esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Evaluation of Quantitative Method
\begin{table}
\begin{tabular}{c l l l l} \hline \hline  & & \multicolumn{1}{c}{**Pupil**} & \multicolumn{1}{c}{**Teacher**} & \multicolumn{1}{c}{**Comp.**} \\
**Item** & **English Version of SUS** & **Responses** & **Responses** & **Science** \\  & & **Mean (Std)** & **Mean (Std)** & **Mean (Std)** \\ \hline Q1 & I think that I would like to use this system frequently. & 3.57 (1.16) & 4.50 (0.71) & 3.77 (1.00) \\ Q2 & I found the system unnecessarily complex. & 1.86 (1.03) & 2.50 (2.12) & 1.79 (0.91) \\ Q3 & I thought that the system was easy to use. & 4.36 (0.63) & 4.00 (0.00) & 3.93 (0.94) \\ Q4 & I think that I would need the support of a technical person to be able to use this system. & 2.21 (0.80) & 3.50 (0.71) & 1.74 (1.05) \\ Q5 & I found that the various functions in this system were well & 4.14 (0.66) & 4.50 (0.71) & 4.14 (0.74) \\ Q6 & I thought that there was too much inconsistency in this system. & 1.71 (1.07) & 1.50 (0.71) & 1.67 (0.99) \\ Q7 & I would imagine that most people would learn to use this system very quickly. & 4.14 (0.86) & 4.00 (0.00) & 4.02 (0.96) \\ Q8 & I found the system very cumbersome to use. & 1.43 (0.65) & 1.00 (0.00) & 1.79 (0.96) \\ Q9 & I felt very confident using the system. & 3.57 (1.09) & 4.00 (0.00) & 3.91 (0.92) \\ Q10 & I needed to learn a lot of things before I could get going with this system. & 2.14 (1.17) & 3.00 (1.41) & 1.79 (0.91) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean values and standard deviations of participants’ responses for the AR app for 6 th grade: Earth as a celestial body. the previous findings, as pupils rated it with the least mean value, in relation to the rest AR apps, while teachers and university students with the highest ones. Taking into account the previous results, all three AR apps obtained satisfactory ratings regarding usability, since the majority of the participants claimed that AR apps are easy to use and felt confident while using them. This could be due to the fact that the majority of the participants, including pupils, are familiar with such devices and technologies-a fact that made the experiment easier to be accomplished. Finally, participants supported that the proposed AR apps are playful because their purpose is to learn through a game. These conclusions are coherent with the literature review, which found that AR games in the domain of Geography are attractive, fun, and satisfactory [19], enhancing the learner's experience [21].
## 4 Application Interface  
The Card3 DFace application was developed in the C++ language and Qt environment, which is widely used for developing multi-platform user interfaces that can run on desktops and mobile devices. This application presents four main areas that encompass the controls (1 on Figure 9) and the three principal stages of the model (2, 3 and 4 on Figure 9). It possesses a block for image acquisition, a block for the generated model and a block for rendering the head views for printing.  
The controls for the application are gathered in a single ribbon at the top. In this command bar, we can select the tasks to perform; namely, uploading from a previously generated model (Figure 9a), camera acquisition (Figure 9b), filtering (Figure 9c), the selection of the number of head views (Figure 9d), the generation of the head views (Figure 9e) and file storage (Figure 9f).  
There is a defined procedure to follow when running this application. Firstly, it is necessary to load a previously generated model (a) or take a picture of a person's face (b). In the next step, it is necessary to apply the filtering technique (c), and after defining the quantity of head views to be generated (d), we can proceed with the generation of head views (e). There is also the possibility of saving the obtained images (f) for later use for printing.  
Figure 8: Lenticular lenses.
