esa-sceva/satcom-chunk-collection huggingface dataset:
# Cleaning of Phototrophic Biofilms in a Show Cave: The Case of Tesoro Cave, Spain
## 3 Results and Discussion
### Composition of biofilms in Different Cave Sectors
#### 3.1.3 Sector T3  
In sector T3, Sala del Aguila, the biofilms developed significantly. Phototrophic colonization adopts different morphologies in the distribution on the mineral substratum, from circular colonies to a homogeneous biofilm covering the rock surface.  
The study of the cultured sample in the laboratory from Sala del Aguila biofilms showed cells of _Synechocystis_ sp., _Diadesmis contenta_, _Phormidium_ sp., _Chroococcidis_ sp., _Pseudococcomyxa simplex_ and _M. sraelensis_, as well as the bryophyte _E. verticillatum_.  
In the samples from the circular colonies, a whitish mass formed by organisms that retained color, as well as many empty sheaths and dead organisms were revealed under the light microscope. This may be due to the alternation of dry and wet seasons.  
We did not study fungi, because given the amount of bacteria and cyanobacteria in the biofilms, which usually produce bioactive (antifungal) substances, and in light of previous studies in other caves, it does not seem that fungi can attain great relevance in these phototrophic communities, as reported for Nerja Cave [15].  
In the gallery accessing to Santuario de Noctiluca, the biofilms were also abundant. Under the light microscope, _M. sraelensis_ and _Phormidium_ sp. were evidenced. The scanning electron microscope study showed a dense web of filaments of both cyanobacteria and associated filamentous bacteria.  
The phototrophic organisms found in Tesoro Cave have been reported in many other European caves [14; 37; 41; 42; 43; 44; 45; 46; 47; 48; 49].
## 3 Results
The first column in Figure 2 presents LOO configuration, the survival prediction of sample LGGs patient's dataset (third row) yielded with leave-one-out cross-validation for rad-path images producing PCC of \(\big{(}R=0.83,\ p=2.2\times 10^{-16}\big{)}\), followed by pathology (path) images with \(\big{(}R=0.76,\ p=2.2\times 10^{-16}\big{)}\) and radiology (rad) images \(\big{(}R=0.72,\ p=2.2\times 10^{-16}\big{)}\). The survival prediction sample HGGs patient's dataset (second row) yielded with leave-one-out cross-validation for rad-path images producing PCC of \(\big{(}R=0.78,\ p=4.6\times 10^{-16}\big{)}\), followed by pathology (path) images with \(\big{(}R=0.73,\ p=8.3\times 10^{-14}\big{)}\) and radiology (rad.) images \(\big{(}R=0.70,\ p=1.8\times 10^{-12}\big{)}\). The survival prediction of sample the LGGs and HGGs patients combined dataset (first row) yielded with leave-one-out cross-validation for rad-path images producing PCC of \(\big{(}R=0.84,\ p=2.2\times 10^{-16}\big{)}\), followed by pathology (path) images with \(\big{(}R=0.73,\ p=2.2\times 10^{-16}\big{)}\) and radiology (rad) images \(\big{(}R=0.72,\ p=2.2\times 10^{-16}\big{)}\). Figure 2: Survival predictions based on different validation methods. Left to right columns: LOO (configuration-I), 5 fold CV (configuration-II), split-train-test (configuration-III). Top to bottom rows: HGGs + LGGs, HGGs, and LGGs.
# Delving Deep into Reverse Engineering of UEFI Firmwares via Human Interface Infrastructure
## 2 Background
### Nvram  
Modern memory hardware is either fast but volatile (DRAM) or persistent but slow (SSD/HDD). As a result, to prevent data loss, applications expend significant effort to persist runtime information onto non-volatile storage mediums. In this process, data structures in main memory often undergo costly transformations to reflect the distinct access characteristics of block storage devices and better utilize limited bandwidth. Non-Volatile Random Access Memory (NVRAM) is the latest advancement in memory technology, representing a unique opportunity to simplify the persistence we know. Unlike DRAM, NVRAM cells do not require periodic refreshes to maintain their state, allowing them to retain content even when power is lost. Performance analysis currently places NVRAM between DRAM and NAND-flash, but with ongoing research and development, its performance is expected to eventually meet or even surpass DRAM. By allocating data structures on NVRAM instead of DRAM, the application state can ultimately be persisted in-place without sacrificing speed or increasing software complexity [20].  
