esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation
### Discussion  
Throughout this evaluation, the performance of the method presented in this work is evaluated in terms of the report interval, moving speed, missing neighbor rate and elevator waiting time under the environments: (1) the anchor nodes are installed both on the boundary of the monitoring area and within it; and (2) a mobile node can move far away from the area without any failures. From the evaluation results, it is clearly understood that the classification between a left node and a failed node is difficult by the fact that it cannot be assured that a mobile node will report its values outside the monitoring area before it is entirely disconnected from the wireless infrastructure (of anchor nodes). The mobile medical asset tracking system can adjust the report interval to increase the classification rate between a failed node and a left node. It is clear that the classification can be near 100% when the report interval is less than 10 s in Figures 5a and 6a. However, a short report interval shortens the lifetime of a mobile node due to frequent data transmissions. Attaching a tilt sensor [29] to a mobile node can solve this problem. Then, the mobile node with the tilt sensor can report its location and neighbor data when it moves continually for more than a predefined time. This predefined time can be configured with respect to the size of the monitoring area and a person's average walking speed. If this scheme is adapted to a mobile node, the report interval of the node can be shorter (by being adaptive) and will only minimally impact the lifetime of the mobile nodes. When a mobile node reports its data every 30 s, its battery lifetime is about 14 days. A lithium battery is attached to each mobile node. The battery and node properties are shown in Table 2. On the other hand, the lifetime of a mobile node with a tilt sensor is estimated to be about 120 days under the two following assumptions. First, a mobile node exchanges a heartbeat with its neighbors every 120 s. Furthermore, if the mobile node recognizes that it consecutively moves for more than the predefined time (e.g., computed as 10 s in the target environment and a walking speed of 1.5 m/s), it assembles and reports the neighbor data. Second, the actual moving time of a mobile node, based on the empirically collected dataset, is estimated to be about 144 min, which is only 10% of an entire day.  
Figure 8: Detection results of the number of inside and outside nodes for real data generated in the emergency room [17]. (With the permission of IEEE publisher).  
Therefore, by disabling reports while the mobile devices are stationary, the number of reports can be decreased and the lifetime of the nodes increased.
## 3 Results and Discussion
Finally, exploratory studies have leveraged NLP to extract TB-related information from EMRs [38; 55], yet none have implemented classification for diagnostic support. These findings underscore the potential for future research to refine and expand this field further. In the specific context of PTB diagnosis, clinical signs and symptoms serve as the initial indicators, identifying a patient as a potential case. According to the Colombian national protocol, at least one of the three diagnostic tests outlined in the introduction is required for confirmation. Smear microscopy, which involves analyzing sputum samples under a microscope to detect Mycobacterium, has a sensitivity of only 40-60% due to the high bacterial load needed for detection [56]. The present results, particularly the models based on embeddings and Type B data fusion, achieved sensitivity values of 73.6% and 71.3%, respectively. These models could serve as viable alternatives when only smear microscopy is available, assisting healthcare professionals in patient assessment. A more sensitive test, such as culture in solid or liquid media, requires at least two weeks and specialized infrastructure [57], which may not be feasible in low-income settings. The most advantageous option in terms of speed and accuracy--the molecular test--is also largely inaccessible in Colombia, where fewer than twenty certified facilities can perform it [58]. Furthermore, reliance on sample transportation presents logistical challenges that may compromise test integrity. Given these constraints, improving infrastructure for advanced diagnostic methods remains a priority in developing countries like Colombia. Meanwhile, the present proposal could provide valuable clinical insights, supporting timely diagnosis and treatment initiation. The main limitation of this study is the dataset size, which impacts the ability to train robust ML models. In AI applications, larger datasets generally lead to better performance. However, the data used in this study were obtained from a real-world setting at Hospital Santa Clara, one of the institutions treating the highest number of PTB patients. Integrating data from multiple healthcare centers and consolidating records prior to confirmed diagnoses remains a significant challenge, even within the same city. Additionally, access to data with consistent characteristics is constrained by national protocols for reporting TB cases. Moreover, while the advancements in LLM architectures, particularly Transformers, offer promising avenues for text-based medical analysis, they were beyond the scope of this study. Future research could explore these architectures for deeper insights. However, a critical challenge with such approaches is the need for a significantly larger dataset to properly train and fine-tune the models. Expanding the dataset depends on the epidemiological characteristics of the disease and the availability of data from medical centers. Although this study's dataset is relatively small, it represents the most comprehensive PTB patient data available from Bogota, D.C.'s leading treatment center.
