esa-sceva/satcom-chunk-collection huggingface dataset:
# Magnetic Resonance Roadmap in Detecting and Staging Endometiosis: Usual and Unusual Localizations
## 6 Atypical Site
### Soft Tissues Localization  
Abdominal endometriosis is often associated with a cutaneous scar from a gynecologic procedure, notably from a cesarean section. Lesions are generally located in subcutaneous soft tissues or inside the muscle layer [79] (Figure 12).  
The patient often realizes the presence of a mass or an abnormal thickening at the site of the scar. Ultrasound is generally the first level diagnostic test, useful to exclude much more common alterations such as lipomas or hernias, followed by a magnetic resonance exam [80].  
MRI increases the specificity for diagnosis while detecting hemorrhagic spots within the fibrotic tissue.  
The other atypical site of cutaneous endometriosis is the umbilicus, followed by the inguinal region [81].  
In the literature, there are also much rarer cases of disease localization within the context of striated muscles. For example, [PERSON] et al. described the case of a 38-year-old woman with endometriosis of the external oblique muscle, complicated by the onset of deep venous thrombosis of the iliac vein [82].  
Figure 12: C-section scar endometriosis in a 38-year-old female with subcutaneous nodule causing increasing pain during menstrual cycle. **(A,B)** Axial T2 WI, T1 WI. Nodular hypointense lesion with spiculated margins in subcutaneous soft tissues of abdominal wall; **(C)** Axial fat-suppressed T1 WI. The lesion shows small hyperintense foci; **(D)** Axial T1 WI post-contrast medium. The lesion shows homogenous contrast enhancement (white arrows).  
The risk of such localizations, especially if asymptomatic or associated with nonspecific symptoms, is the delay in diagnosis and consequently in disease management, with the risk of developing complications [83].
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
## 2 Related Works
### Supervised Learning  
The authors of [2] proposed the first CNN-based method that combined two components: a global coarse-scale network and a local fine-scale network. The former conducted global structure scene estimation based on entire images and the latter refined this estimation locally. The authors of [7] developed a deep ordinal regression network (DORN) that adopted dilated convolution to obtain high-resolution depth maps. In addition, ordinal regression was used to solve the slow convergence problem. Some researchers [12; 13; 40; 14] have focused on using multi-scale CNNs to improve detail depth prediction. BTS [13] was developed as a novel DCNN that strengthened the effectiveness of guidance features and upsampled low-resolution depth maps from coarse to fine. The authors of [12] applied implicit constraints on a multi-scale decoder to guide the generation of adaptive depth estimation features, which could achieve better depth estimation results. LAPD [14] was constructed as a multi-scale autoencoder structure that incorporated a Laplacian pyramid into a decoder architecture that adopted edge and feature maps of different scales to enhance global structure and local detail depth estimation. VNL [40] was proposed as a decoder using adaptive merging blocks to fuse features of different scales and used virtual normal directions to build geometric constraints to improve depth prediction performance. The authors of [34; 35] proposed multi-scale CRFs to enhance feature fusion and refine depth estimation. The authors of [34] used multi-scale depth maps generated by the inner layer of a CNN and fused them using a CRF framework.
# Combination Analysis of Future Polar-Type Gravity Mission and GRACE Follow-On
## 1 Introduction
Among all, the Bender-type SFF, formed by two pairs of GRACE-type inline satellites (in polar and inclined orbits), is most promising from both technological and economic aspects. Therefore, it is selected as the primary candidate for the Next Generation Gravity Mission (NGGM) concepts [40; 41] by the European Space Agency (ESA). During the service period of GFO, other Future Polar-type Gravity mission (FPG) will be probably launched [42]. Therefore, the primary motivation of this article is to investigate the optimal FPG orbit configuration in combination with GFO, under the precondition that the FPG has the same payload accuracy as GRACE and comparable capability of recovering global static and temporal gravity fields to GRACE. With this in mind, the search space for FPG's key orbit elements (i.e., orbit altitude and inclination) is greatly reduced. Considering a five-year nominal mission lifetime for FPG, its orbit altitude is similar to GRACE. As such, the emphasis of FPG orbit adjustment will be put on the orbit inclination selection issue. The rest of this article is organized as follows. Section 2 presents the details of full-scale simulations for FPG orbit selection and performance evaluation. Gravity field results from the standalone FPG, with different inclinations, and jointly with GFO are demonstrated in Section 3. In Section 4, we explain the underlying mechanism of the presented results and further discuss the practical consideration of FPG orbit design. Conclusions are given in Section 5.
