esa-sceva/satcom-chunk-collection huggingface dataset:
# Strategy Based on Two Stages for IR Thermographic Inspections of Photovoltaic Plants
## 1 Introduction
By evaluating three 9.4 MWp PV plants, [23] studies the relationship between the power generated and the faults found with aerial IR thermography, to perform a quantitative estimate of the energy produced. In the reference [24] the authors provide a review of methods reported in the literature to automate different tasks of airborne infrared thermography for the inspection of photovoltaic systems. Studies related to digital image processing, classification and deep learning techniques are reviewed. However, this significant advantage of aerial IR thermography in application time can lead to specific faults going unnoticed, being misinterpreted or not being assessed correctly. For example, with aerial IR thermography, it is possible that the appearance of an intermediate object--such as vegetation or a perimeter fence post--could hide the thermal fault produced. Another situation is that a one-off thermal anomaly smaller than the PV cell could be detected at a distance with an insufficient resolution, causing a temperature value to be taken that is lower than the real temperature. It is also possible that sunlight reflecting on the surface of the PV modules could be interpreted as a non-factual or unreal thermal incident. Two-stage thermographic inspection methods have been applied in other areas. In [25] the authors compare aerial thermography and ground-based thermography on the envelope of a traditional wine cell. Working with UAVs gives great flexibility for the inspection, but the angle of view strongly must be taken into account to avoid disturbances due to specular reflections. In [26] a complete literature review of the use of UAVs in bridge monitoring is carried out and compared with other inspection techniques. The use of hand-held IR cameras allows the validation of results from aerial thermographic inspections. Applying ground-level thermographic inspection for the PV plant as a whole can prevent the problems set out for aerial inspection. Nonetheless, the time required for inspection and subsequent analysis will increase considerably. The disadvantages posed can be minimised by employing a two-stage thermographic inspection strategy. The first stage is carried out as an aerial thermographic inspection while the second stage is carried out as a thermographic inspection on the ground. The first stage is done quickly to locate relevant incidents. The second stage of the inspection is applied to detect relevant incidents to improve the diagnosis.
## 2 Materials and Methods
### Study Design  
The study occurred across two sessions of repeated measurements with 72 h between each evaluation [13]. Two IsoMed-2000 isokinetic dynamometers (D&R Ferstl GmbH, Hemau, Germany) were used in this study: an old one with a longer usage time (well-used/old dynamometer; total number of registered individuals: 2742; number of tests: nearly 4000 (nearly 2500 knee, 1500 other joints); number of strengthening programs: nearly 500 (nearly 200 knee, 300 other joints)), and a new one with less usage time (less-used/new dynamometer; total number of registered individuals: 782; number of tests: nearly 1000 (nearly 600 knee joint, 400 other joints); number of strengthening programs: nearly 10-knee joint). A less-used dynamometer had six months of usage and a well-used dynamometer had three years and nine months of usage. Both dynamometers had annual service maintenance. The torque sensor, signal conditioning hardware, and used software and analogue filters are equal in both dynamometers.  
Tests on both the isokinetic dynamometers were performed separately on both legs according to prior block randomization. The intra- and inter-reliability of the dynamometers was evaluated through a concentric maximal test of the knee flexor and extensor muscles. Athletes came to the laboratory four times with an interval of three days to reduce both the possibility of a learning effect and the harmful effects of fatigue caused by previous tests [14]. Just before each test, the athletes were familiarized with the test by doing the same movement at the same angular speed as the test. Our aim with familiarization was to make the athletes understand the resistance of the angular velocity being tested. At least 8 h before each test, the athletes were informed that they should not take any stimulants or engage in strenuous activity. Apart from that, the athletes were told to continue their daily routines but not to do vigorous physical activity 48 h before each test. Evaluations were done by the same physiotherapist at same time of day (\(\pm\)2 h).  
