esa-sceva/satcom-chunk-collection huggingface dataset:
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 5 Anomaly Detection
### Visual Analysis  
As a first anomaly detection technique, before proceeding to the machine learning analysis, we have aggregated data in different time intervals in order to perform a preliminary visual analysis.  
Figure 6 plots the number of packets received by the SCK node every two hours. Below, Figure 7 plots the average time between consecutive packets in the RPI network. In this second case, data have been aggregated in 4 h intervals. These figures show the effects that the attacks have had on twoWSNs. For example, in the first plot, we can easily identify three moments in which the SCK node has stopped transmitting. The first two times, the data warehouse has stopped receiving SCK data due to the unavailability of the third party internet service from which data are collected. The third time has been due to the saturation of the SCK device.  
A bit more subtle analysis of the plots shows the effect of the jamming attack. In Figure 7, around 10 to 13 June, the time between packets increases. Around the same time, in Figure 6, the number of received packets is greatly reduced.  
This type of visual analysis is useful for smart city IT administrators or data analysts to quickly review the state of their systems. Furthermore, analysts should define certain rules to automatically get alerted in case of a single or a correlation of variables exceeding certain thresholds. However, network data can be very dynamic and their behaviour can evolve over time. Thus, defining thresholds is often too complex or unfeasible in scenarios such as the smart city. Moreover, security attacks can have unexpected effects without a clear detrimental impact on a single variable, creating instead an anomalous situation that can only be detected if several variables are analyzed at the same time. Therefore, as we previously mentioned, the use of more advanced techniques is very important. In the next section, we use machine learning techniques to perform an anomaly detection analysis in the simulation environment we have presented above.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 2 Materials and Methods
### Methods
#### 2.6.4 Methods for Distinguishing Vegetation Types  
Based on the land cover type map of MCD12Q1 (Figure 1) and the IGBP classification data (Table 1), we roughly divided the vegetation types in the Yangtze River Basin into the following two categories: forests and croplands (type_12). In this study, we investigated all types of forests found within the area, such as evergreen needleleaf forest (type_1), evergreen broadleaf forest (type_2), deciduous broadleaf forest (type_4), and mixed forest (type_5), collectively \"forests\". This categorization facilitates the examination of how various vegetation types respond to the intricate drought and heatwave events of 2022, as well as the alterations in ecosystem carbon balance (we did not include grassland in the forest category). We also conducted a detailed analysis of the monthly average changes in the major vegetation indices for these two primary vegetation types for both reference years and 2022 to gain a more intuitive understanding.
# Software Defined Networks in Wireless Sensor Architectures
## 1 Introduction
In addition, they are flexible since they are managed in a centralized way. Then, SDSNs are easily managed due to the simple way to be programmed due to the availability of different open APIs. On the contrary, SDSNs must offer duty cycles since the sensor energy consumption when the sensors are not taking measurements is an important problem to be solved in this technology. Nowadays, smart phones and tablets have increased the use of cellular networks due to the big demand of such devices. Moreover, despite the huge innovation in mobile applications, the infrastructure of cellular networks is quite fragile. Then, supporting several subscribers, user mobility and real-time adaptation offer some scalability challenges. Finally, we review Wireless Cellular Networks (WCN) [15] providing their architecture and how SDN addresses the problem of this technology. WCN provides the integration of multiple technologies as WiFi [16] and WiMAX [17] within the same network and also supports mobility between them. The transition process from one technology to another is known as handover. Handovers are classified depending on the technology and the domain: if the handover is performed between two-different network technologies, the handover is categorized as inter-technology, otherwise (both network technologies are the same), it is called intra-technology. Several SDN solutions have been proposed in WCN to improve some challenges as resource allocation, interference mitigation and handover between networks. Further challenges are congestion control and load balancing. We gather SDN designs that use centralized control, virtualization, resource allocation, tasks distribution and layered controllers. The rest of the paper is outlined as follows. Section 2 provides background on SDN and OpenFlow. This section also reviews related works in SDN and the main Network Operating System that is being used in SDN model. Section 3 describes the Wireless Sensor Networks and their main characteristics. Moreover, the applicability of this type of network is proposed giving a wide view of what they are able to perform. Section 4 presents the union of Wireless Sensor Networks and Software Defined Networks resulting in Software Defined Sensor Networks. Similarly, Section 5 describes Wireless Cellular Networks architectures and the use of SDN models in these networks. Finally, Section 6 concludes with a discussion of the main themes explained in this work and its conclusions.
