esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Non-Encapsulated and Microencapsulated Lactic Acid Bacteria
## 3 Results
### Bile Salts Tolerance  
The viable counts of identified LAB strains were done at 0.3% bile salt, a concentration similar to that of human bile juice. Different results were obtained for strains viability after exposure to bile salt concentration (Table 4). IBNA 26 and IBNA 37 registered the lowest resistance after 3 h, compared with IBNA 64, IBNA 29, IBNA 33, and IBNA 41 which exhibit resistance to bile salt.  
Regarding the bile salt survivability, the effect of 0.3% bile salt involved different percentages of strains survivability with varying degrees. The strain IBNA 29 had around 90% tolerance after 3 h in the presence of 0.3% bile salt, but at pH 2.0, the survival rate declined. At the same time, IBNA 64 had adequate stability and showed \(\geq\)89.88% survivability after 3 h.  
Interestingly, from all identified and confirmed strains, after 3 h, only _L. acidophilus_ (IBNA 64) and _L. salivarius_ (IBNA 33 and IBNA 41) involved higher probiotic resistance and were exposed to the spray-drying process to obtain the microencapsulated products as possible probiotics sources in animal feed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Exposure Time (h)**} \\ \cline{2-7}
**Strains** & \multicolumn{3}{c}{**pH 3.0**} & \multicolumn{3}{c}{**pH 2.0**} \\ \cline{2-7}  & **0 h** & **1:30 h** & **3 h** & **0 h** & **1:30 h** & **3 h** \\ \hline _L. acidophilus_ IBNA 26 & 8.16 \({}^{\text{a}}\) & 8.22 \({}^{\text{a}}\) & 6.56 \({}^{\text{a}}\) & 5.26 \({}^{\text{a}}\) & 0.00 \({}^{\text{a}}\) & 0.00 \({}^{\text{a}}\) \\ _L. acidophilus_ IBNA 64 & 8.41 \({}^{\text{a}}\) & 8.07 \({}^{\text{a}}\) & 7.14 \({}^{\text{b}}\) & 6.67 \({}^{\text{b}}\) & 5.54 \({}^{\text{b}}\) & 5.12 \({}^{\text{b}}\) \\ _L. fermentutum_ IBNA 37 & 8.34 \({}^{\text{a}}\) & 9.04 \({}^{\text{b}}\) & 8.79 \({}^{\text{c}}\) & 7.73 \({}^{\text{c}}\) & 6.51 \({}^{\text{b}}\) & 5.13 \({}^{\text{b}}\) \\ _L. salivarius_ IBNA 29 & 9.31 \({}^{\text{b}}\) & 9.09 \({}^{\text{b}}\) & 8.50 \({}^{\text{c}}\) & 7.26 \({}^{\text{b}}\) & 6.12 \({}^{\text{b}}\) & 5.26 \({}^{\text{b}}\) \\ _[PERSON]_ IBNA 33 & 8.99 \({}^{\text{b}}\) & 8.13 \({}^{\text{a}}\) & 7.64 \({}^{\text{b}}\) & 8.16 \({}^{\text{c}}\) & 7.92 \({}^{\text{c}}\) & 7.72 \({}^{\text{c}}\) \\ _[PERSON]_ IBNA 41 & 9.63 \({}^{\text{c}}\) & 9.38 \({}^{\text{c}}\) & 9.85 \({}^{\text{d}}\) & 9.29 \({}^{\text{d}}\) & 8.18 \({}^{\text{c}}\) & 8.02 \({}^{\text{c}}\) \\ \hline \multicolumn{7}{c}{Main effect} \\ \hline SEM & 0.14 & 0.13 & 0.26 & 0.30 & 0.66 & 0.63 \\ \hline \multicolumn{7}{c}{\(p\) value} & \textless{}0.0001 & \textless{}0.0001 & 0.001 & \textless{}0.0001 & 0.001 & 0.002 \\ \hline \hline \end{tabular} Results are expressed as means of three independent experiments each with duplicate. Means represent viable count (Log\({}_{\textless{}01}\) CFU mL\({}^{-1}\)). \({}^{\text{a}-\text{d}}\) Means with different superscripts in a separately column differ significantly (\(p<0.05\)).  
