esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Therefore, in contexts where precision is less critical, force values can be used as real-time feedback to enhance athlete engagement and to estimate applied force across different loading conditions or throughout the exercise series [4; 5]. However, practitioners should take into account the moderate negative bias and TEE when interpreting these results. In contrast, the power values obtained from HandyGym's encoder do not demonstrate sufficient consistency for comparing performance across different loads due to varying bias dynamics. Power tends to be overestimated in load-multiplying pulley conditions (BlueWP and RedWP) and underestimated in the remaining load configurations. This study has several limitations that may have influenced the validity of the measurements but were not specifically investigated. First, the sample size was limited to 10 participants. Future studies with larger samples from various sporting environments could improve the generalizability of the results. In terms of execution technique, although all participants received identical instructions, slight variations in braking techniques may have occurred due to individual differences. To the authors' knowledge, no studies have specifically examined the validity of performance measurements in isoinertial exercises based on execution techniques, such as delayed braking for eccentric overload versus maintaining constant tension, though this may be a contributing factor [14]. Additionally, due to the system's characteristics, which multiply the load via an internal gear system, greater friction between components may occur compared to other isoinertial systems. Factors such as internal device lubrication could increase variability in resistance between devices, potentially affecting the validity of force and power measurements. A limitation of this study is that only one HandyGym unit was evaluated. A more detailed discussion of the mechanical characteristics of the system, such as friction and gear ratios, would enhance the understanding of the observed biases. However, the system does not provide sufficient technical specifications to allow for in-depth analysis of these factors. Lastly, mechanical factors such as the vertical angle of the rope pull, harness compliance, and rope tension at the starting position of the squat may have contributed to discrepancies in the measured parameters [29]. Nonetheless, as outlined in Section 2, efforts were made to ensure protocol consistency and provide the best possible conditions for accurate measurements.
# Situation-Aware Survivable Network Design for Tactical Environments
## 5 Conclusions  
In this paper, we have discussed the unstable environment of tactical networks caused by the lack of infrastructure, limited network resources, intermittent connections, and frequent mobility. To overcome this problem and provide reliable communication in the military domain, we proposed two algorithms. The situational backoff reset algorithm adjusts the backoff counter value of low-priority traffic by using situation-aware information. A node, which has low-priority traffic to transmit, resets its backoff counter based on the priority of the received packet without performance degradation. The branch node-based routing algorithm avoids an isolated single path and provides resilient connectivity in the event of a link break. The simulation results indicate that the proposed algorithms outperform the conventional schemes in tactical networks. The contribution of our paper can be extended into various fields. Our research is not limited to military domains but can be applied to medical fields that require strict QoS guarantee for medical application and smart factories that require reliable communication. One possible direction of future research is to consider the timeliness of packet delivery on top of the reliability. As future work, we will extend our survivable network design to guarantee robust communication while satisfying the delay requirements of the networks.  
Conceptualization, S.S. and K.-J.P.; methodology, investigation, S.S. and G.W.; simulation, validation, S.S. and G.W.; writing--original draft preparation, S.S.; writing--review and editing, K.-J.P.; visualization, S.S.; supervision, K.-J.P.; project administration, K.-J.P.; funding acquisition, K.-J.P. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development. (UD190033 ED). Not applicable.  
Not available due to the confidentiality issue with the funding agency.  
The authors declare no conflict of interest.
# Data Exchange in Cluster Structure for Longevity of IoT
## 4 Performance Evaluation
### Round of the Last Node  
In order to compare the performances of data transfer methods, we measure the dead round of the last node. The last node indicates the node that is worked on the cluster till the last. A round means the changing period of the cluster header in a cluster. Figure 7 shows the dead round of the last node in two data transfer methods. One method is CPDT we proposed. The other is non-CPDT which is an existing cluster structure method that does not use the property of things. As shown in this figure, the dead round of CPDT is 238 rounds with 100 deployed nodes. Otherwise, the non-CPDT performs the dead round at only 15 rounds.  
Figure 5: Deployment map with 100 things (nodes).  
