esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, studying and predicting mobility patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services (e.g., sharing services). However, predicting the number of incoming and outgoing vehicles for different city areas is challenging due to the nonlinear spatial and temporal dependencies typical of urban mobility patterns. In this work, we propose STREED-Net, a novel autoencoder architecture featuring time-distributed convolutions, cascade hierarchical units and two distinct attention mechanisms (one spatial and one temporal) that effectively captures and exploits complex spatial and temporal patterns in mobility data for short-term flow prediction problem. The results of a thorough experimental analysis using real-life data are reported, indicating that the proposed model improves the state-of-the-art for this task.  
spatial correlation; temporal correlation; autoencoder architecture; time-distributed convolutions; attention mechanism; vehicle flow prediction Article
## 3 System Model and Symbol Definitions
### mMTC Energy Consumption  
The PSM and discontinuous reception (DRX) are two important energy-saving methods used for MTC devices. In our scheme, we chose the PSM rather than DRX because, with the PSM, the RF modules are turned off, with the result that the device becomes unreachable (i.e., no response to paging messages). With DRX, even during that period of unavailability, there is no need to re-establish the connection when it is time to wake up because the device remains registered to the network throughout the entire PSM time. Thus, it is more suitable for our scheme. Without loss of generality, the PSM is represented as a semi-markov model with three modes and four states:  
1. PSM: \(S_{1}\)
2. Connected mode: \(S_{2},S_{3}\)
3. Idle mode: \(S_{4}\)  
The overall energy consumption of a device \(j\) is equal to the sum of energy consumed during each state, as follows:  
\[E_{j}=\sum_{k\in PSM,act,Idle}E_{kj} \tag{5}\]  
\[E_{PSM_{j}}=PSM_{j}\cdot t_{PSM_{j}} \tag{6}\]  
\[E_{Idle_{j}}=Idle_{j}\cdot t_{idle_{j}} \tag{7}\]\[E_{act_{j}}=E_{nch_{j}}\cdot\sum_{q=0}^{R_{max}}y_{j,q}+E_{trx_{j}} \tag{8}\]  
\[y_{j,q}=\left\{\begin{array}{ll}1&\text{if device\,j collided on attempt\,}q\\ 0&\text{otherwise}\end{array}\right.. \tag{9}\]  
where \(E_{PSM_{j}},E_{act_{j}}\), and \(E_{ldle_{j}}\) define the energy consumed by device j during the sleep, active, and idle modes, respectively, while \(R_{max}\) represents the maximum re-transmission [39]. Equation (8) details the energy consumption components during the active mode for MTC devices, encompassing RACH procedures and data transmission.  
In the PSM scheme, after the MME receives the grouping result from the AS, the MME needs to trigger the devices during their scheduled period and send a message about the next active time to the devices in a group. Only one device can transmit the data in a group on the mobile network. The other devices use the PSM to save energy and wait for the active time to connect to the mobile network [26].  
The overall average consumed energy is  
\[E_{MTC_{max}}=\frac{1}{N}\sum_{j=1}^{N}E_{j} \tag{10}\]
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
## 4 Proposed Contextual Multi-Armed Bandit Learning Design
```
1:Input: Number of beams \(B\), Number of IRSs \(S\), Contexts \(C\), Exploration rounds \(T_{e}\), Maximum active beams \(K\), Counter for observed (beam,context) pair \(N\)
2:Initialize: \(Q^{s}_{b}(c)\gets 0\) and \(N^{s}_{b}(c)\gets 0\) for all \(b\in[1,B]\), \(s\in[1,S]\), and \(c\in C\)
3:procedureExploration
4:while\(t\leq T_{e}\)do
5: Observe context \(c_{t}\)
6: Randomly activate a set of beams \(K_{t}\) from \(B\)
7: Randomly select vehicle \(v_{t}\) based on \(c_{t}\)
8:if There exists \(b_{t}\in K_{t}\) such that Beam \(b_{t}\) can reach \(v_{t}\)then