\end{table}
Table 3: Viability of _Lactobacillus_ isolates during 3 h of incubation at low pH.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 50 cm Depth in Bangladesh
#### 5.3.2 Three-Day Lead-Time Forecast  
Similar to Section 5.3.1, in order to avoid the repetition of similar tables, we only report the main accuracy results on the testing datasets. The DT model produced good accuracy when the third input scenario and a five-day time window size were adopted. The RF forecasts using the third and eighth input scenarios consistently performed better than the other input scenarios. It achieved an average RMSE ranging from 1.906 \({}^{\circ}\)C to 2.04 \({}^{\circ}\)C and an average R value ranging from 0.925 to 0.940, where the five-day time window showed the best forecast results. The GBT with the third or eighth input scenario consistently achieved the highest accuracy under all time windows, with the lowest average RMSE (1.903 \({}^{\circ}\)C) and the highest average R (0.939). The performance of the hybrid DT-GBT consistently delivered the lowest average RMSE with the third input scenario and achieved the highest average R values with the eighth input scenario across all time windows. In particular, under a five-day time window, the third input scenario yielded the lowest average RMSE,  
Figure 9: Comparison of observed and optimal forecasted ST50 at four stations.  
and the eighth input scenario yielded the highest average R value. Similarly, the hybrid RF-GBT with the third and eighth input scenario led to the best forecast under all time-window sizes, achieving the best prediction with the lowest average RMSE (1.901 \({}^{\circ}\)C) and the highest average R value (0.940) when using a five-day time-window.  
**Best combination for three-day lead-time forecast:** The combination of hybrid RF-GBT, third and eighth input scenario, and five-day time window achieved the best forecast performance of ST50 with the lowest average RMSE (1.901 \({}^{\circ}\)C) and the highest average R (0.940), respectively. This underscored the best forecast capability of the hybrid RF-GBT for soil temperature at 50 cm depth. These models, employing the third and eighth input parameter combination in the five-day time window, provided the best predictions. Figure 10 demonstrates a strong agreement between optimal predictions and actual observations at four soil temperature observation stations. In particular, the forecast at the Mymensigh station exhibited a minor underestimation, whereas the forecast at the Srimongal station demonstrated a combination of slight overestimation and underestimation.
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
## 3 The Proposed Method
### Deconvolved Conventional Beamforming
Figure 5 illustrates the results of the array beam pattern. The array beam pattern was generated by employing the procedure outlined in Equation (8), through which the angle, \(\theta\), is scanned from \(-90\) degrees to \(90\) degrees. Figure 5 presents a comparison of the conventional beamforming (CB) pattern and the deconvolved conventional beamforming (DCB) pattern, showing that the DCB method significantly enhances angular resolution. Figure 5 displays the DCB patterns obtained under different numbers of array elements, demonstrating that even with fewer array elements, very narrow spatial spectral estimation results can still be achieved. The spatial spectrum plots in Figure 6 showcase the performance of various DOA estimation methods, including Capon, MUSIC, and the proposed method. Figure 6a displays the spatial spectrum results under single-signal-source conditions with an incident angle of \(-2.96^{\circ}\) and a carrier-to-noise ratio (\(C/N_{0}\)) of 45 dBHz. Figure 6b presents the spatial spectrum results under conditions with one direct signal and one multipath signal, where the incident angles for the direct and multipath signals are \(-2.96^{\circ}\) and \(3.06^{\circ}\), respectively. It can be observed that even in the presence of two signal sources, the DCB method maintains good resolution capabilities, while the MUSIC and Capon methods fail to resolve the directions towards the signal sources.
## 7 Numerical Analysis
### Performance Evaluation of DoA Estimation Techniques
The final study investigated the impact of the decorrelation distance \(D_{0}\) on the RMSE \(\sigma(\theta)\) performance, where the \(D_{0}\) values at the BS were varied as \(10^{-6}\)\(\lambda\), \(5\)\(\lambda\), \(13\)\(\lambda\) and \(100\)\(\lambda\), while the \(D_{0}\) value at the MS was fixed. Figure 14 illustrates the findings of this study, which reveal a higher error at \(10^{-6}\)\(\lambda\). However, as the decorrelation distance value increases, the error reduces gradually. Notably, the response at \(13\)\(\lambda\) is similar to that observed at \(100\)\(\lambda\). Hence, the RMSE \(\sigma(\theta)\) performance is significantly influenced by the decorrelation distance, and increasing it can considerably reduce the error.
