esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 4 Merging Method
### Algorithms
#### 4.2.3 Anonymization Algorithm Time Complexity  
To compute the time complexity of the anonymization algorithm, we need to consider the time complexity of Mondrian method [44], incompatible tuple check [22] technique, and the _l-diversity_ verification technique. In the Mondrian algorithm, it requires \(O(nlogn)\) times because the whole dataset must be scanned \(O(n)\) times and the Mondrian algorithm needs \(n\) heights of the taxonomy tree, which is \(O(logn)\). To check incompatible tuples it requires \(O(n)\), and verifying the _l-diversity_ requirement takes \(O(n^{2})\). Therefore, the whole time complexity of the anonymization algorithm is presented by \(O(n^{2}logn)\).
## 2 Conceptual Reliability-Based Decision Support Framework
### Assessment of Contracted Service Based on Key Performance Indicators  
Use of KPIs to measure service performance is common practice in PPP contracts. Similar to all construction projects, the procuring of social PPPs can be seen as a production process [31; 32]. When the private sector signs the contract, they not only provide the construction product, but also the service outlined in the output specifications to meet the government's requirements. [PERSON] [16] illustrated the construction process as a service product, service delivery and service environment. A service product is the service as specified to be delivered in the contract, which usually includes general services, building, maintenance, management service and utility, waste, security service and sometimes furniture, fixture, and equipment; refer to Figure 2. A service environment means the internal environment such as the culture of Project Co (private sector), and the external environment such as the availability of plant, materials, and equipment. If Project Co fails to provide the service quality, the government is eligible to abate or reduce a pre-agreed amount of money from the service payment. The quantum of any abatement forms part of the contract and is linked to KPIs, often on an escalating scale for repeated failures. It is considered that the use of abatements is a reasonable indicator for the acceptability of service performance in line with contractual obligations.  
It is worth noting, as the amount of abatement cost varies a lot across different PPP projects, it is meaningless to express the service delivery performance just in monetary form. The European PPP Expertise Center [33] states that the amount of performance deductions could be limited for a particular service in any payment period at a level that is more closely aligned to the forecast cost to the private partner of providing the relevant service. For example, limiting the private partner's exposure to performance deductions relating to cleaning services. Hence, an advance assessment of delivered service (_AoDS_, termed with variable '_A_') is presented to normalize the monetary abatement cost in the ratio of abatement cost and the performance-related service payment (e.g., operation, cleaning and maintenance payment), the value of which is calculated by its percentage multiple of the total quarterly service payment, i.e., the sum of the actual quarterly service payment through the PPP payment mechanism and the abatement cost.
###### Abstract  
Magnetically coupled resonant wireless power transmission technology (WPT) based on high-temperature superconducting (HTS) coils has gained wide popularity due to its low impedance and high-quality factor Q value characteristics. This technology has greatly improved the energy transfer performance of wireless power transmission (WPT) systems. However, practical applications of conventional WPT, such as wireless charging of autonomous underwater vehicles at mooring points, often encounter spatial misalignment issues due to the complex ocean environment and ocean currents. Nonetheless, few studies have investigated the spatial misalignment of HTS WPT systems, particularly the angular misalignment. This paper presents a solution to address this problem by constructing magnetically coupled resonant wireless energy transmission systems based on HTS coils and copper coils. The study analyzes the relationship between the transmission efficiency of the WPT system and the received power of the load with respect to the spatial orientation of the coil. The performance of the superconducting coil and copper coil WPT systems is compared. The experimental results demonstrate that, under the same spatially misaligned conditions, the WPT system using HTS coils can significantly improve the transmission efficiency and load power compared to the conventional copper WPT system. Moreover, simultaneous adjustment of the lateral misalignment distance and different orientation deflection angles can improve the transmission efficiency and smooth load output power of the high-temperature superconducting WPT system.  
wireless power transfer; misalignment of angles; high-temperature superconducting; system transmission efficiency; the load output power
## 4 Proposed Protocol Design
### Network Model  
The base station is positioned at one end of the network boundary. All sensor node moves in the same direction at a constant speed. The links between the nodes in the network model are represented by an undirected connectivity graph \(G\) and the vertices.  
