esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Pot Experiments  
Chlorophyll plays a key role in biosynthesis processes in the green parts of plants, enabling the conversion of light energy into the energy of chemical bonds in the process of photosynthesis. The indicator of relative chlorophyll content (CCI) in the leaves of maize fertilized with developed foliar fertilizer was the lowest in the control and increased relative to the applied doses of the developed fertilizer (Figure 1). The relative content of chlorophyll in fertilized objects with developed foliar fertilizer was higher by 14%, 24%, and 34%, respectively, according to dose in comparison with the control. The significance of differences was confirmed only in the case of the highest dose of foliar fertilizer. Another relationship was found for soil fertilization, where the relative chlorophyll content was significantly higher in all cases where soil fertilization was applied compared to the control. However, the object with the lowest dose of soil fertilizer had the highest relative content of chlorophyll. According to [PERSON] and [PERSON] [24], the initial fertilization method should be implemented primarily under conditions of application of limited doses of fertilizers. As the applied doses increase, the effectiveness of the initial fertilization decreases. The availability of nitrogen is the main factor limiting the growth and yield of plants. Nitrogen compounds are one of the main building blocks of the plant organism and the basic component of many elements responsible for the proper course of photosynthesis [25]. Nitrogen deficiency significantly reduces the relative content of chlorophyll in leaves, similar to P or Mg. The increase of relative content of chlorophyll in the leaves of plants fertilized with foliar and soil fertilizers compared to the control may indicate improved bioavailability of nutrition from utilized fertilizers. Plant growth and final yields are the result of many processes, among which the intensity of photosynthesis is one of the most important. As shown by the results from the conducted tests, plants fertilized with utilized foliar fertilizer were characterized by higher values of the analyzed gas exchange parameters compared to the control, and these values increased relative to the applied doses of fertilizers (Table 1). In the case of soil fertilization, the values of the analyzed gas exchange parameters were higher compared to the control, however, the highest values of these parameters were observed in plants on which the lowest doses of fertilizers were used (Table 2). Plants fertilized with utilized foliar fertilizer were characterized by higher values of gas exchange parameters in comparison with plants fertilized with soil fertilizers. The increase in photosynthesis intensity under the influence of foliar fertilization is widely documented in the literature [9; 14; 15; 26].
## 4 Experimentation and Results
### Results Analysis and Discussion
#### 4.3.1 The Attitude of Students towards AR
During the AR learning session, we observe the student's behavior and their attitude towards learning with AR technology. Our observations show that students have a very positive attitude towards AR-learning techniques, and they do not show any negative expressions, while performing learning activities. Students are enjoying and playing with the 3D visuals on the surface of their books and the table. Students start learning very easily and quickly in a short time. They explore these applications and do not face any difficulties, while using AR-based applications. It can be concluded that student who learns via AR applications has better attitudes towards the course, and therefore, achieves higher scores at the end of the session. In short, we find the AR applications helpful for the learning purposes of the students. Students are also in favor of the AR techniques, and they achieve a higher number of scores compared to the previous traditional methods of learning based on papers and textbooks.
## 4 Electro-Thermal Modeling
### High-Fidelity Power Loss Model
#### 4.1.2 Switching Power Loss  
Precise estimation of the switching loss in a device is essential. The switching loss offers information about the amount of lost energy because of on-state to turn-off and off-state to turn-on transitions [18; 30; 31; 32; 33; 34]. Application of a complex strategy is needed for quantifying the on and off losses with the small simulation step size. Owing to switching, the lookup table [11] method was preferred to estimate switching power loss due to the complex physics process of switching. With the aid of the switching energy values, i.e., \(E_{sw\ on}\) (mJ) and \(E_{sw\ off}\) (mJ), one could estimate switching power loss in a device. Calculation of switching energy by the use of datasheet specifications has been performed as the function of drain current and the junction temperature [14; 18; 34].  
\[E_{sw_{m}}(k)=f\big{(}I_{ds}(k),\ T_{j}(k),V_{dd}(k)\big{)} \tag{48}\]  
\[E_{sw_{off}}(k)=f\big{(}I_{ds}(k),\ T_{j}(k),V_{dd}(k)\big{)} \tag{49}\]  
where \(k\) denotes the kth PWM switching pulse. Switching characteristics from the datasheet for switching the energy losses (\(E_{sw\ on}\) and \(E_{sw\ off}\)) of MOSFET against the drain current versus the junction temperature have been represented in Figure 13. At each kth time step of a simulation, the total energy is calculated by Equation (50).  
