esa-sceva/satcom-chunk-collection huggingface dataset:
# TRC-Based High-Precision Spot Position Detection in Inter-Satellite Laser Communication
## 6 Discussion  
This article mainly discusses the problem of improving QD's detection accuracy of spot position when the SNR is lower than \(-17\) dB. The key to solving this problem is to provide a sufficiently large SNR gain, so the paper considers using the TRC line spectrum enhancement algorithm to highlight the spectrum of the photocurrent signal of QD. This proposed method changes the energy distribution of the effective signal and broadband noise in the time-domain, and uses a window function to suppress most of the noise, which is very different from the traditional filtering method. It can be seen from Equation (16) that when the length of the sequence \(x(k)\) tends to infinity, the noise energy is all concentrated at time \(0\), and the window function has the greatest suppression of noise. Therefore, the SNR gain provided by the TRC algorithm is proportional to the length of the sequence \(x(k)\). However, if the length \(N\) of the sequence \(x(k)\) is unreasonably selected, resulting in the exact opposite phase between \(x(k)\) and \(x(-k)\), the time-domain correlation between the two sequences will be reduced, thus the value of each element in \(F_{x}(k)\) will decrease. This will cause the output SNR of the TRC algorithm to suddenly drop (as shown in Figure 6). Therefore, when choosing the sequence length \(N\), must consider the sequence \(x(k)\) contains an integer number of the valid signal cycle.  
Moreover, it can be seen from Figure 2 that at time \(0\), most of the noise energy is concentrated, but the energy of the valid signal is also the largest here. Therefore, the window function suppresses noise while also losing greater valid signal energy. This is equivalent to indirect loss of beam energy, resulting in a decrease in the effective radius of the Gaussian spot, and aggravating the nonlinearity of the detection curve, and reducing the detection range of the QD. The linearity can be improved by fitting or interpolation, and a more suitable window function should be found to reduce the loss of effective signal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Spot Radius (mm)**} & \multirow{2}{*}{**SNR (dB)**} & \multicolumn{2}{c}{**TRC Method**} & \multicolumn{2}{c}{**Direct FFT Method**} \\ \cline{3-6}  & & \(\delta_{\text{max}}\) **(mrad)** & \(\delta_{\text{RMSE}}\) **(mrad)** & \(\delta_{\text{max}}\) **(mrad)** & \(\delta_{\text{RMSE}}\) **(mrad)** \\ \hline \multirow{2}{*}{\(0.42\)} & \(-5.93\) & \(0.0124\) & \(0.0065\) & \(0.0131\) & \(0.0063\) \\  & \(-14.58\) & \(0.0138\) & \(0.0069\) & \(0.0443\) & \(0.0212\) \\ \hline \multirow{2}{*}{\(0.53\)} & \(-7.66\) & \(0.0132\) & \(0.0076\) & \(0.0141\) & \(0.0078\) \\  & \(-17.01\) & \(0.0181\) & \(0.0112\) & \(0.0576\) & \(0.0272\) \\ \hline \multirow{2}{*}{\(0.71\)} & \(-7.83\) & \(0.0176\) & \(0.0109\) & \(0.0243\) & \(0.0135\) \\  & \(-17.86\) & \(0.0238\) & \(0.0135\) & \(0.0960\) & \(0.0378\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The errors in different spot radius and SNR conditions.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Dataset** & \(T_{P}\) & \(T_{N}\) & \(F_{P}\) & \(F_{N}\) & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline
1 & 7 & 109 & 6 & 4 & 0.92 & 0.64 & 0.54 & 0.58 \\
2 & 13 & 104 & 7 & 2 & 0.93 & 0.87 & 0.65 & 0.74 \\
3 & 11 & 103 & 10 & 2 & 0.90 & 0.85 & 0.52 & 0.65 \\
4 & 5 & 109 & 7 & 5 & 0.90 & 0.50 & 0.42 & 0.45 \\
5 & 7 & 110 & 7 & 2 & 0.93 & 0.78 & 0.50 & 0.61 \\
6 & 8 & 108 & 9 & 1 & 0.92 & 0.89 & 0.47 & 0.62 \\
7 & 10 & 103 & 10 & 3 & 0.90 & 0.77 & 0.50 & 0.61 \\
8 & 8 & 112 & 4 & 2 & 0.95 & 0.80 & 0.67 & 0.73 \\
9 & 7 & 105 & 12 & 2 & 0.89 & 0.78 & 0.37 & 0.50 \\
10 & 7 & 105 & 7 & 7 & 0.89 & 0.50 & 0.50 & 0.50 \\ \hline  & mean & & & 0.91 & 0.74 & 0.51 & 0.60 \\ \end{tabular}
\end{table} 
Table 7: Efficiency of ensemble classifier models. During classification, it is not uncommon to struggle between discovering all the positive cases in the data while reducing the erroneous results. However, it is often a process where a balance is sought between two opposing forces. Models should therefore be characterized by both high recall and precision. Figure 4 shows the precision-recall curves for all trained and tested models, and for all datasets. The more accurate the model, the more the precision-recall curve is shifted toward the upper-right corner. The large area under the curve means both high recall and high precision. In Table 9, the areas under the precision-recall curves are shown. Figure 4: Precision–recall curves for models of selected machine learning algorithms: (**a**) MLP, (**b**) AdaBoost, (**c**) GB, (**d**) KNN, (**e**) GNB, (**f**) ensemble classifier. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset** & **MLP** & **AdaBoost** & **GB** & **KNN** & **GNB** & **Ensemble Classifier** \\ \hline
