esa-sceva/satcom-chunk-collection huggingface dataset:
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 3 Test Setup
### Models
#### 3.3.1 Link Model
Because this vector exists within a field that is approximately constant for the neighborhood of the manipulator, the direction of the vector in the magnetometer can be considered the same as in the joint; that is \(m_{m_{i}}={}^{i}m\), where \(m_{m_{i}}\) is the magnetism vector in magnetometer \(i\) and \({}^{i}m\) is the magnetism vector in joint \(i\). Given the magnetism vector in frame \(n\) (\({}^{n}m\)), which should be constant in time and only dependent on the manipulator's location on the Earth, the relation between the orientation of the link and the measurement is:  
\[m_{m_{i}}(k)={}^{i}_{n}R^{T}(k)\,^{n}m+\epsilon_{m_{i}}(k) \tag{35}\]  
where \(\epsilon_{m_{i}}\) is the magnetometer measurement noise. The previous equation can be linearized:  
\[m_{m_{i}}(k)={}^{i}_{n}\dot{R}^{T}(k)\,^{n}m+[{}^{i}_{n}\dot{R}^{T}(k)\,^{n}m \times]\eta_{i}(k)+\epsilon_{m_{i}}(k) \tag{36}\]  
_Accelerometer Model_  
Considering a negligible acceleration when compared to the acceleration due to gravity, the accelerometer model is very similar to the magnetometer one. In fact, what is being measured is a rotated constant vector (\(-\,^{n}g\)). The measured specific force is given by:  
\[f_{a_{i}}(k)=-\,^{i}_{n}R^{T}(k)\,^{n}g+\epsilon_{a_{i}}(k) \tag{37}\]  
with \(\epsilon_{a_{i}}\) the accelerometer noise. Clearly, the condition that the IMU acceleration is negligible is false whenever the manipulator is moving. However, the variance of the accelerometers is larger when compared to the gyroscopes. This will have the effect, when applying the MEKF, of being mostly ignored when there is motion. This assumption was also followed in [1], where good joint angle estimates were still obtained despite it. The similarity with the magnetometer model makes it simple to find the linear model of the accelerometer:  
\[f_{a_{i}}(k)=-\,^{i}_{n}\dot{R}^{T}(k)\,^{n}g-[{}^{i}_{n}\dot{R}^{T}(k)\,^{n}g \times]\eta_{i}(k)+\epsilon_{a_{i}}(k) \tag{38}\]  
_Encoder Models_  
Incremental encoders measure the number of pulses produced in a time interval, which, given the resolution of the encoder, can be translated into the angular velocity of a joint. Therefore, their model is:  
\[\omega_{i_{i}}(k)=\left(\omega_{i}(k)-\,^{i}_{n}R^{T}(k)\,^{i-1}_{n}R(k) \omega_{i-1}(k)\right)_{z}+\epsilon_{i_{z}}(k) \tag{39}\]  
However, it can be expanded to describe the lack of angular velocity in the other axes (imposed by the joint):  
\[ \begin{bmatrix}0\\ 0\\ \omega_{i_{z}}(k)\end{bmatrix} =\omega_{i}(k)-\,^{i}_{n}R^{T}(k)\,^{i-1}_{n}R(k) \omega_{i-1}(k)+ \begin{bmatrix}\epsilon_{\omega_{Si},x}(k)\\ \epsilon_{\omega_{Si},y}(k)\\ \epsilon_{i_{z}}(k)\end{bmatrix}  \tag{40}\]where \(\epsilon_{\alpha s_{i}}\) is the error in angular velocity due to slack between the joints that allows them to slightly rotate along other directions.
## 2 Models and Methods  
### Experimental Setup and Procedure  
The DRIVE laboratory's test bench (see Figure 1) is equipped with the Renault D4F port fuel injection (PFI) engine (2000-2014). It allows measuring the exhaust gas temperature at the engine outlet with a type K thermocouple. A Fourier Transform Infrared spectrometer (FTIR) collects gas samples before the catalyst to measure the concentrations of carbon monoxide (CO), unburned hydrocarbons (HC) and nitric oxide (NO) at the engine outlet. A balance of consumption deduces the fuel mass flow rate used to feed the engine. The control parameters close to the engine (spark timing, air/fuel ratio of the mixture and the position of the throttle valve controlling the intake pressure) are adjustable by means of the open engine control unit (ECU).  