## 2 Related Works
### Literature Comparison  
To further elucidate the unique contributions of this study, a comparison with similar research in the field is presented. Table 1 summarizes the key findings from previous studies, outlining the environmental factors, data collection methods, machine learning techniques, and innovative approaches. This structured comparison emphasizes the distinguishing elements of this research, highlighting how it extends and refines existing knowledge in the domain of smart library systems and environmental optimization. \begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Environmental Factors Used** & **Data Collection Methods** & **Machine Learning/ Analysis Methods** & **Contributions and Innovations** & **What is the Added Value of Our Paper?** \\ \hline
[21] & Noise & Surveys and observations & - & Highlights the negative effects of noise on academic performance. & By analyzing noise and other environmental factors together, more comprehensive results are presented. \\ \hline
[19] & IAQ (indoor air quality), light, and acoustics & Physical measurements and surveys & - & Examined the impact of environmental factors in the library on user experience. & Combines environmental factors in the library under feedback and allows more efficient monitoring through IoT sensors. \\ \hline
[22] & Temperature, humidity, CO2, noise, and light & Environmental sensors and portable devices & - & Provides suggestions for monitoring and optimizing various environmental factors. & This study analyzes sensor data with machine learning algorithms and improves environmental factors through more detailed \\ \hline
[23] & Acoustic, visual, and thermal comfort & Portable devices & - & Proposes improvements to comfort levels in library environments. & More personalized environmental improvements are achieved using IoT-enabled sensors and user feedback. \\ \hline
[24] & IAQ (indoor air quality) & Environmental sensors & - & Investigated the impact of the IAQ on student concentration. & IAQ optimization can be conducted dynamically and instantaneously with sensor data and machine learning. \\ \hline
[25] & IAQ, light, and acoustics & Surveys and physical measurements & - & Evaluated the experiences of students and academic staff regarding the indoor environmental quality (IEQ) in library environments. & In this study, more precise environmental control is staff regarding the indoor environmental quality (IEQ) in library environments. & In this study, more precise environmental control is provided, optimized through student and staff feedback. \\ \hline
[26] & Service accessibility, interior design, and environmental factors & Surveys and observations & Regression analysis & Investigated the effect of environmental factors in libraries on user satisfaction. & With IoT- and AI-based solutions, the continuous monitoring of environmental factors and data-driven decision support systems are proposed. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of environmental factors and methods in related studies.
## 1 Introduction
### Motivation  
As mentioned above, protein-protein interactions play a major role in cellular processes and both experimental and bioinformatic high-throughput methods like yeast-2-hybrid assays are widely used for obtaining interaction maps. However, since these methods are not always applicable and often contain a considerable number of false positives [5], there is a need for computational approaches to verify or falsify protein-protein interactions that were predicted by other methods. Since protein-protein interactions are critically dependent on the three-dimensional structures of the individual molecules it seems logical to use this information for judging putative protein-protein interactions. [PERSON] and [PERSON] [6], for example, have suggested a method to model putative interactions on known 3D complexes to investigate the compatibility of a proposed interaction with this complex. In another approach comparative docking together with the analysis of steric clashes is used to analyze putative interactions [7]. In this method information about the interacting residues of both partner proteins is required as well.  
As detailed below, we propose in this contribution a new method based on protein-protein docking where only interface information of one of the partner proteins is sufficient to assess putative interactions. Methods providing the three-dimensional structures of protein complexes from the structures of the individual molecules (docking algorithms) are readily available. With the constant increase of experimental structures deposited in the PDB the individual structures are available in many cases. Additionally, it should be possible to use at least in some cases high quality homology models as input for docking programs.  
Usually docking algorithms are used to predict the complex structure of two proteins that are known to interact. In their scoring step a great amount of different possible complex structures are compared to select those that are near-native. That means discrimination between native and non native interactions. Similarly it should be possible to extend this analysis to protein pairs where it is not known _a priori_ whether they interact in nature or not. That means in other words to perform docking runs with different proteins, even those that do not interact or are not known to do so and finally, after the interpretation of the structures, get as a result whether two proteins are suggested to build complexes in nature or not.  
This is actually a computational method to verify proposed protein-protein interactions. In this contribution we will investigate the general applicability of the suggested approach.
