esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characteristics of PSC and Zeolite  
Compared to zeolite #70 PSC has a lower moisture content (1.6% vs. 6.9%) and a higher pH (11.5 vs. 7.4). Whereas the former is white, hydrophilic, and in powder form, the latter is black, hydrophobic, and pelletal. These physical differences may lead to their different decontamination characteristics below.  
As shown in Figure 1, sodium hydroxide or hydrochloric acid did not affect the pH of PSC, probably due to its high pH and alkaline equivalence (7.68 meq NaOH:g\({}^{-1}\)). Furthermore, mixing PSC with soil would only make the soil alkaline. On the other hand, the pH's of the hydrochloric acid solution and soil were hardly affected by the amount of zeolite added. However, the pH of the sodium hydroxide solution decreased slightly with an increase in zeolite. The results suggest that PSC is better than zeolite in ameliorating soil.  
Components of the PSC and zeolite are given in Table 3. The carbon content of the PSC was 31.7%. Thus, its mineral content was 68.3%, a value higher than that of industrial activated carbons made from coal, coconuts, bamboo, etc. which, in general, is approximately 10% [11]. Compared to PSC, zeolite #70 possessed higher silica, potassium, and sodium contents, but lower calcium, iron, and manganese contents. Among the components of PSC, the high contents were calcium, iron, magnesium, silica, and the low contents aluminum, potassium, phosphorus, sodium, zinc, copper, and sulfur. These are the nutrients for plants and vegetables. Furthermore, no cadmium nor lead was detected in PSC. The results imply that PSC can be used as a fertilizer.  
Figure 1: Effects of paper sludge carbon (PSC) and zeolite (Z) on the pH of hydrochloric acid, sodium hydroxide, and soil.
## 4 Results
### Drilling Verification  
There was an engineering borehole located at the center of the detection array. In order to verify the accuracy of the detection results, the formation structure information obtained using the conversion of the dispersion curve can be compared with the borehole data. The engineering borehole drilling result is shown in Figure 16, and the core sample photo of the borehole is shown in Figure 17.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Lithology** & **Hierarchical Frequency Fusion(m)** & **Superimposed Averaging (m)** & **S-Wave Velocity (m/s)** \\ \hline Miscellaneous fill and silty clay & 0–6.7 & 0–5.9 & \textless{}175 \\ Residual sandy clay & 6.7–18 & 5.9–17.2 & 175–250 \\ Fully weathered granite & 18–30.5 & 17.2–31.5 & 250–400 \\ Strongly–moderately weathered granite & \textgreater{}30.5 & \textgreater{}31.5 & \textgreater{}400 \\ \hline \end{tabular}
\end{table}
Table 2: Stratigraphic delineation results of two methods.  
Figure 15: S-wave velocity structure of hierarchical frequency fusion and superimposed averaging.  
Figure 16: The engineering borehole drilling result.  
In the field of shallow surface microtremor survey, the delineation errors usually need to be within 3 m. The borehole data shows that the delineation between residual sandy clay and fully weathered granite is 17.8 m, with a delineation error of 0.2 m for the hierarchical frequency fusion method and \(-0.6\) m for the superimposed averaging method. The locations of the strongly weathered granite layers delineated using the hierarchical frequency fusion and the superimposed average are 30.5 m and 31.5 m, respectively, with the actual delayed upper interface of the strongly weathered granite being 29.2 m.  
Figure 18 shows the stratigraphic delineation errors for the stratigraphic structures obtained using the two methods. The stratification error can be expressed using Formula (10):  
\[error=depth_{calc}-depth_{act} \tag{10}\]  
where \(depth_{calc}\) means the calculated stratification position, and \(depth_{act}\) stands for the actual stratification position.  
It can be seen that the partitioning of the residual sandy clay-fully weathered granite and the partitioning of the fully weathered granite-strongly weathered granite using the hierarchical frequency fusion method is more accurate and has a smaller partitioning error.  
In summary, it can be seen that hierarchical frequency fusion produces less error in the hierarchy and has a better match with the actual drilling data than the superimposed averaging method.  
