esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Potential Challenges
### Model Selection  
When considering NOMA focusing on DL, the NN architecture stands out as a significant barrier. Several technologies and methods reliant on DL have been created using standard templates. While these models are geared toward DL-based NOMA applications, it is not certain that they will be successful. Generic models are expected to make it possible to implement such systems in practice. For real-world applications, generic models improve the ease of optimizing communication networks and save time and resources spent on selecting a suitable model. As a result, there is a pressing need for extensive research into creating such generic models to achieve overall performance improvement.
## 2 Materials and Methods
### Determination of Total Phenolic Acids  
Phenolic acids--including caffeic acid (CA), chlorogenic acid (CGA), ferulic acid (FA), gallic acid (GA), gentisic acid (GE), protocatechic acid (PCA), p-coumaric acid (pCU), p-hydroxybenzoic acid (pHB), sinapinic acid (SIN), syringic acid (SYN), and vanillic acid (VA)--were quantified using a chromatographic method based on the procedure described by [PERSON] et al. [10], with slight modifications. In brief, 150 mg of each lyophilized extract was mixed with 2.5 mL of 2 mol/L sodium hydroxide, 2.5 mL of a 20 mL/L ascorbic acid solution containing 13.4 mmol/L EDTA (ethylenediaminetetraacetic acid), and 0.5 mL of 0.1 mg/mL isoferulic acid, which was used as the internal standard. The mixture was flushed with nitrogen gas and allowed to hydrolyze under constant agitation for 16 h at room temperature. After hydrolysis, the sample was centrifuged at 2370\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C, and the supernatant was acidified by adding 0.75 mL of acetic acid. Phenolic acids were extracted with ethyl acetate in two steps (4 mL followed by 2 mL). The organic layers containing the liberated phenolic acids were collected by carefully pipetting off the upper layer and then combined. The pooled extracts were evaporated to dryness using a speed-vac concentrator (Thermo Fisher Scientific, Courtaboeuf, France) at 45 \({}^{\circ}\)C for 1 h. The resulting residue was reconstituted in 1 mL of methanol/water (75:25, \(v/v\)) and filtered through a 0.45 \(\upmu\)m membrane before HPLC analysis. The analysis was carried out in duplicate using a Shimadzu HPLC system (Kyoto, Japan) equipped with an LC-20 CE pump, an SIL-10 ADvp autosampler, a CTO-10 ASVP oven, and an SPD-M20A diode array detector. Results were expressed as \(\upmu\)g/g of sample. The limit of quantification was established at 4 \(\upmu\)g/g.
## 1 Introduction
### Rationale behind the New Approach  
Various multilanguage OCR systems are available in the market already with publicly accessible multi-language databases. These systems still focus on classic OCR use cases, without providing a reliable solution for on-screen, digital OCR to support real-time, multilanguage, or cross-sectoral collaborative, supportive works, workshops, and multi-geolocation issues. In our research, we chose and expanded one of the most reliable OCR solutions: PaddleOCR (Figure 1), a practical ultra-lightweight OCR system supporting more than 80 languages.  
PaddleOCR [20] provides promising results for Far-East languages (like Japanese and Chinese) and handles the multilanguage approach with flexible, adaptable AI models [21]. Based on our research, not even PaddleOCR was able to manage accurate phrase recognition in multilingual systems or for synthetic languages used in the IT or sports sector for on-screen simulations.  
The research analyzing the existing solutions available on the market suggests that there is currently no magic formula that can be applied to prepare an optimal training dataset that covers all of the on-screen collaboration tool requirements. Every use case is different, so it is suggested to handle the topics as standalone projects. The training dataset volume will be defined by the use case, the involved languages, and the specific vocabulary to be used. Existing annotated datasets are not golden sets, but they can be used when preparing the method that covers all aspects of our requested OCR system [21].  
The formulation of an optimal, vocabulary-based training dataset for multilingual, real-time OCR systems necessitates a strategic approach to various key elements. These include the careful selection of adaptive vocabulary, implementation of cross-lingual pretraining [22], deployment of dynamic dataset augmentation strategies, ensuring domain-specific adaptation, addressing data imbalance issues, applying multimodal learning, and establishing appropriate evaluation metrics and benchmarks. [23]. A data scientist who addresses  
Figure 1: Adjusted PaddleOCR architecture (adapted from [19]).  
these novel aspects can build OCR systems that are accurate and efficient and capable of handling a wide range of languages and contexts.
