esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Technologies
### SBD Algorithm  
The Shape-Based Distance (SBD) algorithm is an algorithm used to compare and measure the similarity of time series. It effectively handles time series with variations in speed and length on the time axis. The basic idea is to align two time series by finding an optimal alignment to maximize their similarity on the timeline.  
First, we define the distance metric function between two time series \(X=\{x_{1},x_{2},...,x_{n}\}\) and \(Y=\{y_{1},y_{2},...,y_{m}\}\) as \(d(x_{i},y_{j})\), representing the distance between the \(i\)-th data point of the time series \(X\) and the \(j\)-th data point of the time series \(Y\). Then, we define an \(N\times M\) cumulative distance matrix \(D\), where \(D(i,j)\) represents the minimum cumulative distance between the first \(i\) data points of the time series \(X\) and the first \(j\) data points of the time series \(Y\).  
The dynamic programming process of the SBD algorithm can be expressed as  
\[D(i,j)=d(x_{i},y_{j})+\min\{D(i-1,j),D(i,j-1),D(i-1,j-1)\} \tag{1}\]  
Here, \(D(i-1,j)\) represents the minimum cumulative distance between the first \(i-1\) data points of the time series \(X\) and the first \(j\) data points of the time series \(Y\), \(D(i,j-1)\) represents the minimum cumulative distance between the first \(i\) data points of the time series \(X\) and the first \(j-1\) data points of the time series \(Y\). \(D(i-1,j-1)\) represents the minimum cumulative distance between the first \(i-1\) data points of the time series \(X\) and the first \(j-1\) data points of the time series \(Y\).  
Ultimately, by calculating the bottom-right element \(D(N,M)\) of the matrix \(D\), the SBD distance can be obtained, that is, the \(SBDdistance=D(N,M)\). Here, \(N\) and \(M\) are the lengths of the two time series, respectively [31; 32].
## 4 Results
### Dataset X2
#### 4.2.3 Feature Contribution for Dataset X2  
In Figure 11, the feature contributions for Dataset X2 are depicted, outlining significant features and their implications.  
The absolute loadings of the first principal component in Dataset X2 illuminate the significance of each feature requirement in the design of temperature-controlled medicine bags for Songklanagarind Hospital. Through a comprehensive analysis of these feature contributions, several key insights emerge:  
Firstly, FR1 emerges as the highest priority feature, with its highest loading underscoring its pivotal role in ensuring the effectiveness of temperature control in medicine storage. This emphasizes its critical importance in maintaining the integrity of medical supplies. Furthermore, FR4 and FR2 are identified as high-priority features, with substantial loading values highlighting their significance in protecting fragile medical equipment from damage during transport. Additionally, FR10 and FR9, focusing on size and weight, demonstrate notable loadings, indicating their significant impact on the design and usability of medicine bags. Factors such as portability, storage space optimization, and user convenience contribute to their importance in meeting operational needs.  
On the other hand, FR14, FR5, FR13, and FR3 are deemed to have moderate importance, with moderate loadings suggesting their relevance but lesser priority compared to the critical features outlined above.  
Moreover, FR12 and FR7 exhibit mid-range loadings, reflecting their moderate influence on product design. While contributing to the overall assessment, they are considered to have a lesser extent of importance.  
Lastly, FR6 and FR8, focusing on Ziploc enclosure and bag form, are identified as the least priority features, with the lowest loadings indicating their relatively minor impact on medicine bag design. Although offering convenience, they are not as critical as cooling and shock-proofing elements.  
This detailed feature analysis provides valuable insights for optimizing the design of temperature-controlled medicine bags, enabling stakeholders to prioritize features based on their impact on product performance and user satisfaction.  
Figure 11: Absolute loadings of FRs (Dataset X2).
# Monitoring Rainfed Alfafa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
###### Abstract  
The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m\({}^{2}\) surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (\(R^{2}\) = 0.712), whereas the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017-2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management.  
satellite; vegetation indices; semiarid environment; Bardenas Reales; legumes; forage crops; sustainable agrosystems 2021  
22 September 2021  
19 November 2021  
22 November 2021  
###### Abstract  
The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m\({}^{2}\) surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (\(R^{2}\) = 0.712), whereas the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017-2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management.
## 3 Results and Discussion
### Free Amino Group Content  
The free amino group contents of the whey protein modified with oxidized phenolic compounds are shown in Figure 2. The free amino group content of the unmodified whey protein (the control) was 564.87 umol L-leucine/100 g sample, whereas the modified whey protein exhibited lower free amino contents than that of the control (\(p>0.05\)). This indicated that the electrophilic quinone oxidized form of phenolic compounds might have interacted with nucleophilic amino groups and led to the loss of the free amino group content. This result is in agreement with [PERSON] [5], who reported that rosmarinic acid, quercetin, and chlorogenic acid oxidized to quinone radicals can interact with nucleophilic proteins or any amino acids (e.g., lysine, cysteine, and tryptophan) residues of whey protein by covalent interaction, resulting in a loss in free amino group, thiol groups, and tryptophan content of the modified whey protein. The rate of loss in the free amino group of the whey protein was governed by the type and concentration of oxidized phenolic compounds employed.  
