esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Communication  
During our analysis, the following question arose: how does communication with an interlocutor function within the VR environment? The criterion communication seeks to answer that question by differentiating between three types of interaction: (1) scripted communication with an NPC, (2) AI-driven communication with an NPC, and (3) communication with a real person. Scripted communication means that the dialogue between the learner and the NPC within the software is pre-programmed so that the NPC replies according to a predefined pattern. The learner must either follow scripted options or provide free answers based on speech recognition. In contrast, free and open-ended dialogues between learners and interlocutors are possible if the conversation partner is either an AI-driven NPC or a real person.  
For example, in the Talespin application, the learner's communication with an NPC takes place within the framework of a pre-established script. The dialogue follows a path determined by researchers and/or practitioners in advance. Within this dialogue path, various responses to the NPC's statements can be provided, which the learner must follow  
Figure 1: Mursion: Actors using haptic gloves to control avatars.  
Figure 2: Mursion: desktop is used.  
closely. Depending on the response, a predefined reaction is triggered (see also Section 4.8 Authoring Tool).  
Applications such as Bodyswaps (see Figure 3) extend beyond the selection of pre-defined response patterns, as seen in Talespin. In these systems, learners engage with a pr-programmed NPC but can communicate more freely through AI-powered speech recognition. A further enhancement in NPC interaction occurs when the NPC is fully controlled by AI, as exemplified by EngageVR (see Figure 4).  
Although EngageVR is primarily a social VR environment, their clients can design custom learning scenarios. In contrast to NPC dialogues, social VR applications enable interaction with one or more real individuals. VRchat, another social VR platform, facilitates interaction and communication between users across various digital environments, offering a more flexible and open form of communication compared to the other aforementioned approaches.  
Table 1 reveals that many providers offer a combination of different communication styles. A total of 60% of the software providers incorporate AI-powered communication options. Only two out of ten providers focus on scripted dialogues with an NPC. It is worth noting that, due to the rapid development of AI, this may change quickly depending on the provider.
## 3 Methods
### Image Partioning to Parcel Objects and Feature Space Creation  
Image segmentation that makes use of the spatial, temporal, and spectral features of satellite imagery is a complex and computationally demanding process, requiring fine-tuning that depends on the region and involved crop types [24]. Consequently, the adoption of image segmentation for partitioning the image time-series into parcels would not have supported the considerations of operationalization, transferability, and scalability, which the proposed methodology aspires to respect. The study assumes the viewpoint of a paying agency that is in charge of the management and control of CAP payments. The LPIS is one essential computerized database that paying agencies use within their operations and are mandated to frequently update. It should be noted that the reliability of the LPIS data is dependent on the update frequency, given that the evolution of the field limits can be significant over the years. However, in an attempt to exploit the benefits of an object-based approach, while at the same time preserving the scheme's simplicity, it was decided that following a LPIS-based object partitioning approach is an acceptable trade off.  
Table 1 lists the variables that make up the Landsat- and Sentinel-based datasets. The Sentinel-2 feature space, initially, comprises of all multispectral bands, for each individual scene employed (90 variables). In turn, VIs (NDVI, NDWI, and PSRI) for all temporal instances (27 variables) are calculated at pixel level and thereafter incorporated into the feature space (117 variables). In the same manner the Landsat-8 feature space includes a total of 63 variables, including the pan-sharpened multispectral data (42 variables) and the equivalent VIs (21 variables). Then the time-series of imagery is partitioned into parcels via utilizing the geometry of the LPIS vector data. Specifically, the pixel values that fall within the boundaries of the LPIS polygons are averaged, giving a single parcel value for every variable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Sentinel-2 MSI** & ** Landsat-8 OLI** \\ \hline \multirow{4}{*}{References} & Bands: B02, B03, B04 (VIS), B08, BSA (NIR) of 10 & Bands: B02, B03, B04 (VIS), B05 (NIR), B06, B07 \\  & m spatial resolution. B05, B06, B07 (Red-Edge) & (SWIR) of 30 m spatial resolution. B08 \\  & and B11, B12 (SWIR) of 20 m spatial resolution & (panchromatic) of 15 m spatial resolution \\  & Mean values (per parcel) of 10 multispectral & Mean values (per parcel) of 6 pan-sharpened \\  & bands \(\times\) 9 image acquisitions (90 variables) & multispectral bands \(\times\) 7 image acquisitions \\ \hline \multirow{2}{*}{Vegetation Indices} & Mean values (per parcel) of NDVI, PSRI, NDWI & Mean values (per parcel) of NDVI, PSRI, NDWI \\  & \(\times\) 9 image acquisitions (27 variables) & \(\times\) 7 image acquisitions (21 variables) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Landsat-8 OLI and Sentinel-2 MSI variable spaces of multispectral band reflectances and vegetation indices; the pixels values are averaged for each parcel.
