esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Model Generalization and Cross-Domain Validation
#### 4.6.1 Transfer Learning Methodology  
Analysis of prediction errors identified systematic biases in the Singapore-trained model, particularly when applied to non-office environments. The model initially underestimated peak usage during class transitions (error rate: 12.3%) and overestimated during traditional office hours (error rate: 8.7%). To address these limitations, we implemented a transfer learning framework that preserves the base LSTM layers while enabling rapid adaptation to new contexts.  
The methodology involves freezing the first two LSTM layers (64 and 128 units) while fine-tuning subsequent layers using local data. This approach maintains foundational pattern recognition capabilities while optimizing for site-specific characteristics. Testing at the Melbourne facility demonstrated that model adaptation required only 2160 data points (15 days of operation) to achieve performance comparable to the baseline model, with R\({}^{2}\) improving from 0.847 to 0.921 and MAPE decreasing from 15.3% to 8.12%.
## 1 Introduction
In this context, some passive techniques have been proposed to test the conformance of protocols in wired systems. These passive testing approaches are based on the record of the observation during run-time and its comparison with the expected protocol behavior defined by either a formal model or as a set of formally-specified properties obtained from the requirements of the protocol [18]. The observation is performed through a set of points of observation (PO) on monitored entities composing the system under test. The protocol messages observed in execution traces are generally modeled and analyzed through their control and data parts [19]. These approaches are efficient, but are not suitable for wireless environments, like WSN and its own inherent constraints, as mentioned above. Although we already studied and tested wired networks with our formal testing approach [20], we present in this paper an adaptation of this approach to passively test a routing protocol in wireless sensor-based smart systems. Common and specific WSN protocol properties are evaluated on collected execution traces to provide testing verdicts. We successfully assess our approach on an indoor testbed. As far as we know, this is the first work on formal passive testing of routing protocols in a WSN. Our paper's primary contributions are:  
* A logic-based approach for formally specifying some functional requirements of the WSN routing protocol. We provide an algorithm to evaluate these properties on protocol execution traces that are collected at run-time through PO set on the nodes. * With a real indoor testbed and the implementation of a new routing protocol and the implemented algorithm, we demonstrate the efficiency and suitability of our approach by providing relevant testing verdicts. The modeled requirements do not need to be compulsorily standardized, which helps the development life cycle processes. The rest of this paper is organized as follows: Section 2 discusses existing work in the literature. We describe the formal approach of our passive testing framework in Section 3 and provide details of the sensor networking protocol to be tested in Section 4. Section 5 contains details of the experimental setup and evaluation results. We conclude our work in Section 6.
## 4 Measurement Strategy and Equipment  
### Investigated Room and Sources Configuration  
The room for the noise control investigations is in our laboratory and has an approximate volume of 33 m\({}^{3}\) and a floor area of 12 m\({}^{2}\). There is a suspended ceiling of acoustic tiles at a distance of 0.25 m from the concrete to drive the ventilation ducts. Perforated panels backed by thick absorbing material cover the side walls. Three areas are targeted to create quiet zones at the measurement points (MP). Regarding the control positions, there are no other options than placing the compound source in two vacant areas: at a corner or along the edge between the floor and the wall. The floor dimensions of 3.8 m \(\times\) 3.4 m \(\times\) 3.1 m, height of 2.8 m, MP at a height of 1.2 m, and the control positions are depicted in Figure 5a.  
\begin{table}
\begin{tabular}{l l} \hline Population size, \(N\): 40 & Elite solutions: 4 \\ Total generations, G: 40 & Crossover type: single point \\ Chromosome length: 3 & Crossover probability, \(p_{\text{c}}:0.75\) \\ Parent selection scheme: rank selection & Random mutation probability, \(p_{\text{m}}:0.15\) \\ Selected solutions as mating parents: 28 & Adaptive mutation probabilities, \\  & \(\left[p_{\text{low}},p_{\text{high}}\right]:\left[0.3,0.1\right]\) \\ \hline \end{tabular}
\end{table}
Table 1: Selected genetic parameters.  
In the following experiments, the first axial modes cannot be derived directly due to the room shape. These could be a guideline to orientate the quadrupoles to the direction of modes. The dominant frequencies are measured with a low-frequency logarithmic sine sweep in each MP, as seen in Figure 6. The frequency of 50 Hz is the most dominant in the three points. For this frequency, the sound pressure level at eight points was acquired using an NTI XL 2 Audio analyzer and is depicted in Figure 5b to show that no axial mode is profound in the two main axes. For each area, the two most dominant frequencies are considered for attenuation. Therefore, along with the frequency of 50 Hz, noise control should be also implemented for the frequencies of 100 Hz at MP 1, 77 Hz at MP 2, and 108 Hz at MP 3 (Figure 6). Due to the long wavelengths of the investigated frequencies, i.e., \(\sim\)3.2 \(-\) 6.8 m, one error microphone is sufficient for investigating quiet indoor working zones. For each dipole source, \(d=20\) cm. For the longitudinal source, the distance between the acoustic centers of the dipoles is \(D=55\) cm, while for the lateral it is 20 cm. Thus, it applies \(d<<\lambda\) for the shortest investigated wavelength of \(\sim\)3.18 m (108 Hz).  
