esa-sceva/satcom-chunk-collection huggingface dataset:
# Tie-System Calibration for the Experimental Setup of Large Deployable Reflectors
## 2 Experimental Settings for Calibration
### Flexible Ring Truss Support  
The truss support is generally manufactured out of carbon fiber and it is, therefore, reasonable to consider truss deformation under the effect of the tension of the cable net.  
The elastodynamic model of the flexible ring truss support can be found using analytic techniques combined with matrix structural analysis [54; 55; 56; 57], elliptic integrals [58; 59], FEM models [60; 61; 62], and flexible multibody formulations [63].  
This implies the displacement of the nodes connected to the truss support, also called vertices, when the net system is tensioned. By considering the stiffness of the structure, the nonlinear system (4), described in the previous section, becomes as follows:  
\[\left\{\begin{array}{l}\sum_{j=1}^{r}(E_{ij}A_{ij}\frac{Lc_{ij} -Lc_{ij}^{0}}{Lc_{ij}^{0}}-k_{xx_{ij}}(x_{v_{j}}-x_{v_{j}}^{0}))\frac{x_{i}-x_ {j}}{Lc_{ij}}+\sum_{j=1}^{c-r}[E_{ij}A_{ij}\frac{Lc_{ij}-Lc_{ij}^{0}}{Lc_{ij}^ {0}}\frac{x_{i}-x_{j}}{Lc_{ij}}+\\ \hskip 113.811024 pt+k_{ij}(Lt_{ij}-Lt_{ij}^{0}+\delta Lt_{ij})\frac{x_{i}-x_{j }}{Lt_{ij}}]=0\\ \sum_{j=1}^{r}(E_{ij}A_{ij}\frac{Lc_{ij}-Lc_{ij}^{0}}{Lc_{ij}^{0}}-k_{ yy_{ij}}(y_{v_{j}}-y_{v_{j}}^{0}))\frac{y_{i}-y_{j}}{Lc_{ij}}+\sum_{j=1}^{c-r}[E_{ ij}A_{ij}\frac{Lc_{ij}-Lc_{ij}^{0}}{Lc_{ij}^{0}}\frac{y_{i}-y_{j}}{Lc_{ij}}+\\ \hskip 113.811024 pt+k_{ij}(Lt_{ij}-Lt_{ij}^{0}+\delta Lt_{ij})\frac{y_{i}-y_{j }}{Lt_{ij}}]=0\\ \sum_{j=1}^{r}(E_{ij}A_{ij}\frac{Lc_{ij}-Lc_{ij}^{0}}{Lc_{ij}^{0}}-k_{zz_{ij} }(z_{v_{j}}-z_{v_{j}}^{0}))\frac{z_{i}-z_{j}}{Lc_{ij}}+\sum_{j=1}^{c-r}[E_{ ij}A_{ij}\frac{Lc_{ij}-Lc_{ij}^{0}}{Lc_{ij}^{0}}\frac{z_{i}-z_{j}}{Lc_{ij}}+\\ \hskip 113.811024 pt+k_{ij}(Lt_{ij}-Lt_{ij}^{0}+\delta Lt_{ij})\frac{z_{i}-z_{ j}}{Lt_{ij}}]=0\end{array}\right., \tag{7}\]  
where \(c\) is the total number of cables connected to the \(i\)-th node; \(r\) is the number of rods; \(x_{v}\), \(y_{v}\), and \(z_{v}\) are the unknown co-ordinates of the \(j\)-th vertex; and \(x_{v}^{0}\), \(y_{v}^{0}\), and \(z_{v}^{0}\) are the initial co-ordinates of the vertex itself. The stiffness of the truss support can be represented as a three-dimensional bushing, with stiffnesses \(k_{xx_{ij}}\), \(k_{yy_{ij}}\), and \(k_{zz_{ij}}\), connecting the rods to the vertex, as shown in Figure 6.  
The constrained optimization (6) still applies to this flexible ring truss case and the overall algorithm is summarised in the flowchart in Figure 7.  
