esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Scenarios and Illustrative Applications
### Urban Setting: Montreal, Canada
In urban environments like Montreal, the HARISM framework can be customized through targeted sensor deployment at high-density pedestrian zones, transit hubs, and critical infrastructure like external staircases and pedestrian bridges. Urban implementa  
Figure 4: Montreal external staircases (in winter). tions would emphasize interconnectivity with existing smart city infrastructure, including traffic management systems, public Wi-Fi networks, and municipal emergency services. The framework would be configured for high-density data processing using edge computing nodes distributed throughout the urban landscape, enabling real-time hazard detection despite potential interference from building structures and electromagnetic noise. Urban customization would also include specialized HAR algorithms trained to identify complex activity patterns in crowded settings, such as distinguishing between normal commuter movements and distress behaviors that might indicate falls, medical emergencies, or safety hazards. Additionally, the system would integrate with building management systems and public transportation networks to coordinate responses across multiple stakeholders typical of dense urban environments while maintaining strict privacy protocols essential in areas with high surveillance sensitivity.
## 5 Implementation and Evaluation  
### Experimental Setup  
For the evaluation of the effectiveness of the proposed model, the simulation environment had to be created by considering the IoT nodes, edge servers, and a central unit. The factors of hardware limitation, data distribution, and the software environment had been considered during the design of the experimental setup so that real IoT-edge device limitations and behaviors can be represented as accurately as possible.  
The system infrastructure was made up of 20 edge servers, each serving as a distributed node. To emulate the resource constraints that any edge device would normally have, each edge server was configured with four CPUs, eight gigabytes of random-access memory, and one hundred gigabytes of disk space. A centralized unit was put into a high-performance server with 16 CPU processors, 64 GB RAM, and 2 TB disk space to handle the tasks like the administration of the ontology and the global model aggregation.  
Each edge server was assigned a different dataset, (\(D_{j}\)), with 5000 samples in order to represent the diverse real-world applications of IoT devices. For this research, we utilize the dataset from the \"CityPulse EU FP7 project\" [33]. This dataset represents big data collected through IoT sensor devices deployed in smart cities. It includes road traffic, weather, and pollution data gathered from the cities of Aarhus; Denmark; and Brasov, Romania. Eighty percent of the datasets were unique to the local characteristics of each edge server, while twenty percent reflected common shared attributes. The datasets were distributed in a _non-IID_ manner.  
The software environment was built using TensorFlow Federated to implement the federated learning algorithms, specifically FedProx and FedAvg. Protege was used for the design and management of the ontology to ensure semantic consistency across the heterogeneous datasets. Data preprocessing, simulation, and the orchestration of tasks were handled using Python and its associated libraries, such as NumPy, Pandas, and Scikit-learn.  
The simulation environment was containerized using Docker, allowing the edge servers and central unit to be emulated in a controlled and scalable infrastructure. Communication between the edge servers and the central unit was implemented over a simulated network, enabling the evaluation of communication efficiency and bandwidth usage under realistic constraints.  
This setup effectively emulates the conditions of IoT-edge environments and provides a robust platform for evaluating the performance, scalability, and interoperability of the proposed framework.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
###### Abstract  
The propagation delay in mobile underwater acoustic sensor network (MUASN) is asymmetric because of its low sound propagation speed, and this asymmetry grows with the increase in packet travel time, which damages the collision avoidance mechanism of the spatial reuse medium access control (MAC) protocols for MUASN. We propose an asymmetric propagation delay-aware time division multiple access (APD-TDMA) for a MUASN in which periodic data packet transmission is required for a sink node (SN). Collisions at the SN are avoided by deferring data packet transmission after reception of a beacon packet from the SN, and data packets are arrived at the SN in a packet-train manner. The time-offset, which is the time for a node to wait before the transmission of a data packet after reception of a beacon packet, is determined by estimating the propagation delay over two consecutive cycles such that the idle interval at the SN is minimized, and this time-offset is announced by the beacon packet. Simulation results demonstrate that the APD-TDMA improves the channel access delay and the channel utilization by approximately 20% and 30%, respectively, compared with those of the block time bounded TDMA under the given network conditions.  