A decrease in the free amino group content was observed in all samples modified with oxidized phenolic compounds, especially at higher levels of oxidized phenolic compounds (\(p<0.05\)). The free amino group of the whey protein modified with 5% OTA was 366.70 umol L-leucine/100 g sample, whereas that of the whey protein modified with 5% OFA and 5% OGA was 418.41 and 421.34 umol L-leucine/100 g sample, respectively. At the same concentration of oxidized phenolic compounds used, OTA showed more reactivity with the free amino group than OGA and OFA, as evidenced by the greatest decrease in the free amino group content of the resulting modified whey protein (\(p<0.05\)), whereas  
Figure 2: Free amino group content of whey protein modified with different oxidized phenolic compounds at various concentrations.  
they protein modified with OGA and OFA had similar decreases in the free amino group content. The higher reactivity of OTA may be due to the large number of phenolic rings in its structure, which support interaction with the free amino group of whey protein more effectively; in contrast, OGA and OFA contained only one phenolic ring. This is in agreement with [PERSON] et al. [18], who reported that polyphenolics with larger molecular weights and more hydroxyl groups (offering more than one site of interaction) were more likely to have better protein binding affinity.
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications
#### 2.2.6 Sharing and Monetization  
According to a Deloitte Insights report [34], telecom companies have a unique position in managing the relationship between content and customers. Third parties provide most of the content offered to telecom networks, thus enhancing the creation and provision of new content by third parties. This surge in content creation poses challenges for telecom service providers as they consistently strive to maintain a stable network. The new requirements introduced by the new content can be challenging to manage, as they aim to avoid frequently introducing new features into a stable telecom environment network. Blockchain can effectively manage all existing and new requirements, with a process flow built using smart contracts and an autonomous consensus mechanism. Blockchain interoperability has a high potential to enable telecom operators to share and monetize their data assets securely with third-party service providers, advertisers, and researchers. In this context, interoperable blockchain platforms guarantee data privacy, integrity, and consent management while enabling seamless data exchange across networks and third-party content providers.
## 4 System Perspective Compatibility Analysis
### The 5G Interference with BeiDou RDSS Receiver Analysis Results
 \\ \hline
0 & 21.84 & 21 & 0 & 0 & \(-\)0.84 & \(-\)127 & 126.16 & 0.1949 \\ \hline
1 & 23.16 & 21 & 0 & 0 & \(-\)2.16 & \(-\)127 & 124.84 & 0.1659 \\ \hline
2 & 23.53 & 21 & 0 & 0 & \(-\)2.53 & \(-\)127 & 124.47 & 0.1584 \\ \hline
3 & 23.79 &  
According to the analysis results of 5G terminal interference to the RDSS receiver in Table 5, when the frequency isolation of the two systems is greater than 25 MHz, the distance isolation is 19.4 m and no longer changes. As for the interference from 5G terminals, only 194 m of distance isolation is needed to realize coexistence in the worst case of frequency isolation of 0 MHz. Considering that the research scenario of this paper is an airport area, the interference from 5G user terminals has too small of an interference impact on the research environment of the airport, so only the impact of 5G base stations is considered in the subsequent analysis. In summary, the interference of 5G base stations and 5G terminals to the BeiDou RDSS system is analyzed from the viewpoint of the link budget, and the required distance isolation under different frequency isolation is given.
