esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hardware Architecture  
As described in Section 1, the MSP is based on a modular architecture. Internal sensors and a local processing core are integrated into the mainboard. Three sensors are externally connected. An external ancillary module is provided for 5G communication.  
In this section, we define the different core components of the board. The embedded components of the architecture system and the final hardware design of the prototype are highlighted in Figure 3.
## 3 The Proposed Method  
In this work, we propose a novel problem-independent framework for both the fault-type classification and the RUL prediction based on a GA. As we mentioned, the GA was employed to select the close-to-optimal set of data-processing algorithms and optimize the involved parameters in a robust way for a given dataset. As shown in Figure 1a, we first outlined the general process of the data-driven diagnostics and prognostics, which consists of four subtasks of data rebalancing (for classification problems), feature extraction, feature reduction, and learning. We did not explicitly include a feature selection in our framework, although it is a frequently used technique [48]. In fact, an implicit feature selection was already employed in the feature extraction stage because the inclusion and exclusion of created features are dynamically determined by a chromosome in the genetic algorithm (see the Section 3.1.1 for more details). As we explained in Section 2, a variety of algorithms in each subtask can be considered, and the diagnostics/prognostics performance is likely to be highly dependent on the selected algorithm and the specified parameter values. In this regard, it is necessary to select the optimal data preprocessing algorithms and specify the optimal parameter values involved by those algorithms. Hence, we propose a data-independent diagnostic/prognostic genetic algorithm (DPGA) to resolve it. In addition, our DPGA can be easily extended to generate an ensemble result [49; 50] because it runs along with various learning methods, as shown in Figure 1b. We note that four representative machine-learning methods such as the multi-layer perceptron network (MLP), \(k\)-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) were employed in this study. As shown in Figure 1b, our DPGA runs to search the optimal data-processing algorithms for data rebalancing, feature extraction, and feature reduction subtasks and the relevant parameter values over the training dataset for each learning method in the learning phase. Then, a set of best solutions found by each DPGA are integrated into an ensemble to predict the fault-type or the RUL value over the test dataset in the prediction phase. In the following subsections, we explain the details of DPGA and the employed ensemble approach.
## 3 LE Identification Method Considering the Fluctuation of Wind Power Output
### The Conventional Calculation Method of APEV
#### 3.2.2 Method of Calculation  
In the conventional method, the calculation of the APEV is based on the assumption that the bus nodes' output remains unchanged before and after the branch is removed, and it is considered that the APCV of the remaining branches is only affected by the LE caused by the removal of the branch. The mathematical expression for APEV is obtained based on the branch outage simulation method in the static safety analysis. The APCV of each branch within the CLS due to LE can be obtained by Equation (6).  
After the faulty branch is removed, the APEV can be obtained by adding APCV to the active power of the line before the faulty branch is removed, as shown in Equation (8).  
\[\mathrm{P}_{k}^{l}=\mathrm{P}_{k}^{l}=\mathrm{P}_{k}^{0}+\Delta\mathrm{P}_{k }^{l}=\mathrm{P}_{k}^{0}+d_{k-l}\mathrm{P}_{l}^{0} \tag{8}\]where \(\mathrm{P}_{k}^{l}\) is the APEV of line \(k\) after line \(l\) is removed; \(\mathrm{P}_{k}^{0}\) is the active power transmitted by branch \(k\) before line \(l\) is removed;  
The calculation of the APEV in the above conventional method assumes that the bus nodes' output before and after the faulty branch removal is unchanged. In the power system with wind farms, especially under the scenario of high wind power penetration, if the APEV is calculated based on the conventional method without considering the change value of wind power output before and after the faulty branch removal, the error between the APEV and the APMV may be too large, so that the LEE cannot meet the LE identification criterion, resulting in misjudgment of the cause of branch overload. The LE cannot be accurately identified, causing the unexpected tripping of backup protection, which may lead to cascade tripping and subsequent blackouts.  
Therefore, for the power system with wind farms, when calculating the APEV after the faulty branch is removed, the APCV caused by the output fluctuation of the wind farm must be considered. A calculation method of the APEV considering wind power output fluctuation is proposed in the following.