Figure 1: General diagram of proposed model.  
Additionally, the node's speed, direction of motion, transmission range, and location are taken into account as representations of the sensor nodes in the sensing region using Cartesian coordinates. The robotic vehicle with sensor nodes is shown in Figure 2.  
The targeted sensing area is divided into the strip based on the length of sensor/detector holding pole \((d_{l})\) and height from the ground \((d_{h})\). By using the pythagoras theorem, the distance \((d_{c})\) is calculated as given in Equation (1).  
\[d_{c}=\sqrt{(d_{l})^{2}-(d_{h})^{2}} \tag{1}\]  
In this method, the rotating part can rotate the sensor holding pole between \(0^{\circ}\) and \(180^{\circ}\), therefore \(d_{c}\) is considered as radius. And its diameter is used to divide the sensing area into the strips and the number of required mobile sensor nodes \((N_{d})\) is calculated as  
\[N_{d}=\frac{M}{2d_{c}} \tag{2}\]  
where \(M\) is the breadth or length of the network area.  
The number of router nodes required \((N_{d})\) is calculated as  
\[N_{r}=\frac{M}{t_{r}} \tag{3}\]  
where \(t_{r}\) is the transmission range of sensor node.  
The total number of nodes (both mobile nodes and router nodes) required to cover the searching area is calculated as  
\[N_{T}=N_{d}+N_{r} \tag{4}\]  
The network region is splitting into the strips and each strip is covered by a single Mobile Node (MN). From the deployment, each node knows its direct hop neighbors. The mobile node in the central strip is called as Coordinator Node (CN) that performs two operations: (i) coordinating the operation of nodes (ii) deploying the router node. The main role of coordinator is to deploy the router nodes based on the transmission range \(t_{r}\), in which CN tracks the traveled distance, and it drops the router nodes once its traveled distance reaches the transmission range. By this way, the CN expands the network searching area and establishes the communication with the BS. Routers are static nodes which are used to transfer the data to the BS in a multi-hop manner. Also, the coordinator collects the data from other mobile nodes which means that it creates a horizontal path between the CN and other MNs. Then, the coordinator node transmits data vertically to the BS through router nodes. The distance to be traveled by the robotic vehicle at each round is measured depending on the sensing range \(S_{r}\).  
Figure 2: Robotic vehicle with sensor pole.
## 2 Materials and Methods
### Detection and Classification of PSCs  
In a previous work [8], an extensive comparison study of ground-based and quasi-coincident satellite-borne lidar observations of PSCs was performed. To make this comparison, we had to adopt a detection and classification algorithm that follows the same approach and uses the same optical parameters as the v2 CALIOP algorithm (see Figure 7 and [7,20]). This approach seems to be successful and is now also followed by others (see [5]). However, the detection thresholds to distinguish PSCs from background aerosol had to be calculated in a different way. For CALIOP observations, the signal due to background aerosol from measurements is determined from observations at different locations on the same orbit, where no PSCs are present. For the ground-based observations, the background aerosol signal is taken from apparent clear sky observations, which are far less frequent than PSC observations during winter and often have a large time difference with respect to the PSC observations.  
Furthermore, statistical errors inherent in the photon-counting process and thus dependent on the altitude and the possible attenuation in the lower troposphere were taken into account to calculate the errors of the optical parameters \(\mathsf{u}(\beta_{\perp})\) and \(\mathsf{u}(R)\) and create the dynamic thresholds for detection and classification.  