## 2 The Experimental Part  
### Methods and Materials  
#### 2.1.1 A New Experimental In-Situ Acidizing Micro-Model Setup  
The core samples are from the Qamchuqa Formation in the Kirkuk oilfield, northern Iraq (Figure 1). This formation was first defined by [23] at the Qamchuqa Gorge in NE Iraq. It is composed of thick-bedded limestones and strongly dolomitized limestones [24; 25].  
The acidizing micro-model setup consists of prepared thin sections obtained from subsurface carbonate core samples, with a final thickness of polished core samples of around 3 \(\upmu\)m. The thin section is attached to the stage of optical microscope (Olympus type) equipped with different lens sizes in order to monitor and image the micro-alteration on the 3 \(\upmu\)m thick reservoir sample over different times (starting from t = 0) and under high-resolution optical microscope using both PPL and XPL view. A digital camera (Nikon MODEL) is linked between the laptop (data recording) and the microscope in order to image the influence of composite acidizing fluids on the thin section and to record the influence of these fluids on enhanced porous media. Several images were carried out under different lens sizes; later, these images were inserted into ImageJ software (version 1.53t, 2022) to estimate the micro-enhancement of the reservoir. The composite acidizing fluids were heated up to a planned temperature (up to 78.3 \({}^{\circ}\)C) using the hot plate stirrer, then the acid was injected into the thin surface of the reservoir formation with a digital micro-pipette to control the volume of the composite acidizing fluid. The monitoring process was observed starting from t = 0 until t = 30 min under the optical microscope in order to record the high-resolution changes over different times. The thin section under optical microscope was injected directly with a composite acid using micro-pipette.  
The thin sections of the carbonate core samples were prepared at the standard 30 \(\upmu\)m thickness for in-situ injecting through the carbonate core samples with a combined acid formulation in different temperatures. The images were analyzed directly under the optical microscope when the acid combination passed through micro-texture of core samples. The thin sections were petrographically studied under standard polarizing and reflecting microscopes before and after the in-situ acidizing injection to calculate the enhanced pore area percentage. The enhancement carbonate samples were observed under microscale sizes.  
Figure 1: Schematic longitudinal cross-section of the Kirkuk structure (after Dunnington, 1958) including the area from which the samples are recovered.
# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
## 4 Machine Learning Models
### Decision Trees  
The DT algorithm is commonly used for both classification and regression tasks. The DT algorithms recursively partition data into subsets based on the values of input features and then assign labels to each subset based on the majority class or average value of the target variable. The resulting tree structure can be used to make predictions on new data by traversing the tree from the root node to a leaf node that corresponds to a specific class or value. According to [PERSON] [74], decision trees are particularly useful for problems with discrete-valued output variables and can handle both categorical and continuous input features. They are also easy to interpret and visualize, making them a popular choice for exploratory data analysis and decision-making tasks. Figure 6 presents a sample decision tree structure to provide insights into the relationships and decision-making process within the data, aiding in understanding and interpreting the model's predictions. The node numbers are depicted within the boxes, while the input features are represented by the variables (see Sections 1 and 3). The green leaves in the figure represent the target value, \(V_{s}\)The implementation of the DT algorithm for regression tasks is usually performed using the rpart package [75]. The package provides ranges of DT hyperparameters, including complexity parameters, maximum number of trees, minimum number of splits, etc., that can be tuned through grid search or Bayesian optimization. DT algorithms have been used for a variety of geotechnical applicatons, including classification [76; 77] and soil parameters predictions.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background
### Energy Readrage Techniques
#### 2.3.2 Battery Swapping  
This method can be performed either by _swapping_ or by _hot-swapping_, as shown in Figure 2. In the first case, when the UAV battery is depleted, the drone must be directed from its current location to the ground control station (GCS) to replace it with another UAV that has a fully charged battery. The limitation of this method is the number of spare UAVs required and the service interruption time due to flying to and from the ground station [20]. With hot-swapping, the UAV descends to the GCS to have charged batteries inserted immediately, allowing it to resume operations in less time. However, this approach requires human assistance for battery swapping [20].
