esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 4 Collision Avoidance Constraints  
The collision avoidance constraints are imposed to ensure the vehicle avoids obstacles and that it does not violate the edges of the road in order to guarantee the safety of the autonomous vehicle.  
Road boundary constraint: the road boundary is shrunk to account for the vehicle width, and the vehicle is limited to the shrunk road boundary.  
\[R_{l}\leq R_{v}\leq R_{u}, \tag{29}\]  
where \(R_{v}\) is the center of the vehicle, and \(R_{l}\) and \(R_{u}\) are the lower and upper boundary of the road, respectively.  
Obstacle avoiding constraint: this involves the circle obstacles that are in the form \((x-x_{o})^{2}+(y-y_{o})^{2}\leq R_{o}\). The obstacle avoiding constraints can be formulated as  
\[(x_{z}-x_{o})^{2}+(y_{z}-y_{o})^{2}>(R_{o}+R_{1}), \tag{30}\]  
where \((x_{o},y_{o})\) is the center of obstacles, \((x,y)\) expresses the point of the obstacle, \(R_{o}\) is the inner radius of obstacles, \(x_{z}\) and \(y_{z}\) express the projection of the invariant set onto the global coordinate system, and \(R_{1}\) is the circumcircle of the vehicle width.  
Slip angle constraint: In this paper, it was assumed that the slip angle of the tire is small, and the tire lateral force is approximately a linear function of the slip angle. Therefore, the slip angle of the tire is limited to a certain range.  
\[\left|\alpha_{f}\right|\leq\alpha_{df}, \tag{31}\]  
\[\left|\alpha_{r}\right|\leq\alpha_{dr}, \tag{32}\]  
where \(\alpha_{f}\) and \(\alpha_{df}\) are the slip angle and the limited value of the front tires, respectively, and \(\alpha_{r}\) and \(\alpha_{dr}\) express the slip angle and the limited value of the rear tire, respectively.  
The above constraints are written in the matrix form as follows:  
\[h(\varepsilon,u,w)\ \leq\ 0, \tag{33}\]  
where \(\varepsilon\) is the minimal robust positively invariant set, and \(u\) and \(w\) are the input and the disturbances of autonomous vehicles, respectively.
## 3 Selenium Human Uptake and Distribution
### Blood Transfer and Distribution of Se to Tissues via Selenoproteins P  
The supply of Se to tissues depends on the plasma Se transporter, SePP or serum albumin (SeAlb) [59] (Figure 2). SePP is synthesized mainly in the liver, which is the key organ for the homeostasis of Se in the body. SePP is a secreted glycoprotein with a C-terminal domain composed of nine Se-Cys residues in mice and humans. It also has another Se-Cys residue in the N-terminal region in a redox active motif [69; 70]. In rats, studies have identified four types of SePP isoforms. However, in humans plasma, only two isoforms were identified; a complete 60 kDa form and a smaller 50 kDa form [71]. The 60 kDa isoform serves as a transporter of Se to body tissues and the 50 kDa isoform is involved in redox and signaling reactions [72]. The SePP knockout mice show a loss of motor coordination and reduced viability. Their survival time is about 15 days after weaning [73].  
In the body, there is a hierarchy of Se distribution in different tissues or organs for example, Se level is highly conserved in the brain, thyroid and testes. This hierarchy give priority to the synthesis of the most important selenoproteins for the organ [7; 74; 75].  
In humans, a recent study has shown that SePP gene variations affect levels of selenium biomarkers after intake of food with a high content of selenium. Indeed, in middle-aged Danes, CC homozyggotes of the SePP/rs3877899 polymorphism have higher levels of selenoprotein P and whole blood Se compared to T-allele, after consumption of selenium-rich foods [76]. These results emphasize the importance of a more personalized approach to Se requirements [76].
## 3 Antenna Testing and Result Analysis
### Mode 1 (Both \(D_{1}\) and \(D_{2}\) Are Off)  
In mode 1, when both diodes are in the off state, that corresponds to the working of the lower part of the radiator while the stubs do not play any role in the radiation. The proposed antenna offers a wideband mode, having an S11 \(<-10\) dB simulated impedance bandwidth of 3.9-7.15 GHz, while the measured results offer 3.2-8 GHz, as depicted in Figure 7a. The measured bandwidth is wider as compared to the simulated results, which is due to the presence of the biasing cables that consequently induce additional coupling and affect the performance of antenna. To understand the working behavior of the antenna for mode 1, the surface current graphs at 4.5 GHz and 6.4 GHz are presented, as shown in Figure 7b. At both frequencies, the maximum current flows across the lower part of the radiator and CPW feed. Moreover, due to the close presence of the upper part of the radiator, i.e., stubs, a small amount of current is also induced in them; however, the  
Figure 5: Measurement setup for scattering parameters.  