## 3 The Negotiation-Based Approach
### The Winner Determination Problem  
The winner determination problem starts from problem P1 and considers Equation (14). To embed the bids into the model, we consider the following reformulation. The \(k\)-th bid \((\gamma,\tau)^{k}\) can be rewritten by introducing parameter \(q_{it}^{k},\ \forall i\in I,\forall t\in T_{c},\forall k\in K\), such that  
\[q_{it}^{k}=\begin{cases}q_{i},&\text{if customer $i$ is serviced in timeslot $t\in T_{c}$ in bid $k$,}\\ 0,&\text{otherwise.}\end{cases}\]  
Note that \(q_{it}^{k}=0\), \(\forall t\in T_{s}\setminus T_{c}\). Moreover, a bid selection binary variable \(u^{k}\in\{0,1\},\forall k\in K\), is needed such that  
\[u^{k}=\begin{cases}1&\text{if bid $k$ is selected,}\\ 0&\text{otherwise.}\end{cases}\]  
Thus, Equation (3) can be rewritten as  
\[I_{1,t}=I_{1,t-1}+\sum_{i\in I,k\in K}q_{i,t-1}^{k}\cdot u^{k}-x_{1,t-1}\quad \forall t\in T_{s}\setminus\{0\} \tag{31}\]  
To ensure only one bid is the winner, the following constraint is added  
\[\sum_{k\in K}u^{k}=1 \tag{32}\]  
The winner determination problem is then:  
\[\min Z_{\text{c}} \tag{33}\]  
s.t.  
Equations (2), (4), (5), (6), (31) and (32),  
\(u^{k}\in\{0,1\}\), \(\forall k\in K\).
# Neutronic Activity for Development of the Promising Alternative Water-Cooled DEMO Concepts
## 2 WCLL-db Blanket Study
### Development of the WCLL-db MCNP Geometry Model
#### 2.2.2 MCNP Geometry Model  
The WCLL-db MCNP geometry models were built on the basis of the DEMO 2017 baseline model with an empty breeder blanket space [8]. Half of the 22.5\({}^{\circ}\) (11.25\({}^{\circ}\)) toroidal segment was used to accommodate the newly elaborated MCNP geometry models of IB and OB blankets. To simulate DEMO, 360\({}^{\circ}\) torus reflecting boundary surfaces were assigned to the segment. The WCLL-db MCNP model has a modular structure that follows hierarchy rules for different parts of the whole blanket model. A unique universe number was assigned to each local MCNP module. The MCNP model of the blanket, Figure 4, is built in sequential order of the modules filling the whole blanket space. This assumes a strict hierarchy of universes: (1) empty breeder blanket space from DEMO baseline model \(\rightarrow\) (2) first wall \(\rightarrow\) (3) collectors and breeder zone \(\rightarrow\) (4) pipes bundles. Such a modular system enables quick modifications of the design without destroying the whole geometry because each universe is originally the independent MCNP model of the different blanket components. Nevertheless, prompt modifications of the model are limited to the very simple ones: global materials replacement and local geometry variations. Any global geometry change requires repetition of the whole procedure of the MCNP model generation, and thus they are considered unreasonable and meant to be avoided. The MCNP geometry model of the WCLL-db DEMO includes all necessary heterogeneous details, Figure 4, to perform high-fidelity particle transport simulations. Before starting the calculations, the MCNP model was well-tested against geometry errors. The lost particles were detected far away from the plasma outside the vacuum vessel, and their number did not exceed 1 per 10\({}^{6}\). Additionally, all these geometry errors come from the generic model used for the modeling rather than the new blanket descriptions.  
Materials assignments for PbLi, Eurofer, and tungsten were already performed in the MCNP input deck using standard definitions adopted within the EUROfusion DEMO project [9]. For PbBi, a liquid eutectic with a material density of 10.5 g/cm\({}^{3}\) and a 44.5 at%/55.5 at% percentage of Pb and Bi in the mixture were adopted for calculations. The breeder zone volume filled with PbLi has a very complex cell structure due to the presence of numerous water-cooling pipes. The hundreds of geometry cells making up this volume were generated in the SuperMC originally as void cells, and during the preparation of the MCNP input deck, they got the proper material assignment reflecting PbLi properties.