\[E_{total}=E_{sw_{m}}+E_{sw_{off}} \tag{50}\]  
Given a switching frequency \(f_{s}\) (Hz), switching power loss is estimated as represented in Equation (51).  
\[P_{sw_{loss}}=\Big{(}E_{sw_{m}}+E_{sw_{off}}\Big{)}f_{S} \tag{51}\]  
Now, switching loss for a body diode is calculated by reverse recovery characteristics [19]. In that case, reverse recovery loss is a function of the diode current as well as its junction temperature in Equation (52). The \(E_{rr}\) is estimated using the datasheet characteristic according to Figure 14.  
\[E_{rr}(k)=f\Big{(}i_{f}(k),\ T_{j}(k)\Big{)} \tag{52}\]\(I_{f}\) (A) is the forward current of a diode, as depicted in Figure 14. Therefore, the switching power loss calculation across a diode is performed as in Equation (53).  
\[P_{D_{loss}}=E_{rr}f_{S} \tag{53}\]  
The switch's total average power loss and body diode are computed with Equation (54).  
\[P_{total}=P_{D_{loss}}+P_{sw_{loss}} \tag{54}\]
## 3 The Proposed Approach
### Fine-Tuning the CodeBERT Model  
Fine-tuning is performed by extending the CodeBERT model to the NMT architecture. Since CodeBERT is an encoder-only LLMCs, a decoder is added. This construction forms a Sequence-to-Sequence (Seq2 Seq) architecture, which is then fine-tuned using supervised methods. After constructing the NMT model, the training dataset is used in multiple iterations to enable CodeBERT to learn the contextual representation of the buggy lines. As shown in Figure 3, this is an example of CodeBERT learning contextual representations.  
Figure 3: An example of CodeBERT learning contextual representations.  
Figure 2: Example of a CodeTransFix processing context: (**a**) unprocessed bug code and (**b**) context of the bug code.  
The fine-tuning of the CodeBERT model takes as input the context of the preprocessed buggy lines and aims to learn the buggy code context representation. In the process of fine-tuning, the CodeBERT model is trained to understand how to translate the changed context into the fixed code. The context is denoted as \(x_{c}=(x_{1},\dots,x_{n})\) and the correct fixes are denoted as \(y=(y_{1},\dots,y_{m})\), where \(x_{i}\) and \(y_{i}\) represent tokens of the context and the correct fixes, respectively. \(n\) and \(m\) represent the lengths of the context and the correct fixes, respectively. The weight of the Seq2 Seq model is denoted as \(\Phi\). By updating \(\Phi\), the CodeBERT model can be fine-tuned to maximize the average likelihood:  
\[L(x_{c},y)=\frac{1}{m}\sum_{i=1}^{m}\log P(y_{i}|x_{c},y_{1},\dots,y_{i-1};\Phi) \tag{1}\]  
\(P(y_{i}|x_{c},y_{1},\dots,y_{i-1};\Phi)\) is the conditional probability calculated by the Seq2 Seq architecture using weights \(\Phi\), where \(y_{i}\) is the token after the correct repair sequence \((y_{1},\dots,y_{i-1})\) given the context \(x_{c}\). After fine-tuning, only the hidden results of CodeBERT in the Seq2 Seq architecture will be kept and then used as a context for bug fixes. The hidden results will be passed into the Context-Aware NMT Architecture for bug fixes.
# I3 Mote: An Open Development Platform for the Intelligent Industrial Internet
## 3 Problem Statement and I3 Mote Scope
### A Complete Ecosystem  
The I3 Mote is an open hardware platform aimed to fill the gap between the prototyping and early deployment market. In essence, the idea is to provide different form factors (Figure 2) with the same functionality and pin-to-pin compatibility: first, an \"Engineering\" version (left), whose aim is to facilitate in-lab prototyping, and second, a compact and integrated version (center) devoted to field evaluation and deployment. The functionality of the \"Tiny\" board is extended with the \"Daughter\" board (right), which basically provides the debugging capabilities already available in the \"Engineering\" version. This feature facilitates the transition from the prototype to the product.  