1 & 0.76 & 0.80 & 0.75 & 0.59 & 0.67 & 0.79 \\
2 & 0.88 & 0.72 & 0.70 & 0.85 & 0.79 & 0.90 \\
3 & 0.71 & 0.75 & 0.81 & 0.79 & 0.69 & 0.88 \\
4 & 0.77 & 0.62 & 0.77 & 0.70 & 0.66 & 0.72 \\
5 & 0.80 & 0.72 & 0.85 & 0.79 & 0.70 & 0.86 \\
6 & 0.80 & 0.79 & 0.86 & 0.74 & 0.81 & 0.91 \\
7 & 0.82 & 0.69 & 0.76 & 0.86 & 0.73 & 0.84 \\
8 & 0.83 & 0.82 & 0.87 & 0.72 & 0.78 & 0.88 \\
9 & 0.74 & 0.75 & 0.83 & 0.71 & 0.71 & 0.84 \\
10 & 0.72 & 0.66 & 0.62 & 0.66 & 0.69 & 0.72 \\ \hline mean & 0.78 & 0.73 & 0.78 & 0.74 & 0.72 & 0.83 \\ \hline \end{tabular}
\end{table} 
Table 8: Area under ROC curves for models of the selected machine learning algorithms.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 3 Methods  
The workflow of our system, illustrated in Figure 1, consists of two primary pipelines: model training and model deployment. The training pipeline includes several key stages: data collection, data preprocessing, model training, model evaluation, and hyperparameter tuning.  
The methodological decisions made during data preprocessing, model training, evaluation, and hyperparameter tuning were guided by best practices established in previous studies and proven to improve model performance in text classification tasks. Data preprocessing techniques, such as tokenization, the removal of stop words, punctuation, and URLs, and lemmatization were applied based on their demonstrated effectiveness in improving text classification accuracy and reducing noise, as validated by [PERSON] et al. [6] and  
Figure 1: Model training and deployment pipelines.  
[PERSON] et al. [4]. The choice of transformer-based models (e.g., BERT, RoBERTa, Modern-BERT) for training was motivated by their state-of-the-art performance in various NLP benchmarks, particularly due to their ability to capture contextual semantics efficiently, as shown by [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] et al. [22]. For hyperparameter tuning, the selection of parameters such as learning rate, batch size, and maximum sequence length followed the established optimization protocols recommended by [PERSON] et al. [23] and [PERSON] et al. [24], and standard practices detailed in the related literature [4; 20; 21; 22], ensuring stable convergence, minimizing overfitting, and achieving optimal generalization performance. This process ensured that model was effectively trained on disaster-related social media data and optimized for accurate classification.  
The deployment pipeline emphasizes efficient processing and visualization for timely decision support. It begins by streaming social media posts, followed by data preprocessing to clean and prepare the incoming information. The system then generates predictions using the trained model and creates corresponding map markers based on the classification results. Finally, these markers are visualized on a dynamic map, providing an interactive and timely overview of disaster-related information. This visualization allows response teams to monitor ongoing events and make informed decisions based on geographically distributed social media data.
## 2 Transmission Line Ice Cover Monitoring System
### Dynamic Non-Monotonic Focusing Mechanism Bounding Box Loss  
The classification loss for the baseline model uses binary cross-entropy loss, while the bounding box regression employs DFL loss and CIOU loss, transforming the regression problem into a classification problem. Although the convergence value is not as good as other models, this study adopts the dynamic non-monotonic focusing mechanism bounding box loss to replace the DFL loss and CIOU loss of the baseline model, as the LDKA-NET maintains a large model complexity while keeping the parameter count low. The dynamic non-monotonic focusing mechanism utilizes \"outlierness\" instead of IoU to evaluate the quality of anchor boxes and provides a sensible gradient gain allocation strategy. While it reduces the competitiveness of high-quality anchor boxes, it also minimizes the harmful gradient produced by low-quality examples. This allows the model to focus on anchor boxes of ordinary quality, improving the overall performance of the detector.  
