esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Fatigue Performance of Composite Girders with CSW and ST Truss
## 2 Fatigue Performance Test Design  
### Test Model Design  
According to the existing research on composite girders with CSW, the hot spot stress of the composite girders with CSW and ST truss was mainly affected by the geometry of the CSW, including bending angle \(\theta_{\text{b}}\), bend radius \(R_{\text{b}}\), and thickness \(T_{\text{b}}\), as well as that of the chord, including chord diameter \(D_{\text{c}}\) and chord thickness \(T_{\text{c}}\). The geometric parameters are shown in Figure 2. The schematic of the molding process is shown in Figure 2b. Figure 2c depicts a planar view of the intersecting area of the CSW and the chord within a single corrugation wavelength. To facilitate discussions, the intersecting area was divided into two sides, i.e., the left- and right-hand side with respect to the direction of the chord axis. Meanwhile, the endpoint of the inclined web of CSW is defined as point S.  
The test model used in the study adopted a 1:5 scale in consideration of the test objectives and feasibility of conducting the test. The geometric similarity theory was used to determine the specimen dimensions. A detail drawing of the test model is shown in  
Figure 2: Parameters and craft of CSW: (**a**) parameters; (**b**) schematic of molding process; (**c**) schematic of intersecting welds; and (**d**) schematic of chord.  
The concrete top slab was connected to the CSW using studs, and T-shaped joints were used to connect the chord and the CSW by penetration welding. The CSW, chord, and end cross beam were connected by perforation and reinforcement penetration on the CSW at the end cross beam and by the studs welded on the steel tubes. The CSW was manufactured using the same processing technology as that adopted in actual construction projects. In addition, since the intersecting weld was a spatial curve, the CSW was cut and molded by a numerical control (NC) machine to guarantee the applicability of the results obtained from the model test. Moreover, automatic NC welding equipment was applied for welding. Grade Q345 steel was used for the steel structural elements, such as the chord, truss brace, and K-bracing. Grade C50 commercially available concrete was adopted in the concrete top slab. By the carrying out the material tests, the mechanical properties of steel and concrete could be determined.
# Feature Extraction and Evaluation for Driver Drowsiness Detection Based on Thermoregulation
###### Abstract
Furthermore, it is a well-known phenomenon that T\({}_{\text{C}}\) varies periodically in a circadian way. Throughout the day, the temperature peaks in the evening around 21:00, followed by a gradual decrease overnight with a minimum around 05:00 [20]. To facilitate these temperature changes, heat production and dissipation are closely regulated. In thermo-neutral, sedentary conditions, this heat balance is mainly regulated by autonomous control of vasodilation and vasoconstriction of the arterioles in the skin. Accordingly, controlling blood flow to the skin is crucial for managing the human heat balance. Moreover, [PERSON] [21] states that skin blood flow takes up between 5% and 60% of our cardiac output depending on the environmental conditions. With respect to sleep and sleep onset, [PERSON] et al. [19] presented the hypothesis that heat loss via the extremities is linked in feedback with activation of the sleep-promoting areas in the brain. More specifically, efferent warm sensitive neurons (WSN) in the preoptic anterior hypothalamus (PoAH) innervate and stimulate somnogenic brain structures, while other thermo-sensitive neurons innervating wake-promoting brain areas are inhibited. [PERSON] et al. [22] experimentally tested this hypothesis and showed that in conditions of unwanted sleepiness in active subjects there is a negative feedback connection between the distal-to-proximal temperature gradient and sleep onset. In this work, we aim to investigate the use of features extracted from peripheral skin temperatures of the nose and wrist (heat dissipation) as well as the heart rate (heat production) to distinguish between subjects experiencing drowsiness while driving and subjects that remain alert.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### IDEASSat Satellite Subsystems