UEFI provides booting and system management, relying on data stored in UEFI NVRAM. The UEFI environment itself may be susceptible to malware attacks. Moreover, the existence of UEFI NVRAM introduces certain security concerns, such as UEFI NVRAM often being bypassed by security software and computer forensics frequently overlooking the vast data stored in UEFI NVRAM [21]. Attackers could exploit the UEFI NVRAM storage space to stash illicitly obtained data (like credit card details) on the victim's/attacker's computer between the communication lines, without any traditional high-capacity storage access. Another example is data exfiltration in malware scenarios, which can be used to store encryption keys in use, or even for leaking personal, private data, leading to privacy issues. The data would only appear as highly volatile data in memory and network data streams, making it hard to capture for IT forensic purposes unless strategically prepared in advance. Vulnerabilities in NVRAM also pose threats to UEFI itself. Given the rich configuration capabilities of UEFI, attackers might attempt to exploit any vulnerabilities in NVRAM to alter configurations [22]. Unauthorized configuration changes could expose the system to additional risks or allow attackers to bypass security controls.
###### Abstract  
In recent years, Convolutional Neural Network (CNN) has become an attractive method to recognize and localize plant species in unstructured agricultural environments. However, developed systems suffer from unoptimized combinations of the CNN model, computer hardware, camera configuration, and travel velocity to prevent missed detections. Missed detection occurs if the camera does not capture a plant due to slow inferencing speed or fast travel velocity. Furthermore, modularity was less focused on Machine Vision System (MVS) development. However, having a modular MVS can reduce the effort in development as it will allow scalability and reusability. This study proposes the derived parameter, called overlapping rate (\(r_{o}\)), or the ratio of the camera field of view (\(S\)) and inferencing speed (\(fps\)) to the travel velocity (\(\overrightarrow{v}\)) to theoretically predict the plant detection rate (\(r_{d}\)) of an MVS and aid in developing a CNN-based vision module. Using performance from existing MVS, the values of \(r_{o}\) at different combinations of inferencing speeds (2.4 to 22 \(fps\)) and travel velocity (0.1 to 2.5 m/s) at 0.5 m field of view were calculated. The results showed that missed detections occurred when \(r_{o}\) was less than 1. Comparing the theoretical detection rate (\(r_{d,th}\)) to the simulated detection rate (\(r_{d,sim}\)) showed that \(r_{d,th}\) had a 20% margin of error in predicting plant detection rate at very low travel distances (<1 m), but there was no margin of error when travel distance was sufficient to complete a detection pattern cycle (\(\geq\)10 m). The simulation results also showed that increasing \(S\) or having multiple vision modules reduced missed detection by increasing the allowable \(\overrightarrow{v}_{max}\). This number of needed vision modules was equal to rounding up the inverse of \(r_{o}\). Finally, a vision module that utilized SSD MobileNetV1 with an average effective inferencing speed of 16 \(fps\) was simulated, developed, and tested. Results showed that the \(r_{d,th}\) and \(r_{d,sim}\) had no margin of error in predicting \(r_{actual}\) of the vision module at the tested travel velocities (0.1 to 0.3 m/s). Thus, the results of this study showed that \(r_{o}\) can be used to predict \(r_{d}\) and optimize the design of a CNN-based vision-equipped robot for plant detections in agricultural field operations with no margin of error at sufficient travel distance.  
