esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Qualitative Assessment of the Selected Virtual Geosites (VGs)  
All the VGs described in this work represent the volcanic environment because they were produced in a still active volcanic complex. All are aerially well-defined outcrops and landforms, making it easy to define their overall value. Their relevance may be regarded as global because they belong to a volcanic complex that produced a world-renowned eruption that altered the course of ancient history. All the selected VGs are fully representative of several volcanic processes and eruptive phases, which can be observed elsewhere in the world, but here are particularly easy to access and can be found reasonably close to each other within a relatively limited area. All the VGs considered representing currently inactive processes, although the Santorini complex is far from being extinct, as attested by the presence and activity of the Kameni islands described above. Regarding their representativeness, all nine VGs are fully representative of a number of volcanic processes.  
Concerning the second criterion that constitutes the scientific value, i.e., rarity, out of the nine VGs, three may be considered very uncommon: first, VG3, represented by Vlychada beach volcanic deposits of the Minoan (LBA) eruption. Here, three out of the four phases of this gigantic eruption can be observed in stunning detail. Second, VG6, represented by Metaxa Mine, a world-renowned volcanological geosite; third, VG7, located within VG6, i.e., the Late Bronze Age (LBA) famous section, bearing evidence of the wide gamut of volcanic phases that characterized the giant Minoan eruption. The third criterion that composes the scientific value is the level of scientific knowledge about a geosite. In this case, details of seven geosites, VG1 to VG7, are described in international scientific papers, VG3 and VG7 being the most represented in the scientific literature.  
Concerning the so-called \"additional values\" used to assess geosites, two can be identified: economic and educational. Concerning the first, it can be assessed for VG6, Metaxa Mine, which is representative not only of the geological heritage of the island, but of the industrial one, a site that was long exploited for economic purposes. Regarding the educational value, we wish to point out that all of the VGs selected can be used to clearly explain a wide gamut of processes that are typical of the volcanic environment, including subvolcanic bodies such as dikes, which play a key role in feeding magma to the surface. Moreover, it is worth noting that our approach is aimed at targeting a large  
Figure 15: Panorama of the Skaros shield 3D model. The outcrop has an elevation of 240 m.  
number of different-aged end-users, using two strategies: first, all VGs are made accessible on the internet and are navigable through all classical devices, including mobile phones and tablets; second, they are made available on a user-friendly, WebGIS platform.
# Security Risk Analysis of LoRaWAN and Future Directions
## 5 Security Threat and Risk Assessment of LoRaWAN v1.1
### Security Risk Analysis of LoRaWAN v1.1  
Here in this section, we analyze the security threats listed in Section 5.2 with the analysis methods and tools described in Section 5.1, which was devised by ETSI. Figure 6 depicts the security analysis of LoRaWAN v1.1 by using the methodologies described deeply in Section 5.1. Accordingly, the following results are obtained:  
LoRaWAN v1.1 possesses **Minor risk** at security attacks of:  
* Bit-flipping or Message Forgery Attack
* Destroy, Remove, or Steal End-device
* False Join Packets
* Frame Payload Attack
* Network Flooding Attack
* Network Traffic Analysis
* RF Jamming Attack
* Selective Forwarding Attack
* Sinkhole or Blackhole Attack  
LoRaWAN v1.1 possesses **Major risk** at security attacks of:  
* Beacon Synchronization DoS Attack (major risk for availability and minor risk for the rest)
* Impersonation Attack (major risk for availability and minor risk for the rest)
* Plaintext Key Capture (minor risk for availability and major risk for the rest)
* Security Parameter Extraction (minor risk for integrity and availability and major risk for the rest)LoRaWAN v1.1 possesses **Critical risk** at security attacks of:  
* Device Cloning or Firmware Replacement (critical risk for authentication and access control, major risk for confidentiality and integrity and minor risk for availability)
* Self-Replay Attack (critical risk for availability and minor risk for the rest)
* Rogue End-Device Attack (critical risk for authentication and access control availability and minor risk for the rest)  
Figure 6 suggests that LoRaWAN v1.1 is more susceptible to physical attacks such as capture, rogue end-device and rogue gateway attacks, rather than attacks towards higher layers of the communications stack, such as network layer attacks (Sinkhole, Blackhole, etc.).
