esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Works and Discussion  
In our simulations we will be using the design model of our previous work [7,8], where we had modified the original 802.11 DCF to design an improved contention based MC-DCF protocol to perform channel switching in a multichannel single radio environment. We improve on this environment at the sink node, where previously we observed that channel switching among nodes by a single radio at the sink node causes severe degradation. This result in high packet delay and delivery ratio, we will be analyzing the performance at the sink of the MC-DCF protocol by simulations with NS2 [49]. This model uses a graphical user interface which provided various simulation scenarios. Users can select different network protocols, different topology and traffic model as well as users can design their own environment from the TCL script and the C\(++\) library. This model includes radio parameter which incorporates interference information, traffic information and channel utilization information. The 802.11 radio model of the NS2 is used; this model has different topology and traffic generator. Different simulation scenarios will be studied according to three different performance metrics: aggregate throughput, delivery ratio and access delay. The sensor nodes are randomly placed in a 1,000 \(\times\) 1,000 m\({}^{2}\) area. The radio range is set to 50 m and simulations run for 500 s in each scenario. The radio bandwidth is 10 Mbps. We maintained these settings from our previous work [7-9] where we observed MC-DCF performed well in comparison to other range and rates. The number of nodes is 100. The numbers of channels used are three non-overlapping IEEE 802.11 ones that were used in our previous work [7, 8] and which we use to compare with the current result and measure the performance improvement. Since the spectral mask only defines power output restrictions up to \(\pm 11\) MHz from the centre frequency to be attenuated by 30 dB. It is often assumed that the energy of the channel extends no further than these limits. Our simulation uses static nodes to mimic surveillance sensor network with high data rate streaming that would be deployed for organization, parks, and vehicular traffic with nodes that are always powered, as such the energy consumption of the nodes are based on the power output \(\pm 11\) MHz.  
With the improvements made at the sink(s) to receive data directly from sending nodes within range we will be comparing these current simulation results obtained with the previous results from our work in [7, 8] to determined the level of performances in percentages, that is new results minus the previous results divided by the previous results multiply by 100 (NR-PR/PR \(\times\) 100). From the formulated solutions and equations derived to improve the degradation at the sink node encountered from our previous work we will consider the solutions that obtain better performance as the most feasible option to consider for our future MC-DCF.
## 2 Related Work
The study compared several machine learning algorithms, including ANN, SVM, RF, XGBoost, k-NN, Gradient Boosting Machine (GBM), and ARIMA. The most successful model was based on RF, achieving an accuracy of 92%. Additionally, the research analyzed the variables' importance, identifying that internal humidity, dew point, and external temperature are critical factors in predicting energy consumption in the innovative farm. A study in [39] presented a model to predict the temperature inside a greenhouse using time series analysis and the LightGBM. The model considered environmental factors such as humidity, air pressure inside and outside the greenhouse, external temperature, and time series data to make the temperature predictions. The study found that incorporating time series features improved the R\({}^{2}\) and reduced the Mean Square Error (MSE) and the MAE across several prediction models. Other models, such as RNN, SVR, and LR, were also compared, but LightGBM outperformed them all regarding model fit. In [40], a Dense Neural Network (DNN) framework was proposed to predict temperature and relative humidity measurements. According to the results, the framework shows a high correlation of 0.91 and 0.85 for temperature and humidity, respectively. The framework has significantly reduced prediction errors, with a 68.67% reduction for temperature and 46.21% for relative humidity compared to an approach without the DNN model. Several investigations, such as [41; 42; 43; 44], have also contributed relevant studies to agricultural areas using artificial intelligence focused on the geotechnical properties of soil. Researchers have used machine learning techniques to predict crucial soil properties like thermal conductivity and mechanical behavior. Methods like LR, Gaussian process regression, SVR, DT, RF, and adaptive boosting have been employed to predict soil thermal conductivity. Furthermore, researchers have utilized neural network techniques like LSTM to model the mechanical behavior of frozen soils. This comprehensive approach has provided a deeper understanding of soil properties and has become an essential tool in geotechnical engineering.
