esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Section
### Implementation Details
#### 5.2.1 LP Generation  
The code for pix2 pix_cGAN and CycleGAN is written in PyTorch [52] by the same group of researchers. Thus, the network model architecture is different but the training options are nearly identical. The size of the input images was set as 256 pixels, and since the training data was zero-padded and normalized to 256 \(\times\) 256 pixels, the preprocess option in the code was set to 'none'. Other options (number of iterations, batch size, learning rate, etc.) were set to the default values provided by the author. StarGAN, also written in PyTorch, is capable of multi-domain translation, but in this experiment, only used the translation between two domains, label images, and LP images. As was carried out for the other two models, the size of the input image was set as 256 pixels and the domain dimension was set as 2. Other parameters of StarGAN, such as the number of iterations, batch size, and learning rate, etc. were set to the default values provided by the author.  
As mentioned previously, the pix2 pix_cGAN_9k, CycleGAN_9k, and StarGAN_9k datasets were generated from the Label_9k dataset using the three LP-GAN generators. Figure 1 shows some of the resultant images of each proposed LP-GAN generator for the same input label image. Figure 1a shows the input label images; Figure 1b-d, reveal the resultant images from each LP-GAN generator; and Figure 1e,\(f\) present the real LP images acquired by CCTV and web-scraping, respectively. From the results, it can be seen that the proposed LP-GAN generators could translate in the same style as the real LP while maintaining the character information of the input label images. In particular, even when the input label images contained character information that did not exist in the training data (such as  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Dataset** & **Description** \\ \hline Web\_159 & Real LP images from Web-Scraping \\ \hline pix2 pix\_cGAN\_9k & 9000 generated LP images by three state-of-the-art GAN based \\ StarGAN\_9k & LP-GAN from Label\_9k \\ \hline pix2 pix\_cGAN\_3k & \multirow{2}{*}{Randomly selected from each of pix2 pix\_cGAN\_9k, CycleGAN\_9k, StarGAN\_3k} \\ CycleGAN\_3k & & \\ StarGAN\_3k & & \\ \hline Ensemble\_9k & \
\begin{tabular}{l} Combined generated LP images. \\ (pix2 pix\_cGAN\_3k + CycleGAN\_3k + StarGAN\_3k) \\ \end{tabular} \\ \hline Ensemble\_3k &
\begin{tabular}{l} Combined randomly selected 1000 LP images from each of pix2 pix\_cGAN\_3k, CycleGAN\_3k, StarGAN\_3k \\ \end{tabular} \\ \hline Real\_9k & 9000 real LP images for training data \\ \hline Real\_3k & 3000 real LP images randomly selected from Real\_9k \\ \hline Real\_159 & 159 real LP images randomly selected from Real\_3k \\ \hline Test\_13k & 13,117 real LP images for LPCR testing \\ \hline \hline \end{tabular}
\end{table}
Table 3: 13 datasets for experiments.  
the C15 character class) were input into the generators, the character class was correctly translated exactly the same as the other existing character classes.
## 1 Introduction
We conduct a scoping review of 100 peer-reviewed publications related to AI/ML systems and competition settings. Through thematic analysis, we identify and define 18 distinct types of technical debt, each accompanied by rationale, definition, and practical examples grounded in AI/ML competitions. A novel contribution of this work is the proposal of Accessibility Debt as a new category, focusing on addressing the challenges associated with the ease and speed of immediate use of the AI/ML competition platforms. Building on the developed typology, we introduce a stakeholder-oriented assessment framework, including a structured questionnaire designed to facilitate the quantitative evaluation of technical debt across the identified categories. This dual-pronged approach--conceptual mapping and practical measurement--aims to support competition organizers, participants, and educators in systematically detecting, prioritizing, and mitigating technical debt in competitive AI development environments. To clarify the scope of this work, our contribution combines (i) a new typology of 18 technical debt categories, including the novel concept of Accessibility Debt, and (ii) a lightweight, stakeholder-specific questionnaire that enables the assessment of these debt types in practical settings. This study thus offers a unified framework that bridges conceptual categorization and applied evaluation, enabling stakeholders to better understand and manage technical debt in AI/ML competitions. The remainder of this paper is structured as follows: Section 2 reviews related work on technical debt in AI/ML systems and competition platforms. Section 3 describes the scoping review methodology and the process for identifying and categorizing debt types. Section 4 presents the main findings across the identified debt categories. Section 5 introduces the stakeholder-oriented questionnaire and the proposed method for quantifying technical debt. Section 6 discusses the broader implications of our findings, while Section 7 concludes the paper and outlines directions for future research. This article is a revised and expanded version of a peer-reviewed conference paper entitled \"Measuring Technical Debt in AI-Based Competition Platforms,\" which was presented at the 13 th Hellenic Conference on Artificial Intelligence (SETN 2024), Athens, Greece, 11-13 September 2024 [22]. The current version includes a significantly expanded literature review, a comprehensive scoping review methodology, the proposal of an additional technical debt category (Accessibility Debt), and a quantitative assessment framework tailored to AI/ML competition platforms.
