esa-sceva/satcom-chunk-collection huggingface dataset:
# Methods for Mid-Term Forecasting of Crop Export and Production
## 3 Materials and Methods  
### Dataset  
For simplicity we limit our study to the world's 15 top wheat exporters: Russia, Canada, the USA, France, Australia, Ukraine, Argentina, Romania, Germany, Kazakhstan, Bulgaria, Hungary, Czech Republic, Poland, and Lithuania. We also consider 15 primary wheat importers: Egypt, Indonesia, China, Turkey, Algeria, Bangladesh, Brazil, Philippines, Japan, Nigeria, Mexico, Morocco, Korea, Yemen, and Vietnam. We combine several data sources (Table 1):  
* Wheat trade flows and production levels from UN FAOSTAT and UN Comtrade databases [24; 25];
* Fuel prices. They affect the cost of using agricultural machinery; therefore affect the production level and prices;
* Macroeconomic indicators from International Monetary Fund [26] affect demand and consumption throughout the world;
* Normalized difference vegetation index (NDVI) from UN FAO [27] affect the production level. It usually helps to reveal the areas where vegetation is stressed. The values are obtained from the METOP-AVHRR sensor. The use of the whole raw data array would lead to unacceptable growth of the prediction models; therefore we consider per-seasonal county-level average and extreme (max, min) values.
* Topically-relevant messages from Twitter contain reports about deals in the international wheat market, climate anomalies, etc.  
Figure 1 shows the approach we utilize to integrate all the heterogeneous features. The structured data can be normalized and passed directly to neural network-based regression models, while the social media data should be structured first.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Set** & **Frequency** & **For** & **Features** \\ \hline \multirow{3}{*}{Trade flows} & \multirow{3}{*}{Annual} & Country, & Export value \\ \cline{3-4}  & & Country, & Import value \\ \cline{3-4}  & & Commodity & Re-export value \\ \cline{3-4}  & & Re-import value \\ \hline \multirow{3}{*}{Production} & \multirow{3}{*}{Annual} & Country, & \\  & & Commodity & Production value \\ \hline \multirow{3}{*}{Fuel prices} & \multirow{3}{*}{Annual} & \multirow{3}{*}{World} & WTI price, USD \\ \cline{3-4}  & & & Brent price, USD \\ \cline{3-4} \end{tabular}
\end{table}
Table 1: Feature set for the mid-term wheat export and production forecasting.  
It would be naive to expect the tweets to contain precise country-level information about wheat production or trade flows, but they reflect some average market attitudes and expectations. Therefore, we evaluate the per-year country-level sentiment regarding wheat-related conversation topics and consider it as an additional feature to predict wheat production and trade flows. The final dataset is available on [28].
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 7 Conclusions  
In this paper, we have investigated the BH-based resource allocation scheme for the ISTN in the spectrum sharing scenario. To avoid inter-system CCI and improve the reusability of the frequency resources, a BH-based spectrum sharing scheme for the ISTN has been presented, where dynamical protection zones are constructed according to the beam allocation pattern of the satellite. Based on the spectrum sharing scheme, the resource allocation problems for MWSC and MMCDR have been formulated to improve the spectrum efficiency and guarantee fairness, respectively. Optimal solutions have been obtained by reformulating the problems as MILP problems. In order to solve problems with low computational complexity, two greedy suboptimal algorithms have been proposed for MWSC and MMCDR, respectively. The simulation results show that the proposed resource allocation algorithms achieve higher spectrum efficiency in the ISTN and meet the unevenly distributed traffic demands of both the satellite and terrestrial systems. It has also shown that both the greedy algorithms with low complexity perform closely to the optimal algorithms, which are more suitable for real-time resource scheduling in the ISTN. Our future work is to evaluate the impact of the inaccurate location information and capacity requirements on the performance of the proposed algorithms and make corresponding improvements to the resource allocation scheme for ISTN.  
