esa-sceva/satcom-chunk-collection huggingface dataset:
# Gamification with Scratch or App Inventor in Higher Education: A Systematic Review
## 2 Materials and Methods
### Characteristics of the Included Studies  
The eleven articles selected for the review were research studies published in English (N = 10) and Spanish (N = 1) between the years 2016 and 2022, which ensured updated results on the application of Scratch and App inventor programs in universities. The studies were conducted in Colombia, China, Ecuador, Egypt, England, Japan, Nigeria, Spain, and the United States. Six studies used quantitative methodology (54.55%; N = 6), two were mixed studies (18.18%; N = 2), one was qualitative (9.09%; N = 1), one was project-based learning (9.09%; N = 1), and one was real-life testing (9.09%; N = 1). Some assessment instruments overlapped across the studies, with 5 being used in the 11 items selected. These were questionnaires (44.44%), surveys (27.77%), observation (11.11%), objective tests (11.11%), and interviews (5.55%). As seen in Figure 2, the most frequently used instruments were questionnaires, followed by surveys, and the least used were interviews. In all the studies, the selected sample consisted of university students; only one study also included teachers. As for the results, it should be noted that most of the programs met the objectives they had set; only one found a significant sample of students who considered that the program was not fast in its interaction and did not find precision in the personalized exercise recommendations. See Figure 2.  
Figure 1: Document search flowchart. Nine studies used the Scratch language, and two used App Inventor. All the information can be seen in Table 2, specifying the authorship, year of publication, purpose, sample, country, methodology with evaluation instrument, and main results.
# A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
## 1 Introduction
Nowadays, there are still some research gaps regarding models and data utilized for prediction of carbon fluxes. First, few studies have paid attention to the application of deep learning on upscaling one carbon flux in order to derive global products, despite the fact that they outperform conventional machine learning in many cases, though many studies have focused on estimating carbon flux by conventional machine learning methods. Therefore, in our research, the convolutional neural network (CNN), as a robust deep learning model, was applied for prediction of the carbon flux in order to make a comparison with other conventional machine learning methods. Second, available site data are not fully utilized in previous studies [9; 19; 25]. In our study, in addition to the FLUXNET dataset, two more site datasets (i.e., FLUXNET-CH4 [30] and Drought-2018 [31]) with updated data were added to the training target set, which can enhance the representativeness of the site data, and thus lead to a higher quality of the carbon flux estimation. Last but not least, current global carbon flux products are at relative coarse resolutions with limited precision. The widely used FLUXCOM has a resolution of 0.5 degree, while [PERSON] et al. [25] provides a product at 0.1 degree. In our study, we aim to produce a global carbon flux dataset at a much higher resolution, i.e., 30 arc second (\(\sim\)1 km), which can meet the demand of research that requires more spatial details of carbon fluxes. The purpose of this study was to develop a global carbon flux dataset (GCFD) with 1 km resolution using remote sensing and meteorological variables. The main research questions were as follows: (1) Can CNN as a deep learning model achieve better performance than conventional machine learning models such as ANN and RF? (2) Are the spatial patterns and temporal variations of carbon fluxes estimated by CNN reasonable at the resolution of 1 km? The remaining structure of the paper is organized as follows. Section 2 describes the target variables and predictors, models, experiments, model evaluation, and data production. Section 3 gives the validation results, timeseries diagrams, spatial distributions, and variable relation analysis. Sections 4 and 5 present the discussion and conclusions, respectively.
# AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification
## 2 Background and Related Work
### Browser Fingerprinting  
Browser fingerprinting is a technique employed to uniquely identify a browser by collecting specific attributes from its environment. Previous research has predominantly focused on gathering information such as browser version, system time zone, preferred language settings, supported video and audio codecs, installed system fonts, browser configurations, and device display parameters including resolution and dimensions [33; 34]. Such information has been leveraged extensively for applications such as targeted advertising and personalized services by verifying user devices and adapting content to align with individual preferences [35; 36; 37].  
While the majority of studies in browser fingerprinting [33; 36] have focused on user or device identification through static and dynamic browser characteristics, limited attention has been devoted to investigating whether browser-level observations can infer the presence of security software, such as antivirus products.  
