esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 4 Evaluation Metrics of MOEA
### Hyper-Volume Indicator  
The application of the HV index in the scope of evolutionary algorithms is widespread due to its strong theoretical foundation. By calculating the HV value of the space bounded by the reference point and the non-dominated solution set, the overall performance of MOEA was assessed. The non-dominated solution set and the reference point of the multi-objective optimization algorithm measure the volume of the objective space, and this volume is measured by the HV index. The HV as shown in Equation (8) is a measure of quality that is strictly monotonic for Pareto dominance. In this equation, the volume is measured using the Lebesgue measure, represented by the \(\delta\). The count of elements of the non-dominated solution set is represented by the symbol \(|\mathrm{S}|\), and the hypercube created by the reference point z\(*\) and the \(i\)-th solution in the solution set is represented by the \(v_{i}\). A higher HV value indicates an algorithm with superior performance.  
\[HV=\delta(\bigcup_{i=1}^{|\mathrm{S}|}v_{i}). \tag{8}\]  
However, there are two defects with HV. First, the HV index is calculated over a long period. Second, the choice of reference points influences the HV index's accuracy to some amount.
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Model Description
#### 4.1.2 Communication Environment Modeling  
The parameters needed to model the communication between the actors of the environment are presented and described in Table 3.  
**Definition of \(\gamma\)**  
Vehicles are homogeneously distributed over the whole network. Thus, we define the inter-vehicle distance as:  
\[\gamma=\alpha/L_{R} \tag{7}\]  
**Definition of \(\rho\)**  
We have the following expression, which gives the density \(\rho\) of vehicles, with the speed \(V\in[a;b]\):  
\[\rho=\lambda\int_{a}^{b}\left(F(V)/V\right)dV \tag{8}\]  
**Definition of \(\lambda\)**  
Finally, we denote \(\lambda\) the arrival rate of vehicles on one road. Thus, we can compute \(\lambda\) since speed \(V\) is constant.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Designation** & **Description** \\ \hline \(P_{V}\) & Vehicles’ range of communication (m) \\ \(V\) & Vehicles’ speed (m/s) \\ \(\alpha\) & Number of vehicles \\ \(\sigma\) & Data generation frequency (Hz) \\ \(\omega\) & Number of sensors \\ \(\gamma\) & Inter-vehicle distance (m) \\ \(\rho\) & Vehicle density (vehicle/m) \\ \(\delta_{V}\) & Network’s percentage in which vehicles are connected to an RSU \\ \(\lambda\) & Vehicle arrival rate (m/s) \\ \(F_{V}\) & Frequency of new vehicle arrival in range of one RSU (Hz) \\ \(F_{\lambda}\) & Number of new vehicles in the coverage (Hz) \\ \(\theta(A,B)\) & Function which gives A if \(A<B\) and B \\  & otherwise \\ \hline \hline \end{tabular}
\end{table}
Table 3: Communication parameters.  
\[\rho=\lambda/V \tag{9a}\] \[\lambda=\rho*V \tag{9b}\]  
**Definition \(\delta_{V}\)**  
Usually, the range of a vehicle is lower than the range of an RSU. Thus, even if a vehicle is in range of an RSU, the RSU is not necessarily able to sense it. We expressed the number \(\delta_{V}\) as the percentage of the whole network where the RSU are able to receive the vehicle's messages.  
\[\delta_{V}=(\varphi*P_{V}*\beta)/L_{R}*100 \tag{10}\]  
**Definition \(F_{\lambda}\)**  
The number of vehicles entering the coverage area (and also leaving it) every second is obtained by the following expression.  
\[F_{\lambda}=(\lambda*\varphi*\beta) \tag{11}\]  
**Definition \(F_{V}\)**  
By diving \(F_{\lambda}\) (11) by the number of RSUs, we obtain the number of vehicles entering in the range of one RSU every second.  
\[F_{V}=(\lambda*\varphi) \tag{12}\]  
**Definition \(\theta\)**  
\(\theta\) is a function, taking two parameters and returning parameter \(1\) if it is lower than parameter 2 and returning parameter 2 otherwise.  
1. Based on the following observation : \[\forall v<1,[v]/v=0,\lceil[v]/v\rceil=0\] and, \[\forall v\geq 1,[v]\leq v,0<[v]/v\leq 1,\lceil[v]/v\rceil=1\] That is to say, if \(v\) is lower than 0, its integral part is equal to 0, and thus, no matter what its divisor the result will be 0, and its round-up will be 0. Else, we will obtain 1.
