esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Technologies for Carbon Dioxide Capture from the Air
## 2 DAC Technologies
### Adsorption
#### 2.2.1 Physisorbents  
Physisorption occurs between the sorbent surface and CO2, allowing only a physical interaction. Generally, materials with porosity or nanometric dimensions, such as zeolites, activated carbon, MOFs and new materials, have been considered [54]. Activated carbons are characterized by low cost and high thermal stability, showing an adsorption capacity and selectivity lower than that of zeolites at a low CO2 partial pressure but higher at a high CO2 partial pressure [55; 56]. For this reason, activated carbons are not a good choice for direct air capture [6].  
Factors influencing the CO2 capture of zeolites are size, charge density and distribution of cations in the porous structure [57]. Humidity has a negative effect on CO2 capture, and for this reason zeolites are not suggested for direct air capture [6].  
MOFs are characterized by flexibility in shape, pore structure, pore size and surface properties [58]. Similar to activated carbons, adsorption capacity and selectivity are lower at low CO2 partial pressures and in the presence of humidity; MOFs are suitable for CO2 storage and not for CO2 separation. However, in recent years, attention has been directed to MOFs (particularly MIL-101(Cr)) functionalized with amine (adsorption of amines onto metal sites or physical impregnation with liquid amines) which provide better performances for CO2 capture from the air due to their durability under moist conditions [39]. Currently, the high cost of MOFs makes their use in DAC prohibitive [6].  
Among novel materials, boron nitride nanosheets [59] and nanotubes [60] have been investigated for the capture of CO\({}_{2}\) from the air. These materials have the ability to capture CO\({}_{2}\) from the air due to the introduction of electrons and to spontaneously desorb CO\({}_{2}\) when electrons are removed.  
Generally, adsorption with physisorbents is characterized by a low adsorption capacity due to the dilute concentration of CO\({}_{2}\) in the air and the negative effect of moisture. Some physisorbents are shown in Figure 5, while some data about adsorption capacity are reported in Table 1.
## 2 Related Work on Bridging Between Sensors and Applications
### Middleware for Sensor Web Infrastructures
It offers (non-standardized) service interfaces for data querying and sensor tasking, but also auxiliary services for locating, timing, and a concept repository. SOCRADES comprises multiple services providing functionality such as data access, eventing or discovery. The integration of sensors into the infrastructure is done by implementing sensor gateways which hide the communication protocol and expose the sensor functionality as device level web services. In contrast to the SWE framework, the operations of individual services are not standardized. The four approaches described above do not offer service interfaces for tasking of sensors, such as the SPS. Further, only SOCRADES provides push-based delivery of sensor data, as offered by the SAS or SES. Agent based systems for establishing Sensor Web infrastructures are for example IrisNet [12] or the Sensor Web Agent Platform (SWAP) [14]. IrisNet envisions a global Sensor Web by focusing on data collection and query answering. Therefore, it introduces _organizing agents_ to store sensor data in a hierarchical, distributed database and _sensing agents_ which collect the sensor data. SWAP combines the paradigms of a service oriented architecture and multi agent systems. By building on OGC's SWE framework, the proposed architecture improves the integration of arbitrary sensors into workflows on the application level. This is done by introducing a three tier architecture comprising sensor, knowledge and application layer. Different kinds of agents residing on the three layers provide certain functionality and facilitate the development of new applications and the integration of sensors with applications.
## 2 Materials and Methods
### Image Acquisition and Correction  
Each breast fillet was imaged individually in the reflectance mode using the line-scan pushbroom HSI system. The system in the Vis/NIR range consisted of a 12 bit CCD camera (SensiCam QE, PCO-TECH Inc., Romulus, MI, USA), a spectrograph (ImSpector V10E with 30 \(\upmu\)m slit, Specim-Spectral Imaging Ltd., Oulu, Finland), a C-mount objective lens (APO-Xenoplan 1.8/35 mm, Schneider Optics, Hauppauge, NY, USA) with motion control (Newark, CA, USA), two Tungsten halogen lamps that illuminated samples at 45\({}^{\circ}\) from left and right sides, a custom sample holder, and a computer [25]. The exposure time of the camera was set to 24 ms. The breast fillet sample was placed on the translation stage to acquire its hyperspectral image. Based on the configuration of this system, a 2-dimensional image containing the spectral dimension and one spatial dimension was acquired during each line scan. When the whole sample passed through the field of view, a three-dimension hypercube with dimensions of 688 (pixels) \(\times\) 500 (lines) \(\times\) 520 (wavelengths) was acquired. Only wavelengths within the range of 400-1000 nm (total 473 bands) were retained for further spectral analysis, due to low signal-to-noise ratio (SNR) beyond this range of the spectrum.  