Figure 6: Displacement of the vertex due to the deformation of the flexible ring truss support.
## 3 Results and Discussion  
First, reference devices were fabricated to compare the PCE obtained in this work with those reported in the literature. Therefore, devices with the structure glass/ITO/PEDOT:PSS/active layer(BHJ)/LiF/Au with 9 cm\({}^{2}\) active layer area were fabricated. The best results (champion cell) showed a PCE of 4.42% with a V\({}_{\rm{oc}}\) of 0.808 V, J\({}_{\rm{sc}}\) of 15.39 mA/cm\({}^{2}\), and FF of 35.6 (see Figure S1). The efficiencies obtained in this work for the reference cell were equivalent in comparison to the values reported in the literature [13,18]. It is important to note that except for the reference cell, the active layer area used in this work was 25 cm\({}^{2}\) for all the devices, which is considered much larger than the area usually reported in the literature.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 4 Code Division with Processing  
In the development of 3 GPP standardization, two primary groups of schemas were presented for the development of the NOMA direction with code division. These groups are distinguished by the type of processing they perform: either at the bit level (before the modulator) or at the symbol level (after the modulator). Let us investigate these NOMA variants with code division in greater depth, focusing on their structural schemes that illustrate transmission processing and the most common approaches to producing code sequences. Scrambling and interleaving at bit level: The randomization of NOMA subscriber signals at the bit level makes separation possible, and the 3 GPP document [18], takes two methods of randomization, scrambling and interleaving into account to facilitate this separation. Figure 3 is a block schematic of a bit-level processing NOMA system. An encoder that can survive noise is supplied with user input; this is followed by bit-level scrambling or interleaving, modulation, and symbol-level mapping of the received information into the available resources. As an illustration, subcarriers can be employed as sources, followed by a discrete Fourier transform, as discussed in the 3 GPP report [18]. The modulator calls for a block of discrete Fourier transform once the resources are spent, such as subcarriers (DFT).  
Figure 3: A schematic of the sending side of a NOMA system that shows processing at the bit level.  
Figure 2: Illustration of an LDS–CDMA system’s basic architecture.  
Several bit-scrambling NOMA methods, such as LG Electronics' NCMA (network-coded multiple access) and Intel's LCRS (low code rate spreading), have been proposed for 5G standardization. A NOMA system can use the scrambling process described in the 3 GPP specifications [19]. A NOMA system uses subscriber-specific interleaving patterns for bit-level interleaving. Samsung's IGMA, Wireless LLC's IDMA, and Intel's LCRS are illustrations of such NOMA schemes [19]. It should be emphasized that NOMA requires new interleaving blocks in a standard 3 GPP structure, implemented based on the standard low-density block coding (LDPC), and included in the 5G specifications by adding distinct cyclic shifts to segregate signals from different subscribers [19]. IDMA's unique code sequences separate subscriber signals, which is more intriguing. Thus, IDMA is a CDMA with shifting code sequence chips, and by adding a shifting block on the sending side, an iterative multi-user reception method may recover the sequence.
## 2 System Model
### Communication Model  
Let us assume the SUE \(k\) cannot accomplish the computation task \(\phi_{k}\) with the limited embedded resources, or the GUE needs to offload the tasks for computation. In this context, the UE must offload their tasks to either the E-AP or CP tier. Subsequently, the UE denoted as \(k\) transmits its service request to the closest E-AP, identified as \(l\). The signal originating from the UE \(k\) when received at the E-AP \(l\) can be mathematically represented as follows:  
\[y_{k,l}=\mathbf{h}_{k,l}^{H}p_{k,l}s_{k}+\sum_{i\
eq k,l\in K}\mathbf{h}_{k,l}^{H}p_{i,l}s_{i}+n_{l},\forall k,l, \tag{4}\]  
Here, the \(\mathbf{h}_{k,l}^{H}\) represents the channel gain between UE \(k\) and E-AP \(l\), where the path loss, shadowing, and fast fading are considered. The transmission power for the uplink of the users \(i\) and \(k\) can be represented by \(p_{i,l}\) and \(p_{k,l}\), respectively. The received Gaussian noise at the E-AP \(l\) is represented by \(n_{l}\), and it follows a distribution of \((0,\sigma_{i}^{2})\). Moreover, the transmitted signal vector is represented by \(s_{k}\), which essentially refers to the message from the user \(k\)[29].  
