esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Design and Analysis
### Analysis of Entity Recognition Results by Category  
The experimental results of Section 4.5 are the weighted average of evaluation indicators for each entity category, with the evaluation indicators for each entity shown in Table 4.  
To analyze the experimental results of the CSBERT-IDCNN-BiLSTM-CRF model in detail, the experimental indicators of each entity category are merged with the corresponding quantity in the dataset and analyzed in Figure 11.  
Overall, the number of entities in the dataset does not directly correlate with their recognition accuracy. Where \"COMPANY\", \"LOCATION\", \"ORGANIZATION\", \"PER-SON \"COMPANY\", \"LOCATION\", \"ORGANIZATION\", \"PERSON\", \"SOFTWARE\", \"VERSION\" and \"VULNERABILITY\" have higher recognition accuracy, with F1 scores ex  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entity Number** & **Entity Type** & **Precision (\%)** & **Recall (\%)** & **F1(\%)** \\ \hline
1 & ACTION & 75.86 & 78.22 & 77.02 \\
2 & COMPANY & 95.76 & 98.03 & 96.88 \\
3 & HARDWARE & 77.33 & 80.11 & 78.69 \\
4 & LOCATION & 98.91 & 98.55 & 98.73 \\
5 & NETWORKENTITY & 63.35 & 76.57 & 69.33 \\
6 & ORGANIZATION & 85.11 & 89.88 & 87.43 \\
7 & PERSON & 100 & 100 & 100 \\
8 & PROGRAM & 75.81 & 79.21 & 77.47 \\
9 & SOFTWARE & 86.06 & 92.06 & 88.96 \\
10 & VERSION & 89.98 & 89.11 & 89.55 \\
11 & VULNERABILITY & 96.65 & 95.94 & 96.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation index value of each entity.  
ceeding 85%. However, the recognition accuracy for entities such as \"ACTION,\" \"HARDWARE,\" \"NETWORKENTITY,\" and \"PROGRAM\" is relatively lower.  
The low accuracy in identifying the entities \"ACTION\" and \"PROGRAM\" is due to their long lengths, as well as the overlap of some entity content with other entity categories. The model easily confuses the end tags of entities, resulting in incorrect label predictions.  
On the other hand, the entity \"HARDWARE\" has similar features to \"SOFTWARE,\" and both can only be distinguished based on contextual information. However, there are far more instances of \"SOFTWARE\" entities in the dataset compared to \"HARDWARE,\" which leads to higher accuracy in recognizing \"SOFTWARE\" entities than \"HARDWARE\".  
As for the entity \"NETWORKENTITY,\" it contains too many concepts and is prone to being predicted as an \"O\" (non-entity) by the model compared to other entities. Therefore, its recognition accuracy does not directly correlate with its quantity in the dataset.  
Ultimately, we found that the number of entities has a significant impact on prediction accuracy when faced with relatively similar entity types, whereas it is not a major factor in other cases such as entity types with complex concepts.
## 3 Preliminaries
### Reputation System Performance Metrics
#### 3.2.1 Routing Protocol Metrics  
Routing protocol metrics aim to measure how well the routing protocols perform when faced with various percentages of black-hole nodes.  
The delivery ratio indicates the successfully delivered messages from all the messages that were sent as in:  
\[delivery\ ratio=\frac{number\ of\ delivered\ messages}{number\ of\ created\ messages} \tag{1}\]  
Notice that although in the ONE simulator [23], the same message can be delivered more than once to the recipient, only the first time is accounted for in the equation.  
Delivery Ratio for Good Nodes is essentially the same as the delivery ratio. However, only the delivered and created messages from good nodes to good nodes are accounted for, as in:  
\[delivery\ ratio\ for\ good\ nodes=\frac{number\ of\ delivered\ messages\ from\ good\ nodes\ to\ good\ nodes}{number\ of\ created\ messages\ from\ good\ nodes\ to\ good\ nodes} \tag{2}\]  
The latency of a message is the time delay between the creation and the delivery of the message. In the Average Latency for Good Nodes metric, only messages from good nodes to good nodes are accounted for as in:  
\[latency=\frac{\sum_{i=0}^{number\ of\ delivered\ messages}\left(delivery\ time_{i}-creation\ time_{i}\right)}{number\ of\ delivered\ messages} \tag{3}\]The overhead ratio of a protocol indicates the excess of messages successfully transmitted compared to the total number of messages delivered. In the Overhead Ratio for Good Nodes metric, only messages from good nodes to good nodes are regarded, as shown in:  
\[\textit{overhead ratio for good nodes}=\ \frac{\textit{tf}_{\textit{from good nodes to good nodes}}-\textit{d}_{\textit{from good nodes to good nodes}}}{\textit{d}_{\textit{from good nodes to good nodes}}} \tag{4}\]  
where \(t\) represents the number of transmitted messages and \(d\) the number of delivered messages.
