esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of Cyber Threats in EM
### Communication Media
#### 2.1.1 SMS Text Messaging  
SMS Text messaging is often used by the authorities and third parties to rapidly disseminate critical information during emergencies. For example, after the 2004 Indian Ocean Tsunami, a number of SMS-based tsunami warning systems were developed and introduced in South East Asia [15]. However, recent research has shown that these messages could overload the cellular network and cause failures during the emergency that were previously not understood [72]. There is clearly no malicious intent behind emergency text messaging, but it has demonstrated the vulnerability of the cellular network to simple SMS flooding attacks during an emergency.  
SMS messages are not encrypted by default, but the SMS standard does provide optional mechanisms, such as redundancy check, cryptographic checksum and digital signature to verify confidentiality and integrity of data. Each message has a specified validity period after which it is deleted by the network provider.  
_Open issues in security of SMS Text Messaging used in EM_: [PERSON] _et al._[72, 73] have provided a mathematical analysis showing that mobile voice service in a city the size of Washington D.C. can be denied completely with little more than a cable modem generating 240 messages per second. Such rates can be achieved by using External Short Message Entities (ESMEs), including web-based messaging portals at service providers' websites and software applications. As countermeasures, they have recommended the separation of voice and data traffic, strict resource provisioning, direct channel allocation, rate limitation and particular ESME security measures, but a cyber criminal can still overwhelm the network by using a large enough botnet. The same authors' simulation results in [21], on the use of emergency text services in university campuses and backed by historical data, have shown that only 20% of students would receive these messages in a timely manner. The rest would never receive them or would receive them with over five hours of delay. As SMS services are usually configured to retransmit a message automatically, dropped messages increase the load on the network, further disrupting voice communication. Finally, when multiple messages are sent over the course of the same emergency, the loss and delay of messages, as well as the use of different queuing algorithms by different service providers, may cause messages to be received out of order or out of context. A number of examples of real emergencies where confusion was further increased due to out of order emergency text messages have been provided in [21]. Thus, defence against bot-generated SMS floods during an emergency is a significant open issue.
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### Method  
The AD-MRCNN network model proposed in this study is illustrated in Figure 2. It primarily consists of three components: an adapter module, an instance segmentation network, and a frontal line extraction module. The overall workflow of the AD-MRCNN model is as follows: First, six types of meteorological element data at a given time point are input into the adapter, where normalization, data value weighting via weight adjustment, convolutional feature fusion, and mapping to a 0-255 range are performed. Next, the processed data are fed into a pre-trained feature extraction backbone network (FPN) to obtain abstract feature maps. These feature maps are then input into a region proposal network (RPN) to generate a series of candidate ROI bounding boxes. The corresponding regions of these candidate boxes in the feature maps are cropped and sent to the ROI Align layer, where feature maps of varying sizes are resized into uniformly dimensioned abstract feature maps. Finally, these maps are separately input into three branches: one for predicting frontal category grid points, one for bounding box regression, and the mask branch for extracting frontal segmentation information within the identified regions.  
Figure 1: The schematic diagram of the process for generating frontal bounding boxes and category labels from WPC frontal data to train the network. Process A involves resolution enhancement and bilinear interpolation, Process B denotes the dilation operation, and Process C represents the labeling operation (Blue represents the occluded front, green represents the cold front, and red represents the warm front).  
Figure 2: The methodological flowchart of the AD-MRCNN model. The input data are first fed into the adapter for adaptive fusion. The fusion results are then processed by the FPN to extract fused features, followed by the RPN to obtain candidate bounding boxes for fronts. The ROI layer is subsequently employed to process the candidate boxes, and finally, the classification network and mask network generate the front category boxes and front masks, respectively. The post-processing module is utilized to extract the fronts.
