esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Data Preparation
#### 3.2.2 Imaging Data Preparation  
In this case study, there are two situations: the thermal texture resulting from the aerial thermal photogrammetry processing and the image sets resulting from the ground sensors (thermal and UV fluorescence).  
For aerial thermal imaging, the easiest solution was to export the resulting photogrammetric 3D model and its colour texture (colour being the thermal information) and import them into the Blender scene of the complete church 3D model split into sections. After carefully aligning the 'thermal 3D model' with the complete church, the thermal texture could be baked for each of the church sections (Figure 10) as new textures. This set was available only for the areas that were recorded with the UAS, so the west side and the south narthex are not covered.  
For the ground sensors (thermal and UV fluorescence data), considering the complexity of all possible case studies, it was decided that the best way to reference any imaging data to the 3D surface is manually. Any automated solution required too many control parameters and manual verifications, which defeated the purpose of saving time. The reconstructed UV coordinate maps simplified this process. By using any image-editing software, in this case, Adobe Photoshop 2024 v25.1, the imaging data can be superimposed over the texture of the corresponding section. The new texture has the same size and format as the original texture and is transparent in the areas where the imaging data do not cover the surface. Most areas were recorded from different positions, so the final texture would be a mosaic from all corresponding images. Figure 11 presents the final texture, generated from three different thermal images, with an Overlay mode that emphasizes the surface features from the underlying colour texture.  
Figure 9: South nave wall: before and after. The difference between the polygon distribution and colour data in the original UV coordinate map and texture versus its regeneration and texture baking.
## 8 Speaker Direction and Signal Obstruction  
This section explores two factors excluded from above experiments to reduce the number of variables:  
* **Yaw orientation of the phone:** Ultrasound is very directional and orientation of the speaker in relation to a microphone has significant effect on reception quality. Unless the speaker is  
Figure 5: The change of distance (in cm) between MPV and correct position as the speaker is tilted from downward to upward orientation. Each color represents one check point. Vertical axis is distance measured in cm. Horizontal axis is angle of rotation with 0 \({}^{\circ}\) being downward and 180 \({}^{\circ}\) being upward. Note how accuracy increases as speaker angle approaches 180 \({}^{\circ}\)(upwards). In all cases positioning accuracy remains sub-meter.  
pointing directly upward or downward, its yaw orientation (rotation about Z axis) is going to impact reception quality at each of the four microphones. This in turn can influence quality of signal arrival timestamp and therefore trilateration accuracy.
* **User obstructing direct line-of-sight between phone and microphone:** Unlike low frequency sounds that have a long wavelength, ultrasound with it high frequency sound has poor obstacle penetration. This means that maintaining a direct line-of-sight between the phone and all four microphones is important in order to achieve best accuracy. Blocking direct line-of-sight to one of the microphones can negatively impact signal intensity on reception and introduce lag due to the signal arriving via diffraction. Both poor reception and lag can reduce trilateration accuracy. The most likely scenario where one of the microphones would be blocked is when the user himself is standing in the path of the signal. Although microphones were placed on the ceiling to mitigate this problem, in some cases direct line-of-sight can still be blocked.
# Research on Improved Lightweight YOLOv5s for Multi-Scale Ship Target Detection
## 3 Data and Methods
### The Improved Yolov5_ESGW Model  
In order to ensure the accuracy of model detection and reduce the number of parameters and calculations of the model as much as possible, this paper proposes a lightweight ship target detection model. The improved Yolov5_ESGW model structure diagram is shown in Figure 2. Compared with the baseline model, the improved model mainly integrates the ideas of EfficientNetV2 and Ghostnet networks and makes lightweight improvements to the feature extraction network. The Shuffle Attention mechanism is also integrated into the model, which enables the network to more effectively capture the correlation between space and channels, thereby improving the network's expressiveness. In addition, the model's loss function is also optimized to better improve the model's target box prediction ability. The following is a detailed description of the method for improving this model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Size** & **Map** & **Speed** & **Params** & **FLOPs** & **Test** & **GPU** \\  & **(Pixels)** & **(0.5)** & **(ms)** & **(M)** & **(G)** & **Dataset** & **GPU** \\ \hline Yolov5s & 640 \(\times\) 640 & 56.8 & 6.4 & 7.03 & 15.8 & Microsoft COCO & V100 \\ Yolov5m & 640 \(\times\) 640 & 64.1 & 8.2 & 21.2 & 49 & Microsoft COCO & V100 \\ Yolov5l & 640 \(\times\) 640 & 67.3 & 10.1 & 46.5 & 109.1 & Microsoft COCO & V100 \\ Yolov5x & 640 \(\times\) 640 & 68.9 & 12.1 & 86.7 & 205.7 & Microsoft COCO & V100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the detection performance of different Yolov5 models.