###### Abstract  
This paper presents a universal GNSS receiver channel capable of tracking any civil GNSS signal. This fundamentally differs from dedicated channels, each customized for a given signal. A mobile device could integrate fewer universal channels to harvest all available signals. This would allow securing signal availability, while minimizing power consumption and chip size, thus maximizing battery lifetime. In fact, the universal channel allows sequential acquisition and tracking of any chipping rate, carrier frequency, FDMA channel, modulation, or constellation, and is totally configurable (any integration time, any discriminator, _etc._). It can switch from one signal to another in 1.07 ms, making it possible for the receiver to rapidly adapt to its sensed environment. All this would consume 3.5 mW/channel in an ASIC implementation, _i.e._, with a slight overhead compared to the original GPS L1 C/A dedicated channel from which it was derived. After extensive surveys on GNSS signals and tracking channels, this paper details the implementation strategies that led to the proposed universal channel architecture. Validation is achieved using GNSS signals issued from different constellations, frequency bands, modulations and spreading code schemes. A discussion on acquisition approaches and conclusive remarks follow, which open up a new signal selection challenge, rather than satellite selection.  
GNSS; acquisition; tracking; modulation +
Footnote †: journal: Sensors
# Efficient Reversible Data Hiding Scheme for AMBTC-Compressed Images
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Schemes** & **Performance** & **Lena** & **Baboon** & **Peppers** & **F16** & **Goldhill** & **Boat** & **Average** \\ \hline \multirow{3}{*}{[PERSON] et al. [15]} & PSNR (dB) & 33.97 & 26.00 & 33.40 & 31.95 & 33.16 & 31.15 & 31.60 \\ \cline{2-9}  & EC (bits) & 31,011 & 28,146 & 31,208 & 30,520 & 29,874 & 28,784 & 29,924 \\ \cline{2-9}  & Bit rate (bpp) & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 & 2.00 \\ \hline \multirow{3}{*}{[PERSON] et al. [20]} & PSNR (dB) & 33.97 & 26.00 & 33.40 & 31.95 & 33.16 & 31.15 & 31.60 \\ \cline{2-9}  & EC (bits) & 64,516 & 64,516 & 64,516 & 64,516 & 64,516 & 64,516 & 64,516 \\ \cline{1-1} \cline{2-9}  & Bit rate (bpp) & 2.00 & 2.13 & 2.00 & 1.99 & 1.99 & 2.00 & 2.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 9: Comparison of efficiency rate between the proposed scheme and seven previous schemes [16; 14; 17; 18; 19; 20].
## 2 Study Area and Methods
### Methodology
#### 2.3.3 Correlation Analysis between eWUE and Climatic Factors  
From 2001 to 2020, seasonal eWUE data and climatic parameters in the research region were correlated on a pixel-by-pixel basis, and the geographical distribution of the correlation coefficient was derived to show the degree of connection between the seasonal eWUE and climatic factors [32]. The partial correlation coefficient was further obtained by using the simple correlation coefficient, the partial correlation result was used to control one of the variables and the correlation of the other two factors was discussed. The method used to calculate the correlation coefficient \(r\) is  
\[r_{xy}=\frac{\sum\limits_{i=1}^{n}\left(x_{i}-\overline{x}\right)\left(eWUE_{ i}-\overline{eWUE}\right)}{\sqrt{\sum\limits_{i=1}^{n}\left(x_{i}-\overline{x} \right)^{2}}\sqrt{\sum\limits_{i}^{n}\left(eWUE_{i}-\overline{eWUE}\right)^{2 }}} \tag{3}\]  
Figure 3: Flowchart of the study. Pre: Precipitation; Tmp: Temperature.  
where \(n\) represents the length of time; \(\overline{x}\) represents the meteorological element; \(\overline{eWUE}\) represents the average value of eWUE in each season.  
