esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Anonymization for Hiding Personal Tendency in Set-Valued Database Publication
## 4 Proposed Method
### Destination Record Selection for Partial Swapping  
As in Algorithm 2, prior to determining destination record \(t_{k}^{\prime}\) to swap the items from \(t_{k}\), we initially collect all the \(t_{k}\in\mathcal{D}\) which satisfies condition \(WR_{t_{k},g_{j}}>MTV_{t_{k}}\) as the candidate \(ct_{k}\) of destination record and collect them in a bucket \(Ct\). The following step is to compute the Jaccard similarity coefficient of the \(t_{k}\) with each \(ct_{k}\in Ct\) and obtain the minimum \(jc\), \(j_{min}\) of \(t_{k}\), using the following equation.  
\[jc_{min}(t_{k})=\min_{ct_{k}\in Ct}\frac{(t_{k}\cap ct_{k})}{(t_{k}\cup ct_{k})}. \tag{6}\]  
Swapping is performed for such a \(ct_{k}\) denoted by \(t_{k}^{\prime}\). Algorithm 2 outputs a set \(P\) of pairs \((t_{k},t_{k}^{\prime})\) for all \(t_{k}\in\mathcal{D}\), i.e., \(P=\{(t_{k},t_{k}^{\prime})|t_{k}\in\mathcal{D}\}\). There are some requisites to determine \(ct_{k}\in Ct\) that can be the best destination record \(t_{k}^{\prime}\) to swap \(i\in g_{j}\) of \(t_{k}\). Initially, we check if for each \(ct_{k}\in Ct\) the \(ct_{k}\) has the same tendency category \(g\) with \(t_{k}\). Then, we remove such a \(ct_{k}\) from \(Ct\). This step guarantees that the _true swap_ is achieved. The next requisite is that \(ct_{k}\) should result in minimum \(jc\) value. The last, \(ct_{k}\) should not contain the same items as in \(g_{j}\in t_{k}\) to avoid collision which leads to item loss in \(ct_{k}^{\prime}\) that further it impacts to data utility of an anonymized dataset. Once the \(ct_{k}\) is determined as \(t_{k}^{\prime}\), the next phase is selecting \(g_{j}\in t_{k}^{\prime}\) which has the highest weight ratio based on Equation (4) and swap the \(i\in g_{j}\) of the \(t_{k}^{\prime}\) to \(t_{k}\) using Algorithm 3.  
```
INPUT : \(\mathcal{D}\); OUTPUT : \(P\) // P is a set of pairs (\(t_{k},t_{k}^{\prime}\))  Read \(\mathcal{D}\);  Set \(C_{t}\) as an empty set; forall\(t_{k}\in\mathcal{D}\)do  count \(MTV_{t_{k}}\) of each \(t_{k}\); foreach\(g_{j}\in t_{k}\)do  count \(WR_{t_{k},g_{j}}\) ;  end if if\(WR_{t_{k},g_{j}}\) of \(t_{k}>MTV_{t_{k}}\)then \(ct_{k}\gets t_{k}\);  append \(ct_{k}\) to \(Ct\);  end if foreach\(ct_{k}\in Ct\)do if\(ct_{k}\) and \(t_{k}\) have the same tendencythen  remove \(ct_{k}\) from \(Ct\);  calculate \(jc(t_{k},ct_{k})\);  end if  end for  select \(ct_{k}\) which has the minimum \(jc\) out of \(C_{t}\); \(t_{k}^{\prime}\gets ct_{k}\);  end for  end for
```  
**Algorithm 2**Procedure of destination record selection for partial swapping.
## 3 Signal Analysis Method
### Monaural Separation  
At this point, it is evident that the signal has both left and right channels, which complicates subsequent signal processing, leading to increased costs in data transmission and hardware, as well as issues such as channel imbalance due to inconsistent data [32; 33]. However, the left and right channel signals exhibit a correlation and possess sufficient passive acoustic characteristics. Figure 6 presents the signals after monaural separation, demonstrating that both monaural signals exhibit similar trends and characteristics. Therefore, analyzing one of the monaural signals suffices for research purposes. Additionally, it can be observed that the left channel contains richer temporal information, as evidenced by more pronounced changes in relative intensity. Thus, the left channel was selected for further analysis.  
Figure 5: (a) original signal and (b) signal after ADC.  
Figure 6: (a) Left channel after ADC and (b) right channel after ADC.