Figure 6: Far-field measurement setup for the proposed antenna system.  
performance remains unaffected as compared to the antenna without any diodes. Lastly, the gain over the operational wideband is also presented in Figure 7c, and the antenna offered a gain of more than 2.5 dBi, while the measured results also offered the strong comparison as compared with the simulated results. This wideband mode targeted the frequency band spectrum globally that was allocated for C-band applications along with coverage of the sub-band, but this was not limited to Wi-Fi, the ISM upper band, Wi-Fi 6E, and the weather radar system.
## 3 Results and Discussion
### Method Comparison
#### 3.1.3 Limits of Detection  
IDT  
The investigated IDTs are designed to detect amphetamine, methamphetamine, and MDMA. Therefore, IDTs are a suitable example to examine the chosen target compounds. The information gathered from a measurement is that one of the investigated substances is present or not. No statement is made what specific target compound is found if a positive result is obtained. To gain further information about the capabilities of IDTs, the LOD was determined for amphetamine, methamphetamine and MDMA separately. The results of Table 1 reveal that clear visible indication/bar is possible down to 2 ng total target compound. In some  
If, in summary, the LOD is compared between all investigated techniques, no major variations are observed. While IDTs reveal the best overall LODs with 2 ng for all substances, IMS results are slightly higher and deviate between 1 ng for methamphetamine and 9 ng for NFA. In the utilised setup, APLD-MS obtained 2 ng to 21 ng and therefore about two to ten times higher LODs, compared to IDTs.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 4 Detection and Identification of a Drone Using a Machine-Learning Approach
### Training  
The training script is provided directly by the library developers and it is included in the official Github repository. The time required to complete one training step took 4.5 s on average (using TensorFlow with a Central Processing Unit (CPU) support only--3,5 GHz Intel Core i5). Using TensorFlow with a Graphics Processing Unit (GPU) support--NVIDIA GeForce GTX1050 Ti, the time of one training step was on average 0.33 s, so we use this approach. The difference between the training time is, therefore, highly dependent on how the detector is trained. Information about model and training configuration is summarized in Table 3. For more information about model architecture, see publication [23] from [PERSON] et al.  
A model with adequate reliability and feasibility was achieved after a total of 300,000 steps (about 2 training days). The progress of classification and object location is shown in Figure 15. The data on the graph entitled Classification loss show a gradual improvement of the model according to the number of training steps in the task of classifying the object into the correct class. In the case of a model that classifies data into a single class (single class classification), objects that do not contain the drone  
Figure 14: The creation of TensorFlow Records (TFRecords).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Model Configuration**} \\ \hline model name & Faster R-CNN with Inception v2 \\
1 st stage (location) regularizer & L2 \\
1 st stage initializer & Truncated Normal \\
2 nd stage (classification) regularizer & L2 \\
2 nd stage initializer & Variance Scaling \\ \hline score converter & SOFTMAX \\ \hline \multicolumn{3}{c}{**Training Configuration**} \\ \hline learning rate & 0.0002 \\ number steps & 300k \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model and training configuration.  
are considered as incorrectly classified. Another graph called Localization loss shows how the model was improved with the number of training steps in the task of a correctly localized object.  
The graph in Figure 16 shows the improvement of the detection model taking into account all aspects of the success and correct detection of the object.
## 5 Simulation Analysis
### Simulation Results
#### 5.2.3 Performance under Load Imbalance Conditions  
To test the performance of SQL-CBRP under load imbalance conditions, the packet-sending probability of nodes within several clusters is set to 90%, while the packet-sending probability of the remaining nodes within clusters is set to 60%. The latency and packet loss rate of the three algorithms are tested under these conditions.  
Figure 8 illustrates the comparison of delay and packet loss rate for the three algorithms when the number of high-load clusters is 4, 8, and 12. It is evident that under the condition of load imbalance, the SQL-CBRP algorithm has a lower delay and packet loss rate compared to Dijkstra and GPSR. Moreover, compared to GPSR, the delay is reduced by up to 24.5%, and the packet loss rate is reduced by up to 32.7% relatively. This is because SQL-CBRP selects nodes with shorter queuing queues (i.e., nodes with lower load) as the next hop. Therefore, in a network with load imbalance, SQL-CBRP can play a role in load balancing to a certain extent, thereby directing packets to areas with lower load, which in turn reduces the network's latency and packet loss rate.  