The formula used to calculate the partial correlation coefficient is  
\[r_{xy,z}=\frac{r_{xy}-r_{xx}\times r_{yz}}{\sqrt{\big{(}1-r_{xy} ^{2}\big{)}\big{(}1-{r_{yz}}^{2}\big{)}}} \tag{4}\]  
where \(r_{xy,z}\) represents the partial correlation coefficients of the \(x\) and \(y\) variables after the fixed variable \(z\); \(r_{xy}\), \(r_{xz}\), and \(r_{yz}\) are the correlation coefficients between the respective variables; the value range of the partial correlation coefficient is between \(-1\) and \(1\).
## 4 Discussion
### Effects on Pain  
All the studies included in the review report pain reduction, due to the treatment with shockwaves. The VAS score was used to evaluate pain in the studies. Three studies [29; 30; 36] evaluated this score at different conditions (overall pain, morning pain, and activity pain). In the majority of cases [21; 29; 30; 32; 35; 36; 39], this outcome was reported at 12 weeks or 3 months. Only one study [20] considered a single follow-up period of 6 months. Four studies [11; 14; 34; 37] analyzed the outcomes more than once at the follow-up, with the longest period being 6 months. [PERSON], [PERSON] et al. [38] extended the follow-up period to 12 months. Three studies [28; 31; 33] did not consider the timing of the outcomes' assessment, with different follow-up periods. ESWT treatment is widely studied, but the biomechanical mechanisms of pain reduction are still uncertain. SW might stimulate tissue regeneration and neovascularization; they modulate pain transmission by acting on the levels of substance P, destruct unmyelinated nerve fibers and also have a role on destroying calcifications in tendons [18; 41]. Therapy might need up to 16 weeks to actualize these biomechanical modifications [43; 44; 45]. Therefore, this might invalidate any speculation about the long-term effects, as the outcomes were measured at short (3 months) or medium (6 months) follow-up periods. The only study with a 12-month follow-up [38] assessed the effectiveness of medium-energy ESWT.
# Soliton Content of Fiber-Optic Light Pulses
## 1 Introduction  
Light pulses in optical fibers carry the bulk of all telecommunication today. In comparison to electrical pulses in cables, fiber-optic transmission is vastly superior, mostly due to two fundamental advantages: light in fibers suffers extremely low power loss, and fibers provide an extremely wide bandwidth. For the loss, a rough number is 0.2 dB/km. This implies that even after 100 km, still, 1% of the launch power is left, which is unrivaled. The available bandwidth can be estimated as 30 THz (maybe 50 THz if one accepts slightly higher loss), which is several orders of magnitude better than anything that can be achieved with electronics.  
Further comparison of optical and electrical transmission brings us to an imperfection shared by both: in either type of conduit, group velocity depends on frequency; as any signal requires a certain bandwidth, transmitted signals suffer from distortion due to group velocity dispersion. This is a linear distortion, which means that it can be perfectly compensated by adding an element of opposite dispersion.  
In one respect, however, optical fibers are very different from electrical cables. The response of glass to light is nonlinear, whereas that of copper to current is not (within reasonable limits). Therefore, the physics of data transmission with light pulses through optical fibers is fundamentally different in that it involves a nonlinear response of the material to the signal [1; 2]. Nonlinear distortions are not as easily compensated as linear ones. This has led to a widely-held conception that optical power in fibers must always be kept low enough so that nonlinear distortions are avoided.  
To this argument, one can give two responses: One, nonlinearity can actually be put to good use, even be used to an advantage, when the concept of solitons is adopted. It has already been demonstrated even in a commercial setting. Two, the ever-growing demand for data-carrying capacitybrings us close to a capacity crunch, unless means are found in just a few years to improve technology. It may become necessary to look at solitonic formats again.