## 4 Results and Discussion
### Limitations and Future Directions  
While the hybrid modeling framework shows strong potential, several limitations should be noted. The approach depends heavily on the accuracy of the physical model (HEC-HMS), and performance may degrade if the model is poorly calibrated. The bias correction mechanism is tailored to a specific region and seasonal irrigation behavior, which may limit its transferability. This study is limited to a single discharge point; however, the proposed framework could be extended to spatially distributed streamflow forecasting by incorporating sub-watershed delineation or multi-point calibration where data availability permits.  
Future work should focus on scaling the framework to larger, multi-basin datasets and developing a generalized, foundational deep learning model trained across diverse hydrological regimes. Integrating additional data sources--such as remote sensing, land use, canopy evapotranspiration, irrigation records, and high-resolution forecasts--can further enrich the input space. Ultimately, automating the full pipeline and validating it across multiple regions could pave the way for a scalable, operational forecasting system suitable for real-time deployment, especially in ungauged or data-scar basins.
## 4 Discussion
### Clinical Characteristics of Injury  
#### 4.2.1 Anatomical Region and Body Area  
Similar to the findings of the living systematic review, the lower limb was the most frequent anatomical region of injury in our study in both the 12 months (95%) and the two weeks before the race (83%). The study on Spain trail runners in the Al Andalus Ultra trail reported the lower limb as the most common anatomical region for injury (22%) with the knee being the most common (17%) [14]. The study on Greek trail runners reported the lower leg as the most frequent anatomical region (20%), followed by the knee (18%) and foot (15%). These trail runners reported the lower back (43%) as the most commonly reportedinjured body area, followed by the knee (40%) [26]. Although our study also reported the lower leg (26%) and foot (13%) as commonly injured, unlike the two above mentioned studies [8; 26], the knee was not reported as a frequent anatomical region of injury. The difference could be that in Greece, training is mostly on mountainous terrain, according to the study. In contrast, most of our participants trained five or more sessions per week on road surfaces (50%) and less than 1000 m elevation gain per week (50%). Another study on South African trail runners described the most common body area injured as the knee (27%), followed by the ankle (22%) and foot (17%) [10]. Our most reported body area was the lower leg (26%). A study on trail runners in Spain reported the lower limb as the most injured anatomical region (78%), and the most injured body area was the ankle (32%), followed by the knee (14%) and foot/toe (11%). They did however report injuries in the upper limb (18%) where the trunk was the most common (7%) [7]. The difference between the studies may be because the participants of these studies trained for different races in different geographical settings regarding terrain and elevation changes. Participants also trained for different race distances.  
In our study, in the two weeks before the race, the foot (25%) was the most reported injured body area, followed by the hip/groin (17%), knee (17%), and lower leg (17%). Our study is the first to investigate injuries in the two weeks before the race and thus cannot be compared to other studies.
## 2 Smart Meter Data Used  
### The CER Data Set  
This research is conducted based on the Irish Commission for Energy Regulation (CER) data set. The CER initiated a Smart Metering Project in 2007 with the purpose of undertaking trials to assess the performance of Smart Meters and their impact on consumer behavior. It contains measurements of electricity consumption gathered from 4182 households between July 2009 and December 2010(75 weeks in total with 30 min data granularity). Each participating household was asked to fill out a questionnaire before and after the study. The questionnaire contained inquiries regarding the consumption behavior of the occupants, the household's socio-economic status, properties of the dwelling and appliance stock [20].  
Some characteristics of the underlying data are presented in Figure 1, where the normalized consumption observed at different aggregation levels is visualized. Aggregation reduces the variability in electricity consumption resulting in increasingly smooth load shapes when at least 100 households are considered.  
The CER data set, to the best of our knowledge, does not account for energy that is consumed by heating and cooling systems. The heating systems of the participating households either use oil or gas as a source of energy or their consumption is measured by a separate electricity meter. The households registered in the project were reported to have no cooling system installed [20].