## 4 Discussion
### Fish Growth Performance  
During the 30-day study period, red tilapias were fed twice daily ad libitum. For fish, FCR is used for the conventional measure of livestock production efficiency, while SGR is connected to fish growth. Lower FCR values and higher SGR values indicate a suitable growth performance. Red tilapia in the aquaponic systems of the present study showed high growth performance as judged by FCR and SGR values, which were similar along the three treatments. Additionally, the supplementation of Fe and K did not affect fish survival. All the above-mentioned results indicate that the addition of Fe or K did not affect fish growth. There are few articles examining the impact of fertilizers on tilapia growth. [PERSON] et al. [44], working in aquaponics with a combination of lettuce and red tilapia, reported different fish survival rates between the treatment of no inputs and the treatment of Fe, K, Ca, Mg, Mn, P, and Zn addition (58% and 73%, respectively). Additionally, the latter treatment resulted in higher FCR and lower SGR compared to the treatment of no external nutrients. [PERSON] et al. [33] reported 31.5% increased feed consumption and 14.3% higher FCR of Nile tilapia reared in an aquaponic system where nutrient supplementation occurred. The growth performance indices of the present study (survival, FCR, and SGR) indicate better fish growth compared to the two above-mentioned works [44,45]. Moreover, the absence of differences between treatments in all other measured fish growth parameters, such as weight gain, final weight, and length, highlights the neutral effect of Fe and K supplementation on fish development.
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
## 2 Theory Analysis  
In our scheme, the core idea is to use the TCSPC technology to get the information of the number and arrival time of each signal photon. Aided by the range-gated technology, the environmental noise can be significantly reduced. Furthermore, with these information the TOF algorithm and photometric stereo algorithm can be used to reconstruct the 3D model of the object.
# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 3 Compression Experiment of Shipborne Vision Sensor Video
### Coding Complexity Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Configuration Name** & **Type** & **Parameter** \\ \hline File I/O & InputChromaFormat & 420 \\  & MaxCUWidth & 64 \\ Unit definition & MaxCUHeight & 64 \\  & MaxPartitionDepth & 4 \\  & FastSearch & 1 \\  & SearchRange & 64 \\ Motion Search & HadamardME & 1 \\  & FEN & 1 \\  & FDM & 1 \\  & BipredSearchRange & 4 \\  & SAO & 1 \\ Coding Tools & AMP & 1 \\ Quantization & QP & 32 \\ Coding Structure & GOPSize & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main parameters of the HEVC encoder. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Visible Light Camera**} & \multicolumn{2}{c}{**Infrared Camera**} \\ \cline{2-5}
**Type** & **AI** & **LDP** & **AI** & **LDP** \\ \cline{2-5}  & & **\%** & & **\%** & \\ \hline TComYUV & 2.00 & 1.72 & 1.89 & 1.85 \\ TComSlice & 0.01 & 0.02 & 0.02 & 0.02 \\ TComDataCU & 14.67 & 14.77 & 14.21 & 16.44 \\ TComTU & 0.38 & 0.31 & 0.36 & 0.30 \\ TEncSearch & 11.73 & 7.89 & 11.47 & 7.10 \\ TComPrediction & 12.07 & 1.34 & 11.83 & 1.05 \\ TComRdCost & 3.83 & 13.19 & 3.75 & 11.31 \\ TComLopFilter & 0.09 & 0.01 & 0.13 & 0.01 \\ TComTrQuant & 16.80 & 11.00 & 18.58 & 10.49 \\ TComInterpolationFilter & 0 & 19.42 & 0 & 20.40 \\ TEncBinCABAC & 0.88 & 0.47 & 0.89 & 0.46 \\ TEncSbac & 6.98 & 3.43 & 7.28 & 3.25 \\ TEncEntropy & 0.25 & 0.14 & 0.25 & 0.11 \\ else & 30.31 & 26.29 & 29.34 & 27.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Time ratio of major coding processes. intra-frame search and rate distortion cost calculation consume more coding time. When compressing the SVSV using LDP mode, the motion estimation and motion compensation parts consume more time, which leads to the higher computational complexity of the inter-frame prediction part and affects the compression latency.
