esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Group Recommendation Based on the Attention Mechanism
## 3 Methods
### Group Recommendation Method
#### 3.4.3 Predicted Score  
Our goal is to achieve both individual recommendation and group recommendation. Since the neural network has strong data fitting ability, NCF framework is used. Its basic idea is to feed embed users and items into the dedicated neural network to learn interactive functions. Figure 5 is a sketch of NCF framework.  
Figure 4: User embedding aggregation based on neural attention.  
Specifically, given a group-item pair or user-item pair, returning the corresponding embedding vector to each entity in the presentation layer (the specific method is 3.4.2). Suppose the group-item pair we input, the pooling layer performs the element connection, where \(\odot\) corresponds to element-wise multiplication i.e.,:  
\[z_{0}=\Phi_{pooling}\Big{(}g_{l}(j),v_{j}\Big{)}=\left[\begin{array}{c}g_{l} (j)\odot v_{j}\\ g_{l}(j)\\ v_{j}\end{array}\right], \tag{11}\]  
Since group embedding is obtained by user embedding aggregation, they are in the same semantic space, so it is a shared hidden layer on the pooling layer, capturing non-linearity and high-order correlation among users, groups and items.  
\[\left\{\begin{array}{c}z_{1}=ReLU(W_{1}z_{0}+b_{1})\\ z_{2}=ReLU(W_{2}z_{1}+b_{2})\\ \ldots\ldots\\ z_{h}=ReLU(W_{h}z_{h-1}+b_{h})\end{array}\right., \tag{12}\]  
\(W_{h}\), \(b_{h}\), and \(z_{h}\) represent the weight matrix, bias vectors and output neurons of the hidden layer of layer respectively. They are activated by \(ReLU\) function. Finally, the last layer \(z_{h}\) is output to calculate the prediction score:  
\[\left\{\begin{array}{c}\hat{q}_{ij}=W^{T}z_{h},\;if\;z_{0}=\Phi\Big{(}g_{l}( j),v_{j}\Big{)}\\ \hat{s}_{ij}=W^{T}z_{h},\;if\;z_{0}=\Phi\Big{(}x_{i},v_{j}\Big{)}\end{array} \right., \tag{13}\]  
\(W\) represents the weight of the prediction layer, \(\hat{q}_{ij}\) and \(\hat{s}_{ij}\) respectively represent the prediction scores of the group-item pair and user-item pair, respectively.  
Figure 5: Neural collaborative filtering (NCF) interactive learning.
# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 3 Construction of Vehicle Scheduling Optimization Model for Coal Mine Enterprises
### Determination of Customer Order Priority  
#### 3.1.1 Analysis of Customer Order Priority Influencing Factors  
When determining the loading sequence of multi-customer vehicles, it is important to consider the priority of customers. In this paper, the VIKOR method is used to evaluate the priority of customers and calculate the order of customers, which provides the basis for the subsequent vehicle scheduling model. The VIKOR method is suitable for dealing with multi-criteria decision problems with conflicts. In practical applications, different orders may have conflicts on multiple criteria. The VIKOR method is able to deal with these conflicts effectively by calculating the relative disadvantage value of each scheme and provide a balanced solution for decision-makers. When using the VIKOR method, relevant factors need to be selected according to industry characteristics and decision-maker preferences. Five primary factors are taken into account for coal mining enterprises,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Loading Bunker Serial Number** & **Loading Plan** \\ \hline \(k\) & \(n\)-m \(\underset{t_{s}}{\rightarrow}\) \(i\)-\(j\)\(\left(t_{ij}^{k}\right)\)\(\underset{t_{e}(T_{ij})}{\rightarrow}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Loading plan table.  
including the urgency of the delivery date, default penalty cost, cooperative relationship, product profit, and on-time delivery rate, as depicted in Table 2.  
