esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an Open Remote Electrocardiogram (ECG) Service
## 5 Experimental Results
### Data Frame Construction and Delivery  
We instrumented the code running on the ESP12e board in order to inspect the preparation of the JSON array containing the samples and its delivery to the WebSocket interface. To this end, one of the GPIO pins is set high during packet delivery (see Listing 2). In Figure 8, the lower trace represents the data acquisition activity of Arduino (the same as the lower trace in Figure 7, with a different time scale), while the upper trace represents packet delivery on the WebSocket interface. The figure shows a cycle of ten data acquisitions on the Arduino (lower trace), the data of which are delivered as a unique JSON object with a single WebSocket operation.  
The trace focuses on network operation and disregards the activity of the ESP12e serial interface connected with the Arduino: while receiving data, the ESP12e is idle most of the time waiting for data from the Arduino. The relevant events occur just before the pulse in the upper trace when the ESP12e has completed the JSON-encoded frame and delivers it to the WebSocket interface. Figure 9 is an enlarged display of Figure 8 during such transient: it focuses on the last acquisitions before a WebSocket send event. We have evidence--not reported in this paper--that the ESP12e acquires the last data of the frame during  
Figure 8: Timing of a series of 10 data acquisition operations (lower trace high periods) and the delivery of the packet through the WebSocket (upper trace high periods). The lower trace is the same as Figure 7, the other is high during network operation of the ESP12e.  
Figure 7: Timing of the acquisition of a single ECG sample (lower trace high period) and of its delivery to the UART (upper trace high period) on the Arduino.  
the lapse corresponding to the leftmost pulse in the lower trace in the figure. The successive time before packet delivery (2.3 ms) is spent by the ESP12e receiving the last data item across the serial line. Thus, the data produced by the following acquisition will be temporarily buffered in the UART, and delivered when the WebSocket send call terminates.  
The ESP12e is busy almost 100% of the time, but most of the time is spent idling for the next character from the serial line. In this way, it can tolerate WebSocket transmission delays, and there is no interference between operations.  
To summarize the timing of data acquisition and transmission, a data frame is _on air_ 8 ms after completion, and carries information for 40 ms of ECG, with an overall latency of approximately 50 ms.
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 4 Results
### Experimental Analysis  
Primarily, the _ASAD_ algorithm can achieve seasonal adaptation. However, Argus and EGADS require human setting of the seasonal length, and Period can only generate one seasonal length for all current data. Simultaneously, _ASAD_'s seasonal length computation accuracy might reach 75%, which is better than the auto-correction technique.  
Furthermore, the _ASAD_ algorithm performs well with respect to both time consumption and sub-sequence clustering accuracy. Despite the fact that the _ASAD_ algorithm consumes somewhat more time than the Period algorithm, its clustering accuracy rate is significantly higher.  
Finally, because the Period and _ASAD_ outperform the Argus and EGADS in terms of recall, accuracy, and F-score on each KPI, the comparison and analysis of the Period and the _ASAD_ are the focus of attention in terms of dataset features. In Figure 12, it can be seen that _ASAD_ outperforms Period in 80% of KPI datasets.
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 2 Corrosion Detection Method of Substation Grounding Grid
### Electromagnetic Imaging
[PERSON] [48] and [PERSON] [49] proposed the radar imaging method, where the ultrawideband emission signal propagates through the soil medium to reach the grounding body and the echo signal is used to obtain the physical state of the grounding body. Figure 4: Apparent resistivity section of flat steel. Figure 5: Apparent resistivity section with breakpoints. In recent years, the electromagnetic imaging method has become a hotspot in the research of grounding grid corrosion diagnosis. However, this method is limited by problems such as reinforced bar shielding of concrete pavement, cable trench, power equipment impact, and large calculation workload.
# Mobility-Multihoming Duality
## 4 ILNP Implementation in Linux
### Locator Update (LU) Messages  
In multihoming scenarios, multiple L64 values must be active, and thus the LU message must be able to carry multiple L64 values. The LU message implementation is an extended version of RFC6743 [40], as shown in Figure 4.  
