SST can be passively measured from the radiometric TIR or MW components of the electromagnetic spectrum. Examples of radiometers operating in the TIR range are MODIS, onboard NASA Aqua and Terra, and AVHRR, onboard the European MetOp and NOAA Polar Orbiting Environmental Satellites (POES), which can provide a global coverage within 1-2 days at a spatial resolution down 1 km [159]. However, TIR acquisitions depend on a clear sky without clouds or volcanic dust. Alternatively, for guaranteeing all-weather continuous acquisitions, SST can be obtained from radiometer data taken at 4 to 12 GHz, because it is proportional to the surface radiance in this frequency range [160]. However, microwave emissions from the sea surface are weak and coarser in resolution (46 km), and the first high-quality data were possible with the Microwave Imager onboard the Tropical Rainfall Measuring Mission (TRMM), a large space mission [160; 161].
# 3D Facial Plastic Surgery Simulation: Based on the Structured Light
## 3 Results
### Results of 3D Facial Reconstruction and Measurement  
To verify the imaging accuracy of the system face model, 10 subjects were 3D reconstructed, geometric and area measured, and compared with the manual measurement results. Considering the actual impact of the concave and convex surface of the face model in the real measurement, and combined with the relevant esthetic theory, the feature points with obvious locations and not easy to be affected by deformation were selected for markings, such as the nose tip, nose root, and eye kernel.  
An example related to geometric feature measurement of the system for the parts of interest to doctors is shown in Figure 2a, which includes face general position linear measurement, special position linear measurement, and angle measurement. The system intuitively and clearly showed the result. The experimental values measured by the system were compared with the actual values measured by plastic surgeons to determine the measurement accuracy. Taking \"three courtyards and five eyes\" as an example, the plastic surgeon used the soft ruler attached to the face to measure. Figure 2c shows the error values of system measurement and plastic surgeons' measurement, and the average error is about 0.458 mm.  
Take the nose as an example to measure its area. The doctor first laid fluorescent powder on the contour of the nose and then applied the test paper to the nose for data collection, lighting, and showing fluorescent marks (Figure 2b). Finally, the calculated area was 4.42 mm\({}^{2}\). The software calculates the area of the nose as 3.69 mm\({}^{2}\) after selecting the overall area of the nose through the interactive arbitrary measuring tool. The area of key parts such as eyes, eyebrows, nose, and cheekbones of 10 groups of subjects is measured as the above method. Each part above is measured 10 times by one operator, and the average error is 0.65 mm\({}^{2}\) (Figure 2d). Obviously, for the measurement of the face area, because the shape of the face is irregular, the error value is larger than the geometric feature measurement. However, the measurement results can still provide a reference for microplastic surgery and improve the success rate of plastic surgery.  
In addition, the technical parameters and scene reconstruction results of what we designed and the commercial near-infrared 3D imaging system are compared. Table 1 shows the comparison of technical parameters, and we can see that the system has significant advantages over the existing system in terms of resolution, accuracy, scanning time, and size.  
Figure 2: The measurement of the human face using the system. (**a**) geometric feature measurement. (**b**) 3D area measurement. (**c**) average error of geometric measurement. (**d**) average error of area measurement.
# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
## 3 Materials and Methods
### Psychological Scale Selection  
There are various scales available to measure SAD, but the most used is LSAS (Liebowitz Social Anxiety Scale) [39]. The scale is evaluated for its psychometric properties as per standards set in the Structured Clinical Interview for DSM Disorders (SCID) [40]. It is a semi-structured interview guide that explains the process of the assessment of current and lifetime Axis disorders. Some examples of these disorders are anxiety, mood disorders, psychosis, and substance use disorders. The latest version of SCID is called SCID-V. It is considered a standard instrument for the valuation of Axis psychopathology, as explained by the Diagnostic and Statistical Manual-V (DSM-V) [41]. SCID-V also has the LSAS [9], which is a standard clinician-administered interview to assess social anxiety symptoms.  