The measurements were performed over a wide range of operating conditions as described in Table 1. The minimum spark advance value (i.e., spark retard compared to optimal which optimizes engine torque) corresponds to a temperature of 1000 K which should not be exceeded to avoid damage to the systems. The maximum value of spark advance is 25 degrees before the optimum advance. Depending on the chosen intake pressure (which determines the maximum pressure and temperature in the cylinder), some advances cannot be achieved because they may cause knocking. This phenomenon corresponds to a spontaneous-ignition of a part of the end-gas ahead of the flame. This ignition rapidly releases a large energy and causes high-frequency pressure oscillations which may damage the engine [5]. Almost 500 points were recorded in this campaign.  
Figure 1: DRIVE engine test bench layout.  
Each operating point was stabilized during at least one minute before measuring. The data presented in this paper are available in the supplementary materials (see [[https://www.mdpi.com/2076-3417/11/3/971/s1](https://www.mdpi.com/2076-3417/11/3/971/s1)]([https://www.mdpi.com/2076-3417/11/3/971/s1](https://www.mdpi.com/2076-3417/11/3/971/s1))).
## 2 Related Works
### Fog-Cloud Computing for Enhanced Library Systems  
Efficient IoT integration in libraries requires robust computing infrastructure for real-time data processing. Fog computing enhances performance by providing localized processing and storage at the network edge. This model acts as an intermediary between cloud and edge layers, optimizing network efficiency and reducing cloud dependency [40].  
Fog computing plays a crucial role in IoT applications such as smart cities, industrial automation, and library management by processing data closer to the source. It reduces latency and enhances real-time decision-making, ultimately improving the efficiency of smart library systems [41]. Given the increasing number of IoT devices, cloud-based data transmission can create network bottlenecks. Fog computing addresses this challenge by utilizing intelligent gateways at the network edge, optimizing resource allocation and improving overall system performance [42].  
Recent research highlights various applications of fog-cloud computing in enhancing system performance across multiple domains. For instance, air quality monitoring and forecasting systems benefit from fog computing by integrating the IoT, LPWANs, and deep learning models for real-time environmental analysis. The fog-enabled Air Quality Monitoring and Prediction (FAQMP) system utilizes Smart Fog Environmental Gateways (SFEGs) for localized deep learning processing, ensuring real-time predictions while optimizing resource usage [43].  
Similarly, low-cost and automated monitoring systems employing edge AI computing have demonstrated effectiveness in the real-time observation of environmental dynamics. Such systems can be leveraged for library environments to monitor air quality, user occupancy, and energy consumption patterns, contributing to sustainable smart library ecosystems [44].  
Furthermore, the convergence of cloud computing, fog computing, machine learning, and the IoT has opened new possibilities for intelligent data processing. Fog computing alleviates network burdens by enabling resource-intensive computations closer to the data source. Machine learning techniques, including object and text detection models, can be deployed within fog nodes to improve information retrieval and automation in library management systems [45].  
Another emerging challenge is ensuring seamless data migration and synchronization between cloud and fog environments. Existing frameworks, such as FETCH, integrate deep learning and automated monitoring to enhance system compatibility, particularly in health monitoring applications. Applying similar frameworks in library systems could facilitate efficient data handling, reducing latency and improving service reliability through optimized fog-cloud interactions [46].  
By incorporating these technological advancements, this study proposes a novel framework for integrating the IoT and AI in university libraries, aiming to enhance environmental conditions and optimize user experiences.