# Photocatalytic Activity of Monosized AuZnO Composite Nanoparticles
## 2 Experimental
### Characterization  
The structure and morphology of the composite nanoparticles were analyzed by XRD (X'Pert Pro \(\lambda\) = 1.54056 A), TEM (JEM-100 II), SAED, and EDX, and the elemental compositions were measured by Inductive Coupled Plasma Emission Spectrometer (ICAP6200, Thermo Scientific, Cambridge, UK), in which Au and Zn standard solutions were obtained from A Johnson Matthey Company, and aqua regia was used to dissolve the AuZnO composite nanoparticles. The optical properties of composite nanoparticles were characterized by a UV-visible spectrophotometer (UV-Vis near IR spectrophotometer, Hitachi U4100, Tokyo, Japan) and a photoluminescence (PL) spectrophotometer (Hitachi F7000, Tokyo, Japan). Moreover, the AuZnO composite nanoparticles and pure PEO-PPO-PEO polymer were examined by FTIR using an Avatar 360 FTIR spectrometer (Nicolet Company, Madison, WI, US). X-ray photoelectron spectroscopy (XPS) was carried out on a Thermo ESCALAB250 XI photoelectron spectrometer (ThermoFisher Scientific, Waltham, MA, United States) with Al K\(\alpha\) X-ray as the excitation source. The thermogravimetric analysis (TGA) was performed using a thermogravimetric (NETZSCH STA449F5, Netzsch, Gemerny). The heating rate was programmed at 10 K\(\cdot\)min\({}^{-1}\), with the protecting stream of N\({}_{2}\) flow of 40 mL\(\cdot\)min\({}^{-1}\).
# Testing Procedure of Unmanned Aerial Vehicles (UAVs) Trajectory in Automatic Missions
## 4 Discussion
When conducting photogrammetric missions, the phenomenon of lowering the flying altitude may adversely affect the assumed parameters of image overlaps [12; 69; 70]. When considering the fact that the change in the flying altitude is associated with a larger number of parameters that are involved in the calculation of image coverage, with the GSD, matrix size, the focal lengths already being discussed. The analysis has been carried out for an exemplary RGB sensor in the form of a mirror-less device, which is commonly used in UAVs, namely SONY ILCE alfa6000 with three different focal lengths. The 80% forward overlap and 60% side overlap, as recommended by most manufacturers of data processing software, has been used as the initial value [12; 69; 70]. Table 13 presents the values of the forward and side overlaps of the images depending on the size of the GSD, and the reduction of the flying altitude by 2.50, 5.00, and 10.00 m. In the case of the forward overlap, the significant effect of a failure to perform the mission at a given altitude (reduction of the coverage to 73%) only occurs for the 10 mm GSD and the 15 mm focal length. However, in the case of the side overlap using a 15 mm lens, it should be borne in mind that errors in the implementation of the mission, which are greater than 5 m for a 10 mm GSD, and greater than 10 m for a 20 mm GSD, result in a reduction of the coverage to less than 55%. For a 25 mm lens, reducing the flying altitude by 10 m with a 10 mm GSD, reduces the side overlap to 52.7%. In simulations of photogrammetric missions (task 4 and 5), there have been significant deviations from the planned trajectory of the flight in the missions with a stop Table 9, Figure 8, where the accuracy of the mission has been affected not only by the accuracy of the GNSS, but also by the wind. In photogrammetric missions, the trajectories where the UAV does not use the Stop and Turn mode have yielded much better results Figure 10. However, it should be remembered to extend the lengths of the flight lines beyond the extent of the study area for on-board computers of the DJI A2 type Table 11. It is worth mentioning that changing the flight trajectory varies greatly, depending on the manufacturer and the type of the on-board computer. Additionally, the scope of the implemented mission should be expanded in order to take into account the possibility of the occurrence of errors resulting from the determination of the horizontal position.