The hardware ecosystem is complemented by a suite of software tools and ports to popular open-source IoT implementations. This out-of-the-box software package enables the use of I3 Mote as reference design to quickly set up a wireless, wired or hybrid (both wireless and wired) network that performs actuation and data extraction from various types of sensors.  
An additional feature towards ease of use is the separation between the application processor and the communications processor. In a very similar way to Dust Networks module, the CC2650 can operate autonomously, allowing users to write applications directly on top of the network protocol stack. More interestingly, when operating with the applications processor (MSP432), the network protocol stack runs in the CC2650 and the application processor exchanges data with the network processor through a serial port. In this way, the application is completely decoupled from communications (to such an extent that the stack of communications can be supplied in binary form), significantly simplifying the development. Notably, the features of the CC2650 allows for easy exchange of the communications  
Figure 1: I3 Mote platform.  
Figure 2: I3 mote platform versions: ”Engineering” (**left**), ”Tiny” (**center**) and ”Daughter” (**right**).  
standard, following a similar approach to the OpenPicus platform. However, in this case it is not necessary to change the HW module, but simply the SW stack running on the CC2650.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 4 Graph-Based RA in Cellular Homogeneous and Het-Nets  
Most algorithms which utilize graphs are applied for networks of this type, due to their continual prevalence in modern wireless communications. Even with the roll-out of 5G, most mobile operators still rely heavily on macro cells, or their combination with small cells of different range. They need to achieve efficient resource distribution and interference reduction, as the number of users and per-user throughput increase. This section analyzes the development of graph-based RA algorithms in throughput-intensive cellular networks, mostly in the context of the fourth generation of mobile communications. The scenario of homogeneous and Het-Nets is illustrated in Figure 4. It also includes a generalized illustration of the most common graph models for RA problems (usually used independently from each other), and a summary of their subcategories to comprise this Section. A tabular summary of the main design characteristics (such as the graph type and its formulation, and the optimization method used for the solution) of the algorithms reviewed in this section, is given in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Application** & **Graph Model** & **Tasks Solved** & **Graph** & **Optimization** & **Performance** \\  & & **Type** & **via Graphs** & **Formulation** & **Method** & **Assessment** \\ \hline  & & & & & & \(R_{T}\) of \\
[25] & RB allocation in & & & & Vertices— & & 12 b/s/Hz, \\  & an OFDMA & Interference/ & Channel & UEs/user & & and \(R_{u}\) of \\  & an OFDMA & Het-Net & & & clusters; & 0.1 b/s/Hz for \\  & & & & edges—between & & up to 10 small \\  & & & & all UEs/rBs & & cells, 100 UEs \\  & & & & & and 64 channels \\ \hline  & & & & & & \(P_{T}\) of up to \\  & & &
## 3 Results and Discussion
### Electrical Conductivity  
The electrical conductivity of the PPyNPs is investigated to determine the charge transfer with changes in pH (Figure 5). Different protonation levels control the electrical conductivity (128 S cm\({}^{-1}\) at pH 1 to 1.9 S cm\({}^{-1}\) at pH 13), as an increase in acidity increases the carrier density (hole) in polymer chains. This is because proton doping occurs during the acidic treatment and the \(\beta\)-carbon in the polymer chain structure is preferentially protonated [38]. Protonation is essential for inducing the short-range ordering of the polymer backbone and for the efficient proton transport in the conjugated chains, and it is achieved by increasing the amount of C=C stretching [39]. Additionally, a significant decrease in the electrical conductivity is observed under basic conditions due to a drastic reduction in the number of charge carriers during deprotonation.  
Figure 4: (**a–c**) Field emission scanning electron micrographs of PPyNPs and (**d–f**) size distribution of the nanoparticles within a unit area (1 \(\upmu\)m \(\times\) 1 \(\upmu\)m) at different pH values ((**a**,**d**): pH 1; (**b**,**e**): pH 7; (**c**,**f**): pH 13).