Figure 6: First order radio model.  
In particular, in the non-CPDT, as the number of deployed nodes increases, the number of nodes participating in the communication also increases. Consequently, as the dead round of the last node decreases, it is inevitable that the lifetime of the IoT network shortens. However, using the CPDT method, even if the number of nodes increases, the number of participating nodes in the query process does not increase proportionally. On the contrary, as the number of nodes increases, the CPDT prolongs the lifetime of the IoT networks because it significantly reduces the network traffic in IoT networks. In addition, as the number of nodes increases, the probability of each member being elected as a header node decreases. Hence, the energy consumed by the header node can be balanced, and thus, the CPDT method can contribute to the longevity of IoT networks.
## 3 Experimental Results and Discussion  
### Inclusion Criteria and Patient Demographics  
The scraped areas of tissue samples used for isolation of the RNA material had to fulfill some inclusion criteria and were characterized in terms of RNA-Quality, presence of necrosis, viable tumor cells and inflammatory cells such as lymphocytes and granulocytes. Thus, the respective sample of the patient had to meet the following criteria to be included in this study:  
Figure 3: Methodology used to evaluate Q-PCR data. The absolute quantification (1.) relates the PCR signal (cycle number) to the input amount of DNA, using a calibration curve. By use of regression line analysis the amplification efficiency of the whole PCR system can be monitored. Relative quantification (2.) is based on the expression levels of a target gene _versus_ the individual normal tissue of the patient (calibrator). Normalization is undertaken by a non-regulated endogenous gene expression, e.g., derived from classical and frequently described housekeeping genes, here \(\beta 2M\).  
* Low to moderate amount of lymphocytic and granulocytic infiltration
* Low to moderate amount of necrosis
* RNA quality in an acceptable range of 1.8-2.1 [6]
* High Q-PCR assay linearity (correlation of coefficient; r > 0.99) according to [PERSON], 2001 [1]
* A good Q-PCR run will have an efficiency ranging from 90%-110% [10]
* Q-PCR slopes ranging from -3.1 to -3.6 [10]  
Table 2 describes the most important tumor characteristics. The samples were classified as invasive colorectal adenocarcinoma (n = 25) with mostly ulcerated moderately-differentiated tumors. The tumor grading was undertaken according to the American Joint Committee on Cancer (AJCC), pTNM-classifications system and the regression grade as recommended by [PERSON] [11].  
As visible in Table 3, of a series of 25 patients, 13 (52%) samples were from men and 12 (48%) from women. The median age was 67 years (range: 30-84 years).  
\begin{table}
\begin{tabular}{l l} \hline \hline Histological grading (WHO) & \\ G1 (well differentiated) & 1 (4\%) \\ G2 (moderately differentiated) & 22 (88\%) \\ G3 (poorly differentiated) & 2 (8\%) \\ \hline
**Tumor stage** & \\ pT1 & 2 (8\%) \\ pT2 & 8 (32\%) \\ pT3 & 15 (60\%) \\
**Nodal stage** & \\ pN0 & 19 (76\%) \\ pN1-2 & 6 (24\%) \\
**Metastases** & \\ pM0 & 23 (92\%) \\ pM1 & 2 (8\%) \\ \hline Regression grading ([PERSON] _et al._ 1997) & \\
0 & 1 (4\%) \\
1 & 5 (20\%) \\
2 & 9 (36\%) \\
3 & 10 (40\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tumor characteristics.  
\begin{table}
\begin{tabular}{l l} \hline \hline Number of Patients & 25 \\ \hline Age at diagnosis, years (range) & 29 to 84 \\ Median age, years & 67 \\ \hline Gender: Female & 12 (48\%) \\ Male & 13 (52\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Patient demographics.
# Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms
###### Abstract  
Among the various factors affecting long distance driving are the risks imposed by a driver's health, which could result in road accidents. Internet of Things (IoT) and mobile health (mHealth) technologies are increasingly being recognized for their role in monitoring various health parameters of such drivers. IoT devices such as wearable devices could be used efficiently in vehicles by professional long-distance drivers. However, the adoption of such technologies is limited by security and privacy issues. This paper proposes a novel vehicular alarm notification system by integrating mHealth data of the driver with vehicular data for improving vehicle safety. The purpose of the system is to minimize road accidents by integrating the emerging mHealth technologies with the vehicular information system (VIS) using wireless body area network sensors and devices in a secure and lightweight framework. An integrated secure system could facilitate adoptability to provide timely notifications of emergencies to drivers in order to avoid road accidents and take appropriate follow-up actions. Furthermore, the information gathered by the integrated system could assist health service providers to address the driver's root cause of potential health risks. The lightweight security framework adopted in this study is based on an efficient trust model that can improve the adoption of an mHealth integrated vehicular alarm notification system. Our contribution is to provide a real-time alarm notification service to avoid potential traffic accidents by monitoring the health status of long-distance drivers, achieved through incorporating an affordable and lightweight security system to secure health data transfer.  
mobile health (mHealth); mobile healthcare (mHealthcare) system; vehicular information system (VIS); wearable devices; emergency alarm system; road accidents; wireless body area network (WBAN); lightweight security; security algorithm +
Footnote †: journal: technologies
## 2 Materials and Methods
### Introduction to the Theory
#### 2.4.2 Box Dimension Calculation Model  
The fractal dimension is a statistic that describes the complexity of a geometric shape. There are many definitions of fractal dimension, among which the counting box dimension is the most widely used and relatively easy to calculate. The box dimension is the number of boxes consisting of \(N\) squares (with side length \(\delta\times\delta\)) to cover the coal and rock cracks and count the number of non-empty boxes containing pixels of the associated cracks \(N_{\delta}\). It is calculated by the formula:  
\[N_{\delta}=a\delta^{-D} \tag{4}\]  
The prefactor \(a\) comes from the scaling rule for fractal dimensions \(D\). For a fixed crack image, \(a\) is a constant. If Equation (4) is expressed in logarithmic form, the expression reads:  
\[\ln N_{\delta}=\ln a-D\ln\delta \tag{5}\]  
From Equation (5), the slope with respect to \(\ln\delta\) is obtained using linear regression \(\ln N_{\delta}\). The result is the fractal dimension \(D\) of the crack image of the coal and rock.  
The box dimension was analysed using the tool for box dimension analysis in the FracLac plugin in ImageJ (5.0 version) software. In order to reduce the error, the original image was first extracted with cracks using the Ratsnake (1.4 version) annotation software to improve the accuracy and resolution. Finally, it was imported into the FracLac plug-in for box dimension analysis. The specific process is shown in Figure 6. In combination with the Ratsnake software and the FracLac plug-in, a quantitative description of the surface cracks in the coal and rock samples was achieved. In particular, the ImageJ software evaluated whether the crack sketch was a binary image. If it was a binary image, the FracLac plug-in was opened manually for analysis. Otherwise, the image was binarised by setting a threshold. In Fraclac, the control panel parameters for analysing the cracks were set in the region of interest (ROI). Finally, the box dimension was obtained from the image scanning and the statistics of Equation (5).  
Figure 6: Calculation flow chart of box dimension [32].
## 4 Outline of the Approach
### Process of Concept Alignment for Interoperability in Heterogeneous Sources
#### 4.5.3.2 Structure Level Matching Phase  
The structure level matching provides reasoning on structure in order to verify or decline the results obtained in the element level matching phase. This phase consists of three steps:  
1. Select the sets of neighbors \(N_{s}\) and \(N_{T}\) from \(H_{s}\) and \(H_{t}\) which define the context of each concept in the source \(c_{s}\). The ancestors of \(c_{s}\) and siblings that are terminal elements and share the same parent are considered for \(N_{s}\). The set of neighbors \(N_{T}\) from the target hierarchy \(H_{t}\) is populated with the labels of the target concepts the sub collection in which the best suited concept for alignment \(c_{tb}\) is a member. If more than one \(c_{tb}\) is chosen for \(c_{s}\) in the previous element level matching, the labels of all corresponding sub collections are selected for the \(N_{T}\) set. It isimportant to note that neither \(c_{s}\) nor \(c_{tb}\) are included in the sets of neighbors, with the aim of evaluating only the context of these concepts by itself.
