esa-sceva/satcom-chunk-collection huggingface dataset:
# Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors
## 3 Related Work
### CDCL Solvers in SAT Competitions  
The state of the art in CDCL SAT solvers can be thought of as the solvers that have participated in recent SAT competitions [5]. Most participating CDCL SAT solvers typically include at least one version or hack (To hack a base solver means to improve the solver by making only minor changes to its source code.) of MiniSat [32], Glucose [8], CryptoMiniSat [33], CaDiCaL [34], CoMiniSatPS [35], or more often, the most recent SAT competition(s) winners (e.g., [12; 34]). For the latter to show an improvement of a CDCL SAT solver with a new heuristic, the solver must be compared to the base solver without any modifications. Table 1 shows the configuration of the winning CDCL SAT solvers used in this study (MapleLCMDistChronoBT-DL-v3 and LSTech are selected as the base solvers. In addition to the base solvers, the proposed solvers were evaluated against Relaxed_LCMDCBDL_newTech, Kissat_GB, and Kissat_MAB).  
MapleLCMDistChronoBT-DL-v3 [12] is based on the winner of the 2018 SAT competition, MapleLCMDistChronoBT [36], and augmented with duplicate learnt heuristic. In  
Figure 1: A general framework for CDCL-based SAT solvers.  
particular, [PERSON] et al. [12] improved the three-tier clause management by persisting additional clauses through the hash-based detection of repeatedly learnt clauses. They presented their solver MapleLCMDistChronoBT-DL-v3 in the 2019 SAT Race.  
Relaxed_LCMDCBDL_newTech, a relaxed variant of MapleLCMDistCBT-DL, was first introduced in the 2020 SAT competition [15]. Basically, the idea is to relax the backtracking by protecting promising partial assignments from being pruned. Specifically, during the search, whenever a node corresponding to a promising assignment is reached, the algorithm enters a non-backtracking stage (under some conditions); this leads to a complete assignment, which is fed to an SLS solver to search for a solution nearby. In the 2021 SAT competition, its variant LSTech [37] showed a good performance especially on satisfiable instances [37].  
The CDCL solver Kissat [34] is another popular base SAT solver that placed first in the 2020 SAT competition main track. Kissat is a low-level re-implementation of CaDiCaL that features improved data structures, better scheduling of inprocessing, and optimized algorithms and implementation. One configuration of Kissat is Kissat_SAT that targets satisfiable instances. Kissat_SAT is the base solver of Kissat_GB [38] and Kissat is the base of Kissat_MAB [39]. Kissat_GB bumps the variables based on glue centrality of glue variables. Kissat_MAB was augmented with the CHB decision heuristic as specified in [13]. The solver incorporates a reinforcement learning technique under the Multi Armed Bandit (MAB) framework that combines the VSIDS and the CHB branching heuristics by adaptively choosing the relevant heuristic at each restart using the Upper Confidence Bound (UCB) strategy.
# Method for Measuring Surface Charge on Insulating Materials Based on the Vibrating Capacitor Method
## 3 Results and Discussion
### Relationship between Measured Potential and Charge Density Distribution
Although there may be some fluctuations in situations where the surface charge density varies greatly, the curves obtained from both methods are generally consistent with the characteristics of the surface charge density distribution. The calculated values are similar to the simulated values in terms of characteristics, and the numerical values are also close. However, it is more typical for material surfaces to exhibit irregular and non-uniform surface charges. Hence, in the modified configuration illustrated in Figure 1b, the circular regions with charge are omitted and, instead, the central area of the upper surface is partitioned into 25 square regions with side lengths of 2 mm, forming a 5 \(\times\) 5 grid. Random numbers are then generated to denote the surface charge density within these 25 regions. Subsequently, the probe systematically scans from directly above the first square to directly above the last square. The resultant measured potentials, obtained through both model simulations and formulaic computations, are depicted in Figure 7d. On the \(x\)-axis, the probe's position is represented by grid numbers, starting from 1 when situated above the initial grid and incrementing sequentially until 25 as the probe traverses the grid. The \(y\)-axis denotes the measured voltage corresponding to the probe's position above the current grid. The gray line signifies the randomly generated surface charge density values beneath the probe area, while the red and blue lines respectively denote the simulated measured potential values derived from the simulation model and the calculated values obtained using the formula. Observations from the graphical representations indicate that, for instances where the charge distribution exhibits relatively simple characteristics, such as point charge or sinusoidal distribution, the measured voltages obtained through integration calculations closely mirror those derived from simulations, with a minor numerical variance of approximately 50 V. However, in scenarios featuring more random charge distributions, while the measured voltages obtained through integration calculations demonstrate similar characteristics to simulation results, there are notable numerical differences, reaching approximately 100 V. Nevertheless, the overall trends in the measured voltages from both methods remain consistent. Notably, increasing the granularity of partitioning within the charged region may potentially yield more precise outcomes, as it enhances the model's ability to capture nuanced features of the charge distribution, thus reducing discrepancies in measured voltages. However, it is important to emphasize that while greater partitioning may enhance accuracy, the primary focus of this study remains the proposition of a method leveraging integration calculations to optimize complex simulations, thereby achieving computational cost savings.
