esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Selection of Driving Behavior Indicators
#### 2.4.2 LC Characteristics  
In tunnel-interchange connection sections, vehicles execute two types of LC maneuvers after exiting a tunnel: MLC and Diverging Lane Change (DLC). These lateral motion maneuvers are crucial in shaping driving decisions and stability. To characterize LC behavior in these sections, various metrics are utilized, including the LC position relative to the diverging point (PMLC for MLC, PMLC for DLC), LC duration (MLCD, DLCD), LC length (MLCL, DLCL), LC angle (MLCA, DLCA), and the accepted gap.  
The initiation and completion of LC are determined by shifts in lateral vehicle speed, with 0.1 m/s as the threshold [41]. LC duration (LCD) and length (LCL) are calculated using the time and distance differences between these two points. LC angle (LCA) is derived as the mean steering wheel angle during the LC. Particularly, PMLC and PMLC are assessed based on the vehicle's lateral position at the lane crossing.  
The accepted gap, a unique metric in lane-changing behavior, measures the distance between the front and rear vehicles in the target lane at the onset of the LC. Given that DLCs often occur with a single front vehicle in the deceleration lane, our focus is mainly on the accepted gap during MLCs. Furthermore, the Ashworth method [42] estimates the critical accepted gap under various traffic and road conditions, representing the minimum gap that drivers can accept, which reflects the driving demand for LC space under different scenarios. This method is based on the assumption that both the critical gap and the accepted vehicle gap adhere to a normal distribution across different traffic volumes. The critical accepted gap is thereby calculated using the following formula:  
\[\text{Gap}_{\text{c}}=\overline{\text{Gap}_{\text{a}}}-\text{q}\sigma_{\text{ a}}^{2}\]  
where \(\text{Gap}_{\text{c}}\) denotes the critical accepted gap (s); \(\overline{\text{Gap}_{\text{a}}}\) denotes the mean accepted gap (s); q refers to the traffic flow rate (veh/s); and \(\sigma_{\text{a}}^{2}\) is the variance of the accepted gap.
## 5 System Overview
### Data Node  
It is known that data nodes are responsible for storing and managing files that come from the client. The client applications communicate with data nodes via TCP/IP network service. In this work, each of the data nodes has the same priority level to process the filing services for the responsible client of that. The software, dynamic link library developed by us, running in the client provides a secure connection to data nodes via symmetric-key algorithms. However, application servers have a static IP address that are served by corresponding data nodes. Through this IP, it is connected to the data node. Data nodes also control the IP address of requests whether that is on the registration list or not. In addition, the client can encrypt the data that they want to send by the symmetric key algorithm and securely send the encrypted file to the data node optionally. Likewise, both static IP with secure authentication and encryption of files have achieved a strong security level. The data nodes will store requests from the client on the virtual file system that is a large file on the data storage device. One of  
Figure 2: System overall structure.  
the most prominent features of the system is the developed virtual file structure. Also, each of the data nodes can serve in more than one client application. The nodes, to achieve optimal load balancing, are used for file storage in order. Also, the client application can be connected to multiple data nodes, so each node can serve multiple client applications at the same time. With this property, the system has both good scalability and availability.
# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 3 Development of the Magnetotelluric Monitoring Receiver
### Acquisition Software Development  
The acquisition software adopts a layered structure design, enabling plug-and-play management of business modules to enhance the system's robustness, scalability, and maintainability. The overall structural design is shown in Figure 9.  
The calling layer is the interface between Linux and the software. The CLI service thread in this layer listens to input from the Linux shell terminal after the acquisition software starts. When command characters are input, the CLI service thread converts them into corresponding commands and sends them to the command protocol layer for processing.  
