esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Conceptual Framework of AgroTRACE
### Recognition  
Recognition refers to the capability offered by AgroTRACE to distinguish the fresh products, the infrastructure and sites, from the producer to the consumer, by taking into account the GS1 standards. In particular, the following standardization is incorporated:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**IoT System** & \begin{tabular}{c} **GS1** \\ **Standard** \\ \end{tabular} & \begin{tabular}{c} **Supply** \\ **Chain Range** \\ \end{tabular} & \begin{tabular}{c} **Minimum** \\ **Reference** \\ **Level** \\ \end{tabular} & \begin{tabular}{c} **Data** \\ **Transfer** \\ \end{tabular} & \begin{tabular}{c} **Modular** \\ **Structure** \\ \end{tabular} & \begin{tabular}{c} **Waste** \\ **Treatment** \\ \end{tabular} & **Open Access** \\ \hline GR-LIVE & x & \begin{tabular}{c} Field- \\ Retailer \\ Packaging \\ plant- \\ Retailer \\ Packaging \\ Packaging \\ PatchSense \\ \end{tabular} & Product & RFID 1 & \(\surd\) & x & x \\  & x & \begin{tabular}{c} Field- \\ Retailer \\ Packaging \\ plant- \\ Retailer \\ Packaging \\ plant- \\ Retailer \\ Packaging \\ \end{tabular} & Container & \begin{tabular}{c} RFID, \\ RFID, \\ Satellite, BLE \\ \end{tabular} & \(\surd\) & x & x \\  & x & \begin{tabular}{c} Field- \\ 2 \\ \end{tabular} & Container & \begin{tabular}{c} RFID, GSM 3, \\ NFC 4, \\ \end{tabular} & \(\surd\) & x & x \\  & x & \begin{tabular}{c} Field- \\ Consumer \\ Field- \\ Packaging \\ Consumer \\ Field \\ \end{tabular} & Product & RFID & x & x & x \\  & x & \begin{tabular}{c} Field- \\ Retailer \\ Packaging \\ Plant- \\ Consumer \\ Field \\ \end{tabular} & Product & x & x & x & x \\  & x & \
\begin{tabular}{c} Field- \\ Container \\ Field- \\ Product \\ \end{tabular} & Product & x & \(\surd\) & x & x \\  & x &
\begin{tabular}{c} RFID, \\ LoRaWAN, \\ BLE \\ \end{tabular} & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}  
* RFID: Radio-Frequency IDentification; 2 BLE: Bluetooth Low Energy; 3 GSM: Global System for Mobile communications; 4 NFC: Near Field Communication.  
\end{table}
Table 1: Comparison of the available IoT-based traceability systems on the market with the proposed solution (AgroTRACE).  
Global Location Number (GLN): Distinction among the locations of farms, packaging units, wholesalers, distributors, retailers, etc.
* Global Trade Item Number (GTIN): Identifying trade items by providing a single number for each product.
* Electronic Product Code (EPC): Providing serial numbers for the commercial item.
* Serial Shipping Container Code (SSCC): Providing containers' serial codes for the pallets.
* Global Returnable Asset Identifier (GRAI): Distinction of the returned produce.
## 3 Results and Discussion
### Adsorption Efficiency in Batch System
Concerning \(q_{e}\) calculated parameters by pseudo-first and pseudo-second-order model, it is observed quite a difference between the obtained parameters by each model, as well as R\({}^{2}\), having pseudo-second-order more accuracy with experimental data; this may be due to the chemical nature of the interactions between the metal ions and adsorbent active sorption centers [33]. \begin{table}
\begin{tabular}{c c c c c} \hline
**adsorbent** & **Metal** & **Temperature (\({}^{\circ}\)C)** & **adsorbent Dose (g)** & **Initial Concentration (mg/L)** \\ \hline \multirow{2}{*}{YSR} & Cr (VI) & 76.16 & 0.14 & 368.18 \\  & Ni (II) & 70 & 1.19 & 31.82 \\ \hline \multirow{2}{*}{PSR} & Cr (VI) & 40 & 1 & 100 \\  & Ni (II) & 55 & 0.6775 & 368.18 \\ \hline \end{tabular}
\end{table} 
Table 7: Best adsorption conditions for Cr (IV) and Ni (II) on YSR and PSR. Figure 5: Results of Cr (VI) and Ni (II) adsorption on biomass-derived adsorbents. \(n=1\). (**a**) Adsorption capacity; (**b**) Removal efficiency. The fitting parameters of the isotherm models are summarized in Table 9. The Freundlich model achieved the R\({}^{2}\) closest to 1, indicating the heterogeneity of the bio adsorbent surfaces, which is consistent with the results obtained during the biomass characterizations (Figures 1 and 2). Likewise, the adsorption process occurs in multilayer due to the non-uniform heat distribution in the pores of the adsorbents [71]. The Freundlich constant value, \(n\), of 12.82 suggests that there are strong bonds between the Cr (VI) ions and the YSR surface and that the adsorption process is favorable [72].