# Multi-Robot Exploration Employing Harmonic Map Transformations
## 5 Conclusions  
This paper proposes a method for robotic exploration, suitable for both single-robot and multi-robot setups. Firstly, we present a Harmonic Map Transformation for Exploration, and introduce a novel approach for frontier detection and navigation. Then, the concept is extended to distributed multi-robot systems, using map merging to effectively exchange information and a greedy frontier allocation strategy. An algorithm to further optimize the frontier allocation by ensuring that each robot heads to a unique frontier is also presented. The proposed approach is verified through both simulations and real-world experiments and the results are compared with recent multi-robot cooperative exploration methods. It is concluded that the proposed method is efficient as it outperforms existing approaches in terms of path distance.  
The proposed method is heavily reliant on the accuracy of the maps produced by the SLAM algorithm, since the HMT only uses the boundaries provided by the map. Filtering of the map before boundary extraction does reduce sensitivity to noise and improves accuracy. The issue of added noise in the map during the map merging process is also of concern for the aforementioned reasons. This issue is addressed by assuming that each robot's local map is accurate and using it unmodified in the merged map, ensuring that the local region of the robot is correct despite possible inaccuracies in the merged global map. Nevertheless, the experiments confirm that the proposed method is able to perform in realistic conditions while retaining the total PD and ET low when compared to other recent methods.  
In future work, we are considering improving and extending the proposed method. We aim to incorporate map altasing methodologies to facilitate exploration of expansive spaces and integrate sophisticated heuristics for frontier selection to refine the exploration strategy. Finally, it would be of great interest to explore the efficacy of extending the application of the HMT to three-dimensional environments as it would prove useful for aerial robotic navigation and exploration.  
Conceptualization, T.-F.B. and C.P.B.; methodology, T.-F.B.; software, T.-F.B.; validation, T.-F.B.; formal analysis, T.-F.B.; investigation, T.-F.B.; writing--original draft preparation, T.-F.B.; writing--review and editing, C.P.B.; visualization, T.-F.B.; supervision, C.P.B.; project administration, C.P.B. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported by the project \"Applied Research for Autonomous Robotic Systems\" (MIS 5200632) which is implemented within the framework of the National Recovery and Resilience Plan \"Greece 2.0\" (Measure: 16618--Basic and Applied Research) and is funded by the European Union--NextGenerationEU.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.  
Figure 11: The final map produced by each robot. (**a–c**) are the partial maps and (**d**) is the merged map of the robot that produced (**a**).
## 3 Preliminary Knowledge
### Identity-Based Signature  
To solve the defects of power concentration and vulnerability caused by the certificate mechanism, [PERSON] [26] put forth an identity-based cryptosystem (IBC). In this system, the user's public key can be a string containing information about the user's identity, whereas the user's private key is produced by the key generation center (KGC). [PERSON] and [PERSON] [27], in 2001, proposed the first identity-based encryption (IBE) method. The identity-based signature (IBS) derived from IBE is used for device authentication. In this paper, the IBS technology specified in the Chinese national standard SM9 [28] issued by the State Password Administration of China is used to ensure the identity anonymity of the node [29]. The algorithm is described as follows:  
* System master key establishment: KGC selects a random number \(s\in[1,N-1]\) as the main private key and calculates the element \(P_{Pub-s}=[s]P_{2}\) in \(G_{2}\) as the main public key and KGC keeps \(s\) and discloses \(P_{Pub-s}\);
* User key generation: assuming the identity identifier of the user is \(ID_{A}\), KGC calculates \(t_{1}\) on the finite field \(F\). \[t_{1}=H_{1}(ID_{A}||hid,N)+s,\] (1) If \(t_{1}\) is 0, it is necessary to recalculate the master private key and update all users' private keys. If \(t_{1}\) is not equal to 0, calculate \(t_{2}\): \[t_{2}=[s]t_{1}{}^{-1},\] (2) The user's private key is \(d_{A}=[t_{2}]P_{1}\). The user's public key \(pk_{A}\) is \[pk_{A}=H_{1}(ID_{A}||hid)+P_{Pub-s},\] (3) So far, the key pair of the user is \((d_{A},pk_{A})\);
* Signature generation: assuming there is a message \(M\), the signer calculates \(g=e(P_{1},P_{Pub-s})\), selects the random number \(r\in[1,N-1]\), calculates \(w=g^{\prime}\), then calculates \(h=H_{2}(m||w)\), \(l=(r-h)modN\), and finally calculates \(S=[l]d_{x}\). The signer's signature value \((h,S)\) for the message can be obtained.