## 3 Methodology
### Field Failure Data Collection and Pre-Processing  
The required TTF data of an HVAC system is collected from the manufacturer's service books, service stations, maintenance registers, and service engineers. The collected data may be unstructured and noisy; therefore, the data need to be sorted component-wise, analyzed for anomaly detection, and processed. It helps to transform the unstructured and noisy data into a structured and conditioned form that can be used for further modeling and analysis. This step is very crucial as the accuracy of the model depends on the quality and quantity of the data. The data collected and stored consists of the following information: region, zone, area, component code, failure code, failures in kilometers, and failure description. The data pre-processing involved several steps to make failure data structured and conditioned well enough for reliable analysis. Initially, raw data on HVAC system failures in passenger vehicles were collected, covering failure modes, subsystem/component types, failure times, and environmental conditions. Data cleaning was accomplished to remove duplicate entries, correct irregularities, and handle missing values through imputation or exclusion, ensuring data integrity. The outliers were identified and assessed, focusing on extreme failure times potentially linked to data entry errors or atypical conditions.
###### Abstract  
The aim of this study was to evaluate ex vivo the sealing achieved at simulated lateral canals (SLC) and the quality of filling according to their position in the root canal after using the same filling technique. SLC were created at three levels in 55 teeth and divided into two groups depending on the root canal sealer used (1: BioRoot(r) RCS, 2: GuttaFlow(r) bioseal). They filled them with the continuous wave technique and submitted to a diaphanization technique. The samples were analyzed using a magnifying lens (20\(\times\)), pictures were taken, which proceeded to linear measurement with the ImageJ(r) program and used a filling score system with five grades (0 to 4, 0 and 1 not acceptable, 2 to 4 acceptable); BioRoot(r) RCS has got a greater proportion than GuttaFlow(r) bioseal for SLC filled acceptably at 10 mm from the apex (\(p<0.05\)). The highest proportion of SLC filled acceptably was found in the middle third (6 mm) (\(p<0.05\)), followed by the apical third (3 mm) and the coronal third (10 mm). The difference between apical and coronal third could be significant; BioRoot(r) RCS has been better than GuttaFlow(r) bioseal for filling SLC in the coronal third of the teeth. Studies on the characteristics of these cements are missing to explain these differences.  
bioceramics; root canal sealers; simulated lateral canals; warm obturation; extrusion Article
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 3 System Architecture
### LoRa Field Packet Generator  
This section briefly presents the key requirements in realizing the prototype device. The hardware realization is also presented in this section.
## 4 Simulation Results  
### Rate Loss  
This section numerically studies the rate loss of parallel distribution matcher based on CCDM. Take the probability distribution after quantization as \(P_{A}=\{0.4,0.3,0.2,0.1\}\) as an example to demonstrate the calculation process of the rate loss. When n = 10, \(C_{typ}\) = (4, 3, 2, 1), there are 49 complementary pairs (including \(C_{typ}\)) that satisfy the (5), the specific parameters of each complementary pair are shown in Table 1. Only the first 32 kinds of complementary pairs sorted by \(k_{a}+k_{b}\) are listed in Table 1, because there are 49 kinds of complementary pairs, which are equivalent to 97 cases (\(C_{typ}\) is equivalent to only one case) and can index 6-bit binary data at most.  
When the first 32 complementary pairs are chosen, \(p_{l}=6\), the average total number of indexable bits is \(6\times 64+26+25\times 10+24\times 14+23\times 12+22\times 20+20\times 7=1852\), the rate R = \(1852/64/20=1.447\) bit/symbol, and the rate of CCDM is 1.3 bit/symbol, increasing by 0.147 bit/symbol. When the first 17 complementary pairs are selected, \(p_{l}=5\) and the average total number of indexable bits is \(5\times 32+26+25\times 10+24\times 14+23\times 7=933\), resulting in a rate of R = \(933/32/20=1.459\) bit/symbol, an increase of 0.159 bit/symbol. When only the first three complementary pairs are selected, \(p_{l}=2\), the average total number of indexable bits is \(2\times 4+26+25\times 3=109\), and the rate R = \(109/4\times 20=1.36\) bit/symbol, increasing by 0.06 bit/symbol. In the worst case, the complementary pair may not be chosen, and the parallel CCDM is equivalent to a single CCDM.  