The urgency of the delivery date and the cooperative relationship among the mentioned indicators are qualitative. It is necessary to transform them into quantitative measures and evaluate them using the evaluation level membership degree method. The membership degree indicates the degree to which the indicators belong to the set, with a range of [0, 1], as shown in Table 3. Default penalty cost, product profit, and on-time delivery rate are quantitative indicators with distinct units. Hence, they must be normalized to facilitate comprehensive comparison and ranking on a uniform scale.
## 3 System Model of HAP Drones and LEO Satellite Collaborative Networks
### Communication Model
#### 3.1.1 IoT-HAP Drone Communication Model  
The channel gain between terrestrial IoT device \(j\) and HAP drone \(n\) can be modeled as [28]  
\[g_{j}^{n}=(\frac{c}{4\pi d_{j}^{n}f_{c}})^{2}G_{j}^{H}\Big{|}h_{j}^{H}\Big{|}^ {2} \tag{1}\]  
where \(c\) is the speed of light, which is equal to \(3.0\times 10^{8}\) m/s. \(d_{j}^{n}\) is the distance between terrestrial IoT device \(j\) and HAP drone \(n\). \(f_{c}\) is the carrier frequency of transmission signal. \(G_{j}^{H}\) is the attenuation gain, which is related to the environment. In this paper, we set the HAP drone's antenna gain to 17 dBi [31]. \(h_{j}^{n}\) is the small scale fading component that satisfies Rician distribution, and the Rician factor is 10 dB [31].  
For the HAP drone \(n\), the transmission date from the IoT \(j(j\in\mathcal{J}_{n})\) can be expressed as follows [32]:  
\[R_{j}^{n}\leq C_{j}^{n}=b_{j}^{n}\log_{2}(1+\frac{P_{j}g_{j}^{n}}{N_{0}b_{j}^{ n}}), \tag{2}\]  
where \(N_{0}\) is the spectral density of additional white Gaussian noise (AWGN). \(b_{j}^{n}\) is the wireless channel bandwidth between the terrestrial IoT device \(j\) and HAP drone \(n\).
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 3 Data Selection  
To comprehensively study the GLONASS performance, three networks located in high, middle, and low latitude regions were employed. In addition to the differences in latitude, the following three factors were also considered in the selection of the IGS station:  
1. To comprehensively evaluate and compare the performance of GLONASS stand-alone mode, GPS stand-alone mode, and GPS+GLONASS combined mode, the station's receiver should receive both GPS and GLONASS observations. The receivers employed in the three networks are listed in Tables 2-4, respectively.
2. Using the GPS antenna PCC model for GLONASS will introduce systematic bias [9; 27; 28]. To avoid this bias, the station's antenna and radome types should have GPS and GLONASS-specific PCC models in the IGS antenna files. The antenna and radome types used in the three networks are also given in Tables 2-4, respectively.
3. The baseline accuracies are related to the length of the baseline [29]. To precisely assess the performance of GLONASS in terms of DD network processing, the mean baseline lengths of the networks should be similar.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Station Name** & **Receiver Type** & **Antenna + Radome Type** \\ \hline KIRU & SEPT POLARX5 & SEPCHOKE\_B3E6 SPKE \\ MAR7 & TRIMBLE ALLOY & LEIAR25.R3 LEIT \\ METG & SEPT POLARX5 & TRM59800.00 SCIS \\ NYA1 & TRIMBLE NETTR8 & ASH701073.1 SNOW \\ SOD3 & JAVAD TRE\_3 DELTA & JAVRINGANT\_DM SCIS \\ SVTL & JAVAD TRE\_3 DELTA & JAVRINGANT\_DM JVDM \\ TRO1 & TRIMBLE NETR9 & TRM59800.00 SCIS \\ \hline \hline \end{tabular}
\end{table}
Table 2: The GNSS receivers and the antenna + radome types of the high latitude stations.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Station Name** & **Receiver Type** & **Antenna + Radome Type** \\ \hline AJAC & SEPT POLARX5 & TRM115000.00 NONE \\ HERT & LEICA GRX1200 GGPRO & LEIAT504 GG NONE \\ JOZE & SEPT POLARX5 & SEPCHOKE\_B3E6 NONE \\ MATG & LEICA GR10 & LEIAR25 NONE \\ TLSG & SEPT POLARX5 TR & TRM59800.00 NONE \\ WARN & JAVAD TRE\_3 DELTA & LEIAR25.R4 LEIT \\ WTZR & LEICA GR50 & LEIAR25.R3 LEIT \\ \hline \hline \end{tabular}
\end{table}
Table 3: The GNSS receivers and the antenna + radome types of the middle latitude stations.  