## 3 Results
### Role of AMPK in LE-Mediated Apoptosis Inhibition  
The involvement of AMPK in the survival-improving effect of LE on H\({}_{2}\)O\({}_{2}\)-induced apoptosis in C2C12 cells was investigated. The formation of chromatin condensation in typical apoptotic cells was greatly increased by H\({}_{2}\)O\({}_{2}\) treatment, and this effect was clearly ameliorated by LE. However, the protective effect of LE against H\({}_{2}\)O\({}_{2}\)-induced apoptosis was abolished by compound C (Figure 8a). Annexin V-positive cells, which represented the apoptotic population, was increased by H\({}_{2}\)O\({}_{2}\) (to 41.28%) but reduced to 9.28% upon LE pretreatment. However, in the presence of compound C, the annexin V-positive cell population increased to 20.47% (Figure 8b). The induction of genomic DNA fragmentation by H\({}_{2}\)O\({}_{2}\) treatment was reduced by pretreatment with LE but restored upon treatment with compound C (Figure 8c).  
Figure 8: AMPK-dependent effect of LE on H\({}_{2}\)O\({}_{2}\)-induced apoptosis in C2C12 cells. Cells were pretreated with 5 mM compound C, with or without 10 mg/mL LE, for 1 h and then treated with 0.8 mM H\({}_{2}\)O\({}_{2}\) for 24 h: (**a**) Induction of apoptosis was analyzed by measuring cells with condensed nuclei (blue color) later staining with 4′,6′-diamidino-2-phenylindole (DAPI) staining. Representative results of three independent experiments are shown; (**b**) Cells were stained using Annexin V-FITC/PI and then measured using flow cytometry. Representative results of three independent experiments are shown; (**c**) Fragmented genomic DNA was extracted from cells and measured using agarose gel electrophoresis. M, Marker. Representative results of three independent experiments are shown; (**d**) Expression profiles of Bcl-2, Bax, pro-caspase-3, and PARP proteins. Western blot analysis was performed using \(\upbeta\)-actin was as a loading control. Representative results of three independent experiments are shown. Bcl-2, B-cell lymphoma 2; Bax, Bcl-2-associated X protein; PARP, poly(ADP-ribose) polymerase.  
Next, apoptosis-related marker proteins were analyzed by Western blotting. The expression levels of Bcl-2 and pro-caspase-3 were reduced by H2O2 treatment but increased with LE pretreatment, and the effects of LE pretreatment were attenuated by treatment with compound C (Figure 8d). In contrast, expression levels of Bax was relatively unaffected by treatment with H2O2, H2O2 with LE pretreatment, or compound C. In addition, the level of cleaved PARP was increased by H2O2 treatment, decreased with LE pretreatment, and increased by compound C. In summary, these results suggest that the protective effect of LE against oxidative stress-induced apoptosis involves AMPK signaling.
# Interaction in eXtended Reality Applications for Cultural Heritage
## 4 Interaction in Digital Storytelling Applications
### Interaction in VR Applications  
#### 4.3.1 Player's Movement  
The main challenge for player movement interaction in Virtual Reality is the creation of a user-friendly and intuitive way of moving which will not affect immersion in a bad way. Moreover, movement can easily produce a phenomenon called Virtual Reality sickness if not implemented right. In our previous virtual applications, we experimented with a few ways of implementing player movement to get to the one that users would find the easiest to understand and use.  
In the Sarajevo War Tunnel VR [47] and the Mostar Cliff Diving VR [48; 49], we used controller movement imitating hand movements while walking. Thus, in order to move, users had to hold the controller triggers and shake hands as they would when walking or running. This kind of movement appeared strange and was hard to understand for some users, so in future applications, we turned to more common ways of moving.  