Conceptualization, M.Z. and X.Y.; methodology, M.Z. and X.Y.; software, M.Z.; validation, M.Z. and X.Y.; formal analysis, M.Z.; investigation, M.Z. and X.Y.; resources, Z.B.; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, X.Y.; visualization, M.Z.; supervision, Z.B.; project administration, X.Y.; funding acquisition, X.Y. and Z.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Innovation Action Plan of Shanghai grant number 22511100503.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
6G & Sixth-generation \\ BH & Beam-hopping \\ BS & Base station \\ CCI & Co-channel interference \\ CDF & Cumulative distribution function \\ CDR & Capacity to demand ratio \\ CR & Cognitive radio \\ DRA & Dedicated resource allocation \\ EZ & Exclusion zone \\ Gbps & Gigabits per second \\ ISTN & Integrated satellite-terrestrial network \\ LEO & Low Earth orbit \\ LOS & Line of sight \\ Mbps & Megabits per second \\ MEO & Medium earth orbit \\ MHP & Matern hardcore process \\ MILP & Mixed-integer linear programming \\ \end{tabular}
## 1 Introduction
### Theoretical Form Error Induced by Overcut of Vibration in HFUVAS  
The target line is the theoretical tool path to cut the microlens array without consideration of the smoothing tool path in HFUVAS, which can be described by Equation (2):  
\[z_{0}(y)=\frac{\left[y-p_{2}(n-0.5)\right]^{2}}{r+\sqrt{r^{2}-\left[y-p_{2}(n-0.5 )\right]^{2}}} \tag{2}\]  
where \(n\) is the sequence number of the lenslet in the \(y\) direction, \(r\) is the lenslet radius. \(p_{2}\) is the lenslet pitch in the \(y\) direction. During the HFUVAS process, the diamond tool is moved along the target profile. The tool tip without considering tool vibration is determined by Equation (3):  
\[\begin{cases}y_{1}(t)=v_{c}t\\ z_{1}(t)=z_{0}(y_{1}(t))\end{cases} \tag{3}\]  
where \(t\) is the cutting time, \(v_{c}\) is the nominal cutting speed. In addition, the tool tip vibrates at a high-frequency vibration, which is assumed to be linear, and it can be determined by Equation (4):  
\[y_{2}(t)=A\cos(2\pi ft) \tag{4}\]  
where \(f\) is vibration frequency and \(A\) is vibration amplitude. Then, the tool tip trajectory considering tool vibration can be determined by Equation (5):  
\[\begin{cases}y(t)=y_{1}(t)+y_{2}(t)\\ z(t)=z_{1}(t)\end{cases} \tag{5}\]  
When the tool decomposition speed in the \(y\)-axis direction is zero, the front-cut limit and back-cut limit of the tool track appear in each vibration cycle. As a result, they can be determined by solving Equation (6) as follows:  
\[y^{\prime}(t)=0 \tag{6}\]  
Based on Equations (2)-(6), the tool trajectory with the front-cut limit and back-cut limit in HFUVAS of a certain target profile can be obtained. Figure 2a presents the simulated cutting results in HFUVAS of the target profile of microlens array. The vibration frequency and vibration amplitudes are 104 kHz and 0.75 \(\upmu\)m. The radius and pitch of the lenslet are 1 mm and 199.75 \(\upmu\)m, with a lenslet depth of 5 \(\upmu\)m. The back limit of the tool track forms a back-cut line to be left on the workpiece in a downhill line. By contrast, the front limit connects as a front-cut line to leave the workpiece in an uphill line. As a result, the cut line deviates from the target line, leading to a theoretical form error. It should be noted that as the slope angle of the target profile along the cutting direction increases, the theoretical form error also increases. For the target profile of the microlens array, the maximum form error arises at the edge between two adjacent lenslets, as shown in Figure 2b.  
Figure 2: (**a**) Tool trajectory in HFUVAS of the target line of microlens array, (**b**) a comparison of the target line and cut line, with \(r\) of 1 mm and \(p_{2}\) of 199.75 \(\upmu\)m.