## 5 Comparison between Predictive Analysis and Test Results
### Relationship Between Compressive Strength and Surface Resistivity  
In Figure 18a,b, when a CLSM is considered at a fixed water-binder ratio (w/b = 1.5), a hyperbolic function model is used to predict the relationship between the surface resistivity and compressive strength. As shown in Figure 18, the relationship between the surface resistivity and compressive strength of different curing times \(t\) and desulfurization slag contents was compared. The analysis results show the all determinate coefficients R\({}^{2}\) greater than 0.7, and the model based on the hyperbolic model to predict the compressive strength can accurately estimate the compressive strength under different desulfurization slag contents and curing ages \(t\) based on the resistivity data. This model is superior to the linear and logarithmic function regressions, and the trend is shown in Figure 18c,d. The analysis results show that, when the ratio of water to binder is 1.5, the MAPE value is 10.67%. Thus, predicting the compressive strength model of a CLSM by using the surface resistivity set up in this study has a good predictive ability.  
Figure 17: (**a**) Comparison of the surface resistivity between the predicted analysis and test results (under normal scale coordinates). (**b**) Comparison of the surface resistivity between the predicted analysis and test results (after transfer to the semi-logarithm scale coordinates).
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
## 3 Results and Discussion
### Performance Comparison  
The performance of the LR-MPIBS designed in this paper is compared with the MPIBS from existing products and research institutions, as shown in Table 5. According to the table analysis, the endurance of LR-MPIBS exceeds 25 h, and the communication alarm cycle is 30 s. Compared to other MPIBS with alarm cycles exceeding 60 s, LR-MPIBS provides higher position transmission frequency while increasing endurance. It is worth noting that the battery capacity of LR-MPIBS is 1500 mAh, which is one-third of the battery capacity of other comparative systems. Therefore, LR-MPIBS can achieve smaller and more portable designs. LR-MPIBS can provide rescue teams with rescue information for drowning personnel within a 5 km range (with a success rate of over 85%). Key positioning data for the drifting trajectory of drowning personnel (with an accuracy of over 10 m) are also compressed into rescue information, which helps rescue teams improve the accuracy of predicting the drifting trajectory of drowning personnel.  
Overall, LR-MPIBS can achieve rapid alarm for personnel falling into the water, which helps to improve the efficiency of rescue operations for personnel falling into the water at sea.
## 3 CC-VC-UKF
### CC-VC-UKF Algorithm Derivation  
With the definition of the CC cost function with VC, the CC-VC-UKF algorithm can be derived. The specific derivation process of the algorithm is shown in Figure 1.  
The initial value of the nonlinear measurement system, the state equation, and the measurement equation are set, and then the prior state mean value \(a\) and one step prediction covariance matrix \(b\) are obtained by the unscented transformation (UT) according to the state variable and estimated deviation covariance matrix at time \(i-1\) in the time update. Similarly, in the measurement update, the prior mean and measurement cross covariance matrix can be obtained through UT transformation according to these variables. Finally, the Kalman gain and updated covariance matrix can be obtained through indeterminate-point iteration and updated state estimates [21]. The specific discussion on the establishment of the fixed point iterative equation is as follows:  
Figure 1: Schemes following the same format.  
After performing the corresponding mathematical operations through the variable substitution and matrix inversion principle, we can obtain the following:  
\[x_{i}=\tilde{x}_{i|i-1}+\widetilde{\text{K}}_{i}\Big{(}y_{i}-\hat{y}_{i|i-1} \Big{)} \tag{17}\]  
of which,  
\[\widetilde{\text{K}}_{i}=\widetilde{P}_{i|i-1}\overline{H}_{i}^{T}\Big{(} \overline{H}_{i}\widetilde{P}_{i|i-1}\overline{H}_{i}^{T}+\widetilde{R}_{i} \Big{)}^{-1} \tag{18}\]  
\[\widetilde{P}_{i|i-1}=S_{p,i|i-1}C_{x,i}^{-1}\Big{(}S_{p,i|i-1}\Big{)}^{T} \tag{19}\]  
\[\widetilde{R}_{i}=S_{r,i}C_{y,i}^{-1}S_{r,i}^{T} \tag{20}\]  
Finally, the derivation of the CC-VC-UKF algorithm can be completed by updating the state variance and the state estimated value through the relevant formula in Figure 1.