9: Transmit directly via Beam \(b_{t}\)
10: Record reward \(r_{t}\)
11:else
12: Joint transmission via IRS
13: Record reward \(r_{t}\)
14:endif
15: Update \(N^{s_{t}}_{b_{t}}(c_{t})\gets N^{s_{t}}_{b_{t}}(c_{t})+1\)
16: Update \(Q^{s_{t}}_{b_{t}}(c_{t})\leftarrow Q^{s_{t}}_{b_{t}}(c_{t})+\frac{1}{N^{s_{t}}_ {b_{t}}(c_{t})}\cdot r_{t}\)
17:endwhile
18:endprocedureExploitation
19:while\(t>T_{e}\)do
20: Observe context \(c_{t}\)
21: Select a beam \(\mathbf{b}\) with highest \(Q^{s}_{b}(c_{t})\) values for active transmission
22:for Active Beam \(b_{t}\)do
23:if Beam \(b_{t}\) can reach \(v_{t}\)then
24: Transmit directly via Beam \(b_{t}\)
25: Record reward \(r_{t}\)
26:else
27: Joint transmission via IRS
28: Record reward \(r_{t}\)
29:endif
30: Update \(N^{s_{t}}_{b_{t}}(c_{t})\gets N^{s_{t}}_{b_{t}}(c_{t})+1\)
31: Update \(Q^{s_{t}}_{b_{t}}(c_{t})\gets Q^{s_{t}}_{b_{t}}(c_{t})+\frac{1}{N^{s_{t}} _{b_{t}}(c_{t})}\cdot r_{t}\)
32:endfor
33:endwhile
34:endprocedure
```  
**Algorithm 1** Contextual MAB for Beam and IRS Selection
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 5 MIMONet: A DL-Based MIMO Detector
### MIMONet: Training and Testing
#### 5.2.1 Data Generation  
The first step in training and testing the MIMONet detection model is the generation of training and testing datasets. The transmitted signal \(\mathbf{s}\) is drawn randomly from a QAM constellation, and the received signal \(\mathbf{r}\) is calculated using the MIMO channel model Equation (1). After collecting the transmitted data at the receiver end, the received signal \(\mathbf{r}\) and channel matrix \(\mathbf{G}\) are then separated into their real and imaginary components and concatenated into the feature vector \(\mathbf{X}\):  
\[\mathbf{X}=[\text{Re}(\mathbf{r}),\text{Im}(\mathbf{r}),\text{Re}(\mathbf{G}),\text{Im}(\mathbf{G})] \tag{26}\]  
This feature vector is used as input to the neural network during both training and testing.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 1 Introduction
For distributed MIMO, different designs have been proposed [15; 16; 17] and channel sounders and/or testbeds have been built. In this work, we take this one step further and present a design of a truly distributed MIMO channel sounder organized as a mesh network where all the links between each antenna in the distributed array can also be measured and exploited for sensing purposes. With a measurement setup in place, channel measurements are needed to extract the relevant parameters for realistic channel models. In [18; 19; 20; 21; 22], measurements of distributed channels were made. For the topic of joint communication and sensing, work has been done mainly in the mmWave bands in [23; 24]. Finally, theoretical work and simulations have been performed in [25] for a sub-6 GHz RadioWeave scenario for sensing [26; 27]. Most measurements that have been conducted in terms of ICAS have either been performed with a star-shaped design and/or for higher frequencies. Measurements with other topologies and/or sub-6 GHz frequencies are to a large extent lacking.
# Leveraging Blockchain and Consignment Contracts to Optimize Food Supply Chains Under Uncertainty
## 3 Problem Definition, Notations and Assumptions
### Problem Definition  
This study considers a food supply chain platform using a newsvendor model with an uncertain demand scenario. The food supply chain has frequently used blockchain technology. The application of blockchain in the food supply chain has numerous potential advantages, including ensuring the reliability of the product's information. Blockchain also improves the food supply chain's visibility. In this study, we utilize the blockchain technology in a food industry model. We developed four different models including the following:  
1. TS without BT
2. TS with BT
3. CP without BT
4. CP with BT  
TS: Traditional System; CP: Consignment Policy; BT: Blockchain Technology.  
