esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Full Correlated-Power-Noise Generator to Counteract Side-Channel Attacks
## 3 AES: Structure, Implementation, and Vulnerabilities
### Area and Frequency  
The performance of the AES implementation is analyzed in terms of timing and resource occupation using the device Spartan-6 Xilinx Field-Programmable Gate Array (FPGA) device. In order to evaluate the security levels achieved by our designs, a specific platform has been used to develop CPA attacks in an appropriate manner. In this sense, the SAKURA-G has been chosen for its versatility, which has two Xilinx Spartan-6 FPGAs, where the cryptographic module can be implemented in one of its FPGAs, while the other one can be exclusively dedicated to the control and data transmission with the PC. Design specifications are in Table 1. The AES128-256 uses 3616 Look-Up Tables (LUTs) in total so requires 7% of FPGA resources. It is significant to mention that the AES response time depends on the key length and whether an encryption or decryption operation is performed. However, there is no dependency between the response time and the specific value of the key or the data value processed.  
Figure 2: AES encryption algorithm diagram.
## 4 General Description of Adopted IoT-Based System for the Forest Fire Monitoring
### The Choice of Environmental Parameters for the Forest Fire Risk Prediction and Detection
Further research [57; 63; 64] considered human behavioral (date and time) and environmental (road and population density) factors together with weather data obtained from sensors as an input for their fuzzy reasoning system, which gives the probability of fire occurrence. The authors in [57] deployed an AHP-based fire spread estimator, which combines measured data with additional static forest data such as water resources, landscape position, forest tracks, and vegetation to better estimate the forest fire propagation once a fire outbreak is detected. Considering the previous discussion, in this section, we adopted the IoT-based sensor network for forest fire prevention, prediction, and detection, with the access network realized in the form of LoRaWAN system architecture. As already stated, the fuzzy AHP method is applied for the real-time tasks (forest fire risk prediction and fire outbreak detection) and short-term and long-term forest fire susceptibility assessment, which utilizes both real-time (i.e., collected by sensor networks in real-time) and static data.
# Characterization of Nanoparticle Batch-To-Batch Variability
## 4 Discussion
### Crystallinity  
It is well known that differences in the crystalline phase of NMs with the same composition may affect their toxic effects. This has been shown for example, SiO\({}_{2}\) NPs where the crystalline quartz particles are more toxic than amorphous silica NMs [50]. In this study, all SiO\({}_{2}\) preparations were amorphous. The TiO\({}_{2}\) NM in this study were found to consist of 80% anatase and 20% brookite, a similar content of anatase as in the reference NM Aeroxide(r)P25 which consists of 80% anatase and 20% rutile. It is known that the anatase form is chemically more reactive and more toxic than the other crystalline forms [51]. However, only batch 06 (Figure 4F). showed a similar radical formation potential as Aeroxide(r)P25. All other batches induced lower radical formation. Thus, the crystallinity alone does not entirely predict ROS formation.
## 4 DASH Live Broadcast Traffic Model
### Time-Bound Token Bucket Traffic Model  
In the live broadcast scheme, the segment files are entering the token bucket queue every unit time \(\rho\). At this time, it is assumed that the size of the token bucket queue is sufficiently large because there should not be any loss of data. When the proposed token bucket size of the \(i\)th segment is \(L_{i}\), the token generation rate is \(TC\), and the token bucket size is \(TB\), the bandwidth capacity bound satisfies Equation (1) and the bandwidth over-traffic bound Equation (2):  
\[\sum_{i=1}^{N}MIN(L_{i},\ TC)\leq TC\cdot N\cdot\rho \tag{1}\]  
\[\sum_{i=M}^{N}(L_{i}-\ TC\cdot\rho)\leq TB\ (M<N). \tag{2}\]  
Equation (1) indicates the amount of data transmitted by the token bucket, and the data are transmitted within the time limit. In this case, \(N\) denotes the number of segment files to be transmitted. Equation (2) indicates the characteristics of the token bucket, which transmits more data than the token bucket instantaneously at a certain unit time (segment files \(M\) to \(N\) (\(M<N\))). At this time, the amount of data to be transmitted may exceed 1 times the size of the token bucket, but not more than twice that.  