2. Additionally, we generalize this using the following function, which will give A if A\(<\)B and B otherwise: \[\Theta(A,B)=B-(B-A)\lceil[B/A]/(B/A)\rceil\] (13)
## 5 Case Study  
### Scenario 1  
#### 5.1.1 Basic Data in Scenario 1  
In order to evaluate the effectiveness of the proposed model and algorithm, a coalition composed of three MGs and an ESP is studied in this paper, which is shown in Figure 4. The parameters of REs and BESSs are presented in Tables 1 and 2, respectively. The other parameters are set as follows: the voltage of the distribution line among MGs is set to \(U=380\) V; the voltage of the distribution line between MGs and the main grid is set to \(U_{1}=10\) kV; the resistance of unit length distribution line between MGs or MG and the main grid is set as 0.2 \(\Omega/\)km; \(h=-1.5\); \(l=1.3\); for a lead-acid battery with capacity of \(Q\), we have \(A_{total}=390Q\).  
In order to better evaluate the effectiveness of proposed method in this paper, we have collected the data for one week, including load, PV power and wind power of each MG. The simulation was conducted in MATLAB2015b (the MathWorks, Natick, MA, USA) for each day of this week with a time resolution of 15 min, thus there are 96 optimization periods in each day. Based on the forecasting method proposed in [41], we obtain the forecasting results in each time slot of days for one week.
# Reinforcement Learning of Bipedal Walking Using a Simple Reference Motion
## 5 Real-Robot Experiments  
### Real-Robot System  
Figure 7 shows the overview of the real-robot system.  
The humanoid robot KIR-3 HV (ver. 2) used in this study has 22 servo motors. Each servo motor has an encoder inside to measure the rotation angle. Each servo motor was wired to a microcomputer, Teensy 4.0, via an ICS conversion board (Product No. 03121, Kondo Kagaku Co., Ltd., Tokyo, Japan). Teensy 4.0 acquired the data of every joint angle at approximately 250 Hz.  
An Inertial Measurement Unit (IMU), BMI088, was attached inside the backpack on the back of the robot to measure the 3-dimensional accelerations and 3-dimensional angular velocities of the torso. The IMU was wired to a microcomputer, a Raspberry Pi 4, which acquired the data at approximately 400 Hz using the I2C protocol. A one-dimensional Kalman filter algorithm [39] was used to reduce noise in the acquired data.  
Teensy 4.0 was connected to a host PC via a USB cable, and the host PC received the data of the current joint angles at 250 Hz. The Raspberry Pi 4 was connected to the host PC via a LAN cable, and the host PC received the IMU data at 400 Hz. The host PC used the acquired data to create the input to the neural network. Since the neural network required the input at 25 Hz (Section 4.1), the measurement frequencies of 250 Hz and 400 Hz were sufficiently high. Every time the host PC created the input data, it used the most recently acquired data.  
The host PC was equipped with an Intel Core i7-6700K CPU and no GPU. The neural network was not trained in this real-robot experiment; the action \(a_{t}^{\text{RL}}\) was determined using the neural network that had already been trained with our proposed method in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Walking Distance**} & \multirow{2}{*}{**Speed [cm/s]**} \\  & **in 750 Steps (i.e., 30 s) [cm]** & \\ \hline Proposed method & \(53.8\pm 6.9\) & \(1.79\pm 0.23\) \\ Proposed method & \(33.7\pm 2.0\) & \(1.12\pm 0.07\) \\ w/o Bayesian optimization & & \\ RL only & - & - \\ Imitation Learning 1 & - & - \\ Imitation Learning 2 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Walking performance of the learned controllers.  
Figure 7: Overview of the system for control of the real robot.  
simulation environment. The target angles, \(u_{t}^{\text{target}}\) in Equation (5), were sent to the servo motors via Teensy 4.0.  
All communications between the host PC, Teensy 4.0, and Raspberry Pi 4 were performed using a Robot Operating System (ROS) [40].