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 3 Method
### Multi-Temporal InSAR for Bridge Monitoring
Note that deformations mean displacements along the Line of Sight of the satellite. A negative deformation means that the PS moves away from the satellite, while a positive deformation means that the PS moves towards the satellite. Figure 8 shows some results obtained during the MT-InSAR data processing of our dataset. Figure 8: Some preliminary and final results obtained during the MT-InSAR data processing of our dataset. Example of (**a**) coherence and (**b**) phase of an interferogram of two images belonging to the  
dataset; (**c**) reflectivity map; (**d**) DEM in SAR coordinates; (**e**) average spatial coherence for all the interferograms generated--the map is related to the interferograms generated by connecting the images using the Minimum Spanning Tree (MST) graph [11]; (**f**) amplitude stability index; (**g**) map of the PSs of the case-study area (not filtered by coherence threshold); (**h**) detail of the PSs of the A22 Po River Bridge; (**i**) picture of the bridge aligned as in the (**h**).
# Connectivity Analysis in VANETs with Dynamic Ranges
## 1 Introduction
### Contribution  
This paper presents a simple analytical framework for connectivity that accounts for the statistical variations in network topology. Building upon existing research, probabilities of connectivity to encompass the impact of wireless fading channels and obstructions posed by large vehicles--a crucial consideration in real-world vehicular environments are derived. Our approach draws inspiration from well-established wireless fading models, including Weibull, Nakagami-\(m\), Rayleigh and lognormal distributed channels capturing highway, urban and rural networks communication environments in order to improve vehicle connection as in [1]. These models have been instrumental in the field of wireless communication, providing valuable insights into the behavior of signal propagation in varying conditions. Our main contributions are:  
* Firstly, the paper introduces a method for dynamically computing the probability of successful communication between vehicles in VANETs. Utilizing dynamic communication range statistics based on random channel models, the approach effectively accommodates the fluctuating nature of VANET environments, accounting for factors like vehicle density and signal propagation. This contribution enhances the accuracy of inter-vehicle communication assessment in dynamic VANET scenarios.
* Secondly, the paper contributes by introducing and utilizing two distinct traffic models--free-flow and synchronized Gaussian unitary ensemble (GUE)--to simulate nuanced vehicle dynamics on multi-lane roads. The free-flow model depicts independent vehicle movements, while the GUE model represents synchronized behaviors, enhancing the realism and applicability of traffic simulations. This dual-model approach enriches the understanding of multi-lane road scenarios, offering valuable insights for transportation and traffic engineering applications.
* Lastly, the paper also introduces a versatile mapping framework, enabling the calculation of vehicle-to-vehicle connectivity probability across diverse traffic models. The proposed framework offers a generalized approach, making it valuable for assessing connectivity probabilities in various traffic scenarios, particularly beneficial for applications in traffic simulation. This contribution enhances the adaptability and utility of connectivity assessments in dynamic traffic environments.