## 3 Results and Discussion
### Parametric Analysis
#### 3.2.3 Effect of Rock Thermophysical Properties  
Bear in mind that for a constant volume of rock (i.e., constant mass of storage material in the domain), an improvement in the natural capacity of the material to store thermal energy (i.e., \(\rho\) and cp) would lead to an enhancement of the TES system. For that reason, different rock types were investigated for the task proposed. Five types of rock were selected, and their thermophysical properties were taken from literature [43] and are given in Table 2. The results for outlet fluid temperature during the charging cycle can be seen in Figure 11. It is noticeable that the highest impacting factor is the thermal capacity of the rock (the product of density and specific heat). As shown, sandstone (lowest thermal capacity) quickly achieves its maximum temperature, while gabbro (highest thermal capacity) takes the longest time to be fully charged. This can also be noticed from the similarity between the quartzite and the granite temperature profiles, even with very distinct values for k. The thermal conductivity has a subtle effect on the charging rate, being slightly more accentuated for smaller aspect ratios. Except for that, the aspect ratio has a small impact on charging rate, mainly caused by the higher velocities of fluid within the bed (as previously mentioned in Section 3.2.1). However, its effect on the total energy storage is virtually null, as shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rock Type** & **Density (kg/m\({}^{3}\))** & **Thermal Conductivity (W/m-K)** & **Specific Heat (j/kg K)** & **Thermal Capacity (k/m\({}^{3}\cdot\)K)** \\ \hline Quartzite & 2600 & 5.0 & 830 & 2158 \\ Granite & 2600 & 2.8 & 820 & 2132 \\ Sandstone & 2400 & 2.3 & 775 & 1860 \\ Limestone & 2700 & 2.2 & 880 & 2376 \\ Gabbro & 2900 & 2.2 & 980 & 2842 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the distinct rock types analyzed, taken from [43].  
Figure 11: Outlet temperature in charging phase for distinct types of rock.
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
###### Abstract  
In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV's moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.  
path planning: pure pursuit controller; trajectory tracking; deep learning; robot kidnapping detection
# Investigation of Smart Machines with DNAs in SpiderNet
## 3 Methodology and Material
### The Network Structure & DNA
It is important to note, though, that facial recognition technology does not perform flawlessly; research has shown it may struggle with individuals of similar appearance, such as those of the same ethnicity or twins, occasionally resulting in misidentification [18]. Another reliable physiological biometric is the iris scan, as the iris tissue is stable and resistant to change; the scan can be conducted via a camera without the individual needing to be in close proximity [19]. However, iris recognition can be unreliable if part of the iris is obscured by the eyelids [18]. Retina scans, though less researched, use unique blood vessel patterns behind the iris for identification, but require close-up scanning for SpiderNet authentication. Behavioural biometrics, though more susceptible to change over time, remain valuable identifiers. Characteristics like a user's signature, keystroke dynamics, touchscreen usage, device and mouse movements, and voice patterns are dependent on the specific device and require monitoring. Voice recognition, though valuable, presents challenges as voices can change over time, and factors such as accents, speech pace, background noise, or even a cold can impact accuracy [18]. The investigator's location is also vital, as it ensures users are situated in an authorised lab or designated premises. Although both physiological and behavioural biometrics have unique advantages and limitations, SpiderNet recommends using a combination, with some biometrics remaining active throughout an investigation. This integrated approach helps ensure a secure and reliable investigative process. User DNA maybe required for both investigators and device users. For investigators, each step of the investigation process may be authenticated with \(u\)DNA. Jurisdictions may dictate the number of required authentication methods for investigators logging into SpiderNet, whether it involves a single biometric, a combination, or multiple types. Some behavioural biometrics may remain active throughout the acquisition of the evidence for an investigation. Should any discrepancies occur, the investigator would either be logged out or required to verify their identity with an alternate biometric if the behavioural biometrics do not match those initially registered. Thus, regular updates of these biometrics are essential. For device users, if DNA cloning or misuse is detected, SpiderNet enforces an additional layer of authentication through the use of \(u\)DNA. In scenarios where the device itself--such as a car or a phone--is stolen and misused by criminals, SpiderNet employs anomaly detection mechanisms. These anomalies might include unusual locations or deviations from typical user behaviour. Upon identifying such irregularities, the country server triggers a request for biometric authentication to verify the legitimate user and prevent unauthorized access.
# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 3 Materials and Methods
### Spatial Prior Contrast Adapter
#### 3.4.2 Lung Segmentation by MedSAM  
We employed MedSAM to perform automatic segmentation of lung regions in chest X-ray images. The process began by uploading the images to the MedSAM platform, where the lung fields were manually designated as regions of interest. MedSAM then utilized advanced deep learning algorithms in conjunction with image-processing techniques to automatically segment the lungs, effectively isolating them from adjacent anatomical structures such as the ribs and heart. To improve segmentation accuracy, users were able to refine the output by adjusting parameters or manually correcting boundary discrepancies. This semi-automated approach enabled precise delineation of lung regions, which is essential for identifying subtle abnormalities such as infections, tumors, or other pulmonary conditions [28].  
As shown in Figure 5, MedSAM provided accurate and well-defined lung segmentation, thereby enhancing the detection and evaluation of lung-related pathologies and supporting radiologists in making more reliable and efficient diagnostic decisions based on chest X-rays.  