As blockchain technology provides a highly secure and private transaction in information and fund sharing, this would be beneficial as well as popular for parties that are associated in the supply chain. Moreover, uncertain demand in the market is another risk for business. This study thus employs a probabilistic approach to integrate the threat scenario related to demand uncertainty. The problem and the model are formed in this study in such a way that we can analyse the risk and profitability under a blockchain environment with demand uncertainty. We also examine under various cost constraints for running a blockchain platform, how market demand fluctuates under the influence of encryption and privacy concerns. We adopt a supply chain model for the food industry as the maintenance of safety and privacy is crucial in this sector. The models are comparedbased on two business models, namely, a traditional supply chain model and the consumer contract. We investigate how blockchain technology affects profitability in both the traditional and consignment contract. The benefit of implementing the consignment contract and blockchain in the food industry is investigated in this study.  
Figure 1 shows how consumer behavior is affected by the adoption of blockchain technology. With a blockchain platform, consumers can be attracted by two main features, such as no hassle cost and less privacy concern. The hassle cost is the cost of the effort, time, frustration, or complexity that a person has to endure. In this case, the consumer has to pay for gathering information about the authenticity and possible contamination of the food item which is to be purchased. Also, with blockchain adoption, consumers will be less concerned about any transaction privacy regarding funds or information. However, a platform charge must be incurred at both party's (retailer or manufacturer) ends.
###### Abstract  
In the past few years, parallel digital signal processing (PDSP) architectures have been intensively studied to fulfill the growing demand of channel capacity in coherent optical communication systems. However, to our knowledge, real-time timing synchronization in such architectures is until now not implemented on a Field Programmable Gate Array (FPGA). In this article, a parallel timing synchronization architecture is proposed. In the architecture, a parallel First In First Out (FIFO) structure based on an index associated rearranging method, and a dual feedback loop based on the [PERSON]'s algorithm, are adopted. Taking advantages of the FIFO structure, 67% Look Up Table (LUT) is saved in comparison with earlier results, meanwhile the Numerically Controlled Oscillator (NCO) is efficiently improved to meet the FPGA timing requirements for real-time performance. MATLAB simulations are run to evaluate the Bit Error Rate (BER) deterioration of the architecture. The float- and fixed-point simulation results have shown that, The BER deteriorations are less than 0.5 dB and 1 dB, respectively. Further, the implementation of the architecture on a Xilinx XC7 VX485T FPGA chip is achieved. A 20 giga bit per second (Gbps) 16 Quadrature Amplitude Modulation (16 QAM) real-time system is achieved at the system clock of 159.524 MHz. This work opens a new pathway to improve the transmission capacity in real-time wireless communication systems.  
parallel digital signal processing (PDSP); wireless communication; real-time; FPGA; timing synchronization +
Footnote †: journal: Electronics  
0000-0001-5000-00001-5000][PERSON]. [PERSON]  
0000-0001-5000-0000][PERSON]  
1 Microsystem & Terahertz Research Center, China Academy of Engineering Physics, Chengdu 610200, China; [EMAIL_ADDRESS]
## 3 Results
### PLSR Models for Estimating Soil pH and EC with HSI-Resampled Spectra
#### 3.3.2 HSI-Like Band Contribution to PLSR Model  
In the two PLSR models (pH and EC), the final regression coefficients of HSI image bands from 21 to 115 (505-956 nm) are calculated and plotted in Figure 3. The final regression coefficients can denote various contributions of these bands to the PLSR model estimation. To estimate soil pH and EC, the final regression coefficients ranged from \(-0.018\) to \(0.034\) and \(-0.196\) to \(0.454\), respectively. The final regression coefficient curves showed significant troughs and peaks in wavelengths which were sensitive to soil pH and EC. For the soil pH, the regression coefficient curve had two peaks and one trough that indicated the highest importance in the regression model: band 21 (band center: 505.88 nm), band 108 (band center: 896.89 nm), and band 76 (band center: 696.84 nm). To estimate the soil EC, the final regression coefficient curve had similar two peaks and one trough: band 21 (band center: 505.88 nm), band 73 (band center: 682.78 nm), and band 109 (band center: 904.88 nm). All these bands were in green, red, and NIR regions, and those at short wavelengths had high contribution to the models. As a consequence, the three sensitive