esa-sceva/satcom-chunk-collection huggingface dataset:
# Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory
## 3 Method
### Case Study: RF-tree
#### 3.3.4 Write Operation  
**Insert/Update/Delete.** In common Write operations, such as the Insert/Update/Delete operation, the client first finds the target node's address by traversing the search layer in the compute server's SLC. The client reads the target node from the memory server using the target node's address. Next, the client checks the checksum value to guarantee the consistency of the data node. The client performs the Write operation after acquiring the write lock. When the data node is successfully updated, the client writes the unlocked data node to the memory server. Note that the lock of the data node is located on the last 8-byte of the data node.  
**Split.** When the node does not have enough space to store a given key and value pair, the client conducts a split operation of the node. The split operation is also a part of the insert operation, so the overflowan data node is already locked. The client allocates a new node and copies half of the key-value pairs to the new node. The client finds the free node from the local _free list_ and uses it. The node is newly allocated when the free list is empty. After copying the data and connecting the new node, the client atomically updates the variable  
Figure 4: An example of an inconsistent search layer. When RF-tree updates a node, RF-tree writes a pointer (value) and then writes the key. 1 Since DN11 has smaller keys than DN7, 2 the client moves to the DN7.  
Figure 5: An example of a stale SLC. 1 A client moves to the child node when it finds a key that is larger than the target key. 2 The client traverses the child nodes to find the target key.  
for the number of data to half. The client writes the nodes and inserts the parent entry to the parent node of the overflow node.  
**Merge.** When the utilization of the node is too low, RF-tree triggers the merge operation. The client first checks the size of the data of the sibling node. If the sum of the data size of the underutilized node and the sibling node is smaller than the node size, the merge operation is triggered. The client copies the key-value pairs of the sibling node to the underutilized node. The client writes the underutilized node to the memory server. After writing the underutilized node, the client reads and locks the parent node, qnd then removes the sibling's parent key-value pair. The sibling page is added to the _free list_ of the compute server.
## 4 Results
### Pipeline  
In accordance with the expected contributions of this work (aim 1 and 2, outlined in Section 1), this section provides the results of using end-to-end pipelines for defect identification.  
Tables 7 and 8 offer a comparison between the inference times (in seconds) and results, respectively, of two distinct pipelines: Pipeline A, which does not incorporate anomaly detection, and Pipeline B, which does include anomaly detection. The differentiation allows for a rigorous assessment of the performance differences between the two approaches, shedding light on the potential advantages and drawbacks of incorporating anomaly detection in the pipeline. The values presented in the tables were obtained by computing measurements on multiple batches of data and subsequently averaging the results.  
A decline in performance is evident for both vision pipelines with the metrics previously presented, attributed to the propagation of errors. Thus, a true positive in this case must be a piece with all the defects verified in the right location, with the correct type detected in the correct subzone of the piece. The general decline in performance is further amplified with anomaly detection, as the inclusion of an additional step aggravates the effect. The rationale behind incorporating this module was to effectively reduce the number of false positives identified by the defect detection process. However, the introduction of anomaly detection inadvertently leads to misclassification of patches containing defects as \"defect-less\" patches. As a consequence, besides the reduction in false positives, the number of true positives also gets adversely affected. This phenomenon highlights the inherent challenges and trade-offs associated with the application of anomaly detection in defect detection pipelines.
# Challenges in Physical Layer Security for Visible Light Communication Systems
###### Abstract  
This article highlights challenges associated with securing visible light communication (VLC) systems by using physical layer security (PLS) techniques. Motivated by the achievements in PLS studies for radio frequency (RF) communication, many PLS techniques for VLC systems were also rigorously investigated by tailoring the RF techniques to the VLC environment. However, careful consideration of the inherent differences between RF and VLC systems is still needed. By disregarding these differences, an eavesdropper could be given an opportunity to wiretap the VLC systems, even when PLS techniques are employed to protect them. Crucially, the fact that it is often not possible to know the number and locations of eavesdroppers in real VLC systems may allow eavesdroppers to devise various cooperative eavesdropping methods. By examining a few examples of the possible eavesdropper threats that can occur in VLC systems, this article offers novel insights into the vulnerabilities of state-of-the-art PLS schemes for VLC systems. Although the focus of the paper is mostly on these weaknesses, some potential solutions are also briefly proposed with a view to stimulating discourse in the community.  