Figure 5: A sample of contrast and lung segmentation by MedSAM demonstrates original chest X-ray radiograph (**a**), contrast-enhanced chest X-ray radiograph (**b**), and lung segmentation result (**c**).
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 4 Experiments and Results
### Subjective Test Results  
We also conducted a listening test to compare the subjective performance of the proposed algorithm with the conventional scheme. For that, 18 listeners participated and were presented with 42 randomly selected sentences corrupted by the 14 different noises in the SNR values of \(-3\), 0, and 3 dB. In the test, each listener was provided with speech samples enhanced by the \(snT\) model and \(snDT\) model. Listeners could listen to each enhanced speech as many times as they wanted, and were asked to choose the preferred one from each pair of speech samples in terms of perceptual speech quality. If the quality of the two samples was indistinguishable, listeners could select no preference. Two samples in each pair were given in arbitrary order.  
The results are shown in Figure 5. It can be seen that the quality of the speech enhanced by the proposed model was better than the conventional model in all SNR values. With respect to the averaged results, the \(snDT\) model was preferred to the \(snT\) model in 52.78% of the cases, while the opposite preference was 8.20% (no preference in 39.02% of the cases). These results imply that the proposed algorithm enhances not only the objective measures but also the perceived quality.
# Detecting DDoS Attacks in IoT-Based Networks Using Matrix Profile
## 4 Findings and Discussion
### Empirical Findings  
This section presents the results of the empirical experiment. First, we illustrate the results with the MC that consumes streams of video to transform regular TVs. Figure 12 shows the CPU utilisation and matrix profile. As can be seen, the matrix profile was able to detect the start and end of the anomaly in CPU utilisation. The outcome of the matrix profile specified that the attack started at 11:30:32 AM and ended at 11:43:10 AM.  
DDoS attacks cause a fluctuation in voltage usage in some IoT devices such as SC. Therefore, this voltage usage fluctuation can be a symptom of a DDoS attack. This experiment investigated using this feature to detect DDoS attacks. Figure 13 demonstrates the voltage reading and calculated matrix profile. As can be seen, matrix profile managed to determine the anomaly in voltage usage caused by the DDoS attack on the SC.  
Figure 14 depicts memory utilisation and its matrix profile. Memory utilisation is another feature that can be used to detect DDoS attacks that use compromised SCs with additional traffic. The results show that this feature can also be used to detect DDoS attacks. Note that by looking at the top chart only, it is very difficult to identify the anomaly in the data, whereas by looking at its matrix profile, the anomaly (DDoS) is very clear.  
Figure 12: CPU utilisation of Multimedia Centre.  
Figure 13: Voltage usage of Surveillance Camera.
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs
### IPT Batteries Charging in EVs in SCs
#### 5.2.4 EMF Exposure, Charging Modes, and Protection  
The EMF exposure due to ICT stray fields is directly dependent on the 3D relative positions of its two coils (ground to vehicle sides) and, hence, is strongly affected by the charging mode (static or dynamic) and the vehicle space positioning.  
The consequences of such fields on living tissues are concomitant to their location in relation to the ICT position and the relative placing of its coils. The stray fields in dynamic charging mode are variable depending on the moving location of the receiver coil fixed at the EV bottom relative to the transmitter fixed on the ground. In this case, as well as in the case of buses with distant static charging points, the living tissues concerned are those inside the EV in the passenger compartment. In such situations, the protection of passengers could be achieved by shielding the vehicle's upper part, over ICT, including the passenger compartment. This is an easy task using appropriate shapes and material shields [5; 63]. The passenger compartment in the case of static charging mode is normally empty, while for such a mode, the involved living tissues are those located outside the EV near the ICT under its bottom. These involve humans, animals, or plants, and could be affected by stray-field exposure due to open-space static charging. Such a mode is regularly exercised at home for personal EVs and at distant static points related to electric busescharging at their stops. In these circumstances, the ICT shielding (in 3D geometry) is very problematic due to its configuration and position, partly on the bottom of the EV and in the ground. This problem is more difficult for larger or more twisted air gaps corresponding, respectively, to the distances \"\(\alpha\)\" or \"sh\" in Figure 5c. As such, these parameters are difficult to control, and therefore, any element existing below or near the bottom of the EV must be avoided, especially for a significant duration. Thus, for the home-charging case, this should be practiced only in enclosed places or encircled areas in the open space. For the bus stop-charging case, the ICT should be located far from the bus entrance and the passenger waiting area.