AV-Teller introduces a novel dimension to system profiling by detecting antivirus-specific artifacts. These artifacts, including DOM manipulations and injected elements, emerge during browser interactions and provide passive, reproducible signals for remotely identifying antivirus presence. Notably, this detection approach circumvent the need for code execution, privileged system access, or user cooperation.  
This approach diverges from conventional fingerprinting research both in its aim and technical framework. Rather than focusing on profiling users or devices, AV-Teller emphasizes the interaction of browser with system-level security mechanisms, thereby broadening fingerprinting methodologies with a security-aware perspective.  
Furthermore, our method extends prior research by seamlessly integrating with established fingerprinting pipelines. AV-Teller operates concurrently with conventional identification methods, enabling a comprehensive characteristics of client environment. This integration facilitates the development of multi-faceted system profiles that encompass both usability and security features.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 2 Proposed EWS-16T SRAM Cell
### Design and Operation of the EWS-16T Cell
#### 2.1.1 Hold Operation  
During the hold operation, WL and WWL are deactivated, keeping all access transistors turned off. The bit lines BL and BLB are maintained at VDD to reduce wake-up time. Assuming an initial logic state of '1', transistors N2, N3, N5, P2, P4, and P6 are turned on, while the remaining transistors remain off. As a result, the originally stored data in the EWS-16T cell remains unchanged and is reliably retained.
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 1 Introduction  
The fabrication of micro- or nano-particle formulations to address challenges in drug delivery has been of interest for decades. Compared with traditional drug delivery systems, microparticle and nanoparticle drug delivery platforms have a number of advantages [1, 2], including the ability to: (a) load multiple drugs into the micro- or nano-particles in one step, (b) attach targeting ligands, (c) increase the circulation half-life of the encapsulated drug and/or reduce its non-specific toxicity [3, 4].  
To successfully design and develop a polymeric drug formulation, there are three main issues that need to be taken into consideration, namely, the physicochemical properties of the drug(s) of interest, the properties of the polymeric carrier, and the drug delivery system [5].  
In general, polymeric drug delivery systems may be categorised into one of three types, namely, colloidal carriers [6], implantable networks or hydrogels [7, 8], and polymer drug conjugates [9]. Selection of a suitable polymer that is compatible with the encapsulated drug(s) of interest is vitally important for achieving the desired product performance in vivo. In other words, the encapsulated drug candidate's properties including chemistry, hydrophobicity/hydrophilicity and potency, need careful analysis as the drug release profile is influenced by the drug properties, the interaction between the drug and polymer and the release environment [10]. Additionally, the physicochemical characteristics of the polymeric formulation (e.g., particle size and shape, hydrophobicity and hydrophilicity,surface charge) also affect the in vivo performance and biodistribution of the polymeric formulation [11].  
In the following sections of this review, we discuss the evolution of biodegradable polymeric drug delivery systems, methods used for encapsulation of drugs with biodegradable polymers, drug release mechanisms from polymeric formulations, and successful commercial products on the market.
# A Novel Maneuver-Based Driving Envelope Generation Approach for Driving Safety Assessment
## 2 The Motion Constraints
### The Constraint of Vehicle Kinematic  
The maximally allowed acceleration is limited by the friction force, which can be represented by a friction circle on a g-g diagram [30]. Factors including weight transfer, bank and grade of the road will affect the shape of the friction circle. These factors can be considered to obtain accurate vehicle dynamic models under different road conditions [31]. The permissible speed \(v_{lim}\) is limited by the road curvature \(\kappa\) and the maximum allowed speed \(v_{max}\). The longitudinal and lateral motion in the reference frame is limited by the ability of accelerating and decelerating and the friction force.  
\[v_{lim}=min(\sqrt{\frac{a_{lat}}{\kappa}},v_{max}) \tag{1a}\] \[\sqrt{a_{lon}^{2}+a_{lat}^{2}}\leqslant a_{\max}\] (1b) \[a_{\min,lon}\leq a_{lon}\leq a_{\max,lon}\] (1c) \[-a_{\max,lat}\leq a_{lat}\leq a_{\max,lat} \tag{1d}\]  
where \(a_{lat}\) is the lateral acceleration, \(a_{lon}\) is the longitudinal acceleration, \(a_{\max,lon}\) represents the maximum longitudinal acceleration, \(a_{\max,lat}\) represents minimum longitudinal acceleration, \(a_{\max,lat}\) represents the maximum allowed lateral acceleration.
# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 3 Related Work
### State-of-the-Art Open Information Extraction
#### 3.1.2 Hand-Crafted Rules Approaches  
REVERB, introduced by [PERSON] et al. [16], extracts tuples by singling out relation phrases that satisfy syntactic and lexical constraints; for each relation phrase, a pair of noun phrase arguments are identified. REVERB then uses logistic regression trained on 1000 sentences from the web with shallow syntactic features to assign a confidence score to each extracted relation triple. The R2A2 approach [17] upgrades REVERB by adding ARGLEARNER, an argument identifier that makes use of patterns as features to identify the left and right boundaries of each argument.  
KRAKEN [18] is one of the few OIE system that is able to capture N-ary relations. It utilizes hand-crafted patterns to identify relation phrases and their correlated arguments over typed dependency parsers. As a further matter, KRAKEN is able to detect completeness and correctness of the extracted facts, thus increasing the quality of the extracted information. [PERSON] and [PERSON] proposed ClausIE (Clause-based Open Information Extraction) [19], which locates clauses in input sentences by making use of linguistic information of the English language's grammar by computing a dependency parse tree of the input phrase to determine its syntactical structure. Each clause is later classified to be compatible with the grammatical function of its constituents. Unlike the aforementioned OIE systems, ClausIE does not exploit any training data.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 3 Results
### Monte-Carlo Simulation  
The detection efficiencies of LaBr\({}_{3}\) for thirteen radionuclides were also obtained using the Monte Carlo simulation method. Considering that seawater is a good shield for radiation, the contribution of radioactive sources located more than 1 m from the detector is not considered in the study. As the distance between the radioactive source and the detector crystal varies, its contribution to the total energy peak count also varies. Therefore, the simulation was carried out using a sub-shell sampling method for every 5 cm thick spherical shell with a total shell thickness of 100 cm, and finally, the detection efficiency in the total simulated volume can be obtained by weighted summing according to the volume of each layer to the total volume. The simulation results for each nuclide are shown in Table 2. The detection efficiency for \({}^{137}\)Cs at the 662 keV peak of the device developed in this study is 0.0867 cps/Bq L\({}^{-1}\), very close to the efficiency of CeBr\({}_{3}\) detector, another medium-resolution system [12].  
To eliminate the effect of emission probability, we normalized the probability of \(\gamma\)-ray emission for all energies. The relationship between detection efficiency and energy was  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Radionuclides** & **137 Cs** & **234 Th** \\ \hline Activity concentration in tank water (Bq/L) & 0.45 & 5.86 \\ full energy peak count rate (cps) & 0.036 & 0.037 \\ detection efficiency (cps L Bq-1) & 0.080 & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement results in efficiency calibration with spiked seawater.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radionuclide** & **\(\gamma\)-ray Energy (keV)** & **Emission Probability (\%)** &
\begin{tabular}{c} **Detection Efficiency** \\ **(cps/Bq L\({}^{-1}\))** \\ \end{tabular} \\ \hline \({}^{234}\)Th & 92-93 & 4.33 & 0.0062 \\ \({}^{131}\)I & 364.49 & 81.20 & 0.1113 \\ \({}^{134}\)Cs & 569.331 & 15.37 & 0.0165 \\ \({}^{124}\)Sb & 602.728 & 97.78 & 0.1013 \\ \({}^{134}\)Cs & 604.722 & 97.63 & 0.1005 \\ \({}^{110m}\)Ag & 657.75 & 94.38 & 0.0952 \\ \({}^{137}\)Cs & 661.659 & 84.99 & 0.0867 \\ \({}^{134}\)Cs & 795.868 & 85.47 & 0.0791 \\ \({}^{58}\)Co & 810.766 & 99.44 & 0.0924 \\ \({}^{54}\)Mn & 834.855 & 99.97 & 0.0932 \\ \({}^{110m}\)Ag & 884.67 & 74.00 & 0.0680 \\ \({}^{60}\)Co & 1173.24 & 99.85 & 0.0845 \\ \({}^{60}\)Co & 1332.508 & 99.98 & 0.0768 \\ \({}^{40}\)K & 1460.822 & 10.55 & 0.0079 \\ \({}^{124}\)Sb & 1690.984 & 47.46 & 0.0348 \\ \({}^{208}\)Tl & 2614.511 & 99.76 & 0.0590 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection efficiency of different radionuclides obtained with Monte Carlo simulation.  
plotted as shown in Figure 3. The normalized measured values of detection efficiency and the Monte Carlo simulated values are plotted together, as shown in Figure 3.