## 4 System Model
### Energy Ingesting Model  
The energy ingesting model is formulated for the WSN nodes packets transmission and received during multi-tier scheduling, respectively. In this work, the distance among the WSN nodes to LBS, and LBS to SN, is determined in both multipath fading and free space channels, respectively. In our proposed work, the compensation of path loss is achieved by performing clustering, network construction, and switch handling mechanisms, respectively. The formulation of transmission by the WSN nodes to the LBS can be provided as:  
\[\text{En}^{\text{TX}}(\text{z},\text{dis})=\text{En}^{\text{TX}-\text{con}}( \text{z})+\text{En}^{\text{TX}-\text{Amp}}(\text{z},\text{dis}){=}\left\{ \begin{array}{ll}\text{z}.\text{En}^{\text{con}}+z.\epsilon\text{ f}\text{s}\text{dis}^{\text{2}},&\text{dis}<\text{dis}^{\text{*}}\\ \text{z}.\text{En}^{\text{con}}+z.\epsilon\text{ Mpf}\text{dis}^{\text{4}},& \text{dis}\geq\text{dis}^{\text{*}}\end{array}\right. \tag{1}\]  
In a similar manner, the reception energy consumption that can be formulated for z-bits is provided below:  
\[\text{En}^{\text{RX}}(\text{z})=\text{En}^{\text{RX}-\text{con}}(\text{z})= \text{z}.\text{En}^{\text{con}} \tag{2}\]  
In Equations (1) and (2), \(\text{En}^{\text{con}}\) and \(\text{En}^{\text{Amp}}\) represent the amount of energy consumed for transmission and reception and energy amplifier model, respectively. The \(\varepsilon\) fs and \(\varepsilon\) Mpf denote the free space and multipath fading channels, respectively; \(\text{dis}^{\text{*}}\) is the threshold of the transmission distance among entities. The formulation of \(\text{dis}^{\text{*}}\) is provided as \(\text{dis}^{\text{*}}=\sqrt{\varepsilon\text{ fs}/\varepsilon\text{ Mpf}}\).
## 3 Results and Discussion  
First, we simulate the frequency dependence of the maximum output power from the PEC array. In Sections 3.1 and 3.2 the maximum electrical power deliverable by a PEC array is investigated theoretically. This means that each PEC is loaded by the resistance \(R_{\text{L}}=275\) k\(\Omega\) and the power dissipated in each of these resistors is added. The electrical power determined in such a way represents the upper limit of the electrical power deliverable by such an array. In the real applications, the array should work on a single load. Therefore, in Section 3.3 we experimentally investigate connected PECs with individual full wave bridge rectifiers loaded by single resistance. Although the qualitative behavior of the bandwidth and the guaranteed electrical power with respect to the frequency detuning \(\Delta f\) between individual PECs remains the same as in the idealized case (Sections 3.1 and 3.2), there are some quantitative differences observed.
## 4 Methodology
### Create 2.5D Raster Campus  
A number of steps were involved to create a 2.5D raster surface model of the buildings on campus to use with the viewshed calculation. First, each of the 2D building polygons were assigned height values. Using the, ArcMap \"feature to ascii\" tool the XY values for each of the polygon vertices (Figure 6a) were exported into a table and a single Z value from the 3D campus model was assigned to every vertex of a building polygon. As previously explained, by demonstrating that combining 2D vector data with a single elevation value for each building was a suitable substitute for a high density 3D model, it could potentially provide savings in term of survey time, processing time and cost.  