Figure 5: (**a**) Room schematic diagram with the positions of interest. (**b**) Noise level at eight points for the frequency of 50 Hz at the measurement height of 1.2 m.  
Figure 6: Noise level for the three areas in the low-frequency range.
# The Inactivation of Microscopic Fungi in Bakery Products Using Hurdle Technology--A Case Study
## 3 Results
### Effectiveness of Sourdoughs in Preventing the Occurrence of Microbial Infections  
At this stage of the work, starter cultures containing selected strains of lactic acid bacteria (LAB) were used for the production of the examined goods. Their effect on the shelf life of half-baked goods during 30 days of storage was determined by checking the inhibition of the growth of microscopic fungi causing spoilage. The starter cultures used in the study contained strains of lactic acid bacteria isolated from baker's sourdoughs: starter cultures consisting of three different strains of _Lactiplantiobacillus plantarum_ (1) or _Lactiplantiobacillus plantarum_ and _Fructilactobacillus santranciscensis_ (2) (Table 7). After packaging in MAP (gas mixture of 30% N\({}_{2}\)/70% CO\({}_{2}\)), the products were stored for 30 days.  
In the control samples without the addition of sourdough, the growth of microscopic fungi was observed in all three samples after 8 days. In turn, the activity of lactic acid bacteria effectively protected the products, and no mold or yeast growth was observed in any of the samples.  
Analyzing the appearance of baked goods produced with _L. plantarum_ starter and those produced with the _L. plantarum_ + _F. santranciscensis_ starter, clear differences in the appearance of the products were observed (Figure 3). The surface of the product obtained with the starter containing only one species of bacteria was smooth and uniform (Figure 3(1A)), while the usage of the starter containing two LAB species caused cracks to appear on the surface of the rolls during baking (Figure 3(2A)). However, it was considered that such a product might be perceived as a more attractive, rustic type.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Time** [**Day**] & **Control Without** & \multicolumn{2}{c}{**Starter Cultures**} \\ \cline{2-4}  & **Sourdough** & **3 Different Strains of** & **L. plantarum and** \\  & & _L. plantarum_ **(1)** & _F. santranciscensis_ **(2)** \\ \hline
1 & 0 & & \\
8 & 3 & & \\
15 & 3 & & \\
26 & 3 & & \\
27 & 3 & & \\
30 & 3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The number of sourdough-containing samples infected by microscopic fungi during storage.  
The samples prepared with the _L. plantarum + F. santrancisensis_ starter also had a better crumb structure after storage than the samples with the mono-culture starter. In the case of all samples, porosity was poorer than in the control sample, but in the case of sample 2 (Figure 3(2C)), no projection of the crust over the crumb was observed, as in the case of sample 1 (Figure 3(1C)).
## 5 Discussion
### Practical Implications  
From a practical standpoint, the findings of this study offer clear guidance for designing interventions and public policies that promote responsible and equitable use of GAI. The importance of knowledge suggests that educational and digital literacy campaigns may be the most effective tools for increasing use, especially among older individuals or those with less prior exposure.  
Additionally, the strong influence of innovativeness implies that communication strategies should emphasize the disruptive and transformative potential of these tools and present them as opportunities for personal and professional growth. The limited impact of gender and income suggests that barriers to GAI use are not primarily structural but rather linked to individual skills and motivation.  
This study strongly supports the integration of GAI-related content into secondary and higher education curricula in the education sector. It also recommends including GAI tools in adult education programs to promote digital inclusion for groups less exposed to emerging technologies.  
On the other hand, societal risk perception and the perception of a need for regulation (REG) do not clearly inhibit usage but do appear as significant variables in the models. This indicates that social acceptance of GAI also depends on ensuring its ethical and transparent use. Therefore, institutions should accompany technological development with clear regulations that protect citizens' rights and minimize undesirable effects, such as misinformation, bias, or privacy violations.  
Finally, the predictive capacity of the models developed in this study can be leveraged by both public and private organizations to better segment users, anticipate their needs, and design personalized services. For example, public administrations can use these models to identify profiles with a low likelihood of use and target them with specific training or access programs.  
In short, the study provides not only a theoretical understanding of GAI use but also practical tools for its effective and equitable implementation in Spanish society.