## 2 Materials and Methods  
### Sample Collection  
_Mytilus galloprovincialis_ mussels (N = 40 per sampling site) were collected from three sampling sites on the Croatian coastline and three sampling sites on the Slovenian coastline of the northern Adriatic (Figure 1) in February 2014. A few aspects were considered in selecting the sampling sites. One aspect was the types of mussel, which were native and farmed. Native mussels were collected at the pristine site, St. Catherine Island (Katarina, Croatia, 45\({}^{\circ}\)07\({}^{\prime}\)67\({}^{\prime\prime}\) N 13\({}^{\circ}\)62\({}^{\prime}\)96\({}^{\prime\prime}\) E), and farmed mussels were collected at 2 sites, Strunjan and Seca (Slovenia, 45\({}^{\circ}\)816\({}^{\prime}\)24\({}^{\prime\prime}\) N 13\({}^{\circ}\)834\({}^{\prime}\)57\({}^{\prime\prime}\) E and 45\({}^{\circ}\)807\({}^{\prime}\)50\({}^{\prime\prime}\) N 13\({}^{\circ}\)844\({}^{\prime}\)10\({}^{\prime\prime}\) E). The other aspect was the degree of pollution at the sampling sites. In brief, the pristine site is in the open area with less anthropic activities, and polluted sites were considered as highly, moderately, and slightly contaminated sites depending on the anthropic activities and measured data. The site defined as highly polluted was ACI marina Rovinj (Rovinj, Croatia, 44\({}^{\circ}\)87\({}^{\prime}\)55\({}^{\prime\prime}\) N; 13\({}^{\circ}\)84\({}^{\prime}\)67\({}^{\prime\prime}\) E), moderate was ACI marina Pula (Pula, Croatia, 44\({}^{\circ}\)887\({}^{\prime}\)55\({}^{\prime\prime}\) N 13\({}^{\circ}\)884\({}^{\prime}\)67\({}^{\prime\prime}\) E), and the slightly contaminated site was near the Kopar Marina (Kopar, Slovenia, site code 00 TM). The polluted sites were selected according to previous monitoring data available on the water quality, the location, and the environments nearby [87]. The mussels were put in ice bags and transferred to the laboratory.  
Figure 1: Six sampling locations of the mussel _Mytilus galloprovincialis_ in the northern Adriatic: Katarina (native), Strunjan and Seč (cultured), and Kopar, Pula, and Rovinj (polluted—ACI marinas).
## 3 Results and Discussion
### Discussion  
Combining the decrease in single communication static power consumption and the reduction in the number of communications, the single communication power consumption without energy-saving measures is 150.876 J, the number of single-profile communications without data compression algorithm is 75 times, and the total consumption is 11,315.700 J; the single communication power consumption of measures with low energy consumption is 34.276 J, the number of single-profile communications with data compression algorithm measures is 20 times, and the total power consumption is 685.520 J. Therefore, after the energy-saving design of the HTTP, the remote communication energy consumption of a single profile detection data of the buoy is reduced by 10,630.180 J, which is 93.942% lower than the original single profile communication energy consumption.  
The differential coding-Huffman data compression method has been found to be more effective at reducing power consumption than implementing communication management for the Beidou short message service terminal. The analysis suggests that this outcome occurs because the date data (year, month, and day) for individual profile detections exhibit a high degree of repetition, and the variability in temperature, salinity, and depth measurements demonstrates strong regularity, which leads to enhanced data compression results.
## 4 The WBANs Communication Stack
### Routing Protocols
#### 4.3.3 Cluster-based Routing Protocols
It consumes more energy and it does not consider the packet delivery latency. As shown in Table 12, cluster-based routing protocols for WBANs are compared in terms of delay, packet delivery ratio, security, and energy consumption performance metrics.
## 3 Proposed Method
### Specific Structure of SSD7-FFAM  
Figure 2 depicts the specific structure of the proposed SSD7-FFAM. In SSD [4], the feature maps extracted by VGG and additional convolutional layers are respectively used for object location and classification. However, the initial shallow feature maps lack important semantic information, and this problem causes the detection accuracy to be inferior to the two-stage detectors. Therefore, SSD is not conducive to the detection of small objects. Unlike SSD7 [33], the proposed SSD7-FFAM adopts two novel modules: the feature fusion module and the attention module, based on SSD7, to compensate for the reduction of detection accuracy caused by the decrease in convolutional layers. The feature fusion module combines two feature maps of different scales into a new feature map after transformation. This module enhances the semantic information of shallow feature maps. The attention module is a lightweight module that combines channel attention and spatial attention. It dramatically improves network performance while bringing a small amount of calculation and parameters.