#### 2.1.5 Attitude Determination and Control Subsystem (ADCS)  
IDEASSat required high attitude stability when performing scientific measurements using CIP, as well as to orient the S-band patch antenna towards the ground station. Pointing knowledge and control requirements were to 0.25 degrees accuracy. Therefore, ADCS was a more critical part of the IDEASSat spacecraft. IDEASSat used the Blue Canyon Technologies XACT-15 ADCS for attitude determination and control, which is TRL-9 [11]. The XACT-15 includes three magnetorquers and reaction wheels for attitude control; a star tracker, coarse sun sensor, and magnetometer for attitude determination, and an integrated GPS receiver (NovAtel OEM-719) as a source of orbit state and GPS time information that could be used with a built-in orbit propagator, and an inertial measurement unit (IMU) for attitude propagation. Under the requirements of high pointing and high altitude for stable attitude, the XACT-15 met the ADCS requirements of IDEASSat. XACT execution was divided into two modes. Upon separation from the launch vehicle, the spacecraft would enter emergency operation mode at first. The coarse sun sensor and IMU would be used to execute sun point mode to maximize power generation. When the spacecraft entered the nominal operation mode, XACT would switch to the fine reference mode where surface, inertial, and local velocity local horizon (LVLH) pointing capabilities were available. At  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Beacon Type** & **Beacon Size (Byte)** & **Description** \\ \hline \hline \multirow{2}{*}{Lite} & 360 & Normal packet split into 9 consecutive \\  & & 40-byte packets (default). \\ \multirow{2}{*}{Normal} & \multirow{2}{*}{204} & Format similar to AX.25. Content \\  & & includes housekeeping data. \\ \multirow{2}{*}{Short} & \multirow{2}{*}{48} & The short version, only includes critical \\  & & state of health data. \\ \hline \hline \end{tabular}
\end{table}
Table 3: IDEASSat beacon types.  
this time, the information received by the star tracker would be used as the basis for the absolute attitude, and the time and position information obtained by the GPS would be used to determine and propagate the orbit.
## 1 Introduction  
### Background  
Most operation and power generation facilities that are used in power plants have important functional purposes and are independently constructed on concrete slabs applying fixation systems. Post-installed anchors are mainly used as a general fixation system and are widely used for attaching or fixing structures, owing to their flexibility and ease of construction [1]. However, such anchors may affect the safety of the entire structure when exposed to direct events, such as an earthquake, or indirect events, such as concrete cracking due to aging [2].  
According to a Federal Emergency Management Agency (FEMA) report, the frequency of occurrence of earthquakes with a magnitude of 5.0 or higher has been continuously increasing over the last 10 years, and there has been an increasing amount of damage to non-structural elements, such as operation and power generation facilities in power plants [3]. Most of this damage has involved an overturning or sliding of the facility from damaged anchors embedded in cracked concrete. The occurrence of such damage may lead to secondary damage, causing more serious social chaos from the functional loss and malfunction of the facilities [4].  
Despite the increasing importance of anchors used in fixation units, such as in operation and power generation facilities, there are no seismic evaluation or verification criteria for such units in South Korea. In this study, static and dynamic experiments on the anchorages of non-structural elements were conducted using the seismic performance evaluation criteria of anchorages in the United States and Europe.