## 4 Discussion
Studies have shown that interfering anions such as acetate, oxalate, citrate, SO\({}_{4}\)\({}^{2-}\), NO\({}_{3}\)-, PO\({}_{4}\)\({}^{3-}\), K\({}^{+}\) and H\({}_{2}\)Y\({}^{2-}\) can cause deviations in the concentration of heavy metals among measurement methods similar to the AAS and FT-IR [35; 36]. Results of a spectroscopic study on a natural clinoptilolite (a zeolite) comparing the FT-IR and AAS for heavy metal cations (Pb\({}^{2+}\), Cd\({}^{2+}\), Ag\({}^{+}\) and Cr\({}^{3+}\)) observed good metal ion detection and sorption in solution [25]. Lead and cadmium ions were sorbed with comparable ion-exchange and chemisorption process proportions [2; 40]. The amount of lead detectable in aqueous forms is comparable with the cadmium sorption via ion-exchange process. The total amount of Pb\({}^{2+}\) in water sources is significantly higher than Cd\({}^{2+}\) due to domination of lead chemisorption process over cadmium. Similar findings were also observed in results for both the PLS and PCA methods [34]. Individual metals form complexes with organic groups of different energy [8]. Metal bonds formed in aqueous forms have different bond energies [2]. The stronger the new bond is with the metal, the weaker the organic group becomes, which results in a wider frequency of the spectra peak for the pure organic group [8]. Such an effect also influences the solution abundance of heavy metals. This may have been the reason higher concentrations of zinc were observed. Higher values than the recommended threshold levels for heavy metals concentration in water sources have also been observed in studies using the FT-IR [41]. Studies have also proposed that interference can also occur in identifying a sample, especially in a water-based media due to the effect of bonding energies. This might also have been the cause for higher values observed for most metals in Table 3[9]. [PERSON] et al. [2] reviewed that chelating and complexation are possible reactions in the adsorption process which determines availability of metal ions in solution. Moreover, metal ions can also be adsorbed by substitution reactions or by electrostatic forces of attraction to the electron cloud [2]. The FT-IR procedure allows aqueous analysis of the sorption/desorption phenomena which can assist in determination of the speciation of sorbed inorganic anions or inorganic complexes formed [42]. The results obtained in this study can also be beneficial in establishing the transport model of toxic species in natural waters and remediation of liquid wastes [11; 42; 43].
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 3 Background
### Connectivity  
An actual network process is difficult to simulate because of enormous infrastructures and immeasurable nonlinear, nonstationary variables, whereas an inaccurate representation of network statistical characteristics could induce errors in our proposed model performance, as stated in [29]. Simulating a precise network process is inherently challenging due to the sheer size of the infrastructures involved and the unpredictable nature of nonlinear, non-stationary variables. Moreover, an erroneous portrayal of network statistical attributes can detrimentally affect our model's performance, as mentioned in [29]. We navigate these intricacies by operating under the presumption that every TCP connection undergoes a three-way handshake for benign data transmission between a client and target host. Notably, specific connection parameters are essential precursors to the actual data exchange. Typically, certain connection parameters must be established before the exchange of application data.  
The client host randomly picks a sequence number \(a\) and sends a synchronize (SYN) packet with supplementary TCP flags and options. The target host increments \(a\) by one, chooses its random sequence number \(b\), appends distinct flags and options, and replies with a SYN Acknowledgement (SYN-ACK). The client host wraps up the handshake by adding one to both \(a\) and \(b\) and dispatching the final ACK packet. Upon a benign handshake, i.e., when \(ACKFlag==1\), both the client and target host are primed for data exchange, although either could be malicious. This insight equips our methodology to evade network performance mishaps affecting MO-1 and MO-2, ensuring connectivity recognition after the last ACK is sent.
## 3 Candidate Selection and Localization
### Similarity Measurement  
When the robust features are determined, the NCC is used as a similarity measure for selection of the possible candidates. The correlation coefficient for the template image and a searching block at point \((x,y)\) in the inspection image on image pyramid level \(l\) are then defined as:  
\[\delta_{l}(x,y)=\frac{\sum\limits_{r=R_{\text{min}}}^{R_{\text{max}}}\left(H_{p,l }(r)-\overline{H}_{p,l}(r)\right)\cdot\left(H_{S,l}(x,y,r)-\overline{H}_{S,l}( x,y,r)\right)}{\sqrt{\sum\limits_{r=R_{\text{min}}}^{R_{\text{max}}}\left(H_{p,l}(r)- \overline{H}_{p,l}(r)\right)^{2}\cdot\sum\limits_{r=R_{\text{min}}}^{R_{\text{ max}}}\left(H_{S,l}(x,y,r)-\overline{H}_{S,l}(x,y,r)\right)^{2}}} \tag{12}\]  
where \(H_{p,l}(r)\) and \(H_{S,l}(x,y,r)\) are the values of the hybrid-projection transformation for the template and inspection images, respectively. Note that \(l\) is the index of the image pyramid levels. The correlation value \(\delta_{l}(x,y)\) is between \(-1\) and \(1\). It is equal to \(1\) when a perfect match occurs between the template image and searching block. The candidate selection process allows only the most likely candidate to be fed into the refinement process.