## 1 Introduction  
The intracanal fracture of nickel-titanium (NiTi) rotary files is still one of the main concerns during root canal therapy. It has been demonstrated by many authors that rotary NiTi files essentially fail for two main reasons: flexural (cyclic) fatigue and torsional failure [1; 2].  
The cyclic fatigue failure of the file occurs because of the repeated compressive and tensile stresses that accumulate at the file's point of maximum-stress, which is located at the maximum curvature point inside the root canal's anatomy [3].  
Torsional failure occurs when the tip (or some part of the file) binds (or is blocked inside) the root canal, while the motor continues to rotate until the torsional limits are overcome and a fracture occurs [4].  
The mechanical resistance of NiTi rotary files has been tested in two different ways: cyclic fatigue tests and the torsional test. When investigating cyclic fatigue, instruments are usually rotated inside an artificial curved canal until a fracture occurs at the recommended rotation speed. When investigating the torsional resistance, based on the ADA testing recommendations for stainless steel manual files, the tip is usually locked at 2 mm and its rotation speed is 2 rpm. Unfortunately, there is no ADAstandard that describes how to test nickel-titanium rotary files; therefore, studies and their results may differ significantly due to the different testing devices and different methodologies [5; 6; 7].  
Over the past several years, improvements in design, alloys and manufacturing processes have been proposed to increase the mechanical resistance of NiTi rotary files. Several proprietary heat treatment applications have been developed. Studies have shown that thermal treatments (for files with the same geometry and design) enhance the cyclic fatigue lifespan while, at the same time, reducing the torsional resistance of a file [8; 9; 10]. Some studies have also compared heat-treated files and non-heat-treated files with different cross-sectional designs in cyclic fatigue tests, which demonstrate that heat treatment may not be the most influential parameter for flexural resistance [11]. Therefore, many other factors can influence mechanical resistance. Data have shown that different cross-sectional designs, tip and taper dimensions, pitch lengths, and operative motions could affect the resistance to both the flexural and torsional stresses [12; 13; 14]. Additional mass increases the torsional resistance and decreases the fatigue resistance [15].  
However, very few studies have compared instruments of the same design (and different heat treatments) for both torsional and fatigue resistance [16; 17]. Therefore, in the present study, four commercial instruments, with a similar design and tip and taper dimensions, were tested and compared, in order to assess if and how different heat treatments may affect both the torsional and flexural resistance.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 4 Results
#### Obtained Results for Different Numbers of Sensors
Similarly, temperature distribution-based optimization showed a significant reduction in objective function, from 0.23 with a single sensor to 0.12 with three, showing the critical role of comprehensive thermal field sampling.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
###### Abstract  
Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem. To address rain and haze problems simultaneously, we present a robust network-based framework consisting of three steps: image decomposition using guided filters, a frequency-based haze and rain removal network (FHRR-Net), and image restoration based on an atmospheric scattering model using predicted transmission maps and predicted rain-removed images. We demonstrate FHRR-Net's capabilities with synthesized and real-world road images. Experimental results show that our trained framework has superior performance on synthesized and real-world road test images compared with state-of-the-art methods. We use PSNR (peak signal-to-noise) and SSIM (structural similarity index) indicators to evaluate our model quantitatively, showing that our methods have the highest PSNR and SSIM values. Furthermore, we demonstrate through experiments that our method is useful in real-world vision applications.  