Compared to RFC6743, we have the following:  
* The Lifetime field is extended to 32 bits to align with the equivalent field for IPv6 prefixes in IPv6 RAs, which are also 32 bits [39].
* A new LU Code value, ILNP_LU_REPLACE_SOFT, is defined. This allows for a \"soft\" replacement at the CN of the current set of L64 values with the new set of L64 values contained within the LU message (see Section 4.3).  
Figure 4: The ILNP LU message structure based on the message format from RFC6743.
## 3 Results
### Sources of Mercury in Soils
The northern part of the lake showed the highest Hg concentrations due to inflows carrying untreated wastewater from nearby cities such as Wuxi and Changzhou. Non-ferrous metal processing, electroplating, and textile manufacturing were identified as key industrial contributors, while domestic sewage and agricultural runoff added to the diffuse load. Hg pollution was more pronounced near bays and tributary mouths, indicating that both point and nonpoint sources play a critical role in local Hg accumulation. While most reviewed studies focused on natural or transitional landscapes, industrial and undeveloped areas remained underrepresented, comprising only 8.0% and 10.7% of the total, respectively. However, these settings may demonstrate distinct mercury dynamics due to site-specific anthropogenic or geochemical processes. In industrial zones, mercury can be released through historical emissions or metal processing activities, with elevated soil concentrations often persisting near facility boundaries [27; 28]. In contrast, undeveloped lands, despite less disruption, still accumulate mercury through long-range atmospheric deposition and hydrological retention, especially in areas with high organic content or proximity to water bodies [29]. The limited representation of these land uses in existing studies highlights a need for broader site inclusion to improve the generalizability of NbS applications for mercury mitigation. The presence and behavior of mercury in urban green infrastructure systems have remained critically underexplored, despite the widespread adoption of these technologies for sustainable stormwater management. Systems such as bioretention ponds and permeable gardens have been widely implemented in urban areas, yet their effectiveness in retaining or transforming Hg has been evaluated in only a limited number of studies. In a multiyear investigation conducted by [PERSON] et al. [32], mercury was consistently retained in the ponding areas and engineered media of bioretention cells in the San Francisco Bay area. However, MeHg was occasionally exported during baseflow and dry-weather conditions, suggesting that microbial methylation may have occurred under favorable redox conditions. In another study by [PERSON] et al. [34], Hg was effectively retained in permeable gardens within low impact development networks, although saturated zones with high organic matter content were found to facilitate methylmercury formation under anoxic conditions. These findings indicate that while green infrastructure systems can be effective for mercury capture and storage, the potential for MeHg generation under specific environmental conditions must be carefully considered.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 2 RPL: Routing Protocol for Low-Power and Lossy Networks
### P2 MP and P2P Route Construction and Data Packet Forwarding  
RPL can work in two MoPs, namely: \((i)\) storing mode, and \((ii)\) non-storing mode. As the name implies, in the non-storing MoP, nodes do not store and maintain routing table for P2P communication. If a node has a data packet for another node, it forwards the packet to the gateway/root, and the gateway uses source routing to forward the packet to the destination node. RPL uses DAO control messages to construct P2P and P2 MP forwarding paths. In the non-storing MoP, nodes use MP2P forwarding to send their DAO packet to the root. In this way, the root discovers forwarding paths to different nodes in a network. In the storing MoP, each node maintains data forwarding paths to the other nodes in its sub-DODAG, and this is achieved through the transmission of DAO packets. Whenever a node needs to forward a data packet to another node in its network, the node uses the information in its forwarding table to relay the packet to the destination node. If the route to the destination is not present in the forwarding table, the packet is transmitted to the node's parent node. The process continues until the common ancestor is reached, and from there the packet is relayed to the destination node.