# Performance Evaluation of Bedding Sand and Load Transfer Characteristic of Concrete Block Pavements
## 2 Materials and Experimental Methods  
### Material  
#### 2.1.1 Sand  
Three types of sand were selected to evaluate their properties: manufactured sand, quartz sand, and river sand. Table 1 summarizes their basic physical properties. The measured bulk density and absorption of the three materials are similar. The time flow method (JTG E42-2005) was used to measure the sand angularity. River sand was expected to have a low angularity due to water erosion, but the opposite was found. Although the difference was not large, river sand had the highest angularity, while quartz sand and manufactured sand had similar angularities. Regarding the sand equivalent, manufactured sand contained the smallest amount of foreign matter, followed by river sand and quartz sand. The hardness was measured according to JTG E42-2005. To evaluate the hardness of the fine aggregates as a percentage, sand was repeatedly immersed in sodium sulfate liquid and dried before mass changes were measured.  
The experimental parameters to evaluate the hardness by sand type and particle size include types of sand, 0.6 mm passing ratio (20%, 40%, and 60%), and 0.075 mm content (1%, 3%, and 5%). An analysis was carried out on the joint material, bedding sand, and material of the water-permeable course. Figure 2 shows the particle size distribution. The particle sizes of the bedding sand and joint sand met ASTM C 33 and ASTM C 144, respectively.  
Figure 1: Role and requirements of sand in concrete block pavement design.
## 4 Data Processing and Features
### Machine Learning
#### 5.1.2 Unsupervised Learning  
In Unsupervised learning, we train the machine learning algorithm with only the input examples but no output labels. The algorithm tries to learn the underlying structure of the input examples and discover patterns. Unsupervised learning algorithms can be further categorized based on two tasks, namely Clustering and Association. In clustering, an algorithm such as k-means tries to discover inherent clusters or groups in the data.  
Here are some advantages and disadvantages of using machine learning techniques to predict the stock market (Table 11).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name of Method** & & **Advantages** & & **Disadvantages** & **Article** \\ \hline \multirow{5}{*}{**ARIMA**} & \(+\) & This method is effective & \(-\) & Not very effective with \\  & \(+\) & for linear time series. & \(-\) & nonlinear time series. \\  & \(+\) & It gives more reliable and & \(-\) & Requires careful setting \\  & & efficient short-term & & of parameters and relies \\  & & forecasts than similar & & on user assumptions that \\  & & models with more & & may not hold true, \\  & & complicated structural & & leading to erroneous \\  & & assumptions. & & clustering. \\ \hline \multirow{2}{*}{**LR**} & \(+\) & Excellent capacity for & \(-\) & Powerful presumptions \\  & & dealing with complicated & & and a heightened \\  & & nonlinear patterns. & & sensitivity to outliers \\ \hline \multirow{5}{*}{**SVM**} & \(+\) & With its high level of prediction accuracy and capacity to deliver the best global solution. & \(-\) & Depending heavily on \\  & \(+\) & Performs well on many categorization problems, even ones of high & \(-\) & Depending heavily on \\ \cline{1-1}  & & \(+\) & The choices made for the \\ \cline{1-1}  & & \(+\) & Performs well on many categorization problems, even ones of high & \(-\) & Sensitive to outliers. \\ \hline \hline \end{tabular}
\end{table}
Table 11: Advantages and Disadvantages of using ML Techniques to predict stock market.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 2 Network Architecture and Basic Knowledge
### PTP Time Synchronization Principle  
PTP is proposed for distributed network measurements and control systems [16]. It adopts the technology of a physical layer hardware timestamp and is composed of a master clock sending PTP messages and a slave clock receiving the messages. The master and slave clocks determine the clock deviation between them by exchanging PTP synchronization messages with a timestamp, and the slave clock compensates for this deviation. Finally, the time synchronization of the master-slave clock is completed. The interaction process of a PTP time synchronization message is shown in Figure 2.  
In Figure 2, the specific mechanism of the PTP time synchronization process is as follows:  
1. The master clock sends a Sync message to the slave clock in the form of a broadcast at T\({}_{1}\), and the slave clock receives the message at T\({}_{2}\);
2. Then, the master clock sends a Follow_up message to the slave clock in the form of a broadcast, which carries the sending T\({}_{1}\) timestamp of the Sync message;
3. The slave clock sends a Delay_Req message to the master clock in a point-to-point manner at T\({}_{3}\), and the master clock receives the message at T\({}_{4}\);
4. The master clock sends a Delay_Rep message to the slave clock in a point-to-point manner, which carries the T\({}_{4}\) timestamp of the master clock reception time. From the above PTP time synchronization protocol interaction process, we obtain \[\rm{T}_{2}=\rm{T}_{1}+offset+delay\] (1) \[\rm{T}_{4}=\rm{T}_{3}-offset+delay\] (2)  
where T\({}_{i}\) (\(i\) = 1, 2, 3, 4) represents the timestamp of the master-slave clock receiving and sending PTP packets, _offset_ represents the offset of the master and slave clocks, and _delay_ represents the path delay of the packet transmission between the master and slave clocks.  