## 6 Solutions for Security and Trust in IoV
### ML-Based Solutions for Trust
#### 6.3.1 Supervised-Learning-Based Solutions
The authors in [160] focused on identifying potential Sybil vehicles and protecting messages from Sybil attacks. The proposed strategy integrates metaheuristic methods during the establishment of communication to identify possible Sybil nodes and employs trust certification mechanisms to guarantee the integrity of messages. The importance of employing a variety of methods to properly prevent Sybil attacks is highlighted. The author in [161] investigated the expanding field of IoV and the need for trust assessment schemes to defeat insider assailants. For accurate trust assessment, precise weight assignment and the definition of a minimum acceptable trust threshold are essential. The paper employs an IoT dataset, adapted from CRAWDAD to an IoV format, comprising information on 18,226 interactions among 76 nodes, both honest and dishonest. It computes important parameters, including packet delivery ratio, familiarity, timeliness, and interaction frequency. Two feature matrices are generated: FM1, which takes individual parameters into account as features, and FM2, which averages pairwise computations for each parameter. Once the truth has been established by unsupervised learning, supervised machine learning can be employed for categorization. The results demonstrate that FM2 is superior at accurately classifying dishonest vehicles. These references enhance trust and security techniques in vehicular communication networks, addressing important difficulties and making VANETs and IoV systems safer and more reliable. These research will likely develop and enhance these models to address evolving threats.
## 2 Design of the All-in-One Collimating Splitter
### Design of the Unit Cell  
The square unit cell consists of silicon nano-cuboids with different widths (\(d\)), shown in Figure 3a. The height (\(h\)) and period (\(p=p_{x}=p_{y}\)) of the nano-cuboid are fixed at 1 \(\upmu\)m and 0.6 \(\upmu\)m, respectively, and the width (\(d\)) varies from 150 to 450 nm. The refractive index of silicon is set as 3.6451 at the wavelength of 1.55 \(\upmu\)m according to the experimental results. The transmittance and the normalized phase (\(\phi_{T}(d)\)) of the unit cell are calculated with Reticolo [40] using rigorous coupled wave analysis (RCWA). The results are plotted in Figure 3b. According to the optimized phase profile (\(\phi(x,y)\)) above, the nano-cuboid with the proper width (\(d\)) is chosen such that the corresponding cost function (\(f(x,y)\)) is minimized at each location:  
\[f(x,y)=\left|e^{i\phi_{T}(d)}-e^{i\phi(x,y)}\right|, \tag{6}\]  
The width profile of the final results is plotted in Figure 3c for demonstration.
## Materials and Methods
### Generation of Renilla Luciferase-Tagged RSV-A2 and RSV-B1 G/mG Protein Constructs  
A mammalian expression vector, pREN2, was previously described and was used to generate _Renilla_ luciferase (Ruc)-tagged RSV-G\({}_{\rm A}\), RSV-mG\({}_{\rm A}\), RSV-G\({}_{\rm B}\), or -RSV-mG\({}_{\rm B}\) proteins (Ruc-G\({}_{\rm A}\), Ruc-mG\({}_{\rm A}\), Ruc-G\({}_{\rm B}\), or Ruc-mG\({}_{\rm B}\)) [21]. Each construct contained both a FLAG epitope and Ruc-tag at the amino terminus of the G protein. The RSV-G\({}_{\rm A}\) and -G\({}_{\rm B}\) genes were amplified from HEp2 infected RSV-A2 (GeneBank accession no. M11486.1) or RSV-B1 (GeneBank accession no. AF013254.1), respectively, using the primers provided in Table 1. The RSV-A2 and -B1 DNA were cloned in TOPO plasmids and then sub-cloned into pREN2 downstream of _Renilla_ luciferase sequence between _EcoRI_ and _Xho1_ restriction sites as previously described [25].  