Figure 17: Core sample photos of borehole.
## 3 Materials and Methods  
### SegNet-Based Proposed Model  
Semantic segmentation models, in a broad sense, comprise networks responsible for encoding and decoding. Here, the encoder works as a classification network, which is pre-trained. The encoder is composed of different convolution layers. The feature extraction process is done by the encoder, which is composed of convolution layers, as shown in Figure 1.  
Features are taken out of the input through the convolution process. The decoder decodes low resolution feature maps to full input resolution for the purpose of dense classification. The decoder is composed of different deconvolution layers. The aim of the deconvolution layer is up-sampling. Through deconvolution, the features that are reduced in dimensions are up-sampled to image size. Figure 1 shows the SegNet system flow from the input to the output. In SegNet a set of encoder layers encodes the input images based on deep convolution. These images are decoded by corresponding decoder layers. Decoded images are forwarded to a classification layer. This layer acts as a pixel-based classifier. Feature fusion layers combine the features that are collected from the patches. Input images are sliced into patches in a horizontal and vertical fashion. These are passed via the encoder and feature fusion layers. Based on the feature characteristics, the decoder layers produce the output. The decoder output will be the lung nodule segmentation result, as shown in the Figure 1
## 4 Discussion
### Postural Variability and Sitting Strategy  
Our first hypothesis, that postural variability would be lowest during correct sitting compared to comfortable and habitual sitting, was only partially supported. Specifically, KA variability was significantly lower during correct sitting (8.7\({}^{\circ}\)) compared to comfortable (12.8\({}^{\circ}\)) and habitual (12.4\({}^{\circ}\)) sitting. This suggests that participants exercised more conscious control over their lower limb positioning when aiming for a 'correct' strategy. However, variability in HI and TA did not differ significantly across sitting strategies, implying that individuals may focus their efforts on maintaining certain body segments--such as the knees--while allowing for more natural movement in the upper body. This interpretation aligns with previous research that has found that lower-limb symmetry can be modulated through instruction [42] and that clinicians often target specific body regions when promoting postural correction [15].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Joint Angles** & **Males** & **Females** & **Differences** & **\(p\)-Value** \\ \hline Head inclination (\({}^{\circ}\)) & 11.4 (8.7) & 9.2 (5.5) & \(-\)2.2 & \textless{}0.05 \\ Trunk angle (\({}^{\circ}\)) & 8.0 (9.1) & 5.7 (3.2) & \(-\)2.3 & \textless{}0.01 \\ Knee angle (\({}^{\circ}\)) & 12.0 (11.2) & 10.6 (8.0) & \(-\)1.4 & 0.227 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Differences in angle range for HI, TA, and KA between genders.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Joint Angles** & **Observed Mean Range (\({}^{\circ}\))** & **MDC\({}_{95}\) (\({}^{\circ}\))** & **Ratio (Observed/MDC\({}_{95}\))** \\ \hline Head inclination & 10.3 \(\pm\) 7.3 & 2.5 & 4.1 \\ Trunk angle & 6.9 \(\pm\) 6.9 & 2.2 & 3.1 \\ Knee angle & 11.3 \(\pm\) 9.7 & 2.8 & 4.0 \\ \hline \hline \end{tabular} Note: MDC\({}_{95}\) values are based on reliability assessment using intraclass correlation coefficients (ICCs) from the subset of repeated measurements in this study, calculated as MDC\({}_{95}\) = 1.96 \(\times\)\(\sqrt{2}\)\(\times\) SEM, where SEM (standard error of measurement) was derived as SD \(\times\)\(\sqrt{(1-\text{ICC})}\).  
\end{table}
Table 5: Comparison between observed postural variability ranges and minimum detectable change (MDC\({}_{95}\)) values.