# Ensemble Machine Learning Model to Predict the Waterborne Syndrome
## 3 Materials and Methods
### Study Data  
According to UNICEF and WHO, Bangladesh is one of the top 10 low-income countries where around 50,800 child deaths occur annually due to waterborne diarrhea disease [17]. The slum areas of Bangladesh have experienced major healthcare issues, including respiratory, gastric, waterborne and mosquito-borne illness, and hypertension during the ongoing COVID-19 lockdown [18]. In this study, the diarrhea patient's clinical data was formally collected with the assistance of medical college students who volunteered from Varendra University located in the city of Rajshahi in Bangladesh. The data collection process was carried out over five years (2015-2019) through volunteers and in several sessions before the COVID-19 pandemic spread worldwide and reached Bangladesh. The study data was conducted on diarrhea-infected patients at different urban and rural locations, including riverbanks and overcrowded areas. The data consists of 643 unique patients' data which was collected from diarrhea-infected individuals who visited various hospitals, pharmacies, and medical centers for their treatment or to obtain medication.  
As shown in Figure 1, three stages of the knowledge discovery process were employed as part of this study. The data-collection process involved waiting for and collecting patients' clinical data at various hospitals and pharmacies that were prescribed anti-diarrheal therapies. The data was recorded from doctors' medical prescriptions and involved conducting numerous interview sessions. During the sessions, patients were asked questions about their lifestyles and other symptoms that could influence the spread of diarrheal disease. Because most people in urban and rural areas speak their native language, the data collection process was mostly conducted in their native languages. The collected clinical data were then translated into English to maintain uniformity during the data-mining process. The data was collected with the consent of the patients; their written permission documented their consent. The data was then analyzed using different data preparation and classification techniques. These steps involve experimenting with the ensemble model against traditional classifiers to predict the most suitable model. Finally, knowledge extraction was conducted to understand the characteristics, medications, and living conditions.
## 2 Functional Attributes of PGPR and Their Mechanisms
### Soil Bioremediation  
Recently, PGPR have come to the forefront because of their environmental cleanup ability (bioremediation) as a substitute approach to chemical and physical traditional techniques in eliminating (or controlling) pollutants in soils [80]. Although some organic compounds can endure in soil for a long time, they can be degraded by aerobic PGPR or even dechlorinated and mineralized by anaerobic bacteria [81; 82]. For instance, polychlorinated biphenyls (PCBs), as a group of well-known organic contaminants, can be oxidized in aerobic bioremediation processes, where some genera of PGPR (e.g., _Bacillus_, _Pseudomonas_, _Rhodococcus_, and _Achromobacter_) can utilize the biphenyl (a vital primary substrate that supports PCB cometabolism) using several enzymes, such as dehydrogenases, dioxygenases, hydrolases, hydrates, and aldolases [82; 83].  
In anaerobic degradation processes, organic compounds are broken down by anaerobic bacterial strains to release the energy required for their metabolic processes. In this process, reductive dechlorination or dehalorespiration in contaminated soils replaces the normal bacterial respiration using aryl halides as electron acceptors for their respiration, resulting in the formation of less toxic and more biodegradable compounds [84].  
Soil microbes serve various mechanisms for reducing the toxicity for plants and themselves, including intracellular retention, extracellular sequestration, biosorption, biotransformation, bioaccumulation, complexation reaction, precipitation, and enzymatic detoxification (oxidation and reduction) of toxic metals [85; 86; 87], whose efficiency depends on the great variability among the toxicity levels of heavy metals. Among them, the most effective mechanism of PGPR strains is the decline in ROS production through the production of some specific enzymatic and nonenzymatic antioxidants (e.g., hydrolases, dioxygenases, hydratases, dehydrogenases, and aldolases), which can preserve plants from ROS-induced oxidative damage [49; 60].