## 4 Conclusions  
This study investigated the fusion of spectra and texture data of HSI for evaluating different WHC traits--drip loss, expressible fluid, and salt-induced water gain--of chicken breast filets. Key wavelengths were selected by RC, texture variables (including mean, homogeneity, contrast, entropy, energy, and correlation) were extracted by GLCM, and data fusion was used by feature level fusion. Different PLSR models were established with corresponding input variables, including the full spectra, multispectra at selected key wavelengths, and texture variables, as well as the fusion data of multispectral and the corresponding texture data, respectively. The results demonstrated that for drip loss and expressible fluid, texture data was an effective supplement to spectra data, and fusion data as an input variable could effectively improve the predictive ability of the independent prediction set (Rp = 0.80, RMSEp = 0.80; Rp = 0.56, RMSEp = 2.10). While for salt-induced water gain, the model based on multispectral data at selected wavelengths (Rp = 0.69, RMSEp = 18.04) had better performance than the fusion model (Rp = 0.68, RMSEp = 18.16). This was mainly due to the fact that the measurement of salt-induced water gain was based on mince samples, which lacked the important physical structural information of intact meat. In conclusion, fusion data contained more information (i.e., physical structure, chemical composition, and water characteristics of the chicken meat) than spectra or texture data alone and thus influenced the predictive ability of the WHC traits. The results of this study have practical significance and can improve the WHC evaluation ability of chicken meat by HSI. A larger number of samples and a wider range of WHC traits should be used in the future to further improve the model performance.  
This study is supported by the National Natural Science Foundation of China (No. 31772062).  
[PERSON] and [PERSON] conceived of, designed, and performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data and wrote the paper. All authors collaborated in the interpretation of the results and on the preparation of the manuscript.  
The authors declare no conflict of interest.
## 2 The Onion Router Network
Using TOR with MQTT can add anonymity and security to your communications. Here is how TOR can help protect MQTT secure connections from attacks:  
* Anonymity: TOR routes your MQTT traffic through a network of volunteer-operated servers, making it difficult for adversaries to trace the origin of your MQTT messages. This anonymity can be beneficial in scenarios where you want to protect the identity of MQTT clients or servers. * Traffic Encryption: MQTT itself supports encryption through TLS. When combined with TOR, your MQTT traffic is encrypted by TLS as it travels through the TOR network. This double-layered encryption enhances the security of your MQTT communications. * Bypassing Network Restrictions: Accessing MQTT brokers directly may be challenging in some environments, such as restrictive networks or regions with censorship. TOR can help bypass these restrictions by allowing you to access MQTT brokers through its network, even in environments where direct access is blocked. * Protecting Against Network Analysis: TOR's onion routing mechanism ensures that your MQTT traffic is routed through multiple relays, obscuring the connection between the sender and the receiver. This makes it harder for adversaries to analyze traffic and gain insights into your MQTT communication patterns. However, it is essential to consider some limitations and challenges when using TOR with MQTT:  
* Latency: TOR introduces additional latency due to the extra hops your MQTT messages traverse through the TOR network. While this latency might not be significant for many applications, it could impact real-time or latency-sensitive MQTT communications. * Reliability: TOR's reliability can vary based on factors such as network congestion and the availability of TOR relays. While TOR aims to provide robust anonymity and security, occasional network disruptions or slowdowns may occur. * Configuration Complexity: Configuring MQTT clients or brokers to work with TOR can be more complex than traditional network configurations. You may need to adjust network settings and ensure compatibility between TOR and your MQTT implementation. In summary, while TOR can enhance the anonymity and security of MQTT communications, it is essential to weigh the benefits against potential challenges and consider whether TOR aligns with your specific use case and security requirements.
# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
## 2 Materials and Methods
### Proposed Method
#### 2.2.2 Periapical Lesion Classification Model  
Before feeding the cropped PRA images into the classification model for training purpose, we applied the below data preprocessing augmentation techniques to increase our training dataset since we have limited number of unhealthy samples:  
1. Normalized pixel values of each image to range (0, 1)
2. Resize images to 75 \(\times\) 75
3. Random image rotation between 0-20\({}^{\circ}\)
4. Random zoom ranging between 0.8 to 1.2
5. Shuffle images  
We employed a fine-tuned Inception v3 classification model for classifying detected PRAs as healthy (H) or lesion (PL) using Keras framework. We used a balanced subset of the original dataset, which included 3249 PRAs, 1593 of unhealthy PRAs (PLs) and 1656 of healthy PRAs (H). We divided this dataset into training, and testing following the 80:20 ratio. We initialized the model from an ImageNet pretrained inception weights excluding the classification layer. For feature extraction, we froze all layers, stacked and trained only the classification layer. We optimized the network parameters using the Adam optimizer with an initial learning rate of 1 \(\times\) 10\({}^{-}\)5. We decreased the learning rate by half when learning stagnated. We selected a dropout rate of 0.2 to avoid overfitting. We trained for 100 epochs. We then unfreeze and re-trained the top layers to fine-tune the network. We optimized the network using Adam optimizer with learning rate of 1 \(\times\) 10\({}^{-}\)7 and trained for another 100 epochs.  