* Signature verification: assuming the message received by the verifier and its signature are \(m^{\prime}\) and \((h^{\prime},S^{\prime})\), respectively, the verifier first verifies whether both \(h^{\prime}\in[1,N-1]\) and \(S^{\prime}\in G_{1}\) are correct. If one of them is not correct, the verification fails. Otherwise, \(P\), \(u\), and \(w^{\prime}\) are calculated. \[P=[H_{1}(ID_{A}||hid,N)]P_{2}+P_{Pub-s},\] (4) \[u=e(S^{\prime},P),\] (5) \[w^{\prime}=g^{h^{\prime}},\] (6) Finally, \(h_{2}=H_{2}(m^{\prime}||w^{\prime},N)\) is calculated and compared with \(h^{\prime}\). If it is consistent, the verification passes [30]. During the above calculation, \(N\) is the order of elliptic curve used by the SM9 digital signature algorithm. \(P_{1}\), \(P_{2}\) are the generators of the N-order cyclic subgroup \(G_{1}\) and \(G_{2}\). \(hid\) is the signature private key generation function identifier. \(H_{1}\) and \(H_{2}\) are hash functions.
## 2 Measurement Setup  
Figure 1 shows the experimental setup for the stimulated-emission-based JSI measurements. The pump laser is a mode-locked femtosecond (fs) pulse laser (CALMAR FPL-02 CTF) with a repetition rate of 18 MHz. Femtosecond pulses are spectrally filtered by a 100-GHz DWDM filter, which has a flat-top spectral shape with about 0.6-nm full-width at half-maximum (FWHM) bandwidth. After filtering, pulses are amplified by an Erbium-Doped Fiber Amplifier (EDFA) and filtered again by DWDM filter to reduce amplified spontaneous emission (ASE) noise from EDFA (not included in Figure 1). The final pump pulse width is about 15 ps and effective bandwidth is about 0.5 nm. The pump peak power (\(P_{p}\)) is about 500 mW, and the seed (or signal) laser is a continuous wave laser with a power of about 10 mW. The lights from the pump and seed lasers are combined by a 200-GHz DWDM filter and injected into an NLI sample. The NLI samples consist of \(N\) sections of DSF and \(N\)-1 sections of SMF. The length of the DSF and SMF sections is measured using the time-of-flight measurement technique [23]. The polarization states of the pump and seed lights are matched using a polarization controller and an in-line polarizer. The pump light is filtered via a 200-GHz DWDM filter and monitored using an optical power meter to maximize the transmitted power through the in-line polarizer. We discretely vary the wavelength of the seed light while measuring the power of the seed, and generated idler lights using an optical spectrum analyzer. To clearly observe the trend in the shape and position of the JSI spectrum while changing several experimental conditions, we show the normalized spectra. The generated idler power is divided by the input signal power to compensate the system's wavelength-dependent transmittance, and then we normalize it to the maximum value from each measurement's spectrum.  
The spectral purity of bi-photons might be changed according to the pump bandwidth (\(\sigma_{p}\)), but under our experimental conditions, the FWHM pump bandwidth is fixed. Since the pump bandwidth is narrower than that of 100-GHz DWDM filters, we selected the 100-GHz DWDM filters as the target filters of signal/idler photons. In addition, since Raman noise photons increase further away from the pump frequency [24], we decided that 100-GHz DWDM filters, whose center frequency spacing from the pump is \(\pm\)400 GHz, were the target filters for reducing the Raman noise through the NLI optical fiber system.  
