esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Congestion Control of TCP for Constrained IoT Networks
## 2 Related Work
### Studies on TCP for IoT Networks  
[PERSON] et al. explored how to optimize TCP performance by examining the effect of distributed caching and local retransmission strategies where each intermediate node caches TCP segments and retransmits a segment whose ACK (acknowledgement) is considerably delayed, based on RTT estimation [14]. They integrated their implementation named the caching and congestion control (cctrl) module into the uIP stack of Contiki OS and tested it using different radio duty cycling MAC (medium access control) protocols in a real testbed comprising seven TelosB motes. Experimental results showed that the cctrl module increased TCP throughput in many of the examined configurations, but its performance depended heavily on the underlying RDC MAC protocols.  
In Reference [10], [PERSON] et al. conducted an experimental study on performance of TCP over RPL in an IPv6-based testbed which is a low-power and lossy network (LLN) consisting of 30 TelosB devices using the TinyOS BLIP stack, one LBR (LoWPAN border router), and one Linux-based server. They found that TCP incurred significant throughput unfairness among nodes in multihop LLNs, and that RPL's inability to consider traffic load balancing may adversely affect TCP performance.  
To address the TCP fairness problem among LLN endpoints, [PERSON] and [PERSON] proposed TAiM (TCP assistant in the middle) [16], which intervenes in the middle of TCP communication only at LBR and manipulates RTT of the passing flows. TAiM buffers packets and intentionally adds time delay before every forwarding, so that a flow with low throughput can have shorter delay, whereas a flow with high throughput may have longer delay. Experimental results using the BLIP stack showed that TAiM improved TCP fairness while maintaining total throughput.  
In Reference [1], [PERSON] et al. provided recommendations for lightweight TCP implementation and suitable operation in IoT scenarios. In particular, as far as an RTO algorithm is concerned, they recommended using CoCoA for TCP.  
As powerful low-power embedded devices with more processing power and larger memory space emerge, [PERSON] et al. showed that a full-scale TCP can fit well within CPU and memory constraints of modern wireless sensor network platforms by implementing a full-scale TCP, called TCPlp [12; 15], leveraging the full-featured TCP in FreeBSD OS.
## 3 Vehicle-Bridge Interaction Model
### Truck Fleet  
The parameters for the truck fleet were generated based on the normal distributions defined in Table 3. The mean and standard deviation of the distributions for the static mechanical parameters in Table 3 (i.e., geometry and weights) are based on histograms of WIM data recorded in a motorway in Auxerre, France [44]. Particularly, Table 3 adopts the values of the normal distribution fitted to the peak of the histogram associated with fully loaded 3-axle trucks, in line with common WIM practice. Additionally, the mean values of the rest of the parameters in Table 3 (i.e., mass, spring stiffness, damping coefficient) were taken from the literature [43], and their corresponding standard deviations are based on [PERSON] et al. [45]. It is worth mentioning that some other vehicle design standards [46] and road safety guidelines [47] are taken into consideration to have reasonable minimum and maximum values. In other words, the distributions are truncated to ensure that a realistic range of values will be sampled.  
The Monte Carlo method was used to randomly sample values from the distributions in Table 3. As a result, a fleet composed of \(s=1000\) different trucks was generated. Figure 2 shows the histogram of frequencies associated with the first five modes of vibration of the 1000 vehicles. There is reasonable agreement between the histogram of frequencies and the past research by [PERSON] [48] and [PERSON] [49], i.e., frequencies for the body mass bounce and  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Mean** & **STD** & **Min** & **Max** \\ \hline Geometry (m) & & & & \\ \(L_{\text{p1}}\) & 5.4 & 0.2 & 5.2 & 5.6 \\ \(L_{\text{c2}}\) & 1.2 & 0.2 & 1.1 & 1.3 \\ Weights & & & & \\ \(GVW\) (kN) & 241 & 57.5 & 183.5 & 299 \\ \(W_{1}\)(\%) (*) & 26.6 & 2.1 & 24.5 & 28.7 \\ Masses (kg) & & & & \\ \(m_{1}\) & 500 & 50 & 400 & 600 \\ \(m_{2}\) & 1450 & 145 & 1160 & 1740 \\ Spring stiffness (kN/m) & & & & \\ \(K_{11}\) & 1577 & 236.55 & 1103.9 & 2050.1 \\ \(K_{21}\) & 4724 & 1181 & 2362 & 7086 \\ \(K_{12}\) & 3146 & 786.5 & 1573 & 4719 \\ \(K_{22}\), \(K_{32}\) & 4724 & 1181 & 2362 & 7086 \\ Damping coefficients (kN-s/m) & & & & \\ \(C_{11}\) & 22.40 & 6.72 & 8.96 & 35.84 \\ \(C_{21}\) & 66.84 & 20.05 & 26.74 & 106.94 \\ \(C_{12}\) & 13.30 & 3.99 & 5.32 & 21.28 \\ \(C_{22}\), \(C_{32}\) & 10.00 & 3.00 & 4.00 & 16.00 \\ \hline \hline \end{tabular} (*) \(W_{1}\) represents \(P_{1}\) from Table 1 as a% of \(GVW\).  
