esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Architecture Proposal  
In this section, the architecture proposal for exploiting the previously explained framework will be detailed. As introduced before, one of the goals of applying this architecture is to obtain an ecosystem for generating and providing information visualizations as a service.  
The ecosystem is planned to be a holistic set of well-defined components that provide unitary services, but that can also be combined to obtain a complete pipeline. Every service has well-defined interfaces that enable the connection of information flows among them. These services provide support for the generation of information dashboards that compile with the previously presented meta-model.  
One of these services is the dashboard generator (Figure 2), based on plain JavaScript through the D3.js framework to allow better integration with external services avoiding other dependencies. The dashboard generator service accepts HTML requests containing information about the visualization component to craft. Specifically, this service is developed as an API in which the input is a JSON object with the configuration of an entire dashboard or a single visualization:* Information about the dataset or datasets to be displayed. Data sources could be external APIs or files.
* The disposition or layout of the elements.
* The features of the visualization: \(\odot\) Number and type (X position, Y position, size, color, etc.) of visual channels; \(\odot\) Visual mark type (bar, circle, topographic, arc, etc.); \(\odot\) Dataset's variables to be represented; \(\odot\) Interaction events and effects [42].  
The service processes this JSON object; then, the source code is generated using the previous section's code templates. These returned source code files are returned to the client, which could embed them in its own applications or use them standalone.  
On the other hand, the ecosystem might support other information visualization-related tasks, such as data transformations. Formatting data is essential to support some encodings or layouts [43], so a service that carries out this task and unburdens the front-end with these computations can be connected to the dashboard generator component to offer a complete pipeline.  
As the dashboard generator, this service is also developed as an API (Figure 3). In this case, the input data will provide information regarding the computations to perform. Target data must be sent along with the following configuration parameters to enable the service to perform the requested operations:  
* The set of variables from the dataset that will take part in the computations;
* The operation or operations to be performed (summary statistics, regressions, ratios, etc.);
* Filters (optional);
* Groupings (optional);
* Output data layout: tabular (default), nested, linked, etc.  
Figure 2: Schematic view of the dashboard generator service architecture.
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## 2 Preliminaries
### Key-Dependent Message Security  
We now define key-dependent message security by a game played between the challenger and the adversary \(\mathcal{A}\), and KDM security guarantees the direct encryption of the secret key \(sk\) and its correlation function \(f(sk)\). The KDM attack capability of the adversary \(\mathcal{A}\) is mainly determined by the collection of secret key functions \(\mathcal{F}\) that it can query, expressed as \(\mathcal{F}\subset\left\{f|f:\mathcal{K}^{\ell}\rightarrow\mathcal{M}\right\}\), where \(\mathcal{K}\) and \(\mathcal{M}\) are the secret key space and message space of the encryption scheme. Given public keys \(\left\{pk_{1},\ldots,pk_{\ell}\right\}\) and encryption of the key-dependent message \(f(sk_{1},\ldots,sk_{\ell})\), the adversary \(\mathcal{A}\) cannot effectively distinguish it from the ciphertext that is encrypted by the message \(\left\{0,1\right\}\), and so we can call the scheme KDM-CPA secure with respect to \(\mathcal{F}\). \(\mathcal{F}\) is a family of sets of functions parameterized by the security parameter \(\lambda\) and the number of users \(\ell\). The game proceeds as follows:  
1. The challenger chooses a bit \(\mu\leftarrow\left\{0,1\right\}\). Run the scheme's key generation algorithm \(\ell\) times. It gives \(\left\{pk_{1},\ldots,pk_{\ell}\right\}\) to the adversary \(\mathcal{A}\).  