bands were used instead of 95 bands to construct new PLSR models. For pH, the three-band PLSR model also exhibits good performance with R\({}^{2}\) = 0.74, RMSE = 1.01, and RPIQ = 4.02. For EC, the R\({}^{2}\) of the PLSR model is low (R\({}^{2}\) = 0.36), the RMSE value is 19.63 dS/m, and RPIQ is 0.13 (Table 4), indicating the non-reliable model. When model validation was performed, the RMSE values for pH and EC reached 1.26 and 18.96 dS/m, respectively. The performance statistics of the three-band PLSR models were very close to those models constructed by using 95 bands. Therefore we adopt the three-band PLSR method in this study. These reflected the reasonable accuracy of PLSR method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & **Calibration** & & **Validation** \\ \cline{2-7}  & **Bands** & **R\({}^{2}\)** & **Constant** & **Components** & **RMSE** & **RPIQ** & **RMSE** \\ \hline pH & Band 21-band 115 & 0.77 & 3.60 & 3 & 0.95 & 3.84 & 1.06 \\ EC & Band 21-band 115 & 0.48 & -38.39 & 3 & 17.92 & 0.14 & 18.92 \\ \hline pH & Band 21, band 76, band 108 & 0.74 & 2.31 & 2 & 1.01 & 4.02 & 1.26 \\ EC & Band 21, band 73, band 109 & 0.36 & -55.51 & 1 & 19.63 & 0.13 & 18.96 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance statistics of PLSR models for estimating soil pH and EC (dS/m).  
Figure 3: Final regression coefficient curves of PLSR models.
## 3 Proposed Routing Algorithm
### Operation of the Radio-Aware Routing Algorithm
#### 3.2.1 The air time cost  
The air time cost (\(C_{a}\)) represents the cost for occupying the channel when transmitting a single frame on the wireless channel. A route with the smaller air time cost can be considered as a better route than others, the route can be selected with higher priority for data routing. The air time cost can be measured from Equation (1):  
\[C_{a}=\left[\begin{array}{c}{\mathcal{O}+\frac{B_{t}}{r}}\\ \end{array}\right]\frac{1}{1-e_{f}} \tag{1}\]  
\begin{tabular}{||c||l||} \hline Var. & Description \\ \hline \(O\) & Overhead (699 \(\upmu\)s) \\ \hline \(Bt\) & Number of bits in test frame (8,192 bits) \\ \hline \(r\) & Bit rate in Mbps \\ \hline \(e_{f}\) & Frame error rate \\ \hline \end{tabular}  
From Equation (1), as the frame error rate increases, the air time cost also increases according to the frame error rate. The air time cost (\(C_{a}\)) was used in an 802.11b wireless mesh network [26] and it was redefined as a link quality in this paper. In Equation (1), \(O\) is the sum of \(O_{ca}\) and \(O_{p}\). \(O_{ca}\) is the channel access overhead and \(O_{p}\) is the protocol overhead. \(B_{t}\) is the number of bits in the test frame. Some representative values for these constants are: \(O_{ca}=335\)\(\upmu\)s, \(O_{p}=364\)\(\upmu\)s, and \(B_{t}=8,192\) bits in 802.11b network. The input parameters of \(r\) and \(e_{f}\)are the bit rate in Mbps and the frame error rate for the test frame size \(B_{t}\), respectively.
# Content Analysis of YouTube Videos That Demonstrate Periapical Radiography
###### Abstract  
No prior study has evaluated the content of YouTube videos that demonstrated how to take periapical radiographs, one of the commonest radiographic examinations in dentistry. This study provided a content analysis on these YouTube videos. Three separate searches were performed with the following search strings: (1) posterior periapical, (2) molar periapical, and (3) anterior periapical. The videos resulting from the searches were sorted by view count, and their related videos (as listed by YouTube on the right of the screen) were also screened. Exclusion criteria included irrelevance to periapical radiography, no real patient involved, no demonstration of periapical radiography procedures), non-English video, and duplicate videos. Finally, 21 videos were included and analyzed. For each included video, the following aspects were evaluated: general settings (types of image receptor, patient age, and radiographic technique), patient preparation, machine preparation, receptor placement, and operator safety. All but one were demonstrated with an adult patient. The videos were largely based on digital imaging with the use of either intraoral sensors or phosphor plates, and mostly used paralleling technique. Several common issues were identified, such as not removing the metallic objects from the patient's head and neck region, not showing the selection of radiation dose, not placing the orientation dot on the film or phosphor plate on the occlusal side, and not wearing proper personal protective equipment. More instructional videos are definitely required. Future videos should eliminate these issues, and also demonstrate with pediatric patients. Perhaps the dental radiology department of a well-established university should be enticed to produce an instructional video that presents the technique in a flawless presentation, to facilitate the learning of dental students, dental hygiists and dental assistants who have yet to master their skills.  