\end{table}
Table 3: Statistical distributions of vehicle parameters for the truck fleet.  
pitch vary between 1.5 and 3 Hz (as Figure 2a,b), and frequencies for the rear tandem hop and pitch and the front axle hop are found between 7 and 20 Hz (as Figure 2c-e).
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 2 Methodology
### VPN-Encrypted Traffic FL-XGB Identification Model  
In the problem of VPN-encrypted traffic identification, various types of training data are not evenly distributed. Among them, classes with more data account for a larger proportion of the dataset, and classes with fewer data have a smaller proportion in the dataset. The reason is that the usage frequency of different applications is different, and the amount of traffic data generated is different. Thus, the amount of data between different classes is quite different, and there is a problem of imbalance in the amount of data between data classes. Therefore, this article proposes a custom-defined Focal Loss Xgboost ensemble learning model for the imbalance of encrypted traffic classes.
# An Estimation Method of River Dry Runoff Alteration after Upper New Reservoirs Storage
## 3 Results and Discussion
### Method Validation
#### 3.1.2 Min River  
The runoff increment in the driest month, i.e., February, was 434 m\({}^{3}\)/s, which was a proportion of 21.8% among the total increments. Similarly, the theoretical runoff increment of the Min River in the driest month was calculated.  
In the Min River basin, four cascade reservoirs with a total adjustable capacity of 5.5 billion m\({}^{3}\) have also been constructed since 2006. According to the hydrologically recorded data from 2011 to 2020, it was observed that the runoff increment during the dry period ranged from 157 m\({}^{3}\)/s to 541 m\({}^{3}\)/s. This dry period runoff increment accounted for approximately 99% of the total increments in the river.  
Focusing on the driest month, February, the runoff increment was measured at 434 m\({}^{3}\)/s, which constituted around 21.8% of the total increments. Based on this information, the theoretical runoff increment for the driest month in the Min River can be calculated as 492 m\({}^{3}\)/s using the previously mentioned equation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Reservoir** & \multicolumn{2}{c}{**Monthly Average Runoff Increase in February**} \\ \hline
**Name** & **Adjustable Volume (Billion m\({}^{3}\))** & **Theoretical (m\({}^{3}\)/s)** & **Deviation** \\ \hline Ertan + Xiluodu + Jinping 1 st & 15.7 & 1028 & 26\% \\ + Xiangjiaba & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Increase in downstream river flow due to the construction of key projects in the Jinsha River Basin.  
Compared to the practical increment of 434 m\({}^{3}\)/s, the bias \(Dev_{0}\) in the calculated result for the Min River was 13%, which was lower than the deviation observed in the Jinsha River estimation, as seen in Table 3. This could be attributed to the fact that in a smaller basin, the influence of other factors on the runoff increment was relatively smaller compared to the impact of dam operations. Additionally, the calculated result falling within an acceptable deviation of less than 30% further validates the estimation method proposed in this paper. It is important to note that a certain level of bias is a common occurrence in studies related to climate variation and hydrological alteration.
# Internet of Things (IoT) Operating Systems Management: Opportunities, Challenges, and Solution
## 2 IoT OS Key Features and Characteristics
It also provides multithreading with tickless, preemptive and priority based scheduler. Multithreading is designed to reduced inherent drawbacks such as thread management overhead, code stack usage, and inter-process messaging. Native is the emulator or hardware virtualizer that allows the user to run the RIOT code as a linux processes. Hence, it is easier to develop IoT software without the need of actual hardware. Zephyr is originally developed by Intel subsidiary wind river. It provides microkernel for less constrained IoT devices and nanokernel for constrained devices.It supports multithreading with cooperative, priority-based, Earliest Deadline First (EDF), non-preemptive and preemptive scheduling. The programs can be written in C and C++ programming language. Zephyr provides network stack support with multiple protocols. It also support Bluetooth Low Energy (BLE) 5.0. The applications can be develop, build and test using the native posix port. MbedOS [25] the Real Time Operating System (RTOS) is developed by Advanced RISC Machine (ARM) for constrained IoT devices. It is specifically designed for 32 bit ARM architecture. It is based on monolithic kernel and provides preemptive scheduler. It supports C and C++ development. MbedOS features multithreading, 6 LoWPAN, BLE, WiFi, sub-GHz, Near Field Communication (NFC), Radio-Frequency Identification (RFID) and Long Range Low-Power Wide Area Network (LoRaLPWAN). Low memory requirements and various hardware support of mbedOS makes it suitable for IoT research and development. Formerly brillo and now the android things [26] is developed by Google. It is based on android however, it is simplified and trimmed down android version to run on low-power IoT devices. It supports development in both C and C++ programming language. It is built on top of monolithic kernel and provides completely fair scheduler. Android things memory requirements makes it unsuitable for low-end constrained IoT devices rather it is designed for high-end IoT devices.