2. Adversary \(\mathcal{A}\) makes encryption queries of the form \((i,f)\), where \(1<i<\ell\) and \(f\in\mathcal{F}\). To process a query, the challenger computes \(m\gets f(sk_{1},\ldots,sk_{\ell})\), then computes the challenge ciphertexts and returns to the adversary \(\mathcal{A}\).  
\[c=\left\{\begin{array}{cc}Enc(pk_{i},m),&\mu=0,\\ Enc\big{(}pk_{i},0^{\left|m\right|}\big{)},&otherwise.\end{array}\right. \tag{2}\]  
3. Adversary \(\mathcal{A}\) attempts to guess \(\mu\) and outputs \(\mu^{\prime}\in\left\{0,1\right\}\).  
The scheme is KDM-CPA secure if for every probabilistic polynomial-time adversary \(\mathcal{A}\), the distinguishing advantage \(Adv(\mathcal{A})=\left|\Pr[\mu=\mu^{\prime}]-1/2\right|\leq\mathrm{negl}(n)\). This shows that the scheme can securely encrypt any functions \(\mathcal{F}\) of its own secret key, taking the place of a message.  
The KDM-CPA security definition of the symmetric-key scheme is similar. In the first stage, the challenger generates the secret key without giving anything to the adversary \(\mathcal{A}\). In the second stage, it uses the secret key for encryption (and uses it as the input of \(f(sk)\)). Everything else is just the same.
## 2 pH Control Equipment and Control Model
### System Model Building  
Considering the control characteristics of the liquid fertilizer pH value control system comprehensively, the first-order system transfer function and delay link are used to describe the mathematical model of the fertilizer solution's pH value. The parameters to be identified in the model include the system gain \(K\), delay time \(\tau\) and time constant \(T\). The transfer function is shown in Equation (2):  
\[G(s)=\frac{K}{Ts+\tau}e^{-\tau s} \tag{2}\]  
Given the step response of pH = 6.5 as the input signal of the open-loop system, the sampling time interval is set to 1 s, the initial pH value of the mixed liquid is 7.6 and the step response curve of the system is fitted using the first-order approximation method of the computer-fitted system according to the data change of the pH value, then it is identified that the system gain \(K\) is 0.56, the delay time \(\tau\) is 4 and the time constant \(T\) is 39. The pH regulation process has a time lag.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion  
### Data Quality Assessment of WSN Sensors  
The data quality assessment of the WSN soil sensors was performed at the validation locations in site 2 where data logger measurements were accompanied by sensor node measurements at the same time and location. Only the validation results at the midpoint location of site 2 are presented here.  
Figure 7 shows the comparison results of soil temperature, T (Figure 7a), WP, \(\psi\) (Figure 7b), and SM, \(\theta\) (Figure 7c), for the time period between 29 July and 23 August 2016 for the data logger DL2 and the nodes 2282 and 2292 at two depths near the midpoint of the hill in site 2. The soil temperature measurements (based on the MPS-2) from the WSN peak slightly higher than the data logger measurements (light lines in Figure 7a) and are indistinguishable at the validation location at the bottom of the hill (not shown). The WP measurements (also based on the MPS-2) are slightly lower at both depths from the WSN compared to the data logger (Figure 7b). The SM measurements (based on the EC-5) are nearly indistinguishable between the WSN nodes and the data logger.
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 5 Discussion
### Matching Outcomes Affected by Different POS Accuracy  
The proposed method depends on the EO (exterior orientation) of the camera. The POS device adopted in this paper is AEROcontrol which is made by IGI Corp (Integrated Geospatial Innovations, Kreuztal, Germany). Its angle-measuring device IMU (Inertial Measurement Unit) is of high accuracy. Angle-measuring precision is 0.005 degree at pitch and roll direction, separately, and 0.008 degree in yaw direction. However, this device is also expensive. In many applications, ordinary POS is more common, and the platform is more unstable on UAVs (Unmanned Aerial Vehicles). So, it is important to find out how many errors of rotations can be accepted.  
Image pairs 2 and 3 were taken as test objects. Because the IMU angles are the main factors which affect the POS accuracy, different grades of noise were added to the three rotation angles. To this end, 15 different noisy angles ranging from \(0^{\circ}\) to \(20^{\circ}\) were chosen. Figure 18a,b show the number of correct matches and the matching accuracy under different noisy angles, respectively. For a better comparison, the number of correct matches using the LJL method (Figure 18a), which is a constant value of 130, is shown. Similarly, the matching accuracy values of 78.3% are shown in Figure 18b.  
