esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Comparison with Similar Studies  
To better evaluate the predictive performance of the proposed model, this study conducted a comparative analysis with existing research. For example, one study used up to 74 covariates to model and predict SOC. These covariates included bioclimatic data such as temperature and precipitation, soil and biome distribution maps, vegetation indices, and various topographic factors derived from a DEM. Four machine learning methods were applied, namely Random Forest (RF), Cubist, Generalized Linear Model Boosting (GLMBoost), and Support Vector Machine (SVM). The results showed that the RF model achieved the best performance, with an R\({}^{2}\) of 0.32 [57].  
Figure 10: Spatial distributions of model predictive uncertainty and Terrain Ruggedness Index, with selected area highlighted and enlarged for detailed comparison.  
Figure 9: Spatial distribution of model predictive uncertainty.  
Another study constructed a high-dimensional feature set consisting of 130 environmental covariates, including 2 climatic variables, 8 topographic variables, 110 phenological variables (11 \(\times\) 10 years), and 10 EVI time-series variables (1 \(\times\) 10 years). The CNN, Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models were used for SOC prediction. The results indicated that the CNN-LSTM model achieved the highest performance, with an R\({}^{2}\) of approximately 0.35 [58].  
It is important to note that none of the above studies explicitly incorporated geographic location information to account for its influence on the distribution of covariates and the spatial variability of SOC. In contrast, the GPTransCNN model proposed in this study introduces a geo-positional encoding mechanism. This allows the model to perceive both spatial structural patterns and environmental variable information, thereby improving the prediction accuracy of SOM, with an R\({}^{2}\) of 0.4329. Considering the approximate relationship between SOM and SOC (SOM \(\approx\) SOC \(\times\) 1.724), the superior performance of GPTransCNN in SOM modeling indirectly reflects its strong generalization capability and practical potential in SOC prediction tasks.
## 5 Analysis of the Unslotted CSMA/CA Scheme of the IEEE 802.15.4  
This section analyzes the performance of the unslotted CSMA/CA mechanism of IEEE 802.15.4, considering its use to carry traffic generated by the motion capture system. The first data packet from a sensor node is generated randomly in the interval between zero and 100 ms, since there is no time coordination between the nodes. After that, the sensor node generates data packets periodically with an interval of 100 ms. The parameters of the unslotted CSMA/CA algorithm were provided in Table 1. The interval between the data packet and the ACK frame is 192 \(\upmu\)s, since \(t_{ack}=aTurnAroundTime\).  
The results presented in this section were obtained under favorable conditions: an error-free channel and no hidden nodes. Four operation modes were considered: do not retransmit if the transmission fails (Without ACK-0 Ret); up to one retransmission attempt per data packet (1 Ret); up to three retransmission attempts per data packet (3 Ret); and up to seven retransmission attempts per data packet (7 Ret). In this simulation scenario, a transmission can only fail due to collision with a packet transmitted by another node in the same network or due to failure to access the channel (which occurs when the node detects a busy channel during \(macMaxCSMAbackoffs\) attempts).  
Figure 4 presents the delivery ratio as a function of the number of sensor nodes in the network. As the results for the mode without retransmissions show, transmissions start to fail with as low as five nodes and, consequently, the delivery ratio decreases as the number of nodes increases. The retransmissions allow the recovery from failures, up to a certain point. When the number of nodes is relatively small, the more retransmission attempts, the better the delivery ratio. However, as the number of nodes increases, the traffic load also increases, increasing the collisions, as well as the channel access failures. Since retransmissions contribute to aggravate the situation, the delivery ratio starts to collapse after a certain point, a phenomenon that is more pronounced when more retransmissions are allowed. In the considered scenario, the unslotted CSMA/CA protocol can support up to 25 nodes at a delivery rate near 100% (with a maximum of seven retransmission attempts).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(BER_{bad}\) & 10\({}^{-2}\) \\ \(BER_{good}\) & 0 \\ \(T_{bad}\) & 20 ms \\ \(T_{good}\) & 180 ms \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the Gilbert-Elliot model.  
The backoff process and retransmissions also have a significant impact in current consumption of the nodes, as shown in Figure 5, which uses the CC2430 current parameters presented in Section 4.
