esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural and Biochemical Characterization of Thioredoxin-2 from _Deinococcus radiotrans_
## Materials and Methods
### Insulin Reduction Assay  
The insulin reduction activities of DrTrx1 and DrTrx2 were measured using DrTrxR and NADPH by monitoring NADPH consumption, which, in turn, was reflected by the decrease in absorbance at 340 nm [50]. The assay mixture (0.5 mL) contained 50 mM Tris-HCl buffer (pH 7.4), 30 mM bovine insulin (Sigma-Aldrich, St. Louis, MO, USA), 0.1 mM NADPH (Sigma-Aldrich), 0.1 mM DrTrxR, and 0.1 mM DrTrx1 or 0.5 mM DrTrx2. All the measurements were carried out at room temperature using an Epoch2 microplate reader (BioTek, Winooski, VT, USA). The reaction was initiated by adding Trx protein, and the resulting change in absorbance was recorded. All the data provided here represent the mean and standard deviation of three independent experiments.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Data Collection** \\ \hline Data set & DrTrx2 \\ PDB ID & 7 DGL \\ Diffraction source & PAL BL-7A \\ Wavelength (Å) & 0.97933 \\ Space group & C2\({}_{1}\) \\ _a, b, c_ (Å) & 101.89, 57.82, 36.15 \\ \(\alpha\), \(\beta\), \(\gamma\) (\({}^{\circ}\)) & 90, 94.78, 90 \\ Resolution range (Å) \({}^{\star}\) & 50.00–1.96 (1.99–1.96) \\ No. of unique reflections & 14566 (685) \\ Completeness (\%) \({}^{\star}\) & 94.9 (91.3) \\ Redundancy \({}^{\star}\) & 5.1 (3.9) \\ _I/c/(1)_ * & 50.78 (11.63) \\ \(R_{\mathrm{sym}}\) (\%) \(\star\)\({}^{\ddagger}\) & 6.5 (23.2) \\ \hline
**Refinement** \\ \hline Resolution range (Å) & 18.96–1.95 \\ No. reflections & 14526 \\ No. atoms & \\ Protein & 1087 \\ Water & 74 \\ Zinc & 1 \\ \(B\)-factors & \\ Protein & 37.71 \\ Water & 48.57 \\ Zinc & 25.68 \\ \(R\) (\(R_{\mathrm{free}}\)) (\%) \({}^{\star}\), \({}^{\ddagger}\) & 19.11 (22.68) \\ R.m.s. deviations \({}^{\lx@sectionsign}\) & \\ Bonds length (Å) & 0.018 \\ Bond Angles (\({}^{\circ}\)) & 1.126 \\ \hline \hline \end{tabular} \({}^{\star}\) The numbers in parentheses are for the outer shell. \({}^{\dagger}\)\(R_{\mathrm{sym}}=\sum_{\mathrm{slit}}\sum_{i}\ \ \mathrm{l}_{i}(\mathrm{b}\mathrm{l}\mathrm{d})\) - \(\mathrm{l}(\mathrm{b}\mathrm{l}\mathrm{d}\mathrm{l})\)\(>1/\sum_{\mathrm{slit}}\sum_{i}\ \mathrm{l}_{i}(\mathrm{b}\mathrm{l}\mathrm{d}\mathrm{l})\), where _I_(_b_l_) is the intensity of the observed reflection _b_l_l_ and \(\mathrm{c}\mathrm{l}(\mathrm{l}\mathrm{l}\mathrm{d}\mathrm{l})\)\(>1\) the mean intensity of symmetry-equivalent reflections. \({}^{\ddagger}\)\(R=\ \mathrm{\
## 3 Material and Methods
### Background and Challenges
#### 3.1.2 Formalizing the Problem  
Music streaming data (describing the total number of streamings per day) come themselves as a data stream. The data are time series data, and we want to analyse and act on the data immediately, when they arrive from the streaming services. We call the \(i\)'th time series of length \(m\), \(T_{i}^{m}\) and the individual data point, e.g., at time \(j\), \(d_{i,j}\):  
\[T_{i}^{m}=d_{i,1},d_{i,2},...,d_{i,m} \tag{1}\]  
In time series data every observation depends on previous observations based on their relation in time. Therefore new observations can be predicted to some extent based on previous observations. We denote the window that we observe for outliers, the outlier window \(o\) with length \(l_{o}\), and the window from which we predict the expected outlier window, we denote the context window \(c\) with length \(l_{c}\). Starting from day \(j\) we thus have:  
\[(c_{i,j},o_{i,j})=((d_{i,j},d_{i,j+1},...,d_{i,j+l_{c}}),(d_{i,j+l_{c}+1},d_{i,j+ l_{c}+2},...,d_{i,j+l_{c}+l_{c}})) \tag{2}\]  
For example, if users monitor on a weekly basis, we want an outlier ranking of a 7 day window (i.e., \(l_{o}=7\)). Assuming that there are monthly variations the context windows should capture at least roughly a month (i.e., \(l_{c}=30\)).  