## 3 Tree-Based Entropy Structured Self-Organizing Maps--Tessom
### TESSOM Algorithm
At this point, two processes define the label prediction:  
* The winning label of the neuron is identified as the label that had the highest number of occurrences at the time of training, and the test objects have their labels predicted with this result. The predicted label is confronted with the real label indicated by the object for the generation of confusion matrix (matrix that identifies the number of objects classified correctly or incorrectly by their label), and the final result is the set of results in all leaf neurons that were subjected to test objects. * Classifications are generated with the \(kNN\) algorithm, using a number of variable clusters within the range from 2 to 10, and the selection of the \(kNN\) cluster with the best result is done by analyzing the error curve. The cluster with the lowest value is selected. The objects used are the same ones identified by the training neuron, and the subset of new objects that, by the prediction in the **TESSOM** tree, reached the leaf neuron, are used for the test. At this point, the confusion matrix obtained by the test for this neuron will be retrieved as the values for this neuron. Again the accuracy will be based on the sum of all highlighted confusion matrices for **TESSOM**. This solution allows comparing accuracy results measured by various accuracy metrics in order to make adjustments to the model. As the structure for calculating accuracy is preserved, the confusion matrices can be compared at each level of the tree to identify the best results for each node of the tree. Due to the way in which **TESSOM** processes the objects in the training set (SOM training and tree organization), the computational complexity \(T\) of the algorithm should be defined as a composition of complexity computations for the SOM in conjunction with its tree structure. For the SOM, as defined by [57], complexity can be defined as:  
\[T=O(KMDn)\]  
Figure 3: TESSOM flowchart. where \(K\) represents the number of training epochs, \(M\) is the total number of neurons in the net grid, \(D\) is the number of object dimensions (number of attributes) and \(n\) is the number of objects. To complement the definition of the tree-based SOM (TESSOM), the definition presented by [58] identifies the complexity equation for decision trees, which is used in this work as the basis for calculating the asymptomatic complexity, which can be defined as:  
\[T=O(n)+O(mn\log_{2}n)+O(n\log_{2}n) \tag{3}\]  
where \(n\) represents the number of objects and \(m\) the number of attributes. Assuming that \(K\), \(M\) and \(D\) are constants, then Equation (2) can be simplified using \(S=(K\times M\times D)\). Thus, the theoretical computational complexity of the TESSOM algorithm can be represented using the equation defined in (4) :  
\[T=O(Sn)+O(mn\log_{2}(n))+O(n\log_{2}(n))+O(n) \tag{4}\]  
Equation (4) summarizes the theoretical computational complexity for TESSOM, considering the batch processing used by the SOM training, the entropy calculation and the tree generation.
# Development of Open-Source Tools for Event-Based Hydrological Modelling Using GIS and Python
## 3 Results and Discussion
### Discussion
Despite these limitations, we present a general framework for producing exchangeable spatial information that can be easily processed in Python. While this paper details the use of the framework for specific hydrological methods, the overall approach--and, in many cases, the information used and the outputs presented in this paper--can be applied to a wide range of hydrological simulations. Exiting proprietary GIS-integrated hydrological models typically provide user-friendly interfaces and robust toolsets for delineating watersheds, extracting drainage networks,and parameterising hydrological models. However, they often operate as black boxes, limiting the user's ability to customise routines. In contrast, the QGIS--Python approach leverages QGIS's extensive GIS capabilities while allowing users to implement and modify Python scripts for parameter estimation, rainfall-runoff modelling and post-processing of results, promoting transparency and adaptability in the modelling workflow. Practitioners can leverage the presented framework for practical hydrological studies by coding their specific functions depending on the analysis they wish to perform. This can help them develop modelling tools specifically tailored to address questions not covered by proprietary models. Future work could focus on integrating additional Python libraries for hydrological modelling (e.g., PyCatch, PyTOPKPAPI), coupling with hydraulic models for floodplain analysis, and developing user-friendly interfaces within QGIS to facilitate the adoption of the methodology by practitioners and stakeholders without extensive programming experience. However, despite the potential development of user-friendly interfaces, we strongly believe that leveraging Python IDEs to develop tailored applications remains the best alternative for maximising the utility of the presented methods. Similarly, future developments could target the integration of weather information from weather stations by leveraging spatial interpolation tools (i.e., Krigging algorithms) to provide spatially distributed information for hydrological models. Of particular interest for addressing events-based hydrological processes would be to develop of tools capable of processing weather station data to extract storm events and subsequently apply spatial interpolation to generate distributed storm event datasets for hydrological modelling.