Since the training data inevitably contain low-quality instances, we constructed a distance attention mechanism with two layers of attention: WIoU.  
\[L_{WIoU1}=R_{WIoU}L_{IoU} \tag{16}\]  
\[R_{WIoU}=\exp(\frac{\left(x-x_{gt}\right)^{2}+\left(y-y_{gt}\right)^{2}}{\left( W_{g}^{2}+H_{g}^{2}\right)^{*}}) \tag{17}\]  
where \(W_{g}\), \(H_{g}\) is the size of the minimum enclosing box, and to prevent \(R_{WIoU}\) from generating gradients that hinder convergence, \(*\) denotes the separation of \(W_{g}\), \(H_{g}\) from the computation, as shown in the Figure 6 below:  
Figure 6: WIoU loss function.  
This loss function can better capture the complex features in the data rather than overfitting to the noise in the training data, resulting in optimal model performance and good generalization ability.
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 2 Materials and Methods
### Preprocessing of Trajectory Data  
The original taxi trajectory data suffer from inaccuracies and poor quality due to the presence of default values, duplicates, redundant data, noise points, and outliers that  
Figure 1: Technology roadmap.  
deviate from the usual trajectory sequence. If not addressed promptly, these issues can significantly impact subsequent research [24]. The preprocessing involves the following:  
1. Deletion of default and duplicate values: Track data are examined segmentally to remove rows with missing attributes and duplicate records. Table 1 illustrates data defaults (first row) and typical duplicate records (second and third rows). Furthermore, the absence of data in any column can be referred to as a default value.
2. Removal of redundant values: Trajectory data often accumulate redundant points when taxis stop at gas stations, shops, or traffic lights or in traffic jams. To streamline data processing, a novel speed attribute was introduced for calculating taxi speeds using timestamp intervals. A speed threshold was established to eliminate redundant data, setting minimum and maximum speed limits at 6 and 16.7 m/s, respectively. These limits were chosen based on the speed restrictions of various roads in the study area. Any data not falling within this speed range will be discarded.
3. Elimination of noise points: Noise points, typically arising from taxi collection terminal signal anomalies, equipment failures, or unusual driver behavior, deviate noticeably from the normal trajectory pattern. These points are identified and removed [25], as exemplified in Figure 2.
4. Deletion of outliers: Data representing abnormal taxi trajectories, such as unchanged passenger status for an extended period, distances of over 2 km between adjacent points, or frequent, irregular changes in taxi passenger status over a short duration, are identified and removed [26].
## 3 Architecture Proposed
### Rina L2 VPN
Figure 9 shows the message  
Figure 8: RINA L2 VPN’s overall architecture and interfaces. interchanged between the RINA SBI driver and the HTTP-based MAs to create the slice. In order to create an L2 network overlay between two MAs, one takes the role of _master_ and the other takes the role of _slave_. The _master_ will start the flow allocation process and the _slave_ will be awaiting the allocation request from the other MA. By looking at Figure 9, it can be seen that the SBI driver starts the L2 VPN overlay creation by sending a message to MA_A, stating that the remote MA involved is MA_B. In this L2 VPN creation, MA_A will perform the role of _slave_, i.e., it will be awaiting MA_B's flow allocation request. On the other hand, the SBI driver sends a message to MA_B stating that the remote MA is MA_A and its role in the overlay creation is _master_. Immediately, MA_B starts the L2 VPN creation by sending a RINA flow allocation request to MA_A.
## 3 Identifier-Related Challenges
### Identification of an Object with Different Local IDs  
One of the challenges in adopting the CIM framework for seamless data exchanges is the identification of an object with different local IDs (or mRIDs). When an object is instantiated in two different systems, two different local IDs are issued by each system. Suppose that a system wants to obtain data on the same object from another system. To ensure that the objects with different local IDs are confirmed to be identical, the local IDs should be mapped in advance to the data exchange.  