The user wants to see surprises, i.e., music that has a different streaming count in the outlier window than would have been expected based on the context window. To predict the outlier window, we maintain a prediction model _pred_ that takes a context window \(c\) and predicts the corresponding outlier window \(\delta\).  
\[\delta=pred(c) \tag{3}\]  
We calculate the outlier score \(os\) as how much the real streaming counts deviate from the predicted streaming counts, according to some similarity function _sim_:  
\[os=sim(o,\delta) \tag{4}\]  
Using machine learning we can automatically learn the prediction model from historic data and quickly updated the model, when data change significantly, e.g., when new streaming services are added.
## 5 Conclusions  
The emergence of 5G and beyond networks presents unprecedented opportunities and challenges, particularly in resource allocation, network slicing, and the management of highly dynamic environments. This paper introduced the Optimised Network Slice Selection and Orchestration (ONSSO) framework, Enhanced Sub-slice (eSS) model, and a new Company Network as a Service Vertical (CNaaS), a novel approach leveraging machine learning algorithms such as Ray Tune, LazyPredict, and AutoRL to enhance the orchestration of network slices. By focusing on latency, throughput, and energy efficiency, ONSSO delivers superior performance in comparison to traditional resource allocation models. The approach in this study dynamically adjusts slices by sub-slicing and service chaining to orchestrate a bespoke network slice on demand. Supervised learning by automating the selection of best fit machine learning algorithm was leveraged for accurate feature selection and network function classifications to form a network slice. Reinforcement learning was leveraged upon for proactively learning and forecasting the network resource required by time. This framework dynamically adjusts slice resources to match time-varying demands.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 5 Publicly Available Datasets
### MovieLens Dataset  
The MovieLens dataset is a popular dataset used for collaborative filtering. The dataset comprises ratings from over 20 million users for more than 10,000 movies. It contains ratings from 0.5 to 5, with 0.5 being the lowest and 5 being the highest. In terms of FRSs, the MovieLens dataset is used to provide users with personalized movie recommendations based on the ratings of other users. It does this by considering the similarity between users and the ratings they have given to the movies. For example, if User \(A\) and User \(B\) both rated a particular movie highly, then it is likely that the system would recommend that movie to both users. This is performed by calculating the similarity between users and their ratings and then identifying movies that are popular among users with similar tastes.
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 4 Some Studies of Fitts' Law in HCI Scenes
### Touchscreen Scenes
#### 4.1.3. Screens in Vehicles  
The use of in-vehicle information systems (IVIS) by drivers has increased significantly in the past few years, with touchscreens acting as the main user interface [41]. Drivers naturally gravitate toward touchscreen technology because of its natural user-friendliness and intuitive engagement. Touchscreen interactivity is playing a bigger part in driving experiences as electric vehicles continue to gain popularity. Driving is the main priority when operating a vehicle, leaving little time for intricate device interactions. Touchscreen interfaces are therefore the favored option for drivers due to their ease of use and simplicity [61]. For a driving experience to be pleasant, touchscreen activities must be seamlessly integrated with driving movements.  
According to [PERSON] et al., performance on both primary and secondary tasks may suffer when attention is divided between them [62]. Inadequate touch key design in the context of in-car touchscreens might worsen interface issues and possibly intensify driving disturbances [41]. Although it would be ideal to avoid using screens while driving, this may not be possible due to the increasing integration of in-car screens [63]. The impact of button size on movement time was highlighted in [PERSON] et al.'s examination of task completion times and error rates while driving at different speeds, suggesting that larger buttons can enhance interaction efficiency [41]. Furthermore, [PERSON] et al.'s findings from aviation settings showed that particular cocktail postures and touchscreens during turbulent conditions led to longer job execution times, higher error rates, and a perception of workload [64].  