The detection of a PSC is positive if either \(R\) or \(\beta_{\perp}\) exceeds the threshold value. It should be noted that the threshold values are not fixed but vary with the altitude due to the variation in the signal-to-noise ratio of the measured parameters. The classification of the PSCs closely follows the criteria established for the CALIOP data and identifies 5 different classes, STSs (Supercooled Ternary Solutions), NAT (Nitric Acid Trihydrate) mixtures, enhanced NAT mixtures, ice, and wave ice. With the term NAT mixtures, we intend mixtures of NAT with STSs and other nonpolarizing particles. PSCs generally consist of external mixtures of different species. The classification is based on the measurement of optical parameters and as such on the average contribution of several species to backscatter coefficient and depolarization. Enhanced NAT particles correspond to NAT with \(r_{NAT}<3\) um and a volume density of more than 1 um\({}^{3}\)cm\({}^{-3}\)[20] and corresponds roughly with NAT heterogeneously nucleated in wave ice PSCs. Wave ice PSCs are ice PSCs with \(R>50\). Wave ice seldom occurs above Concordia, since no significant topographic features are present. Also, enhanced NAT has a low occurrence. The main PSC classes observed at Concordia Station are NAT mixtures, STSs, and ice.
# Molecular Subtyping and Outlier Detection in Human Disease Using the Paralique Algorithm
## 3 Experimental Results
### A Search for Unrecognized Subtypes  
#### 3.2.1 Asthma  
The incidence of asthma in the U.S. has been on the rise for two decades. It is currently estimated that nearly one in ten children under 18 are asthmatic. The risk for some groups is based largely on ethnicity (particularly African American and Puerto Rican), with incidence among those with lower socioeconomic status rising as high as one in six [40].  
GEO series GSE4302 data were derived from the Affymetrix Human Genome U133 Plus 2.0 Array, and are designed to identify genes associated with response to corticosteroid treatment in asthmatics [41]. They consist of transcriptomic data taken from the epithelial airway brushings of 42 asthmatics, 28 healthy subjects and 16 smokers. To avoid potential confounds, we discarded data taken from smokers and used only the healthy subjects as controls.  
Filtering reduced the number of probes from 54,676 to 2322. Our method produced three paracliques with respective sizes 31, 8 and 3 that were stable until they began to merge as the threshold was lowered below 0.93. The 100 most differentially expressed genes across the two larger putative subtypes included CLCA1, periostin, and ovalbumin, which are all known to serve as markers of a Th2-high endotype of asthma [42].
# Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
## 2 Materials and Methods
### Bioassay with Trifolium pratense  
A short-term pot experiment was conducted in the greenhouse to evaluate the effect of biochar on the bioavailability and phytotoxicity of metal(loid)s in the contaminated soil. Three treatments with RA soil were prepared by adding biochar at different doses: 0 (RA), 4 (RA4B) and 8% (RA8B) of biochar. The mixture of soil and biochar was homogenized by manual agitation. In addition, another treatment with non-amended unpolluted soil (NC) was used as a control. The main physical and chemical properties of the amended soil were included in Table 1.  
Each treatment was placed in a plastic tray and incubated at 60% of its water holding capacity during one week. Thereafter, an aliquot of 400 g of each soil was placed in 12 cells pot-in-frame. For each treatment, 12 pot replicates were prepared (i.e., 144 cells in total). In each cell, five seeds of _Trifolium pratense_ were placed at 0.5 cm depth. The moisture content of the samples was adjusted to 60% of their water holding capacity. Then, samples were placed in the greenhouse at natural sunlight and temperature between 10 and 35 \({}^{\circ}\)C. Moisture content was maintained by weighing and adding distilled water when necessary.  
After two months, soil samples for each pot were air dried at room temperature, sieved through a 2 mm sieve, and stored at 4 \({}^{\circ}\)C before analysis. Plants of each pot were collected and divided in root and shoot, thoroughly washed with distilled water, and dried in an oven (Memmert 100-800, Memmert GmbH, Schwabach, Germany) at 70 \({}^{\circ}\)C for 48 h. Thereafter, the root and shoot biomass was weighted in a precision weighing balance (Gram precision STA-310S, Gram group, Barcelona, Spain). Finally, to obtain data on metals plant uptake, root and shoot biomass were attacked by acid digestion and measured in ICP-OES as described below.