## 3 Results
### Vegetation Dynamics Monitored by Remote Sensing
After the phosphorus application, the NDVI values show a significant increase, especially in treatments such as P(MAP) + N(Urea) and STr + C + N(Urea), which obtained the highest yields and maximum NDVI values, with peaks close to 0.6. In contrast, the control treatment shows a lower NDVI increase, peaking at around 0.5. After the nitrogen application, all treatments show a continuous upward trend in NDVI and reach their maximum values. After reaching their peak, the treatments can be divided into three groups: the high yield treatments maintain NDVI values around 0.8, the medium yield treatments stabilize around 0.75, and the lower yield treatments, including the control and the P(BM + C) + N(Urea) treatment, show NDVI values around 0.65. As the crop enters senescence, the NDVI values decrease sharply, eventually stabilizing between 0.3 and 0.4 at the end of the season. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Phenology**} & **Min** & **Mean** & **Max** & **Min** & **Mean** & **Max** \\  & **GDD** & **GDD** & **GDD** & **NDVI** & **NDVI** & **NDVI** \\ \hline Sowing (\(Z_{0.0}\)) & 0 & 277 & 440 & 0.11 & 0.31 & 0.54 \\ Tillering (\(Z_{2.3}\)) & 464 & 607 & 884 & 0.36 & 0.52 & 0.69 \\ Grain & & & & & & \\ development (\(Z_{8.7}\)) & 1004 & 1406 & 1911 & 0.38 & 0.62 & 0.77 \\ Senescence (\(Z_{9.0}\)) & 1928 & 2340 & 2505 & 0.19 & 0.30 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NDVI and GDD summary for different phenological metrics. A detailed comparison of NDVI values and rankings between MicaSense and PlanetScope across all treatments highlights both consistencies and discrepancies in sensor performance (Table S3). High-yield treatments such as P(St) + N(i) and P(Bm) + N(om) exhibit strong agreements, with MS NDVI values averaging 0.590 and 0.586 compared to PS 0.676 and 0.664, respectively, and minimal ranking differences of 1 to 2 ranks. However, mid-yield treatments like P(Bm) + N(o), with average yields ranging between 6500 to 7500 kg ha\({}^{-1}\), reveal more notable differences, with PlanetScope ranking them higher on average by two to three ranks compared to MicaSense. Across all treatments, 13 plots differ by only up to two ranks, but five show larger discrepancies, with differences exceeding three ranks, particularly for mid-range yield treatments. This reflects consistent alignment for extreme yields but suggests that mid-yield treatments exhibit greater variability in rankings between the two sensors.
# A Robust Localization System Fusion Vision-CNN
## 2 Related Works
### Localization Method Combining Vision and Laser  
Fusing visual and laser information can make use of the advantages in each to improve the robustness and the accuracy of the positioning system. There have been many successful applications in SLAM [5; 35; 36]. In the indoor environment, the fusion of visual information and map-based Monte Carlo positioning has also yielded many results. The paper [37] retrieved similar pictures based on the feature points extracted from the image, thereby reducing the matching area. Then, the laser information was combined with the with Monte Carlo localization algorithm to optimize the pose and the system performed well in the relocalization. In the paper [38], [PERSON] et al. established a hybrid topological map containing metric scale information and object location information. The system first used visual information for object recognition and least squares optimization to obtain coarse pose information; and, then it used particle filtering to obtain its fine estimated pose. To solve the problem of robot kidnapping, [PERSON] et al. [39] used a LSTM network for image association to achieve relocalization, and they used an adaptive Monte Carlo algorithm to achieve precise positioning. [PERSON] and [PERSON] [11] proposed collecting ORB feature points when using laser to build a grid map and then training the collected feature points to generate a visual bag-of-words in the offline stage. The online stage obtained prior information by extracting visual features and generating the proposal distribution. Then, the system removed the occluded information and selected reliable beams of lidar for precise positioning. At present, most of the positioning frameworks that integrate vision and laser are usually carried out in order from coarse to fine, that is, first using visual information for rough estimation and then introducing rough pose estimation into the AMCL system for accurate pose estimation. This process usually requires the robot to be in motion and the algorithm needs several iterative cycles to complete the pose convergence. In order to overcome its shortcomings and improve its iterative convergence speed, this paper will carry out the research of a high-precision, real-time relocalization system toward indoor environments.