# Role of Kir4.1 Channel in Auditory Function: Impact on Endocochlear Potential and Hearing Loss  
[PERSON]  
1 Bioacoustics Research Laboratory, Department of Neuroscience-DNS,  
the knowledge about the wide range of possible causes, for more than 40% of cases, the etiology remains unknown. Estimates suggest that 2 to 3 out of 1000 newborns have a significant hearing disability at birth and over 50% of these cases have a genetic origin. Communication difficulties and a lower quality of life could result from mutations in nuclear or mitochondrial genes, leading to hearing loss [2; 3; 4; 5]. About 75% of the genetic etiology diagnosed results in non-syndromic conditions, while the remaining 25% includes syndromic conditions or chromosomal abnormalities [6]. Hearing loss can be classified into two broad types: conductive or sensorineural hearing loss. Conductive hearing loss occurs when the pathological process affects the middle or external ear, while sensorineural hearing loss happens when the pathology affects the inner ear [1; 2; 3; 4; 5]. The sensorineural hearing loss may arise from either the cochlea or any of the retrocochlear auditory structures. The sound is converted into electrical signals by cochlear hair cells (sound transduction) to travel to the brainstem through the cochlear nerve [5; 7]. Auditory function is primarily ensured by proper inner ear homeostasis, which is determined by maintaining a high positive endocochlear potential (+80-100 mV) and the correct pH of the endolymph. The latter are ensured by the physiological functioning of the cochlear tissues involved in the recirculation of potassium, sodium, and calcium ions [5]. Severe deep recessive congenital autosomal hearing loss in many populations is caused by mutations in the GJB2 gene encoding connexin 26 (gap junction) [8]. EAST/SeSAME syndrome and Pendred syndrome are examples of altered K\({}^{+}\) flows that can occur when the KCNQ genes encoding for potassium channels are altered. Today, there is an urgent need to investigate the proper functioning of transport channels, ion pumps, and cellular junctions because there is a high percentage of individuals who are affected by changes in endocochlear potential [4; 5; 6]. A critical and narrative review of the literature was conducted to verify the current level of knowledge on the role of potassium channels in the function and development of the auditory system. In particular, it has been a priority to raise awareness about Kir4.1 and its correlated pathways, clarifying their mechanisms, and fostering the development of therapeutic goals for medical treatments.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 3 Pressure Support Ventilation (PSV)
### Signal Decomposition Methods  
#### 3.4.1 Empirical Mode Decomposition (EMD)  
EMD is a comparatively recent signal processing method utilized to decay the signals into their parts. The processing of non-linear and nonstationary signals is one of its many applications [44]. In this method, the principal (basis) functions are deduced out of the signal, contrasting the Fourier transform or the wavelet transform, which has a fixed basis function. A process known as sifting is implemented in the EMD method to break down a signal into many intrinsic mode functions (IMFs) and a residue (Equation (1)) [45].  
The IMFs are a collection of AM-FM (amplitude-modulated-frequency-modulated) signal constituents. They fulfil two criteria: (i) the count of maximum and minimum points has to be similar or slightly differ by one to the count of zero crossings, and (ii) the mean of the covers designed by linking local maxima and minima of the signal is null [46]. Many researchers have reported using the EMD method to decompose the HRVs to extract useful parameters [47]. This may be attributed to the non-linear and nonstationary nature of the HRV features [42].  
\[x(t)=\sum_{k=1}^{M}f_{k}(t)+r_{M}(t) \tag{1}\]  
where \(M\) signifies the amount of IMFs, \(f_{k}(t)\) shows the \(k\)th IMF, and \(r_{M}\) links to the residual value.
## 6 Security Analysis and Results
### Additional Security Discussions
#### 6.5.3 More than \(d\) Duplicates  
Thus far, we have presented a scenario where the adversary compromises no more than \(d\) sensor nodes from a particular home zone and deploys as many copies of these nodes in the network. Our scheme is capable of detecting with 100% accuracy (given sufficient time) any of the duplicates deployed in the network, so long as the distinct identities of these compromised nodes from within a specific home zone do not exceed \(d\). The adversary may duplicate a compromised node many times; however, **Disccard** only saves the information of two distinct locations of every duplicate node in its cache. For instance, in the examples before, we have set \(d=5\), which is greater than 10% of the nodes in the home zone. We believe, as do researchers in prior work, that compromising more than 10% of nodes in a home zone imposes a significant effort on the adversary. Furthermore, an adversary that has no limits on the number of nodes he may capture and compromise can defeat any protocol running in the network. To this end,the size of the duplicate cache, \(d\), should be chosen taking this effort into consideration before employing **Disccard** in a sensor network. Additional considerations include the degree of the unattended nature of the network. In a large sensor field, perhaps it is not quite feasible for the adversary to compromise these many nodes, particularly in the presence of some secondary physical monitoring in place. Increasing \(d\) causes increased communication expenses, and this trade-off needs to be analyzed. However, once the adversary is able to compromise more than \(d\) nodes in a home zone, our detection accuracy drops. This is because the sensor nodes will be forced to swap out some of the duplicate node identities with information from their neighbors in the merge process. We have not explored this phenomenon and leave this task for future work, as well.