Figure 3 illustrates the number of tokens in the token bucket at time \(T_{i}\) denoted as \(TB(i)\), and the volume of data remaining in the token bucket queue after transmittal is denoted as \(TQ(i)\). The amount of data \(TQ(i+1)\) remains in the token bucket queue at time \(T_{i+1}\), which satisfies Equation (3):  
\[TQ(i+1)=\max(0,TQ(i)+L_{i}-\min(L_{i},TB(i)+TC\cdot\rho)). \tag{3}\]  
The delay is determined by the amount of data \(TQ(i+1)\) remaining in the token bucket queue. Therefore, if the maximum queuing delay is \(T_{q}\), it can be denoted as follows in Equation (4):  
\[T_{q}=\max\{TQ(i)/TC,\ TQ(i+1)/TC,\ \ TQ(i+2)/TC\,\ \ldots,\ TQ(N)/TC\}\ \ (i=1,2,3, \ldots,\ N). \tag{4}\]  
Figure 3: Time-bound token bucket traffic model.  
Therefore, because the segment file remaining in the token bucket queue is always smaller than the segment file that generates the maximum queuing delay \(T_{q}\), it can be denoted as follows in Equation (5):  
\[TQ(i)\leq T_{q}\cdot TC\ \ \ (i=1,2,3,\ldots,\ N). \tag{5}\]  
The maximum queuing delay \(T_{q}\) increases as the remaining data in the token bucket queue increases.
## 3 Results and Discussion  
### TPC, TFC, and Antioxidant Activities  
It is well-recognized that free radicals contribute significantly to a broad range of clinical symptoms. Antioxidants combat free radicals and shield us from illness. They either remove reactive oxygen species from the environment or defend the antioxidant defense system [54]. In our current observation, _A. malacensis_ contains substantially more flavonoids and phenolic compounds than _A. gallocha_. Additionally, flavonoids have properties that help to stop cell damage [55]. During normal growth, phenolic compounds are also secondary metabolites produced in plants and play an essential role in wound treatment and skin infection [56]. During normal growth, phenolic compounds are also secondary metabolites produced in plants and play a vital role in wound treatment and skin infection [57]. The extract of _A. agallocha_ is a considerably higher phenolic compound than others.  
TPC and TFC were determined by the Folin-Ciocalteu (FC) and aluminum chloride colorimetric methods. Table 1 revealed the TPC and TFC of _A. Agallocha_ and _A. malacensis_extracts where gallic acid and rutin were used as standard, respectively. TPC values of _A. agallocha_ and _A. malacensis_ were 37.203 \(\pm\) 0.14 and 30.026 \(\pm\) 0.11 \(\upmu\) GAE/mg, whereas total flavonoid contents were 276.745 \(\pm\) 0.09 and 292.095 \(\pm\) 0.08 \(\upmu\) RE/mg, respectively.  
According to the DPPH assay, _A. agallocha_ showed better antioxidant activity than _A. malacensis_. We noticed that _A. agallocha_ exhibited 2.394 \(\pm\) 0.009 ug/GAE mg extract, whereas _A. malacensis_ showed 2.271 \(\pm\) 0.006 ug/GAE mg extract. In addition, the reducing power assay assessed the reducing ability of _A. agallocha_ and _A. malacensis_ extracts in a redox reaction. The results of the reducing power assay were revealed as gallic acid equivalent and _A. agallocha_ and _A. malacensis_ extracts exhibited 9.746 \(\pm\) 0.06 and 9.243 \(\pm\) 0.03 \(\upmu\) GAE equivalent, respectively.
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 3 Method
### Fast-Optimal
#### 3.3.1 Path Fusion  
Path fusion refers to intercepting a part of each of two paths to combine them into a better path. In this case, the evaluation index of the path is the path length. Therefore, path fusion is introduced to fuse multiple initial paths to obtain a better path with a shorter length than the two paths. Figure 6 illustrates the algorithm flow chart of path fusion. In each iteration, a random non-optimal path \(P_{new}\) is obtained by the Improved RRT algorithm. Due to the high efficiency of Fast-RRT, \(P_{new}\) can be quickly obtained. Then \(P_{new}\) is fused with the current optimal path \(P_{optimal}\) to obtain a more optimal path, thus being the new \(P_{optimal}\). By continuously generating new paths and fusing them with the current optimal path, the path fusion algorithm will result in the final optimal path.  