According to the above station select criterion, 21 IGS stations were selected and formed 3 networks, distributed in high, middle, and low latitude regions, as shown in Figure 2. The high latitude network locates between 60\({}^{\circ}\)N and the North Pole, the middle latitude network lies between 30\({}^{\circ}\)N and 60\({}^{\circ}\)N, and the low latitude network situates between the equator and 30\({}^{\circ}\)S. The baseline lengths of the three networks are approximately 660 km, 880 km, and 778 km, respectively. The performance evaluation period was from 1 January 2021 (DOY 001) to 30 June 2021 (DOY 181). The GPS and GLOASS observations were downloaded from NASA CDDIS [30].
# Transcriptome-Wide Analysis of Stationary Phase Small ncRNAs in _E. coli_
## 2 Results
### Overview of the Small E. coli Transcriptome  
Our STARPA analysis identified 22,057 reads in total from all growth conditions and RNA pools. A read was kept if it possessed at least 10 reads per million in a single library, and as such, 12,933 distinct reads were retained after this initial filtering step (Supplementary Table S1). Libraries were then grouped by origin (total RNA and ribosome-associated RNA libraries), and reads within the new grouping were further discarded if they did not fulfill the previous filtering criterion. Interestingly, 1755 transcripts were captured by both total and ribosome-associated RNA libraries (Figure 2a, Supplementary Table S2). Furthermore, the majority of the sequenced transcripts from both libraries originated from intergenic or coding regions (Figure 2b), and the overall distribution of represented biotypes was very similar among the two conditions.  
To predict sRNAs involved in stationary phase biology, we conducted a differential expression analysis based on the captured reads per million from STARPA. Of relevant interest were candidates upregulated in stationary phase libraries. Differential sRNA expression analysis revealed a proportion of candidates upregulated in the stationary phase (Table 1, Supplementary Table S3). The majority of the sRNome captured in our sequencing experiments remained unchanged in both the exponential and stationary phase (Table 1, Supplementary Figure S2a-d), and a larger proportion of the differentially expressed transcripts were downregulated in the stationary phase. A portion of the 1267 predicted upregulated transcripts in the stationary phase overlapped between libraries (Figure 2c). As such, STARPA predicted 660 sRNAs to be upregulated in the stationary phase. Reads from similar libraries displayed segregated clustering (Supplementary Figure S2e,f). Interestingly, the biotypes of the upregulated reads had different proportions than those in the total sRNA transcriptome (Supplementary Figure S3), with the majority derived from rRNA and tRNA regions in total RNA libraries and from coding regions in ribosome associated RNA libraries.
# Identification of High-Order Nonlinear Coupled Systems Using a Data-Driven Approach
## 2 Mathematical Tools  
### Ls  
The least squares method was introduced by [PERSON] in 1805 [21]. Roughly speaking, the least squares solution for the problem \(Ax=b\), where \(A\in\mathbb{R}^{m\times n}\), \(x\in\mathbb{R}^{n}\), and \(b\in\mathbb{R}^{m}\), is a vector \(\hat{x}\) such that \(\operatorname{dist}(b,A\hat{x})\leq\operatorname{dist}(b,Ax)\), for all other vectors \(x\in\mathbb{R}^{n}\), where \(\operatorname{dist}(b,Ax)=\|b-A\hat{x}\|\) is the square root of the sum of the squares of the elements of the vector \(b-Ax\). Therefore, a least squares solution minimizes the sum of the squares of the errors between \(b\) and \(A\hat{x}\), hence its name [22].  