The Battle on Neretva VR Application and Crvena stijena VR Application enabled the users to experience events from historical periods presented in museum exhibitions. They executed the users' movement through joysticks of the VR headset controllers. The users had to move one of their thumbs in the direction they wanted to go. Using the thumbs is very intuitive as most users who have used joysticks before know how they work. To avoid motion sickness, diagonal movement was disabled so that the users could only move forward, backward, and sideways. With only forward and backward movements, the users were always looking in the direction of the movement, which proved to produce less sickness. Sideways movement was added after initial user evaluation; users who were used to playing games agreed that the sideways movement was missing, and that it would make the movement of the player easier and more efficient. Even though moving with joysticks or moving thumbs on the touchpad did not imitate natural human movement, it appeared to be more user-friendly in Virtual Reality applications and less likely to decrease immersion as opposed to the hand movements used in the Sarajevo War Tunnel VR [47] and the Mostar Cliff Diving VR [48; 49].  
In the Old Crafts VR Application and Roman Heritage in the Balkans VR, we opted for the static player, with only an option to move between different scenes with the use of command clicks. This way, user placement was strongly controlled, and a view of the  
Figure 2: Holobox setup [46].  
scene was centralized based on its position. This was appropriate in such an application since there was no gameplay, and the exhibition itself was static with regard to the places where the events occurred.
## 3 Results and Discussions
### Cultivation of Microalgae in Outdoor Conditions
CCALA 1094_ microalgae strain in synthetic medium in outdoor 150 L thin-layer photobioreactor under early winter conditions of Central Europe [40]. The specific growth rate over the 22 days of the pilot cultivation reached a value of 0.341 d\({}^{-1}\) which is in the same range as the results of the present study [40]. Similarly, _Chorella_ sp. was cultivated in digestate (non-diluted) by conducting an outdoors experiment in an open pond semi-continuously for 37 days. With natural light in the range between 215-700 umol photons m\({}^{2}\) s\({}^{-1}\) and 0-25 \({}^{\circ}\)C, a productivity of 17.4 mg L\({}^{-1}\) d\({}^{-1}\) of microalgae biomass was obtained [21]. Similarly, [PERSON] et al., (2014) reported biomass yield of 0.79 g TSS.L\({}^{-1}\) when the microalgae _Chroococcus sp._ was cultivated on diluted digestate by 1:30 [36]. [PERSON] et al. (2020) also investigated a microalgae consortium in outdoor batch and semi-continuous experiments in a 4.5 m\({}^{2}\) raceway pond in summer and early autumn seasons on OFMSW liquid digestate fraction diluted at 1:20 for the batch mode and 1:10 for the semi-continuous mode [41]. From day 0 to 35, the biomass concentration and productivity increased until a maximum value of 0.74 g TSS L\({}^{-1}\) and 6.9 g TSS m\({}^{2}\) d\({}^{-1}\). After day 35, the concentration of TSS started to decrease until minimum values close to 0.13-0.31 g TSS L\({}^{-1}\) corresponding to productivity values of 1.2-2.9 g TSS m\({}^{2}\) d\({}^{-1}\)[41]. Finally [PERSON] et al. (2018) investigated seasonal variation of biogas upgrading coupled with digestate treatment in outdoors  
Figure 3: (**a**) Weather condition and temperature data graphs during the running period of the photobioreactor, (**b**) _Monoraphidium_ sp. growth curves in liquid digestate diluted at 1:50, expressed in absorbance at 680 nm and number of cells per \(\mu\)L, (**c**) NH\({}_{4}\)\({}^{+}\) and NO\({}_{3}\)\({}^{-}\) consumption expressed in mg L\({}^{-1}\), (**d**) pH and PO\({}_{4}\)\({}^{3-}\) consumption expressed in mg L\({}^{-1}\). Results are presented for photo-bioreactors for the three batches (B1, B2, B3) and semi-continuous trials. raceway ponds on a microalgae consortium [39]. In this study, process operation was carried out from 1 November 2016 to 30 October 2017. Areal biomass productivity ranged from 0.0 g TSS m\({}^{-2}\) d\({}^{-1}\) in winter to 22.5 g TSS m\({}^{-2}\) d\({}^{-1}\) in summer [39]. The elemental composition of the produced biomass of _Monoraphidium_ sp. was 30.62 \(\pm\) 0.22% C, 4.04 \(\pm\) 0.03% H, 3.27 \(\pm\) 0.26% N and 0.85 \(\pm\) 0.02% S (Table 3). Low carbon and nitrogen content was reported in the microalgae compared with the results of other authors [42; 43]. Lower carbon and nitrogen concentration can be explained by the higher ash content from microalgae but also from mineral residual faction of the digestate as the overall microalgae medium was oven dried. Microalgae was also composed of other nutrients (K, P, Ca, Mg) which are reported in Table 3.