These points were then used to create a Triangulated Irregular Network (TIN) as it is the first step in the creation of a raster DSM for use in the visibility calculation in ArcMap. The TIN process joins all adjacent XYZ points in a network of non-overlapping triangles, but it has difficulties when there are points on top of each other (_i.e._, identical XY coordinates but different Z coordinates) and for this reason a short, 1 m offset (Figure 6b) was incorporated for each building footprint. Once the XYZ values for each polygon vertex were found, the original polygons and the offset polygon were combined so the TIN could be created. The problem presented if the offset was omitted is apparent when the TIN (Figure 7a) and resulting raster DSMs were created (Figure 7b) as there were minimal elevation changes between the majority of surfaces. However, after the 1 m buffer was applied, this resulted in a greatly improved TIN for the buildings on campus (Figure 7c) although certain problematic areas still existed due to issues with the building footprint offsets whereby the building is flagged as larger than it should be. Basic editing of problematic triangles allowed creation of an improved raster DSM (Figure 7d) and this 2.5D raster DSM, created using 2D vector data and basic elevation information, was used to model obstructions in the viewshed calculation. Experimentation with different buffer distances could improve DSM creation, benefiting future applications of the methodology if upscaling to larger test areas.
## 5 Implementation and Testing Environment
### Model Parameters  
Here, we present the model parameters used while GCN and optimized GCN training. These parameters mainly include learning rate, training epochs, batch size, hidden units' size etc.  
As shown in Table 6, we experimented with different learning rates as 0.01, 0.001 and 0.0001. We also varied the number of epochs between 1000 to 5000. The used different batch sizes as 25, 32 and 45. Finally choosing the number of hidden unit is crucial as different sizes can have different impact on the prediction accuracy. Therefore, we experimented with different numbers of hidden units such as 8, 16, 32, 64, 100 and 128.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Distinction between Clear and Cloudy Days  
To distinguish between clear-day and cloudy-day conditions, we utilized the popular clearness index. The clearness index is the ratio of the mean measured GHI (here, ground data) to the mean top-of-atmosphere irradiance (extraterrestrial irradiance) [42; 43; 44]. The extraterrestrial irradiance was calculated using the 'get_extra_radiation' function from the PVLIB v0.10.4 Python package [45; 46], which accounts for factors such as the solar zenith angle, Sun-Earth distance, day length, and solar constant. The 'clearness_index' function of PVLIB v0.10.4 was then used to compute the clearness index, based on the previously calculated extraterrestrial irradiance, measured GHI, and solar zenith angle.  
Days with a mean clearness index value below 0.7 were classified as cloudy, while those with values above 0.7 were considered clear, following the standard procedures defined in [42,43].
## 4 Proposed Models
### Preparation of the Test and Training Samples  
Two types of turns have been distinguished to prepare the sample. The 70-100 degrees turns were tagged by class \"1\" and 40-70 degrees turns were classified as class \"2.\" Everything other than the previously described turn was marked as \"0\" (the turn did not take place). After analyzing the first results of the classification, it was decided, however, to merge classes 0 and 2 into one, as it yields significantly better results. This is because very few turns in the range of 40-70 degrees were identified in the considered sample, making this class significantly underrepresented (because most of the intersections in the mine are  
Figure 3: Input data: (**A**) Raw signal from the gyroscope \(Z\)-axis; (**B**) Yaw angle signal generated from the gyroscope. The increase at the beginning of the signal corresponds to the vehicle leaving the heavy machinery chamber, which subsequently took turns in the same direction. The regular part of the signal corresponds to the haulage cycles, where the haul truck was moving from the mining face to the dumping point, using the same path.  
crossed in angle close to 90 degrees), and difficult to identify by the tested models. On the other hand, merging classes 1 and 2 resulted in a poorer fit of the tested models. Therefore, finally, only two classes were considered: 70-100 degree turns and other.  
Then, the entire sample was split into a 70:30 ratio, creating the training sample used to train the selected classifiers and a test sample to validate the obtained results.  
To better visualize the obtained results, by connecting the speed (obtained from the vehicle's internal monitoring system) with the Yaw angle, the machine position in time on a plane, described by the X and Y axes can be achieved [12]. This enables a very intuitive evaluation of the algorithm's results and is presented later in the article. The speed variable itself is not used in the classification process.