The process of path fusion is shown in Figure 7. It consists of the following steps: the first step is to find the intersection, and the second step is to select the sub-path. In the first step, the intersection points of paths \(path_{new}\) and \(path_{best}\) is be calculated and used to divide the path into multiple sub-paths. Given that the nodes of the two paths do not completely  
Figure 6: Schematic illustration of the algorithm flow chart of path fusion.  
overlap, if the distance between \(point_{1}\) and \(point_{2}\) is less than the set threshold \(r\), they are considered to be overlapped and set the midpoint \(point_{mid}\) as overlap point. In the second step, we select sub-paths from \(path_{new}\) and \(path_{obest}\) to connect each intersection. As shown in Figure 6, the sub-path of \(P_{new}\) connecting \(q_{start}\) and \(q_{near}\) is shorter than the sub-path of \(path_{best}\) connecting \(q_{start}\) and \(q_{new}\). Thus, the sub-path of \(path_{best}\) is used to connect \(q_{start}\) and \(q_{near}\). Similarly, the sub-path of \(path_{best}\) is used to connect \(q_{near}\) and \(q_{goal}\) because it is shorter than that of \(path_{new}\). In this way, the two paths can be combined into a better path than the individual paths. By continuously fusing the current optimal path \(path_{best}\) with the newly generated random path, the algorithm quickly obtains a near-optimal path. The details of the path fusion algorithm are shown in Algorithm 7.
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 3 Proposed Solution
### Validator Election  
The nodes with the highest TVs are the candidates to become validators in the network. To determine which node will be the block creator, we adopt the bully election [23] strategy, which is one of the common election algorithms used in distributed environments. It is called bully election because the nodes with the highest identification numbers make other nodes accept them as coordinators. A node that intends to become the leader contacts any other nodes with higher priorities. If there is a reply from any of those nodes, it gives up on becoming the coordinator. Otherwise, it becomes the coordinator in the network. When the highest priority node directly claims the coordinator role, the communication overhead is the lowest, meaning this is the best scenario. Therefore, this algorithm is appropriate for a MANET environment.  
Similarly, in a MANET blockchain, only the node with the highest TV can become the validator. There is a threshold value \(\theta\) in the network that determines if a specific node is sufficiently trustworthy to become a validator node. Unlike in bully election, a node cannot claim to be a trustful node on its own, meaning it requires a neighboring node. If node \(i\) has a neighboring node \(j\) with a TV above the threshold, it sends a claim message (i, j, TV-Claim, one-hop-count)\(prKey_{i}\), where \(i\) is the follower node of \(j\), \(j\) is the validator node claimed by \(i\) and trust value and one-hop neighbor count of \(j\) is put in TV-Claim and one-hop-count, respectively. \(prKey_{i}\) is the private key of \(i\), which signs the claim message. Similarly, every node with neighbors with TVs above the threshold can broadcast a claim message to the entire network by piggybacking on a TC message. If the TV of node \(j\) is the highest and there are no malicious claims on nodes \(i\) and \(j\) from other nodes, \(j\) becomes the validator. In the claim message, one-hop neighbor count is added to avoid the situation in which two or more nodes have the same trust value and cannot be concluded which node should be selected. Based on the count, the node which has more one-hop neighbors could have more chance to be validator. Here, node \(i\) sacrifices its energy to broadcast a claim message for node \(j\). Because a MANET is a resource-hungry environment, there should also be a reward for the voting node \(i\) (possible rewards for node \(i\) will be discussed in the following subsection).
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 4 Proposed System for Massive Uplink Scenario
### Wake-Up Frame and Data Transmission for Triggering Uplink Transmission from Multiple Users  
The wake-up and data transmission procedure can be operated in unicast transmission (single-user operation) or multicast transmission (MU operation). In unicast transmission systems, the transmission procedure is repeated for each STA. On the other hand, in the multicast transmission procedure, the transmission procedure is based on a group of STAs, and STAs in the same group may communicate with the AP simultaneously.