# Effect of Three Chlorhexidine-Based Mouthwashes on Human Gingival Fibroblastsets: An In Vitro Study
## 3 Results
### Cell Viability  
An overview of cell viability results is presented in Figure 2. The highest values were predictably found in the NaCl group for all time points and exposure times to the mouthwashes. Against our expectations and not in line with the graph (Figure 2), no significant difference was shown between NaCl and CHX + CPC groups at day 0 (120 s), at day 3 (30 s, 60 s, and 120 s), and at day 6 (60 s and 120 s). Moreover, no significant difference was identified between NaCl and CHX 0.2 at day 6 (30 s) (Table 1). The significance level reported in Table 1 is after Bonferroni correction. The uncorrected _p_-values were <0.05 in all these cases but one (CPC + CHX vs. NaCl 60 s at day 6: \(p\) = 0.07).  
After 3 days of culture, the CHX + CPC group exhibited significantly higher cell viability compared to the CHX 0.1 group for both 60 s and 120 s application times; similarly, on day 6 this was observed after a treatment time of 60 s (Table 1).
# Cutting Path Planning Technology of Shearer Based on Virtual Reality
## 4 Virtual Cutting Path Planning Method of Shearer and Design of Prototype System
### Key Technology for Modeling Coal Seam Roof and Floor  
Developing a 3D model that accurately expresses the coal seams is the key to achieving digital, visual, and intelligent production management in the fully mechanized mining face in coal mines. In this study, the discrete detection points are transformed into a series of continuous data using the geological exploration data of a coal mine, through the Kriging [20] interpolation. After processing, the 3D coordinates of \(16\,\times\,40\,\times 2\) points are obtained, so as to build the coal seam roof and floor model in Unity3D.  
The mesh grid component in Unity provides a way to form a surface closely through a large number of small triangles. The MeshFilter records the grid data whereas the MeshRenderer components set the grid material. Since only one side of the triangle generated by the mesh grid component is visible (visible on one side of the clockwise connection), the opposite vertex order is required to make the coal seam top and bottom plates visible under normal viewing angles. During the implementation of the mesh grid of the coal seam roof, the vertices of the triangle are selected according to the specific rules with reference to Figure 6. As shown in Figure 6, assuming that there are altogether 640 3D geological coordinates of the coal seam roof (No. 0-639), the three vertices of the first triangle are 0, 40, 1 in turn, the three vertices of the second triangle are \(1,41,2\) in turn. Similarly, the three vertices of the last triangle were 639, 598, 638 in turn.  
Figure 5: Algorithm of the main process of the virtual cutting of shearer.  
The processed data of the coal seam roof and floor are stored as an XML file, and the mesh is created by a c# script. According to the above method, the final effect in Unity3D is as shown in Figure 7:
# A Review of Cognitive Control: Advancement, Definition, Framework, and Prospect
## 2 Cognitive Control Research Overview
### Cognitive Control Based on CDS
#### 2.3.2 Cognitive Control and CRC for Smart Grids  
Smart grids combine advanced technology with traditional power grids, bringing together power suppliers, consumers, distributed energy resources, and energy storage units to constitute a microgrid. Microgrids facilitate the interaction between power suppliers and electricity users and motivate users to participate in energy management. Demand response management (DRM) is critical for balancing the electricity supply and demand and maintaining microgrid stability. However, the DRM performance depends on the wireless communication quality of the microgrid. In [114], a cognitive control model was formulated on a distributed frame embedded in both the user agents and the microgrid control center agent. Cognitive control was carried out based on the real-time electricity price. The \(Q\)-learning algorithm was selected as the cognitive control algorithm to adjust the wireless communication channel, and the estimation-deviation level was adaptively adjusted. The experimental results showed that the global benefit for all subscribers was maximized; moreover, the electricity demand and supply were balanced.  