## 4 Discussion  
The results obtained by the analytical method could be validated by comparing them with numerical results. Figure 23 compares the maximum stresses and deformations obtained in MF and MH. Tables 4 and 5 compare the most critical stresses and deformations obtained using both methods. Figure 24 compares the maximum numerical stresses and deformations obtained for the complete prosthesis model for cases 3.1 and 3.2, with respect to the maximum ones that can be borne by the selected material. It can be appreciated that the maximum stresses and deformations exhibited by the complete model do not exceed the material yield stress and its allowed deformations. Furthermore, the slippage suffered in the modules of the prosthesis due to the contact is very low, ensuring that the prosthesis does not undergo abrupt geometric changes, providing prosthesis integrity and patient safety.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Prosthesis Section**} & \multicolumn{3}{c}{**Stress in MPa**} \\ \cline{2-4}  & **Analytical Method** & **Numerical Simulation** & **Error Rate** \\ \hline Foot module & 357.984296 & 382.812 & 6.4856\% \\ Heel module & 675.166712 & 639.467 & 5.2875\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of numerically and analytically computed stress.  
Figure 23: Analytically and numerically computed stresses and deformations. (**A**) MF. (**B**) MH.  
Figure 22: Case 3.2: Numerical analysis of MH. (**A**) Stress. (**B**) Deformation.
# The XTRA Option at the NEUTRA Facility--More Than
## 3 Examples of Applications
### Crucible Fragments from Augusta Raurica  
Augusta Raurica was a Roman settlement in Northern Switzerland on the south bank of the Rhine river. The settlement played an important role as an administrative, cultural, commercial, and industrial center in the region. Due to the relatively intact nature of the site, many important finds have been made, such as the silver treasure of Augusta Raurica. In addition to such spectacular finds, the houses and workshops of the common people play a very important role for the understanding of the daily life in such Roman settlements. Metal working and especially bronze casting is of particular interest for the archaeologists as a couple of workshops have been found in good condition. The researchers hope to gather more information on the technologies used for the production of metal objects during this period. One aspect of the whole process is the melting of the metal in melting pots or crucibles. The Roman craftsmen used different types of crucibles, with several layers of differing materials.  
The goal of the presented investigation was to evaluate the structure and build-up of several crucible fragments, all found during excavations in Augusta Raurica [19].  
By means of the XTRA setup at NEUTRA we performed sequential tomography runs with thermal neutrons and 150 kV X-rays. The results are shown in Figure 8 as vertical slices through the stack of ceramic shards. The grey levels are scaled to provide hatching distributions within the bulk ceramic zones. This scaling allows a comparison between the two data sets and the respective deviations from the grey level of the ceramic zones. Both X-ray CT and neutron CT reveal regions that show a bright contrast, but the origin might be different: for neutrons these bright regions originate from organic components with their high hydrogen content, while for X-rays these bright regions are related to enclosures with higher density, such as metals.  
Figure 8: _Cont._The neutron and X-ray data are combined using color channel coding, Figure 8 (right), providing a qualitative overview of the material composition in the shards, indicating organic materials as intense green and metals as red/orange on a background of green/brown, representing the mineral phases of the clay in the ceramic structures.
# Research on Clustering-Based Fault Diagnosis during ROV Hovering Control
## 2 Faults and Methods
### Hovering Control  
To achieve consistent thrust among underwater thrusters with different thrusts, a hovering controller based on RPM control was designed. For the vertical thrusters to maintain the same RPM, a fourth-degree polynomial relationship between the control input (PWM) and RPM was calculated via polynomial curve fitting in \"MATLAB R2024a\". This RPM-based control allows the ROV to maintain its roll, pitch, and depth at reference values through hovering control. The hovering control strategy is shown in Figure 7.  
\[\begin{split} e_{\phi}=\phi_{R}-\phi_{Y}\\ u_{\phi}=K_{\phi P}\ e_{\phi}+K_{\phi I}\int e_{\phi}dt+K_{\phi D }\ e_{\phi}\end{split} \tag{5}\]  
Here, \(e_{\phi}\) represents the error between the reference roll and output roll, \(\phi_{R}\) represents the reference roll, \(\phi_{Y}\) represents the output roll, and \(u_{\phi}\) represents the roll PID calculation value.  