# Automatic Selective Encryption of DICOM Images
###### Abstract  
Securing DICOM images is essential to protect the privacy of patients, especially in the era of telemedicine and eHealth/mHealth. This increases the demand for rapid security. Nevertheless, a limited amount of research work has been conducted to ensure the security of DICOM images while minimizing the processing time. Hence, this paper introduces a selective encryption approach to reduce the processing time and sustain the robustness of security. The proposed approach selects regions within medical images automatically in the spatial domain using the pixel thresholding segmentation technique, then compresses and encrypts them using different encryption algorithms based on their importance. An adaptive two-region encryption approach is applied to single and multi-frame DICOM images, where the Region of Background (ROB) is encrypted using a light encryption algorithm, while the Region of Interest (ROI) is encrypted using a sophisticated encryption algorithm. For multi-frame DICOM images (Approach I), additional time-saving has been achieved by almost 10,000 times faster than the Naive encryption approach, and 100 times better compression ratio, using one segmentation map based on a pre-defined reference frame for all the DICOM frames. For single-frame DICOM image (Approach II), a multi-region selective encryption approach is proposed, where the ROI is further split into three regions based on potential security threats, using a mathematical model that guarantees shorter encryption time in comparison with the Naive and the two-region encryption approaches, with almost 47% and 14% saving times, respectively. Based on the estimated processing time, Approach I outperformed Approach II noticeably. Further, cryptanalysis metrics are utilized to evaluate the proposed approaches, which indicate good robustness against a wide variety of attacks.  
medical image encryption; selective encryption; region of interest; segmentation; LZ4 compression; multi-frame DICOM object Article
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
###### Abstract  
The threat of quantum-computer-assisted cryptanalysis is forcing the security community to develop new types of security protocols. These solutions must be secure against classical and post-quantum cryptanalysis techniques as well as feasible for all kinds of devices, including energy-restricted Internet of Things (IoT) devices. The quantum immunity can be implemented in the cryptographic layer, e.g., by using recent lattice-based key exchange algorithms NewHope or Frodo, or in the physical layer of wireless communication, by utilizing eavesdropping-resistant secrecy coding techniques. In this study, we explore and compare the feasibility and energy efficiency of selected cryptographic layer and physical layer approaches by applying an evaluation approach that is based on simulation and modeling. In particular, we consider NewHope and Frodo key exchange algorithms as well as novel physical layer secrecy coding approach that is based on polar codes. The results reveal that our proposed physical layer implementation is very competitive with respect to the cryptographic solutions, particularly in short-range wireless communication. We also observed that the total energy consumption is unequally divided between transmitting and receiving devices in all the studied approaches. This may be an advantage when designing security architectures for energy-restricted devices.  
Keywords:communication; security; physical layer security; secrecy coding; post-quantum cryptography; energy-efficiency; Internet of Things (IoT); simulation +
Footnote †: journal:
# Frequency-Bounded Matching Strategy for Wideband LNA Design Utilising a Relaxed SSNM Approach
###### Abstract  
This paper proposes relaxed Simultaneous Signal and Noise Matching (SSNM) conditions to address limitations in selecting source degeneration inductors for multistage LNA design, achieved by introducing controlled mismatches at the external ports. Additionally, a novel frequency-bounded mismatch envelope is introduced to guide load termination selection based on desired IM-OM (input mismatch-output mismatch) characteristics across the operating band. Building on these concepts, a systematic, easy-to-follow strategy is presented for implementing wideband multistage low-noise amplifiers (LNAs), significantly reducing reliance on blind CAD-based optimisation. This approach is validated through a three-stage MMIC LNA prototype, fabricated using a 0.15 \(\upmu\)m GaAs process and operating from 28 to 34 GHz. The measured results closely match the simulation, demonstrating a stable gain of 23 \(\pm\) 1 dB and a noise figure of 2-2.5 dB, confirming the practical effectiveness of the proposed design approach for wideband amplifiers.  