The proposed model for computation offloading and resource allocation, which utilizes the A3C approach, dramatically relies on the signal-to-interference-plus-noise ratio (SINR). This metric plays a vital role in assessing the channel capacity and ensuring the quality of services (QoS). Specifically, the SINR for the E-AP \(l\) originating from the user \(k\) is calculated using the following formula:  
\[SINR_{k,l}=\frac{|\mathbf{h}_{k,l}^{H}|^{2}p_{k,l}}{\sum_{i\
eq k}|\mathbf{h}_{i,l}^{H} |^{2}p_{i,l}+\sigma_{l}^{2}},\forall k,l, \tag{5}\]  
where \(p_{k,l}\) stands for the transmission power employed between the user \(k\) and E-AP \(l\). It is worth highlighting that the transmit power \(p_{k,l}\) might not remain uniform across all users, as variations can occur based on factors such as channel conditions and the power constraints imposed by the E-APs. We define \(\gamma_{min}\) as the target SINR to ensure seamless communication in our model. Consequently, for choosing the channel edge, the threshold \(\gamma_{min}\) must be satisfied. The appropriate transmission rate also propels the system to guarantee the QoS and reduce the transmission or communication delay. Taking this into consideration and following the Shannon channel capacity theory, the attainable transmission rate in the edge can be characterized as:  
\[R_{k,l}=W\log_{2}(1+\gamma_{min}), \tag{6}\]
## 1 Introduction
It reflects the fact that researchers are constantly seeking methods to mine the multiple aspects of information that influence prediction. In the context of urban rail transit, there is a relative lack of studies that combine multiple temporal and spatial influences for short-term passenger flow forecasting. It is particularly challenging to accurately predict the passenger flow at each station on a given line. To address this research gap, this paper aims to propose a deep learning-based method for predicting short-term inbound and outbound passenger flows in urban rail transit, called deep spatio-temporal residual attention network (ST-RANet), to improve the accuracy and precision of the predictions. ST-RANet combines the attention mechanisms with residual units and convolutional networks, to extract both multi-temporal and spatial features of passenger flow. In addition to considering the spatio-temporal correlation between stations, some external factors that affect passenger travel are also included in ST-RANet, to determine passenger flow trends with multiple considerations. We have evaluated the performance of our proposed ST-RANet model against multiple advanced models that are widely utilized for predicting passenger flow in railway transit systems. The results of our experiments indicate that our ST-RANet model outperformed all other baseline models in terms of prediction accuracy. The main contributions of this paper can be summarized as follows:  
1. Based on the characteristics of passenger flow at different timescales, the spatio-temporal modules are divided into three categories: closeness, period, and trend. What's more, the model innovatively introduces a convolutional block attention module (CBAM) to allocate different weights for three spatio-temporal modules, enabling deeper mining of spatio-temporal data features. 2. The introduced ST-RANet model is able to predict rail transit passenger flow at all stations of the transportation network simultaneously, not only considering the spatio-temporal characteristics of rail transit passenger flow but also combining holiday and meteorological data to achieve comprehensive and accurate real-time predictions. By utilizing real inbound and outbound passenger flow data from Beijing's metro system to evaluate ST-RANet, this study discovers that various evaluation metrics indicate an enhancement in prediction accuracy by ST-RANet, as compared to the baseline model. The rest of this paper is organized as follows: Section 2 provides the problem definition and data modeling. Section 3 gives a detailed description of the structure of ST-RANet. Section 4 describes the data, parameters, and metric settings required for the experiments. Section 5 conducts the evaluation and discussion of the experimental results. Section 6 summarizes the contributions and limitations of the study and suggests directions for future work.