## 4 LTC Architecture
### PoT Gateway  
#### 4.2.1 PoT Gateway as an OpenVPN Client  
The PoT gateway is built upon an embedded Linux platform, namely, Raspberry Pi 4 Model B (8 GB RAM) board [22]. The PoT gateway is connected to the Internet using a wired connection utilizing the built-in Fast Ethernet port on the Raspberry Pi board. The Raspberry Pi board is configured as an OpenVPN [23] client. At the startup of the board, it automatically connects through the Internet to an OpenVPN server running on a Linux-based virtual private server (VPS) hosted by Vultr [24]. The basic specifications of the VPS instance are listed in Table 1. With scalability, security, and future upgrades in mind, the encrypted VPN tunnel between the PoT gateway and the VPS instance is used to exchange all the system traffic, including VoIP and MQTT traffic. This includes the traffic between the PoT gateway and the VPS on the one hand, and the traffic between many distributed PoT gateways, as the system scales, on the other hand, as depicted in Figure 2.
###### Abstract  
Background: Implant rehabilitation in cases of monoedentulism in the esthetic area is a challenge for the clinician. The aim of our study was to test the diagnostic-therapeutic accuracy of computer-guided implant placement in the esthetic area. Methods: Postimplant surgery cone beam computed tomography (CBCT) scans were retrospectively analyzed to assess the accuracy of computer-guided implant placement compared to the preoperative computer-digital planned implant position. We selectively enrolled CBCT scans of patients who underwent immediate or delayed implant placement of a single maxillary incisor, treated with computer-guided implant surgery through a tooth-supported digitally designed 3D printed guide. Our analysis consisted of three digital measurements: the mean deviation of the implant axis, and the mean mesiodistal implant deviation measured both at the apex and at the head of the implant. Results: A total of 95 implants were placed in 95 patients (60 Males, 35 Females; age from 27 to 45-year-old). Congruence analysis showed a mean deviation of implant axis of 1.04\({}^{\circ}\)\(\pm\) 0.56\({}^{\circ}\) in sagittal projection, a mean mesiodistal implant deviation between adjacent teeth of 0.14 mm \(\pm\) 0.07 mm at implant head level and 0.8 mm \(\pm\) 0.3 mm at the apex in axial projection. Conclusions: computer-guided implant placement through a tooth-supported guide was extremely accurate in the esthetic area because the deviations between the real implant position and the preoperative planning was not clinically relevant.  
diagnostic accuracy of computer-guided implant placement; CBCT analysis of implant placement accuracy in esthetic area
# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
## 2 Proposed Method
### New Map Points Processing Strategy
#### 2.2.2 Repeated Existing Map Points Elimination Operation  
When SLAM runs in a dynamic environment, dynamic landmarks may leave repeated map points in the map point set, whereas the original ORB-SLAM3 has no mechanism to deal with this problem. So, we add a repeated existing map points elimination operation (called REMPE) to the ORB-SLAM. As shown in Figure 3, if the moving map points are not removed in time, they will leave a large number of outdated map points in the map point set of the SLAM system, which represent the objects that have passed through these places. Therefore, before a new map point is added to the map point set, it is necessary to eliminate the existing similar map points.  
Figure 2: Some map points are generated at error coordinates by ORB-SLAM3.  
Directly traversing all the map points in the map point set one by one is time-consuming work, which seriously affects the real-time performance of the SLAM system. As the map has more points, it will take a longer time. The ORB-SLAM3 system uses DBoW2 [47] to accelerate loopback detection, reference keyframe tracking and relocation tracking. In this paper, we use the existing DBoW2 bag of words model in ORB-SLAM3 to calculate the NodeId of each map point when each map point is generated by the SLAM system. Then, each of these new map points is compared with the existing map points that have the same NodeId. A similar score is described as:  
\[sim(d,d_{i})=\|d-d_{i}\|_{2} \tag{2}\]  
where \(d\) is the description of the current map point, \(d_{i}\) is the description of one map point which has the same NodeId as the current map point, and \(sim(d,d_{i})\) is the score function which is in integer form.  
The specific steps of the proposed repeated existing map points elimination operation are: the SLAM system finds the remaining map points whose similarity with the descriptor is within the threshold range (which is set as 0.5 in this study) and then removes them from the map point atlas. The pseudocode of the proposed operation is shown in Algorithm 1.