## 5 Attacks and Countermeasures
### P2P Countermeasures  
The presented attacks may severely affect the availability of services in the P2P network. Therefore, multiple countermeasures were proposed. As a basis for all countermeasures presented in the following, node-IDs in P2P networks have to be distributed by a central entity or randomly (e.g., by hashing the IP-address). Otherwise, malicious nodes could benefit from freely selectable node-IDs (e.g., by selecting an ID next to the object which should be denied).
# Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm
## 2 Materials and Methods  
### Vehicle Dynamics Modeling  
In this research, the bicycle model is adopted to describe the host vehicle dynamic explicitly. The whole control input of this model can be simplified as two parameters, the longitudinal acceleration \(c\), and the front steering angle \(\delta^{*}\). The dynamic bicycle model diagram is presented in Figure 2.  
Figure 1: The kinematic relationship.  
The state vector for this dynamic bicycle model is defined as:  
\[s=\left[V_{y},\psi,\dot{\psi},V_{x},\dot{V}_{x}\right]^{\text{T}} \tag{1}\]  
According to the analysis of longitudinal and lateral force interacting with tires, the first derivative of the state vector with respect to time can be approximated as follows [26]:  
\[\begin{array}{c}\frac{\text{d}s}{\text{d}t}=\left[\begin{array}{c}\mathbf{V}_{ y}\\ \psi\\ \mathbf{V}_{x}\\ \dot{\mathbf{V}}_{x}\end{array}\right]=\left[\begin{array}{ccccc}-\frac{2C_{f}+2C _{r}}{mV_{x}}&0&-V_{x}-\frac{2C_{f}l_{f}-2C_{l_{r}}}{mV_{x}}&0&0\\ 0&0&1&0&0\\ \frac{-2C_{f}l-2C_{r}l_{r}}{I_{x}V_{x}}&0&\frac{-2C_{f}l_{f}^{2}+2C_{r}l_{r}^{ 2}}{I_{x}V_{x}}&0&0\\ 0&0&0&0&1\\ 0&0&0&-\frac{1}{\tau}\end{array}\right]\left[\begin{array}{c}\mathbf{V}_{y}\\ \psi\\ \dot{\psi}\\ \mathbf{V}_{x}\\ \dot{\mathbf{V}}_{x}\end{array}\right]+\left[\begin{array}{c}\frac{2C_{f}}{m}\\ 0\\ \frac{2C_{f}l_{r}}{I_{x}}\\ 0\\ 0\end{array}\right]\delta^{*}\\ +\left[\begin{array}{c}0\\ 0\\ 0\\ \frac{1}{\tau}\end{array}\right]\left[\begin{array}{c}0\\ 0\\ 0\\ \mathbf{V}_{y}\dot{\psi}\\ 0\end{array}\right]\end{array} \tag{2}\]  
where, \(\mathbf{V}_{y}\) refers to the host vehicle lateral velocity, \(\mathbf{V}_{x}\) represents the longitudinal velocity, \(\dot{\mathbf{V}}_{x}\) refers to the derivative of longitudinal velocity, the longitudinal acceleration, \(\psi\) refers to the yaw angle, \(\dot{\psi}\) refers to the variation rate of yaw angle, \(m\) refers to the total mass of the host vehicle, \(\mathbf{I}_{z}\) denotes the yaw moment of inertia of host vehicle, \(l_{f}\) and \(l_{r}\) represent the longitudinal distance from the center of gravity to front and rear tires, respectively, \(\mathbf{C}_{f}\) and \(\mathbf{C}_{r}\) represent the tires cornering stiffness, and \(\tau\) refers to the longitudinal time constant.  