IM-OM circles; wide-band impedance matching; mismatch regions; MMIC; LNA; GaAS pHEMTs; relaxed SSNM; frequency bounded mismatch envelope Article
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 2 Proposed Method Framework  
The flowchart of the proposed ship tracking algorithm is illustrated in Figure 2. The algorithm consists of three steps, which are ship detection, position correction and ship tracking. First, ship detection is realized by a local visual saliency map based on peak signal: noise ratio. In addition, then the ship position is corrected by the RPCs model using AIS data as GCPs. To correct the error of RPCs, AIS data are correlated with the detection of GF-4 satellite in the image coordinates, and then the associated point pairs are used to compensate the error parameters. Finally, an improved MHT framework with amplitude assisted is applied to track ships and estimate the ships' motion parameters. The algorithm is introduced below in detail.
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 4 System Parameter Design
### Sliding Spotlight Factor and Data Acquisition Period  
The sliding spotlight factor \(\eta\) of the SSS-RSSAR determines how fast the beam is steered in azimuth. Similar to the traditional sliding spotlight SAR system, the sliding spotlight factor of the SSS-RSSAR can be estimated by the time of the azimuth resolution improvement with respect to its stripmap mode, the SS-RSSAR, as  
\[\eta=\frac{\rho_{\rm a}}{\rho_{\rm a0}} \tag{47}\]  
Figure 6: Wavenumber data distributed in: (**a**) polar format; (**b**) keystone format; and (**c**) rectangular format when employing the new parameter adjusting manipulation.  
where \(\rho_{\rm a}\) and \(\rho_{\rm a0}\) denote the azimuth resolutions of the SSS-RSSSAR and the SS-RSSSAR images, respectively. Based on the data acquisition geometry of the SS-RSSSAR in [17], \(\rho_{\rm a0}\) can be estimated by  
\[\rho_{\rm a0}=\frac{V_{\rm s}\sin\psi}{B_{\rm a0}} \tag{48}\]  
where \(\psi\) and \(B_{\rm a0}\) denotes the central squint angle and Doppler bandwidth with respect to the swath center, respectively. By assuming the beam width is \(\varphi_{\rm a}\), \(B_{\rm a0}\) can be estimated by  
\[B_{\rm a0}\approx\frac{2f_{\rm c}}{c}V_{\rm s}\varphi_{\rm a}\sin\psi \tag{49}\]  
By substituting Equation (49) into Equation (48), \(\rho_{\rm a0}\) can be rewritten as  
\[\rho_{\rm a0}=\frac{c}{2f_{\rm c}\varphi_{\rm a}} \tag{50}\]  
By substituting Equation (50) into Equation (47) and replacing the carrier frequency \(f_{\rm c}\) with \(f_{\rm c0}\) if the new PA technique is adopted, the sliding spotlight factor \(\eta\) of the SSS-RSSSAR can be set as  
\[\eta=\frac{2f_{\rm c0}\varphi_{\rm a}\rho_{\rm a}}{c} \tag{51}\]  
To ensure that all targets within the ROI can be fully illuminated, the minimum DAP \(t_{\rm a}\) can be estimated as  
\[t_{\rm a}=\frac{W_{\rm a}}{V_{\rm g}}+\frac{R_{\rm c0}\varphi_{\rm a}}{V_{\rm s }\cos\theta} \tag{52}\]  
By substituting Equations (1) and (51) into Equation (52), \(t_{\rm a}\) can be rewritten as  
\[t_{\rm a}=\frac{cW_{\rm a}}{2f_{\rm c0}\varphi_{\rm a}\rho_{\rm a}V_{\rm s} \cos\theta}+\frac{R_{\rm c0}\varphi_{\rm a}}{V_{\rm s}\cos\theta} \tag{53}\]