During inference time, we fed the detected PRAs as input to the classifier; which in turn labeled each PRA as H or PL. On average, it took 5 milliseconds to classify a PRA from our test dataset. We developed a custom visualization function to visualize PRAs, which our classifier labeled as PL by drawing a bounding box around the detected periapical lesion.
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
###### Abstract  
In recent years, non-orthogonal multiple access (NOMA) has become a promising technology for the advancement of future wireless communications. In principle, the relay node with better channel conditions can support others to enhance the system performance by using successive interference cancellation (SIC) technique. In this paper, we take advantage of NOMA in the study of a relaying cooperative system operating in half-duplex (HD) fixed decode-and-forward (DF) relaying scheme. In the two time slots, two data symbols are received at the destination node resulting in a higher transmission rate. Besides that, we study energy harvesting (EH) with power splitting (PS) protocol. For performance analysis, approximate and exact closed-form expressions for outage probability (_OP_) are obtained. Following that, we examine the average bit error probability (ABEP) while expressions for the throughput in delay-limited mode are given. It can be seen that our simulation results match well with the Monte Carlo simulations.  
NOMA; energy harvesting; power splitting; bit error probability; outage probability Article
## 5 Prototype System
### Extraction and Indexing  
#### 5.1.1 Segmentation  
A video can be segmented in many ways utilizing different modalities (detailed in Section 2). The choice of modalities is often domain dependent.  
For the current study, the video is segmented by utilizing the textual modality. TED videos come with text transcripts in the SubRip subtitle file format,.srt, which is a widely supported format. In this format the subtitles are stored sequentially along with the timing information.  
Thus, the transcripts of TED videos were first split into sentences using StanfordNLP toolkit [62] and fed into the C99 text segmentation algorithm [61] in order to produce video segments.  
After the segmentation performed, the time information in the transcript file was used to determine the start and end time of each video segment.
# SambotII: A New Self-Assembly Modular Robot Platform Based on Sambot
## 1 Introduction  
### Background  
A self-assembly or self-reconfiguration modular robot (SMR) system is composed of a collection of connected modules with certain degrees of locomotion, sensing, and intercommunication [1; 2; 3]. When compared with robots that have fixed topologies, SMR systems have some advantages, such as versatility, robustness, and low cost [4].  
The concept of dynamically self-reconfigurable robotic system was firstly proposed by [PERSON] in 1988 [5]. Since then, many interesting robot systems have been proposed. In spite of the significant advances of SMRs, researchers in this field believe that there is a gap between the state-of-art research on modular robots and their real-world applications [3]. As [PERSON] and [PERSON] [6] stated, the applications of self-reconfigurable robots are still elusive.  
One main challenge in this field is how to achieve autonomous docking among modules, especially with higher efficiency and accuracy. Autonomous docking is an essential capability for the system to realize self-reconfiguration and self-repairing in completing operational tasks under complex environments. Various methods, such as the infrared-red (IR) ranging, vision-based ranging, ultrasonic ranging, etc., have been employed to guide the autonomous docking process.  
The IR-based methods generally have high accuracy, simple structure and small size. Thus, they are suitable for self-reconfigurable robotic systems with the shapes of chain, tree or lattices. They were applied in many SMR systems, like PolyBot [7], ATRON [8], ModeRED [9], SYMBRION [10], etc. However, the IR-based methods are unsuitable for mobile robotic systems due to the limited detection ranges.  
The vision-based methods can provide more information than IR-based method do. Some SMR systems like CKBot [11], M-TRAN [12], and UBot [13] utilize the vision-based methods in their autonomous docking process. However, these methods generally involve large-scale and complex image processing and information extraction. This restricts their applications in SMR systems, to some extent.  
Except the IR and vision-based methods, ultrasonic sensors are also used in the docking process. For example, the JL-2 [14] conducts autonomous docking under the guidance of several ultrasonic sensors. In addition, the eddy-current sensors, hall-effect sensors and capacitance meters are occasionally applied in the docking navigation. However, they are easy to be interfered by motors and metallic objects.