The outputs from the vehicle dynamics, such as longitudinal velocity \(\mathbf{V}_{x}\) and lateral velocity \(\mathbf{V}_{y}\) are based on vehicle body coordinates. The goal of steering model is to keep the vehicle within its lane and follow the predefined road path, by controlling the front steering angle \(\delta^{*}\), which is achieved by driving the yaw angle error \(\mathbf{e}_{2}\) and lateral displacement error \(\mathbf{e}_{1}\) to zero, where  
\[e_{1}=\mathbf{V}_{x}\mathbf{e}_{2}+\mathbf{V}_{y} \tag{3}\]  
\[e_{2}=\psi-\psi_{ds} \tag{4}\]  
The desired yaw angle rate is calculated by \(\frac{V_{x}}{R}\), where \(R\) refers to the radius of the road curvature.  
Figure 2: The dynamic bicycle model diagram.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 4 Proposed Congestion-Aware RPL
### Routing Metrics
A \(U_{n}\) is considered as a parent whereas a \(D_{n}\) is considered as a child. It is worth noting that, this metric is not considered in rank computation rather it is used as a selection criterion of a preferred parent when the rank of candidate parents is equal. Even though the ranks of the nodes are equal, the number of children associated may be varied, and thus different criteria for being a parent. The reason for designing this metric is to balance the data traffic by taking into account the number of children for each candidate parent. Unbalance network problem has been addressed in the new draft for optimization of parent-node selection [33], where child node count metric is proposed. \(NI\) is the cost criteria which is used as a tie-breaking metric. We implement the storing mode of operation in this work in which the node stores the information of their neighbors, thus a node is able to calculate the number of child nodes. The \(NI\) is defined as the ratio of the number of \(D_{n}(i)\) to the total number of all neighboring nodes \(N_{n}(i)\in\{U_{n}(i)\), \(D_{n}(i)\}\) as in Equation (5). \[NI(i)=\frac{Number\,of\,D_{n}(i)}{Total\,number\,of\,N_{n}(i)} \tag{5}\]
###### Abstract
Other conservative treatments include footwear modifications [20; 22] and (custom-made) foot orthoses (CFO). Through their geometric features and material characteristics, foot orthoses (FO) may either aim to restore the loadbearing function of the first ray and hallux or redistribute the forces over a bigger surface [8; 22; 23; 24]. A popular FO design feature used in patients with HL is the cut-out FO [16; 25; 26; 27; 28]. This design includes the fenestration of the head of the first metatarsal head. Some authors, in addition to this fenestration, add an extension at the level of the hallux (known as dynamic wedge extension). The few studies that evaluated the efficacy of this FO design reported an increased plantarflexion of the first ray [16; 27]. This plantar flexion allows the proximal phalanx to dorsiflex over the metatarsal head, generating a decrease in joint compression (especially during the propulsion phase) [26] as well as decreasing adduction of the first metatarsal [27]. The effect that these cut-out FO have on the plantar pressure distribution is much less understood since there is contradictory evidence in the literature [16; 25; 26]. [PERSON] et al. [16] reported an increase in pressure at the first MTH and a decrease on the first toe, whereas [PERSON] et al. [26] reported a decrease in peak pressure at the first MTH as well as on the heel, and an increase in pressure at the midfoot and lesser toes. In combination with the cut-out FO, other geometric features have been used. This may include the application of an anteriorly placed rocker element at the forefoot (e.g., Anterior Forefoot Stabilizer Element (AFSE)) or the use of a frontal plane wedge. The AFSE featured a rocker-shaped anterior portion extending from the metatarsal areas to the toes that aims at facilitating the range of motion of the first MTPJ, reducing joint pressures and therefore optimizing the load transfer between the first MTPJ and the hallux. Frontal plane wedges, aim at reducing the external pronatory moments in patients with pronated feetand is therefore a popular approach in patients with FHL [29; 30]. However, the efficacy of these FO still remains unexplored. Given the variability in plantar pressure distribution reported in previous studies and the lack of clarity on the most effective FO designs for patients with HL, the aim of this study is to compare the effects of two distinct FO geometries on plantar pressure distribution in patients with SHL, contributing to a better understanding of how to improve conservative management strategies for this condition. The main hypothesis is that both CFOs will cause a change in the distribution of plantar pressures. The cut-out CFO will have a greater effect at the level of plantar pressures while the AFSE CFO will have a greater effect at the COP, due to the design of both orthoses.