## 4 Experimental Results
### Macula Localization Module Results  
To assess the performance of the macula localization, we utilized the macula annotations provided by [42] for a subset of 1136 images. For quantitative results, we calculated the 1R criterion, which refers to a specific method used in medical imaging, particularly in ophthalmology, for identifying the macula in retinal images. The \"1R\" criterion refers to the difference between the ground truth (GT) foveal position and the predicted foveal position, which should be within one optic disc (OD) radius. We computed the 1R criterion using the ground truth annotations and predictions. Table 6 contains the 1/2R, 1R, and 2R results and a comparison with two previous methods that used the same 1136 images.  
Figure 9 illustrates examples of the macula localization outcomes using the Messidor dataset.  
The value achieved by our method significantly ensures precise identification of the macula, which is crucial for the accurate grading of DME. Such a high accuracy provides consistent and reliable results, reducing variability and enhancing the dependability of diagnoses across different images and patients. Furthermore, automated systems with this level of precision can screen large volumes of retinal images swiftly and efficiently, aiding in the early detection and management of DME. By minimizing errors in identifying the macula and grading DME, these systems yield more reliable results. Finally, precise macula detection facilitates consistent monitoring of the progression or regression of DME, enabling effective follow-up care and treatment adjustments.
## 2 Materials and Methods
### Sensor Installation  
Two weather stations were installed inside the greenhouse: one station was installed in the west area of the greenhouse provided with four VPD control instruments, and the other station was installed in the east area of the greenhouse without the installation of any VPD control equipment (see Figure 1a). Here, the VPD control equipment was an instrument that automatically inserted fogging water for regulating the VPD. Each weather station had sensors to measure different parameters inside the greenhouse such as the air temperature, air relative humidity, carbon dioxide concentration in the air, and incident radiation above the crop. These sensors were programmed to measure data every minute and to estimate average data every 20 min. The data measured with the installed sensors were collected and reviewed weekly, to check their behavior and to elaborate a weekly report describing the data measured and the behavior of the sensors. The collected data were sent to the monitoring platform of the Smart Software Lab. Inc. [26].  
During the period of the field trial in the summer and autumn seasons, the VPD control equipment was turned on during the hottest hours of the day, and it was turned off during the wet hours of the day. During the winter season, when the relative humidity of the air reached high values, the VPD control equipment was turned off all day. Tecnova technicians turned off the VPD control equipment to avoid the incidence of the fungal disease Botrytis cinerea in the tomato crops (the establishment of this disease in tomato crops is higher with high-relative-humidity conditions in the air).
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 6 Conclusions  
This is the first paper that compares NB-Fi, Sigfox, and LoRaWAN not just by their nominal operational parameters but also by PLR, PER, and delays in different scenarios. In this paper, we have compared and analyzed such LPWAN technologies as Sigfox, LoRaWAN, and NB-Fi in three scenarios: the unreliable transmission of short packets, the unreliable transmission of long packets, and the reliable transmission of short packets. Using the simulation, we have demonstrated and analyzed the average delay and the dependence of PLR, PER, and delays on the rate. The analysis has revealed that Sigfox, LoRaWAN, and NB-Fi perform best in different scenarios. Sigfox is the most reliable if all of the transmitted packets are short and sent in the unacknowledged mode only once. If sensors transmit only long packets in an unreliable way, LoRaWAN performs better than NB-Fi and Sigfox. NB-Fi demonstrates the highest reliability if sensors transmit short packets in acknowledgment mode or according to multiple frame procedure. Therefore, the considered technologies have their advantages and disadvantages, which manifest themselves in different scenarios. The results obtained in this paper should be taken into account when selecting an appropriate LPWAN technology for a specific IoT application.  