modeling: simulation; precision agriculture; convolutional neural networks; machine vision; computer vision; modular robot Article
## 2 Configuration and Suspension Principle  
The proposed novel AC six-pole heteropolar HMB includes an inner stator core, outer stator core, permanent magnets, control coils, and rotor. Figure 1 shows the three-dimensional structure and magnetic circuit diagrams. Six magnetic poles denote as A, a, B, b, C, and c, are evenly distributed on the outer stator. Three magnetic poles, a, b and c, extend inward and are connected with cores X, Y and Z by six magnets to form a circular inner stator core. The inner stator core is divided into six poles by six magnets. As a result, there are an annular inner air gap and three outer air gaps. Ignoring the leakage flux, the equivalent magnetic circuit can be obtained. The blue lines denote the control fluxes, and the red lines describe the bias fluxes. It can be seen that one part of the bias fluxes generated by the six magnets passes through the outer air gap, the other parts pass through the inner air gap, and form two closed paths. The coils \(W_{1}\) and \(W_{4}\), \(W_{2}\) and \(W_{5}\), and \(W_{3}\) and \(W_{6}\) of poles \(A\) and \(b\), \(B\) and \(c\), and \(C\) and \(a\) are respectively connected in series as \(W_{1,4}\), \(W_{2,5}\), and \(W_{3,6}\). Then, Y-type connection is conducted to get three-phase symmetrical control windings. One three-phase inverter is adopted to electrify the three-phase windings. It is found that the control fluxes generated by coils \(W_{1}\), \(W_{3}\), and \(W_{5}\) need to pass through the outer and inner air gaps. However, the control fluxes generated by coils \(W_{2}\), \(W_{4}\), and \(W_{6}\) only need to pass through the inner air gaps. Assuming that the rotor has the displacement to the pole \(b\), the three-phase symmetrical currents \(i_{\mathrm{w}1,A}=-2i_{\mathrm{w}2,5}=-2i_{\mathrm{w}3,6}\) flow into three-phase windings. As a result, the air gap flux density increases under poles \(A\), \(a\), and \(c\) while decreasing under poles \(b\), \(B\), and \(C\). Thus, the suspension force pointing to pole \(A\) will pull the rotor back to the balance position.
## 4 Results and Discussions
### Results of DESGM  
The proposed DESGM model integrates the decision outputs of various classification methods. Each of the sub-models makes an equal contribution to the ultimate aggregated prediction. The proposed model yielded the best results, according to the evaluation of the output of this customized ensemble classifier with that of all the base classifiers.  
A common practice among stacking classifiers is to employ logistic regression (LR) as the meta-classifier. Furthermore, the testing performance of each model, as shown by the F1 score, precision, recall, and accuracy, is provided in Figure 4 and presents a comparative analysis of the proposed model alongside two other ensemble models, focusing on metrics such as accuracy and other relevant testing measures. Compared to the conventional ensemble model, the proposed model demonstrated improved accuracy by 0.026 and F1 score by 0.02. When comparing the deep learning model to the proposed DESGM, it is seen that the ensemble model demonstrates outstanding precision and other notable achievements with a margin of 0.06. The DESGM performed excellently compared to the individual base classifiers and different ensemble classifiers, as seen in (Figure 4) with its high accuracy of 0.9817 and F1 score of 0.9904. Therefore, it can be inferred that the stacking ensemble classifier (Table 5) exhibits a substantial performance improvement compared to other classifiers when applied to the NHANES dataset.  
In Figure 5a, the area under the curve (AUC) is the best way to measure how accurate a depression diagnosis is. The receiver operating characteristic (ROC) graph displays that the ensemble voting AUC increases a little when the cut-off values are changed. This, in turn, changes the number of patients who are identified as having the disease. Even though XGB boosting makes a considerable difference in the number of true positives, it still does not reach an AUC of 1.0. On the other hand, the proposed method DESGM obtains a true positive rate of 1, which means that no cases of depression are mistakenly identified, as shown in Figure 6. To provide a comprehensive evaluation of the model's performance, we included metrics that are sensitive to class imbalance, such as the F1 score, precision, recall, and the area under the ROC curve. These metrics offer a balanced perspective on how well the model performs on both classes, beyond the overall accuracy.  
Figure 4: DESGM: performance analysis on each feature subset.  
Figure 5: **(a,b)**: Comparison of all classifiers (ML and ensemble) in the overall dataset through accuracy and AUC.