## 3 Approaches
### MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
In order to overcome the problem of class imbalance, data augmentation techniques, such as translation, scaling and rotation were used. Pre-processing: Initially, the images were parted into two, obtaining two images containing one femur each. All images were resized from \(2500\times 2048\) pixels (original size) to dimensions compatible to the input size of the deep learning model used. For each image, the coordinates of a squared bounding box (marked by specialists) surrounding the femur head area were stored. Methods: At first, the task was addresses in a two-class approach between fracture and no-fracture, secondly between no-fracture, A type and B type and thirdly between no-fracture and each subclass of A and B fractures. The authors did not consider class C fractures defined in the AO classification. At the beginning, the classification task has been pursued using full X-ray half-images, down-sampled to \(224\times 224\) pixel, as the network used was ResNet50. CAM were also used to confirm the network's learning. Next, they investigated the influence of localizing a relevant ROI prior to the classification. The cropped image containing only the ROI was then used as input to the same network instead of the full image. To make this process automated, the authors leveraged the bounding box annotations manually provided by our experts to formulate the problem as a regression, in which the goal was to find the ROI in the radiograph. For this purpose, an auxiliary network based on AlexNet [29] was trained to predict the extreme points of the bounding box. For this architecture, full X-ray images were down-sampled to \(227\times 227\) pixel. Results: Using full images, accuracies for each type of classification were: 0.83 for binary classification, 0.89 for three-class classification, and 0.72 for the fine-grained classification. Using cropped images, accuracy for binary classification improved to 0.94 and to 0.91 for three-class classification. Improvements for fine-grained classification with cropped images are not stated in the paper. Results are slightly lower using the automated-cropping method. Conclusion: Using AO foundation classification is a perfect approach to define a generalized method. In addition, as we already notice in previous papers, it is shown that using ROI is useful for improving classification accuracy. The main obstacle broached by the authors is the large imbalance in the frequency of appearance of the classes in the fine-grained classification. A method to deal with unbalanced data must be defined.
## 3 IoT Standards and Protocols for Precision Agriculture
### IoT Protocols in Precision Agriculture (RQ2)  
In order to select the appropriate standard in precision agriculture, it is first necessary to understand the most widely used communication protocols for transmitting information in the IoT ecosystem [58]. Communication protocols formally define the formats and rules that must be followed for the transmission of digital messages. Various protocols exist, some of which have been specifically designed for environments with limited available resources, such as the sensors we have analysed. These protocols enable the generation of a consistent and understandable data flow from most network-connected devices. The most commonly used protocols in precision agriculture are described below.
## 3 Definition of Indicators
### Parameters for the Construction of Indicators  
Based on the diagnosis carried out, the most relevant parameters identified for creating the indicators are the following: absorbed water, received water, rooftop water, unused water, needed water, and available water. These parameters consider the demographic, urban, and hydrological factors relevant to the study.  
**Absorbed water (_aw\({}_{i}\)_)**: It is the vector representing the volume of water retained in the subsoil in m\({}^{3}\). It considers the absorption of green areas and permeable areas. It is the result of soil type (\(st_{i}\)) in m\({}^{2}\) by annual rainfall (_ar_) in \(m\) by the coefficient of absorption per soil type (_ast\({}_{i}\)_). For \(1\leq i\leq k\), \(k\) is the number of different soil types (Equation (3)).  
\[aw_{i}=(st_{i}*ar*ast_{i}) \tag{3}\]  
**Received water (_rcv_)**: It is the volume of received water in m\({}^{3}\) corresponding to annual rainfall (_ar_) per surface area of the study (_sa_) in m\({}^{2}\) (Equation (4)).  
\[rcw=ar*sa \tag{4}\]  
**Rooftop water (_rw\({}_{i}\)_)**: It is a vector corresponding to the water volume in m\({}^{3}\) accumulated by the different types of rooftops. It is defined by the result of the total area vector of each type of rooftop surface (\(rs_{i}\)) (dwelling, commercial, and services areas) in m\({}^{2}\) by annual rainfall (\(ar\)); it is affected by the runoff coefficient corresponding to 0.85 for concrete. For \(1\leq i\leq n\), where \(n\) is the number of different types of building rooftops (Equation (5)).  
\[rw_{i}=(rs_{i}*ar*0.85) \tag{5}\]  
**Unused water (\(uw\)):** It is the vector of the volume of water lost in sidewalks, roads, and common areas in m\({}^{3}\). It is obtained by the different soil types \(st_{i}\) by the annual rainfall (\(ar\)), affected by the runoff coefficient per surface type (\(rc_{i}\)), for \(1\leq i\leq k\) (Equation (6)).  