\[\begin{split} e_{\theta}=\theta_{R}-\theta_{Y}\\ u_{\theta}=K_{\theta P}\ e_{\theta}+K_{\phi I}\int e_{\theta}dt+K_ {\theta D}\ e_{\theta}\end{split} \tag{6}\]  
Here, \(e_{\theta}\) represents the error between the reference pitch and output pitch, \(\theta_{R}\) represents the reference pitch, \(\theta_{Y}\) represents the output pitch, and \(u_{\theta}\) represents the pitch PID calculation value.  
\[\begin{split} e_{D}=D_{R}-D_{Y}\\ u_{D}=K_{DP}\ e_{D}+K_{DI}\int e_{D}dt+K_{DD}\ e_{D}\end{split} \tag{7}\]  
Figure 6: Expected data distribution when applied DBSCAN.  
Here, \(e_{D}\) represents the error between the reference and output depths, \(D_{R}\) represents the reference depth, \(D_{Y}\) represents the output depth, and \(u_{D}\) represents the depth PID calculation value.  
\[u_{t}=u_{\phi}+u_{\theta}+u_{D} \tag{8}\]  
Here, \(u_{t}\) denotes the sum of the calculated values for roll, pitch, and depth PID control. The calculated \(u_{t}\) as an RPM value, performs RPM control according to the thrust direction of each thruster and implements hovering for the maintenance of attitude (roll and pitch) and depth.  
Figure 8 shows the results of the hovering control for depth only, from the bottom of the engineering tank to the target depth (2.5 m), with the gain values related to the roll and pitch set to zero. In simple depth-hovering control, it can be observed that the four vertical thrusters are identical to those in RPM control.  
Figure 8: Results of reaching the target depth from the tank bottom and performing hovering: depth, RPM, and attitude data for only depth hovering control.  
Figure 7: Hovering control: PID computation of roll, pitch, and depth errors based on RPM control.  
Figure 9 shows the overall strategy for the proposed ROV system. Before faults are diagnosed, the selected fault features (RPM and current consumption of the underwater thrusters) are acquired and subjected to a preprocessing step. According to the preprocessed data, faults are diagnosed using the DBSCAN method described in Section 2.3 to distinguish between normal, breakage, and entanglement.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Relationship Between Quiet Eye and Shot Accuracy  
#### 5.2.1 Correlation Analysis  
A Pearson correlation analysis indicated a strong positive relationship between QE duration and shot accuracy (r = 0.72, \(p\) < 0.001). Additionally, QE onset showed a moderate negative correlation with shot accuracy (r = \(-\)0.58, \(p\) < 0.001), suggesting that earlier QE onset was linked to better accuracy.  
\[r=\frac{\sum\bigl{[}\left(X-\overline{X}\right)\left(Y-\overline{Y}\right) \bigr{]}}{\sqrt{\sum\left(X-\overline{X}\right)^{2}}.\sqrt{\sum\left(Y- \overline{Y}\right)^{2}}} \tag{5}\]  
Table 4 provides a visual representation of the correlations discussed in this section, including the relationships between shot accuracy, QE duration, QE onset, and other key variables.
# Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR
## 2 Design and Manufacturing
### Actuation and Control
#### 2.2.4 Microcontroller  
To control the four motors and collect sensor data, we used an Arduino Uno microcontroller board as the main controller and two BASICMICRO RoboClaw drivers which provide a current of up to 15 Amperes. The driving motors, fitted with encoders, are directly attached to the RoboClaw ESC drivers which control their speed based on the Arduino controller's input. The sprawl of BSTAR is instrumented with a potentiometer which is used to measure the sprawl angle and control its orientation in PID closed-loop using the Arduino controller (see Figure 8). The analog voltage is then transformed to a digital value by the controller. The controller evaluates the error between the actual and the target angle and using the PID controller it controls the sprawl angle with the duty cycle of the motor. The gains are experimentally tuned to minimize the error and overshoot (the normalized gain values are: kp = 1, ki = 0.5, and kd = 0.25). The Arduino is also responsible for controlling the position of the servo motor which actuates the ramp. The BSTAR is powered with two 11.1 Volt 5200 mAh LiPo batteries. The Arduino is connected to a receiver to respond to commands from a human operator but is also preprogrammed to perform basic tasks (motion sequences of the sprawl, ramp and driving motors) to dispatch or recover the RSTAR for example.  
Figure 7: The ramp is actuated by a motor aligned with its rotation axis.
###### Abstract  
This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system's 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas.  
smart aquaculture monitoring; remotely operated vehicle; beyond 5G; communication multi-platform control; IoT-based water quality sensing +
Footnote †: journal: Marine Science and Engineering  
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