Figure 1: Experimental setup for stimulated-emission-based joint spectral intensity measurement. PUMP: A 15-ps pulse laser with a repetition rate of 18 MHz, PROBE: CW laser for seed and its wavelength is swept during measurement, PC: polarization controller, C: 200 GHz DWDM filter for pump and seed combining, NLI: nonlinear interferometer sample, ILP: in-line polarizer, F: 200 GHz DWDM filter for pump filtering, PM: power meter for pump power monitoring, OSA: optical spectrum analyzer for measuring seed power and generated signal spectrum.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 3 Proposed Method
### RSCS6D_Pruned: A Lightweight Version
#### 3.5.2 Network Slimming Pruning for RSCS  
Figure 3 compares the network architectures before and after pruning. The original structure in Figure 3a uses a full-feature-map overlay fusion strategy, expanding the point cloud keypoint feature channels to 2256 dimensions. While this design enhances feature representation, it also significantly increases computational demands. The optimized structure in Figure 3b employs NS pruning with a 50% pruning rate. This reduces the maximum feature channels from 2256 to 1077, while maintaining algorithm performance and improving computational efficiency.  
NS pruning adaptively reduces channels based on task needs. It decreases model parameters and computation significantly. However, retraining is needed to restore pre-pruning accuracy. By integrating a lightweight pose estimation algorithm based on point cloud keypoints, a compact network can be built. This network has few parameters, low memory usage, and broad applicability.
## 2 Methods and Tools
Drone-based photogrammetry and LiDAR minimize temporal decorrelation through localized studies, though hybrid workflows combining satellite data are often necessary for broader context. Environmental instabilities, such as fluctuating water levels, are addressed through regular recalibration in ground-based methods like ERT and seismic surveys and by integrating real-time observations with LiDAR-derived DEMs to dynamically adjust models. Cloud-based processing and real-time data fusion can further enhance responsiveness, ensuring accurate and timely analysis for hazard mitigation and environmental monitoring.
## 3 Results and Discussion
### Results
#### 3.1.2 PCDD/Fs and dIPCBs
The mean values in our study (mean 656 fg WHO-TEQ\({}_{2005}\)/m\({}^{-3}\)) are also below the recommendations by the Environment Minister in Ontario (Canada) (5000 fg I-TEQ m\({}^{-3}\)) [3] and slightly over the atmospheric standard of Japan (600 fg WHOTEQ\({}_{2005}\) m\({}^{-3}\)), but far higher than the standard of Germany (150 fg WHOTEQ\({}_{2005}\) m\({}^{-3}\)) [59; 60]. It is noteworthy that the results in this study referred only to the particulate phase, and the calculated WHO-TEQ values may be underestimated. For the dlPCBs, the total particulate concentrations (expressed as the sum of m.o. and n.o. congeners) were 975-4083 fg m\({}^{-3}\) (median 1438 fg m\({}^{-3}\)), and the WHOTEQ\({}_{2005}\) concentrations were 53-512 fg m\({}^{-3}\). As it is clear from Table 5, the concentrations and WHOTEQ\({}_{2005}\) of dlPCBs are in comparable values with studies in industrial sites of Dunkirk, France (743-1747 fg m\({}^{-3}\), particulate phase) [31], and Shanghai, China (340-7607 fg m\({}^{-3}\), particulate plus gas phase) [55], but significantly higher concentrations have been reported in gas phase under the vicinity of a steel complex in Korea (6100-61,800 fg m\({}^{-3}\)) [61] or in the urban atmospheres (particulates and gas phase) in Hochiminh, Vietnam (1570-8300 fg m\({}^{-3}\)) [62] and Brescia, Italy (11,600-708,400 fg m\({}^{-3}\)) [23].
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 4 Simulation Results
### Jamming Suppression Performance  
In this subsection, the overall jamming suppression performance is evaluated in terms of BER in both single-tone and partial-band jamming scenarios. In the simulations, the  
Figure 11: The detection performance of conventional FCME and the proposed algorithm under AWGN channel and EVA channel.  
Figure 10: The false alarm probability of partial-band jamming in an OTFS system.  
proposed jamming detection algorithm described in Section 3 is utilized, with the target false alarm probability set to \(P_{fa}=0.05\).  
Figure 12 illustrates the BER performance of an OTFS system when the proposed jamming detection method is applied, followed by different jamming suppression algorithms. To enable a more systematic performance comparison, we also evaluated the performance differences in jamming detection with and without the use of a smoothing window. It can be observed that both nulling suppression and clipping suppression improve BER performance regardless of whether the smoothing windowing process is applied. However, when the jamming suppression method is fixed, the introduction of the smoothing windowing technique surely enhances BER performance. This improvement can be attributed to the smoothing window's ability to create a more uniform amplitude distribution of the signal in the frequency domain, effectively mitigating the adverse effects of noise fluctuations and reducing the false alarm probability. Furthermore, nulling suppression, when combined with the proposed jamming detection method, outperforms other combinations.  