## 2 Results
### Function Potentials of Rhizsphere Microbial Community in Response to H\({}_{2}\)  
As shown in Figure 3A and Supplementary Table S5, subsequent results showed that, under our experimental conditions, _ACAT_, _tktA_, and _coxL_ were the most abundant genes involved in C fixation, where the relative abundances of many genes were significantly higher in the HNW group than those in SW. These genes in Proteobacteria, for example, include _PRK_ (2.25-fold) and _rbCL_ (1.86-fold) related to the Calvin cycle, _porA_ (1.53-fold) and _frdA_ (3.69-fold) related to the rTCA cycle, _coxL_ (1.60-fold) related to the Wood-Ljungdah pathway, _ACAT_ (1.26-fold) related to the HP/HB cycle, etc. (\(p<0.05\) or 0.01). However, there were no such significant differences between the HNW + F and SW + F groups.  
Conversely, for C degradation, most genes exhibited lower abundances in the HNW group than those in SW, including _amyA_ (0.26-fold) and _malZ_ (0.49-fold) involved in starch degradation, _bgIX_ (0.64-fold), and _bgIB_ (0.68-fold) associated with cellulose degradation, _abfA_ (0.47-fold), _lacC_ (0.54-fold) and _bgaB_ (0.36-fold) related to hemicellulose degradation, etc. (Figure 3B and Supplementary Table S5; \(p<0.05\) or 0.01). By contrast, only four genes were altered significantly in response to HNW under the fertilized conditions, including _katG_ (1.27-fold), _treZ_ (0.89-fold), _SGA1_ (0.87-fold), and _glCD_ (0.87-fold). Notably, the genes involved in C degradation were more abundant in Proteobacteria, Acidobacteria, and Bacteroidetes compared to other phyla.  
In the absence of fertilizers, _nupA_ (2.63-fold), _nupB_ (3.07-fold), _nrfA_ (2.01-fold), and _nrfA_ (2.81-fold) for dissimilarity nitrate reduction to ammonium (DNRA) displayed higher abundances in the HNW group compared to SW in Proteobacteria, Chloroflexi, and Bacteroidetes (Figure 4A and Supplementary Table S5, \(p<0.05\) or 0.01). In contrast, _nirA_ (0.30-fold) and _narB_ (0.02-fold) for assimilatory nitrate reduction to ammonium (ANRA), _nirK_ (0.80-fold), and _norB_ (0.67-fold) for denitrification were less abundant in the HNW group. By contrast, fewer genes exhibited significant differences in the abundance when fertilizers were, respectively, added. For instance, only the relative abundance of _narB_ (2.46-fold) for ANRA was higher, while \(pmoA/amoA\) (0.37-fold), \(pmoB/amoB\) (0.55-fold), and \(pmoC/amoC\) (0.38-fold) related to nitrification was lower in the HNW + F group, compared to those in SW + F treatment (\(p<0.05\) or 0.01).  
For P cycling, _ugpA_ (1.33-fold), _ugpB_ (1.89-fold), _ugpC_ (2.17-fold), and _ugpE_ (1.36-fold) for P transportation, _ppx-gppA_ (1.16-fold) for inorganic P solubilzation, and _phoD_ (1.22-fold) and _appA_ (7.72-fold) for organic P mineralization displayed more abundant in the HNW alone group compared with its control (SW) in Proteobacteria (Figure 4B and Supplementary Table S5; \(p<0.05\) or 0.01). In the presence of fertilizers, _phmc_ (1.51-fold), _phoA_ (1.44-fold), and _phnA_ (2.48-fold) involved in P transportation and mineralization were enriched by H\({}_{2}\)-based irrigation (\(p<0.05\) or 0.01).