Figure 3 shows rate loss over block length for the probability distribution as \(P_{A}=\{0.4,0.3,0.2,0.1\}\). The rates under different a are shown in Table 2. We observe that the parallel distribution matcher achieves a lower rate loss than CCDM for all block lengths n. When the block length n = 100, the rate of CCDM is 1.75 bit/symbol and the rate loss is 0.1, while when the parallel distribution matcher is n = 70, the maximum rate can reach 1.76 bit/symbol (\(p_{l}=8\)) and the minimum is 1.74 bit/symbol (\(p_{l}=4\)), saving 30% on block length n. When the block length of the parallel distribution matcher is n = 100, the maximum rate loss is 0.0847 bit/symbol and the minimum rate loss is 0.0693 bit/symbol, both better than CCDM's rate loss of 0.1 bit/symbol.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Pair Number** & \(C_{I}\) & \(\overline{C_{I}}\) & \(k_{a}+k_{b}\) &
# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 2 Materials and Methods
### Identification of Principal Variables  
Despite the fact that eleven variables commonly employed at the time to describe ship accidents were selected, it is of particular interest to identify the most relevant variables associated with the type of accidents. Some of these studies used classical statistical methods like hypothesis tests (ANOVA), and others were based on artificial intelligence (AI) algorithms like PCA (principal component analysis) or random forest algorithms. Despite this, the PCA study only let us simplify the computation time but did not let us identify the most relevant variables, and random forest (200 decision trees) is a well-recognised procedure to be employed to improve the information obtained from the hypothesis test.
# Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols
## 3 Wireless Protocols Architecture
### LTE-M  
LTE-M is another protocol based on the traditional LTE [47]. However, it was designed to transmit bits at a higher data rate at the expense of comparatively higher power utilization and lower battery life compared to NB-IoT [48]. It was designed to cater to applications  
Figure 3: Network architecture for IoT protocols based on 3 GPP licensed spectrum (NB-IoT and LTE-M).  
Figure 2: Network architecture for IoT protocols based on unlicensed ISM-band spectrum (Sigfox, LoRa, Z-Wave).  
requiring relatively higher speeds and lower latency [13]. This protocol is useful for certain scenarios where the other protocols do not meet the data-rate requirements.  
Figure 3 illustrates the network connectivity for 3 GPP licensed band-based protocols such as NB-IoT and LTE-M. This architecture can be divided into two parts, which are the User Plane and Control Plane. The User Plane is concerned with the process of sending and receiving user data. The Control Plane manages the control processes that are implemented to establish communication and authentication of the end devices. The data are transmitted wirelessly from the UE IoT devices to the eNodeB by adopting the LTE-based IoT protocols. The bits are then transferred over a wired network to the Evolved Packet Core (EPC) network. The data are received by the Mobility Management Entity (MME) within the EPC, which focuses on eNodeB signaling, mobility, and security. The Home Subscriber Server (HSS) is associated with user authentication and profile. The Serving Gateway (SGW) handles the routing and packet forwarding towards the uplink and the downlink. The Packet Gateway (PGW) connects the packet core network with the external IP network. The data then flow through the IP network towards the IoT servers [49; 50].