The business layer includes the command protocol layer, status monitoring layer, task execution layer, CLI execution layer, and control logic layer. The command protocol layer, similar to the control software's command protocol layer, mainly implements the encapsulation, sending, receiving, and parsing of command data packets, providing a checksum mechanism to verify the integrity of data packets. The status monitoring layer is responsible for reading the basic status of the hardware layer in real time and updating the corresponding status parameters in the software, such as the GPS lock status, battery voltage, temperature, humidity, and disk space. The task execution layer is responsible for parsing commands from the command protocol layer or CLI execution layer into multiple steps and executing them sequentially or conditionally. The CLI execution layer is responsible for executing single commands from the command protocol layer or  
Figure 8: IoT module network configuration page.  
forwarding commands to the task execution layer after execution. The control logic layer is responsible for converting single-step operations from the upper layer into operation codes recognizable by the underlying hardware and controlling the hardware to execute the corresponding actions.  
The hardware layer includes hardware control interfaces, communication modules, analog boards, GPS boards, and power boards. The hardware control interface layer is the interaction layer between the underlying hardware and upper applications, encapsulating the functions of the underlying hardware into unified interfaces for upper applications to call. This approach shields the differences in underlying hardware details, facilitating module expansion. The communication module, consistent with the control software's communication module, is responsible for reliable communication between the control software and the acquisition software. The analog board, GPS board, and power board are the basic circuit modules of the instrument, with the analog board mainly implementing analog-to-digital conversion, the GPS board providing positioning and synchronization, and the power board handling voltage conversion.
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 2 NVIS Characteristics  
In this section we explain in more detail the principal characteristics of the NVIS propagation, especially those of the ordinary and extraordinary waves. Moreover, we explain the use of these ionospheric waves to perform polarization diversity and diversity combining.
## 4 Performance Evaluation
### Simulation Environment
\(E_{rx}\) denote the energy consumed to transmit and receive a bit over a distance \(r\), respectively. \(\alpha_{11}\) is the energy/bit consumed by the transmitter electronics. \(\alpha_{2}\) is the energy dissipated in the transmit op-amp and \(\alpha_{12}\) is the energy/bit consumed by the receiver electronics.
## 5 Experiments and Results
### Quality Assessment Metrics
#### 5.2.2 Explained Variance (EV)  
The explained variance score measures the extent to which the model accounts for the variance of the prediction. It is defined as  
\[\text{EV}=1-\frac{\text{Var}(Y-\hat{Y})}{\text{Var}(Y)}, \tag{11}\]  
where \(\text{Var}(Y-\hat{Y})\) and \(\text{Var}(Y)\) represent the variances of the prediction residuals and the actual data, respectively. A higher EV score signifies a better fit of the model to the data.
## 5 Simulation
### Four-Path Environment  
In severe multipath environments like \"urban canyons\", there are possibly more than one multipath. Here we construct a complicated scene with three multipath signals. MP1 is shifting as in the simulations before. MP2 and MP3 are two static multipaths. The power of the LOS signal in the LHCP channel is set to \(-15\) dB. Table 7 shows the parameters of each path.  
In this multiple-path environment, though all of the algorithms are biased as Figure 13 shows, the DP-SIMLE algorithm again shows significant improvements in reducing 41% more of the multipath bias in the short delay range ((5.13 m-2.68 m)/5.95 m) as illustrated in Figure 14.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameters** & **LOS** & **MP1** & **MP2** & **MP3** \\ \hline RHCP power & \(0\) dB & \(-6\) dB & \(-9\) dB & \(-15\) dB \\ LHCP power & \(-15\) dB & \(-9\) dB & \(-13\) dB & \(-12\) dB \\ path delay & \(0\) m & \(0\) to \(40\) m & \(15\) m & \(36\) m \\ carrier phase & \(0\) & \(\pi/4\) & \(\pi/6\) & \(-\pi/6\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The parameters of the signals in the four-path simulation.  
Figure 14: The RMSE of different multipath mitigation algorithms in the four-path simulation. **(a)** The RMSE in the entire simulation; and **(b)** the RMSE in the short delay range.  
Figure 13: The residual multipath error of different multipath mitigation algorithms in a severe multipath environment with three multipaths.  