Ahletes participating in the study were randomly divided into two groups. The measurements took a total of 4 days for each athlete. Table 2 shows that, in terms of groups, the athletes were evaluated according to which dynamometer was used on which day. Dynamometer adjustments in the evaluation carried out on the first day for each athlete were also provided for other measurements. Thus, the results of the concentric muscle strength measurement of the knee joint flexor and extensor muscles of each athlete were obtained four times in total, two with the old dynamometer and two with the new dynamometer.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Mean** & **SD** & **Median** & **Range** [Min-Max] \\ \hline
**Age (years)** & 16.7 & 0.8 & 17.0 & 4.0 [15.0–19.0] \\
**Body Mass (kg)** & 66.3 & 10.1 & 66.1 & 43.1 [48.4–91.5] \\
**Height (m)** & 1.65 & 0.07 & 1.67 & 0.26 [1.51–1.78] \\
**Experience (year)** & 4.8 & 1.4 & 5.0 & 5.0 [3.0–8.0] \\ \hline \hline \end{tabular}
**SD:** Standard deviation.  
\end{table}
Table 1: Descriptive statistics (\(n=37\)).
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 2 Literature Review  
[PERSON] et al. [1] provide a comprehensive overview of IoRT with SATCOM focusing on the architectural patterns of interconnection between satellite and sensors/actuators. Three typical applications, in which satellites could play an important role, are outlined: smart grid, environmental monitoring and emergency management. In terms of the satellite standards and protocols, the authors consider digital video broadcasting - return channel via satellite (DVB-RCS2) as the main feasible option that can guarantee a certain Quality of Service (QoS) level, only briefly mentioning low Earth orbit (LEO) constellations.  
In [4], the authors study the applicability of satellite communication for time-critical IoT applications, such as in smart grid or e-health services. It is concluded that most of today's applications have delay tolerances that are too stringent to be met by satellite links. However, there are certain types of applications that may still benefit from using SATCOM. A similar issue is addressed by [PERSON] et al. [17]. In their research paper, the authors divide the possible IoT application scenarios into two groups: Delay-Tolerant Applications (DTA) and Delay-Sensitive Applications (DSA). It is argued that Low Earth Orbit satellite constellations in many cases offer more advantages compared with Geostationary Earth Orbit (GEO) systems. They also discuss a number of questions related to system design of terrestrial IoT and satellite networks, including constellation design, network architecture and access and routing protocols.  
[PERSON] and [PERSON] [12] propose an Internet of Arctic Things architecture, which is characterized by a freely drifting swarm of small satellites and satellite-aware routing as one of the advanced options.  
The researchers conducted an experimental assessment with several ground nodes and satellite orbits, as well as lightweight communication protocols, which resulted in a low overall number (<5%) of packet losses.  
In terms of the engineering design idea, [3] provides the closest setup to the prototype discussed in the present paper. The work describes an IoT system design based on ZigBee that uses the Iridium satellite constellation for remote monitoring and control purposes in remote Himalayan villages.  
The research of [PERSON] et al. [28] focuses on the comparison between two IoT/Machine to Machine (M2M) protocol stacks in relation to data transfer over a random access satellite channel based on the DVB-RCS2 standard. The simulation showed that the Constrained Application Protocol (CoAP) outperforms MQTT on low and moderate traffic rates.  
In summary, all of the aforementioned works study the feasibility of satellite communication for IoT, formulating many open issues and addressing some of them. We see a potential gap in data transfer optimization on the edge of the two network parts--the terrestrial part and the satellite part. That is the primary aim of our study.