Figure 18: Matching outcomes with different angles of noise: (**a**) The number of correct matches; (**b**) Matching accuracy.  
From the above discussion, it can be seen that no obvious changes for matching outcomes occur with noisy angles varying from \(0^{\circ}\) to \(2^{\circ}\). It presents a slight decrease, with noisy angles varying from \(2^{\circ}\) to \(5^{\circ}\) and \(5^{\circ}\) to \(10^{\circ}\), whereas a drastic decrease happened during tuning up noisy angles beyond \(10^{\circ}\). To avoid such uncertainties, it is a good choice to fix the threshold at \(5^{\circ}\). That is, when the IMU on a platform can provide an angle-measuring precision higher than \(5^{\circ}\), the proposed method can be adopted. Therefore, it is applicable for general UAV aerial photogrammetry.
## 6 Challenges and Future Research  
The future development of nano-EOR faces many challenges. The main challenges for the petroleum industry are the limitations of technology, economic aspects, and environmental and health issues. Even though numerous studies have shown that NPs could increase oil recovery, most of them are limited to small laboratory scales and are not yet applicable for large-scale field implementation. Several limitations hinder the application of NPs on a field scale:  
* Stable nanofluids for larger-scale application are not difficult. As NPs tend to agglomerate under reservoir conditions (high temperature, pressure, and salinity), the preparation of homogenous NP suspensions is still a challenge.
* Several mechanisms for improving recovery in nano-EOR have been proposed. However, these mechanisms and the influencing parameters are not thoroughly understood. The interactions between NPs and rock surfaces need to be studied further.
* A fundamental understanding of nano-EOR is limited. The lack of theoretical and numerical investigation into nano-EOR seems to be one of the reasons for this. Most numerical studies refer  
Figure 6: Glass micromodel picture of the oil displacement process with nano-polymer solution at different NP concentrations. Reproduced with permission from reference [83]. Copyright 2013 Taylor & Francis.  
to the colloidal particle model, which does not perfectly describe NP behavior. The models are also limited to the physical interactions of NPs and have not yet considered the chemical interactions.
* Due to the fast advancement of nanotechnology, health and safety studies are lagging behind. One of the important challenges for NP development is the lack of knowledge on the effects of various types of nanoparticles on the human body. Due to nano-scale size, NPs can be easily inhaled by humans and potentially deposited inside the lungs.
* In light of these challenges, future research on nano-EOR should focus on the following aspects:
* Research on the preparation of stable nanofluids in large quantities needs to be performed in consideration of economic aspects.
* Several NPs have proved to exhibit different characteristics and mechanisms for EOR. However, no research has yet been proposed on the application of mixtures of nanofluids. New applications and better performance may be possible by enabling two or more nanofluids to function together.
* Pilot projects of nano-EOR need to be carried out. These projects will improve the understanding of the nano-EOR processes in real-world conditions. Optimization studies on nano-EOR parameters are also recommended to improve recovery and cost efficiency.
* Experimental work needs to be done to confirm the adsorption and desorption behavior during NP flow. Reversible and irreversible adsorption need be studied further, both experimentally and theoretically, because they will affect the deliverability of NPs to the target zone.
* Integrated research on the health and safety of NPs must be done to prevent the risk to humans and the environment.
## 4 Beam Steering/Beam Forming Using Variable Source Antenna
The maximum gain of the transparent transmitarray is 19.5 dBi, corresponding to the minimum SLL \(<-\)12.4 dB, ranging from 27.5 GHz to 29.5 GHz. This work is a remarkable candidate for reducing the visual impact for 5G communication systems and smart beam steering technology. A planar multi-focal source transmitarray antenna beam scanning performance is explored in [113]. The proposed planar multi-focal transmitarray antenna pros are described in this article over the traditional transmitarray. A quad-focal phase shift distribution was studied by applying the particle swarm optimization technique for \(\pm\)60 degrees beam scanning range for Ku-band transmitarray. The findings of this study show that a multi-focal transmitarray antenna can be an excellent low-cost option for high gain beam forming applications. In normal conditions, multi-focal transmitarray constraints give an extra degree of freedom that cannot be achieved without variations in the lattice geometry or transmitarray curvature. However, the phase error can be addressed by optimizing the array aperture phase distribution mechanism. At 14.5 GHz, a 260 mm aperture diameter with F/D equal to 1 is built to exhibit quad-focal transmitarray. The azimuth plane \(\pm\)60 degrees beam scanning performance is examined. This phenomenon shows exceptional beam scanning accomplishment for a quad-focal transmitarray.