## 4 Technological Challenges and Integration Issues
### Addressing the Challenges  
The European Union's approach to addressing these technological challenges is multifaceted. Through initiatives such as TwinEU and the collaborative efforts of the Joint Task Force, use cases are being clearly defined to provide direction for technology development [4]. For each use case, such as real-time grid observability or operational planning with high levels of distributed energy resources, the specific requirements related to data, models, computational speed, and other factors are identified. This helps guide developers in prioritizing efforts, often focusing on manageable subsets of the overall complexity [5]. In addition, a roadmap for technology development is expected, which will likely phase the deployment of the digital twin. The initial focus may be on coupling transmission system operator capabilities, followed by the gradual inclusion of distribution system operators, and eventually the integration of cross-sector components [3].  
In the area of interoperability, European research is placing strong emphasis on open platforms. The InterConnect project under Horizon 2020, for example, produced a blueprint for interoperability on the Internet of Things within the energy sector, which can guide how devices and subsystems connect to the digital twin [46]. The European Union is also engaging with international standardization bodies. One of the key recommendations from the ENTSO-E and DSO joint report is to collaborate on interoperable digital twin standards and platforms using a system of systems approach [25]. Regarding data and high-performance computing, the alignment with European data spaces and the Destination Earth initiative will provide key technological assets, including data catalogs and exascale computing capabilities, which the digital twin can adopt rather than develop independently [13; 17].  
In summary, the technological challenges involved in developing a digital twin for the EU electricity grid are significant but can be overcome through coordinated effort. These challenges span the entire technology stack, including hardware such as sensors and computing infrastructure, software such as models and user interfaces, and system integration involving standards and cybersecurity. Europe's strategy is to tackle these issues through collaborative innovation by sharing knowledge and prototypes through Horizon projects and by progressively aligning common solutions. Through this approach, the digital twin can move from a conceptual framework to an operational reality that is robust, accurate, and fully embedded in grid management practices.  
To provide an integrated overview of the technological challenges analyzed in this section, Table 2 presents a structured summary of the main technical barriers, critical observations on existing limitations, and the authors' recommended solutions. The table serves to distill the complex, multi-layered integration issues that must be addressed for the digital twin to achieve scalable, secure, and real-time operability across Europe.
# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
## 3 Experiments and Results
### Pretraining and Finetuning  
To validate the intra-domain transfer learning strategy and the proposed fault diagnosis model, we introduce two more learning schemes for comparison: train from scratch (TFS) and transfer from ImageNet (TFI). The proposed intra-domain transfer learning method is abbreviated as TFC, signifying transfer from the CWRU dataset.  
The weights of the ResNet-50 model are randomly reinitialized in TFS. We can imagine the dilemma the TFS scheme ran into with small samples to train such a complex model. All of the convolutional layers of TFI and TFC inherit weights from the off-the-shelf ResNet-50 model, and their batch normalization layers and the fully-connected layers are reinitialized.  
With prior knowledge that the target-domain samples differ significantly from natural images, the convolutional layers of TFI are trainable but at a relatively small learning rate. Therefore, the convolutional layers of TFC are frozen to reuse the feature extractors learned from intra-domain samples. The final soft-max layer of each of the above three schemes is customized to match the number of desired classes in our target task. We choose Adam optimizer in our pretraining and finetuning, and the learning rate is set to \(1\times 10^{-4}\). All models are trained with mini-batch samples of a size of 16.  