encoder-decoder network; dilated convolution; image restoration; guided filter; de-haze; d
## 4 Optimization Problem Modeling
### Optimization Constraints
#### 4.4.4 Flow Conservation
This metric is crucial for prioritizing tasks during scheduling conflicts as tasks with higher accumulated energy are considered to have greater investment and are, therefore, prioritized to minimize system cost and resource waste. 2. Modify Transmission Strategy in Accordance with Priority:The aforementioned data task \(D^{o}_{n^{\prime}}\), enjoying higher precedence, will implement the strategy encapsulated in \(\pi^{o}_{n,t^{\prime}}\). Here, \(\pi^{o,*}_{n,t}\) denotes the designated set of strategies for transmitting the data task \(D^{o}_{n}\). Consequently, the optimal scheduling strategy set is \(\hat{\pi}^{o,*}_{n}=\hat{\pi}^{o}_{n}\). For the data task \(D^{o}_{n}\) possessing a subordinate priority, it is imperative to revert to the observation satellite \(OS_{n}\) and reformulate the transmission scheduling strategy, as detailed in the Dijkstra-based algorithm developed above. To circumvent subsequent conflicts, careful consideration must be given to arranging the set containing strategy \(\hat{\zeta}^{r}_{n,k,k^{\prime},t^{\prime}}\), ultimately achieving the refined strategy set \(\hat{\pi}^{o,*}_{n^{\prime},t^{\prime}}\).
## 3 Generation of All-Weather LST:
### Validation Metrics  
The performance of the generated all-weather LST was evaluated using validation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R\({}^{2}\)). These metrics are defined as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{9}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{10}\]  
\[R^{2
## 2 Study Area and Datasets  
### Location of XXNA and Land Use Planning  
XXNA is located at the southeast of the coastal region in China. It belongs to Xiang'an district, Xiamen, China, 25 km to the west of the heart of Xiamen (Figure 1). XXNA will be built in the east of Dadeng Island (Figure 1), aiming to be as regional hub airport and major gateway airport.  
According to the land use planning of XXNA in Figure 2 (adapted from [PERSON] [7]), the total planned area on Dadeng Island is about 43 km\({}^{2}\), including eighteen different land use types. The area for airport is about 16.30 km\({}^{2}\), which accounts for 37% of all planning area on Dadeng Island. The construction plan for XXNA is divided into two phases: short-term goals and long-term goals [7; 29]. In the short-term target plan (target year 2025), the construction of two long-distance parallel runways is planned, and the annual passenger throughput is expected to reach 45 million passengers, and cargo and mail throughput of 750,000 tons. The long-term goal (target 2045) is to add a short-range parallel runway to each of the two long-distance parallel runways. By then, XXNA will have four runways, which can meet the annual passenger throughput of 70 million passengers, and cargo and mail throughput of 1.6 million tons of transportation requirements.  
Figure 1: The geographical location of the study area and synthetic aperture radar (SAR) data coverage. (**a**) Study area position on the map of the southeast coast of China marked with a red rectangle; (**b**) the ALOS World 3D 30 m digital elevation model of the study area, the black rectangle represents the coverage of the SAR dataset used, the red rectangle is the position of Xiamen Xiang’an New Airport (XXNA).  
Figure 2: The land use planning of Dadeng Island (adapted from [7]).
# Deep Learning-Enhanced Autoencoder for Multi-Carrier Wireless Systems
###### Abstract  
In a multi-carrier (MC) system, the transmitted data are split across several sub-carriers as a crucial approach for achieving high data rates, reliability, and spectral efficiency. Deep learning (DL) enhances MC systems by improving signal representation, leading to more efficient data transmission and reduced bit error rates. In this paper, we propose an MC system supported by DL for operation on fading channels. Deep neural networks are utilized to model the modulation block, while a gated recurrent unit (GRU) network is used to model the demodulation blocks, acting as the encoder and decoder within an autoencoder (AE) architecture. The proposed scheme, known as MC-AE, differs from existing AE-based systems by directly processing channel state information and the received signal in a fully data-driven way, unlike traditional methods that rely on channel equalizers. This approach enables MC-AE to improve diversity and coding gains in fading channels by simultaneously optimizing the encoder and decoder. In this experiment, we evaluated the performance of the proposed model under both perfect and imperfect channel conditions and compared it with other models. Additionally, we assessed the performance of the MC-AE system against index modulation-based MC systems. The results demonstrate that the GRU-based MC-AE system outperforms the others.  