physical layer security; visible light communication; beamforming; secrecy capacity; secrecy outage probability Article
## Appendix A
### Average Precision (AP)  
An object detection algorithm has two tasks: (1) propose bounding boxes that might contain objects of interest and (2) identify the contained objects. To test the performance of the algorithm on a dataset, each prediction (sorted in decreasing order by the confidence score) is compared to the ground truth bounding boxes for each class and is labeled as:  
* True Positive (_TP_) if:
* The Intersection over Union (_IoU_) between the predicted bounding box and the ground truth is greater than or equal to a set threshold (_IoU_\(\geq\) threshold) and the bounding box has been classified correctly
* False Positive (_FP_) if:
* The Intersection over Union (_IoU_) between the predicted bounding box and the ground truth is below a set threshold (_IoU_\(<\) threshold) and the bounding box has been classified correctly
* A detection with a higher confidence score has already been assigned to the ground truth bounding box
* False Negative (_FN_) if:
* No detection (_IoU_ = 0)
* The bounding box has been classified incorrectly  
After each prediction labeling, the precision (_P_) and recall (_R_) over the accumulated statistics (_TP_, _FP_, _FN_) are computed, where:  
\[P=\frac{TP}{TP+FP}=\frac{TP}{\text{all detections}},\] (A2)  
\[R=\frac{TP}{FP+FN}=\frac{TP}{\text{all ground truths}},\] (A3)  
which are used to construct the precision-recall (PR) curve (for each recall value, find the maximum precision value).  
The Average Precision (AP) metric for a single class is defined as the area below the PR curve at a set _IoU_ threshold and as such, is usually written as AP@threshold, with the most common notations being [EMAIL_ADDRESS] (AP at _IoU_\(\geq\) 0.5) and [EMAIL_ADDRESS]:0.95 (the average of AP values at 10 different _IoU_ thresholds, ranging from 0.5 to 0.95 in 0.05 increments). mAP@threshold is the mean AP@threshold over all classes.
## 5 Conclusions
The proposed method can help in the actual 5G network design and deployment when dealing with the coexistence of the 5G system and the satellite system. The exclusion and restriction zones can be achieved by loading the actual parameters of the ES and 5G BSs. Consequently, cases in which no BSs can exist and where BSs can be deployed are differentiated. With the proposed power control scheme, the maximum Tx power of each BS in the restriction zone can be achieved either using the 3 GPP network layout or by customizing the BS site selection. In future work, we expect to focus on developing guard band protection measures to mitigate the interfering beams, decrease the size of the exclusion and restriction zones, and decrease the BS Tx power within the restriction zone. Finally, hybrid technologies can also be considered in future generation systems, such as 6G, because nonterrestrial networks will become part of it. [PERSON] and [PERSON] contributed to the main idea of this research work. [PERSON] and [PERSON] wrote the computation codes and performed the simulations, experiments, and database collection. [PERSON], [PERSON] and [PERSON] confirmed the numerical results of the work. The research activity was planned and executed under the supervision of [PERSON]. In addition, [PERSON], [PERSON] and [PERSON] contributed to the writing of this article. All authors have read and agreed to the published version of the manuscript. This work was supported by the Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government (6011-2018-00016; a study on the supply of 5G additional frequency). The authors declare no conflict of interest.
# HTSA-LSTM: Leveraging Driving Habits for Enhanced Long-Term Urban Traffic Trajectory Prediction
## 3 Experiments and Evaluation
### Formatting of Mathematical Components  
To assess the effectiveness of the HTSA-LSTM model in real-world applications, this section separately trains and tests the model using real-world driving data from urban city roads (CRs), urban expressways (UEs), and highways (Hs). The following evaluation metrics are employed to evaluate the model's performance on the test set:  
1. Root Mean Square Error (RMSE): \[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{i=1}(Y_{i}-f(x_{i}))^{2}}\] (21)  
_RMSE_ is a common metric for evaluating the accuracy of a model's predictions. However, it has certain limitations when assessing the accuracy of multimodal predictions, as _RMSE_ tends to favor models that average the prediction results. In some specific scenarios, the mean value may not accurately reflect the quality of the prediction.  