## 3 Code Roots Recognition and Generator Polynomial Reconstruction  
### Principles of the Code Roots Recognition  
As mentioned in Section 2, for a given valid codeword \(C_{j}\), the elements in \(\mathbf{A}\) have higher probabilities of being the code roots of \(C_{j}\) than the elements in \(\mathbf{\ \overline{A}}\), where \(\mathbf{A}\) is the set of the roots of the generator polynomial. So after the code length and synchronization position estimation, we can compare the Log-Likelihood Ratios (LLR) of \(P_{r}(\mathbf{S}_{i}=0)\) on different elements in GF (\(2^{m}\)) from \(\alpha^{1}\) to \(\alpha^{2^{m-2}}\) and choose the elements which make the LLR of \(P_{r}(\mathbf{S}_{i}=0)\) be obviously higher as the estimated code roots. Finally, we propose a method to judge whether the element \(\alpha^{2^{m-1}}=1\), be a root of the code.  
In Equation (24), we define the LLR of \(P_{r}(\mathbf{S}_{i}=0)\) as follows, which is also written as \(L(\alpha^{i})\) :  
\[L(\alpha^{i})=L\big{[}P_{r}(\mathbf{S}_{i}=0)\big{]}=\sum_{j=1}^{M}L[P_{r}( \mathbf{S}_{j,i}=0)]=\sum_{j=1}^{M}\Bigg{[}\frac{1}{rank(Hb_{\min}(\alpha^{i} ))}\sum_{k=1}^{rank(Hb_{\max}(\alpha^{i}))}L(S_{j,k}=0)\Bigg{]} \tag{24}\]  
where:  
\[L(S_{j,k}=0)=\log\frac{P_{r}(S_{j,k}=0)}{P_{r}(S_{j,k}\
eq 0)}=2\mathrm{artanh}\Bigg{[}\prod_{u=1}^{n _{u}}\mathrm{sign}(r_{u})\times\Bigg{[}\prod_{u=1}^{n_{u}}\tanh(r_{u}\ /\ \sigma^{2})\Bigg{]}^{\frac{1}{n_{u}}}\Bigg{]} \tag{25}\]  
But for a computer, the \"previous higher\" is difficult to follow. For the realization of the computer automatic recognition of the code roots, we propose the procedure includes the following steps:  
**Step1**: Let \(l\) be the estimated code length, calculate the LLRs to form a vector: \(L_{l}=\left[L(\alpha),L(\alpha^{2}),\cdots,L(\alpha^{2^{m-2}})\right]\).  
**Step2**: Rank the vector \(L_{l}\) from the lowest to the highest, in order to form a new vector \(L_{lR}\), and record the indexes.  
**Step3**: Calculate \(dL\), which is the difference of \(L_{lR}:dL(i)=L_{lR}(i+1)-L_{lR}(i)\) (\(1\leq i\leq l-1\)).  
**Step4**: Find the maximum of \(dL\), record the corresponding value of \(i\).  
**Step5**: Select the \((i+1)^{th}\) to the \(l^{\mathrm{th}}\) elements in \(L_{\infty}\), get their positions in the vector \(L_{l}\) and find corresponding GF elements \(\left\{\alpha^{\hat{n}},\alpha^{\hat{n_{l}}},\cdots\right\}\) as the estimated roots.  
As an example, we consider a BCH (63, 51) code which is corrupted by an AWGN with SNR E\({}_{g}\)N\({}_{0}=5\) dB, and the corresponding hard decision BER is \(10^{-2.19}\). The recognizing procedure is shown in Figure 2.  
Figure 2a is the original LLRs calculated in **Step 1**. By ranking the original LLRs from the lowest to the highest according to **Step 2**, we can get \(L_{{}_{IR}}\) as shown in Figure 2b. We can see that the change between \(L_{{}_{IR}}\)(39) and \(L_{{}_{IR}}\)(40) is the largest, _i.e.,_ the difference \(dL(50)\) on \(i=50\) in Figure 2c is the largest. Thus, we propose \(L_{{}_{IR}}(i)\) (\(i>50\)) as the LLRs of the generator polynomial roots, which are \(\alpha^{1},\alpha^{3}\) and their conjugate roots.
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 4 Crowd Related Research Approaches
### Crowd Monitoring
#### 4.2.2 Crowd Localization  
Localization of crowds in crowded images received less attention from the research community. With localization information, one can figure out how people are distributed in the area, which is very important for crowd managers [43]. Information about localization can be used to detect and monitor an individual in dense crowds [61]. In order to identify the location of head in an image a regression guided detection network (RDNet) has proposed for RGB-Datasets that can simultaneously estimate head counts and localize heads with bounding boxes [62]. Similarly in [27], a density map has been used to localize the heads in dense image with accurate results. In [63], localization has been identifiedwhile using LSC-CNN with the help of a metric named as Mean Localization Error (MLE). This model has achieved a remarkable performance in terms of its accuracy. Compressed Sensing based Output Encoding (CSOE) has been proposed which can help to improve the efficiency of localization in highly dense crowded situation [64].