# Ontology-Based Feature Selection: A Survey
## 5 Ontology-Based Feature Selection
### Other Applications  
In this section, we review a number of other interesting applications that integrate ontology-based feature selection methods. Specifically, the analysis concerns manufacturing processes, recommendation systems, urban management, and information security. Table 3 summarizes the basic characteristics of these approaches. An important application of ontology-based feature selection algorithms is the selection of manufacturing processes. [PERSON] et al. (2018) describe an ontology-based Decision Support System (DSS), which aims at assisting the selection of a Suitable Manufacturing Process (MPS) for a new product. In essence, selected aspects of MPS are mapped to ontological concepts, which serve as features in rules used for case-based reasoning. Traditionally, MPS has relied on expert human knowledge to achieve the optimal matching between material characteristics, design specifications, and process capabilities. However, due to the continuous evolution in material and manufacturing technologies and the increasing product complexity, this task becomes more and more challenging for humans. The proposed DSS consists of two components, namely the ontology and the Case-based Reasoning Subsystem (CBR). The purpose of the ontology is to encode all the knowledge related to manufacturing in a way which enables the reasoner to make a recommendation for a new product design. It consists of three main concepts, the Manufacturing Process (MfgProcess), the Material (EngMaterial) and the Product (EngProduct). The MfgProcess concept captures the knowledge about manufacturing in subconcepts, such as casting, molding, forming, machining, joining, and rapid manufacturing. The properties of each manufacturing process are expressed in terms of shape generation capabilities, which describe the product shape features a process can produce, and range capabilities, which express the product attributes that can be met by the process such as dimensions, weight, quantity, and material. The EngMaterial concept captures knowledge about materials, in terms of material type (e.g., metal, ceramic) and material process capability (e.g., sand casting materials). The EngProduct concept encodes knowledge about products, defined in the form of shape features and attributes. The ontology facilitates the construction of rules, which associate manufacturing processes with engineering
## 4 Conclusions  
This paper presented a machine and deep learning-based evaluation framework as a support in decision-making in industrial environment to detect defective weld joints according the electrical properties using thermographic image sequences acquired from IR camera recorded videos. The authors compared inference ability of different classifiers in combination with three different feature groups, fostering precision, recall, and f1 score on the positive class as key merit factors. The proposed adaption in the CNN with classification layer yields good results in the prediction of the weld resistance defects with high accuracy, precision, and f1 score. It was demonstrated that this implementation allows inline and real-time labeling of rare cases of defective welds that subsequently undergo repair or rewelding before the final assembly, thus avoiding the propagation of defects and improving efficiency. Additionally, the quality assurance of the metal products plays a pivotal role in satisfying customer quality specifications. From a manufacturer's standpoint, the authors have further demonstrated the feasibility of inline anomaly detection systems to continually improve production methods.  
Conceptualization, D.B., N.L., G.D.S., V.B. and A.B.; methodology, D.B., S.G., A.R. and N.L.; software, D.B., M.P., S.G., A.R. and N.L.; validation, D.B., M.P., S.G., A.R., N.L. and A.B.; formal analysis, D.B., M.P., S.G., A.R., N.L. and A.B.; investigation, D.B. and N.L.; data curation, S.G., N.L. and G.D.S.; writing--original draft preparation, All authors; writing--review and editing. All authors; visualization, D.B., M.P., S.G. and N.L.; supervision, D.B. and N.L; funding acquisition, G.D.S. and V.B. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Public-Private Laboratory \"Cognitive Diagnostics\" between Polytechnic University of Bari and Comau(r) S.p.A. company.  
Data Availability Statement: Data are not available since Data Protection.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{4}{c}{**Inference Time [ms]**} \\ \cline{2-4}  & **Workflow I** & **Workflow II** & **Workflow III** \\ \hline DT & 0.27 \(\pm\) 0.01 & 2.46 \(\pm\) 0.37 & 0.21 \(\pm\) 0.13 \\ KNN & 2.21 \(\pm\) 0.32 & 1.53 \(\pm\) 0.75 & 1.24 \(\pm\) 0.25 \\ SVM & 0.36 \(\pm\) 0.01 & 0.33 \(\pm\) 0.01 & 0.03 \(\pm\) 0.06 \\ CNN-C & 100.50 \(\pm\) 5.20 & - & - \\ CNN-W & 61.60 \(\pm\) 22.13 & - & - \\ CNN-EW & 48.03 \(\pm\) 1.80 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average and standard deviation time in milliseconds elapsed for single sample inference belonging to five-fold testing sets during prediction phase performed by classification methods and workflows illustrated. Measurements confirm the feasibility of inline testing and real-time quality assessment.
## 3 Experimental Methods and Numerical Verification
### Finite Element Method  
The finite element method is the most commonly used technique to solve practical problems and mathematical models.  