## 2 Methodology
### Feature Extraction  
After the signal was pre-processed, the noise was attenuated while the stride segments were obtained. However, the sequence data of these segments could not be directly fed into the SVM model due to the large computation cost and the poor correlation between the class and the original sequence data, which would have negatively affected the classification performance. Instead, some statistical features extracted from the pre-processed signal were the appropriate choices for the input of the SVM model.  
A total of 21 statistical features, including 12 EMG features and 9 GRF features, in each step were extracted to classify the terrain. The feature symbols used in this paper and their specific meanings are shown in Table 3. These features can be divided into three categories as follows:  
1. Force and time features extracted from the GRF curve. Such features reflect the variation and relationship of the two GRF measures during a stride or between two consecutive strides and they are depicted in Figure 4a.
2. Time-domain features extracted from the filtered EMG data, including the mean absolute value (MAV), standard deviation (STD), root mean square (RMS), and waveform length (WL), which are broadly used features, such as in [28; 49]. Such features reflect the overall activation level of the muscle in a stride.
3. Muscle force features extracted from the EMG and LE, including the TA peak, TA 80, SL peak, SL 25. Such features reflect the muscle force level at a particular timing of a stride and they are depicted in Figure 4b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **The Meaning of the Features** \\ \hline \multicolumn{3}{c}{**GRF-Based**} \\ \hline
**Hallux Max** & The max value of hallux GRF \\
**Heel Max** & The max value of heel GRF \\
**Max T** & The time interval between the two peaks \\
**Hallux ON** & The duration of Hallux GRF above the threshold \\
**Heel ON** & The duration of Heel GRF above the threshold \\
**Hallux OFF** & The duration of Hallux GRF below the threshold \\
**Heel OFF** & The duration of Heel GRF below the threshold \\
**Start T** & The time interval between Hallux ON and Heel ON \\
**End T** & The time interval between Hallux OFF and Heel OFF \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extracted features of the EMG and GRF, where \(i\) is the index of EMG filtered amplitude and \(N\) is the length of the sequence in a stride.
## 3 Results and Discussion  
### L\({}^{*}\) Measurements  
The obtained results of L* are shown in Figure 2. After calculating the average value of L* for 8-day samples with different antioxidants, these were plotted using bar graphs. Error bars representing standard deviation (n = 10) were also added.  
A slight decrease in the L* values for blank samples can be observed, which is consistent with expectations. Considering the error size in the samples treated with antioxidants, especially those treated with sodium metabisulphite (A1) and anthocyanins present in carrot extract (A3), it was challenging to conclude whether a particular active agent protected against oxidation or not. It seemed that mushrooms protected with antioxidants A1 and A2 maintained the colour white, while A3 did not have a positive effect on extending the shelf life of the analysed samples.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 2 Autonomous Driving Based on Dead Reckoning
### Dead Reckoning Based on Differential Drive Kinematics  
Without positioning sensors, differential drive kinematics can generally be used for calculating the relative position of the vehicle in real time. The basic principle of differential drive kinematics involves using the speed of the left and right wheels and the difference between them for calculating the trajectory of the vehicle.  
There are two basic localization methods that are based on wheel speed: Rear wheel position-based feedback and front wheel position-based feedback. According to research [1], the control stability of rear wheel position-based feedback control law is unaffected by the sign of wheel velocity, so it is much more suitable for use in dead reckoning.  
In this model, the speed of the rear wheels is used for calculating the real-time position of the vehicle, which is demonstrated schematically in Figure 2.  
In Figure 2, ICC represents the instantaneous center of curvature, meaning that when the velocity of each wheel changes, the vehicle rotates about this point. (x,y) is the coordinate of the midpoint between both wheels in the world coordinate system. \(R\) represents the radius of curvature, \(r\) is the wheel radius, \(L\) is the distance between the two wheels, \(Vc\) is vehicle velocity, \(Vl\) is left wheel velocity, and \(Vr\) is right wheel velocity. \(\theta\) is the angle of the forward direction of the vehicle in the world coordinate system, and \(\omega\) is the rate of  
Figure 1: Overview of the path tracking system.  