# A Review on Internet of Things for Defense and Public Safety
## 3 Target Scenarios for Mission-Critical IoT
### C4 isr  
C4 ISR systems use millions of sensors deployed on a range of platforms to provide advanced situational awareness. Radar, video, infrared or passive RF detection data are gathered by surveillance satellites, airborne platforms, UAVs (Unmanned Aerial Vehicles), ground stations, and soldiers in the field. These data are delivered to an integration platform that analyzes them and delivers information up and down the chain of command. These platforms provide a Common Operational Picture (COP) allowing for enhanced coordination and control across the field.  
High-level military echelons are provided with comprehensive situational awareness through central operations centers, which receive data feeds from platforms. Lower levels (i.e., platoon, soldiers) also have access to the data in their area. In the case of combat pilots, they receive prioritized data feeds integrated with data from their own sensor systems.
## 2 Machine Learning Applications in Big Data
### Transportation and Smart Cities  
ML applications in transportation are reshaping urban mobility and the development of smart cities. Autonomous vehicles, one of the most significant advancements in this domain, rely on ML algorithms to process data from a multitude of sensors, including cameras, light detection and ranging (LiDAR), and global positioning systems (GPSs). These algorithms enable vehicles to perceive their environment, make real-time decisions, and navigate complex traffic conditions safely and efficiently. The continuous influx of data from millions of miles driven allows these models to learn and improve, reducing the likelihood of accidents and enhancing the reliability of self-driving cars [50; 51; 52; 53].  
In addition to autonomous driving, ML is applied to optimize traffic management systems in smart cities. By analyzing data from traffic sensors, surveillance cameras, and social media, ML models can predict traffic patterns, optimize traffic light sequences, and reduce congestion. This leads to more efficient use of infrastructure, lower emissions, and improved quality of life for urban residents [54; 55; 56; 57].  
ML is also crucial in the logistics and supply chain sectors, where it is used to optimize routing, inventory management, and demand forecasting. Companies use ML to predict delivery times, optimize delivery routes, and manage inventory levels in real time, reducing costs and improving customer satisfaction. The integration of ML with IoT devices in smart cities further enhances these capabilities by providing real-time data on traffic, weather conditions, and other factors that influence logistics operations [58; 59; 60; 61].
## 4 Results
### Evaluation and Optimization  
The best models identified from the previous experiments were tested using a held-out testing data set. To further optimize performance, we adjusted the n_gram_range parameter to include 2- and 3-grams. Figure 2 shows the results of testing before and after optimization. The results indicated that, while incorporating 3-grams slightly improved the NPMI score, it negatively impacted the overall performance. An analysis shows that the CAMeL model configured with 2-grams achieved the best balance, attaining high NPMI scores while maintaining efficient and acceptable performance. This suggests that 2-grams offer a more optimal configuration for achieving coherence and efficiency in the topic modeling task, when compared to 3-grams.  
Figure 1: NPMI coherence score.