From a series of simulations we can find that: the proposed DP-SIMLE contributes much to mitigating short delay multipath whose relative delay to the LOS signal is less than 25 m when the power of multipath dominates the LHCP channel. Either the LHCP antenna with a bad AR or the low multipath power received by the LHCP antenna degrades the performance of DP-SIMLE. The performance of DP-SIMLE is superior over RHCP-MEDLL in most of the cases. However, DP-SIMLE has its own drawbacks in that its short delay multipath mitigation performance strongly depends on the LHCP antenna's receiving more of the power of the short delay MP signal than the LOS signal, and it is not easy to ensure this kind of reception every time.
## 3 Results
### Gene Expression of Key Enzymes of EO Main Components  
The different treatments evaluated, of inoculation, drought stress and their combinations, affected the amount of EOs in _M. piperita_. Therefore, to achieve a deep understanding of their effects on the synthesis of major monoterenes, the relative gene expression of the enzyme _Lim s_ and _Pr_ was analyzed by q-PCR (Figure 4). The main compounds presented in the EOs of the peppermint plants used in the present study are menthol, menthone, limonene, menthofuran and pulegone. In the first step of monoterenes biosynthesis in the Mentha species, the enzyme _Lim s_ is involved [23], which is responsible for the cyclization process of geranyl diphosphate (GPP) to form limonene [24]. Then, limonene is transformed into pulegone through several reactions [25]. The _Pr_ enzyme was previously found to reduce pulegone to menthone or isomenthone in a 2.5:1 ratio [26; 27]. In the current study, we considered the _Lim s_ gene, the first gene responsible for the synthesis of the monoterenes, and also Pr, which is particularly important for reducing pulegone (and a major component in our plants).  
The expression of _Lim s_ was downregulated in plants subjected to drought stress (Figure 4A). In contrast, the inoculation of regularly irrigated plants affected it positively, with GB03 being overexpressed by 2-fold more than the control (\(p<0.05\)), while the rest of the treatments did not present differences with the control (\(p>0.05\)). In relation to the transcription of the gene that codes for the enzyme _Pr_ (Figure 4B), positive alterations were observed in the different treatments carried out, with drought stress MS and SS upregulating _Pr_ expression at the same level as regularly irrigated inoculated plants, but with these values not being statistically different (\(p>0.05\)) (Figure 4B). MS plants inoculated with GB03 and WCS417r showed an upregulation in _Pr_ expression of 3- and  
Figure 4: Limonene synthase (_Lim s_) (**A**) and pulegone reductase (_Pr_) (**B**) gene expression of peppermint plants subjected to moderate (MS) or severe drought stress (SS) and inoculated with different PGFR strains (mean \(\pm\) SE). Values are fold changes relative to the control. Different letters indicate significant differences between treatments based on [PERSON]’s LSD test (n = 5, \(p<0.05\)).  
4-fold, respectively. _Pr_ in inoculated SS plants was upregulated for both evaluated strains, but was markedly stimulated for GB03 by almost 7-fold (_p_ < 0.05), which was the highest expression of _Pr_ observed in the present study.
## 3 Results and Discussion
### Soil Quality Assessment
#### 3.2.2 Evaluation by Pollution Indices
Of the elements with EF values that were >5, 2.16% (34 sites) of As, 4.51% (71 sites) of Cd, 0.44% (7 sites) of Cu, 4.00% (63 sites) of Hg, 0.06% (only 1 site) of Ni, 1.97% (31 sites) of Pb, and 0.64% (10 sites) of Zn, these were classified as having a significant contamination or higher level. Specifically, the maximum EF values of As (49.51), Cd (32.14), Cu (16.06), Hg (75.48), Ni (5.54), Pb (62.66), and Zn (17.47) were examined. Similarly, 0.95% (15 sites) of As, 3.30% (52 sites) of Cd, 0.25% (four sites) of Cu, 2.60% (41 sites) of Hg, 0.06% (one site) of Ni, 1.14% (18 sites) of Pb, and 0.57% (nine sites) of Zn showed moderately to highly contaminated or higher levels, with Igeo values >2. The maximum Igeo values of As (5.02), Cd (4.77), Cu (3.27), Hg (5.34), Ni (2.06), Pb (5.52), and Zn (3.69) were examined. About 5.84% (92 sites) of As, 12.77% (201 sites) of Cd, 0.19% (only three