Under the settings of the three training schemes and five splitting ratios of target-domain samples, we train the ResNet-50 model for 100 epochs, each repeated five times. The training history is shown in Figure 8. From the figure, we observe the proposed method converges faster than the alternatives, and it consistently plateaus at the highest diagnostic accuracy in our target task. Given adequate training samples, all three learning schemes can achieve a satisfactory result if the model is sufficiently trained. But when training samples are scarce, intra-domain transfer learning can boost the performance in terms of both convergence speed and accuracy in the target task.
# Review of Automatic Estimation of Emotions in Speech
## 7 Neural (Machine Learning) Techniques for Emotion Classification of Speech
### Supervised and Self-Supervised Methods
The 1D CNN converts input (raw) speech into low-level features; the Transformer generateshigh-level features via self-attention. Both wav2 vec2 and HuBERT have a CNN feature extractor and Transformer encoder. This SSL approach has been applied to various speech applications, and appears to extract relevant phonetic patterns that can fruitfully be used. Like most machine learning methods, however, any intuitive interpretation of features is extremely difficult. Thus, one assumes that relevant features are being obtained because the final classification outcome may be improved over earlier methods. Typically, the output features of this SLL training are about 1000 for each 1 s time window of input; their models often have hundreds of millions of parameters. So, much recent SER pre-trains a model with SSL (a general upstream operation, which applies to many speech processing applications) using a large amount of unlabeled speech data (e.g., wav2 vec 2.0 or HuBERT), and then fine tunes the general model with the contextual and acoustic information for SER (a downstream task) [56; 89]. For SER, the model that derives from training that applies to various speech tasks is used, with fine tuning for the specific SER task. A smoothed pooling layer transforms the frame-level context representations from the wav2 vec encoder into representations at the sentence level, and classification uses a fully connected layer for each speech sample by emotion [115; 126]. Transfer learning [127] exploits models from other speech areas (e.g., ASR or ASV) to improve SER. Joint training or multi-task learning between ASR and SER can consider ASR and SER loss simultaneously during training [88; 128].
# A New Concept of Reconfigurable Antenna Structure Based on an Array of RF-MEMS Switches
## 5 Conclusions  
This paper presents a reconfigurable structure that can be set by modifying its geometry to be a reconfigurable antenna or microwave component. The structure works like a field-programmable gate array (FPGA), a configurable integrated circuit that can be repeatedly programmed after manufacturing. It consists of an array of square gold blocks with a connecting set of electronic MEMS switches. The gold blocks can be configured to interconnect each other to perform various radio frequency or microwave components, such as frequency reconfigurable antennas, impedance transformers, and other basic RF components. As a proof of concept, an experimental prototype consisting of \(7\times 7\) gold patches interconnected by means of a set of RF-MEMS switches is designed, fabricated, and numerically and experimentally assessed. The proposed programmable prototype is an attractive candidate for modern wireless communications such as mm-wave devices and CubeSat satellites. The obtained results have shown the potential versatility of the proposed reconfigurable structure. Future advances will be devoted to increasing the dimensions and number of patch elements and simplifying the control structure of the MEM switches.  
For research article Conceptualization, J.I. and M.D.; methodology, M.D. and M.M.; software, M.D. and M.M.; validation, M.D. and M.M.; formal analysis, M.D. and M.M.; investigation, M.D. and M.M.; resources, M.D. and M.M.; data curation, J.I.; writing--original draft preparation, M.D.; writing--review and editing, M.D., M.M., and J.I.; visualization, M.D., M.M., and J.I.; supervision, M.D.; project administration, M.D. and J.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors would like to thank [PERSON] for the revision of the manuscript.  
The authors declare no conflicts of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.  
Figure 13: Simulated vs. experimental measurement for a \(7\times 7\) rectangular patch configuration.