To minimize the influences of illumination and sensor sensitivity, two extra images named dark and white reference image were acquired before sample image acquisition [26]. The standard white reference image was obtained by acquiring the spectral image of a 75% reflectance Spectralon(r) target (SRT-75-050, Labsphere, North Sutton, NH, USA), and the dark reference image was obtained by recording the spectral image when the camera lens was completely covered by its opaque cap [27]. The calibrated image (R) was calculated using Equation (1).  
\[\mathrm{R}=\frac{I_{m}-I_{d}}{I_{r}-I_{d}}\times 75\% \tag{1}\]  
where \(I_{m}\) is the measured raw value, \(I_{r}\) is the reference value on the surface of the 75% reflectance panel, and \(I_{d}\) is the measured dark current (\(\sim\)0% reflectance).
# Magnetocaloric Properties and Critical Behaviour of the Sm\({}_{2}\)Ni\({}_{17}\) Compound
## 3 Results and Discussion
### Critical Phenomenon and Spin Interaction
#### 3.4.3 Spin Interaction  
Comprehending the interaction range and characteristics of this material is crucial. The universality class of the magnetic phase transition in a homogeneous magnet is determined by \(J(r)\), the exchange interaction. Based on analysis of the re-normalization group theory, \(J(r)\) is expected to decline as the distance increases, and specifically, it follows a pattern of \(J(r)=r^{-(d+\sigma)}\). In this formula \(d\) stands for the dimension of the system. At the same time, \(\sigma\) represents the scope or extent of the interaction, and it is known to be greater than zero [75]. In addition, [PERSON] et al. suggested that the range of \(\sigma\) and \(\gamma\) meet the re-normalization group approach as follows [76]:  
\[\gamma=1+\frac{4}{d}\frac{n+2}{n+8}\Delta\sigma+\frac{8(n+2)(n-4)}{d^{2}(n+8)^ {2}}*\left[1+\frac{2G\left(\frac{d}{2}\right)(7n+20)}{(n-4)(n+8)}\right]\Delta \sigma^{2} \tag{10}\]  
where \(n\) is the dimension of the spin and \(\Delta\sigma=\left(\sigma-\frac{d}{2}\right)\) and \(G\left(\frac{d}{2}\right)=3-\left(\frac{1}{4}\right)\left(\frac{d}{2}\right)^ {2}\).  
As per this model, the extent of the spin interaction is contingent upon the value of \((\sigma)\), indicating whether it is long- or short-ranged, where in a 3D isotropic system, if \(\sigma\geq 2\), \(J(r)\) may decline quicker than \(r^{-5}\) with short-range spin interactions. Whereas if \(\sigma\leq 3/2\), \(J(r)\) decays more slowly than \(r^{-4.5}\) with long-range spin interactions; thus, the MFM is the applicable model. In the intermediate range, \(3/2\leq\sigma\leq 2\), \(J(r)\) decays slower than \(r^{-5}\) and faster than \(r^{-4.5}\), implying the system exhibits characteristics of different classes, with coefficients assuming intermediate values that depend on the value of \(\sigma\).  
We have used all possible combinations of \(n\) and \(d\), and for each combination we look for the \(\sigma\) value so that the \(\gamma\) values are the same as determined by conventional methods. The values of \(\alpha\), \(\sigma\), and \(\beta\) found for different pairs of \(n\) and \(d\) are given in Table 3.  
In this case, \(\sigma=2.0\) is found, resulting in \(J(r)\) spin interactions decayed as \(r^{-5}\), which is a short-range spin interaction.  