[PERSON] [42] presented the idea of finite element analysis as a computational method used to find approximate results of boundary value problems in engineering. It shows when a component failure wears out or performs the way it was made to. It is used in the process of design and development to predetermine what will happen when a product is being used.  
The idea of FEM was originally created by members of the structural dynamics unit at the Boeing Airplane Company. The main feature of the developed procedure is the evaluation of the stiffness properties of structural elements based on the assumed places of displacement interpolation functions [43].  
In the FEM procedure, the three main steps are: (1) pre-processing--the creation of a finite element mesh. In the computational method, any type of material can be used with user-defined properties and boundary conditions. (2) Solution--the computational software used for the finite element analysis originates the matrix equations from the geometrical model and individually solves for the key measures. (3) Post-processing--an examination of the results of all primary quantities, such as displacement in a particular direction, which are obtained after the solution.
# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
## 4 Experiments
### Experiments Based on Untrained Models
#### 4.2.4 Work Efficiency  
As shown in Figure 12, under the condition that the proportion of correspondence points was 0.05 to 0.95, the average values of the total time for estimating the correspondence points and calculating the rigid body transformation matrix using the three algorithms for all models in the test set was recorded. Then, the values were used to measure the efficiency of the point cloud registration of the three algorithms.  
Figure 12 shows that as the proportion of correspondence points increases, the average time of each algorithm rises; this is because the number of point cloud data to be processed increases. Furthermore, as the proportion of correspondence points increases, the time-consuming growth rate of the MPCR-Net is slightly lower than that of the PRNet and RPM-Net. This is because the MPCR-Net dynamically adjusts the iterations based on the rigid body transformation matrix difference calculated by two consecutive iterations. As the proportion of correspondence points increases, the iterations required to obtain the optimal rigid body transformation matrix gradually decrease, thereby reducing the time consumption.  
Figure 11: Average registration error with different proportions of correspondence points: (**a**) Average calculation error of rotation angle; (**b**) Average calculation error of translation distance.
## 3 Results
### Accuracy Assessment of Orthophotos
As a final comparison of the orthophotos derived from different acquisition geometries, we utilized the variations of the dimensions of the aluminum targets. These dimensional variations, regarding the orthophotos covering the landslide of Zachlorou, are presented in Table 5. As can be observed, the synergistic use of nadir and oblique photos increase the accuracy of the orthophoto. The true length of the aluminum target is 0.538 m and the respective measured value from the oblique and nadir imagery is 0.542 m, meaning that the overall difference is only 4 mm. The difference value for the nadir imagery is 8 mm and for the oblique imagery is 10 mm.
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### gRPC in IoT for Healthcare  
gRPC [76] is a communication framework built on top of HTTP/2 that enables high-throughput, bidirectional data exchange between healthcare IoT devices and backend systems. This approach supports both request-response interactions and continuous data streaming, ensuring minimal transmission delays for critical patient information. The Request-Response Model gRPC is defined as follows:  
\[R(t)=\{\text{Req}(t),\text{Resp}(t)\}, \tag{6}\]  
where \(R(t)\) captures a discrete request at time \(t\) and its corresponding response. In a healthcare context, each \(\text{Req}(t)\) may represent a specific query for clinical data, while \(\text{Resp}(t)\) holds the information returned by the device. The Streaming Model is defined below:  
\[S(t)=\int_{t_{0}}^{t_{1}}f(\text{Metrics},\,t)\,dt, \tag{7}\]  
where \(f(\text{Metrics},\,t)\) denotes a time-varying function of health-related data (e.g., heart rate or ECG signals). The integration from \(t_{0}\) to \(t_{1}\) aggregates these continuous measurements, which are then delivered in near real time via gRPC streams. Because healthcare applications often require immediate insights, we focus on end-to-end latency. Let \(T_{i}^{s}\) be the sending time for a data chunk \(i\) and \(T_{i}^{r}\) its receiving time. Then, the latency \(L_{i}\) for chunk \(i\) can be defined as  
\[L_{i}=T_{i}^{r}-T_{i}^{s}. \tag{8}\]  
\[\overline{L}=\frac{1}{N}\sum_{i=1}^{N}L_{i} \tag{9}\]  
Minimizing the average latency is crucial to ensure fast feedback and timely clinical decision-making.  