## 5 Existing Cybersecurity Solutions for Smart Manufacturing
### Cybersecurity Countermeasures
#### 5.1.2 Intrusion Detection Systems  
Vulnerabilities are inevitable regardless of the layers of defense in the system. To avoid this, it is necessary to set a standard behavior of the system and detect any abnormality. IDS are classified into two categories, host-based or network-based and signature-based or anomaly-based. In the cases of smart manufacturing, host-based IDS implementation is quite impractical, as the IoT devices are incapable of hosting an IDS mechanism within the equipment, because of their compute-intensive nature. Whereas a network-based intrusion detection system, being a separate standalone device can capture the evidence from the whole segment of the network in which it is placed.  
Signature-based IDS first creates a standard, based on the knowledge of previous attacks or known vulnerabilities and then compares the traffic activities to identify an intrusion. In the case of anomaly-based detection techniques, the IDS looks into the network traffic for any deviation from the standard set of expected system behavior. In the case of the manufacturing industry, setting a standard behavior is convenient by modeling the system operation which does not change very often. In the scenarios where the equipment transactions are modified by the supply chain, there can be a machine learning technique implemented for the anomaly-based IDS.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 4 OFDMA in Wi-Fi 6
### Advances on the PHY Layer  
We first explain how Resource Units (RUs) are used in Wi-Fi 6 to make spectrum usage more flexible, and how a longer symbol duration in Wi-Fi 6 improves efficiency. Finally, we discuss the new frame format.
## 5 Simulation and Results
### Analysis of Indicator Simulation Results
#### 5.3.2 Average Cluster Head Duration Under Interference  
The average cluster head duration is an important indicator for measuring the performance of cluster head selection algorithms. The longer the cluster head duration, the higher the stability of the cluster head, which helps to improve the reliability, resource allocation efficiency, and task coordination ability of the network. A longer cluster head duration can reduce the communication burden and energy consumption caused by frequent replacement of cluster heads, especially in interference environments, where the stability  
Figure 14: The ratio of the maximum and minimum number of nodes within the cluster.  
Figure 13: The coefficient of variation of nodes within the cluster.  
of cluster heads has a more significant impact on the overall performance of the system. As the level of interference increases, frequent changes in cluster heads can easily lead to network instability, which in turn affects task scheduling and data transmission. Therefore, comparing the average cluster head duration under different interference levels reveals the stability and interference tolerance of each algorithm.  
We compared the average cluster head duration of CWCA, K-means++, WCA, RS, and IWCA algorithms in an environment with increasing simulated interference level in Figure 15. The number of nodes is set to 200, divided into 12 clusters. The simulation is repeated 20 times at each interference intensity to obtain the average simulation results. The results showed that the improved CWCA algorithm with external interference factors exhibited longer cluster head duration under various levels of interference conditions, significantly better than other algorithms, especially in high interference environments, indicating that the selected cluster heads were more stable and had stronger interference tolerance. Compared with the other algorithms, K-means++ relies on global spatial distribution to select initial cluster centers. Although it offers certain advantages in clustering efficiency, it lacks adaptability to node mobility and energy fluctuations, resulting in frequent changes of cluster heads. Consequently, its average cluster head duration remains relatively low among all evaluated algorithms and outperforms only the RS method. In contrast, the WCA algorithm incorporates multiple weighted factors such as node degree, residual energy, and mobility, which enhances its responsiveness to network state variations. This leads to a significantly longer cluster head duration compared with K-means++. Furthermore, the hybrid algorithm IWCA retains the spatial initialization advantage of K-means++ while integrating the multi-factor selection strategy of WCA, thereby improving the rationality and stability of cluster head selection. As a result, IWCA demonstrates superior overall performance among the four baseline algorithms and is second only to the proposed CWCA.
## 5 Discussion
### Endoscopic Retrograde Cholangiopancreatography (ERCP) and EUS  
ERCP is another diagnostic option and is often considered the gold standard. ERCP has long been considered the gold standard for diagnosing pancreas divisum, as it provides both diagnostic and therapeutic capabilities by allowing direct contrast injection into the pancreatic ducts and potential minor papilla intervention. However, its invasive nature is a major drawback, with post-ERCP pancreatitis being a significant risk, particularly in patients with pancreas divisum due to increased ductal pressure and altered drainage. Additionally, ERCP is limited in cases where minor papilla cannulation is challenging. EUS, on the other hand, is a less invasive modality that offers high-resolution imaging of the pancreatic ductal system without the risks associated with ERCP. Advances in EUS technology, including contrast-enhanced EUS and elastography, have further improved the ability to differentiate normal and pathological pancreatic ductal anatomy. Moreover, EUS-guided pancreatography can complement standard imaging, and in select cases, even guide therapeutic interventions such as EUS-guided minor papilla drainage when ERCP is not feasible.