Figure 4 shows an example of data exchange between two different systems. Systems A and B in the figure represent the distribution management system (DMS) and customer information system (CIS), respectively. It is assumed that data on switches need to be exchanged between systems A and B since both systems have the data on switches for the internal applications of each system. As shown in Figure 4, the local IDs represented by'switch_id' for switches are different; therefore, local ID mapping is required to identify the origin of data acquired by another system. Data both systems have in common such as 'pole_no' in Figure 4 can be used for the local ID mapping. For instance, 100 of'switch_id' in the DMS database can be mapped to 200 of'switch_id' in the CIS database since both local IDs have the same pole number which is 123A123. Similarly, the switches with local IDs 101 and 102 in the DMS can be matched to ones with local IDs 201 and 202 in the CIS, respectively. However, the mapping scheme that uses common data cannot be applied if there is a human error in the common data such as typos and missing or duplicated data. Furthermore, it is hard to guarantee that the common data can be always available in all cases with the necessity of local ID mapping.  
Figure 4: An example of data exchange between DMS and CIS.  
Figure 3: Three regional sets separated by three boundary sets.  
The concept of regional MAS cannot be utilized for the mapping of local IDs where several authorities can be involved in the same region, as in the example depicted in Figure 4. In addition, one and only one authority issuing mRIDs is not applicable to legacy systems that have numerous local IDs already issued. In cases where data exchange between legacy systems is required, it could be time-consuming to perform the local ID mapping for numerous objects. Table 1 summarizes the identifier-related challenges described in Section 3.
## 4 Case Study
### Energy Storage Parameters Impact on System Cost  
The ESS has both charging and discharging active power characteristics, which play an important role in peak-shaving.  
For the UC model considering ESS, different energy storage parameters have different influences on the total cost. Table 3 illustrates that the overall system cost decreases when the active power capacity charging and discharging power limit increase.
## 2 Three-Dimensional Image and Brightness Loss  
### Theory of Stereo Vision  
The 3D visual phenomenon is generated by the parallax effect, which includes both binocular parallax and motion parallax. Binocular parallax is due to the different perspectives of the eyes, resulting in slightly different image messages being received by the left and right eyes. The two received images are combined in the brain to synthesize the stereoscopic effect [8; 9], as shown in Figure 1.  
Motion parallax is due to changes in the observation point, and is affected by the distance of an object relative to a moving background, as shown in the simplified illustration of this parallax in Figure 2. When observed from point A, the object appears to be left of the butterfly, but for observation from point B, the object appears to be to the right of the butterfly.  
Using either of the parallax effects, human eyes can produce 3D visual effects when viewing an object. To construct an image display device that is capable of producing 3D visual effects, one can utilize the parallax technique to adjust the brightness of light, color, and viewing direction, so that the messages received by the left and right eyes have a difference in viewing angle, causing a stereoscopic effect. The brain activity during the synthesis process depends on complex factors related to human evolution, psychology, and medicine. However, the current plane 3D imaging technology remains focused on the concept of the \"right and left eyes receiving different images,\" which is related to  
Figure 1: Two images sent to the brain to synthesize the stereoscopic effect.  
Figure 2: Motion parallax.  
binocular parallax. In other words, provided some analogy of the \"right and left eyes receiving different images\" of the visual environment is employed, viewers can observe 3D images on a 2D plane screen [10,11]. Currently, on the basis of the binocular parallax theory, 3D image display technologies are roughly divided into stereoscopic and auto-stereoscopic types.