## Appendix C Bubble Simulations Using Log-Periodic Power Law Singularity (LPPLS)  
In order to have a better understanding of the dynamics between prices and bubble predictors (long-term returns and slopes), we carry out several simulations that attempt to replicate a financial bubble by following the LPPLS model and then calculating the long-term returns and slopes to appreciate these predictors over a long period before the crash \(t_{c}\). In all cases, we simulate the S&P500 index for the first 1200 days with a Geometric Brownian Motion (GBM). We estimate the simulation parameters \(\mu\) and \(\sigma\) from the daily prices of the index between January 2008 and September 2024. In that period, \(\hat{\mu}=0.00443\) and \(\hat{\sigma}=0.012774\). Then, the next 1000 days are simulated through the LPPLS model. We add Gaussian noise with the same standard deviation \(\hat{\sigma}\) to the latter. The day following the last day of the bubble simulation equals the critical time \(t_{c}\) (not shown in the Figure) when the shift to an instability basis occurs. We ensure that the first value of the price series with LPPLS is the last value of the process with GBM so that the time series is consistent. Then, the long-run returns \(r_{t}\) and slopes \(s_{t}\) are calculated (See Section 2.4). In each of the simulations we carry out, the GBM process and the parameters of the LPPLS model are always different.  
Figure A2 shows three simulations as an example. Naturally, the dynamics vary depending on how many days we look backward in the index (\(\delta t\)) to calculate the value of long-term returns and slopes. However, independent of that and the parameters of the bubble simulation, the divergence \(r_{t}-s_{t}\) appears to become increasingly negative as the bubble process becomes more acute. In other words, as the index continues to rise at an accelerating rate, the slopes exceed the long-term cumulative returns in value, signaling an impending situation of overvalued prices. This is not surprising since the LPPLS model has the characteristic of rising at an increasing rate as we approach the critical time \(t_{c}\).
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 2 The Proposed Method
### Rotation-Equivariant Networks  
Convolutional neural networks (CNNs) typically employ fixed-size, two-dimensional convolution kernels to extract features from images. During convolution, these kernels apply the same weights to every position in the image, thereby allowing the detection of the same feature, even if the image is horizontally or vertically shifted. This property is known as translation invariance. However, when an image undergoes a rotation, the pixel arrangement of all objects in the image changes, causing the features that can be extracted by the CNN to also change and potentially become distorted. This lack of rotation equivariance in CNNs poses challenges in accurately recognizing rotated objects.  
The absence of rotation equivariance implies that as an object in the input image changes its orientation, the features extracted by the CNN also change, adversely affecting the accuracy of the rotated object detection. This effect is particularly pronounced for elongated objects, such as buses and ships.  
Traditional CNNs are designed based on the assumption of translation invariance in input data. However, for objects with rotational symmetry, the extracted features may change, resulting in the loss of important information and poor training outcomes. E2-CNNs provide a solution to this problem. Their core is rooted in group theory and convolution operations, enabling them to extract features from an image in multiple directions simultaneously, thereby ensuring rotation equivariance throughout the convolution process. This property ensures that objects yield the same feature output regardless of their orientation or angle. Since rotation involves a continuous operation that entails substantial floating-point computation and information storage, it is impractical and inefficient to include the entire 2D rotation group in the computation. Thus, optimization becomes necessary in this context.  