## 5 Experiments and Results
### Sensitivity Analysis of Fitness Parameters  
Table 6 presents a sensitivity analysis of the (\(\alpha\)) parameter of the fitness function of the SSA algorithm. In Equation (12), the fitness constitutes a weighted-sum of the accuracy and the ratio of the selected subset of features. The weighted-sum approach is a classical methodology to handle a multi-objective optimization problem as a single-objective problem. Where the aim is to optimize the weighted-sum of the utilized objectives. Herein, two objectives were considered; the accuracy, and the ratio of the selected features. In which, the accuracy is weighted by the (\(\alpha\)) parameter, and the ratio of selected features was weighted by the multiplier (1-\(\alpha\)). The coefficient (\(\alpha\)) has a controlling behavior for selecting the optimal salp solution, which quantifies the importance of the corresponding objectives. However, there is no guarantee that the randomly chosen value of (\(\alpha\)) will produce the optimal solution; therefore, tuning the (\(\alpha\)) parameter is imperative.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Metrics**} & \multicolumn{2}{c}{\(\alpha\)} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{2-6}  & **0.001** & **0.01** & **0.05** & **0.1** & **0.2** \\ \hline Accuracy & **0.980** & 0.972 & 0.972 & 0.971 & 0.970 \\ Recall & **0.980** & **0.980** & 0.974 & **0.980** & **0.980** \\ FPR & **0.020** & 0.036 & 0.030 & 0.038 & 0.040 \\ TNR & **0.980** & 0.964 & 0.970 & 0.962 & 0.960 \\ Precision & **0.980** & 0.965 & 0.970 & 0.963 & 0.962 \\ G-mean & **0.980** & 0.972 & 0.972 & 0.971 & 0.970 \\ AUC & **0.980** & 0.972 & 0.972 & 0.971 & 0.970 \\ Average No. of features & 220.3 & 224 & 154.9 & 154.9 & **143.1** \\ Reduction rate & 29.9\% & 28.7\% & 50.7\% & 50.7\% & **54.4\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The effect of alpha parameter on the fitness of the SSA algorithm.  
Figure 11: Convergence curves of the SSA algorithm at different population sizes.  
Table 6 shows performance of the SSA algorithm at five different values of (\(\alpha\)), where it belongs to \(\{0.001,0.01,0.05,0.1,0.2\}\). It is clear from the table that when \(\alpha=0.001\), it obtained the best results in terms of accuracy, recall, TNR, precision, g-mean, and AUC, which all attained (98.0%). The minimal FPR value was obtained at a percentage of (2%). Although, when \(\alpha=0.001\), the algorithm could achieve drastically better performance. In contrast, in terms of the reduction rate of features, when \(\alpha\) equals 0.2, the algorithm achieved extremely better. So, the best reduction rate is obtained when (\(\alpha=0.2\)) with percentage of 54.4%. Whereas, when (\(\alpha=0.001,0.01\)), it achieved a reduction rate of 29.9%, and 28.7%, respectively.
# MINC-NRL: An Information-Based Approach for Community Detection
## 5 Experiments
### Comparison Results with the Other Algorithms  
#### 5.4.1 Non-Overlapping Community Detection  
The following Tables 4 and 5 show the CN and NMI of running the MINC-NRL algorithm on the data sets, compared with non-overlapping community detection algorithms GN [2], FN [40], Louvain [41], and EdMot [16]. Note that the NMI values quantify the differences between the result partitions with the ground-truth partitions of the data sets.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Karate** & **Dolphins** & **Football** & **Polbooks** & **LFR500** & **LFR2000** & **LFR10000\_a** \\ \hline MINC-NRL & **2** & **2** & **12** & **3** & **3** & **4** & **4** \\ GN & 5 & 5 & 10 & 5 & - & - & - \\ FN & **2** & 3 & 7 & **3** & **3** & 8 & - \\ Louvain & **2** & **2** & 10 & **3** & **3** & 5 & **4** \\ EdMot & 4 & 5 & **12** & 5 & **3** & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CN of MINC-NRL compared with other algorithms.  
Figure 6: Network representation of Football using Deepwalk.  
The results show that MINC-NRL performs well on both real-world networks and LFR synthetic networks. The numbers of communities of the networks are correctly figured out by MINC-NRL. It also obtains the highest average NMIs on most of the data sets compared with the other algorithms.