# Impact and Control of Reflected Noise from an Overpass Bottom
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sound Receiving Point** & \(\mathbf{L_{Aeq,1h}}\) **Simulation** & \(\mathbf{L_{Aeq,1h}}\) **Measurement** & **Deviation (1)–(2)** \\  & **Value (1) dB(A)** & **Value (2) dB(A)** & **dB(A)** \\ \hline Steel overpass (sound meter & 78.7 & 78.2 & 0.5 \\
1.5 m above the ground) & 81.0 & 79.1 & 1.9 \\ \hline RC overpass (sound meter & 78.6 & 77.9 & 0.7 \\
1.5 m above the ground) & 80.9 & 79.5 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation values and measurement values with different materials on the bottom of the overpass (steel and reinforced concrete (RC)). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**Steel Overpass**} & \multicolumn{3}{c}{**RC Overpass**} \\ \hline  & **Floor** & \(\mathbf{L_{Aeq,1h}}\) **dB(A)** & \
\begin{tabular}{c} **Reflected** \\ **Noise dB(A)** \\ \end{tabular} & \(\mathbf{L_{Aeq,1h}}\) &
\begin{tabular}{c} **Reflected** \\ **Noise dB(A)** \\ \end{tabular} \\ \hline  & 1F & 78.2 & _7.7_ & 78.2 & 7.8 \\  & 2F & 80.9 & 6.9 & 80.8 & 6.9 \\ Noise level & 3F & 82.7 & 4.3 & 83.1 & 4.8 \\ on each floor & 4F & 84.8 & 3.5 & 84.6 & 3.6 \\  & 5F & 84.5 & 1.1 & 84.1 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation results of overall noise level and reflected noise caused by the overpass with different bottom materials. and 0.8-5.3 dB(A), respectively. Study results show that as the sound absorption rate of the sound absorbing material increased, noise reduction improved. Since human ears can perceive a more than 3 dB(A) change in sound level [34; 42], a sound absorbing material with a sound absorption rate of at least 0.60 is recommended to reduce reflected noise level. The WSDOT [27] indicated that on a double-decker highway, when the bottom of the upper decker had a sound absorbing material, noise from the lower decker was absorbed, reducing reflected noise by around 0-4 dB(A). Therefore, these sound absorbing materials can effectively reduce the level of reflected noise.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 7 Discussions of Results and Research Findings  
### Comparison of Contribution to Other Published Methods  
We proposed a new software-based method that uses typical/existing HW blocks in coherent optical transponders and software-based feedback loop to continuously optimize both Tx and Rx FIR filters based on estimated channel response and signal-to-noise ratio (SNR) at the Rx. Comparing to published methods, we are highlighting the following key differentiators:
## 2 Methodology
### SRP Model
#### 2.1.2 ECOM1 Model  
ECOM1 can be cooperated with analytical SRP models. The acceleration of SRP at 1 astronomical unit (AU, 1 AU = 149,597,871 km) described by the simplified five-parameter ECOM model is decomposed into three directions in a Sun-oriented DYB frame.  
\[a_{RPB}=a_{D}\cdot e_{D}+a_{Y}\cdot e_{Y}+a_{B}\cdot e_{B} \tag{6}\]where \(e_{D}\) is a unit vector pointing from the satellite to the Sun, i.e., \(e_{\bigodot};e_{Y}\) is a unit vector pointing along the satellite's solar panels axes; and \(e_{B}\) completes a right-handed system. The DYB frame can be expressed as  
\[\begin{array}{ll}e_{D}&=e_{\bigodot}\\ e_{Y}&=\frac{e_{\bigodot}\times r}{\|e_{\bigodot}\times r\|}\\ e_{B}&=e_{D}\times e_{Y}\end{array} \tag{7}\]  
where \(r\) is the satellite position in the Earth-centered-inertial (ECI) frame. The five-parameter ECOM is described as [16]  
\[\begin{array}{ll}a_{D}&=D_{0}\\ a_{Y}&=Y_{0}\\ a_{B}&=B_{0}+B_{c}cosu+B_{s}sinu\end{array} \tag{8}\]  
where \(D_{0}\), \(Y_{0}\), and \(B_{0}\) are the constant acceleration along \(e_{D}\), \(e_{Y}\), and \(e_{B}\) directions. \(B_{c}\) and \(B_{s}\) are the coefficient of the periodic term. \(u\) is the argument of latitude of the satellite.