Figure 2: Differential drive kinematics.  
rotation, which is identical for both wheels. Based on the above model and assumptions, the following equations are obtained:  
\[\omega\bigg{(}R+\frac{L}{2}\bigg{)}=Vr \tag{1}\]  
\[\omega\bigg{(}R-\frac{L}{2}\bigg{)}=Vl \tag{2}\]  
Based on the above equations, it can be solved for \(\omega\) and \(R\) at any moment:  
\[\omega=\frac{Vr-Vl}{L} \tag{3}\]  
\[R=\frac{L}{2}\frac{Vr+Vl}{Vr-Vl} \tag{4}\]  
If the rotational speeds of the left and right wheels are known, the horizontal and vertical velocities of the vehicle in the coordinate system at a given moment can be obtained:  
\[v_{x}=v_{c}\cos(\theta)=R\omega\cos(\theta)=\frac{L}{2}\frac{Vr+Vl}{Vr-Vl} \times\frac{Vr-Vl}{L}\cos(\theta)=\frac{Vr+Vl}{2}\cos(\theta) \tag{5}\]  
\[v_{y}=v_{c}\sin(\theta)=R\omega\sin(\theta)=\frac{L}{2}\frac{Vr+Vl}{Vr-Vl} \times\frac{Vr-Vl}{L}\sin(\theta)=\frac{Vr+Vl}{2}\sin(\theta) \tag{6}\]  
Therefore, the position and angle of the vehicle at moment t can be calculated using the following equation:  
\[x=\int_{0}^{t}v_{x}dt+x_{0} \tag{7}\]  
\[y=\int_{0}^{t}v_{y}dt+y_{0} \tag{8}\]  
\[\theta=\int_{0}^{t}\omega dt+\theta_{0} \tag{9}\]  
As the wheel radius is known and the rotation speed of both wheels can be obtained through the CAN bus, the speed of both wheels at any given moment can be calculated. Therefore, the final vehicle position and angle in real time relate only to the rotation speed of both wheels.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method
### Depth Position Mapping and Transformer Integration
#### 3.3.2 Transformer Decoder  
The Transformer Decoder is designed to integrate depth-sensitive features with contextual information, enabling robust representation refinement for downstream tasks [29]. By employing a multi-layer structure, each layer consists of self-attention, cross-attention, and a feed-forward network. Self-attention operates on the input queries to capture internal dependencies, while cross-attention aligns these queries with encoder outputs, ensuring the effective fusion of spatial and contextual features. Specifically, cross-attention uses depth-sensitive features as queries and leverages contextual embeddings as keys and values, allowing the decoder to focus on relevant spatial-depth relationships. Each layer applies residual connections and normalization to stabilize feature updates, while a feed-forward network further enhances the refined representations. Through iterative processing across multiple layers, the decoder progressively aligns and integrates depth-sensitive features and contextual cues, resulting in task-specific predictions with enhanced accuracy and robustness.  
The computational complexity of the transformer is as follows. Let the input feature map have spatial dimensions \(H\times W\), batch size \(N\) and per-position embedding dimension \(C\), and flattened sequence length \(L=H\times W\). Each attention layer uses \(h\) heads with per-head dimension \(D=C/h\). For each attention computation, three linear projections contribute \(3\times N\times L\times C^{2}\) multiply-accumulate operations (MACs), while the linear Attention mechanism avoids the explicit \(L\times L\) similarity matrix and requires only \(N\times\big{(}L\times C+C^{2}/h\big{)}\) MACs, which are negligible compared to the projections. An output projection adds another \(N\times L\times C^{2}\), resulting in a dominant per-attention complexity of approximately \(4\times N\times L\times C^{2}\). Each layer also includes a feed-forward network contributing \(2\times N\times L\times C^{2}\). Thus, the encoder layer (one attention + FFN) requires \(6\times N\times L\times C^{2}\) MACs, and the decoder layer (two attentions + FFN) requires \(10\times N\times L\times C^{2}\). The total complexity of the transformer is therefore \(16\times N\times L\times C^{2}\), which scales linearly with \(L\). In contrast, standard softmax attention incurs \(O\big{(}NL^{2}C\big{)}\) complexity due to the quadratic term in sequence length. This linear formulation yields substantial computational benefits for high-resolution inputs.