###### Abstract  
(1) The aim was to evaluate the predictability of treatment outcomes using clear aligner therapy (CAT) and ClinCheck Web 1.4 (Align Technology, Inc., San Jose, CA, USA) software in mandibular incisor intrusion in both children and adults with deep bite malocclusion. (2) This study included healthy children and adults with skeletal Class I or mild to moderate Class II/III malocclusions, mild to moderate dental crowding (<5 mm), and deep overbite (OB) who underwent CAT. Pre-treatment (T1) and post-treatment (T2) orthodontic treatment records were assessed and compared to initial planned ClinCheck movements. The cephalometric parameters evaluated included bodily intrusion (Centroid-C point), IMPA, L1-NB, and L1-A-Po. Clinical expression of CAT was compared to ClinCheck predictions using paired sample \(t\)-tests, and differences between growing and non-growing groups were assessed using a repeated measures ANOVA with Tukey post hoc analysis. (3) The sample included 48 patients (mean age 19.79 \(\pm\) 11.78 years), including 18 adults (mean age 30.28 \(\pm\) 13.79 years) and 30 children (mean age 13.5 \(\pm\) 2.05 years). The predicted vertical mandibular incisor movement (intrusion) was significantly higher using ClinCheck (2.32 mm) compared to clinical treatment (0.22 mm). The angular movements of IMPA, L1-NB, and L1-A-Po were all significantly higher using ClinCheck (4.6\({}^{\circ}\)) compared to clinical expression of 0.79\({}^{\circ}\), 0.55\({}^{\circ}\), and 1.21\({}^{\circ}\), respectively. There were no significant differences between children and adults with respect to vertical or angular tooth movements. (4) CAT and ClinCheck software significantly overpredicts orthodontic tooth movements related to mandibular incisor intrusion in both adults and children with no statistical difference between the groups.  
cephalometric analysis; clear aligners; intrusion; orthodontic treatment Article
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 10 Biofilm Formation of Gram-Negative Organisms in Diabetic Foot Ulcer
### Mechanism of Biofilm Formation in Klebsiella pneumoniae  
_Klebsiella pneumoniae_ shares similarities with _Escherichia coli in_ that it cannot produce autoinducer 1 (AI-1) molecules. Instead, it possesses the _sdiA_ gene, which produces the SdiA protein, a homolog of the LuxR protein that senses AI-1 molecules from other bacteria [109]. AI-2 is produced through a LuxS synthase-dependent mechanism, as observed in _E. coli_[110]. This system is crucial for biofilm formation and lipopolysaccharide-LPS generation in _K. pneumoniae_. The SdiA protein also represses fimbiae production, thereby reducing biofilm formation. Specifically, the genes _sdiA_, _fimK_, and _kpfR_ promote type 1 fimbiae formation; _fimK_ reduces cyclic di-GMP levels, and lower cyclic di-GMP concentrations decrease type 3 fimbiae formation, ultimately reducing biofilm formation [111; 112]. The _mrkH_ and _mrkl_ genes are essential for inducing biofilm formation, as they encode type 3 fimbiae [113]. The _wbbM_ gene is responsible for LPS formation, the _wzm_ gene is involved in transport, and the _wcaG_ gene contributes to the cell surface properties [114; 115]. MrkH and MrkI can actively function in the presence of cyclic di-GMP due to their cyclic di-GMP binding domains. In this context, MrkH activates MrkA, which induces type 3 fimbiae formation [116]. The IcsR protein acts as a repressor within this system [113; 117].
## 5 Illustrative Example
### Scenario 2: Fetch  
During the assembly process, the worker occasionally needs additional tools or components that are not immediately at hand. The cobot assists by fetching these items from storage locations. The system stores in its memory all the standard tool locations for the various storing devices (such as racks, bins, etc.). Thus, the worker does not need to specify the location of the tool and the empty spot on the table. The following steps describe a fetch scenario example.  
1. Initial Setup: Dedicated spaces for frequently used tools are identified and labeled on the workstation map. The cobot's control system stores the coordinates for these storage locations.  
Figure 2: The tool, component, and waste bin locations for the illustrative example.  
2. Task Execution: - Worker: \"Cobot, fetch the soldering iron from Tool Rack B\". (Figure 2). The cobot has the soldering iron coordinates stored in its memory. - Cobot: \"Fetching soldering iron from Tool Rack B\". - The cobot moves to Tool Rack B, identifies the soldering iron, and picks it from Rack B. - The cobot identifies an empty space on the table near to the worker and moves to place it on that space. - Cobot: \"Soldering iron delivered\". 3. Follow-up: The worker uses the soldering iron to solder components onto the board, instructing the cobot to fetch other tools as needed.