## 4 Experimental Results and Discussion
### Hyperparameters Search
#### 4.1.5 Size of Neighboring Vertices in \(k\)-NN  
Lastly, we investigated the influence of different \(k\)-neighbors in refining the classification results of RF(CSI). Table 2 presents the performance of the RF model with suitable hyperparameters and eight different \(k\) sizes. The accuracy and F1 score are highest at \(k=60\). Considering the case when \(k=60\), the accuracy and F1 score of the classification results are higher than the other cases. Although the application of the \(k\)-NN does not significantly improve the result, it provides a good visualization as the relief, background, and degraded region are well clustered, as shown in Figure 11. The second and third columns of Figure 11 were obtained using the RF models trained using SI and CSI, respectively. The fourth column was obtained after applying \(k\)-NN to the results in the third column. The \(k\)-NN fills the holes and gets rid of isolated points. Once the relief, background, and degraded region are well clustered, it is possible to change the degraded region to the background region, thereby making the classification a two-class label consisting of relief and background regions.  
Figure 9: Performance of RF models (in terms of F1 score) for various \(d_{CSI}\) values.  
Figure 11: Subjective comparison of the classification results before re-labeling. The second and third columns were obtained using the RF models trained using SI and CSI, respectively. The fourth column was obtained after applying \(k\)-NN to the third column. The \(k\)-NN fills the holes and gets rid of isolated points.  
\begin{table}
\begin{tabular}{c|c c c c} \hline \hline \(k\) & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \(10\) & 0.9610 & 0.8907 & 0.8836 & 0.8851 \\ \(20\) & 0.9612 & 0.8921 & 0.8839 & 0.8860 \\ \(30\) & 0.9615 & 0.8929 & 0.8843 & 0.8866 \\ \(40\) & 0.9619 & 0.8944 & 0.8846 & 0.8875 \\ \(50\) & 0.9622 & 0.8959 & 0.8848 & 0.8883 \\ \(60\) & 0.9625 & 0.8985 & 0.8832 & 0.8889 \\ \(70\) & 0.9623 & 0.9006 & 0.8793 & 0.8880 \\ \(80\) & 0.9622 & 0.9026 & 0.8757 & 0.8873 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Search for acceptable size of \(k\).  
Figure 10: Feature importance of RF (CSI) models \(d_{CSI}=1\) and \(3\).
# Strength Analysis of Real-Life Passwords Using Markov Models
## 3 Background
For e.g., if we have a transition matrix with probabilities for the various kinds of weather, we can denote the probability for the weather being rainy, sunny or snowy two, three or four days from now. This probability is denoted by \(P^{n}_{\alpha;\alpha_{i}}\), where:  
* \(\alpha_{i}\) denotes the current state of the model
* \(\alpha_{j}\) denotes the next state of the model
* \(n\) denotes the number of steps  
The probabilities \(P^{n}_{\alpha;\alpha_{i}}\) are calculated by setting the transition matrix to power \(n\)-the number of steps. When the above statements are applied to the passwords, the transitional probabilities simply represent the probability of a character \(c_{i}\) appearing after a character \(c_{i}\) in a certain position in the password, which corresponds with the number of steps \((n)\). Having this in mind we can transform Equation (2) into:  
\[P(\text{``}c_{1}c_{2}...c_{l}^{n})=P(c_{1})P(c_{2}|c_{1})^{1}P(c_{3}|c_{2})^{2 }...P(c_{l}|c_{l-1})^{l-1}. \tag{5}\]  
From the above equations, we can see that the probability \(P\) basically represents the probability of occurrence of a sequence of characters and the order of the characters in the password. This probability is obtained by analysing the frequency distribution (the number of occurrences) of these characters in a suitable training dataset. It is important to note that the probabilities of the first characters \(P_{0k_{i}}\) are represented by an _initialization vector_. The initialization vector basically holds the probabilities of every character occurring in the first position in a password. The final score assigned to the password is a number that is computed by using the following equation [15]:  
\[S(\text{``}c_{1}c_{2}...c_{l}^{n})=-10 log_{10}P(\text{``}c_{1}c_{2}...c_{l} \text{''}), \tag{6}\]  
so that the less likely it is to produce the password, the stronger it is according to the model. Needless to say, the frequency distributions of the characters used in keyspace compression via Markovian filtering is language-specific, and the distribution used in this paper applies only to passwords chosen by English-speaking users (presumably, similar distributions can be found for other alphabet-based languages).