The LSAS has shown exceptional divergent and convergent validity [18]. Scores on the LSAS can distinguish between patients with SAD and those without SAD [33]. It can also classify the participants into four more classes as follows. A score of less than 55 on the LSAS scale means that the participant belongs to the \"No SAD\" group. A participant with a score exceeding 55 on the LSAS scale means that he/she belongs to a SAD group. It further has criteria or thresholds for the symptoms of SAD. It includes the severity and length which are needed for a symptom to qualify for the diagnosis. The total score shows the severity of the individual's SAD in terms of 1) \"No SAD\" (\(\leq\)55), 2) Marked (\(\geq\)55 and \(\leq\)65), 3) Moderate (\(\geq\)65 and \(\leq\)80), 4) Severe (\(\geq\)80 and \(\leq\)95), and 5) Very Severe (\(\geq\)95). The comparison of SAD subclasses can be useful for three reasons:  
1. To better understand the relationship between SAD and gaze interaction by comparing all classes as each class might have a different kind of gaze interaction pattern,
2. For correct prediction of SAD using gaze interaction for each class separately, and 3. To supply effective treatment of SAD for each class as each SAD person in a different class might not need the same kind of treatment.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 2 Theory and Prior Studies
### Motivation and Differentiation of Research  
To summarize the related research, travel speed and traffic environment information is needed to estimate the speed limit according to the road and traffic conditions. If the appropriate speed limit is estimated for the road situation, the vehicle-specific speed deviation decreases, reducing the likelihood of traffic accidents occurring.  
However, the existing research relating to selecting a speed limit has not developed due to the limitations of data collection, and some methods select the speed limit only according to the function of the road. As shown in Figure 3, since detectors are installed only on some roads, it is not possible to collect data on all urban roads. This is a problem not only in Korea, but in all countries.  
Due to the limitations of detectors' collection space and accuracy, until now, the total traffic volume of a city was predicted using loop detector data and studies aiming to improve the accuracy were conducted [26; 27].  
Recently, it has become possible to acquire driving information for each driver (speed, location, route, etc.) using cell phones, and the method of collecting the data has also changed. When application collection data are applied to GIS, they can be applied as traffic volume and speed data, as shown in Figure 4.  
Figure 3: Location and collection method of traffic detectors in Seoul.  
However, the road environment information that affects the speed limit is not managed as a DB by the road management agency. Therefore, geometry data are obtained by collecting data through sample field surveys (72 sections).  
When integrating the collected data, as shown in Figure 5, it is possible to provide a safe speed limit considering the road geometry and traffic characteristics rather than applying the existing uniform speed limit.  
Therefore, this study aimed to develop a speed limit estimation model equation by analyzing application-collected data and field survey data from urban roads (one-way roads with two or more lanes and a speed limit of 80 km/h or less) in Seoul.
## 1 Introduction
Therefore, and in order to improve the achievable throughput of the forward direction of communication of the GEO satellite system under different working conditions, this paper studies three different relaying architectures, namely: (1) _Detect-and-Forward_ (a non-regenerative strategy), where the GEO satellite only detects the symbols of the NB-IoT frames that modulate the intensity of the optical beam, and forwards them to the NB-IoT devices after \(M\)-PAM to \(M\)-QAM mapping; (2) _Decode-and-Forward with NB-IoT_ (a regenerative strategy), where the GEO satellite detects and decodes the symbols of the NB-IoT frames that are transported on the optical feeder link, and forwards them to the IoT devices after the NB-IoT frame regeneration for the downlink radio transmission is over; (3) _Decode-and-Forward with NB-IoT/DVB-S2(X)_ (a regenerative strategy), where the NB-IoT frames are encapsulated into DVB-S2(X) satellite frames for uplink transmission and, in the GEO satellite, the DVB-S2(X) decoding is performed to recover the NB-IoT frame that is then transmitted to the NB-IoT terminals. We note that the previously listed relaying architectures are also applicable to LEO and Medium Earth Orbit (MEO) satellites. However, to adapt the study presented in this paper from GEO satellites with fixed positions in the sky to LEO/MEO satellites that change their position on the sky at different speeds, the modeling of the optical channel and its link budget must be adapted accordingly, reducing the (distance-dependent) path loss attenuation that is experienced at lower orbits but adding an additional effect of the atmosphere at low-elevation angles, as well as pointing errors that may be incurred when the satellite is moving. The remaining part of this article has been structured as follows: Section 2 summarizes the key concepts to model the MCS defined in the NB-IoT standard, the three proposed relaying architectures using GEO satellites with optical feeder links (one non-regenerative and two regenerative), and the details of the time-packing equalization and low-complexity Log Likelihood Ratio (LLR) computation for soft decoding. Section 3 studies the effect of the turbulent atmosphere in the optical feeder link, with emphasis on the beam wander and scintillation that is introduced in the uplink transmission. Section 4 presents the simulation set up and the figures in terms of end-to-end Block Error Rate (BLER) and throughput. Finally, conclusions are drawn in Section 5.