When the columns of \(A\) are linearly independent, the least squares solution of  
\[Ax=b \tag{1}\]  
is unique and it is given by [22]:  
\[\hat{x}=(A^{T}A)^{-1}A^{T}b. \tag{2}\]  
Equivalently, if the \(n\) columns of \(A\), namely, \(a_{1}\), \(a_{2}\), \(\ldots\), \(a_{n}\), are orthogonal, then the least squares solution of (1) is [22]:  
\[\hat{x}=\bigg{(}\frac{a_{1}\cdot b}{a_{1}\cdot a_{1}},\frac{a_{2}\cdot b}{a_{2 }\cdot a_{2}},\ldots,\frac{a_{n}\cdot b}{a_{n}\cdot a_{n}}\bigg{)}. \tag{3}\]  
Moreover, the \(QR\) factorization can be applied to the matrix \(A\), and the Gram-Schmidt algorithm can be used to obtain the orthogonal \(Q\) and upper triangular \(R\) matrices, which can be used to obtain the least squares solution of (1). For a thorough analysis of the least squares method, the \(QR\) factorization and the Gram-Schmidt algorithm, the reader can refer to [22; 23].
## 3 Simulation Model
### Modelling Adverse Weather  
The safety of automobile driving is significantly influenced by adverse weather. The ponding caused by strong rainfall on the pavement may induce the hydroplaning potential, thus resulting in an obvious reduction of the friction coefficient of pavements [15; 53]. The variation of real road friction due to hydroplaning is not only related to the depth of water accumulation and driving speed [15; 54], but also concerned with factors such as the tire properties and pavement surface properties [55], which is a comprehensive research issue. Additionally, it will become worse if the impact of side wind on the lateral stability of automobiles is considered, which may form the combined effect of rainfall, ice, and crosswind, thereby greatly increasing the complexity of the loading characteristics of automobiles and the possibility of safety accidents. Additionally, the present study focuses on the analysis of different factors affecting vehicle stability, so the impact of hydroplaning phenomenon under a rain scenario is simplified by reducing the friction coefficient. As a result, regarding the effects of rain, ice, and snow on road surface conditions, they generally increase the driving risks by decreasing the friction coefficient between the pavement surface and tire. So, different friction coefficients were input to indicate adverse weather, according to findings in [13], as given in Table 4.  
In terms of modeling wind velocity and wind direction in CarSim aerodynamics, the linear interpolation and extrapolation function was used to model wind velocity and wind angle. Wind velocity is defined as zero in 0-2 s, increases linearly to the target speed in 2-4 s, maintains a constant wind speed, starts to decrease linearly at 23 s, and decreases to 0 at 25 s. Additionally, the wind angle is set as consistent with wind speed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weather Condition** & \begin{tabular}{c} **Normal** \\ **Condition** \\ \end{tabular} & **Light Rain** & **Heavy Rain** & **Ice and Snow** \\ \hline \begin{tabular}{c} Pavement surface \\ condition \\ Friction coefficient \\ \end{tabular} & Rather dry & \
\begin{tabular}{c} Wet with less \\ ponding \\ 0.8 \\ \end{tabular} &
\begin{tabular}{c} Wet with massive \\ ponding \\ 0.6 \\ \end{tabular} & Frozen \\ \hline \hline \end{tabular}
\end{table}
Table 4: Moisture conditions of pavement under different weather conditions.  
Figure 4: Vehicle parameters setting.