# Routing Schemes in FANETs: A Survey
## 3 Taxonomy
### Characteristics  
Routes in FANETs can be characterized by the following:  
* Routing decision can be made at either the source node or the intermediate node. There are two options as follows:
* Hop-by-hop routing includes the next-hop node and destination node information (i.e., node addresses) in the route request (RREQ) message. Hence, routing tables must be maintained at intermediate nodes to store the information [105].
* Source routing includes the complete route information (i.e., the node addresses of all nodes of a route) in the RREQ message. Hence, routing table is not embedded in intermediate nodes, and these nodes can forward packets to destination nodes using the complete route information in RREQ.
# Sequential Polar Decoding with Cost Metric Threshold
## 4 Simulation Results
The improved SC-Creeper builds upon the basic SC-Creeper algorithm, achieving the best performance at high code rates. While it may exhibit poorer performance at low code lengths and rates compared to SCL-8, it remains competitive and may surpass SCL-8 in certain cases. Despite its enhanced performance, the complexity of the improved SC-Creeper is comparable to that of its basic version, with potential reductions in operations for specific scenarios, leading to an overall complexity reduction. In summary, the performance of polar decoding algorithms varies based on factors such as the code rate, block length and complexity constraints. While SCL-8 remains a strong performer across a wide range of scenarios, algorithms like SC-Creeper and its improved version excel in high-code-rate regimes, offering superior performance with comparable complexity. However, for low code lengths and rates, SCL-8 and SCL-8-like algorithms may still maintain a competitive edge, with SC-Creeper variants exhibiting comparable performance. Figure 4: Comparison of FER values, \(N=1024\). It can be noted that all methods are \"between\" SC (in other words, SCL-1) and SCL-8 in complexity. Thus, as \(E_{b}/N_{0}\) increases, we can say that the complexity of the SC-Creeper approaches is comparable to that of an SC decoder using \(O(N\log N)\) operations. However, the modified SC-Creeper uses slightly more operations due to the application of additional conditions and different stopping criteria compared to the original SC-Creeper algorithm. Nonetheless, for \(\Delta=0\), both algorithms have similar complexity and are identical.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 4 Results and Analysis of Experiment
### Experimental Design
#### 4.1.2 Pre-Processing  
This step is essential to obtain consistent, integrated, and processable data using the machine learning techniques. The preprocessing starts with data cleaning and data transformation, and it ends with data reduction [38], as illustrated in Figure 6. The data cleaning process is used to clean the dataset by eliminating irrelevant and useless records and parameters. Afterward, data transformation is performed to convert all features. The step ends with data reduction, where PCA is utilized with 13 components. The results prove the validity of the findings of the preprocessing in [38] by achieving a balance between completeness and simplicity and an accuracy of the clustering process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Duration** & **Packet Rate** & **Byte Rate** \\ \hline
0–30 s & 250 p & 1024B \\ \hline
30–60 s & 500 p & 2097152B \\ \hline
60–120 s & 1000 p & 12582912 \\ \hline
120–200 s & 1000 p & 14582912 \\ \hline
200–260 s & 1000 p & 20971520 \\ \hline
260–300 s & 1500 p & 20971520 \\ \hline \hline \end{tabular}
\end{table}
Table 1: UDP Packets Generated During 300 s of Simulation.  
Figure 5: Throughput of the Network During 300 s of Simulation.
## 5 Conclusions  
The data in this study support the suitability of the _Salmonella_ spp. kit for commercial use on different food samples, including egg products and poultry meat, which are the foods most associated with salmonellosis.  
Therefore, the validated LAMP kit provides an accurate method for the rapid detection of _Salmonella_ spp., offering significant advantages over the traditional method, as it is characterised by a high sensitivity (up to 0.4 CFU/g), easiness of use for laboratory testing, and a large reduction in the analysis time (about 26 h to obtain definitive results), making it a valuable asset to the food industry. Despite the LAMP kit being less sensitive than the traditional methods, the great rapidity and ease of use suggest that the LAMP assay can be a valid alternative for routine examination in the food sector and for screening of large numbers of food samples.  
In our study we isolated the \(S\). Infants strain from the positive sample. In the EU, an increased occurrence of various serotypes implicated in human infections, including \(S\). Infants, has been reported, related to poultry meat [24]. The increase in \(S\). Infants has been associated with the propagation of various clones of broiler origin in different European countries, including the dominant Hungarian clone [25].  
Continuous monitoring to detect _Salmonella_ along the food chain is of critical importance for public health, above all in the poultry meat industry, as poultry meat is one of the most consumed meals globally and thus one of the most traded meat products.  