Conceptualization, D.B., E.K. and A.L.; Formal analysis, D.B. and E.K.; Investigation, P.L. and D.B.; Methodology, E.K. and A.L.; Software, P.L. and D.B.; Validation, E.K. and A.L.; Writing--original draft, P.L.; Writing--review and editing, D.B., E
## 5 Experimental Analysis and Discussion
### QoS in Different Routing Protocols Corresponding to Mobility Models
#### 5.3.3 Throughput  
Throughput is the number of packets delivered in a specific period. Every protocol exhibits the same behavior as we increase the number of nodes, i.e., throughput value keeps on decreasing with node value increment. AOMDV here also performs better than the other two routing protocols. When increasing the number of nodes, the performance of AOMDV (0.68%) increases in the case of the RDM mobility model. During the RWM models with the increasing number of nodes, the performance of RWM (0.191%) for AOMDV is better than the other two routing protocols. The performance of RWM and RDM corresponding to different routing protocols in terms of throughput with a different number of nodes has been shown in Figure 6.  
Figure 4: Performance analysis of RWM and RDM on various routing protocols in terms of PDR.  
For the analysis, we have considered two different categories of routing protocols, i.e., proactive and reactive. DSDV, being a proactive routing protocol, maintains a routing table for every possible route before a node has data transmitted, whereas the other two perform route discovery on demand, i.e., whenever a node has data to send, a suitable route is discovered. DSDV, being proactive, performs well in terms of average end-to-end delay, as shown in Figure 4. For every case, DSDV performs well, AODV exhibits a sudden increase in delay as the number of nodes passes the value of 50 because of more route requests, whereas the other two exhibit a normal increase in delay with an increasing number of nodes because DSDV is table-driven and AOMDV contains Multiple paths for a single destination.
# Cost-Aware IoT Extension of DISSECT-CF
## 3 Cost Modeling in DISSECT-CF
### IoT Pricing
#### 3.1.2 IBM Bluemix  
IBM Bluemix IoT platform's pricing follows the \"pay as you go\" approach. Bluemix only charges after the MiB of data exchanged. They differentiate three categories in terms of data usage and each of them comes with a different price per MiB. The more we transfer the less our price will be per MiB.
# Photonic Crystal Cavity with a Thin Low-Index Layer for Silicon-Compatible Nanolight Source
## 4 Design of Photonic Crystal Cavity Mode
In order to consider mechanically stable, practical devices in the low-index dielectric substrate, PDMS (\(n\) = 1.396), SiO\({}_{2}\) (\(n\) = 1.47), we calculated the effects of the substrate on the \(Q\) factor for the optimized PhC cavity (\(r\) = 120 nm, \(H\) = 1000 nm, \(t\) = 10 nm, \(r_{d}\) = 90 nm, \(d\) = 60 nm). The \(Q\) factor decreases to 750 and 490 for PDMS and SiO\({}_{2}\) due to leakage loss into the substrate, respectively. In order to overcome leakage loss, the height of the rod can be increased by increasing the distance between the high-intensity region, SiO\({}_{2}\) layer, and substrate. When the height of the rod becomes 1400 nm and the distance from the SiO\({}_{2}\) layer to the substrate increases to 895 nm, the \(Q\) factor is largely improved to 2200 and 870 for PDMS and SiO\({}_{2}\) substrate, respectively.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 3 Materials
### Semantic Segmentation Network  
Semantic segmentation distributes semantic categories to pixels of the input image to obtain pixel-level classification. Since 2015, intelligent image segmentation methods have been extensively applied in bio-medicine [40]. A common semantic segmentation framework is an encoder-decoder network: (1) Encoder: usually a pre-trained classification network, such as VGG [27], ResNet [30], or other deep neural network; (2) Decoder: the decoder semantically projects the discriminative features (lower resolution) to the pixel space (higher resolution) to obtain pixel-level classification.  
Under this structure, we down-sample the spatial resolution of the input image to develop low-resolution feature maps that can efficiently classify object classes. The feature representation is then up-sampled to form a full-resolution segmentation map. Semantic segmentation requires not only discriminative capabilities at the pixel level but also a mechanism that can project the discriminative features at different stages to the pixel space [41] (i.e., map back to the original image size). Different architectures employ different mechanisms (skip connections, pyramid pooling [42], etc.) as part of the decoding mechanism.