## 2 Background
### Ensemble Learning Approaches Overview
#### 2.2.4 Hybrid Methods  
Similarly to stacking, hybrid methods combine different ensemble techniques to exploit their advantages. These methods often show superior performance but can be complex to implement and tune [29]. The difference between the two approaches lies in the fact that stacking focuses on combining predictions from multiple models using a meta-model, while a hybrid ensemble leverages different types of models or techniques together for enhanced performance.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 5 Evaluation
### Additional Experiments
#### 5.5.1 Observation 7  
Figure 8 shows the throughput perceived by small flows of different durations. The results indicate the following: The proposed RSF MPTCP was robust in a highly dynamic environment in which MPTCP flows joined and left the system frequently.  
Figure 8: Throughput results for Scenario 7: short-lived flows. Both paths had 20 Mbps bandwidth, 150 ms delay, and 2% packet loss probability. Short-lived flows arrived and departed at random times according to Poisson processes.  
Figure 7: Throughput results for a 10 MB file transfer in Scenario 6: heterogeneous loss paths. One path had 3% packet loss while the loss probability of the other one was varied. Both paths had 20 Mbps bandwidth and 150 ms delay.  
The results in Figure 8 illustrate RSF MPTCP's ability to handle the frequent establishment and termination of flows. This feature reflects the increased responsiveness of our MPTCP algorithm to network changes. The two main enhancements in our design, namely, balanced pricing and adaptive congestion control, enabled this responsiveness.  
In Scenario 8, we investigated a case in which one of the two paths suddenly became unavailable (e.g., WiFi path disappeared, but the LTE connection was still fine). The results are shown in Figure 9, where we compare the throughput obtained by the intermittent subflows of different MPTCPs.
## 5 Case Study Applications
To examine the scalability of the proposed methodology, evaluating the Hub-and-Spoke modeling effect in SSS transportation and the algorithms' robustness in providing efficient solutions for larger applications, an extension of the previous case study consisting of 49 ports of the same island cluster has been considered. The appropriate number of vessels for transferring passengers from the mainland to the cluster is four; thus, three alternative scenarios employing four, five and six vessels were tested. These scenarios are as follows:  
* C5: Four vessels depart from central ports (three from Piraeus and one from Rafina);
* C6: Four vessels depart from central ports and one from a hub connector (selected by algorithm among several alternatives);
* C7: Four vessels depart from central ports and two from hub connectors (selected by algorithm among several alternatives). In case C5, the optimal traveling distance is 1666 nm while the optimal passenger hours is 26,347. Due to the limited number of vessels, the longest route facilitates 18 vessels. As the number of vessels increases, adding ferries from hub nodes (cases C6 and C7) decreases the traveling distance and passenger hours, resulting from the gradual split up of the flows after the passengers transfer deeply into the cluster. To avoid long-lasting trips, a constraint on the maximum number of intermediate stops is set at 20, 12, and 12 stops for cases C5, C6, and C7, respectively. From the results presented in Table 2, it is concluded that by employing ferry boats from hub nodes, both the traveling distance and passenger hours decrease, providing a more comfortable journey to the passengers and reducing the traveling costs (fewer miles and partly involvement of smaller ferries with lower cost). An indicative travel plan for case C7b is schematically illustrated in Figure 10.1  
The selection of the appropriate number of vessels, in accordance with the number of islands and passengers served, is important in optimizing the set goals. Conversely, an ineffective solution may lead to a dramatic increase in the goal parameter values. To account for such discrepancies, the special case of one vessel, corresponding to the simple traveling salesman problem, is considered for the latter case of the 49 island cluster. The total distance is not much increased since, when several vessels depart from the central port, they travel in parallel for some distance before diverging to different directions. In contrast, the cumulative passenger hours on board are three to five times larger than those in Table 2, as indicated in Figure 10.2 In this extreme case, a trade-off analysis with different sub-goal weights results in notable solution deviations and a well-structured Pareto front. Indicatively, the total distance and passenger hours at the edges of the three point clouds of solutions, derived by different weights of the optimization sub-goals, are (1912-67,210), (1523-76,460), and (1415-108,710), respectively.