###### Abstract  
Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications.  
optimized search algorithm; adaptive balance; search performance evaluation; path planning applications
## 1 Introduction
Moreover, by-products generated in the juice or pulp processing of fruits and vegetables can be a valuable source of functional compounds, considering that the peels, cores, and seeds have a higher antioxidant content than the pulp [16]. As a result, numerous studies have aimed to find technological solutions for obtaining food natural additives from these alternative raw materials [17]. Extracts from fruits and vegetables by-products have also been evaluated as alternative antioxidants for use in various oils [18; 19]. Tomatoes are known as a rich source of carotenoids, mainly lycopene (70-80%) [20], which are very efficient scavengers of singlet oxygen and other reactive oxygen species [21; 22]. Tomato processing generates about 3-7% of by-products consisting of skins, seeds, and residual pulp, which are typically used as livestock feed or often discarded, thereby leading to environmental pollution. However, the industrial tomato by-products represent one of the most promising sources of nutrients and bioactive compounds, including polyphenols, carotenoids, minerals, proteins, fibers, and minerals [23; 24; 25]. The extraction of carotenoids from tomato by-products is attracting worldwide interest with a view to their use as food colorants and functional food ingredients. However, the traditional use of organic solvents poses several drawbacks to both human health and environmental safety [25]. Given the lipophilic properties of carotenoids, several previous studies proposed the use of vegetable oils as the extracting solvents for carotenoids to increase the functionality and thermal stability of the oils and to use them in food applications [26; 27]. The present study aimed to investigate the physicochemical and sensory properties, as well as the oxidative stability of soy milk mayonnaise enriched with carotenoids and other lipophilic bioactive compounds from dried tomato by-products directly extracted in the vegetable oil used in mayonnaise preparation.
# High Optical Feedback Tolerance of a Detuned DBR Laser for 10-Gbps Isolator-Free Operation
## 5 Conclusions  
In summary, we studied the OFT of a three-section DBR laser in both static and modulated states. The results confirmed the significant influence of wavelength detuning from the Bragg wavelength on OFT. When the DBR laser is working in the red-detuned region, the OFT can be greatly enhanced, which is favorable for both continuous and direct-modulation applications. The excess RIN induced by optical feedback was remarkedly suppressed in the CW state under the feedback level of \(-9\) dB. In the DM state, the red-detuned DBR laser also demonstrated superior stability against feedback. An error-free operation at a feedback level of \(-15\) dB was realized in the BtB configuration. For 20 km fiber transmission, the eye diagram was still wide open at a feedback level of \(-12\) dB, and the power penalty at FEC limit is lower than 1 dB under \(-15\) dB feedback. The DBR laser working in the red-detuned region has demonstrated its outstanding stability against feedback, which makes it a promising candidate as a colorless ONU transmitter for XG(S)-PON and NG-PON2.  
Conceptualization, D.L. and Q.Y.; methodology, Q.Y.; validation, Q.Y.; formal analysis, Q.Y.; investigation, Q.Y.; writing--original draft preparation, Q.Y.; writing--review and editing, D.L. and Y.H.; visualization, Q.Y.; supervision, D.L. and L.Z.; project administration, D.L. and L.Z.; and funding acquisition, D.L. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Municipal Natural Science Foundation, grant number 4212056, and National Natural Science Foundation of China (NSFC), grant number 61975197.  
Not applicable.  
The authors declare no conflict of interest.
# Empowering a Broadband Communications Course with a Unified Module on 5G and Fixed 5G Networks +
## 2 Approach and Learning Objectives
### Methodology
#### 2.1.1 Section A: Timeline of Evolution and Relation with Industrial Revolutions  
In the first section, the timeline of the evolution of modern telecommunications and its interconnection with the industrial revolutions was presented. In particular, we pointed out that modern telecommunications are a direct child of the Second Industrial Revolution, which commenced with the deployment of the Public Switched Telephone Network (PSTN), in the second half of the 19 th century. The third Industrial Revolution led to great advances in electronics and the introduction of new services, especially Internet services, exerting a profound impact on the way people communicate, work, and live. Nowadays, we have already entered the fourth Industrial Revolution (I4.0), which leads to the convergence of various processes, like fixed/wireless communication, computing, and sensing, into a single network entity. A deep understanding of this timeline is essential for all students in the fields of electrical and electronic engineering, in order to understand their position, role, and impact on modern society. The timeline of evolution offered to the students is pictorially described in Figure 1.  