Figure 13 illustrates the BER performance of an OTFS system when the proposed jamming detection method is applied, followed by different jamming suppression algorithms. In the simulations, the partial-band jamming is configured with a JSR of 10 dB and \(\xi=0.1\). In the low SNR region, the BER is primarily influenced by noise, and the jamming suppression method fails to effectively mitigate the jamming in the received signal. Similar to the single-tone jamming scenario, the introduction of smoothing windowing significantly enhances the BER performance of both jamming suppression methods when \(\text{SNR}>13\text{ dB}\). Additionally, nulling suppression combined with the proposed jamming detection technique outperforms the other methods.  
Figure 12: BER performance of OTFS with different jamming detection and suppression techniques under single-tone jamming.
## 3 Results  
### Most Appropriate Projection Angle \(\Delta\theta\)  
The treatment time for BNCT-SPECT was approximately one hour, and the statistical error of the measurements was assumed to follow a Gaussian distribution. To confirm the most appropriate \(\Delta\theta\), 50 numerical experiments were conducted. In each experiment, the estimation was repeated 20 times, and the image with the smallest MAE was adopted as the best result. A performance comparison of the image reconstruction methods was conducted using the average values of the estimates obtained from the 50 experiments.  
The mean values of the MAE derived from the estimated values, calculated 50 times for each \(\Delta\theta\), are shown in Figure 3, and the mean values of the SSIM are shown in Figure 4.  
Figure 3 shows that the MAE of the proposed method was lower than that of the ML-EM method for all \(\Delta\theta\). Additionally, the minimum MAE of the proposed method was 0.0568 \(\pm\) 0.0002 at \(\Delta\theta\) = 30. Meanwhile, the MAE of the ML-EM method exceeded 0.12 in all cases, and at \(\Delta\theta\) = 30 and 45, it was more than double that of the proposed method.  
Figure 1: Spherical head phantom and cancerous lesion within the imaging area (50 \(\times\) 50 pixels).  
Figure 2: Projection conditions in this simulation.  
From Figure 4, it can be observed that, for the SSIM, the ML-EM method outperformed the proposed method at all \(\Delta\theta\). However, when prioritizing the accuracy of estimates in normal cells, adopting \(\Delta\theta=30\) was optimal for the proposed method based on the trend of the MAE.  
Based on these findings, it was concluded that, under the expected conditions of BNCT-SPECT, performing image reconstruction using the proposed method on projection data captured with a 30-degree angular step was optimal.
## 3 Numerical Results and Discussions
### Influence of the Absorber Materials  
The aim of this part is to analyze the influence of the separation plate material. Our first thought was that a separation plate made of aluminum will increase the thermal performance of the solar collector. Indeed, an aluminum separation plate will collect the solar radiation passing through the perforated absorber plate and then release this energy to the air flowing through the collector.  
However, an absorber plate made of a material such as aluminum is more expensive and has a higher environmental impact than an absorber plate made of based-wood, plastic, or recycled material. Thus, the aim of this chapter is to determine the energy gain provided by an aluminum absorber plate compared to other materials.  
In addition, by using plastic in place of aluminum, we reduce the weight of the solar collector and then facilitate the use and implantation of such a system.  
For this study, we analyzed the data collected from CFD simulations. Two materials were analyzed, namely, aluminium and high-density polyethylene with the specifications presented in Table 4.  
Three parameters were analyzed--the heat transfer coefficient, the heat exchanger efficiency, and the solar collector efficiency results are presented in Figures 17-19, respectively. The results found clear support for the slight impact of low-conductivity materials in TSC, as shown by [PERSON] and [PERSON] (2011). They are only slight differences between low and high thermal conductivity materials regarding the thermal performance of TSC. This is an important fact when you consider the cost of metal materials compared to plastic or plant fiber materials.  
Figure 20 shows the non-uniformity of the temperature distribution caused by low-conductivity materials. We can notice that the HDPE perforated absorber plate slightly overheated in the extreme upper and bottom part. However, non-uniform thermal behavior does not have a major impact on the solar collector efficiency, as reported before.  
Figure 19: Evolution of the solar collector efficiency for Aluminum and HDPE absorber.  
Figure 18: Evolution of the heat exchanger efficiency for Aluminum and HDPE absorber.