# Displays Based on Dynamic Phase-Only Holography
## 1 Introduction  
Holographic displays are one of the strong contenders for realizing true 3D displays. The first display holograms were based on off-axis holograms recorded on a photographic plate [1]. In order to develop dynamic displays, computer-generated holograms (CGHs) are written on to a spatial light modulator (SLM). The current complexity of SLMs limits the size of the CGH. However, prototypes have been developed using SeeReal [2], Microsoft Research [3], Light Blue Optics [4], and the European Real3D project [5]. This work has focused on the reconstruction of 2D scenes and has grown from student work [6] to commercialization within 10 years.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Driving Behavior Characteristics Analysis Results
#### 3.1.5 Cluster Analysis of Driving Behavior
This approach is likely to have fulfilled the criterion of our concern, which was to ensure that objective safety measures and real-world results accurately represented the value of risk levels. In Table 7, the clustering results are outlined by displaying the centroid values associated with each behavioral variable for the four driver categories. These clusters correspond to different driving styles with varying levels of associated risk:  
Category 1: Cautious and Steady Drivers (28% of the sample)  
This group has the lowest average speed of 45.2 km/h and moderate acceleration of 1.68 m/s\({}^{2}\), with a steering angle of 18.6 degrees. They also have the second-longest headway time of 2.25 s, which is above the safety threshold of 1.0 s. Instead of speed, these drivers prioritize safety and make sharp turns, having full control over their vehicles. Category 2: Aggressive and Adventurous Drivers (22% of the sample)  
These drivers can maintain the highest speed while making turns at 67.8 km/h, which is far greater than the design speed of 40 km/h. They also exhibit a high acceleration of 1.52 m/s\({}^{2}\), with a headway time of 1.12 s, which is dangerously close to the critical safety threshold. This combination indicates vigorous behavior with very few safety limits. Category 3: Rough and Reckless Drivers (18% of the sample)  
Within this category, while the average speed was not the highest recorded (58.4 km/h), the participants exhibited the most erratic control activity, which was highlighted by the highest recorded acceleration values (1.85 m/s\({}^{2}\)) and average steering angles (38.2 degrees). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Category 1** & **Category 2** & **Category 3** & **Category 4** \\ \hline Average speed (km/h) & 45.2 & 67.8 & 58.4 & 52.6 \\ Average acceleration & 0.68 & 1.52 & 1.85 & 0.73 \\ (m/s\({}^{2}\)) & 2.25 & 1.12 & 0.96 & 2.68 \\ Average steering Angle & 18.6 & 32.5 & 38.2 & 15.4 \\ (deg) & 28 & 22 & 18 & 32 \\ Driver ratio (\%) & 28 & 22 & 18 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Clustering results of driving behavior. Alarmingly, they have the quickest headway time (0.96 s), which is below the safety threshold of 1.0 s. These drivers lack proper foresight and do not seem to adjust adequately to the anticipated road conditions. Category 4: Careful but Sluggish Drivers (32% of the sample)  
Of all the categories, this is the most populous group. They retain average speeds of 52.6 km/h while keeping their rate of acceleration extremely low (0.73 m/s\({}^{2}\)) along with equally low average steering angles (15.4 degrees). They also keep exceedingly large headway distances (2.68 s), far surpassing what is necessary for safety. The behavior displayed suggests that there is a great amount of caution, which could be attributed to a lack of confidence or ability.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 2 Methods
### Virtual Experiment of Flood Monitoring
#### 2.3.2 Operation Rule of Satellites  
In this virtual experiment wherein the designed SAR small-satellite constellation was operated to monitor flood fluctuation, we investigated the maximum potential of that observation. Due to the various satellite locations and the finite swath width--30 km in this case, based on the properties of the reference small satellite--in some instances, the entire flood extent area could not be observed in this analysis. Therefore, we adjusted the observation angle from 15\({}^{\circ}\) to 30\({}^{\circ}\) and between left- and right-looking at each observation time so that each satellite could orient its SAR instrument toward a target region and observe the largest possible extent of flood area from its location.  
Concerning the limitations of the observations of SAR-equipped small satellites, we make the following two assumptions. First, disaster observation is prioritized, given that even satellites owned by private companies are typically used in emergencies. Second, concerning limitations of the power system, energy generation must be equal to or greater than the energy consumption of the SAR imaging mission sequence [22]. On the basis of these assumptions and the assumption that the interval between observations at daytime and night is about 12 h, small satellites can store sufficient power if disaster management is prioritized.
# AT-BOD: An Adversarial Attack on Fool DNN-Based Blackbox Object Detection Models
## 4 The Proposed Model
### Perturbation-Based AT-BOD
#### 4.2.3 Generating Perturbations  
We implement a Blackbox attack against an object detection model. The attack is able to decrease the mAP value of detector that measures the detection accuracy among dataset classes over different recalls. Since we are implementing a Blackbox attack, it means that we do not know the inner network structure and parameters including the model target weights. Hence, it is not possible to use gradient-based iterative calculations to generate adversarial examples. In order to apply the attack, perturbations transferability is adopted.  
