esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applying the Methodology to the Engineering Case
### Engineering Background
#### 3.1.2 Adjacent Excavation  
As shown in Figure 3, the excavation is laterally adjacent to the tunnels, with a minimum horizontal distance of 40 m, which is less than the critical distance of 50 m for risk management, as specified by DB33/T1139-2017 [25]. The total area of the excavation was 11,000 m\({}^{2}\), and the excavation depth was 10 m. To support the excavation, retained piles (\(\updownarrow\)1000 mm at 1200 mm spacing) and two levels of horizontal internal struts made of reinforced concrete were employed. The cross-sectional dimensions of the struts varied across different zones, ranging from 700 mm to 900 mm in width and depth. As illustrated in Figure 4, the excavation was carried out in three vertical steps, to depths of 2.9 m, 7.6 m, and 10 m. Correspondingly, two levels of reinforced concrete struts were installed at depths of 2.5 m and 7.5 m. It took 40 days to excavate down to the first level of strut, 36 days to reach the second level of strut, and 55 days to complete the excavation to the base of the foundation pit. Additionally, to enhance the safety of the retaining structure, minimize the internal forces in the retaining piles and the deformation of the retaining structure, reduce costs, and make optimal use of the surrounding site, a suspended support system was adopted. The capping beam was set 1.0 m below the natural ground level. Monitoring points were arranged around the perimeter of the excavation site to analyze the impact of excavation on the surrounding environment. Point C1 was located on the retaining pile, while point C2 was positioned 15 m outside the excavation.
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 4 Discussion
### Limitations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**MLR**} & \multicolumn{6}{c}{**GWR**} \\ \cline{3-8}  & & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multicolumn{2}{c}{**Lwr**} & \multirow{2}{*}{**Medium**} & \multicolumn{2}{c}{**Upr**} & \multirow{2}{*}{**Mean**} \\  & & & & & **Quartile** & & & **Quartile** & \\ \hline EDS & \(-\)0.102 ** & \(-\)0.330 & 0.517 & \(-\)0.218 & \(-\)0.138 & \(-\)0.021 & \(-\)0.109 \\ AGRI & \(-\)0.135 ** & \(-\)0.484 & 0.213 & \(-\)0.147 & \(-\)0.100 & \(-\)0.075 & \(-\)0.114 \\ INDS & 0.034 & \(-\)0.143 & 0.318 & \(-\)0.025 & 0.030 & 0.062 & 0.017 \\ PDS & 0.046 & \(-\)2.720 & 4.899 & \(-\)0.109 & 0.071 & 0.169 & \(-\)0.072 \\ URB & 0.039 & \(-\)0.481 & 0.321 & \(-\)0.045 & \(-\)0.018 & 0.019 & \(-\)0.015 \\ PDUC & 0.030 & \(-\)1.004 & 0.351 & \(-\)0.052 & \(-\)0.012 & 0.069 & 0.002 \\ LIVE & \(-\)0.028 & \(-\)0.736 & 0.301 & 0.000 & 0.088 & 0.145 & 0.071 \\ PROT & 0.006 & \(-\)0.094 & 0.336 & \(-\)0.046 & 0.027 & 0.115 & 0.046 \\ TEMP & 0.607 *** & \(-\)0.682 & 0.944 & 0.296 & 0.457 & 0.628 & 0.440 \\ PREC & 0.376 *** & \(-\)0.095 & 0.624 & 0.192 & 0.398 & 0.506 & 0.345 \\ ELEV & 0.612 *** & \(-\)1.604 & 1.799 & 0.343 & 0.504 & 0.951 & 0.658 \\ SLOP & 0.356 *** & \(-\)0.107 & 0.315 & 0.050 & 0.158 & 0.236 & 0.136 \\ SOIL & 0.032 & \(-\)0.270 & 0.153 & \(-\)0.027 & 0.048 & 0.104 & 0.027 \\ AIC & \(-\)766.555 & & & & \(-\)811.911 & & \\ R\({}_{\rm adj}^{2}\) & 0.488 & & & & 0.698 & & \\ \hline \hline \end{tabular} Note: **, *, and * indicate significance at 0.01 and 0.05 levels, respectively (two-tailed). Refer to Table 2 for definitions of acronyms.  
\end{table} 
Table 5: Results of robustness test. not spatially flip the natural endowment. This is true at least in the Yellow River basin of China. It is regrettable that this study does not capture changes in forest ecological functions over time, and thus cannot clarify whether the human activities facilitate or inhibit forest ecological function in the end. For the time being, we can only suggest that local governments encourage those human activities that can improve local forest ecological functions. We need to supplement relevant studies on time series in the future. Further refinements for future research can also be made from the following perspectives. First, the indicator selection for natural factors can be more detailed. In addition to temperature, precipitation, and elevation, the soil type, slope direction, and slope gradient may also affect forest ecological function. Second, the portrayal of forest ecological function in this paper is rather general and does not focus on certain ecological service functions, such as carbon sequestration and water conservation, even if these are hot topics in current research. We will also expand the related contents in our future research.