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 4 Experimental Evaluation
### Experimental Setup  
#### 4.1.1 Data Workloads  
To test the impact of vectorial instructions, we use K-Means, a well-known clustering algorithm for knowledge discovery and data mining, as our benchmarking application. K-means is a computationally intensive application, and hence it is well-suited for measuring the impact of instruction vectorization. When applied to a dataset of \(n\) records of \(d\) dimensions, its computational complexity is O(ndk), assuming a bounded number of iterations. In the scope of this paper, we wanted to assess how efficiently the computation can be performed in our target environment. With this goal in mind, we ran the algorithm under different conditions to test the impact of using vector hardware instructions on key performance properties: speed-up, scale-up, and size-up. In all these cases, we fed K-means with three different kinds of synthetic input datasets. These datasets have the same size, but different internal structures. They consist of tables of a fixed number of rows and columns (dimensions), and random content. There is a specific reason behind this choice: although the algorithmic complexity of _K-means_ remains the same, even different data formats can significantly modify the total execution time, as we will describe more extensively in the remainder of this section. We considered the following cases: (i) 10M rows of 1000 dimensions (10M1000d), (ii) 100M rows of 100 dimensions (100M100d), and (iii) 1000M rows of 10 dimensions (1000M10d). In the following section, we go into more details of how these datasets were used to measure specific properties of the computation.
## 3 Experiments, Results and Discussion
### Kinematic Surveys Under Adverse Conditions
Environment (f) presents consistently adverse conditions for the reception of GPS signals. Some characteristic solutions are presented in Figure 10. The dashed line represents the path travelled, while the blue outline depicts part of the roof overhang. The survey included two 60 second rounds. Once again, the custom positioning procedure offers the best results. If the atmosphere models are applied, the positions shift by approximately 4 metres to the south, although the heights are considerably closer to the real values. The difference in the height component is more than 30 metres. If the elevation angle weighting is applied, the positions shift almost 20 metres to the north. The cDGPS cannot be performed since only 1 epoch fulfils the required conditions for cDGPS. Figure 8: Solutions in a mixed-condition environment (e): **(i)** autonomous Doppler positioning with custom procedure; **(ii)** Doppler smoothing of code observations with the Hatch filter; **(iii)** custom positioning procedure with C1&D1, plus elevation angle weighting and SNR weighting; **(iv)** internal positioning of the receiver. Units are in meters. Figure 10: Solutions under adverse conditions (f): **(i)** autonomous code positioning; **(ii)** custom positioning procedure with C1&D1; **(iii)** custom positioning procedure with C1&D1, plus ionosphere and troposphere models; **(iv)** internal positioning of the receiver. Units are in meters. Figure 5:0 Conditional DGPS inenvironment (e): (\(\bullet\)) cDGPS positions; (\(\bullet\)) custom positioning procedure with C1&D1; (\(\bullet\)) recalculated positions. Units are in meters.
## 2 Related Work
### System Optimization for Reducing Dialogue Rounds  
The optimization of dialogue systems to minimize the number of interaction rounds has received some attention in the field of natural language processing (NLP). Although not a dominant research area, efforts in this direction aim to enhance efficiency by reducing redundant conversational turns while complementing the primary goal of generating coherent and contextually relevant responses.  
One line of research explores reinforcement learning (RL) for dialogue policy optimization. For instance, studies such as those leveraging deep Q-networks (DQNs) [25] aim to identify optimal strategies for task-oriented dialogues. These approaches reward concise and effective interactions, guiding the system to achieve the desired outcome in fewer turns [26; 27]. Reinforcement learning frameworks often incorporate reward shaping to penalize unnecessary dialogue steps, thereby fostering more efficient exchanges. However, applying RL to modern large-scale language models like GPT and BERT-based architectures is challenging due to the substantial computational resources required and the complexity of training such models. The high cost of reinforcement learning optimization in these advanced systems highlights the need for more efficient methodologies.  