\[uw_{i}=(st_{i}*ar*rc_{i}) \tag{6}\]  
**Needed water (\(tw_{i}\))**: The vector is specified by the needed water consumption per land use (\(clu_{i}\)) in m\({}^{3}\) multiplied (element by element) by the number of users per land use (\(ulu_{i}\)), where \(1\leq i\leq m\) and \(m\) is the number of different land uses. The specific water consumption varies depending on the land use, such as domestic, commercial, or services (Equation (7)).  
\[nw_{i}=(clu_{i}*ulu_{i}) \tag{7}\]  
**Available water (\(vw\))**: It is the water volume resulting from rooftop water (\(rw_{i}\)) and the real water supply by land use (\(rws_{i}\)) in m\({}^{3}\) (Equation (8)).  
\[vw=\sum_{i=1}^{m}rw_{i}+rws_{i} \tag{8}\]  
The real water supply (\(rws_{i}\)) is the result of the water supplied by land use (\(lu_{i}\)) in m\({}^{3}\) by the number of users (\(ulu_{i}\)), considering the effect of a leakage loss factor (\(llf\)) (Equation (9)).  
\[rws_{i}=slu_{i}*ulu_{i}*llf \tag{9}\]
## 3 Results
### Competitive Dye Displacement Assay  
Competitive displacement assay was performed to study the mechanism of interactions between LEAPs and CT-DNA. EB is a well-known fluorophore with a planar structure, which binds to DNA in an intercalative fashion. EB shows weak fluorescence in aqueous solution, but its fluorescence intensity is enhanced drastically upon intercalation within DNA base pairs. In this assay, any molecule that binds to DNA in a fashion similar to EB is expected to replace EB from the DNA helix in a competitive manner, which results in a decrease in the fluorescence intensity of the EB-DNA complex. With continuous addition of LEAPs to the EB-CT-DNA system, a significant decrease in the fluorescence intensity was observed (Figure S1 in Supplementary Data), likely due to the displacement of EB from the EB-CT-DNA complex by the LEAPs (nLEAP-1, nLEAP-2, eLEAP-1, and eLEAP-2). This suggested that LEAPs replace EB from the DNA helix, thereby indicating that LEAPs bind to DNA in an intercalative mode.  
The binding affinity of LEAPs with CT-DNA was calculated in terms of K\({}_{\text{d}}\), using the Stern-Volmer plot, as shown in Figure S1 (Supplementary Data), with the help of Equation (1). The K\({}_{\text{d}}\) values of nLEAP-1, nLEAP-2, eLEAP-1, and eLEAP-2 were found to be 4.1, 6.44, 6.08, and 6.21 uM, respectively. The K\({}_{\text{d}}\) values of LEAPs were lower than that of the classical intercalator EB (K\({}_{\text{d}}\) = 7 uM), as calculated previously by [PERSON] et al. (2018), with nLEAP-1 showing the lowest K\({}_{\text{d}}\) value. The high K\({}_{\text{d}}\) value of EB compared with nLEAP-1, nLEAP-2, eLEAP-1, and eLEAP-2 indicates that EB has a lower CT-DNA binding affinity than these LEAPs. These results indicate that nLEAP-1, nLEAP-2, eLEAP-1, and eLEAP-2 interact with CT-DNA double-helix, similar to EB, but with a higher CT-DNA binding affinity.
## 3 Results  
### Laboratory Results  
The present article focuses solely on the methodology employed for sample 1, which yielded the most significant results.  
Sample 1 is a coarse sandy soil that was collected from the foot of a slope at the site of the railway duplication. It was visually identified as LA (lateritic sand) according to the MCT classification, and its characteristics are presented in Table 1, as reported by [PERSON] [12].  
The retention curve of sample 1 was obtained from the test carried out in the HYPROP equipment and was adjusted by the bimodal Fredlung-Xing model [29] using the HYPROP-FIT program, which was found to provide the best fit among the five available models (Figure 4).  
By including the complementary points from the WP4-C test in the HYPROP-FIT program, a new curve with the appearance shown in (Figure 5) was obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Retention Curve Points**} & \multicolumn{2}{c}{**Conductivity Curve Points**} \\ \hline
**Pressure (cm)** & **Volumetric Humidity (m\({}^{3}\)/m\({}^{3}\))** & **Pressure (cm)** & **Hydraulic Conductivity (cm/h)** \\ \hline \(0\) & \(0.37\) & \(0\) & \(0.0074\) \\ \(-5\) & \(0.36\) & \(-5\) & \(0.0077\) \\ \(-11\) & \(0.36\) & \(-11\) & \(0.0083\) \\ \(-25\) & \(0.35\) & \(-41\) & \(0.0081\) \\ \(-49\) & \(0.33\) & \(-63\) & \(0.0045\) \\ \(-67\) & \(0.31\) & \(-72\) & \(0.0051\) \\ \(-76\) & \(0.29\) & \(-89\) & \(0.0016\) \\ \(-2570\) & \(0.12\) & \(-128\) & \(0.0004\) \\ \(-3019\) & \(0.15\) & \(-264\) & \(0.0001\) \\ \(-645\) & \(0.06\) & \(-642\) & \(0.0000\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data from the Multilinear constitutive model of sample 4 (NA?).  