Finding adversarial examples able to attack a network over Blackbox settings can be solved by formulating it as a ZO optimization problem. In our work, the victim model is YOLOv3, a regression-based detector uses an end-to-end neural network that divides the image into region of interest and then predicts the bounding boxes alongside with a confidence score that presents the probability of the predicted bounding box. The model takes the entire image and divides it into a grid of size 13 \(\times\) 13, each cell in the grid predicts 5 bounding boxes that can be described as coordinates of a rectangle encloses an object. We formulate the problem of finding adversarial examples using ZO-ADAMM explained in Section 4.2.2.  
There are two types of perturbations: per-image and universal [49]. The universal perturbation describes how flexible an adversarial attack is for different inputs. Adversarial attacks mostly have individual perturbation which means that the perturbation is applicable only to a particular source. For other sources, we recalculate the attack and receive a different perturbation. For example, if an attacker is able to bypass a facial recognition system, we will need to create glasses for each attacker, meanwhile the perturbation is universal, we will be able to create glasses to spoof the facial recognition system, and no matter who is wearing them. In our work we generate the universal perturbation using Algorithm 2 below.  
```
Input: data sample \(X\), CNN model \(F\), \(l_{2}\)-norm perturbation \(\varepsilon\), adversarial example \(v\) Output: universal perturbation vector \(v\)
1: Initialize: \(v\gets 0\)
2: for each datapoint \(x\) in data sample \(X\) do
3: If \(F(x+v)=F(x)\) then
4: Calculate the minimal perturbation \(v\) that makes \(x+v\) goes to decision boundaries
5: Update \(v\)
6: end if
7: end for
8: return \(v\)
```  
**Algorithm 2** Universal perturbation algorithm  
As shown in Figure 4, after generating the adversarial examples, we built an adversarial network model that sends these examples along with original images to the targeted object detection model, then we calculate the adversarial loss. The model runs iteratively to reduce the loss with the smallest perturbation.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 4 Handover Management for Drone Networks
### Handover Types  
Handover in cellular networks can be classified into different types, based on technique, network type, network management, operating frequency, and scenario. _For example_, handover can be classified into two main handover technique types: hard and soft handover techniques. The hard handover requires the UE to terminate the connection from the serving BS before it switches to the target BS. The soft handover imposes a more gradual connection termination, simultaneously maintaining a connection with two or more BSs for a short period of time [69]. The drones' network can apply two different handover techniques depending on the mobile communication technology.  
Handover also can be classified into different types based on the technology of the serving and target networks. The two main types are horizontal handover and vertical handover. In the horizontal handover, the access points use the same technology and the network interface remains unchanged. In vertical handover, the access technologies are different from each other, and multiple network interfaces are employed. For instance, the user switches from the terrestrial cellular network to satellite technology, as illustrated in Figure 4.  
Furthermore, handover in cellular networks can be classified into three methods depending on the network management system: (i) Network-Controlled Handoff (NCHO), (ii) Mobile-Assisted Handoff (MAHO), or (iii) Mobile-Controlled Handoff (MCHO) [70; 71]. The handover control system is extensively described in [72]. For example, if the recipient signal is the mechanism triggering parameter, two handover scenarios will occur: absolute or relative. The former occurs when the serving BS signal strength becomes lower than a pre-defined threshold value, whereas the latter occurs when the serving RSRP is lower than that of the target BS. The relative handover technique may cause handover to occur earlier than needed yet provides higher quality. Absolute handover, however, causes what is referred to as the \"ping-pong\" effect. This phenomenon occurs from frequent variations in the RSRP value, prompting frequent handovers. These various handover types can also be applied with drones' networks.
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 5 Results  
We trained our model using only Sentinel-2 pixels that are not marked as cloud, cloud shadow, water or snow in the the Sen2 Cor [72] scene classification layer. Furthermore, we masked-out pixels that have a cloud probability \(>50\%\) for training, which allows the model to observe different cloud coverage conditions. At inference time we used only pixels with cloud probability \(<10\%\) to avoid possible noise coming from different cloud conditions.  