## 2 Literature Review
### Traffic Signal Timing Optimization  
Signal timing control schemes directly impact the operational effectiveness of intersections. In foreign research, many scholars [9; 10; 11] often construct mathematical function models targeting one or more parameters in the evaluation criteria for signal timing optimization. They utilize methods like genetic algorithms to solve the established models, followed by simulation. Results indicate that the proposed schemes can reduce intersection delay. [PERSON] et al. [12] formulated an optimization model targeting the minimization of average delay at signalized intersections. [PERSON] [13] and [PERSON] [14] proposed a multi-objective control model for single intersections based on fuzzy logic methods, selecting multiple performance indicators as optimization objectives. [PERSON] [15] introduced a multi-objective optimization and decision-making method to optimize signal timing at individual intersections. Additionally, [PERSON] et al. [16] applied data fusion technology to propose a new multi-objective signal control parameter optimization model for urban intersections. [PERSON] j et al. [17] proposed an optimization method for traffic intersection signal control based on an adaptive artificial fish swarm algorithm, which reduces average delay, parking frequency, and travel time. [PERSON] et al. [18] proposed a model-based demand response traffic control system that significantly reduces intersection delays and can adapt to different traffic demands. [PERSON] et al. [19] proposed an optimization control model for signal parameters on main traffic roads, which optimizes signal cycles, green light times, and phase offsets.  
There has been a significant amount of research in China, as well. Scholars [20; 21; 22] are constructing signal timing models for intersections, aiming to optimize multiple objectives by targeting one or more parameters in the intersection evaluation criteria and assigning different weights to these criteria based on varying traffic flow conditions. [PERSON] et al. [23] investigated signal control issues at urban arterial road intersections, focusing on multiple intersections and establishing signal control function models. The results of model solutions effectively reduced average delay per vehicle and improved intersection capacity. [PERSON] et al. [24] proposed a signal timing optimization model aimed at minimizing average delay per person at intersections based on total delay for motor vehicles, non-motor vehicles, and pedestrians, while considering the differences in two crossing modes for non-motor vehicles.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Implementation Specifics  
Library Integration Details: TenSEAL context management ensures that contexts are thread-safe but not serializable across processes. Kyber keys are generated using _PyPQC_kem_k_k_yber512_keypair()_. All FHE objects use TenSEAL's protobuf-based serialization format.  
Our implementation uses carefully chosen FHE parameters that balance security, precision, and computational efficiency:  
* Polynomial Degree: We use \(n=8192\), which provides 4096 SIMD slots while maintaining 128-bit security against known quantum attacks.
* Modulus Chain: We employ a modulus chain \([2^{31},2^{26},2^{26},2^{26}]\) with equal-sized middle primes. This choice is critical for TenSEAL's stability and ensures consistent noise growth across operations.
* Scale Parameter: We set the scale to \(2^{26}\) to match the size of the middle prime moduli. This alignment is essential for TenSEAL's rescaling operations and helps maintain precision throughout computations.
* Security Level: The parameter set achieves \(\lambda\geq 128\) bits of security, exceeding NIST's minimum requirements for quantum-resistant schemes.  
These parameter choices reflect TenSEAL's requirements and best practices, ensuring reliable operation while maintaining a sufficient noise budget for our image processing operations. The equal-sized middle primes and matching scale factor are particularly important for TenSEAL's internal optimizations and noise management strategies.  
Algorithm 7 illustrates our comprehensive error handling approach. We implement structured error handling to manage three critical failure modes in homomorphic operations: noise budget exhaustion (which triggers NOISE_BUDGET_EXHAUSTED when ciphertexts degrade beyond reliable computation thresholds), parameter mismatches between encryption contexts (resulting in PARAMETER_ERROR), and other cryptographic failures (yielding CRYPTOGRAPHIC_ERROR). This robust error handling system is essential for maintaining system reliability and security throughout all homomorphic operations.  