Figure 4: Sample 1 retention curve obtained in HYPROP (Menezes [16]).  
The addition of supplementary points did not substantially alter the appearance of the retention curve for sample 1. The sample exhibited the typical curve shape of sandy soils, with a low air entry value followed by a sharp decline.  
However, despite being identified as lateritic sand, the retention curve of the sample does not exhibit the typical bimodal shape of lateritic soils. It only shows a single air entry value, around 1 pF (10 cm of water column), after which the soil behaves like an unsaturated soil, with moisture content decreasing as the suction increases. At the optimum moisture content (12.42 m\({}^{3}\)/m\({}^{3}\)), the suction reaches a value of approximately 1.7 pF (50 m of water column).  
The hydraulic conductivity curves, obtained using the bimodal Fredlung-Xing model to adjust the retention curve (Figure 6) in HYPROP, indicated a saturated conductivity of 4.6 \(\times\) 10\({}^{-6}\) cm/s and 1.2 \(\times\) 10\({}^{-7}\) cm/s at the optimum moisture for sample 1. These values are considered low for sandy soils. The same process was carried out to obtain the retention and conductivity curves of sample 4 (Figures 7 and 8).  
Figure 5: Sample 1 retention curve obtained in HYPROP and WP4-C (Menezes [14]).  
Figure 6: Conductivity curve of sample 1 obtained in HYPROP (Menezes [14]).
## 6 Results and Discussions
### Validation of the Present Method  
As there are limited studies on the water entry of 3D wedges, verification data for the impact of a 3D elastic wedge with a constant velocity has not yet been found. Hence, the present method is first validated by comparing the present numerical predictions with the results of 2D rigid and elastic wedges reported by published literature. Then, the applicability of the present method to 3D wedges is validated by comparing the present numerical predictions with results obtained from the commercial software.
## 4 Results and Analysis
### Fixation
#### 4.2.1 Data Preprocessing  
Examples of fixation maps are presented in Figure 7. It can be inferred from Figure 7 that two drivers had different vision because of the distinct positions of their heads and angles from which the driver's vision is viewed. Therefore, the view images needed to be rotated and cropped before performing the fixation map's analysis. Thus, a datum line is necessary to adjust view images to the same angle. When the driver was sitting in the vehicle, the same vehicle body contour appeared on the scene camera. Thus, the car body contour was selected as the datum line (green lines in Figure 8). Images used in the subsequent analysis were all rotated with the intersection of the two green lines. Additionally, the redundant edges were cropped to acquire the same image size. The original and calibrated images are presented in Figure 8.  
Figure 6: Pupil area versus luminance level.  
Figure 7: Fixation maps of two participants.
## 2 Related Work
### Problem Description  
During the operation process, the traditional LEACH protocol executes the cluster re-construction process constantly in cycles. The re-construction process is described in rounds, and each round needs to create clusters first. The process is mainly about how to select cluster heads. The protocol uses each sensor node to select a random number between 0\(\sim\)1. If the selected number is less than T(n), then this node becomes a cluster head. This method of selecting cluster heads does not guarantee that cluster head nodes will be distributed over the entire network, and it is very possible for selected cluster heads to concentrate in a certain part of the network, resulting in no cluster heads around some nodes. The phenomenon of uneven distribution of cluster heads may result in premature death of nodes inside clusters, thus becoming blind nodes.  
In Figure 3, nodes A and B belong to the same cluster. Suppose that \(R_{I}\) is the distance from node A to the cluster head with energy \(E_{A}\), and \(R_{2}\) is the distance from node B to the cluster head with energy \(E_{B}\), and that \(E_{A}\approx E_{B}\). \(E_{T}\), the communications energy, is proportional to the square of the distance to the target [8], and obviously \(E_{A}-E_{TA}<E_{B}-E_{TB}\). The margin of the remaining energy increases as the margin between the distances from two nodes to the cluster head increases. If the location of the cluster head is selected improperly, resulting in that the margin between the distances to the cluster head is too large; the uneven level of energy of nodes inside the cluster may be very serious.  