## 5 Discussion
### Integration and Optimization of Multi-Source LST Data for Geothermal Anomaly Detection
#### 5.1.2 Multi-Perspective Integration and Extraction Analysis of ASTER NLST Data  
Although ASTER data are less suitable for time-series analysis than Landsat data, this study adopted a multi-angle extraction strategy combined with the D-S evidence fusion method, achieving promising results in geothermal anomaly identification. The results show that, although the global threshold method (Figure 7b) can rapidly delineate large-scale geothermal anomalies, it tends to produce overly broad regions, reducing its effectiveness in identifying smaller-scale anomaly zones.  
In contrast, the approach in Figure 7c, which applies regional segmentation with localized threshold settings, enhanced the extraction of small-scale anomaly regions. Notably, it was more effective in detecting moderate-to-low temperature anomalies, allowing for more comprehensive identification of localized geothermal signals.  
Figure 7d further accounts for the influence of elevation on temperature variation, enhancing anomaly detection in high-altitude mountainous areas while significantly reducing false positives in low-altitude regions.  
With the introduction of a fault structure buffer zone, areas located more than 1 km from faults were considered outside the influence of thermal diffusion halos. Even anomaly regions identified by all three preceding methods were excluded if located beyond this range, as they were considered unrelated to geothermal activity. Comparison with known geothermal hotspots confirmed that none of the excluded regions overlapped with known hotspots, further supporting the validity of the method.  
Ultimately, the D-S evidence fusion method effectively integrated the strengths of multiple extraction approaches, improving anomaly concentration and significantly reducing noise and false positives. This method yielded more stable results and was better suited for integration with daytime Landsat anomaly data, providing a more reliable foundation for subsequent comprehensive analysis.
# Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools +
## 1 Introduction
Overall, in this paper, we make the following contributions: (_i_) we initiate by presenting a comprehensive overview of the context, challenges, and motivations behind the development of advanced botnet detection methods; (_ii_) we provide a practical case study within Cloud-based environments that highlights how our framework can be used for botnet detection within a big data ecosystem; (_iii_) we conduct an in-depth state-of-the-art analysis, by providing a clear synthesis of the most relevant work in botnet detection and also highlighting limitations of current GNN-based methods; (_iv_) we introduce and detail the proposed _Structural Iterative_ Representation for _Graph Nodes_ methodology, by presenting its architecture, key functionalities, and the novel approach to robust node representation across diverse network topologies; (_v_) we also provide a detailed description of the dataset used in our experimental campaign; (_vi_) finally, we perform extensive experimental evaluations using real-life and synthetic datasets to rigorously validate the effectiveness, generalizability, and scalability of SIR-GN, which demonstrates its superiority over existing botnet detection models. The remaining part of this paper is structured as follows: Section 2 presents an innovative case study focusing on detecting bot nodes within a Cloud-based environment. Section 3 reviews a set of related work in the context of botnet detection. Section 4 presents the _state-of-the-art_ methodologies in this field. In Section 5, we describe datasets employed in our experimental campaign. After that, the inferential SIR-GN methodology is demonstrated in Section 6, while Section 7 covers its evaluation and the results obtained. Finally, Section 8 concludes the paper and discusses potential directions for future research.