While driving, drivers need to visually engage with the information displayed on screens, adding to the cognitive load. In a study by [PERSON] et al., they investigated the visual requirements of HCIs and developed predictive equations to estimate the visual demands associated with using these interfaces while driving [65]. Additionally, the application of Fitts' law in designing and evaluating icons for in-car screens provides valuable insights into optimizing user interactions. Although the study by [PERSON] et al. was primarily focused on single-finger touch-pointing tasks with touchscreens in driving scenarios, the findings are broadly applicable beyond driving environments. The results offer guidance for interface design and decision-making interactions across diverse settings where users interact with interfaces to make choices.
## 2 Related Work
### Mixed Reality Using Passthrough  
To date, no MR application using passthrough technology has been developed for therapeutic education in diabetes. Moreover, only a few MR applications using passthrough technology have been presented and validated. The first of these MR applications was for learning to play the piano using Meta Quest 2 and passthrough in grayscale [17]. The MR application described in [17] used passthrough in grayscale to create an immersive learning experience. Two visualization modes were implemented: Wireframe and Solid. In Wireframe mode, the piano keys were outlined with border lines, while in Solid mode, a solid color lid the real keys. A comparison of both modes revealed that, although users recognized the limitations of grayscale passthrough, both visualization methods provided a satisfactory user experience. The second of these MR applications was also for learning to play the piano, but using Meta Quest 3 and color passthrough [18]. A study compared the MR application with Synthesis, showing both were effective, but students performed significantly better with MR. The MR application provided a more satisfying experience, independent of age and gender, with high presence and minimal adverse effects. Students preferred MR, as it allows direct keyboard view with integrated visual aids, keeping their focus on playing. The third of these MR applications aimed to enhance the ecological validity of eating and sensory behavior research [19]. They developed an MR application that used passthrough VR, using the headset's external cameras to capture and display the real-world environment within the virtual space. To blend real-world objects into the virtual space, specific surfaces were chosen to project the passthrough feed instead of standard textures. In their work, two passthrough planes were attached to a virtual tabletop (one flat on the surface and the other perpendicular), allowing users to see real-world objects as if they were part of the virtual scene. Experts assessed the MR application to study eating and sensory behavior. An initial evaluation compared three virtual environments: a lab booth, a basic restaurant, and a fully textured restaurant. Feedback led to refining the virtual restaurant, which was later re-evaluated. Experts recommended adding social cues for greater realism. The updated environment was preferred over a sensory booth but rated lower than a real restaurant.  
Our research seeks to advance beyond prior studies on technology-assisted carbohydrate counting. No previous studies have applied MR to carbohydrate counting using both grayscale and color passthrough.
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 5 Statistical Analysis of Randomness  
Traditionally, all the cryptanalytical attacks launched against TinyJambu have been conducted in block cipher cryptanalysis, (e.g., differential and linear cryptanalyses). Nevertheless, the cryptosystem TinyJambu can be considered a stream cipher cryptosystem in that both encryption and decryption are concerned. This is the reason why an analysis of the sequence generated using TinyJambu (the so-called keystream sequence) deserves a section in this work. To our knowledge, a deep study of such a sequence cannot be found in the literature. The analysis of this sequence is a way of evaluating TinyJambu's strength.  
The encryption/decryption procedure in TinyJambu is a typical stream cipher procedure that is carried out as follows:  
* The encryption is just the bit-wise XOR operation between each 32-bit block \(M_{i}\) of plain text and the contents of stages \(S_{\{64...95\}}\) in the NLFSR.
* The decryption is just the bit-wise XOR operation between each 32-bit block \(C_{i}\) of ciphered text and the same contents \(S_{\{64...95\}}\) in the NLFSR stages.  
Notice that TinyJambu is not a keystream generator in a classical sense [3] (pp. 29-30); that is, it does not generate a long binary sequence for cryptographic purposes. Nevertheless, we can concatenate the contents of stages \(S_{\{64...95\}}\) used in the successive \(M_{i}\) encryptions and generate the corresponding keystream sequence. It is over such a concatenated sequence that we applied the different statistical tests.  