The traditional LEACH protocol assumes that all nodes carry the same energy. The selection of cluster heads is regardless to the remaining energy of nodes. However in the actual network operation, the remaining energy of nodes is often very different. It is necessary to take these factors into account for optimizing the selection of cluster heads.  
Figure 3: Diagrams for Reasons of Blind Nodes.
## 3 System Model Design
### Collecting Information about Nodes in Network
#### 3.1.1 Defining and Collecting Information about Mobile Nodes  
Opportunistic social networks are sparse wireless networks and there is no direct full link from the source node to the destination node. Therefore, opportunistic social networks use the random movement of the node to establish a suitable temporary communication link for data exchange. In the data transmission process, we use the intermediate node to realize the effective transmission of the message from the source node to the destination node. Therefore, we have the concept of relay node and neighbor node.  
For the MSSN algorithm, we also need to collect information about many nodes. In our proposed algorithm strategy, there is a preparation period of a network node, in which all network nodes acquire the latest network node status and update the corresponding status. In a real-life scenario, the user may perform the same behavioral activities every day, such as entering a conference room at a certain time, from home to the company, etc. Therefore, we set this time period \(\Delta t\) to 24 h. During this period, we also need to collect the historical movement trajectories of all nodes and their social attributes. In addition, this mechanism collects information of the two encountered nodes in the cycle; and obtain and update in real-time. Therefore, in order to better normalize this information, we will define the data by formula:  
\[list(node)=(m\_track_{node},att_{node},\textit{*list\_num}) \tag{1}\]  
where \(m\_track_{node}\) is the distribution of the movement trajectory of the node in the network, \(att_{node}\) is a feature vector, represents the social attribute of the node, and \(list\_num\) represents the sequence of node states encountered by the node during the time period. Therefore, the entire information network is defined by a sequence of motion trajectories, social attributes, and historical encountering information states.
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 2 Methodology and Materials
### Experimental Setup  
Figure 2 shows the experimental setup of this work. Instead of conducting the experiment on a real ocean, we used a water-filled rubber duct of various lengths to simulate the effects of an ocean. As Figure 2 shows, the 10 m long water filled rubber duct was terminated with two water-filled containers, one being labeled as the transmitting end and the other as the receiving end. This setup allows a high-voltage surface electromagnetic wave to be delivered from the transmitting end to the receiving end through a 10 m long water-filled rubber duct.  
Upon detection of a power at the receiving end, the neon lamp in the modified live-wire detector will be on, and the two resistors connected to this neon lamp will consume the power. Of these two resistors, one was 1 ohm at the top of the modified live-wire detector. The voltage across this 1 ohm resistor was used to calculate the current flowing through the live-wire detector circuit. The power consumed by the 1 ohm was used to calculate the transmission efficiency.  
The components in the experimental setup together with the experiments are separately discussed in more detail in the following paragraphs.
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 5 Results
### Analysis of Models' Features
#### 5.3.3 Evaluating Model Performance in Continuous 72-h Wind Power Forecasting  
The performance of continuous wind power forecasting is important due to the accumulation of errors over time. A model's ability to continuously predict all 72 h demonstrates its stability in handling long-term data, assuring reliable long-term predictions.  
Figure 14 shows the hourly MAE change for XGBoost, LTC, and Hybrid models. XGBoost and Hybrid models have the lowest error in the first 3 h, while the LTC model displays consistent errors across all 72 h. This highlights models' ability to consistently forecast wind power and makes LTC more reliable than other models in long-term wind forecasting. However, the Hybrid model consistently maintains the lowest errors throughout all hours, reinforcing its superior forecasting accuracy and robustness.  
Figure 15 shows the MAE error with its confidence range, as well as its regression. LTC model has a consistent regression slope, whereas XGBoost exhibits a steeper slope. The hybrid model's regression slope is 64% steeper than the LTC model's regression slope, but 52% shallower than the XGBoost model's regression slope. However, the hybrid model has a 12% initial error.  
Confidence ranges further indicate that the LTC model maintains a stable error range over time, while both XGBoost and Hybrid models display an initial error with an almost negligible confidence range. This indicates that the LTC model is the most stable and reliable for long-term predictions, while the Hybrid model serves as a balanced compromise, offering a tradeoff between initial error and long-term error stability.  
Figure 14: The 72 h forecast error dynamics for XGBoost, LTC, and Hybrid models.  
Figure 15: Regression graphs of 72 h errors.