# Application of Machine Learning Models for Baseball Outcome Prediction
###### Abstract  
Data science has become an essential component in professional sports, particularly for predicting team performance and outcomes. This study aims to develop and evaluate machine learning models that accurately predict game outcomes in the Chinese Professional Baseball League (CPBL). Method: A total of 859 games from the 2021 to 2023 regular seasons were analyzed, using both traditional baseball statistics and advanced sabermetric indicators such as the Weighted Runs Created Plus (wRC+), Weighted Runs Above Average (wRAA), and Percentage of Leadoff Batters on Base (PLOB%). Five machine learning models--decision tree, logistic regression, Neural Network, Random Forest, and XGBoost--were constructed and assessed through a five-fold cross-validation. Evaluation metrics included accuracy, F1 scores, sensitivity, specificity, and the AUC-ROC. Results: Among the models, logistic regression and XGBoost achieved the highest performance, with an accuracy ranging from 0.89 to 0.93 and an AUC-ROC from 0.97 to 0.98. The feature importance and SHapley Additive exPlanations (SHAP) analysis revealed that the wRC+ and PLOB% were the most influential predictors, reflecting the offensive efficiency and pitching control. Conclusion: The results suggest that combining interpretable machine learning with sabermetrics provides valuable insights for coaches and analysts in professional baseball. Furthermore, incorporating performance weighting based on game context may further enhance model accuracy. This research demonstrates the potential of data-driven strategies in sports analytics and decision-making.  
Chinese Professional Baseball League; performance analysis; sport big data; weight index; explainable AI +
Footnote †: journal: Applied sciences  
Article
## 2 Materials and Method
### Inclusion and Exclusion Criteria  
We selected original articles published in English that examined the relationship between diet and adiposity in female adolescents from South Africa. The inclusion criteria encompassed articles that investigated the associations between diet (including food and nutrient intakes, dietary patterns, and practices) and various adiposity indices as outcomes. These indices included weight, Body Mass Index for age (BMI-for-age), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), abdominal, central or visceral obesity, subcutaneous fat, skinfold thickness, body fat percentage, and lean body mass. For this review, adolescence was defined as the developmental stage between childhood and adulthood, spanning from 10 to 19 years of age [11]. We included studies of any design, provided that they reported on the association between the exposure (diet) and the outcome of interest (adiposity). Studies that involved both males and females were also considered, provided that the associations were reported separately by gender.
# Augmented Reality: A Systematic Review of Its Benefits and Challenges in E-learning Contexts
## 5 Discussion
### Recommendations for AR Development  
As mentioned previously, this systematic review describes some of the technical problems that challenge the mass adoption of AR in educational settings, such as e-learning contexts. To circumvent such issues, developers must take advantage of advancements in technology to enhance various aspects of AR technology. For instance, most mobile and laptop cameras are currently made for capturing 2D videos and images, and thus, do not render good 3D images. Similarly, the current GPS readings are precise, but only up to 6 m; however, greater accuracy is needed for deploying AR markers. AR developers in academic settings need to enhance accelerometer reading using protocols, such as the exponential smoothing technique, as described by [59]. Camera performance can also be enhanced by using approaches, such as the 2D QR and bar-code markers, as suggested by [60]. In terms of cumbersome (heavy) devices, developers should focus on using lightweight materials to reduce their weight significantly and address health-related concerns, such as neck pain. Currently, Microsoft Compact is developing a lightweight AR prototype that has benefits for academic environments, both online and on-site. During the development of new AR devices, methods for complete eradication of potential eye problems also need to be considered. Many issues with software  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key Challenges** & **Authors** \\ \hline Information/cognitive overload & [14, 54, 55] \\ Complex technology for both students and teachers & [55] \\ May be confusing and overwhelming to both students and teachers & [56] \\ Lack of necessary skills to use AR technologies & [6] \\ Teachers’ resistance to adoption & [9, 14] \\ Incompatibility with the usual pedagogical approaches & [6, 57] \\ Lack of training and knowledge among teachers about using AR in education & [14, 54] \\ Technology is expensive & [14, 58] \\ May cause health and discomfort concerns for users & [14] \\ Technical problems, such as poor connections and Global Positioning System (GPS) errors & [8] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the key challenges.  
inter-operability in academic settings must be addressed, as does how the current technology can be seamlessly integrated with traditional pedagogical approaches.