## 2 Materials and Methods
### Preparation of Hydrolate Extracts  
Extraction of the hydrolate was carried out in glass containers and in triplicates using hexane and ethyl acetate as organic solvents. The ratio of hydrolate/hexane was 9:1 (\(v/v\)), with 9 mL of the hydrolate and 1 mL of the organic solvent, and the ratio of hydrolate/ethyl acetate was 8:2 (\(v/v\)), with 8 mL of the hydrolate and 2 mL of the organic solvent. The mixtures were shaken overnight at room temperature. Then, 0.5 mL of the upper (organic solvent) fraction was taken for the GC-MS analysis (Section 2.12). After the analysis, the relative standard deviations were calculated, and the results were averaged.  
Profile of Volatile Substances Determined by Gas Chromatography Coupled with Mass Spectrometry (GC-MS)  
The unprocessed hydrolate samples and hydrolate extracts were analyzed using a Shimadzu GC-MS system (GCMS-QP2010 Ultra, Sao Paulo, Brazil) equipped with an MS detector and an Rxi-5 Sil MS capillary column (Restek, Pennsylvania, USA; 30 m \(\times\) 0.25 mm, film thickness 0.25 \(\upmu\)m). The injector and ion source temperatures were set to 250 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, respectively. The column temperature was programmed to increase from 40 \({}^{\circ}\)C to 220 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, holding the initial and final temperatures for 15 min each. Helium (99.99%) was used as the carrier gas at a flow rate of 1 mL/min. Hydrolate samples of 0.5 \(\upmu\)L and 1.0 \(\upmu\)L, and extract samples of 1.0 \(\upmu\)L, were injected using an autosampler in split mode, with split ratios of 1:5 and 1:10 for the hydrolate samples, and 1:5 for the extract samples. The MS detection was performed in electron ionization mode with an ionization energy of 70 eV, and the MS transfer line temperature was set to 250 \({}^{\circ}\)C. The mass-to-charge (\(m/z\)) range was from 40 to 400, with a scanning frequency of 5 Hz.  
The compound identification was based on the comparison of their mass spectra and the retention indices with those of synthetic compounds in the National Institute of Standards and Technology (NIST11) and the Flavors and Fragrances of Natural and Synthetic Compounds (FFNSC2) spectral libraries. The phenylacetaldehyde peak was confirmed using a reference standard. The linear retention indices were determined relative to a homologous series of n-alkanes (C6-C24). The components' relative concentrations were calculated from the GC peaks without correction factors.
## 3 Underwater Routing Protocols
### Sor  
The work in [65] designs a self-organizing (SOR) protocol where every node forwards the data packets of the nodes above it towards the gateway in a radial network. A hello packet is broadcast by the gateway to its one-hop neighbors. Once the neighbors respond, the gateway sends a route request to the neighbors, which process it until the last node sends an acknowledgment to the gateway. This information is used by the gateway to construct strings that forward the data packets. This process reduces the energy consumption by avoiding unnecessary forwarding of the data. It also shortens the end-to-end delay and increases the throughput. However, the protocol suffers from void problems when a node in the string dies, which results in packet loss. Furthermore, the mobility of nodes does not make it easy to form strings of nodes and forward the packets in a smooth manner.  
DVRP  
The diagonal and vertical routing protocol (DVRP) addressed in [66] reduces the number of forwarders among the source, relay and destination. Relay selection is accomplished by the adjustable angle from the source to the destination. A reduction in the number of forwarder nodes enhances the energy efficiency. The selection of the adjustable angle ensures the selection of routing paths with low end-to-end delay. In sparse conditions, the angle is increased to include one or more forwarder nodes. The protocol achieves a better packet delivery ratio than the counterpart scheme. However, the energy consumption of the protocol degrades when the number of sinks increases in the network.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 3 Hardware-Assisted Dynamic Information Flow Tracking
### Gate-Level DIFT  
[PERSON] et al. in [15] presented a DFIT scheme on gate-level netlist to track all information flows. This design resolves the problem of implicit flow and coverts flow to some extent because all tracking logic is implemented as gate logic. The tracking gate logic can reflect how untrusted data influence trusted data in different branches. Both data and code can be tracked because all information flow is visible from the viewpoint of the hardware. The tracking operations run in parallel with the normal operations. The information flow checking operations are combined with the basic ISA. Therefore, the software does not need to be instrumented or modified. The gate-level DIFT can be easily configured to enforce different security rules. However, extra tracking operations introduce high hardware overheads.  