# Multicell Interference Management in Device to Device Underlay Cellular Networks
## 6 Performance Evaluation
### Comparative Results
Figure 9: Comparison of Rate per Hertz (initial, Algorithm 1, Rate Max [26]). Similarly in Figure 12, the total number of users who satisfy their SINR requirements as a function of increasing number of D2D users per eNB is presented. It is clearly observed that our approach sustains its significant capacity improvement in terms of number of users that can be supported by the network resources even both for low and high density networks (i.e., increasing number of D2D users per eNB). Figure 11: Average number of satisfied users (in terms of Signal to Interference and Noise Ratio (SINR) requirement) after each stage of the Resource Block (RB) and power allocation procedure. Figure 12: Average number of satisfied users (in terms of SINR requirement) for the combined approaches, for increasing number of Device to Device (D2D) users per evolved NodeB (eNB).
###### Abstract  
This study explored the impact of short rest intervals on resisted sprint training in elite youth soccer players, specifically targeting enhanced initial-phase explosive acceleration without altering sprint mechanics. Fifteen U19 soccer players participated in a randomized crossover design trial, executing two sprint conditions: RST2M (6 sprints of 20 m resisted sprints with 2 min rest intervals) and RST40S (6 sprints of 20 m resisted sprints with 40 s rest intervals), both under a load equivalent to 30% of sprint velocity decrement using a resistance device. To gauge neuromuscular fatigue, countermovement jumps were performed before and after each session, and the fatigue index along with sprint decrement percentage were calculated. Interestingly, the results indicated no significant differences in sprint performance or mechanical variables between RST2M and RST40S, suggesting that the duration of rest intervals did not affect the outcomes. Horizontal resistance appeared to mitigate compensatory patterns typically induced by fatigue in short rest periods, maintaining effective joint movement and hip extensor recruitment necessary for producing horizontal ground forces. These findings propose a novel training strategy that could simultaneously enhance sprint mechanics during initial accelerations and repeated sprint abilities for elite youth soccer players--a methodology not previously employed  
resisted sprint; sprint mechanics; repeated sprint; rest interval time; soccer
# Application-Oriented Input Spectrum Design in Closed-Loop Identification
## 6 Average Performance Input Design Problem Solution
### The Parameter Estimation of the Upper Tank
The results of the model parameters estimation using frequency (18) and KYP (30) methods for the upper tank system are shown in Figure 4. For parameter estimation purposes, 100 Monte-Carlo independent runs have been made. From Figure 4 it can be observed that the upper tank model (41) parameter estimates, for both presented methods, are clustered inside the assumed contour (i.e., for \(\gamma=0.1\)). In fact, about 95% of the estimated parameters from the frequency method and about 92% of the estimated parameters from the KYP method satisfy the ellipsoidal constraint (12). The greater dispersion of the estimated parameters observed for the KYP method (Figure 4) results from numerical issues related to the assumed frequency grid. The mean absolute percentage error (MAPE) of the estimated model parameters, considering the frequency method, does not exceed 3.12% for \(\theta_{1}\) and 0.27% for \(\theta_{2}\), respectively. The MAPE obtained for the KYP method is larger and has the following values: 8.43% for parameter \(\theta_{1}\) and 0.72% for parameter \(\theta_{2}\).
