esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Bit Repair Fast Reroute Mechanism for Smart Sensors IoT Network Infrastructure
## 2 Related Works
### Problem Areas
#### 2.1.2 Custom Alternative Path and Cost-Based Calculations  
In some situations, a network administrator should also have the possibility to manually specify an alternative path. The administrator can define a custom alternative path that can avoid a group of routers potentially affected by the failure.  
Existing FRR solutions such as LFA [33], R-LFA [16], TI-LFA [49] calculate alternative FRR paths according to the link metrics of used routing protocols. The calculation and construction of alternative paths must usually meet specific algorithm conditions. Only paths that satisfy them can be then selected as valid alternative routes. In certain topological situations, problems may arise where an alternative path exists, although it is possible that based on its metrics it does not meet mathematical conditions of an algorithm and cannot be used as an alternative path. Therefore, some existing paths are unnecessarily excluded under the given conditions. The alternative path is defined in each of the existing FRR mechanisms differently (tunneling, adding specific bits in the IP header).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solution** & \begin{tabular}{c} **Full Repair** \\ **Coverage** \\ \end{tabular} & \
\begin{tabular}{c} **Proactive** \\ **Behavior** \\ \end{tabular} &
\begin{tabular}{c} **Dependency on** \\ **Routing Protocols** \\ \end{tabular} & **Prediction** \\ \hline BIER-TE & Yes & Yes & No & No \\ Directed LFA & No & Yes & Yes & No \\ LFA & No & Yes & No & No \\ MPLS-TE & No & Yes & No & No \\ MRC & Yes & Yes & Yes & No \\ MRT & Yes & Yes & Yes & No \\ Not-Via & Yes & Yes & Yes & No \\ Remote LFA & No & Yes & Yes & No \\ TI-LFA & Yes & Yes & Yes & No \\ SWIFT (BGP) & Yes & Yes & Yes & Yes \\ M-REP & Yes & No & No & No \\ EM-REP & Yes & No & No & No \\ \hline \hline \end{tabular} BIER-TE-Bit Index Explicit Replication-Traffic Engineering. LFA-Loop-Free Alternate. MRC-Multiple Routing Configurations. MRT-Maximally Redundant Trees. TI-LFA–Topology Independent Loop-Free Alternate.  
\end{table}
Table 1: Comparison of FRR solutions.
## 2 Collision Grid Map Scheme and Convolutional Neural Network
### Convolutional Neural Network Algorithm Using the Collision Grid Map  
The Convolutional Neural Network algorithm can be shown robustly in the office and road environment. However, there is required big data due to the different appearance. In an automatic farm system, typically fences and livestock have same appearance. Unlike the office and road environment, networks can be learned with less data that is defined as a collision situation. In order to predict the collision situation, the non-collision situation is defined when there is a fence only and the collision situation is defined when cows stick their head out of the fence. In these defined situations, performance of segmentation is guaranteed even without deep networks due to normalized data of the appearance.  
Figure 1: (**a**) The example of generating the first channel in a Collision Grid Map using raw point cloud and (**b**) an overall Collision Grid Map.  
The convolutional layer is learned features of local data in a Convolutional Neural Network algorithm. The proposed Collision Grid Map transforms 3D point cloud data to grid data without destroying spatial characteristics of point cloud data. When networks are learned, a generated Collision Grid Map can be effectively applied to the Convolutional Neural Network to predict the collision situation.  
When weights of convolutional layers are w in N by N size, Equation (7) is shown as the learned convolutional layer for a generated Collision Grid Map. \(c\) is the convolutional layer value and \(m^{*}\) means the generated Collision Grid Map.  
\[c_{ij}=\sum_{a=0}^{N-1}\sum_{b=0}^{N-1}\left(w_{ab}m^{*}_{(i+a)(j+b)}\right) \tag{7}\]  
Figure 2 applies the Collision Grid Map to Convolutional Neural Network. The network is a learned spatial feature about collision and a non-collision situation in the convolutional layer. In order to reduce computation, data size is reduced in the pooling layer. In a practical barn environment, measured data about the defined situation is filtered by the learned network to detect the local feature of the x-y plane and safety distance. Then, the SoftMax function transforms the filtered data to a probabilistic value. A high probability score is selected as an appropriate situation. When the Collision Grid Map is applied to a Convolutional Neural Network, there are some advantages that the network is learned not only in local features in the x-y plane data but also on a safety distance in the z direction to distinguish between the collision and non-collision situation.