# Is Running Technique Important to Mitigate Hamstring Injuries in Football Players?
## 4 Discussion
The reduced ipsilateral pelvic elevation is particularly beneficial as it decreases the tension applied to the proximal hamstrings due to lower activity of the muscles crossing the hip joint, particularly affecting the biceps femoris [52; 53; 54]. The decreased lateral flexion and external rotation of the thorax help in maintaining an optimal alignment of the trunk musculature without increasing their length-tension relationships [55], contributing to a more efficient energy transfer. This leads to enhanced performance and a reduced hamstrings injury risk among footballers [18; 56]. Another surprising finding was the increase in pelvis external rotation at touchdown, eventually indicating adaptations to optimize biomechanical efficiency or compensate for other alterations induced by the running technique improvement intervention. The pelvis acts as a pivot between the thorax and the lower limbs to enhance the stability of the center of mass during sprinting, reducing the risk of hamstring injuries [18; 46; 57]. Despite our expectations that the running technique development would decrease external pelvic rotation (as observed in the reduction of 3.22\({}^{\circ}\) at toe-off), to improve the performance while conserving energy and maintain efficiency at higher velocities [29; 58], there was a slight increase in variability of pelvic rotation at the touchdown. The joint angles analysis at the toe-off also revealed a decreased flexion of the thorax concerning the global coordinate system in the experimental group. The forward lean of the trunk during sprinting has been associated with hamstring stretching muscles, potentially increasing the risk of strain injury [18; 21]. Therefore, a more upright trunk may be beneficial for both hamstring injury prevention and performance acknowledgement [10; 36]. A more upright posture, often associated with front-side mechanics, contributes to a reduced backside elliptical running cycle, minimized ground contact time and increased sprint velocity [12; 59]. Different studies indicate an optimal forward lean ranging from \(\sim\)8-11\({}^{\circ}\) for high-speed running [21; 60; 61]. Despite the comprehensive approach of the current study, there are some limitations that warrant consideration. Firstly, this sample size, particularly of the control group, limit the statistical power of the findings. In addition, it only included under-19 male football players, restricting the generalizability of the results to other populations. The lack of long-term follow-up data limits the understanding of the durability of the observed changes in running technique and their effects on injury prevention and performance outcomes overtime. Therefore, future research should aim to address these limitations through larger sample sizes, diversified participant demographics, longer intervention durations, and long-term follow-up assessments to provide a more comprehensive understanding of the relationship between running technique, hamstring injury prevention, and performance in football players of all ages and levels.
# Development of Polymer-Cored Akaganeite Adsorbent for Phosphate Adsorption
## 3 Results and Discussion
### Adsorption Experiments
#### 3.3.2 Isothermal Adsorption Experiment  
Experimental data were expressed by q\({}_{e}\) (amount of phosphate anions attached to adsorbents) versus Ce (equilibrium concentration of phosphate anions in the solution) plots, and the parameters were calculated by applying Freundlich and Langmuir isotherm models for each powdered/granular akaganeite (Figures 10 and 11). The adsorption isotherm model of powdered/granular akaganeite followed the Freundlich isotherm model with R\({}^{2}\) > 0.88 for both powdered and granular akaganeite, using the Sigma plot 12.0 program. This result was analyzed using the Freundlich model with a high R\({}^{2}\). It showed that powdered akaganeite has an affinity for phosphate anions, as shown by \(n\) > 1.0; conversely, granular akaganeite showed a lower affinity, as shown by \(n\) < 1.0. The maximum adsorption  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Powdered Akaganeite** & **Granular Akaganeite** \\ \hline BET surface area (m\({}^{3}\)/g) & 231 & 54 \\ Pore volume (cm\({}^{3}\)/g) & 0.249 & 0.060 \\ Pore size (nm) & 4.3 & 4.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of physical properties of the powdered/granular akaganeite.  
Figure 9: Effect of contact time on the adsorption of phosphate onto granular akaganeite applied by the pseudo-second-order kinetic model.  
capacity was calculated to be 21.27 mg/g for powdered akaganeite and 3.65 mg/g for granular akaganeite. The reason for the lower adsorption capacity of granular akaganeite was that the BET surface area was decreased by the immobilization of the particles from the PES polymer, and only the outer surface had effective adsorption. Thus, it is possible to remove phosphate at a concentration of 5 mg/L, which a biological treatment process cannot remove.  
Figure 11: Adsorption isotherms for granular akaganeite obtained using the Freundlich model (red dash line) and Langmuir model (black dash line).  
Figure 10: Adsorption isotherms for powdered akaganeite obtained using the Freundlich model (red dash line) and Langmuir model (black dash line).
## 4 Conclusions  
This study discussed the problem of unsupervised rail fastener anomaly detection, addressing the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions. The proposed knowledge-distilled generative adversarial network demonstrated precise anomaly localization using only normal samples. Additionally, to address the feature degradation problem caused by successive downsampling operations, a multi-scale attention-coupling feature-enhancement mechanism was proposed for robust defect representation against background interference. Moreover, to boost detection sensitivity for small-scale anomalies, an enhanced anomaly discriminator was constructed to preserve fine-grained details for detecting small anomalies. Finally, the proposed method achieved an AUC of 94.0%, ACC of 92.5%, and F1-score of 91.6% on the MNIST dataset. On the rail fastener dataset, it attained an AUC of 94.7%, ACC of 90.1%, and F1-score of 87.8%. These results conclusively demonstrate the method's superior anomaly detection capability. Current limitations include dependency on sufficient normal samples and increased computational complexity. Future work will focus on multimodal dataset construction for railroad switch-and-rail components and lightweight architecture development for real-time deployment.  