## 1 Introduction  
Wireless Sensor Networks (WSN) have gained growing research interest in the last years. The possibility to monitor different physical quantities even in dangerous and hard-to-reach areas has found applications in several fields, including medical, industrial and surveillance environments [1]. WSNs are made of small nodes, where each node often relies on small size and light weight batteries. As a consequence, both energy consumption and area occupation are important aspects in the design of nodes. Although nodes feature a limited energy budget, they embody not only sensing but also computational and transmit/receive circuits. Thus, energy consumption issues are critical and ought to be minimized at every design level. As an example in [2] several system level techniques, including modulation, Media Access Control (MAC) protocols and channel coding techniques are analyzed to achieve energy efficiency in WSNs.  
In [3] it is shown that in WSNs the transmission energy can be lowered accepting to receive error-affected data. In this case the receiver should embed error correction strategies to recover the original data. In particular, the amount of energy spent to perform error correction should be significantly lower than the energy saved at the transmitter side. As an example, in [4, 5] an energy efficient error correction scheme for WSNs is proposed. In particular, in [5] the physical layer of the IEEE 802.15.4 standard [6] is augmented introducing interleaving and forward error correction. In [2, 3] several classes of codes are investigated, including Reed-Solomon codes, convolutional codes, turbo codes and Low-Density-Parity-Check (LDPC) codes [7, 8]. Experimental results in [3] show that LDPC codes are good candidates for WSN applications as they feature a significant coding gain as compared with other codes. However, they consume about one order of magnitude more than simpler codes as the extended Hamming ones. Most of previous works proposing error correction codes for WSNs assume that networks contain at least two classes of nodes: sensing nodes and central nodes. Sensing nodes feature lower computational capabilities and available energy than central nodes. Thus, sensing nodes send coded information to a central node which performs the decoding operations. On the contrary, this work investigates homogeneous WSNs where each node can both transmit and receive coded information. A similar idea is proposed in [9] with focus on turbo codes. In particular, in [9] it is shown that the energy consumption of homogeneous WSN is reduced by about 70% resorting to turbo codes. In this work we show that even higher energy saving and smaller area can be achieved with LDPC codes. In particular, this work shows that small block length LDPC codes are adequate for typical throughput and data transmission requirements of WSNs.  
The paper is structured as follows: Section 2 deals with LDPC coding and decoding algorithms whereas Section 3 concentrates on modeling the WSN environment. Section 4 details the proposed LDPC decoder architecture and Section 5 shows the experimental results. Finally, in Section 6 conclusions are drawn.
## 4 Design Considerations  
The key parameters of the bidirectional flyback converter in the proposed cell balancing system are listed in Table 1, where the lithium-ion battery module has a 6S1P configuration with a rated capacity of 2500 mAH and a voltage range of 2.5 V-4.2 V for each cell. To achieve the desired bidirectional flyback converter with adjustable current, some component values should be selected or determined, such as operating voltage, balancing current which refers to the current I\({}_{1}\) of the low side and is limited by the specification of the phototMOS (40 V, 2.5 A) for the safety, operating frequency, and duty cycle. The related design considerations are described below.  