The backbone network employed in our model, ReResNet [21], is constructed based on E2-CNNs. Specifically, the 2D rotation group is discretized initially, establishing a discretized 2D rotation group with eight discrete parameters, representing eight rotation angles. This discretization significantly reduces computational complexity and memory resource consumption. Subsequently, each convolutional kernel extracts features based on this discretized 2D rotation group, resulting in feature maps (K, N, H, W) as depicted in Figure 2, where K denotes the number of convolutional kernels, N represents the number of directions (eight), and H and W denote the height and width of the feature maps, respectively.
## 1 Introduction  
Two dimensional atomic solids such as graphene and transition metal dichalcogenides (TMDs) have attracted a great amount of interest due to their thickness-dependent electrical and optical properties. Of TMDs, Molybdenum disulfide, (MoS\({}_{2}\)), with its indirect to direct band gap transition as a function of layer thickness has been of particular interest for digital and optoelectronic applications [1; 2; 3; 4]. MoS\({}_{2}\) has already been used to fabricate functional electronic circuit elements [5; 6], as well as opto-electronic devices [7], and coupled electro-mechanic systems [8; 9]. Recent research has also shown its applicability for high frequency applications [10; 11]. Also, the recent progress towards the wafer-scale growth and batch fabrication of field-effect transistors (FETs) on chemical vapor deposited (CVD) monolayer 2D solids such as graphene [12] and recently MoS\({}_{2}\) and WS\({}_{2}\)[13], has shown great promise for realizing commercial applications of these materials.  
However, the performance of FETs fabricated on these wafer-scale films are largely impacted by device to device variability, including gate hysteresis and threshold voltage variations, e.g. threshold voltage of \(\sim\)100 tested MoS\({}_{2}\) FETs, fabricated on 100-mm silicon substrates, ranges between 10 V and 45 V with the reported mobility in 1-40 cm\({}^{2}\)/Vs range and high variability in the current on-off ratio and gate hysteresis. This significant performance variability are often caused by the charges trapped between the 2D device channel and the gate dielectric as well as oxygen and moisture chemisorption at the basal plane and edges of MoS\({}_{2}\)[14; 15; 16; 17]. Therefore, one way to reduce performance variability and gate hysteresis is taking the measurements under vacuum or long-term annealing of MoS\({}_{2}\) FETs at elevated temperatures prior to testing the device [18]. Both of these techniques leads to removing the adsorbed oxygen/moisture content from the MoS\({}_{2}\) channel surface. A second method to reduce the channel interaction with the ambient, is to apply hydrophobic capping layers to the top surface of the device. Previous reports have shown that the performance variations of graphene and carbon nanotube (CNT) FETs can be reduced by applying a monolayer self-assembled hexamethyldisilazane (HMDS) layer between the back gate dielectric and the device channel or on the top surface of the device channels [18; 19; 20]. Other reports showed that passivating the surface of graphene and phosphorene FETs by a thicker hydrophobic film, such as fluoropolymers, results in more lasting positive changes and improved consistency in device to device performance [21; 22; 23].  
Here, we report the impact of encapsulation of MoS\({}_{2}\) FETs by a hydrophobic fluoropolymer, Teflon-AF. Our results show significant improvement in key device performance metrics such as current on-off ratio, subthreshold swing and gate hysteresis. Key performance metrics, such as threshold voltage, subthreshold swing, gate hysteresis and on-off ratio also show improved consistency after applying the fluoropolymer cap.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 2 Related Works
### ElGamal Public-Key Cryptography
#### 2.2.1 Key Generation  
System initially chooses two global parameters \(p\) and \(g\), where \(p\) is a large prime number and \(g\) is the primitive root of \(p\). The user \(U_{1}\) generates a public key and a private key as follows.  
1. \(U_{1}\) arbitrarily chooses an integer \(x_{1}\) as a private key;
2. \(U_{1}\) computes the public key \(y_{1}=g^{x_{1}}\bmod p\);
3. \(U_{1}\) publishes the public key \(y_{1}\) and keeps the private key \(x_{1}\) secret.  
The difficulty of solving the discrete logarithm problem can be defined as follows. When \(y_{1}=g^{x_{1}}\bmod p\) and only \(y_{1}\), \(p\) and \(g\) are known, it is hard to retrieve \(x_{1}\).