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 2 Materials and Methods
### Model Integration to the Raspberry Pi 4 for Online Disease Assessment  
#### 2.3.1 Hardware  
For the current research, a Raspberry Pi 4 (RPi) model B was used (Figure 5) that has a quad-core ARM Cortex-BCM2711 CPU of 15 GHz and 8 GB of RAM. Additionally, there are 40 GPIO pins, a full HDMI port, 4 USB ports, an ethernet port, a 3.5 mm audio jack, a video camera interface (CSI), the display interface (DSI), and a micro SD card slot. The RPi is connected with a U-Blox Neo-6M GPS Module to record the coordinates for each capture. Finally, RPi was connected to an 8 MP Raspberry Pi Camera Module 2 (RB-CAMERAV2) via a flex cable (Figure 5). RPi was chosen for model integration as it has a relatively higher computational power compared to other microcontrollers, like Arduino or STM32 controllers, and it has a powerful CPU and a GPU that can be leveraged for certain types of parallel computations. Additionally, it can run a full-fledged Linux OS, providing access to a wide range of software libraries and tools, like TensorFlow. Finally, it provides higher built-in networking capabilities, making it ideal for IoT projects that require network communication.  
\begin{table}
\begin{tabular}{c c} \hline
**Data Augmentation Parameter** & **Possible Values** \\ \hline Rotation & \([-30^{\circ},30^{\circ},5^{\circ}]\) * \\ Image Shearing & - \\ Image Zooming & 0.8, 0.9, 1.1, 1.2 \\ Image Flipping & Horizontally, vertically \\ \hline \end{tabular}  
* can take any integral value in the specified range, with a step of +5\({}^{\circ}\).  
\end{table}
Table 2: Data augmentation parameters and selected values.  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameter** & **Possible Values** \\ \hline Epoch number & 10, 20, 30, 50 \\ Optimization algorithm & RMSprop, Adam, SGD \\ Learning rate & 0.1, \(5\times 10^{-2}\), \(10^{-2}\), \(10^{-3}\), \(10^{-4}\) \\ Batch size & \(16,32,64\) \\ Dropout rate & 0.1, 0.2, 0.3 \\ \hline \end{tabular}  
\end{table}
Table 3: Tested values of the different deep learning hyperparameters for their tuning using the grid search method.  
A robust steel case was used for the protection of the RPi, ensuring the safety of the device under harsh conditions, such as exposure to sunlight and occasional raindrops. The total cost of the proposed system with all components was approximately EUR 160.
# Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS
## 7 Conclusions  
A new method to suppress VFTO was proposed, called a damping busbar. The mechanism of this new method was analyzed. A calculation method for the self-inductance of the damping busbar at high frequency was presented. Moreover, the inductance effect of the damping busbar on VFTO suppression was studied as well. As a result, the amplitude of the VFTO was reduced by approximately 27% after applying the damping busbar. A further enhancement of the suppression effect was investigated by designing a spiral litz coil connected in series with the busbar, which increased the total inductance value. Consequently, a good damping effect was achieved by utilizing the inductive and resistance arrangement which was formed by the improved design of the spiral tube damping busbar. The conclusions are as follows.  
1. We proposed a new design which has a significantly better damping effect when compared with other existing suppression methods. The proposed damping busbar can be easily implemented in any existing GIS without any change in its structure and is therefore very cost-effective.
2. We investigated a calculation method for the self-inductance at high frequency.
3. We improved the performance of the damping busbar by adding a spiral litz coil connected in series with the busbar in order to enhance the suppression effect, and we developed an algorithm based on air gap calculation to design the litz coil.
4. We compared the damping effect with and without the damping busbar and the improved design.  
However, research on improving the proposed design to increase the equivalent inductance value by increasing the length of the busbar and the number of turns has to be considered. Many different adaptations, tests, and experiments should be investigated in future work (i.e., experimental study of a 1000 kV GIS station VFTO after installing the improved damping busbar).  
Conceptualization, R.A.A., Y.-X.S., and W.X.; formal analysis, R.A.A.; investigation, R.A.A., Y.-X.S., and W.X.; writing--original draft preparation, R.A.A. and W.X. supervision, Y.-X.S., and W.X.  
The authors declare no conflict of interest.