[PERSON] et al. in [16] presented an architectural framework to support verifiable information flow tracking. The whole system is partitioned into a trusted part and an untrusted part. Based on the partition, the time needs to be multiplexed between the trusted part and the untrusted part to prevent the sensitive information leakage. In order to support the above functionality, this framework includes a verifiable critical function in gate-level implementations, e.g., pipeline, cache and I/O systems. Moreover, the microkernel contains a scheduler to support the context switch between trusted and untrusted partitions.  
[PERSON] et al. in [17] presented an information flow tracking (IFT) method to support the verification of security properties in RTL code. This work provides two tracking libraries with different precision levels to generate information flow tracking logic. However, the IFT logic introduces false positives/negatives due to the tag propagation rule, e.g., through the _OR_ rule and the _AND_ rule. For example, the _OR_ rule introduces a high false positive value because the security tag is set whenever one of the inputs is set.  
[PERSON] et al. in [18] presented an architecture to enforce non-interference between trusted entities and untrusted entities and allow the related security properties be verified at gate level. They designed an execution lease between a caller (leaser) and a callee (leasee). The control flow is bounded between two contexts using a timer. The program counter is reset to restore PC once the timer expires. Additionally, the address space is bounded by using extended instructions to enforce access control on different parties.
## 4 Results and Validation
### Validation on Daily Time Scale  
An example of the calculated daily \"bias maps\" is shown in Figure 11a, whose date corresponds to Figure 10a (15 January 2008). With the default orbital configuration, most regions are characterized by a daily mean bias within +/\(-\)7.5 Wm\({}^{-2}\) (Figure 11a). As expected, the largest biases can be found in regions with highly dynamic weather systems (requiring frequent temporal sampling), and are in general also related to RSF's absolute magnitude (driven by insolation and reflectance). The configuration without early afternoon orbit (Figure 11b) has several regions with pronounced biases (+/\(-\)30 Wm\({}^{-2}\)), often consisting of swirls with positive alongside negative bias.  
Figure 11: Bias of daily mean RSF with respect to CERES-SYN1 deg-Day for 15 January 2008, retrieved using (**a**) all available satellites, and (**b**) excluding early afternoon satellite NOAA-18.  
The configuration using all satellites is characterized by a stable mean bias (\(MB\)) around 0 Wm\({}^{-2}\) (red triangles in Figure 12). The bias corrected RMSB also shows a relatively stable performance of about 7 Wm\({}^{-2}\) (red dots in Figure 12). The annual average of these statistics is shown in Table 5, where the colors refer to the same configurations used in Figure 12.  
The configuration excluding NOAA-18, that is, without early afternoon orbit (blue in Figure 12), has a significantly degraded performance, with a bias corrected RMSB ranging seasonally between 10 and 14 Wm\({}^{-2}\), which is about 12 Wm\({}^{-2}\) annually (Table 5). Excluding the mid-morning orbits (NOAA-17 and MetOp-A) causes the RMSB to increase even more, to 12-18 Wm\({}^{-2}\) (green in Figure 12), with an annual average of 14.6 Wm\({}^{-2}\) (Table 5). The mean bias is less stable, with seasonal deviations up to \(-2\) Wm\({}^{-2}\) (light green triangles in Figure 12).  
Some additional orbital configurations were validated to test the performance sensitivity to individual satellites. First, the very early and very late orbits (NOAA-15 and NOAA-16) were excluded, resulting in only a small performance degradation (second row in Table 5). Furthermore, the mid-morning orbits were tested individually, by excluding each of them separately (third and fourth row in Table 5). The resulting performance was only slightly affected. Finally, the single-satellite configurations (last five rows in Table 5) were not able to achieve the performance obtained by any of the combination configurations. However, there are significant differences, with the best performance by the afternoon satellite (\(RMSB\) of 15.5 Wm\({}^{-2}\)), followed by each of the mid-morning satellites (around 18 Wm\({}^{-2}\)), and at a large distance the early and late satellites (28-30 Wm\({}^{-2}\)).  