2. Calculate a new similarity matrix between the two sets of neighbors \(N_{s}\) and \(N_{T}\) using similarity equation 1 determining \(R_{\left\{c_{s}^{\cdot},\,c_{tb}\right\}}\forall c_{s}\in N_{s},\forall c_{tb} \in N_{T}\).
3. Given the highest relation \(R_{0}\) (\(c_{s}c_{tb}\))obtained from the element level matching phase and the highest relation \(R_{\left\{c_{s}^{\cdot},\,c_{tb}\right\}}\) resulting of the structure level matching similarity calculation, the next step is apply IF THEN rules to determine the one-to-one mappings and decision recommendation for the inclusion of new concepts, sub collections and collections in the ubiquitous user model concept scheme allowing it to evolve over time.
## 2 Materials and Methods
### Experiment Design and Field Management  
Qiliangyou 2012 and Meixiangzhan 2 (high-quality rice) were selected for sowing in the early and late seasons. Two N application levels (120 and 150 kg ha\({}^{-1}\) (N\({}_{1}\) and N\({}_{2}\)) for early rice and 120 and 165 kg ha\({}^{-1}\) (N\({}_{1}\) and N\({}_{2}\)) for late rice) and three planting densities (86, 143 and 200 plants per m\({}^{2}\) (D\({}_{1}\), D\({}_{2}\) and D\({}_{3}\)) for the early season and 57, 114 and 172 plants per m\({}^{2}\) (D\({}_{1}\), D\({}_{2}\) and D\({}_{3}\)) for the late season) were applied to Shanggao and Xingguo. The N application rates and plant densities were selected based on the local recommendations of rice production. Treatments with 150 kg ha\({}^{-1}\) and 86 plants per m\({}^{2}\) (N\({}_{2}\)D\({}_{1}\)) and 165 kg ha\({}^{-1}\) and 57 plants per m\({}^{2}\) (N\({}_{2}\)D\({}_{1}\)) represented the rice farmers' high-yield models and control plots in the early and late seasons. Models with 120 kg ha\({}^{-1}\) and 143 or 200 plants per m\({}^{2}\) (N\({}_{1}\)D\({}_{2}\) and N\({}_{1}\)D\({}_{3}\) in early season) and 120 kg ha\({}^{-1}\) and 114 or 172 plants per m\({}^{2}\) (N\({}_{1}\)D\({}_{2}\) and N\({}_{1}\)D\({}_{3}\) in late season) represented an increased plant density with a decreased N rate. The area of a plot was 30 m\({}^{2}\), the treatments were conducted in triplicate and the planting spacing was 25 cm \(\times\) 14 cm. The treatments were divided by the field ridge covered with plastic film and were drained and irrigated separately. The sowing dates of the early season in 2018 and 2019 were 24 March and 23 March, respectively, and the sowing dates of the late season in 2018 and 2019 were 23 June and 25 June. In the N\({}_{1}\) treatment, 30, 36 and 54 kg ha\({}^{-1}\) was divided into basal, tiller and panicle fertilizer in the early season. In the N\({}_{2}\) treatment, 60, 36 and 54 kg ha\({}^{-1}\) was divided into basal, tiller and panicle fertilizer in the early season. In the N\({}_{1}\) treatment, 15, 42 and 63 kg ha\({}^{-1}\) was divided into basal, tiller and panicle fertilizer in the late season. In the N\({}_{2}\) treatment, 60, 42 and 63 kg ha\({}^{-1}\) was divided into basal, tiller and panicle fertilizer in the late season. The application rates of the phosphorus and potassium fertilizers in each plot were the same. The application rates of the phosphorus (P) fertilizer were 75 and 80 kg ha\({}^{-1}\) P in the early and late season, respectively, all of which were applied as basal fertilizer. The application rates of the potassium (K) fertilizer were 150 and 165 kg ha\({}^{-1}\) K in the early and late season, respectively, which were divided into basal and panicle fertilizers in the same amount. Other management practices were operated according to conventional high-yield cultivation measures and were consistent in the two ecological regions.  