# How to Isolate Non-Public Networks in B5G: A Review
## 3 Spectrum Isolation

Table 1: Performance metrics and application scenarios of the seven NPN deployments. needs to be transmitted in time window \(i\). Then, the requirement for spectrum isolation is denoted by \(z_{g}\). It is calculated by dividing the amount of time during which the QoS requirement is met \(t_{g}\) by the total observation time \(T_{g}\); i.e., \(z_{g}=t_{g}/T_{g}\). Then, the index of spectrum isolation performance for NPN \(g\) denoted by \(s_{g,i}\) is defined to represent the dependence on the resources provided by other NPNs. Thus, \(s_{g}\) can be calculated by dividing the amount of resources it owns and uses by the amount of resources it uses; i.e., \((r_{g}-\alpha_{g,i})/(r_{g}-\alpha_{g,i}+\beta_{g,i})\). Spectrum assignment among NPNs is designed to meet quantized isolation requirements. For spectrum isolation based on QoS parameters, the isolation indicators can be used to decide whether to reallocate spectrum among NPNs or not, since isolation indicators can reflect whether QoS requirements are met or missed intuitively. The spectrum assignment of NPNs can converge gradually to the minimum amount of resources when the isolation requirements are met [17]. For spectrum isolation based on resource sharing, the isolation indicators can be used to guide the sharing of the spectrum among NPNs, since they reveal the impact of resource sharing on QoS performance metrics. According to the guidance, more or less spectrum will be shared among NPNs, aiming to meet their QoS requirements as best as possible [22]. However, the spectrum shared among NPNs should fulfill the spectrum isolation requirements. After adjusting the spectrum allocation, if the QoS requirements or the isolation requirements are still not fulfilled, the QoS requirements and/or the number of NPNs served by the system should be reduced. The advantages, challenges and solutions for spectrum isolation are summarized in Table 2.
# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 4 Results  
### Finger Vein Datasets Description  
In this section, we first discuss the details of the datasets used, followed by the setup of the experiments. The effectiveness of our proposed ViT-Cap model was evaluated on four publicly available finger vein datasets: MMCBNU from Jeonbuk National University in South Korea [33], SDUMA from Shandong University in China [34], FV-USM from University Sains Malaysia [35], HKPU from the Hong Kong Polytechnic University [7], and MIXFV. The primary reason for using these specific datasets was that most existing finger vein recognition methods are evaluated by tests performed on one or more of these datasets. The details of these four public finger vein databases are shown in Table 1.  
Information from the MMCBNU database was collected from 100 volunteers, including 83 men and 17 women. Each volunteer provided the index, ring, and middle fingers of both hands, and the collection was repeated 10 times per 6 fingers, for a total of 60 finger vein images from each volunteer.  
The SDUMA database was created by Shandong University in China, with a dataset of finger veins from 106 volunteers, each of whom provided the index, middle, and ring fingers of both hands, so a total of 3816 images were collected with a resolution of 320 \(\times\) 240 pixels.  
The FV-USM database was collected from 123 volunteers, including 83 men and 40 women, ranging in age from 20 to 52. Each volunteer provided four fingers: left index finger, left middle finger, right index finger, and right middle finger, resulting in a total of 492 finger classes, and the finger image resolution captured was 640 \(\times\) 480.  
The Hong Kong Polytechnic University dataset consisted of finger vein images collected from men and women volunteers. About 93% of the participants were under 30 years old. Finger images were obtained over two separate time periods, with a minimum interval of one month and a maximum interval of more than six months. In each session, each volunteer provided six image samples from the index and middle fingers, and each sample consisted of a finger vein and finger texture image from the left hand.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Database**} & **Sampling** & **Image** & **Number of** & **Number of** & **Total Images** \\  & **Object** & **Resolution** & **Classes** & **Fingers** & **Total Images** \\ \hline MMCBNU & 100 & 320 \(\times\) 240 & 600 & 6 & 6000 \\ SDUMA & 106 & 320 \(\times\) 240 & 636 & 6 & 3816 \\ FV-USM & 123 & 640 \(\times\) 480 & 492 & 4 & 5904 \\ HKPU & 156 & 513 \(\times\) 256 & 624 & 4 & 3132 \\ \hline \end{tabular}
\end{table}
Table 1: Details of public finger vein datasets.  