Conceptualization, H.C. and Z.L.; methodology, H.C.; validation, H.C.; formal analysis, H.C. and Z.L.; resources, Z.L.; data curation, X.X.; writing--original draft preparation, H.C.; writing--review and editing, X.X. and Z.L.; visualization, H.C.; supervision, Z.L. and X.X.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China (62173222).  
Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article.  
The authors declare that there are no conflicts of interest regarding the publication of this paper.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MSAC** & **E-JPU** & **AUC/\%** & **ACC/\%** & **Recall/\%** & **F1/\%** \\ \hline BaseLine & \(\times\) & \(\times\) & 87.2 & 83.8 & 83.2 & 84.3 \\ Method1 & \(\bigvee\) & \(\times\) & 92.1 & 89.2 & 86.5 & 86.9 \\ Method2 & \(\times\) & \(\bigvee\) & 88.9 & 87.2 & 89.3 & 86.3 \\ Proposed method & \(\bigvee\) & \(\bigvee\) & 94.7 & 90.1 & 93.5 & 87.8 \\ \hline \hline \end{tabular} Note: where ’\(\bigvee\)’ indicates that the module is used and ’\(\times\)’ indicates that the module is not used.  
\end{table}
Table 7: Quantitative results of the model ablation experiments.
## 4 Construction of the Cascaded Model and Training
### Transformer for Regression  
The accuracy of equalizer parameters and COM values directly predicted by channel feature parameters is relatively low. In this work, the Single-Bit Response (SBR) before and after equalization is first predicted based on the feature parameters of the channel, and then the equalizer parameters and COM values are determined according to the predicted pulse response. The pulse response needs to be used as a time series for prediction. Traditionally, RNN or LSTM models are used for time-series prediction, but these networks have certain limitations. The Transformer model can effectively improve the gradient explosion problem that occurs in LSTM by using attention mechanisms, thereby enhancing prediction precision [36].  
The formula of an attention mechanism is shown in Equation (6). It mainly consists of three inputs: \(Q\) (Query), \(K\) (Key), and \(V\) (Value), along with a softmax module. When \(Q\), \(K\), and \(V\) are identical, it is referred to as a self-attention mechanism.  
\[\text{Attention}(Q,K,V)=\text{softmax}(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{6}\]  
The complete Transformer architecture primarily includes an embedding layer, a positional encoding layer, an encoder layer, a decoder layer, and an output layer. In this work, since our task is a time-series prediction task rather than an NLP task, the positional encoding layer has been removed. The essence of time-series prediction can be viewed as a regression task; the model only needs to generate a single output value corresponding  
Figure 7: The structure of this study’s DNN.  
to each time point and does not need a decoder to generate sequence outputs. Using only the encoder layer is sufficient, which simplifies the model structure and reduces the computational load. As shown in Figure 8, our final design keeps the embedding layer, the encoder layer, and the final output layer. Additionally, in order to achieve faster convergence, the Gaussian Error Linear Unit (GELU) was chosen as the activation function.  
\[f_{\text{GELU}}(x)=xP(X\leq x)=x\times\frac{1}{2}[1+\text{erf}(x/\sqrt{2})] \tag{7}\]
## 3 Method
### Rules Database  
After defining the membership functions for both the inputs and outputs of the FIS, the next step is to define the rules that infer the outputs from a given set of inputs. The rules database was then designed based on the knowledge of the expert. The usual approach is to define at least one rule for each combination of the inputs, which would result in \(5^{3}=125\) rules in the knowledge base. However, a preliminary analysis of these 125 rules shows that most of them cannot indicate any stall event alone and those rules were discarded to avoid cluttering the database. The rules database is described in Table 6 for a total of 39 rules. Taking rule #12 as an example if the delta_CP input is VH (Very High), the CP_deviation is H (High) and CTW_deviation is L (Low) then the PDM Status will output VH (Very High) and the Flow Restriction will output N (Normal). By correlating with Tables 4 and 5, the FIS will infer that the PDM is probably stalling or has low rotational speed and there is no other flow restriction in the system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rule** & & **Inputs** & & **Outputs** \\ \hline \# & **Delta\_CP** & **CP\_Deviation** & **CTW\_Deviation** & **PDM Status** & **Flow Restriction** \\ \hline
1 & N & N & N or H or VH & N & N \\ \hline
2 & VH & VH & N or H or VH & N & EH \\ \hline
3 & H & VH & N or H or VH & N & VH \\ \hline
4 & VH & H & N or H or VH & N & H \\ \hline
5 & H & H & N or H or VH & N & H \\ \hline \hline \end{tabular}
\end{table}
Table 6: FIS Rules Database.  
Figure 8: Graphical description of variable Flow Restriction.