## 5 Ionospheric Observation Anomalies  
In agreement with the timing of the temperature fluctuations before the earthquake (Figure 3), the electron density measured by the electromagnetic satellite Zhangheng-1 also showed an abnormally high value on 14 January 2020, with the anomaly region indicated by Letter \"A\" (Figure 6b). In the ascending orbit, the satellite observed the region  
Figure 4: Short-term abnormal changes in bedrock temperature at different observation stations. The red vertical lines indicate an abnormal start time. The green area is an abnormal change in shape. 0 denotes the time of earthquake occurrence.  
Figure 5: Evolution of distance between geothermal anomaly observation station and epicenter. \"+, \(-\)\"represent the opposite direction.  
closer to the epicenter and measured abnormally high values in the study area, which has latitude and longitude ranges of 79.0-81.5\({}^{\circ}\) E and 38.0-46.0\({}^{\circ}\) N (the thicker part of the orbit in Figure 6b). The electron density time series in Figure 6a indicates relatively stable electron density on this orbit from 5 December 2019 to 9 January 2020, with fluctuations of approximately 1.2 \(\times\) 10\({}^{10}\) m\({}^{-3}\). However, on January 14, the maximum average value was 2.01 \(\times\) 10\({}^{10}\) m\({}^{-3}\), which exceeded the background value by 67%; this observation was synchronous with the rock temperature anomaly. The variation of spatial electron concentration is generally related to solar activities and near-surface disturbances, such as cyclones and volcanic eruptions. Earthquakes can also affect it through atmospheric fluctuations, electric field disturbances, and magnetic field disturbances. Before the earthquake, due to stress accumulation, rock fracture, fissure increase, gas escape, greenhouse gas release, heat source, and others, the near-surface atmosphere, electric field, and magnetic field should be changed. The electron concentration oscillates when it reaches the ionosphere, which may be caused by acoustic gravity waves [(35; 57)].
# Extending Cooling Rate Performance of Fast Scanning Chip Calorimetry by Liquid Droplet Cooling
## 2 Experimental
### Implementation of the Liquid Droplet Cooling into the FSC Electronics  
In droplet cooling mode, for temperature recording and triggering the switching off of the heater voltage, the thermopile of the sample loaded sensor is connected to a high input impedance JFET voltage preamplifier SRS SIM910 (see amplifiers in Figure 5). The temperature signal from the preamplifier is monitored and recorded at the desired rate in the ring buffer of the NI DAQ (ADC/DAC) board. The data sampling rate was 1,000,000 samples per second per channel. In order to independently prove the validity of the thermopile readings, particularly during cooling with liquids at low temperature, the heater resistance was recorded too by applying a small voltage to the heater. Heater resistance can be calibrated to temperature and, thus, gives an additional confirmation of the temperature, e.g., when the thermopile cold junction temperature is unknown or changes during the droplet cooling.  
Figure 3: Methodology of liquid droplet cooling.  
Figure 4: Details of the droplet cooling step.  
Figure 5: Liquid droplet cooling realized as a part of the fast scanning calorimeter described in [30] Thermo-chim. Acta 2010.  
The heater, which is needed to set up the initial temperature, must be switched off synchronous with the droplet arrival in stage II.B in Figure 4. If it remains powered, the temperature will not drop at maximum speed and levels off above the environmental temperature. Thus, an adjusted triggering of the heater voltage off is needed to increase the cooling rate.  