2. Negative Log-Likelihood Loss (NLL):  
To address this limitation, another evaluation metric is introduced: the negative log-likelihood loss (NLL) of the model's predicted trajectory relative to the corresponding true trajectory [29]. Although the NLL value cannot be directly interpreted as a specific physical quantity, it provides an intuitive comparison between unimodal and multimodal prediction distributions.  
\[\text{NLL}=-\frac{1}{N}\sum_{i=1}^{i=1}logp_{i} \tag{22}\]  
The trajectory information input into the HTSA-LSTM model is denoted as \(T=X_{i},Y_{i}:i\in[1\dots n]\), comprising a series of trajectory points \((x_{i},y_{i})\) continuously distributed over time. For each \((x_{i},y_{i})\), there is a corresponding predicted point \((x_{i-pre},y_{i-pre})\). The probability distribution of the predicted trajectory points at each position, represented as \(p(y_{i-pre},x_{i})\), reflects the prediction accuracy at point \((x_{i},y_{i})\), abbreviated as \(p_{i}\). The goal is for the HTSA-LSTM model's prediction accuracy \(p_{i}\) to be as close to 1 as possible for each trajectory point. The objective of the negative log-likelihood loss (NLL) is to minimize the negative log of \(p\big{(}y_{i-pre},x_{i})\), or \(-logp_{i}\), which encourages the prediction accuracy to approach 1, thereby minimizing the loss and reducing the discrepancy between the predicted and true values. The NLL of the input data is the average NLL across all samples, where NNN represents the number of trajectory points in the input data.  
3. Mean Absolute Error (MAE): \[MAE=\frac{1}{n}\sum_{i=1}^{n}|\hat{y_{i}}-y_{i}|\] (23)  
_MAE_ ranges from \([0,+\infty)\), where smaller _MAE_ values indicate that the predictions are closer to the true values. An _MAE_ of 0 suggests that the model is perfect; conversely, larger prediction errors result in a higher MAE.  
R-Squared (R\({}^{2}\)) Coefficient of Determination:  
\[R^{2}=1-\frac{\sum\limits_{i}(\bar{y}_{i}-y_{i})^{2}}{\sum\limits_{i}(\overline{y }_{i}-y_{i})^{2}} \tag{24}\]  
\(R^{2}\) ranges from [0, 1] and is used to characterize the fit of the model's predictions to the actual data.
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Adsorption Technique
#### 3.1.2 Anion-Exchange Resin Adsorption  
Resin adsorption gradually attracted researchers' attention due to its strong adsorption, regeneration ability, and convenient operation. The carbon chain length (or hydrophobicity) and terminal functional groups of PFASs could influence the adsorption of anion-exchange resin for PFASs. [PERSON] et al. explored the adsorption of large pore anion-exchange resins about IRA910 for single PFASs (PFBA, PFHxA, PFOA, PFBS, PFHxS, and PFOS), showing that the chain length had a great influence on PFCAS adsorption compared with PFSAs [34]. The adsorption efficiency of PFSAs was better than PFCAs, in which the optimum adsorption efficiency of PFBS could be up to 1023.32 mg/g. Moreover, in order to investigate the treatment effect and regeneration capacity of anion-exchange resin, [PERSON] et al. eluted the IRA67 resin that saturated adsorption with PFOA and PFHxA using the mixture solution of NaCl and methanol, the recovery could achieve 98% and 40%, respectively. This phenomenon indicated that short-chain PFASs were difficult to remove from the resin. In addition, the properties of ion-exchange resin also played an effect on the adsorption for short-chain PFASs [25]. For instance, the adsorption capacity of the ion-exchange resin was superior to the non-ion exchange resin, and most of them were better than ACs.  