multicarrier; autoencoder; deep learning; wireless communication; block error rate; gated recurrent unit (GRU); modulation; demodulation  
Msc: 94A14
## 4 Results and Discussion  
To reflect the superiority of reliable robust \(H_{\infty}\) controller in improving the vehicle and the motor performances, the responses of the hub-driven EV under different controllers are investigated in both the frequency domain and time domain. The parameters of the hub-driven EV are given in Appendix A. We assume \(u_{max}=2000\) N and \(z_{max}=0.11\) m. Moreover, the uncertain parameters about the sprung mass and the tire stiffness are supposed to satisfy \(|\lambda_{1}|\leq 0.3\), \(|\lambda_{2}|\leq 0.2\).  
A conventional robust controller \(\mathrm{K}_{\mathrm{C}}\) is designed firstly, as described by Theorem 2, in which the actuator failure and the model uncertainties are not considered. The gain matrix of the conventional controller is  
\[\mathrm{K}_{\mathrm{C}}=10^{3}\times[-1.092-0.326-0.12-5.802-4.6833.91]\]  
Then, a parameter-dependent controller \(\mathrm{K}_{\mathrm{P}}\) is designed for parameter uncertainties by applying the method in Theorem 1 with the \(\xi_{0}\) and \(\mathbf{J}\) terms dropped out. This design does not consider the actuator failure, and its corresponding gain matrix is  
\[\mathrm{K}_{\mathrm{P}}=10^{3}\times[-0.865-0.425-0.109-6.747-3.6634.018]\]  
Lastly, a reliable robust controller \(\mathrm{K}_{\mathrm{PF}}\) is designed for active suspension in the hub-driven EV according to Theorem 1. in which the actuator failure and the parameter uncertainties are both considered. The gain matrix of a reliable robust controller is \(\mathrm{K}_{\mathrm{PF1}}\) and \(\mathrm{K}_{\mathrm{PF2}}\) when the actuator thrust loss is 0% and 40% respectively.  
\[\mathrm{K}_{\mathrm{PF1}} =10^{3}\times[-1.082-0.531-0.136-8.434-4.5795.022]\] \[\mathrm{K}_{\mathrm{PF2}} =10^{3}\times[-0.649-0.319-0.082-5.061-2.7473.014]\]  
Figure 7 illustrates the control structure for the active suspension system. The control structure can be divided into two parts: the outer control loop and the inner control loop. In the inner control loop, the states which are sampled by sensors act as an input to the reliable robust \(\mathrm{H}\circ\) controller. The ideal control force is first generated by the controller based on control strategy. Then, the linear motor is controlled to produce thrust force (detailed control process can be found in the literature [52]). In the outer control loop, with the change of the sprung mass, tire stiffness or actuator thrust losses exceed a certain limit, the gain matrix of the controller will be updated by solving proposed convex optimization problem. This convex optimization process takes 20 s. However, the degree of actuator thrust loss, the sprung mass, and the tire stiffness do not change rapidly in real time. In other words, the gain matrix of the controller is not updated in real time. In addition, in inner control loop, theabsolute CPU-time (0.00081 s) per time step is shorter than the real-time simulation time-step (0.01 s), thus illustrating the viability of its real-time implementation.
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
### Motivation  
A wireless self-organizing network made up of learner nodes using smart devices on campus, the campus opportunistic network allows each learner node to communicate with other nodes. Although many academics have made progress in this area, few have concentrated on the campus context and have failed to suggest efficient routing algorithms or manage the cache in a way that is suitable for the specific movement of nodes in the campus. The predictability of node movement trajectories is also determined by how regularly learner nodes travel around campus.  
The blind nature of message delivery is one of the major issues when performing message transmission. If the node delivers the message directly to its neighbors, this results in a significant message transmission delay, as messages must pass through numerous relay nodes in order to reach their destinations and they cannot deliver to the recipient before the message survival time ends sometimes. On the other hand, network resources are wasted because some nodes carrying copies of messages do not eventually connect with the destination node, resulting in a large number of duplicate messages in the network that are truly unnecessary. Additionally, since copies of individual messages are forwarded numerous times, they can take up a lot of memory, preventing the reception of recently arrived messages. Since nodes typically have limited memory, it is important to manage the cache because a multi-copy-based routing strategy leaves a lot of copies in the network. This can even have a significant impact on the success rate of message delivery, making it a vital study in this paper. A certain amount of message copies exists in the network to ensure that messages get to their destination quickly, the network needs a certain number of message copies, so when the node cache is too small to accommodate new messages, it should choose which messages to delete initially to make room for new arrivals.