The daily \(MAB\) is another measure for regional uncertainty, and its annual average statistics (Table 5, third column) confirm the orbital-specific performance assessment obtained by \(RMSB\) (second column).
# Shape Memory-Enhanced Electrical Self-Healing of Stretchable Electrodes
## 2 Materials and Methods  
The bi-layer structure conductive composites were fabricated via the transfer process. Briefly speaking, the conductive network was firstly dip-coated onto a release substrate, followed by transferring into the shape memory polymer matrix [10,11]. The AgNWs were in the size of 25 \(\upmu\)m length and 80-120 nm diameter. The resultant electrodes were cut into the average size of 75 \(\times\) 25 \(\times\) 0.05 mm (length\(\times\) width\(\times\) thickness). The shape memory polymers utilized in this study were synthesized with the PCL as the soft segment and the diphenyl-methane-diisocyanate as the hard segment. Additionally, 1,4-butylene glycol (BDO) was utilized as the extender. According to our previous study [11], the melting transition temperature of the PCL was determined to be around 58 \({}^{\circ}\)C by the Differential Scanning Calorimetry (DSC ) measurements and 40 wt. % of the PCL content was arbitrarily chosen in order to obtain the optimized shape memory effect and healable performance. What's more, undesirable electrical-thermal effect due to the Joule heat should be avoided in the measurements. Based upon our previous infrared thermography study [9], the temperature of the AgNW-containing electrodes kept at the ambient temperature till the applied voltages was up to 1-3 V. Thus, the voltages utilized in this study were limited to be 0.1V, aiming at avoiding any phase transitions due to the Joule heating. In the electromechanical testing, the samples were extended to the temporary shapes at the rate of 10 mm/min, whose current was monitored with a constant voltage of 0.1V via the platform composedofan electrochemical workstation and a tensile machine(CHI800D, CH Instruments Ins, Shanghai, China). The samples were labeled as CSHP-5, CSHP-10, CSHP-15 and CSHP-20 where the numbers stood for the per-stretching percentages. The cracks were made by razor, followed by the investigations of Scanning Electron Microscope (Zeiss Evo 18 Special Edition SEM, Germany) with accelerating voltage of 15 kV) and UV Laser Raman Spectroscopy(LabRAM HR 800, HORIBA Jobin Yvon, France).
## 3 Results and Discussion
### Surface Performance Testing  
The hardness test was carried out on the surface of the cleaned specimen and the results are shown in Figure 24. Compared to the microhardness value of 150 HV obtained after direct laser action on the surface, microhardness of the original substrate of 102 HV was observed at a 0.2 mm height of water and 0.1 mm height of SiO\({}_{2}\). SiO\({}_{2}\) accounted for a ratio of 2% of the mixture of the three measured microhardness of the surface of 119 HV, 135 HV, and 131 HV, compared to the microhardness of the original substrate. Media-assisted cleaning played a role in surface strengthening, but compared to the 150 HV obtained by direct laser treatment, there is still a large gap, the most significant gap, being about 15 HV. However, compared to the untreated surface, SiO\({}_{2}\)-assisted cleaning has the most significant impact--the microhardness has been strengthened to a certain extent--improving the HV to 32 or so, an increase of 30%. The effect of water was the smallest, with an enhancement of only about 20 HV, or about 20%. Description of the laser cleaning process: the laser's direct action on the surface of the microhardness obtained is higher than the microhardness of the added active agent (SiO\({}_{2}\)), and the water-dry cleaning laser strengthening effect is the best.  
Figure 23: Solid–liquid mixing cleaning principle.  
Although solid-liquid mixture-assisted laser cleaning can reduce the thermal impact on the substrate, there are still certain risks involved, especially in high-power laser applications, where a low water layer height can exacerbate surface burning. Further optimization of laser parameters, the types and sizes of solid particles, and liquid selection can enhance cleaning efficiency and surface quality while reducing damage to the substrate.