# Nanomechanical, Structural and Antioxidant Characterization of Nixtamalized Popcorn Pericarp
## 3 Results and Discussion
### Antioxidant Activity
#### 3.4.4 Iron Chelation  
The results of iron chelation are shown in Figure 5. The activity of the pericarp is compared with the activity shown by Trolox, caffeic acid, ferulic acid, and coumaric acid (0.1 M). The pericarp exerts iron chelation of 134% compared to the Trolox (0.1 M), 116% compared to coumaric acid (0.1 M), 116% compared to ferulic acid (0.1 M), and 141% compared to caffeic acid. The activity showed no significant difference between ferulic acid and coumaric acid (\(p\) < 0.05). A study found values of iron chelation in six medicinal plant extracts, and _Clinacanthus nutanshad_ had high values of around 88.2 \(\pm\) 0.4 [42]. The values found in another study demonstrated that the pericarp has iron chelation significant enough to apply to the food, nutraceutical and cosmetic industries as a new source of antioxidant compounds [33].  
Figure 4: Percentage of hydroxyl radical of pericarp comparison with Trolox, caffeic acid, ferulic acid, coumaric acid (0.1 M). Values reported as percentage (%). Values presented are the average of three replicates \(\pm\) standard deviation. Different letters indicate that the values are significantly different (\(p\) < 0.05).
## 2 Theoretical Description
### Method Summary  
The flowchart of the proposed method is shown in Figure 1. Given an observed signal \(g\), the corresponding wavelet coefficients \(w\) are obtained by wavelet transform firstly. Next, the standard deviation of the independent Gaussian white noise \(\sigma_{n}\) is estimated by Equation (11). Then the parameters \(k_{1}\), \(k_{2}\), \(\sigma_{1}\), \(\sigma_{2}\) are estimated by Equation (27) to Equation (30), and the new estimated wavelet coefficients \(\hat{w}\) are calculated by the Equation (17). Finally, the new wavelet coefficients \(\hat{w}\) are reconstructed to obtain the estimated signal \(\hat{x}\), and the fault feature can be obtained through envelope spectrum analysis.
## 3 Data Processing
### SAR-AIS Correlation  
After SAR processing, there is an additional processing step that correlates ship detection and classification reports with AIS polls. This step permits, first, the development of user-customized alarms and, second, the provision of a manner for validating ship classification performance. Certainly, the cross-check of the category assigned by SIDECAR with that provided by AIS poll helps assessing algorithm precision. The AIS-SAR correlation process starts by gathering the AIS information over the same location and period covered by the SAR image. The temporal window under analysis depends on the temporal resolution of the AIS service, which is almost continuous in coastal AIS and discontinuous in satellite AIS. All the detections for the same vessel are grouped into route tracks, and the locations expected at image acquisition time, calculated. This process solves two undesired effects:  
* Ship location shifts in SAR images due to Doppler effects
* Low or discontinuous number of AIS tracks.  
Both are compensated with a route propagation algorithm that estimates the tentative position of a ship projected into the SAR image. For each successful correlation between SAR-AIS ship positions, a confidence value is calculated from the distance between SAR and AIS locations, and the similarity between ship macro-scale features (ship length, breadth and heading).  
Figure 6: Illustrative description of fuzzy logic processing. The plots refer to a hypothetic input signature that is cross-checked against the Oil Tanker category.  
The following figure presents an example of this algorithm showing the AIS track (shown by yellow squares), the expected position at image acquisition time projected into the SAR geometry (shown by the yellow cross) and the actual detection from the SAR image (shown by the green circle).  
With previous AIS-SAR correlation procedure, a test has been carried out for an ENVISAT image of the surroundings of the Strait of Gibraltar, Spain. Table 2 summarizes the cross-correlation of categories for the SAR-AIS pairs having the highest confidence. The results show that the category assigned by the algorithm matches the category of the observed vessel in around 60% of the cases. This result can be considered good for the medium resolution single-polarimetric images managed. Although this preliminary test is encouraging, further studies are required for validating the results and for exploiting the improved resolution and polarimetric capabilities of new sensors.