## 3 Results
### Comparison of Model Complexity  
In this study, a performance comparison was conducted among five models--UNet, DeepLabv3+, HRNet [25], PSPNet, and the proposed MHC-PSPNet. MHC-PSPNet offers a clear advantage in model efficiency, with only 2.38 M parameters--approximately three times fewer than the smallest HRNet models in the study. Additionally, the total floating-point operations for MHC-PSPNet are just 5.28 billion, about five times fewer than those  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Extractors** & **Resnet50** & **MobileNetV2** \\ \hline IoU/\% & 82.51 & 79.74 \\ Accuracy/\% & 96.89 & 96.07 \\ Total GFLOPS/G & 103.65 & 5.28 \\ Memory usage (CPU)/G & 14.53 & 7.39 \\ Memory usage (GPU)/G & 9.32 & 2.63 \\ Memory usage (TPU)/G & 3.41 & 1.26 \\ Total params/M & 46.71 & 2.38 \\ Model size/M & 178.18 & 9.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experiments of the two backbone networks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modelling** & **Feature Extractors** & **IoU\%** & **Accuracy/\%** & **Total Params/M** \\ \hline \multirow{4}{*}{PSPNet} & ShuffleNetV1 & 74.51 & 95.08 & 7.02 \\  & ShuffleNetV2 & 76.48 & 95.47 & 4.4 \\ \cline{1-1}  & MobileNetV1 & 74.97 & 95.16 & 8.89 \\ \cline{1-1}  & MobileNetV2 & 79.74 & 96.07 & 2.38 \\ \cline{1-1}  & MobileNetV3 & 77.35 & 95.39 & 2.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Lightweight backbone network performance comparison.  
required by the HRNet model. These results demonstrate that MHC-PSPNet is more efficient in terms of computational resource usage and storage requirements, making it well suited for resource-constrained environments [26]. In rescue scenarios, it can effectively operate under low-power and limited hardware conditions. The results are presented in Table 4.
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 4 Experimental Results
### Quantitative Evaluation
#### 4.3.3 Evaluation on Challenges  
Occlusion is a difficult issue as the object might be occluded by other objects or disappear from the scene. Inspired by [56; 57], we evaluated the proposed algorithm on motion blur and different levels of occlusion and background clutter, as shown in Table 7. We divided these challenges into partial occlusion (PO) [57], heavy occlusion (HO) [57], slight background clutter (SBC), heavy background clutter (HBC), motion blur (MB) and deformation (DEF). We have found 84 sequences in total with occlusion in OTB-100 and TColor-128 datasets, including 41 partial occlusion and 43 heavy occlusion sequences. Almost all the targets in these sequences with heavy occlusion were completely occluded and disappeared. We also found 31 sequences with slight background clutter, 27 sequences with heavy background clutter, 53 sequences with motion blur and 63 sequences with deformation in OTB-100 and TColor-128.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Jumping** & **Car4** & **Singer1** & **Walking1** & **Walking2** & **Sylvester** & **Deer** & **FaceOcc2** \\ \hline OPM [41] & 2.7 & 4.1 & 3.9 & 2.6 & 0.5 & 2.4 & 0.9 & 4.7 \\ Ours & 3.0 & 2.4 & 7.0 & 3.4 & 8.2 & 6.6 & 4.4 & 9.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average center location error comparison with occlusion probability mask-based tracker (OPM) [41] on some sequences in OTB-100. The red number is the best result.  
Figure 7: The evaluation results on TColor-128 (Temple Color). The left shows the precision plots, and the right is the success plots.  
Our proposed method was more robust to PO, SBC and DEF, as shown in Table 7. This shows that the MBD-based object descriptor could effectively alleviate the influence of background information in the bounding box. ACFN [18] performed best for handling HBC due to its powerful deep features. As mentioned in Section 3.1, we regard boundary patches of bounding box as seeds. If the boundary patches have heavy blur, they will affect the initialization of the seeds and lead to our algorithm not performing best on MB. In our future work, we will improve on this shortcoming to make our tracker more robust to these challenges.