The MIXFV contains 100 categories selected from the top 100 categories of SDUMLA, FV-USM, MMCBNU and HKPU. The MXIFV database is divided as follows: the training set has 27 training samples for each category from four different public finger vein databases, and the test set has 13 test samples for each category, also from four different public finger vein databases.
## 5 Towards Enhanced Use of High Resolution EO Data in Earth System Modelling
### Links with EO Satellite Data Providers  
Many important land surface geophysical parameters can be inferred from satellites including: soil moisture, snow cover, surface temperature and LAI. Due to their indirect nature and given the currently used assimilation systems, remotely sensed observations are still significantly under exploited. Used products are generated by complex external processing levels (L2 to L4) which can potentially introduce inconsistencies with other atmospheric observations and processes. Coupled data assimilation systems would address these issues by allowing a consistent handling of these observations across systems, provided systematic differences (e.g., due to calibration) are removed by bias correction schemes, that can be integral part of the data assimilation system [350]. Moreover, a stronger link between observations and data assimilation, will by design enable a quality control system with reduced latency.
## 2 Methodology  
### Extraction of Cellulose via (NaDES) and Microwave-Assisted Process  
The following method of cellulose extraction was adopted from our previous study [8]. Empty fruit bunch date palms were transported from a compound within the vicinity of King Saud University in Riyadh, Saudi Arabia. The biomass was washed several times with tap water and then dried for 48 h at 80 \({}^{\circ}\)C in a laboratory dryer. The biomass was then ground with a cutting mill and sieved into powder form. The time required to obtain 1 kg of the material having a particle size of 0.25 mm was approximately 1 h. To process the biomass, 1 g was mixed with 30 mL of an alkali reagent (NaOH, 5% \(w/v\)) in a TANK eco microwave extraction system (SINEO, Shanghai, China) for 10 min at 90 \({}^{\circ}\)C. After irradiation, the solution was allowed to cool to room temperature (24 \({}^{\circ}\)C) for about 30 min before centrifugation. The dark liquid from microwave extraction was separated using a high-speed centrifuge for 10 min. Successive washings with deionized water and ethanol by centrifugation were performed to adjust the pH neutral (pH 7), as shown in Figure 1, and to remove unwanted residuals. After washing, the sample was dried for 8 h at 85 \({}^{\circ}\)C.  
Figure 1: Process flow diagram of the extraction process from raw date palm biomass.  
A chlorine-free, environmentally safe bleaching reagent, 30% H\({}_{2}\)O\({}_{2}\)\(v\)/\(v\) (pH 5.9), was subsequently mixed with the alkali-treated dry biomass in a microwave reactor for 10 min at 65 \({}^{\circ}\)C. The bleached sample was then spun in a centrifuge and rinsed with deionized water and 96% \(v\)/\(v\) ethanol to remove any remaining excess reagents and non-cellulosic components until a neutral pH was achieved. The sample was dried again for 8 h at 85 \({}^{\circ}\)C. Following this, 15 mL of 1 M H\({}_{2}\)SO\({}_{4}\) and 6.5 g of NaDES (in a 1:1 molar ratio of choline chloride-to-citric acid anhydrate) were added, serving as a hydrolytic medium. The dried, bleached solid was further mixed and placed in a microwave reactor with NaDES and H\({}_{2}\)SO\({}_{4}\) at 90 \({}^{\circ}\)C for 10 min. Subsequent washing of the resulting solid was performed with deionized water and 96% \(v\)/\(v\) ethanol using a high-speed centrifuge. The DES-treated sample was dried for 8 h at 85 \({}^{\circ}\)C. The dried solid product was collected from the dryer and then manually ground into a fine powder using a ceramic or glass mortar. Finally, the extracted microcellulose powder was stored in a well-sealed container in a dry place for further characterization. The surface and morphological analysis of the synthesized cellulose samples was conducted under vacuum conditions using a scanning electron microscopy (SEM) Model VEGA II LSU (Tescan, Brno-Kohoutovice, Czech Republic).