# A Sustainable Composite Cementitious Material Manufactured by Phosphogypsum Waste
## 3 Results and Discussion
### Non-Eavorable Water Content of Composite Cementitious Material  
The non-evaporable water content of composite cementitious material paste comprising 10% to 90% phosphogypsum is shown in Figure 2. It is noticed that the non-evaporable water content of composite cementitious material was increased initially and then was decreased with the addition of phosphogypsum, clearly reaching its highest level with phosphogypsum inclusion of 10% among all mixtures. The non-evaporable water content of the composite cementitious material paste was steadily dropped in comparison with the paste with 0% phosphogypsum, displaying a quasi-linear relationship. The lower the non-evaporable water content of the paste, the more phosphogypsum was added. For example, the non-evaporable water content of the composite cementitious material paste at 3 days was 14.19%, 14.28%, 10.87%, 8.65%, 6.76%, and 5.88%, respectively, with 0%, 10%, 30%, 50%, 70%, and 90% phosphogypsum. The main cause of this phenomenon is the low phosphogypsum reaction degree. The more phosphogypsum is employed, the less cement is utilized. The excitation impact of calcium hydroxide is reduced. As a result, the more phosphogypsum was added, the lower the non-evaporable water content of the paste became, which was consistent with the reaction degree experiment. Furthermore, when the curing time was extended from 3 to 28 days, the non-evaporable water content of the composite cementitious material paste rose in all combinations. Except for the 10% phosphogypsum inclusion, the non-evaporable water content of the paste with 30%-90% was lower than that of pure cement paste for all curing ages and mixes. For example, the non-evaporable water content of the composite cementitious material paste was 20.21%, 21.13%, 18.28%, 17.95%, 15.48%, and 11.19% after 28 days with 0%, 10%, 30%, 50%, 70%, and 90% phosphogypsum, respectively; in addition, it was at its highest with phosphogypsum incorporation of 10%. Because the reaction degree of phosphogypsum was low, despite showing certain pozzolanic activity due to the dissolution of calcium ions and the presence of sulfate radical in phosphogypsum, the non-evaporable water content of composite cementitious material decreased steadily as the incorporation of phosphogypsum was increased from 10% to 90%.  
Figure 1: Reaction degree of phosphogypsum.  
Figure 3 depicts the relationship between non-evaporable water content and the composite cementitious material reaction degree, which exhibits a good linear relationship. The linear equation of \(y=0.96827+1.05412x\), R\({}^{2}=0.97509\) can be obtained through 15 points in the scatter plot. The non-evaporable water content may be predicted simply by the reaction degree of composite cementitious material containing 0-90%, based on these easy-to-find experimental results.
## 4 Discussion
Early research indicated that the use of cell-free supernatant from lactic acid bacteria might be a solution for biopreservation [49]. Another advantage of using these peptide extracts from lactic bacteria isolated from acidic fruits is that both bacterial strains originate from wild tropical fruits. A recent study reports the successful utilization of _Lactobacillus rhamnus_ GG as a preservative agent for maintaining the quality of fresh-cut bell pepper, preventing its microbial colonization [19]. In our study, a significant reduction of _Salmonella_ and _Shiegella_ was detected when mango wedges were treated with T5-cocktail (Figure 3). This might be related to the overloading of the UTNGt28 peptide extract in the samples. Recently, we demonstrated that UTNGt28 peptide extract induced lysis and the formation of ghost cells as a secondary killing event in _Salmonella_[30]. Similar to our previous study in pineapple [22], in mango wedges, the acidity (pH 3.3) does not influence the target cell survival during storage (Figure 4). For the control wedges, a slight increase of pH from 3.3 to 3.43 was observed, while a decrease with 0.1-0.2 units was detected in all antimicrobial cocktail treatments. Considering that the pH of PP-cocktail and nisin at the time of application was 6.80, it seems that the acidity of the fruit increased their solubility, however, they might rapidly bind the target membrane and induce killing. pH is known to be a factor that increases peptide solubility [50]. The outer membrane of Gram-negative bacterial cells constitutes a semi-permeable barrier that is responsible for preventing antimicrobials from reaching their cytoplasmic membrane and eventually inducing membrane disruption [51]. A recent study indicated that killing occurs only when bacterial cell membranes are completely saturated with peptides [52]. In this study, T5 and T6 permeated the cytoplasmic membrane while T7 showed minor effects (Figure 5). This indicates that nisin did not permeate the membrane to the extent required for membrane disruption as it did not cause leakage of cell contents from the target cells (Figure 6). T5- and T6-cocktails alter the cell membrane integrity demonstrated by the detection of DNA/RNA molecules (Figure 6). The result was in agreement with our previous findings showing that the extract containing more than one peptide from _L. plantarum_ UTNGt2 showed a bactericidal mode of action toward _Salmonella_ while nisin does not have any effect on the membrane integrity [22,28]. Nonetheless, the mechanism of action of peptide cocktails against Gram-negative bacteria is different from nisin, as nisin needs an extra permeabilization agent that perturbs the outer membrane of target bacteria and sensitizes Gram-negative bacteria to several antibiotics [53].