## 3 Listening Test 1
### Results
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Pearson Correlation–Listening Test 1 (Tonality)**} \\ \hline Annoyance & 1 & 0.963 ** & 0.763 ** & \(-\)0.200 & -0.260 * & 0.249 * & 0.354 ** & 0.225 * & 0.454 ** \\ Loudness (ISO) & & 1 & 0.778 ** & \(-\)0.133 & \(-\)0.155 & 0.247 * & 0.386 ** & 0.324 ** & 0.469 ** \\ Sharpness (ISO + Aures) & & & 1 & \(-\)0.091 & 0.031 & 0.046 & 0.272 ** & 0.247 * & 0.439 ** \\ Tonality LOW (DIN) & & & 1 & 0.349 ** & \(-\)0.019 & 0.692 ** & 0.321 ** & 0.018 \\ Tonality MID (DIN) & & & & 1 & \(-\)0.238 & 0.184 & 0.696 & 0.034 \\ Tonality HIGH (DIN) & & & & 1 & 0.342 ** & 0.047 & 0.572 ** \\ Tonality LOW (HMS) & & & & 1 & 0.543 ** & 0.539 ** & \\ Tonality MID (HMS) & & & & 1 & 0.322 ** & 1 & 0.322 ** \\ Tonality HIGH (HMS) & & & & 1 & \(\phantom{-}\) & 1 & \(\phantom{-}\) \\ \hline \hline \end{tabular}  
* Correlation is significant at the 0.05 level (2-tailed) ** Correlation is significant at the 0.01 level (2-tailed).  
\end{table} 
Table 6: Correlations between annoyance estimations and selected psychoacoustic parameters with defined tonality regions. Figure 13: Mean annoyance evaluations vs. single value tonality calculations, based on (**a**) Aures model; (**b**) hearing model. Listening Test 2 (Comparison of Different Test Methods and Loudness vs. Sharpness Factorial Design)  
The second listening test was performed to investigate the possible interaction between loudness and sharpness. Stimuli were generated in the form of a factorial design, with selected loudness and sharpness values. One sample of vacuum cleaner noise without tonal components was selected, and its loudness and sharpness values were systematically changed by filtering. Four different sub-tests were conducted to investigate this possible interaction. In addition, three different experimental methods were used in these sub-tests to investigate the possible bias due to the experimental method. Nine participants were asked to rate annoyance of the vacuum cleaner noise signals in these three experiment methods. Finally, the results from the different test methodologies were compared and a repeated measures ANOVA was conducted to investigate the possible loudness sharpness interaction.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 3 6G-IoT: New Technologies and Emerging Services
### Four-Dimensional Communication  
Geographical coverage will exceed current boundaries, and communication will occur in four dimensions: on the ground, underwater or underground, in the air, and in space. Thus, a unified communication environment will be formed in which connectivity and services will be uninterruptedly provided by 6G-IoT [46]. Unmanned Aerial Vehicles (UAVs) and satellites are the key enabling technologies that will make this scenario feasible. Satellites provide wireless coverage in various earth orbits, including low (LEO), medium (MEO), and geostationary (GEO) earth orbits, thereby enhancing the current terrestrial telecommunications infrastructure and enabling the efficient network coverage of rural, remote, and geographically isolated areas [65]. Furthermore, new satellite systems in Very Low Earth Orbit (VLEO) will be developed, which will provide considerable advantages over traditional satellite systems, such as reduced satellite size and improved energy efficiency. VLEO systems also offer lower signal path losses and reduced latency communications, making them ideal for telecommunications applications [46].  
In the airborne networking scenario, UAVs will be utilized as relays or even as mobile base stations in the event that a portion of the terrestrial network is compromised or destroyed during an emergency situation [66]. Additionally, they can be combined with satellite systems, forming a hybrid non-terrestrial network with advanced capabilities [47]. In addition, communications between submarines and specialized equipment for various ocean life monitoring services will be feasible in the underwater sector thanks to transponders linked to the terrestrial infrastructure. Following the concept of four-dimensional communication, underground sensors or devices embedded in the soil can also be employed in order to collect data on various parameters like moisture levels and nutrient content. In both cases, the main research challenge is to mitigate the increased signal attenuation caused by soil or water layer interference on the transmission path. This, in turn, impacts network topology, as there is a tradeoff between the energy consumption and communication range of underground/underwater sensor nodes. Thus, in this context, new technologies, such as the Internet of Underground Things (IoUT) [67], Wireless Underground Sensor Networks (WUSNs) [68], and Internet of Underwater Things (IoWTs) [69], are gradually advancing. Consequently, the enhanced monitoring, analysis, and prediction services that Agriculture 5.0 will offer will be substantially aided by the global continuous connectivity and reliable low-latency communication of 6G-IoT Non-Terrestrial Networks (NTNs) [38; 70].