## 3 Data Description
### Characteristics of the Experimental Data  
The raw GPR data were processed and converted to decibels (dB). The complete data set was achieved in a matrix with 288 rows and five columns. Columns 1 to 5 stored information on five properties, namely temperature (T), relative humidity (RH), chloride contamination level (CL), rebar surface corrosion condition (CS), and amplitude of the signal from rebar (A), respectively. The range of these parameters is shown in Table 2. Note that the rebar corrosion status data were converted into a readable format for the DBN model (i.e., numerical number) with 0 represented for the non-corroded rebar and 1 designated for the corroded rebar.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **Relative Humidity (\%)** & **Chloride Level (\%)** & **Corrosion Status** \\ \hline
5 & 55 & 0.00 & 1 \\
10 & 70 & 0.02 & 0 \\
15 & 85 & 0.04 & \\
20 & & 0.06 & \\
25 & & 0.08 & \\
30 & & 0.10 & \\
35 & & & \\
40 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters related to GPR data.  
Figure 3: Restricted Boltzmann machine architecture.  
Before employing data for training and testing the DBN model, the data were normalized within the range [0, 1] using Equation (3)  
\[X_{norm}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{3}\]  
where \(X\) and \(X_{norm}\) represent the original and normalized values, respectively. \(X_{max}\) and \(X_{min}\) indicate the largest and smallest values of \(X\), respectively.  
Figure 4 presents the histogram of the output. The detailed relationship between the output with each input parameter in the entire experimental data is shown in Figure 5.
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 1 Introduction
### Knowledge Gap  
To assess the impacts of tropical cyclones to support management approaches, typical traditional field-survey approaches include methods such as questionnaires to affected populations, in-situ water level measurements, and surveys [8]. However, these techniques prove to be very expensive and time-consuming to fully grasp the extent of landscape and property damage when, for example, areas of interest are flooded, and communication systems are damaged [20]. Current high-resolution remote sensing (10-30 m) has the potential to be used in effective flood responses and facilitate flood prevention and/or mitigation strategies at a very fine scale.  
Many studies have investigated the impacts of tropical cyclones on natural and coastal habitats using remote sensing, such as in the USA [2] or India and Bangladesh [21], but very few have offered insights into tropical cyclone impacts in the Mozambique Channel.  
Studying the impacts of tropical cyclones and storms in the Mozambique Channel is therefore of the utmost importance in countries underrepresented in scientific research and applications, but overrepresented in vulnerability and exposure to climate change-related risks [8].
# Potential Study of Solar Thermal Cooling in Sub-Mediterranean Climate
## 4 Economic Feasibility
### Cost and Tariffs  
In order to determine the economic feasibility of the absorption chiller system, an estimation of the various components of the system was performed. A spreadsheet was tailored specifically to the absorption chiller system to perform the economic mathematical analysis. Various parameters were taken into account through the economic calculations. The system cost is defined through the cost of the absorption chiller according to the cooling capacity per watt, and the cost of the auxiliary component in the local market. Figure 8 shows the component share of the total cost.  
As shown, the solar field has the highest cost of the solar cooling system with a share of 42%. The solar field at GJU was overdesigned regarding the supply of hot water to the generation of the weak H\({}_{2}\)O-LiBr solution, since the solar collector is also used for heating domestic hot water for Building C. In addition, the solar field was overdesigned to ensure that the chiller was working with a COP of 0.79 regardless of the outlet conditions. The installation and the operational cost are considerably lower as a result of low local wages in Jordan compared to developing countries.  
Table 2 shows the cost of the system components. The total cost of the system was 243,000 JD (Jordanian Dinar). The absorption chiller has a cost of 546.8 JD/kW and a total of 87,480 JD. The solar field total cost was equal to 102,060 with the highest cost share of the system. The heat rejection in the condenser and the absorber has a cost share of 19,440 JD and the auxiliaries have a cost of 34,020 JD.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 2 Detailed Analysis of GDPR Articles Challenging for LLMs
### Article 20--Right to Data Portability  
Article 20 [10] of the GDPR grants individuals the right to receive the personal data they have provided to a controller in a structured, commonly used, and machine-readable format, as well as the right to transmit that data to another controller [10]. However, this right specifically applies to data directly provided by the individual--such as account information or feedback--and not necessarily to data collected indirectly through methods like web scraping or from publicly available sources [2; 73].  