# Systematic Review of Mecanum and Omni Wheel Technologies for Motor Impairments
## 1 Introduction
Beyond offering enhanced maneuverability, these wheels show promise in addressing several longstanding challenges associated with conventional assistive mobility systems. Conventional electric wheelchairs often struggle on irregular or soft surfaces, and they gen  
Figure 1: Design of wheels: (**a**) Mecanum wheel [8]; (**b**) omni wheel [9]. erally require substantial user effort to reposition or navigate tight spaces. Moreover, typical joystick-based controls may not be accessible to users with severe physical limitations, highlighting the need for multimodal or even hands-free interfaces. The present systematic review addresses these limitations by compiling and synthesizing research on Mecanum and omni wheel-based devices specifically designed for users with motor impairments. To our knowledge, no prior systematic review has comprehensively synthesized the design, usability, and impact of Mecanum and omni wheel-based assistive mobility devices. This review addresses that gap and aims to provide practical recommendations for researchers, engineers, clinicians, and policymakers. The key contributions of this review are as follows:  
1. It identifies and categorizes the main mechanical, control-related, and usability challenges associated with Mecanum and omni wheel-based assistive mobility systems;
2. It highlights recent trends in design and interaction technologies, including AI-driven navigation, multimodal user input, and adaptive mechanical configurations;
3. It provides strategic recommendations for future research aimed at improving terrain adaptability, user accessibility, and system affordability. The remainder of this article is structured as follows: Section 2 presents the methodology used to conduct the systematic review. Section 3 summarizes the key findings from 26 included studies. Section 4 discusses the challenges and technological trends emerging from the literature. Section 5 outlines the limitations of the review, and Section 6 offers conclusions and directions for future development in assistive mobility technologies.
### Case Study No. 3: OpenPLC Linked with Node-RED Acting as a Software IIoT Gateway
#### 6.3.1 Control of Discrete-Event System  
As in the previous case studies, we will need to control discrete-event system. In this third case study, we use open-source editor and runtime OpenPLC. In OpenPLC, we again use Ladder logic. However, in this case study we use only four global variables named **Sensor_1** and **Sensor_2**, **Conveyor_1** and **Conveyor_2**.  
**Sensor_1** checks if the product is on the conveyor belt, based on which we switch on the **Conveyor_1** belt. **Sensor_2** stops **Conveyor_1** by sensing the product on the belt. We have the **Conveyor_2** variable for the purpose of checking if we are communicating, so it is set to TRUE by default. So we can think of the whole process as moving the product from point A to point B, where it stops.
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## 2 FTN Signaling with Turbo Equalization  
### FTN Signaling with OBM  
The concept of FTN signaling was introduced by [PERSON] [19] in 1975. In FTN signaling, binary \(\text{sinc}(t/T)\) pulses can be transmitted faster than typical orthogonal (Nyquist) pulses without decreasing the square of minimum Euclidean distance between symbols, thus theoretically not affecting the bit error rate (BER) of demodulation. The binary baseband modulation of FTN signaling is linear and based on pulse shaping as follows:  
\[s(t)=\sqrt{E_{b}/T}\sum_{n}a_{n}h(t-n\tau T), \tag{1}\]  
where \(E_{b}\) is the average symbol energy, \(a_{n}\) represents \(\pm 1\), \(h(t)\) is a \(T\)-orthogonal unit energy-shaping pulse filter such as sinc or root-raised-cosine (rRC), the symbol interval of \(a_{n}\) is \(\tau T\), the acceleration factor is \(\tau\leq 1\), and \(n\) is the symbol index. Regarding Nyquist signaling, \(\tau=1\).  
We use discrete-time ISI and the additive white Gaussian noise (AWGN) channel model in [2] as the following chain of signal processing.  
Data \(\{a_{n}\}\) at \(n\tau T\rightarrow\) linear modulation and pulse shaping by \(h(t)\) at rate \(1/\tau T\rightarrow\) AWGN \(\rightarrow\) matched filter \(\rightarrow\) sample at \(n\tau T\rightarrow\) discrete-time post filter \(B(z)\rightarrow\) frame reverse \(\rightarrow\) channel observation \(\mathbf{y}\).  