## 4 Simulation Settings and Results
### Construction of Polar Codes  
For computer simulations, we use the modified Monte-Carlo method from [59] to construct polar codes. At each designed \(E_{b}/N_{0}\), we transmit \(10^{9}\) all-zero codewords over a quasi-static Rayleigh fading channel and decode the codewords with polar SC decoding. Since polar codes are linear codes, this can be viewed as treating all bits as frozen bits. With all-zero codewords, the encoding step can be omitted, and the decoding process is simplified. As the decoding proceeds, if bit \(m_{i}\) is decoded incorrectly as 1, we record an error at bit index \(i\) and correct it to 0. Then the decoding process continues. We collect the errors of each bit index from 1 to \(N\) at each \(E_{b}/N_{0}\), and the \(L\) bit indices with the largest numbers of errors are selected to be the frozen bit indices. This ensures that the frozen bits indices correspond to least-reliable bits.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 4 Results and Discussion
### Multi-Factor Analysis of Vegetation Cover
After considering the complex factors, the following inference is made: the western part of the study area benefits from the high mountain barrier effect, with more rainfall on the windowed side and less rainfall on the leeward side, and the classical rain shadow effect comes into play [75; 76]. In addition, soil erosion is serious with rainfall, and insufficient soil depth makes it difficult for vegetation to thrive. Moreover, increased rainfall will increase the leaching of nutrients from the soil [77] and enhance soil acidity [78], so that the windward slopes of the undulating steep mountains will not be a good environment for vegetation growth. The data in Table 1 also confirm the emergence of this phenomenon.
###### Abstract  
Near-infrared spectroscopy (NIRS) is a non-ionizing optical technique that can be used to quantify proteins, carbohydrates, fats, and other organic and biological substances. The aim of this study was to determine the ability of NIRS to identify different concentrations of L-fucose and L-proline solutions by utilizing different NIR spectral regions. NIR spectra of solid L-fucose and L-proline, their aqueous solutions in different concentrations, and the spectra of saliva samples collected from two patients with oral squamous cell carcinoma (OSCC) were studied. Differences in spectra of the pure solid reference samples and water were most noticeable in spectral regions 800-1250 nm and 1418-1867 nm. The saliva sample with an atypically high concentration of oral cancer biomarkers showed a similar spectral feature between 1530-1650 nm as the liquid samples with cancer biomarkers. In addition, a fine k-nearest neighbors (kNN) classifier was trained to differentiate the aqueous solutions and achieved 75.97% validation accuracy. The preliminary study presents that NIRS can be utilized to detect differences in spectra between the different biomarker concentrations in aqueous solutions. However, the qualitative measures may have resulted in limited sensitivity, which could be enhanced by additional samples and using a measurement probe dedicated to fluid measurements.  
near-infrared spectroscopy; L-fucose; L-proline; saliva; oral cancer +
Footnote †: journal: Applied Sciences  
0000-0001-6600-0000][PERSON]  
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0000-0001-6600-0000][PERSON]  
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0000-0001-6600-0000][PERSON]. [PERSON].  
clinicians [4; 6; 7; 8; 9; 10]. Bioimpedance spectroscopy (BIS) can detect oral lesions, but the diversity of probe designs makes it difficult to draw reliable conclusions on its diagnostic potential [5]. Genetic analysis can help in the selection of potential locations for biopsies, and add value to histopathological diagnosis [11]. Optical coherence tomography (OCT) in the detection of OPMDs and OSCCs has been studied by [PERSON] et al. [12]. They concluded that the ability of the OCT to distinguish different oral lesions is poor. [PERSON] et al. [13] concluded that fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable in planning of the patient's treatment strategy.  
In biomedical applications, near-infrared spectroscopy (NIRS) can be used in vivo to distinguish healthy tissues from pre-malignant and malignant tissues and aid the diagnostics due to its excellent penetration depth and its minimal requirement for sample preparation [14; 15; 16; 17; 18; 19; 20; 21; 22]. Furthermore, optical spectroscopy can be performed without sample extraction, enabling the non-invasive assessment of tissue [23; 24; 25; 26; 27]. For example, spectroscopic techniques are used to assess periodontal inflammation [28], and various cancers, including skin [15; 16; 17; 18; 19; 29], breast [30; 31], oral [32; 33], oropharyngeal, and laryngeal cancer [23].
## 5 Computational Analysis
### Grid Independence Study
#### 5.4.1 CFD Model Validation  
The above experimental arguments are also sustenance by the CFD simulations. CFD cases were simulated for three distributor plates with similar operational conditions as of experiments. Fluidization curves were also plotted for CFD results and compared with the experimental data, and CFD results agreed reasonably with experimental results within 10% of error, as shown in Figure 16. Moreover, qualitative results were also analyzed to perceive the insight flow behavior of the fluidized bed. Finally, bubble dynamics were also analyzed in comparison with each distributor plate.  
Figure 16: Comparison of CFD results with experimental (L/D = 0.5, particle size = 520 \(\upmu\)m) (hybrid plate).  
Figure 15: Time-averaged solid volume fraction with radial distance for different grids.