In practice, distinguishing between data directly provided by a data subject and data gathered from other sources is particularly challenging with LLMs due to the massive and diverse nature of the training datasets. Additionally, LLMs transform personal data into high-dimensional abstract representations, making it technically complex to extract the original data in a structured and portable format [25; 37]. The absence of standardized formats further complicates interoperability, while the sheer scale of data processed by LLMs creates additional resource challenges [31].  
Organizations can enhance compliance by maintaining separate records of personal data provided directly by data subjects and offering original submission data in structured formats (e.g., JSON or XML). Comprehensive documentation of data sources and processing methods can also aid in identifying personal data relevant to portability requests. Collaborative standardization efforts with industry bodies and regulatory authorities may eventually help establish universal protocols for data portability. It is important to recognize, however, that the right to data portability may be limited to data explicitly provided by individuals, and the practical extraction of data from LLMs remains a significant technical challenge.  
**Legal Implications and Considerations:** Failure to comply with Article 20 can result in legal action and significant fines, as it undermines individuals' control over their personal data [10]. Organizations must, therefore, implement robust data governance practices that ensure the accurate and secure transfer of personal data. In doing so, they should assess the extent to which Article 20 applies to their operations, engage legal counsel to interpret these rights in their specific context, and explore technological solutions that balance data portability with privacy and security.
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 3 Related Work
### Detecting a Predefined Solar Spot Group with a Pre-Trained Convolutional Neural Network  
[PERSON] et al. [26] detected sunspots doing some pre-processing and using a CNN developed for sunspot and sunspot group characterization and classification. The dataset consists of 853 images, generated from 72 images from the HMI, in Intensitygram Flat format to allow easier manipulation of the image, which facilitates the pre-processing mentioned above. Regarding the dataset partition, 600 out of the 853 figures were used for training, while the other 253 were used for the testing phase.  
Before being fed to the CNN, the images were pre-processed. The pre-processing consisted of the following steps:  
1. **Image adjust (contrast stretching):** method used to increase the contrast of the output image;
2. **RGB to gray:** converts the RGB images into gray level images (uni-dimensional scale);
3. **Image to black and white:** considering a threshold, the image is converted to a binary matrix, where the values above the threshold are converted to white, while the pixels below the threshold are converted to black;
4. **Image open (growth):** this method performs an erosion followed by a dilation on the gray scale or binary image, using the same structuring element for both operations;
5. **Image complement:** computes the complement of the image, inverting black and white colors;
6. **Region properties:** used to measure a set of priorities for each connected object in the binary image;
7. **Image crop:** obtains a region of an image in determined coordinates, within a box of certain dimensions;
8. **Image write:** writes an image with a name specified by the user, in the determined format, in the current folder.  
The authors present an example of the pre-processing stage, showing the resulting image of each step mentioned above, and including the final result with the detection of the sunspots.
## 1 Introduction
In contrast, the Ti-Nb-Sn stem attains a continuous and predictable Young's modulus gradient solely by localized heat treatment of a homogeneous Ti-Nb-Sn alloy, maintaining consistent material composition while modulating mechanical properties. This strategy ensures both biomechanical optimization and clinical safety, offering a practical and scalable solution for reducing stress shielding in cementless total hip arthroplasty. Furthermore, to our knowledge, the Ti-Nb-Sn stem represents one of the first modulus-graded stems to be evaluated through mid-term clinical radiographic follow-up in human subjects [21; 22]. However, reducing the stiffness of the femoral stem presents a trade-off with maintaining the mechanical strength necessary to support loads acting on the neck region [23]. Excessive reduction in stiffness can compromise structural integrity, making it difficult to implement. To address this issue, a novel stem has been developed that achieves both high strength at the neck and reduced stiffness distally, through a heat treatment process that creates a gradient in the Young's modulus along the stem. The Ti-Nb-Sn stem, designed with high strength in the proximal region and a low Young's modulus distally, is considered optimal for minimizing both fatigue-related failure and stress shielding. In Japan, the Ti-Nb-Sn stem has progressed through clinical trials and has been adopted for clinical application. Its beneficial effect in reducing stress shielding has been reported at both the one-year and three-year postoperative evaluations [21, 22]. This study investigates the biomechanical performance of a Ti-Nb-Sn alloy stem with a validated Young's modulus gradient applied across its geometry. By constructing a highly precise gradient finite element model (FEM) and performing FEM analysis using patient-specific bone density data, we aim to assess the in vivo functional performance of the Ti-Nb-Sn stem and its clinical potential in suppressing stress shielding. Furthermore, we also evaluate the actual occurrence of stress shielding based on follow-up results from clinical cases in which the Ti-Nb-Sn stem was implanted.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 5 Experimental Results  
To evaluate the effectiveness and performance of the proposed algorithm, we used existing laboratory equipment and a self-constructed experimental platform. Representative real-world scenarios on campus were selected for mapping experiments, which demonstrated the algorithm's validity.