To evaluate the performance of density estimation, we aggregate predictions into 1 ha blocks and compare them to reference data. The evaluation on 1 ha blocks sidesteps the influence of residual co-registration errors between reference data and Sentinel-2 images. On validation blocks, our Mean Absolute Error (MAE) after training only with the initial 37 training blocks was 8.9 Trees/ha, and dropped to 5.9 Trees/ha after including the actively selected samples. This represents a reduction of \(\approx\)34% (see Figure 3). For a fair comparison we applied the same robust thresholding in both cases, as we will explain below. These results are in line with our previous study about oil palm density, where active learning lead to a reduction of 28% with respect to the initial MAE.  
Figure 3: Performance of coconut palm density estimation as predicted by our method (right axis) compared to manually labelled validation data (vertical axis). Each green dot indicates a comparison per block of size 300 ha each, while the unit on both axes is Trees/ha. Values with densities below 1 Trees/ha were trimmed for visualisation.  
We present a visual comparison of reference and predicted density maps at two different locations in Figure 4. The main structure of the plantations is correctly mapped and the density values in general agree. However, pixels in areas of high density (close to 2 trees) exhibit some under-estimation bias, as in the middle image of Figure 4.  
As mentioned in Section 4, we considered only confident estimates according to the threshold \(CV>\tau\). It is expected that a lower value of \(\tau\) should increase the accuracy, but reduce the overall recall. Which exact value of \(\tau\) to use depends on the use case. In our case, we aim for high map coverage and remove only the most uncertain locations. See Table 1 for an evaluation how varying values of \(\tau\) affect the output. For these results, we used an \(\epsilon=0.7\) to compute the \(CV\) and have tested different values of \(\tau\). We observe that at \(\tau=0.45\) the MAE improves by 9.1% at the cost of leaving out the <2% most uncertain samples, which we regard as the best compromise.  
We applied the chosen threshold \(\tau=0.45\) to all predictions in our study area. Due to cloud cover especially in high-altitude regions in several states in the _before_-map, the overall recall dropped to 89.1% for the complete study area. Fortunately, most discarded pixels lie in mountain areas, where no coconut is grown, consequently the recall over the validation areas, where coconut plantations are found, was a lot higher (>98%).
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Experimental Evaluation  
This section presents an in-depth performance evaluation of the proposed offloaded QoS routing framework. The assessment compares the proposed method (\(EX\)) with two heuristic baselines under varying QoS constraints, network settings, and topological configurations. All the reported results are averaged over 20 randomized simulation runs on synthetic WSN topologies derived from the existing literature.
## 4 Materials and Methods
### Major Phenolic Composition  
High-performance liquid chromatography (HPLC) was used to determine phenolic composition in wines [39]. Wine samples were filtered with 0.45 \(\upmu\)m nylon membrane (PALL, Washington, NY, USA). Five microliters of the filtrate was injected into the HPLC system. The HPLC system was equipped with an Agilent 1100 series diode array detector (Palo Alto, CA, USA). Data acquisition and processing were completed using ChemStation software (Agilent, v.10.02). Separation was carried out on a Kinetex XB-C18 column (100 A, 2.6 \(\upmu\)m, 150 \(\times\) 4.6 mm, Phenomenex, Torrance, CA, USA) under wavelengths of 280 nm, 360 nm, and 520 nm. Solvent A (0.5% formic acid in milli-Q water) and solvent B (100% methanol) were used as mobile phases under a flow rate of 0.8 mL/min. Solvent gradient: 0-5 min (3-16% B); 5-15 min (16-35% B); 15-20 min (35-55% B); 20-25 min (55-100% B); 25-27 min (100% B); 27-29 min (100-3% B); 29-35 min (3% B). The phenolic compounds (caffeic acid, caffeoyltartaric acid, catechin, epicatechin, ferratic acid, gallic acid, malvidin-3-o-glucoside, p-coumaric acid, resveratrol, trans-coutaric acid, and vanillic acid) were identified through comparison with UV spectra and retention times of analytical standards. Quantitation was conducted through external calibration curves. Caffeic acid, caffeoyltartaric acid, catechin, epicatechin, ferratic acid, gallic acid, p-coumaric acid, resveratrol, trans-coutaric acid, and vanillic acid were quantified under 280 nm and malvidin-3-glucoside was quantified under 520 nm. Wavelength 360 nm was used as an additional reference for the identification of phenolic acids.