Figure 13: Scaling plots above and below T\({}_{C}\) for the Sm\({}_{2}\)Ni\({}_{17}\) alloy. The inset shows the same plot in the ln-ln scale.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
###### Abstract  
Satellite-mediated quantum key distribution (QKD) is set to become a critical technology for quantum-secure communication over long distances. While satellite QKD cannot be effectively eavesdropped, we show it can be disrupted (or 'jammed') with relatively simple and readily available equipment. We developed an atmospheric attenuation and satellite optical scattering model to estimate the rate of excess noise photons that can be injected into a satellite QKD channel by an off-axis laser, and calculated the effect this added noise has on the quantum bit error rate. We show that a ground-based laser on the order of \(1\) kW can significantly disrupt modern satellite QKD systems due to photons scattering off the satellite being detected by the QKD receiver on the ground. This class of laser can be purchased commercially, meaning such a method of disruption could be a serious threat to effectively securing high-value communications via satellite QKD in the future. We also discuss these results in relation to likely future developments in satellite-mediated QKD systems, and countermeasures that can be taken against this, and related methods, of disruption.  
quantum key distribution; satellite; security +
Footnote †: journal:
## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Comparative Radiography
#### 3.3.4 Virtopsy  
Virtopsy (or virtual autopsy) [158] is a minimally invasive procedure to perform an autopsy, that employs radiological imaging methods routinely used in clinical medicine, such as CT and MRI, to find the reason for death. Virtopsy is a multi-disciplinary technology that combines forensic medicine and pathology, roentgenology, computer graphics, biomechanics, and physics.  
Some contributions presented preliminary studies about the possible application of ML to virttopsy [159; 160; 161], highlighting the importance and suitability of interactive ML methods [162], while others present simple image processing techniques to detect forensically relevant information in the images (e.g., the detection of metal objects embedded in a cadaver in [163]). However, in [164], the authors actually employ DL methods to detect and segment a hemopericardium (i.e., blood in the pericardial sac of the heart) in post-mortem CTs to better identify cases with a possibly non-natural cause of death (in particular, the presence of hemopericardium often leads to the diagnosis of pericardial tamponade as a cause of death). Their best performing deep network classified all cases of hemopericardium from the validation images correctly with only a few false positives, while most segmentation networks tended to underestimate the amount of blood in the pericardium. Also, in [165], a semi-supervised DL pipeline is employed to localize and classify orthopedic implants in the lower extremities (specifically the femur) on a large database of whole-body post-mortem CT scans. For the localization component, Dice scores of 0.99, 0.96, and 0.98 and mean absolute errors of 3.2, 7.1, and 4.2 mm were obtained in the axial, coronal, and sagittal views, respectively. For the classification component, test cases were properly labeled with an accuracy >97% (the recall for two of the classes was 1.00, but fell to 0.82 and 0.65 for the other two). Despite these examples, we conclude that this field remains largely unexplored.
## 3 Results and Discussion
### Effect on the Physical Characteristics
#### 3.1.4 Effect on Dry Density  
The dry density of the tested sandstone under different F-T cycles had a decreasing trend in Figure 9. The curve showed that the change range of the first two data was significantly larger than that of the later data, showing a fast change, followed by a slow trend. This phenomenon suggested that F-T damage of the samples that was caused by fewer F-T cycles occurs mainly on the surface of the samples, because some mineral particles on the surface are easily detached in the early stage of F-T cycles, as shown in Figure 5a. The change curve also indicated the development process of F-T damage from the surface to the inside [17].  
In general, the F-T damage of rocks is mainly affected by the following factors: lithology, moisture content, number of F-T cycles, ionic solution, temperature range, and stress state [26; 27]. When the samples were completely frozen, the frost heave force occurred in the pore sides for the increase of ice volume (about 9%), promoting further development and expansion of initial defects to form new micro-cracks [5]. On the other hand, when the temperature decreased, inconsistent deformation appeared among different mineral particles due to their different thermal expansion properties [9]. At the same time, the expansion of ice volume aggravated this deformation, resulting in huge local crystallization pressures (i.e., frost heave force) among the mineral particles or micro-cracks. The pressures acted on the microscopic scale of mineral particles or micropores of the rocks. Therefore, the presence of pore water had a profound effect on the rock damage under the F-T cycle conditions [28]. If there is no water in the pores or micro-cracks, there is no so-called F-T  
Figure 8: The average P-wave velocity and loss rate with cycles in the tested sandstone.  