For a better understanding of the triggering conditions, preliminary liquid droplet measurements were performed without switching off the heater. It was observed that few microseconds before contact with the droplet, the sensor thermopile detects a temperature deviation, probably caused by cold gas pushed in front of the moving droplet. This deviation can be used to program the trigger using the pre-amplified thermopile voltage as the source and switch off the heater voltage output of the DAQ board internally. The functionality of the NI DAQ 6356, used in the current version of the FSC, allows us to carry this out. After several tests, it was confirmed that the trigger delay is negligible and suitable for liquid cooling purposes. Thus, direct implementation into the FSC hardware was realized, avoiding additional electronics, such as the Schmidt trigger used previously [29].
# DRUNet: A Method for Infrared Point Target Detection
## 2 Related Work  
### Infrared Single-Frame Small Target Detection by CNN-Based Method  
[PERSON] et al. [16] proposed IR small target detection with a generative adversarial network (IRSTD-GAN) in 2021, using a generative adversarial network, where the generator outputs the grayscale prediction of the target and trains the discriminator for indirect supervision while directly supervising it. [PERSON] et al. [17] proposed the asymmetric contextual modulation (ACM) algorithm, which contrasts the fusion between deep and low-level features and proposes an asymmetric contextual modulation module. The high semantic features use the channel attention feature weight distribution for the low-level features, and the low-level features use the pointwise convolution spatial attention to distribute the feature weights for the high semantic features. Attentional local contrast (AlcNet) was proposed in the paper [18] to address the problem of intrinsic features in a purely data-driven approach, using a model-driven combined with a data-driven approach, inputting the multilevel feature map of the network into a nonparametric nonlinear local contrast model and then performing fusion regression, and performing enhancement or suppression operations on different regions of the feature map based on small target characteristics. The author also discloses the single-frame small infrared target (SIRST) dataset used, which is also used in our training and experiments. [PERSON] et al. [19] proposed dense nested attention network for IR small target detection (DNAnet). They designed a densely nested interaction module (DNIM) to implement progressive interactions between high-level and low-level features. Using repetitive interactions in DNIM, deep IR small targets can be maintained. Based on DNIM, a cascaded channel and spatial attention module (CSAM) is further proposed to adaptively enhance multilevel features, which can integrate and fully utilize the contextual information of small targets by iterative fusion and enhancement. [PERSON] et al. [20] designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. It consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism.  
Although the performance of the CNN-based method has improved in recent years, the problem of small target loss remains. The complexity of some models is relatively high, and there is still space for optimization in terms of detection rate and detection efficiency [21].
## 4 Discussion
### Analysis of Time-Varying Characteristics of Glare in the Access Zone of East Oriented Tunnel  
An analysis was conducted on the time-varying characteristics of glare in the access zone of an east oriented tunnel, as shown in Figure 15. From Figure 15, it can be observed that the overall glare value of an east oriented tunnel shows a trend of first increasing and then decreasing. In the morning, the glare value is higher than in other time periods. The uncomfortable and even intolerable glare phenomenon caused by the luminance difference between inside and outside the tunnel occurs from 9:00 a.m. to 11:00 a.m. The luminance outside the tunnel at 10:00 a.m. and 11:00 a.m. is too large, and the corresponding _PGSV_ glare value is 8.41 and 7.6, respectively, which is far beyond the intolerable glare value limit.  
Figure 14: Road tunnel light and dark background block diagram.  