As the initialization of TinyJambu depends on several parameters (key, nonce, associated data, plain text), for our randomness analysis, we generated multiple keystream sequences originating from different choices of random keys, nonces and AD, as well as distinct types of plain texts (e.g., text files, images, videos, etc.). The lengths of our sequences were \(2^{23}\) bits. Moreover, we made use of three kinds of tests: graphical tests [18; 19], the Diehard battery of tests [20; 21] and the family of statistical tests FIPS 140-2 developed by the NIST [22]. For more details concerning the meaning and assessment of such tests, see references [18; 19]. In the following subsections, we show the obtained results.
## 2 Materials and Methods
### Analysis  
Since the spatial and temporal resolutions of NDVI, SM, PAR, and fPAR are not 0.05\({}^{\circ}\) and monthly. Firstly, the spatial resolution of these parameters was resampled to 0.05\({}^{\circ}\) using nearest neighbor interpolation, and the temporal resolution was integrated monthly. Subsequently, SPEI was utilized to identify drought events in the Xilingol grasslands. The monthly anomalies of SPEI, SM, PPT, LST, GPP, SIF, SIF\({}_{\text{yield}}\), and NDVI were calculated pixel-by-pixel for the 2007-2015 period. The relationships between SIF, SIF\({}_{\text{yield}}\), NDVI, and GPP under drought and no\({}_{\text{drought}}\) conditions were analyzed using Pearson's correlation method, and it was assessed whether SIF has the ability to capture the variations in GPP due to drought.
## 2 Materials and Methods
### LST Calculation  
With the aim of determining surface temperature, NDVI was computed by using both the red (\(RED\)) and the near-infrared (NIR) images band [49]:  
\[NDVI=\frac{NIR-RED}{NIR+RED} \tag{4}\]  
In fact, \(NDVI\) remained a key quantitative interpreter for the green cover. It varied from \(-1\) to \(+1\), in which the lesser (\(+\)) figures suggest built-up or bare ground, and larger (\(+\)) estimates suggest flora [50], while (-) estimates indicate non-vegetated surface types or water [50]. Conversely, surface emissivity (\(\varepsilon\)) was assessed based on [PERSON] et al. [51]:  
\[\varepsilon=m\ P_{V}+n \tag{5}\]  
where \(n\) = 0.004, \(m\) = 0.986, and \(P_{V}\) represents the amount of vegetation [15], and it was given by the following formula:  
\[P_{V}=\left(\frac{NDVI-NDVI_{min}}{NDVI_{max}+NDVI_{min}}\right)^{2} \tag{6}\]  
Ultimately, LST in \({}^{\circ}\)C was calculated according to [PERSON] and [PERSON] [15], making use of the identified NDVI and \(\varepsilon\) driving forces:  
\[LST=\left(\frac{T_{B}}{1+\left(\lambda\times\frac{T_{B}}{\rho}\right)Ln \ \varepsilon}\right)-273.15 \tag{7}\]  
where \(\lambda\) denotes the radiance's released wavelength. \(\rho\) = \(h\)\(\times\)\(c\)/\(\sigma\) (1.438 \(\times\) 10\({}^{-2}\) m K), \(h\) is a constant of Planck which equals 6.626 \(\times\) 10\({}^{-34}\) js, \(c\) is the sunlight speed, which equals 2.998 \(\times\) 10\({}^{8}\) m/s, and \(\sigma\) is a constant of Boltzmann, which equals 1.38 \(\times\) 10\({}^{-23}\) J/K.  
To obtain the changes in LST and LULC, the stage change of a provided pixel timewise was evaluated using a cross-tabulation process by the analysis of gain-and-loss [52].
## 6 Evaluation
### Experiments and Results
#### 6.2.2 IoT Device Battery Energy Consumption  
Figure 3 shows an experiment executed for Application 2 with 10,000 tasks, 100 IoT devices, and 2 MEC servers. Initially, Figure 3a shows the tasks are allocated according to their type. The \(x\)-axis shows the time in seconds, and the \(y\)-axis indicates the number of tasks. Regular tasks run on the IoT device itself due to the lower cost among all allocation policies, while critical tasks run on the server, as the total time is reduced compared to the IoT device, even though the energy cost is higher. Thus, tasks are distributed for local processing in the IoT device and in the MEC server.  