Y 202  
consulted source among students and clinicians for educational videos on anatomy [10] and surgery procedures [11]. It is well-understood that misinformation/inaccurate information may exist among materials posted on social media and online platforms [12; 13]. Recent studies by our team have identified issues on the content accuracy of YouTube videos that demonstrate how to take panoramic radiographs and how to identify anatomical landmarks on panoramic radiographs [14; 15]. These studies implied a need to evaluate the content of other YouTube videos on dental radiology. Since periapical radiography is one of the commonest radiographic examinations in dentistry, it is important to assess the content of the videos that demonstrate its procedures so that the dental schools can better understand the quality of them as potential learning resources for students. Hence, the aim of this study was to evaluate YouTube videos that demonstrated how to take periapical radiographs, to identify the correct procedures that could be followed and incorrect settings that should not be avoided.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 3 Characteristics of the Internet of Vessels
### Information Processing and Information Fusion
Following the process in Figure 10, the information collected from sensors is processed by the Kalman algorithm to get the trajectory, then the information from the AIS system and VTS system is gathered and classified in the fusion center. In the fusion center, the temporal and special alignment technology and the track correlation technology solve the problems of the unity of the time domain and spatial domain between the AIS system and VTS system. Finally, with the fusion of related trajectory data, the administration could get more stable and more precise ship position information and movement information. Meanwhile, the information fusion technology could help the IoV system choose the correct strategy and prediction. [PERSON] [50] presented an algorithm of ship movement trajectory prediction through data fusion which considers the measurements of the ship's current position from different kinds of information sources such as the ARPA (automatic radar plotting aid), GNSS (global navigational satellite system), AIS (automatic identification system) and ENC (electronic navigational chart). Based on the information fusion, [PERSON] verified the algorithm to improve the reliability and accuracy of prediction. Currently, the navigation data fusion has been implemented in navigational decision support systems and used on board many ships. Similarly, there are many other sub-systems of the IoV system using the information fusion technology such as the fusion between the Radio Frequency Identification (RFID) system and the Zigbee wireless networking system [51]. The main goal of the IoV system using the information fusion technology is to improve efficiency and accuracy.
# Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
## 2 Model's Background
### Performance Indices  
This paper develops four models, i.e., DT, RF, XGBoost, and AdaBoost, to forecast flyrock induced by blasting. To conduct the modelling, flyrock datasets were randomly divided into training (75% of the datasets) and testing (25% of the datasets). Several renowned indices were used to analyze the performance of each model, including R2, root means square error (RMSE), variance adjustment factor (VAF), and the A-10 index. These indices, which were used by other researchers [79; 80; 81; 82; 83; 84], are presented as follows:  
\[\mathrm{R}^{2}=1-\frac{\sum\left(y_{\mathrm{act}}-y_{\mathrm{pre}}\right)^{2}} {\sum\left(y_{\mathrm{act}}-y_{\mathrm{act}}\right)^{2}} \tag{1}\]  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{\mathrm{pre}}-y_{\mathrm{act }}\right)^{2}}{n}} \tag{2}\]  
\[\mathrm{VAF}=\left[1-\frac{var\left(y_{\mathrm{act}}-y_{\mathrm{pre}}\right)} {var\left(y_{\mathrm{act}}\right)}\right] \tag{3}\]  
\[a10-index=\frac{m10}{N} \tag{4}\]  
where \(y_{act}\) and \(y_{pre}\) are measured and predicted values, respectively. \(N\) is the total number of datasets, and \(m10\) is the number of samples with values of the rate measured/predicted value (range between 0.9-1.1).