Currently, the luminance difference between inside and outside the tunnel is 97% and 96%, which occurs at the moment when the luminance difference between inside and outside the tunnel is the largest in a day. As mentioned above, the overall trend of L\({}_{20}\)(S) brightness outside the hole is to increase first and then decrease, and the maximum value appears at 10:00 a.m. The overall variation trend of the luminance difference degree inside and outside an east oriented tunnel also increases first and then decreases. The time-varying characteristics of glare are roughly consistent with the L\({}_{20}\)(S) value of the luminance outside the tunnel and the overall variation trend of the luminance difference degree. When the east-west oriented tunnel entrance and the surroundings are directly exposed to sunlight, the luminance outside the tunnel increases accordingly. Because the luminance of the tunnel entrance remains extremely low, the degree of luminance difference between the inside and outside of the tunnel increases on approach. This leads to the phenomenon of inappropriate luminance in the visual environment at the access zone of an east-west oriented road tunnel, resulting in uncomfortable glare at the access zone. Therefore, when the access zone is exposed to direct sunlight, the increase in luminance outside the tunnel will not only affect the design of the tunnel lighting distribution curve and increase the lighting energy consumption, but also increase the difference in luminance inside and outside the tunnel, leading to uncomfortable glare. In severe cases, it will affect the driver's driving comfort and safety.
# Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication
## 2 Background
### Presentation Contexts  
Presentation Contexts in DICOM define the combination of a specific SOP Class and one or more supported Transfer Syntaxes that an AE can handle for communication purposes. When two DICOM devices establish a connection or association, they negotiate the set of services and data formats that they can support. This negotiation is accomplished using Presentation Contexts. A Presentation Context consists of the following components [42]:  
* **Abstract Syntax:** this identifies the class or type of DICOM object being transmitted or requested, and represents a specific SOP Class, such as CT Image Storage or MR Image Storage.
* **Transfer Syntaxes:** these define the encoding and compression techniques used to represent and transmit the DICOM data, and specify how the DICOM object is serialized into a byte stream. Multiple Transfer Syntaxes can be associated with a single Abstract Syntax, allowing for flexibility in data formats and compression options.  
The process of association negotiation in the context of medical imaging involves the exchange of information between two devices. During this negotiation, both devices share a list of supported Presentation Contexts. Each Presentation Context is composed of an Abstract Syntax along with one or more Transfer Syntaxes that the device is capable of supporting for that specific Abstract Syntax.  
The two devices then compare the lists of Presentation Contexts they have shared and work towards finding a mutually supported Presentation Context. When this mutual agreement is reached, it signifies that the devices can effectively communicate and conduct operations involving DICOM objects. This is done using the designated Abstract Syntax, while the agreed-upon Transfer Syntax is employed for the communication.  
For instance, consider a scenario in which two devices establish a Presentation Context with an Abstract Syntax. This agreement indicates that the devices are now equipped to exchange and manipulate magnetic resonance image data using the JPEG Baseline compression algorithm. This enables seamless and efficient communication between the devices while ensuring compatibility in handling medical image information.
## 3 Results
### Future Flood Risk Evolution Driven by Multiple Assessment Factors
#### 3.2.2 Attribution Analysis of Risk increase  
Understanding the key drivers of risk increase is crucial to implementing targeted actions to address challenges in advance. Therefore, we conducted an indicator contribution analysis on the driving factors for every grid cell with a risk increase, as shown in Table 3.  
Figure 11: Statistical results of risk change in the PLEEZ in different scenarios. (**a**) Amount and percentage of area changed for each risk level in different periods and scenarios of Figure 9 (compared with the baseline map). (**b**) Amount of area changed for each intensity of risk change in different periods and scenarios of Figure 10 (compared with the baseline map). Almost all scenarios reveal that RMAX3 would be the dominant factor contributing to higher future flood risk, followed by GDP and land use, while population would play a minor role. This finding implies that extreme rainfall events caused by climate change will directly exacerbate flood disasters in the PLEEZ in the near- and mid-term future. The relatively lower contribution of RMAX3 in the 2030s in the SSP2-RCP4.5 scenario (46.58% of the total; Table 3) is caused by the low and even negative change ratio of RMAX3 in many areas in this scenario (Figure S7a), resulting in the risk mitigation that we would like to see in the 2030s (Figure 9a). In other words, the weak extremity of RMAX3 in the near future would lead to a delayed arrival of the risk peak under the SSP2-RCP4.5 scenario compared with SSP5-RCP8.5. Another important finding is that the key causal factor for the significant increase in risk in local areas is the change in land-use type (Table 3). Further statistics in Table 4 reveal that this change is mainly from formerly unused lands such as wetland and bare land to cropland, as its ratio exceeds 65% in all scenarios, reaching a maximum of 81.25%. The remaining change type is basically the conversion of forests into cropland. Thus, relevant territorial planning authorities need to ensure the protection of wetland resources, and potential flood risk should be fully considered when reclaiming new cropland resources. Moreover, the total contribution rate of the land-use factor to risk increase in the 2030s in the SSP2-RCP4.5 scenario reached 36.04%, underscoring the importance of rational land planning in the near future.