## 4 Discussion
### Weight Distribution and Postural Stability  
The comparability of our results with previous studies is limited because we used different assessments to measure postural stability. For example, [PERSON] et al. [14; 36] used the single leg stand on a force platform to judge the balance of patients with OA (n = 49) prior to and 12 months following medial opening wedge HTO. In line with our longitudinal study design, they also found only small (d \(<0.34\)) and not significant (\(p>0.05\)) changes regarding center of pressure and standing balance following HTO. As in our study, these patients showed a reduction in pain as quantified by the WOMAC despite no changes in standing balance [14]. [PERSON] et al. [36] found that the amount of varus malalignment was inversely related to single-legged standing balance. Presumably, neuromuscular control is reduced in the presence of this malalignment. The authors discussed a reduced joint innervation or muscle reflexes as possible causes [37]. It seems, that asymptomatic subjects with varus alignment exhibit more changed postural control strategies than subjects with neutral alignment [38]. [PERSON] et al. [39] also used the IBS and investigated 80 patients with primary OA, classified by the Kellgren-Lawrence score (mild vs. moderate to severe) and 40 age-matched controls. These authors detected a higher postural sway of the moderate to severe group compared to those of the mild or control groups.
## 6 **YefM-YoeB and Axe-Txe: Diverse Transcriptional Control of Homologous TA Complexes**
Three component TA modules include the _pasABC_ complex encoded by plasmid pTF-FC2, _paa4-parE-paaR_ of _E. coli_ O157:H7, and the _e-_\(\zeta\)-_o_ system specified by streptococcal plasmid pSM19035. The _pasA_ gene codes for an antitoxin and _pasB_ encodes a toxin [94]. The _pasABC_ promoter undergoes full autorepression by the PasAB complex, and the PasC protein has little effect on promoter expression [95]. Instead, PasC enhances the neutralizing effect of the antidote [94]. In the case of the _paaR-paa4-parE_ operon, the PaaA antitoxin and ParE toxin form a complex that autorepresses the main promoter only partially. The transcriptional regulator encoded by an upstream gene within the same operon, PaaR, is required for full down-regulation of transcription. These different repressor complexes probably act independently [96]. The PaaR repressor also is essential in maintaining an appropriate PaaA:ParE ratio [96]. Another tripartite type II TA system is _e-_\(\zeta\)-_o_ which was discovered initially on plasmid pSM19035 of _Streptococcus pyogenes_. In this case the \(\zeta\) toxin and \(e\) antitoxin have no roles in transcriptional control. Instead, their expression is inhibited solely by the \(o\) protein encoded by the first gene in the operon. In addition to being a repressor of its own promoter, \(o\) is encoded widely by plasmids belonging to the Inc18 family and acts as a global negative regulator that also controls transcription of genes required for plasmid copy number control and stable inheritance, thereby promoting accurate plasmid segregation [97, 98, 99, 100]. Like certain other type II TA transcriptional regulators, including CcdA (Figure 2A) and RelB, \(o\) is a RHH protein [101]. However, the binding site recognized by \(o\) is distinctive, comprising palindromic and non-palindromic DNA heptad repeats (_5'-_NATCACN-3'_) in the cognate operator sites [98]. The VapC subfamily of PIN domain proteins forms the toxin component of a widespread type II TA complex. PIN domain proteins cleave single-stranded RNA and are characterized by a cluster of strictly conserved acidic amino acids within the active site. VapC is counteracted by the VapB antitoxin [102]. Expression of the _vapB-vapC_ genes is autoregulated negatively by the VapB-VapC complex which specifically binds inverted repeat sequences within the _vapBC-1_ operator region [103, 104]. However, the _vapBC-1_ cassette of nontypeable _Haemophilus influenzae_ presents notable differences to the typical pattern. First, contrary to other TA systems described to date, the VapC-1 toxin possesses DNA binding activity whereas the VapB-1 antitoxin does not directly interact with DNA. Nevertheless, VapB-1 increases VapC-1 interaction specificity with the operator region [62]. Second,the Fis protein upregulates _vapBC-1_ expression during nutrient upshift in \(H\). _influenzae_. The influence of Fis on _vapBC-1_ expression is thought to occur indirectly by altering DNA structure in the promoter-operator region [62].