mobile underwater acoustic sensor networks (MUASNs); asymmetric propagation delay; medium access control; time-division multiple access (TDMA) +
Footnote †: journal: Applied Sciences
# Analysis of Factors Influencing the Prices of Tourist Offers
## 3 Selecting Optimal Price Predictors
### Preliminary Analysis of Method Parameters
#### 3.3.1 Series Length for Time Varying Features  
The aim of this step was to evaluate the influence of parameter \(k\) on the quality of time-varying features, calculated as the best fit slopes on the basis of the last \(k\) values. Mutual information coefficient was chosen as a measure of usefulness of these features in the defined classification task. It measures the amount of information shared by the two variables together and is defined as follows [25]:  
\[I(X;Y)=\sum_{x_{i}\in X}\sum_{y_{i}\in Y}P(x_{i},y_{i})log\frac{P(x_{i},y_{i})}{ P(x_{i})P(y_{i})}, \tag{1}\]  
where \(P(x_{i},y_{i})\) is the joint probability of \(x_{i}\) and \(y_{i}\). In this case, variables \(X\) and \(Y\) are the feature and the class.  
Figure 3 presents the values of the mutual coefficient for the seven features calculated on the basis of series of values depending on series length \(k\). The dependency has been demonstrated for each value of \(n\).  
It can be observed that MI for _barrel_price_serie_ and _usd_avg_serie_ almost does not change for different values of \(k\). For other features based on the series, higher values of \(k\) lead to higher MI, suggesting that longer series lead to better features in our classification problem. This regularity is less clear only for _destination_from_current_temp_serie_. However, it should be noted that the inflation and unemployment indices are updated no more than once a month. It means that the best fit slope calculated on the basis of values from the last several days is usually 0. The longer the series, the higher chance that any change in inflation or unemployment rate is observed. The low variance of these features based on the short series is the reason for lower MI. Another important observation is that usually lower values of \(n\) are associated with higher MI values, which means that the proposed features seem to be better predictors in the case of shorter forecast horizons.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Split**} & \multirow{2}{*}{**Training Data**} & \multicolumn{4}{c}{**Test Data**} \\ \cline{3-6}  & & \(n=7\) & \(n=14\) & \(n=21\) & \(n=28\) \\ \hline
1 & 1 January–30 April & 7 May–6 June & 14 May–13 June & 21 May–20 June & 28 May–27 June \\
2 & 1 January–31 May & 7 June–7 July & 14 June–14 July & 21 June–21 July & 28 June–28 July \\
3 & 1 January–30 June & 7 July–6 August & 14 July–13 August & 21 July–20 August & 28 July–27 August \\
4 & 1 January–31 July & 7 August–6 September & 14 August–13 September & 21 August–20 September & 28 August–27 September–28 September \\
5 & 1 January–31 August & 7 September–7 October & 14 September–14 October & October & October \\
6 & 1 January–30 September & 7 October–6 November & 21 October–20 November & 28 October–27 November \\ \hline \hline \end{tabular}
\end{table}
Table 3: Splitting data into training and testing subsets.
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 3 Method
### Cross-Scan Mechanism  
The key to the success of the Mamba method lies in its use of the Selective State Space Model (S6 model), originally designed for natural language processing (NLP) tasks. The S6 model reduces quadratic complexity to linear by enabling each 1D vector element to interact with previously scanned information. However, since visual signals lack the inherent order of text sequences, the S6 model cannot be directly applied to visual tasks.  
To address this, we introduce the cross-scanning module proposed in VMamba, which adapts the S6 model for extracting global feature information from images. As illustrated in Figure 4b, this module is implemented as the 2D-SSM block. To process visual images in the S6 block, we propose a four-direction scanning strategy. Taking the one direction as an example, the 2D feature map is flattened into a 1D sequence of \(L\) blocks. The State Space Model (SSM) updates the state \(h_{k}\) at each block \(k\) according to the following equation:  
\[h_{k}=\bar{A}h_{k-1}+\bar{B}x_{k} \tag{10}\]  
where \(h_{k}\) is the state vector at block \(k\), \(h_{k-1}\) is the state vector at the previous block \(k-1\), \(x_{k}\) is the input feature at block \(k\), and \(\bar{A}\) and \(\bar{B}\) are the discretized parameters (refer to Equation (2) in the VMamba paper).  