## 3 Nanoparticles in EOR
### Silica-Based  
Silica-based NPs are among the most often studied for EOR application since they can be produced easily, have well-known physical-chemical properties, and can be engineered with different characteristics such as hydrophobicity to hydrophilicity [79]. They are also one of the most abundant non-toxic inorganic materials [109] and have lower production costs than other NPs [110]. Many types of engineered silica NPs have been synthesized. These can be categorized based on their wettability behavior into three types [9]: lipophobic and hydrophilic polysilicon (LHP), hydrophobic and lipophilic polysilicon (HLP), and neutral-wet polysilicon (NWP). Silica-based NPs have been proved to change the wettability of porous media when adsorbed. In addition, silica NP itself has good thermal stability, as observed using infrared spectroscopy, x-ray diffraction, and SEM analysis, excluding the thermal instability for relatively high temperature applications [111]. [PERSON] et al. [3] conducted experimental and theoretical studies on the utilization of hydrophilic silica NPs (LHP) for EOR. By injecting 2.0-3.0% of LHP NPs, they concluded that NPs could be adsorbed onto pore walls, changing the wettability of the surface and apparently improving the oil recovery. [PERSON] and [PERSON] [12] performed a further study on LHP, HLP, and NWP silica NPs dispersed in different fluids (water, ethanol, and alcohol). NWP and HLP silica NPs dispersed in ethanol could alter the wettability of the water-wet core and behave as surfactants by reducing the oil-water interfacial tension. [PERSON] et al. [84] studied the effect of LHP silica NPs by implementing a coreflood experiment on the Berea sandstone core with various NP concentrations from 0.01 up to 0.1 wt % dispersed in brine (NaCl 3.0 wt %). They showed that the contact angle decreases when the concentration of NPs increases, but a higher concentration tends to block the pore throat of the low-permeability Berea core sample. Moreover, earlier nanofluid injection is favorable since it gives the highest ultimate oil recovery compared to the post-waterflood and sequenced water-nanofluids
## 3 Results and Discussion  
### Results for the CVCC Method  
The values of the test results obtained and the standard deviations of the measurements for MPR, ID and CD uncertainties are shown in Table 6. For each fuel sample, the values of the standard deviation of the mean were determined from 15 tests (combustion cycles). Table 6 also includes data related to fuel injection for each fuel sample. Figure 9 shows the changes in pressure in a constant volume chamber for the fuels tested. These are graphically represented average values from 15 combustion cycles.  
The results of comparative tests conducted in a constant volume chamber and under fixed conditions showed that the addition of n-hexane to canola oil has little effect on changes in the values of ignition delay (ID) (Figure 10) and combustion delay (CD) (Figure 11). Accordingly, small changes in the value of the derived cetane number (DCN) (Figure 12) were also noted. The results of these tests were obtained under fixed test conditions, i  
The pressure changes in the combustion chamber (p\({}_{\rm ch}\)), shown in Figure 9, show a significant difference in the pattern of pressure increase for diesel fuel and the blends of canola oil and n-hexane analyzed. At the same time, it can be noted that in the case of blends, the pressure in the combustion chamber builds up significantly more slowly than in the case of diesel, and an increase in the proportion of n-hexane in canola oil resulted in a slight increase in the rate of build-up of this pressure in relation to pure canola oil. As a result, changes were noted as to the value of the CD parameter and the MPR parameter (Figure 13).