# A Deep-Learning Approach for Identifying a Drunk Person Using Gait Recognition
## 2 Materials and Methods
### Experimental Design
#### 2.1.2 Setup  
The experiment was divided into a questionnaire survey phase and a walking phase. Personal information, such as gender, age, and drinking habits, was collected through a survey.  
The subjects' gaits were recorded 10 times each, which consisted of five drunk and five non-drunk walkings. At this time, since there are many limitations to measuring walking in an actual drunken state, goggles (Drunken Busters Goggles(r)Drunk Busters of America LLC, Brownsville, TX, USA) that simulate the drinking state were used. Drunk walking consists of walking while wearing the goggles, and non-drunk walking is defined as normal walking without wearing goggles.  
Regarding recording, the distance that the subjects walked was 10 m, and the distance between the camera and the floor was 1.2 m. The distance between the camera and the subject was 5 m, and the camera was placed in the center of the 10 m walking distance. The actual walking distance recorded by the camera was about 4 m, which comprised the middle section. To extract only natural human walking, the first and last 3 m were not included in the recorded range. To minimize noise caused by shadows, a shaded area with an illuminance of 3000 lux and which was free from obstructions was selected as the recording location. When recording indoors, it is often difficult to remove the background due to shadows on the wall and sudden changes in lighting; thus, we filmed outdoors in the shade. Regarding the camera, the rear camera (UHD 30 fps) of a mobile phone (Galaxy S21+) was used, and the resolution was 3840 \(\times\) 2160.
## 3 Experiments
### Experimental Steps  
1. Step 1: Scene Initialization. In the experimental environment, obstacles and target points representing the substation layout were arranged. The setup included defining the positions of fixed obstacles, the movement trajectories of dynamic obstacles, and the coverage area of electromagnetic interference zones. The distance between the robot's starting point and target points ranged from 6 to 12 m, ensuring the evaluation of both short-range and long-range navigation tasks.
2. Step 2: Sensor Data Collection. The robot sensor modules were activated to collect the following data: * Beidou navigation data: Global positioning data were generated through the virtual RTK module with a frequency of 1 Hz. * Binocular vision data: Left and right images and depth maps were generated with a frequency of 30 Hz. * IMU data: Attitude information was output with a frequency of 100 Hz.
3. Step 3: Navigation Task Execution. The Beidou-Binocular Cooperative Navigation Model was activated, fusing multi-sensor data to perform path planning and navigation tasks. The GNSS, Vision-SLAM, and GNSS-Vision models were run in the same scenario to complete navigation tasks.
4. Step 4: Data Recording and Processing. For each navigation task and model configuration, the experiment was repeated 10 times. The global positioning error, actual path length, dynamic obstacle avoidance outcomes, and system processing delay were recorded for each run. The final reported metrics are the average of these 10 trials, and statistical data, including standard deviation, were generated for analysis.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 6 Conclusions  
We combine the methods of soil moisture data assimilation and fitting corrections in improving precipitation estimates from the GPM IMERG early product. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the Antecedent Precipitation Index (API) model; three fitting methods--i.e., linear, nonlinear and cumulative distribution function (CDF) fitting corrections, both separately and in combination with the SMAP soil moisture data--are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the SMP, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference due partially to an intrinsic correlation between reference rainfall dataset (CMPA) and the GPM product. Because CMPA was produced based on satellite observations in addition to ground observations, CMPA and GPM have components in common, which makes it harder to further improve the correlation between the two. In addition, leaf area index and the frequency of the SMAP overpasses may also influence the correction effect. This study highlights that combining soil moisture and precipitation information can effectively improve satellite-based precipitation products in near real time, which will have great impact on flood and landslide monitoring.  
[PERSON] carried out the experimental design, analyzed the data, and wrote the paper. [PERSON] proposed the idea, and led the writing of the paper with contribution from [PERSON]. [PERSON] and [PERSON] helped to revise the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51779278. U1811464, and 51379224.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Studied Cases for the Proof of Concept (POC)
#### 3.1.2 Case 2  
The main principles and objectives of the second studied case were mostly similar to case 1 as case 2 focused on the management of the DCCs of temperature measurement instruments and their transfer between the instrument owner and calibration provider. This work was carried out in alignment with the German Gemimeg II research project, in which the DCC-related development work has been led by PTB [5].  