At each block \(k\), the output \(y_{k}\) is computed as follows:  
\[y_{k}=\bar{C}h_{k}+\bar{D}x_{k} \tag{11}\]  
where \(y_{k}\) is the output at block \(k\), and \(\bar{C}\) and \(\bar{D}\) are the discretized parameters.  
In the horizontal direction, all outputs \(y_{k}\) for blocks \(k=1,2,\ldots,L\) are concatenated to form the output feature \(Y_{d}\):  
\[Y_{d}=[y_{1},y_{2},\ldots,y_{L}] \tag{12}\]  
where \(Y_{d}\) is the output feature in the horizontal direction, \(y_{k}\) is the output at block \(k\), and \(L\) is the number of blocks in the horizontal direction.  
The cross-scan mechanism simultaneously scans the feature map from four directions. The outputs from these directions are then combined to construct the final 2D feature map as shown in Figure 5. This strategy ensures that each feature element integrates information from different directions, forming a global receptive field while maintaining the linear computational complexity.
## 3 Method
### Pre-Processing  
The pre-processing of the proposed system is mainly carried out to extract log-mel spectrum features from the input audio waveform through signal processing method, which involves time-frequency transformation and auditory feature simulation. First, the raw audio signal \(x(n)\) is divided into short frames (20 ms duration) with an overlapping of 50%, and each frame is multiplied by a Hamming window to reduce spectral leakage; this window function is  
\[w(n)=0.54-0.46\cos(\frac{2\pi n}{N-1}). \tag{1}\]  
After that, short-time Fourier transform (STFT) is applied to all frames to obtain the frequency representation of the \(x(n)\), given by  
\[X(k)=\sum_{n=0}^{N-1}x(n)w(n)e^{\frac{-2\pi n}{N}}, \tag{2}\]  
followed by calculating the power spectrum  
\[P(k)=|X(k)|^{2}. \tag{3}\]  
In addition, a mel filterbank is designed to mimic human auditory perception, where linear frequency is converted to mel scale via a function, as follows:  
\[m=2595\log_{10}(1+\frac{f}{700}), \tag{4}\]  
where \(m\) and \(f\) are the frequency in linear and mel scales, respectively. A group of triangular filters are uniformly spaced on the mel scale. Each filter's response \(H_{m}(k)\) forms a triangular shape in the frequency domain. The power spectrum is projected onto these filters to compute the energy in each mel band. That is,  
\[E(m)=\sum_{k}P(k)H_{m}(k). \tag{5}\]  
Finally, the logarithmic operation \(\log(E(m)+\epsilon)\) (where \(\epsilon\) is a small constant to prevent numerical instability) is applied to compress the dynamic range and align with human perception of sound intensity. The time-frequency matrix is the log-mel spectrogram, which provides a perceptually relevant representation of audio signals.  
Figure 1: The framework of the proposed anomaly detection system.
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 2 Design, Composer, and Experimental Setup
### Kirigami Structure Design  
The kirigami structures were fabricated with arrays of patterns into polyester plastic sheets (PET) (\(E=4.33\) GPa, \(\
u=0.4\)), chosen for their balance of flexibility and durability. The intricate kirigami patterns enabled the creation of designs tailored for specific frictional and locomotive roles: linear cuts as a baseline with high stability but minimal friction; triangular cuts, inspired by the Puff Adder's sharp-edged scales (\(\sim\)5-8 mm [7; 18; 24]), for enhanced anisotropic friction and anchoring; and trapezoidal cuts, inspired by whiptail lizard ventral scales (\(\sim\)4-7 mm [25; 26]), for balanced flexibility and support (Figure 1a). Prior biological studies have shown that snakes exploit directional friction coefficients (forward vs. backward vs. lateral) to optimize motion, often producing passive anchoring at specific body segments through contact angle modulation and scale placement [4]. This behavior has been quantitatively linked to scale geometry and surface interaction. Our asymmetrical kirigami cuts are explicitly designed to emulate these biomechanical strategies by deforming asymmetrically under compression, thereby maximizing anchoring in the desired direction of motion. Proposed design parameters for kirigami pattern analysis for optimization included a lattice unit defined by width \(L=2l\text{cos}(\pi/6)\) and height \(H=2l\text{sin}(\pi/6)\), where \(l\) is the cut length and \(\delta\) is the hinge width separating cuts at an angle \(\gamma=\pi/3\) (Figure 1b). Fabrication involved cutting with the Silhouette Cameo 5 (Silhouette America, Inc., Lehi, UT, USA), producing expandable kirigami structures (Figure 1e,f).  