sites) of Cr, 0.76% (12 sites) of Cu, 8.07% (127 sites) of Hg, 0.25% (only four sites) of Ni, 3.94% (62 sites) of Pb, and 1.59% (25 sites) of Zn showed considerable contamination or higher levels, with Cf values > 3. The maximum Cf values of As (48.53), Cd (40.98), Cr (3.54), Cu (14.43), Hg (60.66), Ni (6.25), Pb (68.72), and Zn (19.38) were also determined. Contour maps using inverse distance-weighted (IDW) interpolation were created to analyze the locations of the contaminated PTEs and their affected region, since IDW is a simple, intuitive, and efficient interpolation method and demonstrates good results when the distribution of the sample sites is uniform (Figures 3-5). The spatial distributions of Cu, Pb, and Zn with EF values > 5 (significant contamination or higher levels) coincided with the Cu-Pb-Zn deposits. Cd showed a similar distribution pattern, but it also coincided with the carbonatite rock distribution. The distributions of As and Hg with EF values > 5 are consistent with the distribution of the Hg and Cu-Pb-Zn deposits and a portion of carbonatite rocks (Figure 3). Similar distributions were found using Igeo and Cf values (Figures 4 and 5).
###### Abstract  
The Jurassic Tamulangou Formation in the Hongqi Depression has favorable hydrocarbon generation conditions and great resource potential. This study systematically analyzes the geochemical characteristics and thermal evolution history of the source rocks using data from multiple key wells. The dark mudstone of the Tamulangou Formation has a thickness ranging from 50 to 200 m, with an average total organic carbon (TOC) content of 0.14-2.91%, an average chloroform bitumen \"A\" content of 0.168%, and an average hydrocarbon generation potential of 0.13-3.71 mg/g. The organic matter is primarily Type II and Type III kerogen, with an average vitrinite reflectance of 0.71-1.36%, indicating that the source rocks have generally reached the mature hydrocarbon generation stage and are classified as medium-quality source rocks. Thermal history simulation results show that the source rocks have undergone two major thermal evolution stages: a rapid heating phase from the Late Jurassic to Early Cretaceous and a slow cooling phase from the Late Cretaceous to the present. There are differences in the thermal evolution history of different parts of the Hongqi Depression. In the southern part, the Tamulangou Formation entered the hydrocarbon generation threshold at 138 Ma, reached the hydrocarbon generation peak at approximately 119 Ma, and is currently in a highly mature hydrocarbon generation stage. In contrast, the central part entered the hydrocarbon generation threshold at 128 Ma, reached a moderately mature stage around 74 Ma, and has remained at this stage to the present. Thermal history simulations indicate that the Hongqi Depression reached its maximum paleotemperature at 100 Ma in the Late Early Cretaceous. The temperature evolution pattern is characterized by an initial increase followed by a gradual decrease. During the Late Jurassic to Early Cretaceous, the Hongqi Depression experienced significant fault-controlled subsidence and sedimentation, with a maximum sedimentation rate of 340 m/Ma, accompanied by intense volcanic activity that created a high-temperature geothermal gradient of 40-65 \({}^{\circ}\)C/km, with paleotemperatures exceeding 140 \({}^{\circ}\)C and a heating rate of 1.38-2.02 \({}^{\circ}\)C/Ma. This thermal background is consistent with the relatively high thermal regime observed in northern Chinese basins during the Late Early Cretaceous. Subsequently, the basin underwent uplift and cooling, reducing subsidence and gradually lowering formation temperatures.  
**Keywords:** Hailar Basin; Hongqi Depression; Tamulangou Formation; source rocks; geochemical characteristics; thermal evolution history
# P-Distill: Efficient and Effective Prompt Tuning Using Knowledge Distillation
## 4 Experiments
### Ablation Study  
To further verify the effectiveness of the proposed method, we conducted ablation studies using the following variants of P-Distill.  
**P-Distill\({}_{-init}\):** instead of training with prompt initialization, it focuses exclusively on leveraging the two types of distillation losses designed to transfer the knowledge from the teacher to student model in different ways.  