Figure 3b represents the battery energy consumption of one IoT device of the system. The \(x\)-axis indicates the time in seconds, and the \(y\)-axis shows the battery energy consumption in Joules. At around 15 s, the battery energy consumption level reaches the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & **A** & **B** & **C** \\
**Variables** & **One MEC** & **Two MEC** & **W/o MEC** \\ \hline \(E_{CPU}(J)\) & 2752.26 & 1725.18 & 5074.35 \\ \(E_{Trans}(s)\) & 835.60 & 1725.17 & 1166.21 \\ \(E_{mce}(I)\) & 3587.86 & 3450.35 & 6240.56 \\ \(T_{CORE}(s)\) & 956.21 & 1225.64 & 347.07 \\ \(T_{Trans}(s)\) & 199.75 & 159.69 & 290.31 \\ \(T_{TOTAL}(s)\) & 1155.96 & 1385.33 & 637.38 \\ \hline \(T_{Trans}=T_{mce-up}+T_{mce-down}\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The MEC server benefit related to the energy consumption.  
Figure 2: Task allocation for Application 1 and Application 2.  
LSL. It corresponds to 10% of the maximum battery capacity. From this moment forward, TEMS no longer allows tasks to run on the IoT devices, causing a sudden increase in the number of allocations to the MEC server for the newly created tasks.  
Our analysis indicates that low battery levels quickly reach LSL and make IoT devices unavailable for processing. High computational workloads also negatively affect the battery level. Therefore, a battery with a healthy energy level and adequate task processing workloads allows the allocation to be performed on the IoT device, without making it unavailable due to lack of battery, contributing to total cost reduction.
# Testing Concrete for the Construction of Winemaking Tanks
## 2 Materials and Methods
### Testing of Concrete Specimens  
For these two mixtures, further testing of physical and mechanical properties, durability tests, as well as reachability tests were carried out. More specifically, the flexural and splitting tensile strengths were determined according to EN 12390-5 [19] and EN 12390-6 [20], respectively. The open porosity was measured according to EN 12390-7 [21] and RILEM CPC11.3 [22] (in water under vacuum) regulations and the static modulus of elasticity was according to BS 1881-121 [23].  
Durability testing after freeze-thaw cycles is of particular importance for tanks as they may be placed in low-temperature areas for wine preservation after being exposed to higher ambient temperatures, with a potential risk of deterioration or cracking. After subjecting the specimens to freeze-thaw cycles, tests are carried out on mass loss (weighing), concrete structure (by determining the dynamic modulus of elasticity) and finally on the compressive strength of the specimen. All these properties are compared with the corresponding properties of the specimens before they were subjected to cycles, in order to determine the potential deterioration of concrete. The test follows the ASTM C666 [24] standard. Each freeze-thaw cycle lasts 4 h and covers the temperature range of \(-18\)\({}^{\circ}\)C to 4 \({}^{\circ}\)C.  
One of the main benefits of concrete wine tanks is the increased thermal resistance of concrete (and therefore low thermal conductivity), which leads to more subtle temperature changes inside the tank, despite greater external temperature changes, as compared to, for example, metal tanks. Thermal conductivity testing was carried out on concrete samples measuring 200 \(\times\) 200 \(\times\) 25 mm (tiles). The apparatus used for the tests was a THERMTEST MODEL HFM 100 thermal conductivity measuring device for building materials (\(\lambda\)), which follows the EN 12667 [25] standard. The calculation of thermal conductivity was carried out using the method of permanent heat flow \"Heat Flow Meter Technique\" (heat flow measurement method). The instrument provides results at two different temperatures 10 \({}^{\circ}\)C and 20 \({}^{\circ}\)C, and since these are possible working temperatures, both records are given in the Results section.  
The permeability of concrete to water vapor was also measured as an important parameter of the material of a winemaking-maturation tank, for the micro-oxygenation of wine. The test was carried out following EN 1015-19 [26]. The tiles were maintained for 2 days in a chamber with constant temperature and humidity conditions (20 \({}^{\circ}\)C and 95% RH) and then for 26 days in a chamber with 21 \({}^{\circ}\)C and 50% relative humidity, according to the standard, with the aim of stabilizing their hygroscopic properties.