Synthetic DNA constructs representing a mutated form of G protein (mG) were purchased (Genscript, Piscataway, NJ, USA). Genbank sequences M11486.1 and AF013254.1 were used to design RSV-A2 and -B1 mG constructs, respectively, and included amino acid substitutions in both constructs at positions 48 (M48I) and 49 (I49V) to abolish the secreted form of G [27]. Synthetic constructs were amplified, restriction enzyme digested, gel purified, and then cloned into pREN2 as described above. The sequence and integrity of all DNA constructs were confirmed using automated DNA sequencing.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Primer Name** & **Sequence** \\ \hline RSV-A2-G Forward & AAGGAATTCAACATGTCCAAAAACAAGGACCAACGCACC \\ RSV-A2-G Reverse & GGGCTCGAGTTAAAGTAACTGCGCGTGGT \\ RSV-B1-G Forward & AAGGAATTCACATGTCCAAAACAAAGAATCAACGCACT \\ RSV-B1-G Reverse & GGGCTCGAGGAATAACTAAGCATGTGATTGGG \\ \hline \hline \end{tabular}
\end{table}
Table 1: RSV-G primer sequences.
## 2 System Architecture and Methods
### Implementation Details
#### 2.3.2 Semantic Analysis Module  
After the system transcribes the child's voice command, it tasks an LLM system with interpreting the message and reforming it into a structured dictionary with five fields: Result, Action, Object, Color, and Size. The Result field indicates the success of the interpretation, returning true for success and false otherwise. Action specifies the robot's required action, encompassing six possibilities: go to a bucket or object, go down, go up, grab, release an object, and restart (an internal command). The Object field identifies which object the action targets. Color and Size are optional, and dependent on the scene's objects. For instance, Color becomes critical when differentiating between multiple cube objects of varying colors. Similarly, the need for Size arises under similar circumstances. We intentionally avoid test scenes with objects of similar color and size to prevent ambiguity in our experiments.  
The prompt was the following: Interpret the voice command from a child, converted to text, to control a robot (Little hand). Respond with Result, Action, Object, Color, and Size. If the color or size is not specified, respond with not specified. If it was impossible to understand the phrase, respond with Result: error. Otherwise, respond with Result: ok. Possible actions include go down, go up, release, grab, go to, and restart. Possible colors are red, white, blue, and yellow. Possible sizes are large and small. Possible objects are star, cylinder, cube, and box; any other object is considered an error. The voice command is: {**STT transcript**} Note: Commands may include variations in expression and common language errors made by children. Normalize the response to the established categories. The output is a JSON file with the following fields: result, action, object, color, and size. Additionally, children might use terms like grab, suck, suction, or similar to indicate the action of grabbing an object.  
The previously described prompt is a translation of the prompt used in our experiments, originally in Spanish. {**STT transcript**} represents the output from the STT module, which converts the child's spoken voice command into written text. For example, an STT transcript might be as follows: \"Move the robot to the blue cube\".  
The output is a JSON file containing the fields previously described (see Figure 3). In the results section, a thorough evaluation is conducted comparing the Gemini [19] LLMservice with ChatGPT [20] versions 3.5 and 4. We ultimately chose Gemini due to its superior runtime speed and performance. We did not test open-source LLMs locally due to the need for sufficiently powerful GPUs to run these models.
## 4 Discussion
### Dense Reward Assignation Policies  
Our agent converges to sub-optimal policies by carefully designing a reward schema as the one presented in Section 2.2.2. Our algorithm assigns a specific reward at each MDP transition considering the optimality of VNF assignments in terms of QoS, hosting costs, and data transfer costs. This dense-reward schema enhances the agent's convergence. In fact, in our experiments, we have also noticed that the dense-reward algorithms improve the results of their sparse-reward counterparts. In other words, we see in Figure 2(b) that NFVDeep-Dense performs slightly better than NFVDeep, and NFVDeep-Dense-D3 QN performs better than NFVDeep-D3 QN. This improvement exists because dense rewards provide valuable feedback at each assignation step of the SFC, improving convergence of the DRL agents to shorter RTTs. On the other hand, we have also observed that even if cost-related penalties are sparsely subtracted in our experiments, the proposed DRL agent learns to optimize SFC deployment not only with respect to QoS but also taking into account the operational costs.