In [115], optimal state estimation of smart grids was performed based on cognitive control. The entropy state was used to identify and detect bad data from sensory measurements. To improve the detection accuracy, an entropic state calculation method combining least-squares estimation, a Kalman filter, and a cumulative sum was proposed. When a sensor's working environment is noisy, its corresponding weight is reduced by the cognitive controller to mitigate its influence on the state estimation of the system. The simulation results showed that the weight readjustment could be completed within a few PACs.  
Furthermore, the authors showed that when faced with cyberattacks, known as false data injection (FDI) attacks, it was also possible to use the entropic state as a metric to detect FDI attacks. The disadvantage of this method is that it can only detect FDI attacks but cannot reduce or even eliminate its impact. Additionally, this algorithm is not suitable for large-scale smart grids, which have too many sensors (thousands), because this will increase the dimension of the matrix of the system model, leading to an excessive amount of computing resources being consumed during the calculation process, which will adversely affect the system performance in real-time.  
In [116], CRC was employed to address the risk associated with FDI attacks. When an FDI attack occurs, the risk-sensitive subsystem is activated by the task-switch control module, which adjusts the system matrix to defend against FDI attacks.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 5 Simulations Verification
### The Stability of Platoon  
Finally, the effect of performance metrics on platoon stability is evaluated. The term \"string stability\" is usually utilized interchangeably with \"platoon stability\" in the study of platoon, which suggests any non-zero acceleration, velocity, and position changes of vehicles in a platoon do not amplify when they propagate upstream, i.e., \(\parallel\epsilon_{2}\parallel_{\infty}>\parallel\epsilon_{3}\parallel_{ \infty}>\cdots>\parallel\epsilon_{n}\parallel_{\infty}\).  
We set up the following simulation scenario: A platoon with eight vehicles is placed on a straight one-way road, and the length of the road is 5000 m. According to [38], \(q_{1}=0.34\), \(q_{2}=1.2\), \(q_{3}=0.53\), and \(q_{4}=0.6\). The standstill spacing is 10 m and the vehicle length is 5 m. Moreover, \(\lambda=150\), \(p_{b}=3\times 10^{-4}\) and \(K=20\). The leader vehicle starts with an initial velocity of 30 m/s and maintains this velocity until \(t=5\) s. Meanwhile, member vehicles and the tail vehicle have a fixed velocity of 30 m/s, and the spacing between any neighborhood is also fixed. At time \(t=5\) s, the leader vehicle accelerate with an acceleration of 2.5 m/s until the velocity of leader vehicle reaches to 55 m/s.  
Figure 7: Results for varying \(\lambda\): (**a**) delay; and (**b**) packet dropping rate.  
At time \(t=25\) s, we let the leader vehicle decelerate with acceleration \(5\) m/s\({}^{2}\) until the velocity of the leader vehicle reaches to \(5\) m/s.  
Figure 8 depicts the vehicle's behavior when the leader vehicle accelerates or decelerates. The results demonstrate that accelerations and spacing errors of the seven following automated vehicles in the platoon gradually decline upstream, and the string stability is guaranteed perfectly, which indicates excellent tracking performance of the following automated vehicles with the transmission delay and packet dropout. Thus, it suggests that the IEEE 802.11p-based communication can support the timely delivery of vehicle information among platoons for diverse on-road applications.
# Electrochemically Activated Screen-Printed Carbon Electrode for Determination of Ibuprofen
## 3 Results and Discussion
### Application  
In order to evaluate the ability of the DPV procedure with aSPCE for the IBP analysis in real samples and to investigate the possibility of interfering matrix effect, the IBP was determined in pharmaceuticals by the standard additions method. The results were presented in Table 2. According to the results, the obtained standard deviation (SD) and relative error values were satisfactory and acceptable. These results confirm the good precision and accuracy of the proposed analytical procedure and indicating that the aSPCE had potential in the real sample analysis and is free from interferences of substances found in pharmaceutical formulations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Linear Range (uM)** & **LOD (uM)** & **Reference** \\ \hline Spectrophotometry & 1940.0–5800.0 & 134.0 & [6] \\ \hline RP-HPLC & 3880.0–5820.0 & 0.39 & [7] \\ \hline RP-HPLC & 1.21–1210.0 & 0.29 & [8] \\ \hline CZE & 24.20–969.0 & 7.17 & [9] \\ \hline DPV (BDDE) & 0.949–66.90 & 0.41 & [10] \\ SWV (BDDE) & 0.80–30.0 & 0.35 & [12] \\ \hline DPV (aSPCE) & 0.50–20.0 & 0.059 & This work \\ \hline RP-HPLC–reversed-phase high-performance liquid chromatography; CZE—capillary zone electrophoresis; BDDE—boron-doped diamond electrode; CNF/SPCE—screen-printed carbon electrode modified with carbon nanofibers. & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different methods for IBP determination in pharmaceutical formulations.  