Figure 1: Bioinspired kirigami design and fabrication workflow. **(a)** Triangular and trapezoidal scale patterns of the Puff Adder and whiptail lizards used as biological inspiration [15; 24]. **(b)** Geometric definition of kirigami unit cells, showing linear, triangular, and trapezoidal types. **(c)** CAD designs of kirigami patterns, including linear, triangular, trapezoidal, and hybrid cuts. **(d)** PET sheets with different thicknesses selected to tune flexibility and stiffness. **(e)** Fabrication of kirigami sheets using a Silhouette Cameo 5 digital cutting machine. **(f)** Resulting physical kirigami samples displaying clean, high-precision cuts across different patterns.
# Localizing the First Interstellar Meteor with Seismometer Data
## 3 Discussion  
The locations reported by the DoD in the CNEOS catalog are rounded to the tenths place in longitude and latitude. This suggests that the reported location, \((-1.3^{\circ},147.6^{\circ})\), corresponds to a square connecting \((-1.35^{\circ},147.65^{\circ})\), \((-1.35^{\circ},147.55^{\circ})\), \((-1.25^{\circ},147.55^{\circ})\), and \((-1.25^{\circ},147.55^{\circ})\). The area of this region is \(\sim\)120 km\({}^{2}\). The acoustic localization described here implies a distance of \(83.9\pm 0.7\) km from the AU MANU seismometer at \((-2.0432^{\circ}\), \(147.366196^{\circ})\), and overlaps with the DoD-provided location only within a \(\sim\)16 km\({}^{2}\) region connecting \((-1.337^{\circ},147.650^{\circ})\), \((-1.350^{\circ},147.647^{\circ})\), \((-1.318^{\circ},147.55^{\circ})\), and \((-1.305^{\circ},147.55^{\circ})\). This reduces the uncertainty in the location of the fireball by a factor of \(\sim\)7.5 in terms of area. This reduction in the geographic uncertainty of the IM1 fireball improves the search efficiency in the forthcoming ocean expedition to recover its fragments [3]. In addition, the direction in which the IM1 was traveling through the atmosphere fortuitously aligns with the distance constraint derived here, due to the serendipitous location of the AU MANU seismometer. This is beneficial for the ocean expedition search, as the fragments are expected to fall along the extrapolated post-fireball ground track trajectory of the meteor [4]. More precise modeling work can be undertaken in the future, along the lines of [12].  
Conceptualization, A.S. and A.L.; writing--original draft preparation, A.S.; writing--review and editing, A.S. and A.L. All authors have read and agreed to the published version of the manuscript. This research was funded in part by a grant from the Breakthrough Prize Foundation and by research funds from the Galileo Project at Harvard University.  
Not applicable.  
We thank [PERSON] and [PERSON] for providing the crucial meteorological data used in this work. The authors declare no conflict of interest.
###### Abstract  
To proactively respond to changes in droughts, technologies are needed to properly diagnose and predict the magnitude of droughts. Drought monitoring using satellite data is essential when local hydrogeological information is not available. The characteristics of meteorological, agricultural, and hydrological droughts can be monitored with an accurate spatial resolution. In this study, a remote sensing-based integrated drought index was extracted from 849 sub-basins in Korea's five major river basins using multi-sensor collaborative approaches and multivariate dimensional reduction models that were calculated using monthly satellite data from 2001 to 2019. Droughts that occurred in 2001 and 2014, which are representative years of severe drought since the 2000s, were evaluated using the integrated drought index. The Bayesian principal component analysis (BPCA)-based integrated drought index proposed in this study was analyzed to reflect the timing, severity, and evolutionary pattern of meteorological, agricultural, and hydrological droughts, thereby enabling a comprehensive delivery of drought information.  