Another strategy focuses on improving dialogue state tracking and response generation. Enhanced state tracking mechanisms allow the system to maintain a more accurate representation of user intent and conversation history, reducing errors and misunderstandings that can lead to prolonged interactions [28]. Meanwhile, advancements in pre-trained language models, such as GPT and BERT-based architectures, enable better contextual understanding and response formulation, indirectly contributing to fewer dialogue rounds [3; 29]. However, for scenarios where computational resources are limited, optimizing existing model architectures rather than pursuing entirely new and resource-intensive designs remains a highly valuable approach.
## 3 Comparison of Numerical Simulations with Laboratory Measurements
### Determination of Micro-Parameters  
Some technologies such as the indentation test have been applied to measure the strength parameters of rock minerals in a local area, but it is extremely difficult to obtain the micro-parameters of shale matrix and beddings. In our simulations, all micro-parameters used in the numerical model are calibrated through a trial and error approach. The slope of the stress-strain curve, the peak strength and the macro-fracture morphology of the rock specimens were used as characteristic indicators to calibrate the strength parameters in the numerical model [44; 45]. By adjusting the Young's modulus and stiffness ratio of the particle and contacts, the slope of the simulated stress-strain curve becomes parallel to the laboratory curves [46]. Further, both shear and tensile strengths at the contact are adjusted to match the UCS. The micro-parameters are finally determined when the simulation results match well with the experimental ones.  
The stress-strain responses between the laboratory results (after the elimination of discreteness curve) and numerical simulations are compared in Figure 5. Figure 5a shows that the UCS, slope and the evolution process of the simulated stress-strain curves agree well with the experimental ones at the dip angle of 0\({}^{\circ}\). Figure 5b shows some differences between the laboratory and numerical results when the dip angle is 90\({}^{\circ}\), but the simulated peak strength still approaches the average of the experimental ones. Further, the fracture morphology of shale at 0\({}^{\circ}\) and 90\({}^{\circ}\) is similar between the experiments and simulations, as shown in Figure 6. The calibrated micro-parameters are listed in Table 1 for the rock matrix and in Table 2 for beddings. These calibrated parameters will be used in subsequent simulations. It is noted that the bedding strength is usually weaker than the shale matrix. In this specimen, the bedding strength parameters are about half of the shale matrix after calibration. With these parameters, we will explore the influences of bedding strength parameters  
Figure 4: Laboratory testing on shale specimen: (**a**) testing machine and (**b**) shale specimens.
###### Abstract  
The critical role of divalent ions (M\({}^{2+}\)) in the self-assembly of SbpA S-layer proteins (from _Lysinibacillus phaericus_ CCM 2177) into crystalline structures has been reported in several studies. Hence, ions such as magnesium, barium, nickel and, most commonly, calcium (Ca\({}^{2+}\)) have proven to trigger both protein-protein and protein-substrate interactions involved in the two-stage non-classical pathway recrystallization followed by SbpA units. As a result, two dimensional, crystalline nanometric sheets in a highly ordered tetrameric state (p4) can be formed on top of different surfaces. The use of iron in its _ferrous_ state (Fe\({}^{2+}\)) as self-assembly inducing candidate has been omitted so far due to its instability under aerobic conditions, tending to natural oxidation to the _ferric_ (Fe\({}^{3+}\)) state. In this work, the potentiality of assembling fully functional S-layers from iron (II) salts (FeCl\({}_{2}\) and FeSO\({}_{4}\)) is described for the first time. A combination of chemical (oxidation retardants) and electrical (\(-1\) V potential) factors has been applied to effectively act against such an oxidizing trend. Formation of the respective crystalline films has been followed by means of Electrochemical Quartz Crystal Microbalance with Dissipation (EQCM-D) measurements and complementary Atomic Force Microscopy (AFM) topography studies, which prove the presence of squared lattice symmetry at the end of the recrystallization process. Both techniques, together with additional electrochemical tests performed over the ion permeability of both types of S-layer coatings formed, show the influence of the counterion chosen (chloride vs. sulphate) in the final packing and performance of the S-layer. The presence of an underlying Secondary Cell Wall Polymer (SCWP) as in the natural case contributes to pair both systems, due to the high lateral motility freedom provided by this biopolymer to SbpA units in comparison to uncoated substrates.  
S-layers; self-assembly; iron salts; QCMD; electrochemistry +
Footnote †: journal: Applied Soft. 2018