Figure 1: Solar radiation, maximum and minimum temperatures and precipitation of the early and late seasons in the two ecological regions in 2018 and 2019.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 2 Related Literature
### Post-Quantum Cryptography (PQC)
#### 2.2.3 Standardisation and Governmental Response  
The risks highlighted by quantum computing have focused the attention of governments, industry, and the research community, prompting a wide range of response activities.  
The research presented in this paper was selected to contribute to the development and validation of new post-quantum cryptography (PQC) techniques and standards, specifically examining the practical reality of energy use. PQC methods currently appear to be more resilient to both classical and potential quantum computing attacks than existing key-generation algorithms including RSA and ECC [9]. Governments worldwide are establishing timelines for the transition away from quantum-vulnerable algorithms to quantum-resistant techniques. Australia's approach is among the most ambitious, with a target to complete the transition by 2030 [22]. Many other countries and administrative domains are also setting ambitious deadlines in the 2030s, including the UK [23], the US [24], and the EU (through individual agencies in member states) [25; 26].
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 3 Results
### Principal Component Analysis of Main Active Ingredients  
PCA, a multivariate statistical technique, reduces data dimensionality while preserving key variance, enabling pattern recognition and sample differentiation [38; 39]. To assess the impact of drying methods on AS fruit bioactive compounds, PCA was conducted (Figure S3) using quantified compounds: protocatechuic acid, chlorogenic acid, rutin, hyperoside, isofrazidin, quercetin, eleutheroside B, and eleutheroside E. The first two principal components (PC1: 58.0%, PC2: 25.1%) explained a cumulative 83.1% variance, demonstrating distinct compositional differences among drying methods.  
VFD samples were positioned on the far right of PC1, indicating the most distinct characteristics among all drying methods. As PC1 integrates multiple variables, it reflects differences in volatile retention and chemical integrity, both of which are better preserved under VFD's low-temperature, low-pressure conditions. The high PC1 score of VFD suggests superior retention of key bioactive compounds, particularly phenolics. Similar findings were reported in dried Omnja fruits, where PCA effectively differentiated drying effects on volatile and bioactive compounds [40].
## 3 Sensor Upgrade
### The KATERINA II Underwater Radioactivity Sensor
For the needs of the current deployment, the data-logger of the system was adequately programmed to operate via RS232 interface. The data-logger consist of a compact stand-alone digital Multi-Channel Analyzer with a fully controlled micro-processor to perform data acquisition using digital signal processing algorithms and other functionalities for communication with other sensors in the same monitoring platform. Furthermore, software functions were also developed to achieve communication with the external controller of the observing system. In the laboratory, this was done at different levels using commands in hexadecimal format and performing all steps of communications with the system (via RS232 communication protocol). In order to achieve an effective communication between the sensor and the computer station, the desirable option is to send the commands individually. The second step consisted of the following tasks: (a) a dedicated program is developed to program the flow of the individual commands, and (b) the quality control of commands are validated from the response of the system as an acknowledgement receipt. The KATERINA II detection system as installed in the W1-M3A observing system, provides three different data sets with a time-lag (acquisition time) of 1-h: real-time data of total gamma-ray rate (gross counts per second), real-time qualitative data of \({}^{222,220}\)Rn progenies and \({}^{40}\)K (counts per second in ROIs corresponded with each radionuclide without background subtraction) and post automated quantitative activity concentration of \({}^{222,220}\)Rn progenies and \({}^{40}\)K (background subtraction and efficiency consideration for each photo-peak). The latter is accomplished after the retrieving of the whole energy spectra from the internal memory during maintenance operation. The counting rates of specific photo-peaks, after the subtraction of the Compton background, are converted into absolute units of Bq I\({}^{-1}\) or Bq m\({}^{-3}\) using the photo-peak detection efficiency, which is an energy-depended parameter and its estimation is described elsewhere [29].