In this section, the main learning objective was developing a deeper understanding of the evolutionary process of modern telecommunications, its interrelation with industry, and the future trends of key requirements, such as transported capacity per fiber, bit rate per channel/antenna, spectrum usage, and latency.
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 6 Conclusions  
To improve the continuity and smoothness of high-altitude and long-endurance UAV transpolar flight and to optimize autonomous navigation performance, a SINS/SRS/CNS multi-information fusion global autonomous navigation method was proposed. The system equations in the middle-low latitudes and high latitudes were established. The navigation and filter parameter conversion relationships between different coordinate frames were derived. The simulation and experiment verified that the SINS/SRS/CNS multi-information fusion global autonomous navigation method with parameter conversion can effectively improve the accuracy and smoothness and realize non-oscillation switching in transpolar navigation. In the vehicle experiment, the proposed algorithm improved the horizontal position accuracy by more than 29% compared with the multi-information fusion global autonomous navigation method without filter parameter conversion.  
Conceptualization, B.Z. and Q.Z.; methodology, B.Z.; software, B.Z.; validation, B.Z., Q.Z. and C.G.; formal analysis, X.Z. and W.Q.; investigation, C.G. and X.Z.; resources, J.L.; data curation, C.G. and W.Q.; writing--original draft preparation, B.Z.; writing--review and editing, Q.Z.; visualization, B.Z.; supervision, Q.Z.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grants 61533008 and 61603181), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX18_0268), the Qing Lan Project of the Jiangsu Higher Education Institutions (No. 2022), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 19 KJD590001).  
Not applicable.  
Figure 9: The horizontal position RMSEs of multi-information fusion autonomous navigation.  
**Acknowledgments:** The authors would like to thank all the editors and anonymous reviewers for their helpful comments and valuable remarks.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Home Automation System Based on Intelligent Transducer Enablers
## 8 Results
### Self-Configuration and Self-Registration Time  
The first test was performed to estimate the time spent at each of the states shown in Figure 5. The objective consisted of determining possible bottlenecks. We performed 20 independent realizations in each of the four scenarios described in the previous subsection.  
In order to evaluate the impact of the AP on the whole plug-and-play process, two different devices were tested: a Linksys WRT54G and an SMCWEB-N. Figure 12 shows the time needed to perform the self-configuration and the self-registration protocols (the x-axis labels correspond to the different protocol states explained in Section 5.2). This figure shows the central point and the confidence intervals with a confidence level of 95%. It can be seen that most of the time is spent on the authentication and the association between the AP and the ESP8266 (shown in red).  
Analyzing the results obtained for the Linksys WRT54G (on the left in Figure 12, it can be concluded that the confidence intervals corresponding to the authentication and association steps are the most variable ones. Both the self-configuration (States 4 and 5-A) and self-registration protocols (States 6 and 5-B) are completed in less than 2 s. Considering all of the states, the average time of all 80 realizations was 10.4412 s with a variance of 1.8241 s.  
The results obtained using the SMCWEB-N AP (on the right in Figure 12) show that the authentication and the association times are greater than the ones obtained with the Linksys WRT54G, but they oscillate less. With the SMCWEB-N, the average time of the whole process was 12.4871 s and the variance 0.2571 s.  
After observing the results of both APs, it can be concluded that both self-configuration and self-registration protocols play a minor part in the times of the whole process, the time of actually connecting to the AP being the most relevant one. It also can be seen that the selection of an AP directly affects the total time and variance. In both cases, the time required for the entire process was less than 13 seconds, measured from the time the ITN is connected to the power supply, until it is available to be accessed through the network.