###### Abstract  
This study describes the use of erythrocytes (RBCs) of loggerhead turtles as in vitro models for evaluating their toxicity to methylmercury. Blood samples of loggerhead turtles that were born in the Colombian Caribbean were used. The LC\({}_{50}\) of RBCs to methylmercury was determined at 96 h using methylmercury concentrations of 0.5-100 mg L\({}^{-1}\). Next, the viability of the RBCs and the activity of the enzymes superoxide dismutase (SOD), glutathione S-transferase (GST), and lipid peroxidation by malondialdehydedehyde (MDA) at 6 and 12 h of exposure to acute concentrations of 0, 1, and 5 mg L\({}^{-1}\) were evaluated. The LC\({}_{50}\) for loggerhead turtle RBCs was 8.32 mg L\({}^{-1}\). The cell viability bioassay of RBCs exposed for 12 h only showed 100% cell viability. Increasing in vitro MeHg concentrations caused a corresponding increase in MDA concentration as well as decreases in the activities of SOD and GST. The RBCs represent an excellent model for ecotoxicological studies and SOD, GST, and MDA are biomarkers of environmental pollution and oxidative stress in loggerhead turtles. This was the first study conducted on loggerhead turtle where the response of RBCs to MeHg-induced oxidative stress is evaluated.  
Cytotoxicity; erythrocytes; methylmercury; malondialdehydedehyde; in vitro; superoxide dismutase
# Generalized Beam Theory for Thin-Walled Beams with Curvilinear Open Cross-Sections
## Numerical Results  
### Definition of the Case-Studies  
A thin-walled cantilever of length \(l=1\) m is considered for numerical analysis. The cross-section is semi-annular, with radius \(R=0.1\) m, uniform curvature \(\bar{k}=1/R=10\) m\({}^{-1}\), and constant thickness of the wall \(h=0.003\) m (see Figure 3a). Defining the curvilinear abscissa \(s\) from the bottom end of the semi-annular, the curve \(\tilde{\gamma}\) turns out to be described by:  
\[\begin{split} y(s)&=\frac{2R}{\pi}-R\sin\left(\frac {s}{R}\right)\\ z(s)&=-R\cos\left(\frac{s}{R}\right)\end{split} \tag{32}\]  
with \(s\in[0,\pi R]\).  
Two different load conditions are considered, namely load condition 1: \(\mathbf{p}(x,s)=-p\mathbf{e}_{z}\) in \(s\in[\frac{\pi}{2}R,\pi R]\) (Figure 3a); load condition 2: \(\mathbf{p}(x,s)=p\mathbf{e}_{y}\) in \(s\in[0,\pi R]\) (Figure 3b). In both cases \(p=3\times 10^{3}\) Pa. It is worth noting that the second load case respects full symmetry about the \(\mathbf{e}_{y}\) axis.  
The material data used for the calculations correspond to the aluminium alloy having longitudinal elastic coefficient \(E=84\) GPa and Poisson ratio \(\
u=0.33\).