## 3 Conclusions
(6) International Collaboration: Given the global nature of T/O issues, international collaboration and knowledge sharing are essential to develop comprehensive management strategies. This could involve sharing best practices, conducting joint research projects, and developing standardized monitoring and assessment protocols.
## 3 Test Equipment and Test Design
### Similarity Relationship and Similar Materials  
The prototype secondary lining of the shallow buried loess tunnel was set to C25 concrete structure. The mechanical parameters of lining are based on actual engineering. The elastic modulus of 28.5 GPa, the ultimate compressive strength of 19 MPa, and the ultimate tensile strength of 2 MPa. The prototype tunnel is two-lane, of which cross-section is a three-centered circle, with a lining thickness of 0.40 m, a span of 11.80 m, and a height of 9.69 m.  
In a scaled model test, the results reliability firstly depends on the similarity of geometric dimensions, and then on the similarity of similar materials. For the physical model test of tunnel lining, strict control of the geometric similarity is a necessary condition to reasonably and accurately reflect the characteristics of the lining prototype. The greater the geometric similarity ratio of the lining model, the smaller the difference in the bearing capacity between the lining model and the lining prototype [28; 29]. Additionally, the width of the cracks changes with the size of the model, and the number of cracks also reduces as the size decreases. Existing experiments have pointed out that if the material properties meet the similar relationship, the influence of the model size on the model cracks can be ignored [30]. In this paper, the geometric similarity ratio \(C_{L}=10\) is determined according to the model making ability and test system. Based on the geometric similarity ratio \(C_{L}=10\), and the unit weight similarity ratio \(C_{\gamma}=1\), the elastic modulus and strength of the material are controlled to be similar. The other similarity is derived from the dimensional analysis method, as shown in Table 4.  
In scaled model test of tunnel lining, it is very difficult for the similar material of lining to simultaneously satisfy the similarity relationships of elastic modulus, strength, and unit weight. In this paper, the similarity of the lining model materials only considers the similarity of elastic modulus and strength and does not consider the similarity of the unit weight. The gypsum and concrete are both brittle materials with relatively close properties. Their tensile strength is much less than the compressive strength, and their Poisson's ratio is about 0.2. In addition, the Gypsum is easy to form model, and often recognized as a kind of good concrete-model material and widely used to conduct some laboratory experiments, as reported in previous studies [31; 32; 33]. By conducting a series of lab mixing proportion tests, it was found that when the mass ratio of gypsum to water is 1:1, the elastic modulus of the gypsum specimen is 2.73 GPa, the compressive strength is 2.02 MPa, and the tensile strength is 0.22 MPa. Therefore, the similarity ratio of elastic modulus is \(C_{E}=28.5/2.73=10.4\approx 10\), and the similarity ratio of strength is \(C_{R}=(19.0/2.02)/(2.0/0.22)=9.4/9.1\approx 1\), meeting the requirements of similar accuracy and scaling laws.
## 3 Reducing Transmission Overhead without Compromising Throughput
### LARep Design
#### 3.2.4 Phase 4: Message Replication Phases
This allows for higher throughput, while controlling excessive transmission overhead. Notice that neither phase gives a criteria for determining more suitable relays, as that is the responsibility of the forwarding utility in use. Rather, the replication phases proposed here try to adjust the number of copies forwarded per message to a more optimal value based on current network conditions. Hence, LARep, our proposed replication technique, is flexible enough to be incorporated into existing encounter-based routing protocols.
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 3 Experimental Results
### Comparison of Spectral Efficiency and Overhead  
The proposed scheduling technique is compared with the related methods, namely QuAS and PSS, according to the performance metrics of spectral efficiency and overhead. The proposed scheduling algorithm has been implemented in eNodeB to enhance the spectral efficiency with guaranteed quality of service. Figure 9 demonstrates that the proposed technique has better performance regarding the amount of spectral efficiency compared to the PSS and QuAS techniques. The result of the simulation shows that, when the number of users is small (10 as in simulation), there were a small difference between these algorithms, but when the number of users is increasing, the proposed method shows better results than the other. The quality of the transmission through the channel for the user depends on the transmission range and the obstacles within the base station and the user. The spectral efficiency is computed to estimate the throughput of every user according to the SINR value in eNodeB and the quality of the signal.  
In the simulation, the overhead while eNodeB transmits packets using the proposed LTE downlink scheduling technique is also observed. It is directly linked with the channel bandwidth for providing efficient transmission in LTE. The small amount of overhead demonstrates a better quality of service. Figure 10 illustrates the proposed scheduling technique has minimized overhead compared to the related techniques.  
Figure 8: Fairness Index versus different UEs.