This chain produces a minimum phase sequence of channel observation \(\mathbf{y}\) and can be applied to turbo equalization as detailed in the next subsection. Furthermore, the orthogonal basis model (OBM) receiver uses a sequence of wider band orthogonal pulses to express \(h(t)\) as follows:  
\[h(t)=\sum_{k=-K}^{K}c_{k}\phi(t-k\tau T), \tag{2}\]  
\[c_{k}=\int h(t)\phi(t-k\tau T)\ \text{d}t. \tag{3}\]  
It is worth mentioning that basis function \(\phi(t)\) is \(\tau T\)-orthogonal, and the matched filter is matched to \(\phi(t)\) rather than \(h(t)\); thus, the noise it outputs is white, which solvesthe colored noise problem in other receivers. The signal processing chain can be modeled as discrete-time system \(\mathbf{v}\) such that  
\[\mathbf{y}=\mathbf{a}*\mathbf{v}+\mathbf{w}, \tag{4}\]  
where \(\mathbf{a}=\{a_{n}\}\), \(*\) means convolution, and \(\mathbf{w}\) is a vector of independent identical distribution (i.i.d.) white Gaussian noise.  
Regarding \(\tau=0.703\), we use \(\mathbf{v}=[0.553,\,0.793,\,-0.084,\,-0.171,\,0.154,\,-0.064,\,0.006,\,0.010,\,-0.012, \,0.015,\,-0.016,\,0.013,\,-0.008]\) in [2], which is shortened to reduce memory usage during the simulations to the following:  
\[\mathbf{v}=[0.553,0.793,\,-0.084,\,-0.171,\,0.154,\,-0.064]. \tag{5}\]  
Consequently, turbo equalization (the BCJR detector) utilizes a minimum phase model of ISI and white noise. The detector branch label \(l\) at trellis stage \(n\) is generated from data \(\mathbf{a}\) as follows:  
\[l=\sum_{n=0}^{m}a_{n-k}v_{k}, \tag{6}\]  
where \(m\) is the length of \(\mathbf{v}\) taken into account (which may be shortened due to memory limitations).
## 2 Modeling and Analysis of Electric Actuator Control System
### Dead-Zone Non-Linearity Influence Analysis
#### 2.3.1 Dead-Zone Non-Linearity Effects  
The load is set at 15 N-m and the control angle is set at 4\({}^{\circ}\), the dead-zone voltages are set to 1 V, 2 V, 3 V and 4 V, respectively. The simulation results are shown in Figures 4-7.  
Figure 4 shows that under PID control, when the dead-zone is small, the output deflection angle of the actuator can quickly track the command value and the overshoot is small. However, with the dead-zone widening, the adjustment time becomes longer and the overshoot gradually increases. When the dead-zone is 4 V, the system enters into the limit ring oscillation state. When analyzing it with a description function, it can be seen that the system enters a critical steady state. The phase locus of the limit cycle can be observed through the phase plane in Figure 5. It indicates that PID control has a weak ability to suppress the dead-zone non-linearity. When the dead-zone exceeds a certain degree, the system will enter into the oscillation state. Figure 6 shows that when ADRC control is adopted, the overshoot of dynamic response increases with the increase of dead-zone, but the increment is small, and its dynamic response curve is basically consistent within a large range of dead-zone variation, with good stability and small overshoot. Figure 7 shows that the state observer can observe the disturbance caused by the non-linear dead-zone within the system, and the estimated value of the disturbance quantity increases with the increase of the dead-zone.  
Figure 4: PID control responses under different dead-zones.  
Figure 5: Phase locus curve when the dead-zone is 4 V.  
Figure 6: Active disturbance rejection control (ADRC) responses under different dead-zones.  
Comparing the two control methods, when the load is fixed, the dead-zone non-linearity has a greater impact on the stability of the system under PID control, and when the dead-zone is large, limit cycle oscillation will occur. The ADRC control is less affected by the dead-zone, and the performance is better than PID control.