# Programming Agents by Their Social Relationships: A Commitment-Based Approach
## 3 Social Relationships as Commitments
A first way to model the relationships among all these agents is to consider the following set of commitments. \[ \begin{array}{l}\mathbf{c_{1}}:\mathsf{C}(\mathit{seller},\ \mathit{customer},\ \mathit{pay}(500,\mathit{seller}),\ \mathit{at}(\mathit{goods},D))\\ \mathbf{c_{2}}:\mathsf{C}(\mathit{trk1},\ \mathit{seller},\ \mathit{pay}(50,\mathit{trk1}) \wedge\mathit{at}(\mathit{goods},A),\ \mathit{at}(\mathit{goods},B))\\ \mathbf{c_{3}}:\mathsf{C}(pln1,\ \mathit{seller},\ \mathit{pay}(200,\mathit{pln1}) \wedge\mathit{at}(\mathit{goods},B),\ \mathit{at}(\mathit{goods},D))\\ \mathbf{c_{4}}:\mathsf{C}(\mathit{trk3},\ \mathit{pln1},\ \mathit{pay}(50,\mathit{trk3}) \wedge\mathit{at}(\mathit{goods},C),\ \mathit{at}(\mathit{goods},D))\end{array} \]  
Commitment \(c_{1}\) represents the offer that _seller_ proposes to _customer_: if _customer_ pays 500 Euros for some _goods_, _seller_ will deliver the bought goods at _customer_'s place \(D\). Since _seller_ cannot directly deliver the goods, however, it creates commitment \(c_{1}\) only after having inspected the environment (i.e., the social state) and having observed the commitments \(c_{2}\) and \(c_{3}\). With the former, the _seller_ getsa means for moving the goods from \(A\) to \(B\) by using \(trk1\). With the latter, instead, the \(seller\) has an agreement with \(pln1\) to ship goods from \(B\) to \(D\). In these two commitments, the \(seller\) plays the creditor role, and hence, it can detach them (by paying the asked amount) only after the \(customer\) has detached \(c_{1}\) by paying for the goods. The last commitment, \(c_{4}\), encodes an agreement between \(pln1\) and \(trk3\) for the shipping of goods from \(C\) to \(D\), but the \(seller\) does not need to know how \(pln1\) will satisfy \(c_{3}\). From the \(seller\)'s perspective, in fact, it is sufficient to know that \(pln1\) has taken on a commitment to ship the goods directly to the _customer_. Of course, \(pln1\) creates \(c_{3}\) only after \(c_{4}\) has been created. This scenario, despite being very simple, encompasses several alternative solutions, each consisting of a different set of commitments that agents create to entice the others. To keep the discussion simple, here we introduce a first, plain solution, whereas in Section 6, we will discuss alternative execution paths and demonstrate the flexibility we get when commitments are available as resources.
## 3 Results and Discussion
### Analysis of Gin, Rum, and Whiskey Samples by HS-SPME-GC-Ms
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Chemical Class** & **Compound** & **exp. RI** & **th. RI \({}^{\text{a}}\)** & **Gin \({}^{\text{b}}\)** & **Rum \({}^{\text{b}}\)** & **Whiskey \({}^{\text{b}}\)** \\ \hline  & propyl decanoate & 1491 & 1492 & - & - & tr \\  & 2-methylpropyl decanoate & 1523 & 1546 & - & + & + \\  & methyl dodecanoate & 1524 & 1526 & - & - & tr \\  & (Z)-3-hexenyl benzoate & 1568 & 1570 & tr & - & - \\  & ethyl dodecanoate & 1599 & 1594 & - & ++ & ++++ \\  & dodecyl acetate & 1614 & 1607 & - & - & tr \\  & 2-phenylethyl hexanoate & 1640 & 1650 & - & tr & tr \\  & 3-methylbutyl decanoate & 1645 & 1645 & - & ++ & + \\  & 2-methylbutyl decanoate & 1651 & 1647 & - & tr & + \\  & propyl dodecanoate & 1692 & 1685 & - & - & tr \\ ester & ethyl tridecanoate & 1696 & 1687 & - & - & tr \\  & methyl tetradecanoate & 1722 & 1725 & tr & - & - \\  & benzyl benzoate & 1757 & 1763 & tr & - & - \\  & octyl octanoate & 1776 & 1779 & - & tr & - \\  & ethyl tetradecanoate & 1794 & 1793 & - & + & + \\  & isopropyl myristate & 1824 & 1825 & tr & + & - \\  & 2-phenylethyl ester + & 1842 & - + 1847 & - & + & - \\  & 3-methylbutyl dodecanoate & 1846 & 1851 & - & - & tr \\  & methyl