Conceptualization, V.C. and C.D.B.; methodology, V.C., A.C., S.S., G.O., D.C., R.P., C.L. and G.A.; software, G.L.B.; validation, F.G.; formal analysis, R.A.; investigation, G.C. and A.A.; resources, C.D.B.; data curation, R.A.; writing--original draft preparation, V.C.; writing--review and editing, V.C. and D.L.M.; supervision, C.D.B.; project administration, C.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.  
We wish to thank our colleagues involved in this scientific collaboration at the Istituto Zooprofilattico Sperimentale della Sicilia (IZSSI).  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. In the interest of transparency, it should be pointed out that authors [PERSON], [PERSON] and [PERSON] are employed by Enbiotech srl. They offered only a methodological background contribution but had no role in the experimentation; in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Method  
### International Height Reference System and Vertical Height Reference System  
The International Height Reference System (IHRS) is a geopotential reference system that co-rotates with the Earth, as defined by the International Association of Geodesy (IAG) in 2015 [36]. According to the IHRS, the geopotential on the geoid (simply, the geoidal potential) is a constant value of \(W_{0}=62,636,853.4\) m\({}^{2}\)/s\({}^{-2}\), and the vertical coordinates are defined as [2]  
\[C_{P}=-\Delta W_{P}=W_{0}-W_{P}, \tag{1}\]  
where \(C_{P}\) is denoted as geopotential number, \(\Delta W_{P}\) is the geopotential difference between the potential \(W_{P}\) at the considered point \(P\) and the geoidal potential \(W_{0}\).  
A vertical height reference system (VHS) is defined by the geographic elevation or depth compared with a reference surface (which is usually the local sea level) [37; 38; 39]. It has a close connection with the concept of the IHRS, because its reference surface can be a geoidal surface with \(W_{0}\), and the value of \(C_{P}\) (given in \(\mathrm{m}^{2}/\mathrm{s}^{-2}\)) can be converted to a physical height \(H_{P}\) (given in \(\mathrm{m}\)) by the following equation [40; 41]:  
\[H_{P}=\frac{C_{P}}{\hat{g}}=\frac{W_{0}-W_{P}}{\hat{g}}, \tag{2}\]  
where \(H_{P}\) can be the orthometric height (OH), normal height, or dynamic height, depending on the types of \(\hat{g}\) applied.  
The OH is a geometric length measured along the plumb-line from the ground point \(i\) (\(i=P,Q\)) to its corresponding point \(i^{\prime}\) on the geoid (\(i^{\prime}=P^{\prime},Q^{\prime}\), see Figure 1). For the OH case, the \(\hat{g}\) in Equation (2) is expressed as  
\[\hat{g}=\bar{g}=\frac{1}{H_{P}}\int_{0}^{H_{P}}g(h)\,\mathrm{d}H_{P}, \tag{3}\]  
where \(\bar{g}\) is the \"mean value\" of gravity \(g(h)\) along the plumb-line. The normal height and dynamic height are approximations of the OH [40], and the OH is, for practical purposes, the height above sea level (in fact, the height above the geoid). In this paper, we regard the OH as the vertical coordinates of the VHS.  
Currently, it is challenging to unify many different local VHSs, because the global mean sea level is not an equipotential surface. An essential component of realizing the IHRS is to unify the global VHS, which requires a global reference surface that is assumed to be available all over the world to be defined [2].
## 3 Materials and Methods
### Sensitivity Phenomenon and Criterion on Navigation Receiver
Therefore, the sensitivity criterion was finally defined as \"loss of positioning within the first 4 s from the beginning of electromagnetic radiation, and the loss of positioning duration lasts for 30 s\". Why in the first 4 s? The option is that once the initial positioning time exceeds 4 s, the positioning can be recovered within 30 s even if the loss of positioning duration could last for a certain time (less than 30 s). Moreover, it also takes into account the interference response time of the navigation receiver in 4 s. With this criterion, the measurement of critical sensitivity level is relatively stable under electromagnetic interference.
# Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach
## 3 Experiment and Results
### Results and Analysis
#### 3.3.2 Comparison with Different Backbone Architectures  
The training and validation box loss curves for various object detection algorithms were analyzed, as depicted in Figure 6. The results indicate that the proposed method achieves the lowest box loss on both the training and validation datasets, demonstrating superior performance in terms of convergence speed and generalization ability. Specifically, the proposed model exhibits a lower final loss compared to other models, suggesting better fitting and reduced overfitting. YOLOv11_RepViT also performed well, but its validation loss was slightly higher than that of the proposed method. In contrast, other models, such as YOLOv11_PE-YOLO and YOLOv11_Retinexformer, exhibited relatively higher validation losses, which may imply limited generalization capabilities. Overall, the proposed method outperforms the other YOLOv11 variants, underscoring its effectiveness for object detection tasks.  