In addition to Aalto University, the organizations involved were:  
1. PTB, the German NMI and developer of the DCC concept and XML schema;
2. Testo Industrial Services GmbH, a calibration service provider with an accredited laboratory;
3. Boehringer Ingelheim, a pharmaceutical company;
4. Beamex Oy Ab.  
Based on the requirements defined in case 2, PTB developed a DCC good practice for temperature certificates based on the DCC XML schema version 3.0.0. The DCC good practice for temperature certificates has been further developed in the Gemimeg II project [65].
## 3 Results
### Example of Scenario States Visualization  
#### 3.2.1 Introduction  
The second example will demonstrate the problem of visualizing a scenario and how our algorithm can help in solving it. A task for this example is composed of three areas and two robots of the same type. The initial state of the system is presented in Figure 7. The tracking bigraphical reactive system for the purpose of this example consists of two reaction rules, \(r1\) and \(r2\), shown in Figure 8a,b, respectively. The goal of the task is to move the two robots from the area \(AT1\) to the area \(AT3\).  
Tracking Transition System generated from this TBRS is defined in Table 17.  
The tracking Transition System from Table 17 can be transformed into a state space as in Figure 9. Now, suppose that a walk chosen for the behavior policy is of the form:  
\[S_{0}\xrightarrow{f_{1}}S_{1}\xrightarrow{f_{3}}S_{2}\xrightarrow{f_{5}}S_{4} \xrightarrow{f_{8}}S_{5}\]  
The above walk never \"passes\" through a state where both robots are in \(AT2\) area. (that is, through the state \(S_{3}\)). Such a situation must occur for the following reasons. For a walk representing four activities (because it consists of four arcs), that can correspond only to reaction rules from Figure 8 a course of a mission for each robot must take the form of moving from an AT1 area to an AT2 area and then from AT2 to AT3 area. Since the activities represented by the reaction rules are not cooperative (each of the reaction rules involve only one agent) the movements will be performed in parallel. We also know that the time required to perform both activities will be the same for both agents (because agents are of the same type and perform the same type of activity) so the successive movements will end at the same moment. Because of all that, during a mission there must occur a situation where both robots are at an AT2 area at the same time. Therefore, the algorithm for constructing subsequent scenario states must be able to construct states that are not \"on\" a provided walk.  
Figure 8: Reaction rules for the example of scenario visualization. (**a**) Reaction rule \(r1\). \(\tau=\{(0,0),(1,2),(2,1)\}\). (**b**) Reaction rule \(r2.\tau=\{(0,0),(1,1),(2,2)\}\).  
Figure 7: The initial state of a system for the scenario visualization example.
# Dispersion Management and Pulse Characterization of Graphene-Based Soliton Mode-Locked Fiber Lasers
## 6 Dispersion Management of the Mode-Locked Laser  
The dispersion coefficient, \(\beta_{2}\) of the SMF-28, Hi-1060 fiber and EDF at 1550 nm are \(-\)22 ps\({}^{2}\)/km, \(-\)7 ps\({}^{2}\)/km, and 23 ps\({}^{2}\)/km, respectively. The estimated net group velocity delay (GVD), according to a different total length of SMF-28 that ranges from 10.4 m to 6.4 m in the cavity, is shown in Table 1. Starting from the net GVD value of \(-\)0.121 ps\({}^{2}\) for 10.4 m long SMF-28, it can be seen that every decrease of 0.5 m length results in a constant increase in the net GVD of approximately 0.011 ps\({}^{2}\). This corresponds to a net GVD of \(-\)0.033 ps\({}^{2}\) at the shortest SMF-28 length of 6.4 m. It can be deduced that while all the net GVD values obtained lie in the anomalous dispersion regime, the value trends towards zero-dispersion with shorter lengths of the SMF-28. For a real dispersion managed fiber laser, the net cavity dispersion is made to switch from anomalous dispersion to near-zero dispersion, and, subsequently, to net-normal dispersion, as reported in Refs. [10; 41]. This results in a large change in pulse operation regime from soliton to stretched-pulse, and to dissipative soliton mode-locking, respectively.  
Figure 6: Experimental setup of graphene-based mode-locked EDFL.