```
1:Set of capabilities \(C=\{c_{1},c_{2},\ldots,c_{n}\}\), client request \(R=\{r_{1},r_{2},\ldots,r_{m}\}\)
2:Processed operations \(P\), unsupported operations \(E\)
3:Initialize supported capabilities: \[S\leftarrow\{c\in C\mid\text{device supports }c\}\] (10)
4:Advertise \(S\) during <hello> message exchange.
5:Receive client request: \[R\leftarrow\{r\in C\mid\text{requested by client}\}\] (11)
6:Validate requests: \[P\gets R\cap S\quad\text{(supported operations)}\] (12) \[E\gets R-S\quad\text{(unsupported operations)}\] (13)
7:for all\(p\in P\)do
8: Execute operation \(f(p)\)
9:endfor
10:for all\(e\in E\)do
11: Notify client: \"Operation \(e\) not supported.\"
12:endfor
13:if device capabilities change then
14: Update \(S\): \[S\gets S^{\prime}\quad\text{(new capabilities set)}\] (14)
15: Re-advertise \(S\) during the next <hello> exchange.
16:endif
```  
**Algorithm 2** NETCONF-Light
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Update Mechanisms
#### 6.3.2 Adaptation Methods  
The adaptation of the model can be conducted in an informed, or blind manner. Note here that these adaption methods are also called active or passive learning in other works, e.g., by [1]. In the former case, the update happens after a triggering event. This event can be, e.g., the deflection of a change detector or the excess of a threshold (e.g., model error). In blind model adaptation, the update happens without any special trigger event on a recurrent time basis. Incremental classification models are updated blindly by nature since they evolve with data. Again, the VFDT is an exemplary algorithm. Without any strategy to explicitly detect concept drift, the model adapts to the most recent data, e.g., compare Section 4.2 for references on segmentation. It updates itself as soon as a new labeled instance arrives and thus reacts to the current concept without triggers. In this case, it may also make sense to degrade older data records with a suitable fading factor. The idea is to weigh old labeled data points less over time in the training process. Informed (or active) classification methods can be implemented by including a drift detection algorithm.  
For more detailed information, several surveys or extensive studies present different aspects of drift detection and update mechanisms in data streams [4; 36; 154]. The authors structure groundwork algorithms based on their possibilities for drift detection and maintenance. In addition, they discuss various synthetic as well as real-world datasets [36]. Researchers interested in stream data with specific drift characteristics can find a comprehensive overview of relevant datasets in their study.
# Effects of the October 2024 Storm over the Global Ionosphere
## 3 Solar and Geomagnetic Activity
The Kp index declined significantly afterwards, marking the storm's recovery phase. High-latitude geomagnetic activity was evaluated using the Polar Cap Index for the Northern Hemisphere (PCN), which is calculated from magnetic field variation recorded at the Qaanaaq station in Greenland. PCN data averaged over 15 min intervals are used to detect geomagnetic disturbances in the polar caps caused by the solar wind's dynamic pressure and orientation of the IMF [21]. As expected, PCN showed a marked increase in response to the CME impact and tracked closely with Kp, particularly when Kp exceeded level 7, indicating enhanced auroral electrojet activity. On 10 October at approximately 15:00 UT, coinciding with the SSC, solar wind speed jumped sharply from 409 km/s to 740 km/s (Figure 2c), confirming the CME's arrival. At the same time, IMF--Bz turned strongly negative (Figure 2b), while the total magnetic field strength (IMF--B) showed multiple successive increases, consistent with the passage of a complex CME structure. These conditions facilitated prolonged and intense dayside magnetic reconnection at the magnetopause, enabling massive energy transfer into Earth's magnetosphere. After 00:30 UT on 12 October, IMF--Bz returned to near-zero, and IMF--B weakened to about 4 nT, signalling the end of the storm's active phase. The long duration and sustained southward orientation of IMF--Bz was a consequence of one of the most geoeffective geomagnetic storms in the recent solar cycle.