**P-Distill\({}_{-pred}\):** This approach does not implement prediction-layer distillation loss. Following the application of the prompt initialization method, it trains the student model based on hidden-state distillation loss. This method aligns the internal representations of the student model with those of the teacher model without focusing on the final output predictions.  
**P-Distill\({}_{-hidden}\):** This variant does not consider the differences between the hidden-state outputs of the teacher and student models. Instead, it focuses on training based on the differences in the prediction-layer outputs. This approach aligns the final predictions of the student model closely with those of the teacher model without directly focusing on their internal representations.  
**Results** Figure 4 shows that all three variants of P-Distill underperformed compared to the original P-Distill among various tasks. This shows that each component of P-Distill contributed to the performance achieved on downstream tasks.  
Given these results, we further observe that the extent of degradation varied among different variants. First, P-Distill\({}_{-init}\) exhibited the most significant performance degradation across various tasks. While not utilizing prompt initialization and only conducting prediction-layer distillation and hidden-state distillation still led to performance improvements over P-tuning v2, the degradation in performance was clear compared to other variants and the original P-Distill. This indicates that prompt initialization, based on the teacher model's prompt, is crucial in prompt-based knowledge distillation. Second, P-Distill\({}_{-hidden}\) and P-Distill\({}_{-pred}\) exhibited decreased prediction performance. This demon  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fine-Tuning** & **P-Tuning v2** & **P-Distill** \\ \cline{2-4}
**CoNLL05 WSJ** & 0.885 & **0.890**\({}_{(128)}\) & **0.890**\({}_{(64)}\) \\  & & & 0.888\({}_{(32)}\) \\  & & & 0.885\({}_{(16)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of P-Distill performance across varying prompt compression ratios. Bold values indicate the best performance.  
Figure 4: Comparison of ablation study results across various tasks, with different colors and bar styles representing distinct variants of P-Distill.  
strates that integrating prompt initialization with the hidden-state or prediction-layer distillation techniques enhances the stability and effectiveness of knowledge distillation.
## 3 Proposed Approach
### Explainability-Guided Vote (EGV) Approach
#### 3.2.2 Replacing High-Influence Tokens with Synonyms  
After identifying influential tokens, we determine the number of tokens to substitute based on a predefined substitution rate \(p\). The number of tokens to be replaced is calculated as length\((x)/2*p\), as shown in Steps 4 and 5 of Algorithm 1. This ensures that half of the substitutions are made for positively influenced tokens and the other half for negatively influenced tokens. Tokens with high positive and negative influence are substituted with synonyms to observe changes in predictions. Stopwords are excluded from substitution even if they have high IG attribution scores, ensuring meaningful modifications. Figure 1 shows how tokens are selected and substituted, while Algorithm 1 outlines the specific steps to construct synonym sets from Steps 6 to 14. We adopt the RS&V method to combine synonyms from WordNet [40] and neighboring words located within a specified Euclidean distance using the GloVe embedding space, which is postprocessed using counter-fitting [41]. For a given input text, we generate \(k\) number of variations by substituting random synonyms for each selected word \(w_{i}\in x\).
## 4 Results and Discussion
### Landsat Change Detection Methods
However, recently, there is an increasing interest in using nonparametric flexible classifiers, which produce more reliable results by holding a fewer assumption compared to parametric approaches (see Figure 19). With the availability of freely preprocessed data, recent studies prefer to use ARD or surface reflectance physical values, which almost eliminates the preprocessing stage (see Figure 20). Cloud computing platforms allow for the application of change detection analysis over very large study areas. In addition, computational powers allow for the application of state-of-the-art algorithms to solve different prediction problems. As such, several studies use all available data for monitoring abrupt and gradual changes together or composite many images to assure cloud-free annual reliable data. Figure 19: Usage of different processing units and classifier categories over time. Note that early classifiers include Mahalanobis, minimum distance, nearest neighbor, ISODATA, K-means, maximum likelihood, whereas advanced classifiers include support vector machine, artificial neural network, random forest, decision tree and classification and regression tree, or object-based methods.