The mechanism of anion-exchange resin mainly included hydrophobic effects, electrostatic effects, and ion-exchange effects. Generally, the resin with stronger hydrophobicity possessed a virtuous adsorption ability; however, the regeneration capacity was deprived by contrast [4]. Furthermore, the adsorption of short-chain PFASs might be influenced by pH via changing the resin surface potential or morphology [34]. For instance, in various ranges of pH, strong base anion (SBA) resin was impregnable due to its ionization form. In contrast, weak base anion (WBA) resin was influenced significantly [31], which could take effect when the amine group was protonated under acidic conditions. Moreover, the main mechanism for short-chain PFAS removal was single-molecule anion-exchange by the analysis of transmission electron microscope; thus, the inorganic anions in solution would compete with PFAS anions for the ion-exchange sites and then decrease the removal efficiency. The ion-exchange reaction equations are shown in (1) and (2), where [R4N+] and [R3N] indicate the ion-exchange site [15].  
\[[\text{R}_{4}\text{N}^{+}]\text{Cl}^{-}+\text{PFAS}^{-} \leftrightarrow [\text{R}_{4}\text{N}+]\text{PFAS}^{-}+\text{Cl}^{-} \tag{1}\] \[[\text{R}_{3}\text{N}]+\text{H}^{+}+\text{PFAS}^{-} \leftrightarrow [\text{R}_{3}\text{NH}^{+}]\text{PFAS}^{-} \tag{2}\]
# Space Weather Effects from Observations by Moscow University Cubesat Constellation
## 4 Solar Energetic Particle Injection into Polar Cap
These changes are caused by the filling of polar caps by energetic solar particles such as electrons and protons that were accelerated in flares. The wide and intensive peaks near  
Figure 3: Time dependences of solar proton fluxes in different energy ranges from GOES data: \(>\)1 MeV (brown), \(>\)10 MeV (blue), \(>\)30 MeV (violet). the geomagnetic equator, which correspond to the minimal \(L\) values, are connected with satellite crossings of the South Atlantic Anomaly (SAA). The curves on the bottom panel of Figure 4 demonstrate the decreasing count rates in polar caps in both DeCoR instrument channels. It is caused by a decrease in the general SCR fluxes on 3 November, as it can be seen in Figure 3. Periodic variations of the counting rate in polar caps, which could be seen in the middle and bottom panels of Figure 4, are caused by satellite rotation and indicate the strong anisotropic pitch-angle distribution of detected particles, because in this case, counting rate values will depend on the instrument axis orientation relative to the magnetic field vector. The general time dependence of charged particle fluxes in polar caps during the period from 25 October 2021 to 6 November 2021, obtained from DeCoR Norbi data, are presented in Figure 5. For comparison, the time dependence of flux of electrons with energies in the range of 175-315 keV is also presented according to the data of the EPAM instrument (ACE satellite, [[http://sd-www.jhuapl.edu/ACE/EPAM/](http://sd-www.jhuapl.edu/ACE/EPAM/)]([http://sd-www.jhuapl.edu/ACE/EPAM/](http://sd-www.jhuapl.edu/ACE/EPAM/)) (accessed on 30 March 2022)) obtained via [[https://cdaweb.gsfc.nasa.gov](https://cdaweb.gsfc.nasa.gov)]([https://cdaweb.gsfc.nasa.gov](https://cdaweb.gsfc.nasa.gov)) (accessed on 30 March 2022). We can see, in general, a good fit between both DeCoR Norbi and ACE dependences. It means that sub-relativistic  
Figure 4: Time dependences of gamma-quantum (blue line), electron (red line) intensity from DeCoR Norbi data before (**top panel**), soon after (**middle panel**) and after (**bottom panel**) SCR arrival in end of October. Time dependence of Mcllwine parameter \(L\) presented by black line on all the panels. and relativistic electrons give significant input in both DeCoR channels and fill the polar caps effectively. There are two discrepancies in the ACE and DeCoR Norbi data. The last demonstrates very intensive increases on 31 October and 3 November (before the peak caused by the CME), which are not seen in the ACE curve. These peculiarities may be connected with the local increase of solar proton flux on 31 October and the beginning of proton flux growth near 18.00 UT on 3 November. In this case, it reflects the fact that solar protons also fill the polar caps.
# Underground Mine Road Detection Using Deep Learning Technique
## Materials and Methods
### STDC Backbone Network  
#### 2.1.1 STDC Module  
Figure 4 shows the structure of the STDC module. Each module is divided into several blocks, and ConvXi is used to represent the operation of the i block. The output of block i can be calculated by the following formula:  
\[\mathrm{x_{i}=ConvX_{i}\big{(}x_{i-1,k_{i}}\big{)}} \tag{1}\]  
The input of i module is xi-1, and the output is xi. ConvX consists of three parts: convolution layer, normalization layer and ReLU activation layer. Ki is the size of the convolution kernel. The kernel size of the first ConvX of this module is 1 \(\times\) 1, and the remaining ConvX kernels are 3 \(\times\) 3. Assuming that N is the number of channels output from the STDC module, the number of filters in the ith convolutional layer can be calculated from N/2i.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 3 Results
### Validity  
Most GNSS units recorded a total distance greater than the known distance of 45.72 m (Table 2). While statistical significance was only found in two units, the effect sizes of the differences were large across all units. These results indicate that GNSS units, although belonging to the same system, do not always measure distance with the same degree of accuracy.  