Figure 7 illustrates the convergence trends of various YOLOv11 models (including YOLOv11_PE-YOLO, YOLOv11_Retinexformer, YOLOv11_RepViT, YOLOv11_SwinTransformer) and the proposed model (\"Ours\") in terms of precision, recall, mAP@IoU = 0.5, and mAP across the IoU range of 0.5-0.95. The results indicate that all models rapidly improve to high-performance levels in the early stages of training and gradually stabilize. The proposed model demonstrates superior performance across all evaluation metrics, particularly in precision and mAP@IoU 0.5-0.95, suggesting enhanced detection accuracy and robustness. This indicates that the proposed model has stronger generalization capability and localization precision across different IoU overlap conditions. In comparison, the other models show similar performance, with YOLOv11_Retinexformer exhibiting slightly lower precision and recall, possibly due to limitations in the suitability of its backbone. Overall, the proposed model demonstrates superior performance in complex detection tasks.  
Figure 6: Training and validation loss curves for different backbone networks.  
Table 2 presents a comparison of the test set performance for different backbone architectures, including precision, recall, [EMAIL_ADDRESS], and FPS. The proposed model achieves the highest precision (0.932) and recall (0.934), indicating superior accuracy in identifying true positives. Additionally, \"Ours\" exhibits the best [EMAIL_ADDRESS] score of 0.958, outperforming all other models, which implies better overall detection accuracy and robustness. In terms of inference speed, the proposed model significantly outperforms other backbones with an FPS of 59.6, which is nearly double that of the next fastest model (YOLOv11_SwinTransformer at 30.4 FPS). These results demonstrate the proposed model's advantage in terms of both detection performance and computational efficiency, making it well-suited for real-time and complex detection tasks.
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 3 Materials and Methods
### Data Preprocessing  
Regarding the applicable data of the interpretable model KAN, we were inspired by [PERSON] et al. [16] and improved the data preprocessing process of MIT-BIH, as shown in Figure 2.  
The original data were segmented using a 10 s time window, followed by normalization, filtering of the maximum value set for first-order derivative zero features, threshold-based filtering of the R-peak candidate set, setting a 1.2 T heartbeat cycle, and standardizing to a uniform dimension. Additionally, feature extraction was conducted using a combination of sample balanced and variance reordering.  
Each selected segment was zero-padded to ensure a fixed length of 188. The range [0, 186] represents the ECG signal, while the 187 th position serves as the arrhythmia category marker. Variance feature analysis was applied to both the original and sample-equalized ECG data. For both data types, the datasets were reordered based on the variance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**AAMI ECG** & **N** & **S** & **V** & **F** & **Q** \\ \hline \multirow{4}{*}{Class description} & Normal heart that & Supraventricular & Ventricular ectopic & \multirow{2}{*}{Fusion beat} & \multirow{2}{*}{Unknown beat} \\  & not included in S, & & & & \\  & V, F or Q classes & & & & \\ \hline \multirow{4}{*}{MIT-BIH ECG classes} & Normal beat (N) & Atrial premature & \multirow{4}{*}{Premature premature ventricular and ventricular sequence beat (\(\prime\))} & \multirow{4}{*}{Fusion of Pasco and normal beat (\(\prime\))} \\  & Left bundle branch & beat (A) & & & \\  & block beat (L) & & & & \\  & Right bundle & & & & \\  & branch block & & & & \\  & beat (R) & & & & \\  & Atrial escape & & & & \\  & beat (e) & & & & \\  & Nodal (junctional) & & & & \\  & escape beat (j) & & & & \\ \hline Training set & 72,471 & 2223 & 5788 & 641 & 6431 \\ \hline Test set & 18,118 & 556 & 1448 & 162 & 1608 \\ \hline Total & 90,589 & 2779 & 7236 & 803 & 8039 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ECG categories and data volumes of the MIT-BIH arrhythmia dataset defined based on the AAMI standard.  
of their time series. This reordering resulted in a pseudo-time series representation of the ECG. Figure 3 illustrates the time feature distribution before and after sorting.