## 3 Results and Discussion  
### Model Horizontal Resolution  
The terrain height from the NDTM inside the LES domain is shown in plot a) of Figure 4, where the red circle marks the location of the meteorological mast. Although a large part of the domain consists of ocean, the terrain also includes mountains to the west and east of the meteorological mast which have steep inclines as seen by the dense contour lines at 100-metre height differences.  
Plots (b), (c) and (d) in Figure 4 show the model terrain height for the same location from D03, D04 and D05, respectively. The \(3\times 3\) km resolution in plot (b) is clearly visible in the D03 terrain model, and the large grid smooths the terrain so that the mountains are lowered to 450 masl from the original 950 masl. Increasing the resolution to \(1\times 1\) km in the model, shown in plot (c), increases the height and steepness of the mountains, but they still have a rounded shape. The LES is run with the terrain shown in plot (d), where the mountain shape is more distinct with a steeper increase going up to 900 masl. Note that the GMTED 30-arcsecond resolution corresponds to approximately 900 m horizontal resolution, and that it is important to ensure that the terrain input resolution is equal to or better than the model resolutions. A LES model with GMTED input will result in terrain heights close to the plot in (c), and similarly for a mesoscale model with a 1 km resolution based on NDTM input.
## 5 Data Processing and Results
### The Coordination Degree Between Technological Innovation and Financial Development  
Figure 19 shows the coordination degrees between the technological innovation and the financial development subsystems calculated based on our dataset \(O\) for all 30 regions in 2002, 2012, and 2023. As can be seen, all regions have been improving their coordination degrees over the 21 years. The patterns of the coordination degrees for the regions are roughly aligned with expectation. In 2002, Beijing, Shanghai, and Guangdong have the highest coordination degrees and are higher than the remaining regions by a big margin. In 2012, Jiangsu and Zhejiang were catching up with Shanghai. In 2023, Jiangsu already surpassed Shanghai.  
Figure 20 shows the evolution of the coupling degrees for Beijing and Qinghai over the years 2002-2023. Both Beijing and Qinghai had a steady increase in their coordination degree over the years. Beijing increased from slightly above 0.5 to close to 0.8. Qinghai increased from under 0.2 to close to 0.35.  
Figure 19: The coordination degree between the two subsystems for all 30 regions in China in 2002, 2012, and 2023 calculated using our dataset \(O\).  
Figure 20: The coordination degree between the two subsystems for Beijing and Qinghai over the years 2002–2023 calculated using our dataset \(O\).  
Figure 21 shows the coordination degrees for the 30 regions in 2002, 2012, and 2023 using the reference dataset \(R\). Although the overall patterns of the coordination degrees contain no apparent issue, the increase in the coordination degrees over the years is less prominent than that predicted using our set of predictors.  
As shown in Figure 22, the evolution over the years for Beijing and Qinghai revealed more issues. First, for Beijing, other than a significant jump in coordination degree from 2006 to 2007, the coordination degrees are fairly stagnant, i.e., there were less than 0.1 increases from 2008 to 2023. This does not appear to match the rapid technological and financial development during this time period. Second, for Qinghai, the coordination degrees drop in three periods: (1) from 2002 to 2006, (2) from 2009 to 2011), and (3) from 2020 to 2023. There does not appear to be any reason for the drop in the first two periods. Perhaps the third period may be attributed to the pandemic. Again, the issues could be due to the set of predictors failing to capture the actual development level of each subsystem.  
Figure 21: The coordination degree between the two subsystems for all 30 regions in China in 2002, 2012, and 2023 calculated using the reference dataset \(R\).  
Figure 22: The coordination degree between the two subsystems for Beijing and Qinghai over the years 2002–2023 calculated using the reference dataset \(R\).