According to the analysis above, it is evident that since the SQL-CBRP algorithm selects the neighbor with the shortest queue as the next hop, it reduces queuing delay and effectively avoids packet loss due to queue overflow. Therefore, compared to the Dijkstra algorithm and the GPSR algorithm, the SQL-CBRP algorithm has lower packet loss rates and delays for services with different data rates. Specifically, when the network has high loads or the load distribution is imbalanced, the advantages of SQL-CBRP become more pronounced.
# Development of a New Test-Rig for Wheel-Rail Contact Experiments under Large Slip Conditions
## 1 Introduction
### Adhesion Recovery in Large Slip Conditions  
Degraded adhesion occurs due to wet or contaminated surface conditions. Large creepage in the wheel-rail contact may then be used to restore adhesion. This is probably because the large slip changes the surface condition, removes the surface layer, affects the roughness, increases the temperature and causes water evaporation, all of which help adhesion recovery. The increase in adhesion leads to a higher level of available traction or braking forces.  
Adhesion recovery in large slip conditions is observed by [PERSON] ([PERSON], 2013) and [PERSON] ([PERSON], 2014). This phenomenon is shown in Figure 1. The initial low adhesion creep curve (dotted) is changed into the creep curve (red) with a second positive slope and a second maximum by applying large creepage, feeding energy into the wheel-rail contact interface. The adhesion recovers with increasing energy input between wheel and rail. [PERSON] ([PERSON], 2015) believes that this phenomenon can be attributed to the influence of friction heat, which reduces the moisture and thus increases the friction coefficient. On the other hand, [PERSON] and [PERSON] (2016) seek explanations in the wear process and the subsequent changes in surface conditions.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 5 Improved Scheme
### General Authentication  
The general authentication procedure is performed between \(OBU_{i}\) and \(LE_{j}\) after \(U_{i}\) has completed the login procedure. The general authentication procedure is shown in Figure 4 and the steps are described as follows.  
1. \(OBU_{i}\) chooses a random number \(r_{i}\) and computes its alias \(AID_{i}=h(r_{i})\oplus ID_{i}\). Then, it produces the request messages \(M_{1}=h(A_{i})\oplus r_{i}\) and \(M_{2}=h(r_{i}||AID_{i}||C_{i}||y_{i})\), where \(A_{i}\) is obtained from the login procedure.
2. \(OBU_{i}\to LE_{j}\): The authentication messages (i.e., \(AID_{i}\), \(M_{1}\), \(M_{2}\), \(C_{i}\), and \(y_{i}\)) are sent from \(OBU_{i}\) to \(LE_{j}\).
3. Upon receiving the authentication request messages (i.e., \(AID_{i}\), \(M_{1}\), \(M_{2}\), \(C_{i}\), and \(y_{i}\)), \(LE_{j}\) uses \(PSK\) to retrieve \(A_{i}=C_{i}\oplus h(PSK||y_{i})\) and \(r_{i}=M_{1}\oplus h(A_{i})\) and then checks whether \(M_{2}=h(r_{i}||AID_{i}||C_{i}||y_{i})\) holds. The authentication request will be rejected if it does not. Otherwise, \(LE_{j}\) produces a random number \(r_{j}\) to calculate \(AID_{j}=ID_{j}\oplus h(r_{j})\) and \(SK_{ij}=h(r_{i}||r_{j})\). Finally, \(LE_{j}\) calculates the response messages \(M_{3}=r_{j}\oplus h^{2}(r_{i})\), \(M_{4}=PSK\oplus r_{j}\), and \(M_{5}=h(AID_{j}||SK_{ij}||r_{j}||PSK)\).
4. \(LE_{j}\to OBU_{i}\): \(LE_{j}\) return response messages (i.e., \(AID_{j}\), \(M_{3}\), \(M_{4}\), and \(M_{5}\)) to \(OBU_{i}\).
5. \(OBU_{i}\) computes \(h^{2}(r_{i})\) to retrieve \(r_{j}=M_{3}\oplus h^{2}(r_{i})\), \(PSK=M_{4}\oplus r_{j}\), and \(SK_{ij}=h(r_{i}||r_{j})\) and checks whether \(M_{5}=h(AID_{j}||SK_{ij}||r_{j}||PSK)\) holds. \(OBU_{i}\) terminates the process if  
Figure 3: Normal vehicle registration procedure.  
it does not. Otherwise, \(OBU_{i}\) calculates the reply message \(M_{6}=SK_{ij}\oplus h(r_{i})\); computes \(C_{inew}=h(PSK||r_{i})\oplus A_{i}\) and \(E_{i}=h(PW_{i})\oplus PSK\); replaces \(C_{i}\) and \(y_{i}\) with \(C_{inew}\) and \(r_{i}\), respectively, and stores \(E_{i}\) in its secure hardware.