Figure 4: (**A**) DPVs recorded at the SPCE in 0.25 M acetate buffer solution of pH 4.5 \(\pm\) 0.1 containing increasing concentrations of IBP: (a) 5.0, (b) 10.0, (c) 20.0, (d) 50.0, (e) 100.0, (f) 200.0, (g) 500.0 \(\upmu\)M. (**B**) Calibration plot of IBP at the SPCE. (**C**) DPVs recorded at the aSPCE in 0.25 M acetate buffer solution of pH 4.5 \(\pm\) 0.1 containing increasing concentrations of IBP: (a) 0.5, (b) 1.0, (c) 2.0, (d) 5.0, (e) 10.0, (f) 20.0, (g) 50.0, (h) 100.0, (i) 200.0, (j) 500.0 \(\upmu\)M. (**D**) Calibration plot of IBP at the aSPCE. Stirring solution of 45 s under open circuit. The DPV parameters: \(\Delta\)E\({}_{\rm A}\) of 150 mV, \(\
u\) of 200 mV s\({}^{-1}\) and \(t_{\rm m}\) of 25 ms.
## 5 Methodology
### Development of the Two-Phase Heuristic
Once selected \(u^{*}\), in the capacity loop \(W_{t}\) the vehicle \(m_{u^{*}}\) allocation phase of the auto-carrier \(k\) starts, this is Step 2 of the heuristic algorithm. If the accommodation of \(m_{u^{*}}\) is feasible and there are still spaces of \(W_{t}\), return to Step 1 of the heuristic algorithm, to obtain the next vehicle \(m_{u^{*}}+1\), the capacity loop ends when \(W_{t}\) is equal to 0 or does not exist \(u^{*}\). Then, the route \(R_{k}\) and the arrangement \(A_{k}\) of the auto-carrier \(k\) add to the _Solution_, and finally a new route \(R_{k+1}\) is started. A requirement of the logistics company is to add to the solution the remaining demand \(M\), in the case that the number of auto-carriers \(K\) were not enough to transport the demand \(M\), line 17. Algorithm 1 ends when there is no demand \(M\) or auto-carriers \(K\) available for routing. In the allocation phase (Algorithm 2), to accommodate the vehicles in the auto-carrier, \(A_{k}\) is abstracted as a vector of size \(W_{t}\) (capacity of the \(t\)-type auto-carrier), to simulate and delimit the levels of the auto-carrier a variable called _level_ is created. If \(t\) is pair, the index \(i\) of the upper level initializes at \(i=0\) and ends at \(i=level-1\), while the lower level initializes at \(i=level\) and ends at \(i=W_{t}\), as shown in Figure 8a. If \(t\) is odd, the index \(i\) of the upper level initializes at \(i=0\) and ends at \(i=level\), while the lower level initializes at \(i=level+1\) and ends at \(i=W_{t}\), as shown in Figure 8b. Once \(A_{k}\) is defined, the load and assignment of the vehicle \(m_{u}\) is defined by its height \(h_{m_{u}}\), Algorithm 2 starts by obtaining \(level=\frac{W_{t}}{2}\). If \(h_{m_{u}}>2.5\) m, the vehicle \(m_{u}\) will occupy spaces in the two levels of the auto-carrier, then first determine if there is available space in the lower level \(i<level\), otherwise \(m_{u}\) is assigned in the next auto-carrier with available space, the case that is met \(i<level\), _parity_ verifies if \(t\) is even or odd, depending on this result obtain the spaces that comply with Equation (8) and if are available, perform the allocation of \(m_{u}\) (e.g., see Figure 5) to these spaces. If \(h_{m_{u}}>1.8\) m and \(h_{m_{u}}<2.5\) m, to assign the vehicle \(m_{u}\) verify if there is available space in the lower level \(i>level\) as shown in line 22 of Algorithm 2, otherwise \(m_{u}\) is assigned to the next auto-carrier with available space in the lower level. Finally, for \(h_{m_{u}}<1.8\) m it is only determined if there is space available in the auto-carrier and \(m_{u}\) is allocated, with the assignment of \(m_{u}\) in any of the cases and the return of \(A_{k}\), the allocation phase ends.