Through Equations (1) and (2), the time _delay_ and _offset_ values can be calculated as follows:  
\[delay=\frac{\rm{(T_{2}-T_{1})+(T_{4}-T_{3})}}{2} \tag{3}\]  
Figure 2: PTP time synchronization message interaction process.  
\[offset=\frac{\left(\mathrm{T_{2}-T_{1}}\right)-\left(\mathrm{T_{4}-T_{3}}\right)}{2} \tag{4}\]  
According to the _delay_ and _offset_ values obtained from Equations (3) and (4), the size of the local clock is continuously adjusted to complete the time synchronization process.
## 4 Experiment Results and Comparison
### Performance Metrics  
In this section, we compare the performance of the purposed SAL approach and the baseline algorithms by considering the three metrics: overall accuracy, recall, and F-measure [31].
## 3 Feature Selection Algorithms
### Empirical Mode Decomposition
#### 4.2.4 Unsupervised Learning Algorithms  
Unlike the supervised machine-learning methods discussed in the previous sections, unsupervised learning-based methods use training data samples without the need for class labels. Intuitively, these types of methods avoid the problem of defining possible multiple structural damage cases, including multiple structural damage scenarios. By applying unsupervised methods, two types of detections can be performed from the data obtained by the sensor network measurements of SHM systems: novelty detection and anomaly detection.  
To address the drawbacks of supervised machine-learning approaches applied to structural damage identification, including sample labeling and the need for data from multiple damage scenarios, [PERSON] and [PERSON] [51] presented an unsupervised ML approach that uses an autoencoder to extract features from the raw measurement data. An SVM was trained with the extracted features, allowing it to detect even small damages (<10% stiffness reduction) in a structural member of a steel bridge.  
[PERSON] and [PERSON] [127] proposed two new damage indices for damage localization and quantification from time series modeling using an AR model. In this approach, thresholds are defined to detect possible structural damage without establishing damage patterns. [PERSON] and [PERSON] [128] explored an unsupervised anomaly-based method for short- and long-term SHM using an anomaly score and also setting an alarm threshold to establish damage. The core of the method implemented unsupervised feature selection through a one-class nearest neighbor rule.  
[PERSON] et al. [129] proposed a damage detection framework based on outlier detection in the acquired data. The data descriptors used in this proposal included expected data value, data variability, data symmetry, and data flatness. In this proposal, a k-medoid clustering technique was used to detect outlier data using a proposed symbolic distance-voting scheme. [PERSON] and [PERSON] [130] developed a post-earthquake damage detection and localization approach that combines intensive cumulative measurements with an unsupervised learning algorithm. This approach consists of two stages: in the first stage, a feature extraction process is performed to extract the cumulative measures and in the second stage, joint probabilities are used as damage indicators obtained through the kernel multivariate maximum entropy method.  
This review will emphasize supervised learning methods such as ANN, CNN and SVM in combination with preprocessing techniques such as WT, EMD, HHT and PCA. Despite the advantages of using unsupervised techniques in the problem of structural damage identification, the use of supervised techniques is the most adopted branch in the literature.
## 5 Experimental Progress
### Introduction of the Test Satellite Constellation  
In March 2022, the company GalaxySpace launched six LEO broadband communication satellites into orbit, building China's first LEO test constellation for broadband communication together with GalaxySpace's first satellite (YINHE-1). The constellation is capable of providing uninterrupted, low-latency broadband communication services for more than 30 min each time. The distribution of the satellites is shown in Figure 6, where GS2, AP01, AP02, BP01, BP02, and AP03 are in the same orbit with phase intervals of 15\(\sim\)18\({}^{\circ}\). Four of the satellites are named \"BUPT-YINHE\" (\"BUPT\" is the abbreviation of Beijing University of Posts and Telecommunications.), \"Nantong-1\", \"Yitu Space Wish\", and \"Xuancheng-1\". The BUPT-YINHE satellite participated in this experiment as a shared platform for communication and remote sensing payloads.