```
0:Ciphertext \(ct\), operation parameters params
0:Result or error code
1:try:
2:\(result\leftarrow\textsc{CkksOperation}(ct,params)\)
3:return\((result,\textsc{SUCCESS})\)
4:catch TenSEAL_Error \(e\):
5:if \"noise budget\" \(\in\textsc{String}(e)\) then
6:return\((\bot,\textsc{NOISE\_BUDGET\_EXHAUSTED})\)
7:else if \"parameter mismatch\" \(\in\textsc{String}(e)\) then
8:return\((\bot,\textsc{PARAMETER\_ERROR})\)
9:else
10:return\((\bot,\textsc{CRYPTOGRAPHIC\_ERROR})\)
11:endif
12:end try
```  
**Algorithm 7** Error Handling for CKKS Operations
# Fluoride Risk Assessment from Consumption of Different Foods Commercialized in a European Region
## 1 Introduction  
Fluorine is a highly electronegative chemical element necessary for the human organism. It is widely distributed in nature as part of minerals, as well as in water, where it is found in its ionized form [1]. The main sources of fluoride in the environment are natural, such as volcanic emissions and marine aerosols [2]. Volcanic regions or regions with geothermal activity present high levels of fluoride in soils and water [3, 4]. However, anthropogenic activities such as the use of pesticides, sewage, or industrial discharges cause an increase in fluoride concentration in the environment [5, 6].  
Fluoride is absorbed by the gastrointestinal route [7, 8]. Thus, diet is the main fluoride source. Plant foods, such as cereals, fruits, and vegetables, among others, absorb fluoride mainly from the soil and irrigated water. [PERSON] and [PERSON] [9] showed that after adding 22 mg/L of fluoride in an experimental tank to aquatic plants, the concentration of fluoride in these plants increased up to 35 times in 24 h. The absorption and accumulation capacity of fluoride in plant organisms depends on factors such as soil type and pH. The absorption process is favored in soils with acidic pH due to fluoride's ionic form [10]. Food fluoride levels vary considerably from country to country [11].  
Consumption of plant-based foods, such as cereals, fruits, and vegetables, can lead to high intakes of fluoride [12, 13]. Fluoride is an essential element that is necessary for enzymatic reactions in the body, as well as in the reduction of dental caries topically [14, 15, 16]. The European Food Safety Authority (EFSA) established recommended daily intake (DRI) values for fluoride according to age and sex [17]. The recommended daily intake is defined as the minimum required amount of a nutrient or essential element for the proper functioning of the organism [18]. However, a prolonged and high intakeof fluoride over time can trigger a series of toxic effects such as myopathy, neurological damage, dental fluorosis, or skeletal fluorosis [2,19]. Currently, Europe does not have an acceptable daily intake limit (ADI) for this ion. However, the Food and Nutrition Board of the American Organization Institute of Medicine established ADI values according to age and sex [20]. The acceptable daily intake is defined as the estimate of the total amount of a chemical substance or element contained in food and/or drinking water that can be ingested daily throughout the life of a person, without appreciable risk to health [18].