hexadecanoate & 1924 & 1926 & tr & - & - \\  & ethyl 9-hexadecenoate & 1975 & 1976 & - & - & + \\  & ethyl hexadecanoate & 1993 & 1993 & tr & + & tr \\ \hline lactone & \(\gamma\)-butyloctone & 913 & 916 & - & - & tr \\  & (E)-whiskeylactone & 1308 & 1302 & - & tr & tr \\ \hline  & hexanal & 799 & 801 & - & - & tr \\  & benzaldehyde & 953 & 962 & - & tr & tr \\  & 2-nonanone & 1086 & 1092 & - & - & tr \\  & nonanal & 1098 & 1104 & tr & + & tr \\ carbonyl & decanal & 1192 & 1206 & tr & + & - \\  & 2-decanone & 1285 & 1294 & + & - & - \\  & 2-undecanone & 1288 & 1294 & - & - & + \\  & undecanal & 1301 & 1307 & - & - & tr \\  & \(\beta\)-damascenone & 1365 & 1386 & - & + & tr \\ \hline  & diethylocarbethane & 700 & 658 & - & tr & - \\  & 1,1-diethoxyethane & 724 & 728 & tr & - & tr \\  & 1,1-diethoxybutane & 921 & 901 & - & tr & tr \\ \hline acid & acetic acid & 609 & 610 & tr & - & - \\  & octanoic acid & 1206 & 1180 & - & - & + \\  & dodecanoic acid & 1594 & 1567 & - & - & + \\ \hline  & methyl salicate & 1176 & 1192 & - & + & - \\  & ethyl salicate & 1248 & 1270 & - & tr & - \\  & dihydroeugenol & 1337 & 1373 & - & tr & - \\  & dillapiol & 1621 & 1622 & tr & - & - \\ \hline  & furfural & 827 & 833 & - & + & + \\  & 4,7-dimethylbenzofuran & 1200 & 1220 & tr & - & - \\  & vitispirane & 1272 & 1271 & - & - & + \\ \hline  & toluene & 758 & 763 & tr & - & tr \\  & ethylbenzene & 856 & 855 & - & - & tr \\  & styrene & 885 & 893 & - & tr & tr \\  & 1,2,4-trimethylbenzene & 987 & 990 & - & - & tr \\  & cadalene & 1671 & 1674 & + & - & - \\  & 1,4-dimethyl-7-(1-methylethyl)- & 1769 & 1775 & tr & - & - \\  & azulene & (1-methyldodecyl)benzene & 1909 & 1916 & - & - & tr \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 3 Methodology of TSDCA-BA Model
### Structural Design of the TSDCA-BA Model
#### 3.1.3 Three-Layer BiGRU Convolutional Structure  
Figure 4 shows the basic structure of the model, which is composed of bidirectional GRU layers and one-dimensional convolutional layers, with Dropout layers placed between them to reduce overfitting. The input first passes through a bidirectional GRU layer with  
Figure 3: The overall architecture of the proposed SDCA-BA Module.  
L2 regularization, maintaining the full output sequence. Then, a Dropout layer randomly deactivates some neurons to improve the model's ability to generalize. Afterwards, the features are processed by a one-dimensional convolutional layer with L2 regularization to capture local patterns. The convolution output is normalized using batch normalization to stabilize training, followed by a ReLU activation to increase nonlinearity and output stability. As shown in Figure 1, the architecture combines three bidirectional GRU layers and convolutional layers. Two bidirectional GRU layers with L2 regularization are applied first, each returning the full sequence and followed by Dropout layers to help prevent overfitting. This arrangement is designed to capture temporal dependencies and enhance generalization. Next, the input flows through two one-dimensional convolutional layers with kernel size 3 and L2 regularization. Each convolution is followed by batch normalization and ReLU activation to improve training stability and nonlinear modeling. After the second convolutional layer, a Squeeze-and-Excitation module is added to adaptively adjust channel weights and emphasize important features. A third bidirectional GRU layer with Dropout is then introduced to further refine temporal representations. Finally, a convolutional layer with kernel size 1 performs feature fusion, together with batch normalization and ReLU activation to promote convergence and stable outputs. Overall, this design effectively integrates temporal sequence modeling with local feature extraction, improving the model's ability to handle complex sequential data.