* \(OBU_{i}\to LE_{j}\): The message \(M_{6}\) is sent to to \(LE_{j}\) by \(OBU_{i}\).
* \(LE_{j}\) uses \(SK_{ij}\) to retrieve \(h(r_{j})\). Then, it checks whether the retrieved hash value is equal to the pre-computed hash value using the chosen \(r_{j}\). In this way, a replay attack from an illegal OBU is avoided.  
At this time, the state of \(OBU_{i}\) changes to trusted since \(OBU_{i}\) has been authenticated successfully and has obtained the parameter \(PSK\). Now, not only \(LE\) but also \(OBU_{i}\) can authenticate other mistrusted OBUs.
## 3 Single Channel PAM4 Amplification Results
The signal distortion at higher input power levels is due to deterministic SOA gain saturation and signal pre-distortion techniques can be applied when modulating the PAM4 signal at the transmitter in order to avoid signal distortion [7]. The biggest source of detection errors occurs for the upper levels, as evident in Figure 7d; and therefore increasing the separation of the upper levels of the PAM4 signal would improve the BER performance at the higher input power levels. Figure 6: BER performance of pre-amplified 28 Gbaud PAM4 signals using a single section SOA (blue) and multi-section SOA (red). The multi-section SOA outperforms the single SOA with improved IPDR from 12.91 dB to 16.04 dB. Figure 7: We display eyediagrams derived from the output intensity waveforms from the SOA. The eyediagrams are taken over a wide range of input power levels to the SOA in order to display different signal impairments. (**a**) \(-21\) dBm; in this power regime the ASE noise dominates, note the larger splitting of the upper stronger PAM4 symbols due to signal-spontaneous beat noise; (**b**) \(-11\) dBm; the eyediagram is clearly open with four distinct levels and no errors were detected in the 20,000 symbols used in the simulation. In this power regime, the signal is strong enough to overcome the ASE noise added by the SOA while being weak enough to not induce nonlinear gain saturation; (**c**) \(-6\) dBm; in this power regime, there is little influence of the ASE though the signal is just strong enough to begin inducing distortions by saturating the gain of the amplifier; (**d**) \(0\) dBm; in this power regime, the clearly visible signal distortions are due to strong gain saturation within the SOA.
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 5 Machine Learning Adaptability for WSNs
### Reinforcement Learning  
In Table 1 many WSN initiatives, including collaborative communications, routing, and flow control, use \"Reinforcement Learning (RL)\" to guarantee that sensors and detecting nodes can observe optimum operations and execution networks in a suitable operating environment. There has been further development of the app's features. The term \"trial and error\" refers to the way that knowledge is gained via experience rather than through theoretical models. This model is notable for how easily it can be modified and put into practice. Processing and memory requirements are minimal, yet the potential applications are high. As such, RL for WSNs is concerned with a wide range of issues, including clustering protocols, medium-access protocols, and routing protocols; additionally, hyper-parameter optimization is addressed within the context of RL, with applications including architectural network design and application-level collaboration between nodes. To improve the effectiveness of standard learning algorithms for WSN routing issues, the Q-learning [40] approach is useful [41].
## 3 Object Detection in High-Resolution Images Using OPAIs
### Object Patch Aggregation Image (OPAI) Algorithm  
However, these methods still require processing the entire image data with the deep learning network, resulting in slow processing speeds for high-resolution images. To address this issue, this paper proposes an effective method for reducing the size of high-resolution input images to match the input size that deep learning models can handle, optimizing processing efficiency. Typically, the size or number of objects to be detected in high-resolution images occupies a very small area compared to the entire image. Therefore, pixel data from non-target areas can waste the computational resources of the deep learning network model, and these non-target areas often constitute a significant proportion of the image. By applying a nonlinear transformation that reduces the number of pixels in non-target areas while maintaining the pixels of the objects to be detected, we can reduce the size of the high-resolution image. This approach focuses on retaining the critical object pixels, allowing for the creation of a smaller input image that the deep learning model can handle.  
Figure 7 demonstrates an example where objects within a large image can be effectively gathered into smaller OPAIs. Four objects are distributed within a 637 \(\times\) 885 image, and each object's area is extracted and repositioned within a 256 \(\times\) 256 image. The algorithm for creating a smaller OPAI from the original image involves using the area size of the objects to determine the paste positions within the OPAI, starting from the top-left corner.  