## 3 Shake Table Model Tests
### Results and Analysis
#### 3.4.4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Anti-Shock** \\ **Measures** \\ \end{tabular} } & \multicolumn{5}{c}{**Maximum Reduction Ratio (\%)**} \\ \cline{2-7}  & **Waveform** & \begin{tabular}{c} **Circumferential** \\ **Strain** \\ \end{tabular} & \begin{tabular}{c} **Longitudinal** \\ **Strain** \\ \end{tabular} & \begin{tabular}{c} **Earth Pressure** \\ **(**)** \\ \end{tabular} & \begin{tabular}{c} **X-Axis** \\ **Acceleration** \\ \end{tabular} & \begin{tabular}{c} **Y-Axis** \\ **Acceleration** \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Surrounding \\ rock grouting \\ \end{tabular} } & El-Centro wave & \(-54.8\%\) & \(-45\%\) & \(-40\%\) & \(-27.8\%\) & \(-56.6\%\) \\  & Taft wave & \(-60.8\%\) & \(-53.6\%\) & \(-34\%\) & \(-27.8\%\) & \(-27.5\%\) \\  & Artificial wave & \(-53.3\%\) & \(-47\%\) & \(-41.4\%\) & \(-28\%\) & \(-43.8\%\) \\  & Average & \(-56.3\%\) & \(-48.5\%\) & \(-38.5\%\) & \(-27.9\%\) & \(-42.6\%\) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Seismic joints \\ \end{tabular} } & El-Centro wave & \(-47.9\%\) & \(-30.64\%\) & \(-24.3\%\) & \(-27.8\%\) & \(-9.4\%\) \\  & Taft wave & \(-38\%\) & \(-36.2\%\) & \(-9.3\%\) & \(-11.4\%\) & \(-7.7\%\) \\  & Artificial wave & \(-34.7\%\) & \(-30.5\%\) & \(-15.2\%\) & \(-17.3\%\) & \(-15.8\%\) \\  & Average & \(-40.2\%\) & \(-32.4\%\) & \(-16.3\%\) & \(-18.8\%\) & \(-11.0\%\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Shock \\ absorbing layer \\ \end{tabular} } & El-Centro wave & \(-30.1\%\) & \(-24.2\%\) & \(-16.2\%\) & \(-12.5\%\) & \(-9.4\%\) \\  & Taft wave & \(-38\%\) & \(-36.2\) & \(-20.9\%\) & \(-12.5\%\) & \(-11.5\%\) \\ \cline{1-1}  & Artificial wave & \(-34.7\%\) & \(-28.6\) & \(-26.1\%\) & \(-12.5\%\) & \(-15.8\%\) \\ \cline{1-1}  & Average & \(-34.3\%\) & \(-29.7\%\) & \(-21.1\%\) & \(-12.5\%\) & \(-12.2\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of anti-vibration effect. \(-21.1\%\), \(-12.5\%\), and \(-12.2\%\), respectively. These results indicated that the seismic effects of seismic joints and shock absorption layers were similar, with the grouting reinforcement of surrounding rock exhibiting more significant effects than the former two.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis of the acquired data was done using InStat Statistical Program (Version 3.05, GraphPad Software, San Diego, CA, USA). Descriptive statistics of the variables (push-out force, histomorphometric BA% and BIC%) were expressed as mean and standard deviation (SD). First, an unpaired Student's t-test was conducted to evaluate differences in the mean values between the saline groups in OVX compared to SHAM rats. Second, one-way analysis of variance (ANOVA) with Dunnett post-hoc test was used for comparison of the variables in each treatment group with saline group as control. Third, a non-parametric Mann-Whitney test was used for statistical comparison between OVX and SHAM rats of the pooled BIC% and BA% data of the drug-receiving groups. The level of statistical significance was set at \(p<0.05\).
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 4 System Design
### LoRa Mesh Protocol Design
#### 4.2.2 Route Discovery and Maintenance  
As mentioned before, our LoRa mesh protocol is a kind of on-demand routing protocol, when there is no load in the network, all of the nodes keep silent. When there is a load, the network starts the update and maintenance process. As shown in Figure 6, when starting to transmit a message, the node will look up the route table to find a valid route to its destination. If the route exists, the packet will be transmitted following the path, if not, the node will start finding a route by sending a broadcast lookup packet. Each node that received the broadcast packet without a valid route to the destination will relay this broadcast. If received by the destination node or any node with a valid route to the destination, they will reply to the source node according to the path of the broadcast packet. After sending the lookup packet, the source node will add it to the waiting list for timeout re-transmission. Once the lookup packet reply is received, the suspended message packet will be transmitted to the destination through the discovered route.  
Figure 5: The packet structure.  
Figure 6: Flowchart of route lookup.  
When the network is active, each node can update its route table by capturing the packets of the surrounding nodes. Each route table entry has a valid time field, which will be refreshed by a new packet from the route path, and the RSSI field can also be updated by any captured packet. If the entry's valid time ends, the entry will be set to be invalid. If one route entry fails on several transmission tasks, it will also be set to be invalid.