remote sensing; integrated drought monitoring; meteorological drought; hydrological drought; agricultural drought; Bayesian principal component analysis (BPCA); statistical simulation 2021  
3
## 2 Materials and Methods
### Binary Evaluation of M-Scan Fringe Washouts  
The SD-OCT M-Scans were examined for fringe washout using an automated OCT dosimetry algorithm. In the evaluation, which took place post-treatment, the automated OCT dosimetry algorithm detects fringe washouts in three basic steps, whereas each M-scan is processed separately (Figure 3). First, the algorithm performs an intensity scaling and gamma correction for contrast enhancement and sums up the intensity values of each A-scan within the M-scan. Second, the data are convolved with a dedicated filter kernel to create a peak response to possible fringe washouts. Third, a dynamic threshold that adapts to noise and intensity differences is applied to the processed M-scans to identify fringe washouts based on the peak response from step two. According to the equation depicted in step No. 3. in Figure 3, the threshold (\(T\)) is based on a central tendency measure (\(M\)) of the processed M-scan signal, a dispersion measure (\(D\)) of the central tendency and a sensitivity factor (\(\kappa\)) referred to as the \(\kappa\)-value. The sensitivity factor describes that the value of the peak (fringe washout) must be \(\kappa\) times the dispersion D away from the central tendency M [37].  
In view of a future in vivo application, the \(\kappa\)-value was defined as a variable parameter that statically modulates the response and that must be selected manually by the operator. This will give the dosimetry algorithm a certain flexibility to adapt to different anatomical and optical eye conditions of patients. To check which algorithm setting could be suitable for future in vivo applications, the M-scan data were analyzed for fringe washouts with different \(\kappa\)-values. Therefore, all acquired M-scans were evaluated with \(\kappa\)-values ranging from 6 to 40 (\(\kappa\) = 6, 9, 10, 11, 12, 13, 14, 15, 20 and 40). The selection of this range is based on previous [PERSON] correlation coefficient classifications [37]. The outcome of the M-scan evaluation was compared to the binary calcein-AM evaluation of the porcine RPE-choroid-sclera explants and classified by using the confusion matrix presented in the following section. Statistical measures were then analyzed  
Figure 3: Flowchart describing how an M-scan is processed by the automated OCT dosimetry algorithm. Step 1: intensity scaling, gamma correction and intensity summation. Step 2: signal convolution with filter kernel. Step 3: dynamic threshold for peak detection (fringe washout).  
characteristic (ROC) curve. The ROC of an algorithm shows its performance as a tradeoff between selectivity and sensitivity, with the optimum performance in the top-left corner of the graph. Therefore, we used the ROC curve to determine the dosimetry algorithms optimal \(\kappa\)-value by checking the optimal operating point (OOP) for each pulse duration. Furthermore, we calculated the area under the ROC curve (AUC) in order to determine for which pulse duration, the dosimetry algorithm showed the best overall performance.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 2 Related Work
### Standardization Activities
#### 2.1.8 Summary  
ETSI MEC has been working on a reference architecture for the evolution of the mobile network edges, in order to create a cloud environment close to the RAN, while ETSI MANO has been developing a MANO framework for the provisioning of VNFs and NSs in data centers. Furthermore, ETSI oneM2M has been designing an end-to-end high level architecture for M2M communications, while OMA LwM2M is working on the same path, but focused on device management and security functionalities. Additionally, the cloudlet paradigm is extending the cloud paradigm by placing resources close to mobile devices, while the OpenFog Consortium by using the fog computing paradigm is placing cloud resources at the edges of the network to meet the strict requirements introduced by IoT use cases. Finally, TOSCA is complementing all these standardization efforts by designing a standard language for modeling service orchestration in cloud environments in a highly extensible and flexible manner.  
However, all the cited standardization activities still face industry challenges for the full definition of management and orchestration technologies. Most of the discussed reference architectures are still immature and require further investigation and development before they can be operationalized and used by network operators. Research efforts relevant for the specification and implementation of a MANO framework for 5G-enabled smart cities are detailed next.
## 5 Experiments and Results
### Comparison of SC-M* to Baselines  
We next compared the SC-M* to other SC-based MAPP algorithms, including SC-A* (optimal) and SC-CBS (suboptimal), in the one-resource-one-type environment.