## 2 Properties of SDPPG Signals and Problems in Measurement
### Noise in SDPPG Measurement  
Using peak to peak intervals (PPIs) of an SDPPG signal, a heart rate can be detected. PPI is defined as the time interval between the maximum values of one signal and the next period signal, as shown in Figure 3. As explained earlier, the SDPPG signal contains not only PPI but also other information that can help diagnose diseases. Hence the shape of the SDPPG signal is crucial. However, noises in measurement such as background noise and movement noise distort SDPPG signal shapes and can result in incorrect diagnoses. Therefore, a noise cancellation algorithm is required to remove the background noise and movement noise.  
The background noise maintains the main shape of SDPPG signals, but PPI can be affected. The background noise can be filtered out with a low-pass filter [5]. When there is movement noise in a measurement, the SDPPG signal shapes are significantly distorted, as shown in Figure 5. The movement noise affects SDPPG measurement differently, according to the type of movement, such as walking, vertical motion, or horizontal motion [9]. Hence, it is difficult to analyze and remove movement noise.  
Figure 4: Spectrum of PPG and SDPPG signals.  
Figure 3: PPG and second derivative of photoplethysmography (SDPPG) signals.  
In this paper, an algorithm is proposed to detect and remove movement noise in SDPPG signals using wavelet transform and multi-path signals. To that end, we briefly review the wavelet transform and diversity combining in the next section.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 4 Data Plane Programmability Using P4
### Variables  
For arithmetic operations, P4 supports only signed integers. Therefore, we utilize extern functions for floating point operations and store floating point numbers as fixed-size bit strings. Metadata and header fields carry information throughout the processing pipeline, but they are bound to individual packets. To store information persistently, registers may be used. They can be allocated at program start and accessed and updated in actions of the ingress and egress pipeline. An example for the use of registers is keeping connection state.
## 2 Materials and Methods
### Statistical Analysis  
The model's performance was assessed using the coefficient of determination (R\({}^{2}\)), absolute and normalized root mean square error (RMSE), the Wilmot d index [49], and modeling efficiency (ME). The RMSE and d-index values indicated the model's capacity for predicting experimental data. Following are the equations for measuring model efficacy:  
\[\text{R}^{2}=\left[\frac{\left[\sum_{i=1}^{n}(\text{O}_{i}-\overline{O})\times \left(\text{S}_{i}-\overline{S}\right)\right]^{2}}{\sum_{i=1}^{n}\left[\text{ O}_{i}-\overline{O}\right]^{2}\times\sum_{i=1}^{n}\left[\text{S}_{i}-\overline{S} \right]^{2}}\right] \tag{2}\]  
\[\text{RMSE}=\left[n-1\sum_{i=1}^{n}\left(\text{P}i-Oi\right)^{2}\right]^{0.5} \tag{3}\]  
\[\text{D}\;\text{Index}=1-\left[\frac{\sum_{i=1}^{n}(\text{P}_{i}-O_{i})^{2}}{ \sum_{i=1}^{n}\left[\left|P_{i}^{\prime}\right|+\left|O_{i}^{\prime}\right| \right]^{2}}\right] \tag{4}\]  
\[\text{ME}=\left[\frac{\left[\sum_{i=1}^{n}(\text{O}_{i}-\overline{O})-\sum_{ i=1}^{n}(\text{P}_{i}-\text{O}_{i})^{2}\right]}{\sum_{i=1}^{n}\left[\text{O}_{i}- \overline{O}\right]^{2}}\right] \tag{5}\]  
where \(S_{i}\) and \(O_{i}\) represent the predicted and observed values, respectively, n is the number of observations, \(\overline{O}\) and \(\overline{S}\) are the means of the observed and simulated values, respectively, and \(P_{i}=P_{i}-\text{M}\) and \(O_{i}=O_{i}-\text{M}\) (M is the mean of the observed variable) [50].