# An IoT System for Remote Monitoring of Patients at Home
## 4 Data Classification and Multiclass Q-Learning (MQL) Algorithm
Because UM1 and UM2 are existing messages and are pushed back by one position in UrgentQ[\(*\)] owing to the arrival of UM3, their priorities are increased to 3.9 (=\(3+0.9\)1) and 1.81 (=\(1+0.9\)2), respectively. The priority increase is a type of aging technique that prevents a message with a lower priority from suffering indefinite postponement. The explanation for a non-urgent message is more complicated. First, by looking at Qtable[\(*\), \(*\)], the algorithm finds the QTable[At, \(k\)] that obtains the maximum reward in QTable[At, \(*\)] to select the transmission time of a non-urgent message whose arrival unit time is At. QTable[At, j] contains the reward obtained when a non-urgent message whose arrival time is unit time At is actually scheduled to transmit at unit time j. The message is then inserted in NonUrgentQ[k, \(*\)]. The remainder of the explanation is similar to that of the urgent message case, and has been omitted here. At unit time t, urgent messages in UrgentQ[\(*\)] are scheduled to transmit immediately. However, non-urgent messages in NonUrgentQ[\(t\), \(*\)] cannot be scheduled to transmit when UrgentQ[\(*\)] is not empty. Therefore, an attempt to transmit a non-urgent message is rewarded negatively and Qtable[\(*\), \(*\)] is updated appropriately. Conversely, an attempt to transmit a non-urgent message when UrgentQ[\(*\)] is empty is rewarded positively. Algorithm 2 (Transmission scheduling algorithm for non-urgent messages) explains the transmission scheduling algorithm for non-urgent messages. Let us suppose that Table 3 shows the contents of QTable[*, *] and message queues at unit time 5. When a non-urgent message M18 whose priority is 3.0 arrives at unit time 5, Algorithm 1 determines that QTable[5,9] is the optimum reward (=9.46). The algorithm then inserts M18 into NonUrgentQ[9,1] and the priority of the existing non-urgent message M09 is increased to 2.9. \"M18/3.0\" in the lower part of Table 3 indicates that a non-urgent message whose ID is 18 and priority is 3.0 is waiting at the front of NonUrgentQ[9, *]. Let us suppose it is unit time 9. At this time, messages in UrgentQ[*] and NonUrgentQ[9, *] are ready to be scheduled. Using Algorithm 2, an urgent message M17 in UrgentQ[1] is scheduled to transmit immediately. However, because NonUrgentQ[9, *] is not empty, the reward of QTable[5,9] is decreased to 7.51 (=Qtable[5,9] \(\times\) 0.9 - 1). This means that, owing to the existence of the urgent messages scheduled at unit time 9, a non-urgent message whose arrival unit time is 5 is now less likely to be scheduled to transmit at unit time 9. If three urgent messages are transmitted successfully at unit time 9, the reward of QTable[5,9] will be decreased to 4.18. If there is still sufficient time to schedule a non-urgent message M18 to transmit at unit time 9 after the transmission of M17, M01, and M02, the reward of QTable[5,9] will be increased to 5.76.
## 2 Related Work
### Energy Efficient Routing
#### 2.1.2 Hierarchical Routing Protocols  
These protocols consider the clustering in order to improve scalability and reduce the traffic. LEACH [13] is one of the first hierarchical routing protocols for sensor networks. It chooses the cluster head based on its signal strength and utilizes randomized rotation of local cluster heads to evenly distribute the energy load among the sensors in the network. Only cluster heads, whose optimal number is estimated to be 5% of the total number nodes, route data to the sink, which saves sensor nodes energy. However, LEACH assumes that any node is 1-hop away from the sink. TEEN [14] is another example of hierarchical routing protocols. It is also data-centric. It builds clusters of different levels until reaching the sink node. The cluster head broadcasts two thresholds to the nodes: only a value of the sensed attribute higher than the hard threshold can force the sensor node to transmit, and if this sensor was already transmitting, only changes higher than the soft threshold should be transmitted. This mechanism considerably reduces the number of transmissions. However, TEEN is not adapted to periodic queries. An extension called APTEEN [15] has been proposed.
# Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System
## 4 Results and Analysis
### Classification Accuracy of the Machine Vision
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Actual Defects** & \multicolumn{5}{c}{**Predicted Defects**} \\ \hline  & **Normal** & **Black** & **Sour** & **Broken** & **Very Long Berry** & **Small** & **Accuracy** \\ \hline Normal & 157 & 0 & 0 & 2 & 2 & 0 & 97.52\% \\ Black & 0 & 164 & 5 & 0 & 0 & 0 & 97.04\% \\ Sour & 0 & 13 & 152 & 0 & 0 & 0 & 92.12\% \\ Broken & 9 & 0 & 0 & 157 & 0 & 0 & 94.58\% \\ Very long berry & 3 & 0 & 0 & 0 & 151 & 0 & 98.05\% \\ Small & 3 & 0 & 0 & 0 & 3 & 125 & 95.42\% \\  & & & & & Average accuracy & & 95.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for defect types classification using \(k=10\).