Compared with the reference speed data obtained from video analysis, Unit 4 measured significantly higher peak speed (\(p\) = 0.010, large effect size, Table 3). No significant differences were identified between other GNSS units and video analysis, with data of 4 units approaching statistical significance (Units 1, 3, 7, 8). In general, most GNSS units measured a lower peak speed (7.25 (0.51) m/s) than the video-based value (7.78 (0.90) m/s) and the effect sizes of the differences were medium to large.
## 3 Methods for Assessing the Aesthetic Quality of Images
### Traditional Methods of Assessing the Aesthetic Quality of Images
[PERSON] [57] used the color attributes, energy distribution, and structural and edge properties of images as aesthetic features of images and achieved prediction of the aesthetic quality of images by training multiple-classification prediction models. [PERSON] [59] selected sharpness, depth, subject clarity, hue, and color as image aesthetic features and used aesthetic regression to evaluate the aesthetic quality of images. To obtain image features with higher relevance to image aesthetics, researchers tried to evaluate image aesthetics from the perspective of composition and proposed a series of evaluation methods based on composition rules [61; 62; 63; 64; 65]. [PERSON] [54] used different methods to extract image subject areas and backgrounds with different semantic contents to obtain global features and regional features of images and fuse them to obtain comprehensive and integrated image aesthetic features to train SVMs and complete image aesthetic-quality classification and evaluation. In response to the above methods, researchers have proposed the use of generic image features for image-quality aesthetic evaluation [67; 68; 69; 70; 71; 72]. [PERSON] [68; 69; 70] trained a hybrid Gaussian model to simulate the local feature distribution using scale-invariant feature transform (SIFT) descriptors and color descriptors as local descriptors. Local features are encoded using visual bag-of-words [71] as well as Fisher vectors, and the encoded features are concatenated via spatial pyramids to obtain image aesthetic attributes. The above methods and theories have achieved certain results, but the relevant features still have major defects in representing image aesthetics, and the inquiry into the essence of aesthetics is not deep enough to adequately represent the essence of aesthetics. The evaluation method of image aesthetics based on composition rules also has deficiencies and defects, so the above evaluation methods still have great limitations. In the evaluation of image aesthetic quality, researchers have been trying to find evaluation methods that go beyond the traditional evaluation methods and are closely related to the aesthetic characteristics of images. In recent years, with the gradual development and maturity of technologies such as deep learning and neural networks, researchers have continued to introduce neural networks and deep learning into image aesthetic-quality evaluation, and have achieved a series of results.
## 3 Results and Analysis of Influencing Factors
### The Impact of Casing Properties on Signal Attenuation
#### 3.6.2 The Impact of Casing Magnetic Permeability on Signal Attenuation  
Casing magnetic permeability is a crucial attribute. When studying its impact on the signal, the casing conductivity is set at 1 \(\times\) 10\({}^{7}\) S/m, and the relative magnetic permeability is adjusted within a range of 1 to 1000. As shown in Figure 11, taking the transmission of electromagnetic waves over 100 m as an example, we can observe: when \(\mu\) = 1, the  
Figure 10: The impact of casing conductivity on signal attenuation.  
Figure 9: The impact of drill string dimensions on signal attenuation.  
signal attenuation is 109 dB; when \(\mu\) = 10, the attenuation is 114 dB; when \(\mu\) = 100, the attenuation reaches 130 dB; when \(\mu\) = 500, the attenuation is 142.3 dB; and when \(\mu\) = 1000, the attenuation is 145 dB. These data show that with the increase in casing relative magnetic permeability, signal attenuation actually tends to intensify. In other words, the greater the magnetic permeability, the smaller the amplitude of the received magnetic field and the greater the attenuation.