The process involves pasting the object regions into the OPAI sequentially and updating the start position for each subsequent object. If the next object fits within the remaining space of the OPAI, it is copied to that location. If the space is insufficient, the start position is moved down by the maximum height of the pasted objects and the x-coordinate is reset to the left edge to continue the process. If the new start position exceeds the height of the OPAI, a new OPAI is created and the process continues from the top-left corner of the new image. The algorithm can be summarized as follows:  
1. Calculate the paste positions (_Stick_pos_) for each object within the original image.
2. Sequentially paste object regions into the OPAI.
3. Update _Stick_pos_ after each object is pasted.
4. If the space is insufficient, move down and reset the x-coordinate of _Stick_pos_.
5. If _Stick_pos_ exceeds the OPAI height, create a new OPAI and continue.  
In the example, the size ratio of the OPAI is 12% compared to the original large image, which shows a drastic reduction in pixel data, offering a significant possibility of computational reduction in deep learning model calculations.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
## 4 Multi-UAV Collaborative Search Algorithm
### Improved Multiple UAVs Collaborative Search Algorithm Based on Binary Search Algorithm with Energy  
We define an improved multi-UAV collaborative search algorithm based on the MUCS-BSAE algorithm called IMUCS-BSAE. We use the \"square wave signal\" curve path as the working path of the UAV and use the MUCS-BSAE algorithm to assign the working path of each UAV. The following changes are made when the route planned by the above exploration strategy occurs: When the UAV reaches the cell with coordinates \(C_{(n-1)h}(h=0,1,2,\ldots,m-1)(h\leq m-2)\), there is not enough energy left or time left to reach the next cell with coordinates \(C_{(n-1)(h+1)}\). The next UAV will fly from the starting location \(S\) to the cell with coordinates \(C_{(n-1)(h+1)}\) and continue its mission from cell \(C_{(n-1)(h+1)}\) to \(C_{2(h+1)}\) following the route. It can be seen that, in this case, the UAV wastes unnecessary energy as a result. Therefore, we improve on the \"square wave signal\" curve path. When the UAV reaches the cell with coordinate \(C_{(n-1)h}\) and cannot reach the next cell with coordinate \(C_{(n-1)(h+1)}\), the next UAV will enter the cell with coordinate \(C_{2(h+1)}\). The next UAV will enter the cell with coordinates \(C_{(2(h+1)}\), and, according to the distribution of the remaining cells in the space area, the remaining cells will be regenerated into a new path \(P\) according to the rules for generating the \"square wave signal\" curve path (the last column of the exploration region is not covered by this rule). Table 2 summarizes the parameter values used in this paper.
```
1:P, S, E, V, SEL, ST, STEL, N, \(Maxtime\)
2:Output: output MovePath, Time
3:N: Number of UAVs
4:P: Path
5:_Maxtime_: The time it takes for a single UAV to fly back to its origin
6:S: Starting point of UAVs
7:E: Energy of UAVs
8:V: The speed of the UAVs
9:SEL: Energy consumed per second at the current speed
10:ST: Hovering time of the UAVs
11:STEL: energy consumed per second while staying
12:LastPath \(\leftarrow\)\(\emptyset\)
13:L \(\leftarrow\)\(0\)
14:U \(\leftarrow\)Maxtime
15:while True do
16:t \(\leftarrow\frac{L+U}{2}\)
17:Set an empty matrix Matrix
18:Set an empty list List
19:for\(i=1\) to N do
20:MovePath \(\leftarrow\)TimeMove(\(P,S,T,V,ST\))
21:Remove MovePath element from Path and generate new Path
22: Save the path of the current UAV
23:Matrix\([i]\)\(\leftarrow\) MovePath
24:List \(\leftarrow\) L + MovePath
25:if\(\text{Matrix}\
eq\text{LastPath}\) and \(\text{List}=\text{P}\)then
26:U \(\leftarrow\) t
27:LastPath \(\leftarrow\) Matrix
28:elseif\(\text{Matrix}=\text{LastPath}\) and \(\text{List}=\text{P}\)then
29:Flag, T \(\leftarrow\) EnergyMove(Matrix, P, S, E, V, SEL, ST, STEL)
30:if\(\text{Flag}=\text{False}\)then
31:U \(\leftarrow\) t
32:else
33:break;
34:else
35:L \(\leftarrow\) t
36:return Matrix, T
```  
**Algorithm 6** MUCS-BSAE (Binary Search and Energy)