## 5 Results and Discussion
### Predictive Models Developed Using ANN
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Data Training Percentage**} \\ \cline{2-4}  & **70\%** & **80\%** & **90\%** \\ \hline I1–H4 & 9.506876 & 1.911992 & \(-\)6.917437 \\ \hline I2–H4 & 6.985396 & 10.440174 & \(-\)1.723676 \\ \hline I3–H4 & \(-\)4.915390 & 11.821140 & \(-\)2.475820 \\ \hline I4–H4 & \(-\)5.293412 & \(-\)2.478422 & 10.804223 \\ \hline B1–H5 & 10.342958 & \(-\)6.097498 & 5.354134 \\ \hline I1–H5 & \(-\)5.340765 & 4.321218 & \(-\)2.957952 \\ \hline I2–H5 & \(-\)12.073148 & 6.730848 & \(-\)7.420634 \\ \hline I3–H5 & \(-\)1.495051 & \(-\)1.673032 & \(-\)1.183222 \\ \hline I4–H5 & \(-\)9.681449 & 7.487540 & \(-\)4.575078 \\ \hline B1–H6 & 1.1958247 & \(-\)10.231457 & 5.565350 \\ \hline I1–H6 & \(-\)12.1059627 & \(-\)1.590068 & \(-\)4.021247 \\ \hline I2–H6 & 0.0344864 & 8.865150 & 5.553954 \\ \hline I3–H6 & \(-\)9.2652208 & \(-\)4.616111 & 5.455334 \\ \hline I4–H6 & 12.0185146 & 12.299163 & \(-\)10.527717 \\ \hline B2–O1 & 14.765571 & \(-\)11.104739 & \(-\)0.62485862 \\ \hline H1–O1 & \(-\)6.662708 & 5.525194 & 0.90165390 \\ \hline H2–O1 & \(-\)10.577831 & \(-\)6.403159 & 5.92878925 \\ \hline H3–O1 & \(-\)7.284002 & 1.918726 & \(-\)9.87260412 \\ \hline H4–O1 & \(-\)8.744731 & 8.004559 & 0.08648525 \\ \hline H5–O1 & \(-\)12.319315 & 8.997052 & \(-\)7.63236367 \\ \hline H6–O1 & \(-\)4.108802 & \(-\)12.639489 & \(-\)0.07664491 \\ \hline B2–O2 & \(-\)8.337048 & \(-\)0.07675744 & \(-\)3.107592 \\ \hline H1–O2 & \(-\)9.444444 & 8.49491407 & \(-\)9.655582 \\ \hline H2–O2 & 8.975756 & \(-\)7.48027294 & \(-\)6.024728 \\ \hline H3–O2 & \(-\)8.726450 & 9.93562367 & 5.738516 \\ \hline H4–O2 & 4.006490 & \(-\)7.01414123 & 9.385994 \\ \hline H5–O2 & 10.649440 & \(-\)10.11985671 & 2.076983 \\ \hline H6–O2 & 14.583526 & 11.58227775 & 8.594578 \\ \hline B2–O3 & \(-\)4.4530081 & \(-\)1.13586835 & 1.294068 \\ \hline H1–O3 & 9.7557132 & 0.02180394 & 8.395259 \\ \hline H2–O3 & \(-\)3.0576919 & 8.99669282 & \(-\)1.187068 \\ \hline H3–O3 & 13.5887864 & \(-\)9.10282385 & 4.191767 \\ \hline H4–O3 & \(-\)0.8085856 & \(-\)6.27224615 & \(-\)8.351199 \\ \hline H5–O3 & 0.8808544 & \(-\)1.02923910 & 5.886176 \\ \hline H6–O3 & \(-\)12.4993457 & 4.83479909 & \(-\)8.863369 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._
## 3 ML Application in O-RAN
### Dynamic Function Split
In [43], the authors propose a novel DRL-based algorithm to jointly solve the optimal placement of network functions between the CU, DU, and user RU in an O-RAN architecture. In the meantime, the proposed algorithm aims to minimize the end-to-end delay and deployment cost, while considering constraints such as processing capacity and link bandwidth. The proposed method evaluates the impact of user mobility on the proposed DQN-based joint user association and CU-DU placement scheme (DJRCD) using the SUMO traffic simulation tool. The algorithm is tested using a mixed highway-urban region in the north of the Greater Toronto Area in Canada, and it is found to be superior to existing methods, achieving a reduction of up to 30% and 40% in end-to-end delay and deployment cost, respectively. The dynamic nature of O-RAN environments often necessitates VNF reconfigurations during operation, resulting in additional overhead costs and traffic instability. To tackle this challenge, the work in [59] introduced a multi-objective optimization approach aimed at simultaneously minimizing VNF computational expenses and the periodic reconfiguration overhead. This solution relies on constrained combinatorial optimization with deep reinforcement learning, where an agent works to minimize a penalized cost function derived from the proposed optimization problem. The evaluation of this solution demonstrates \begin{table}
\begin{tabular}{c l l} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2021 & [41] & Applied RL to dynamically perform function split decisions for DUs and CU in a virtualized O-RAN architecture. \\ \hline
2023 & [42] & Applied DRL to propose an energy-efficient RAN disaggregation and virtualization approach across edge sites, including DUs, and a cloud site, including CUs. \\ \hline
2022 & [43] & Proposed a DRL-based algorithm to jointly solve the optimal placement of network functions between the CU, DU, and user RU in an O-RAN architecture. \\ \hline
2023 & [59] & Proposed a DRL-based algorithm for a multi-objective optimization to minimize computational costs and the overhead associated with periodically reconfiguring dynamic VNFs splitting. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of current efforts on ML applications for dynamic function splitting in O-RAN. substantial improvements, including a remarkable 76% reduction in VNF reconfiguration overhead, accompanied by a modest increase of up to 23% in computational costs. Furthermore, when compared to the most resilient O-RAN system, Centralized RAN (C-RAN), which does not necessitate VNF reconfigurations, this solution delivers savings of up to 76% in bandwidth while revealing a 27% overprovisioning of CPU resources.