Figure 9: The average dry density and loss rate with cycles in the tested sandstone.  
damage, i.e., dry rocks are hardly affected by F-T cycles [29]. When the temperature rose, the ice in the pores or micro-cracks melted, accompanied the release of frozen stress and the migration of water, thus accelerating the damage [30]. After repeated F-T cycle tests, therefore, numerous defects inside the samples caused the changes in physical properties that were mentioned above.
# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 5 Technical Data Lake Architecture Proposal for Fish Farming Data-Driven Strategy
### Trusted Zone  
To pass this data to the trusted zone, we rely on different jobs depending on the data type. For structured data, it is directly stored in structured tables. Apache hive is a data warehouse system based on two types of tables: External and managed. External tables point directly to a remote data directory with a defined structure (fields, delimiter, etc.). Moreover, it has to be noted that Hive does not manage the storage of the external table; it only manages the metadata, which means, in case of deleting the external table, the data remains in HDFS, and only the table definition is deleted. For managed tables, also known as internal tables, both the storage and the metadata are managed by Hive, and deleting the table implies deleting the data from HDFS. The table creation is a one-time action. Still, the data ingestion into the conformed zone is an automated process using an orchestrator such as Apache Oozie that allows the execution of sequential and parallel tasks with a defined frequency to match the frequency of data collection.  
Semi-structured data is not collected in a conventional or tabular form but is not completely unstructured. It contains some tags or key values that can be used to analyze that data. In our data lake use case, semi-structured data may refer to JSON files, XML files, or other markup language-based files. This data is transformed using dedicated Apache Spark jobs to match a defined structure at the level of the conformed zone. A dedicated spark job is developed for each semi-structured data source to transform and store data values in the conformed zone in a structured hive table. The spark jobs are executed systematically using the orchestrator to ensure a continuous semi-structured data integration.  
In the fish farming data lake use case, unstructured data, such as images and geospatial data, is usually used for data science use cases. For this purpose, data transition scripts are developed to create dedicated data science repositories for each use case.
## 3 Methods
### Disentangled Representation
#### 3.2.2 Style Encoder  
Global average pooling eliminates spatial correlation information and encodes global information by averaging feature values and flattening the feature map into a vector. To eliminate content information from the style encoder, average pooling and global pooling are often utilized [13]. As shown in Figure 2c, the style encoder consists of four \(3\times 3\) convolution layers, downsized by a factor of 2. Subsequently, a global average pooling layer is employed to suppress spatially correlated information. Then, a \(1\times 1\) convolution is used to project the output of the global average pooling layer into an 8-dimensional vector.
## 3 Results
### Thermal Desorption Spectra  
TDS is used to investigate the deuterium trapping information. Figure 6 shows the evolution of the TDS spectrum measured on W bulks and W films exposed to different fluences of deuterium plasma with ion energy of 100 eV at 500 K. The amount of deuterium desorption as a function of temperature for various implantation fluences is shown in Figure 6. To measure the deuterium retention, the samples are heated with a linear ramp of 10 K/min to a top temperature of 1273 K. Distinct desorption peaks correspond to distinct trap energies. In general, a high peak position corresponds to a high activation energy, and a low peak position is the opposite of a high peak position. Desorption from a deeper position of samples would result in the appearance of a high-temperature desorption peak.  
In Figure 6a, two kinds of desorption peaks of the W bulk can be distinguished, which are located at about 550 K and 850 K. The analysis of the TDS spectra of W films exposed to deuterium plasma shows the presence of peaks at 500 K and 720 K, as in Figure 6b.  
The deuterium retention in the W film is 1.17 \(\times\) 10\({}^{19}\) D/m\({}^{2}\) and in the W bulk is 2.23 \(\times\) 10\({}^{19}\) D/m\({}^{2}\) exposed to the deuterium plasma with a fluence of 1.30 \(\times\) 10\({}^{25}\) D/m\({}^{2}\) at 500 K. Increasing the fluence at the same exposure temperature results in a significant increase in deuterium retention, as shown in Figure 7.  
Figure 5: Focused ion beam (FIB) cross-section imaging of nanocrystalline W film after deuterium plasma irradiation.  