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
## 5 Experimental Results
### Scattering Attenuation of Coherent Waves  
The scattering attenuation computed using the signal processing method described in Section 3 is illustrated in Figure 8. In Figure 8, four cases of standard particle doses (0, 0.004, 0.008, and 0.012 g) are indicated. In all cases, the scattering attenuation increases as the frequency increases. The three particle dosage cases (0.004, 0.008, and 0.012 g) exhibit higher attenuation values than the zero-dosage case across the presented frequencies. The increase in attenuation is probably triggered by the particles dosed into the air in the test chamber, which interact with the incident acoustic waves. In the zero-dosage case, the attenuation exhibits non-trivial values above zero across frequencies, thereby indicating that other environmental factors, such as atmospheric moisture, and other dust particles in the air can contribute to the attenuation of coherent acoustic waves.  
To quantitatively investigate the effects of particle dosage on the coherent wave attenuation, attenuation values at several frequencies were obtained from Figure 8. Figure 9 presents the obtained values of attenuation at the frequencies (4-6 MHz with an increment of 0.5 MHz) across a range of standard particle doses. The presented results (Figures 8 and 9) indicate that the attenuation of coherent waves can be used to distinguish clean air and air from dust particles. In Figure 9a, although attenuation is observed to generally increase with the particle dosage increases, this increasing trend is not monotonic. For example, attenuation decreases from 0.004 to 0.008 g of particle dosage at 5.5 and  
Figure 8: Coherent wave attenuation over the excitation frequency region.  
Figure 7: Example of linear regression using the assumed model: (**a**) least-squares fit, and (**b**) coefficient of determination.  
6 MHz. We inferred that the frequency range does not possess sufficient wave energy by the applied ultrasonic transducers, and that the corresponding linear regression is limited, as illustrated in Figure 7. Therefore, attenuation is averaged over the range of frequencies exhibiting a significant positive relationship with the regression in Figure 9b. Then, the increasing trend of the attenuation is clearly observed until the particle dosage of 0.008 g. In the 0.012 g dosage case, attenuation slightly decreases. This might indicate an experimental limitation because the amount of particles was not optimally distributed over the measurement, as presented in Figure 4b.
# A Comparative Study of Web Application Security Parameters: Current Trends and Future Directions
## 4 Comparison of Selected Web Assessment Tools
### Limitations  
Penetration tools have some limitations as well, some of which are described here:  
* These tools cannot detect all the vulnerabilities in web applications. Therefore, these tools are not efficient enough to find vulnerabilities such as information disclosure and encryption flaws.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Evaluated Scanner**} & \multicolumn{2}{c}{**CSS**} & \multicolumn{2}{c}{**SQLI**} & \multirow{2}{*}{**Precision**} \\ \cline{2-2} \cline{4-5}  & **TP** & **FN** & **TP** & **FN** & \\ \hline Acunetix & 139 & 0 & 66 & 0 & 100\% \\ \hline IBM APPSCAN & 6 & 0 & 49 & 0 & 100\% \\ \hline Nessus & 200 & 0 & 43 & 0 & 100\% \\ \hline BurpSuite & 136 & 1 & 62 & 0 & 99.2\% \\ \hline Wapiti & 11 & 2 & 4 & 0 & 90\% \\ \hline Arachni & 136 & 1 & 60 & 0 & 99.5\% \\ \hline WebInspect & 8 & 0 & 11 & 0 & 100\% \\ \hline Nikto & 9 & 1 & 11 & 0 & 99.5\% \\ \hline Netsparker & 136 & 0 & 64 & 0 & 100\% \\ \hline W3 af & 81 & 0 & 19 & 0 & 100\% \\ \hline OWASP-ZAP & 136 & 0 & 63 & 0 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recall rate of evaluated scanners.  
Access control flaws are harder to detect with these tools. Detecting hard-coded back doors or identifying multi cover attacks are difficult to detect [57].
* There are no specific goals associated with automated penetration testing tools. Therefore, these automated tools must check every possible flaw in web applications [58].