Figure 6: Thermal desorption spectroscopy (TDS) spectra of D\({}_{2}\) release in (**a**) W bulks and (**b**) W films exposed to 100 eV deuterium plasma at different irradiation fluences.
## 3 MPC Controller Design and Longitudinal Speed Compensation
### Longitudinal Speed Compensation
#### 3.4.3 Longitudinal Speed Compensation  
A large number of existing studies have taken the steering angle or the lateral force of the front tire as the control output. The above analysis shows that when the longitudinal speed changes in the prediction horizon, the vehicle states produce prediction errors in the prediction horizon in each optimization cycle, and the stability constraints of the closed-loop system also change. Thus, the difference in control output is produced, which further generates the tracking error throughout the path tracking process. Therefore, systematically considering the convex optimization problem and the variation of longitudinal speed in prediction horizon, a speed compensation strategy is proposed to reduce tracking error. The longitudinal speed compensation model can be expressed as:  
\[\left\{\begin{array}{l}v_{\rm x}*=f(v_{\rm x},v_{\rm x,max},a_{\rm x},T_{\rm T },\tau)\\ f=\min(v_{\rm x},v_{\rm x,max},a_{\rm x},T_{\rm T},\tau)\\ v_{\rm x,max}=\sqrt{\mu g/k(s)}\\ a_{\rm x}\leq\frac{\sqrt{(\mu g)^{2}+\left|(v_{\rm x}*)^{2}(k(s)\right)}^{2}}{v _{\rm x}*}\end{array}\right. \tag{32}\]  
where \(\tau\) is the compensation factor, \(T_{\rm T}\) is the time length of prediction horizon, \(a_{\rm x}\) is the longitudinal acceleration.  
In order to accurately capture the propagation of \(v_{\rm y}\) and \(r\) at high frequency, sampling time \(T\) is set to be 0.02 s, considering the accuracy of the vehicle information captured. Note that the value of the control horizon \(N\)c and prediction horizon \(N\)p are set to be \(N\)c = 15 and \(N\)p = 50, based on the consideration of higher control accuracy and calculation efficiency. The compensation factor \(\tau\) is set to be 0.5. This choice is made because in the high-speed driving, the speed of compensation cannot exceed the actual speed of vehicle. The weighting matrices are obtained by iteratively tuning by the principles mentioned in [26; 27], \(R=1\), \(Q\)= diag[1000, 5], \(W\) = [10; 10; 10; 10].
# Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
## 1 Introduction
[PERSON] et al. [27] showed that 100 Hz PPP (recorded by Topcon Net-G5 receiver) derived displacements with the help of a filter. In addition, the high-rate GNSS
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 1 Introduction
However, existing privacy-preserving approaches, such as encryption, anonymization, and access control, often suffer from limited user control, lack of transparency, and vulnerability to inference attacks. Moreover, most privacy frameworks are not designed to deal with the constraints and heterogeneity of IoT environments. These challenges emphasize the need for user-centric architectures that empower individuals with control over their data across the entire lifecycle. Figure 1 summarizes the structure and logical flow of this study, starting from the identification of privacy threats in the IoT context, moving through privacy protection via the ART approach (Awareness, Regulation, and Technology) and culminating in the exploration of PDSs as a user-centric privacy solution. Hence, in this paper, we discuss the main characteristics of data privacy challenges in the IoT context and explore the role of PDS-based solutions in enhancing user privacy. This way, it makes significant contributions in the following ways:  
* Identifies and examines the critical privacy challenges in the context of the IoT, which arise from the increasing collection and sharing of large volumes of personal data;
* Discusses the triad of privacy solutions: privacy awareness, privacy regulation, and privacy-enhancing technologies;
* Introduces and explains the PDS concept and explores it as a promising solution to mitigate privacy threats by presenting relevant works in this way;
* Discusses the challenges of PDS implementation in relation to GDPR complaints. The remainder of this paper is organized as follows: Section 2 presents the related work relevant to our study. Section 3 explores the main concepts related to the IoT and examines its key privacy challenges, including common threats and methods for protecting user data. Section 4 discusses the concept of Personal Data Stores and their role as a potential solution to IoT privacy issues; finally, Section 5 concludes the paper by summarizing the key contributions.