(1) Determine the turn ratio \(N\) of the transformer  
The proposed system considers that it has a worse case in boost mode than in buck mode. According to the high-side voltage and low-side voltage, the turn ratio \(N\) from high side to low side will be designed in the worst case for operating in boost mode when high-side voltage \(V_{pack(max)}\) is 25.2 V and low-side Voltage V\({}_{\text{Bn(max)}}\) is 2.5 V. Therefore, in the case of the maximum duty cycle \(D_{Lmax}\) of the boost mode as 0.8, we can calculate the turn ratio as  
\[N=\frac{V_{Bn(max)}(1-D_{Lmax})}{V_{pack(max)}D_{Lmax}}=\frac{2.5\text{V}*0.8}{ 25.2\text{V}*(1-0.8)}=0.396 \tag{14}\]  
The turn ratio \(N\) as 0.396 means if the low side has one turn, the high side will have about 2.5 turns. In the practical design, the turn ratio \(N\) will be selected as 0.333 to make the high side have more turns to overcome the worse case and convenient for implementation.  
(2) Determine the inductance of the field inductor in the transformer  
The designed bidirectional converter set the crossing power \(P_{critical}\) between DCM and CCM in the condition as 20% load. The equation is shown as  
\[\beta=\frac{P_{critical}}{P_{Bn(max)}}=0.2 \tag{15}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Specifications** \\ \hline High-side voltage & 15–25.2 V \\ Low-side voltage & 2–5–4.2 V \\ Balancing current & 500–2000 mA \\ Switching frequency \(f_{s}\) & 100 kHz \\ Duty cycle & 20–80\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related parameters of the bidirectional flyback converter.  
\[P_{Bnt(max)}=V_{Bnt(max)}I_{1(max)}=4.2\mathrm{V}*2\mathrm{A}=8.4\mathrm{W} \tag{16}\]  
\[L_{critical}=\frac{V_{pack(min)}^{2}D_{Hmax}^{2}}{2 rf_{\mathrm{sw}}P_{Bn(critical)} }=\frac{15\mathrm{V}^{2}*0.45^{2}}{2*0.2*100\mathrm{kHz}*10\mathrm{W}}=108.4 \mathrm{\mu H} \tag{17}\]  
where \(\beta\) represents the ratio of the power \(P_{critical}\) to maximum low-side power \(P_{Bnt(max)}\), the low-side maximum voltage V\({}_{\mathrm{Bn(max)}}\) is 4.2 V, the maximum balancing current I\({}_{1(\mathrm{max})}\) is 2 A, the high-side minimum voltage V\({}_{\mathrm{pack(min)}}\) is 15 V, the maximum duty cycle D\({}_{\mathrm{Hmax}}\) of the buck mode is 0.45, and the operation frequency is 100 kHz. The actual inductance of the implemented field inductor of the transformer is 111.7 uH.
# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 7 Collaborative Node-Centric Misbehavior Detection Based on Hybrid Trust Values  
This section discusses the collaborative determination of anomalous vehicle nodes through the cooperation of the vehicle node layer, cluster head layer, cloud layer and by issuing alerts and monitoring through the RSU layer in vehicular networks. Vehicle nodes in vehicular networks are primarily categorized into three types: trusted vehicles (with normal behavior), neutral vehicles (with suspicious behavior), and malicious vehicles (with anomalous behavior). A vehicle is considered trustworthy when its trust value exceeds a certain threshold. Neutral vehicles, due to their suspicious behavior, are determined through periodic checks at varying frequencies (from 300 milliseconds to 5 min) based on their trust values. If their behavior remains abnormal after a certain period, they are classified as anomalous vehicles. Vehicles with trust values below a certain threshold are directly classified as anomalous. The process for collaborative determination of anomalous nodes and periodic checks is illustrated in Figures 7 and 8. Through these methods, this section achieves collaborative determination and monitoring of vehicle nodes, enhancing the trustworthiness and security of nodes in vehicular networks. The comprehensive evaluation combines the vehicle node layer, cluster head layer, cloud layer, and RSU layer to help identify and address anomalous vehicle nodes.  
This summary explains the methodology for identifying anomalous behavior in vehicles within a vehicular network, emphasizing a collaborative approach across different layers of the network. Vehicles are classified based on their trust values, and a special emphasis is placed on consistency in trust assessments. The specific formulas for these calculations and the detailed process of determination are mentioned but not provided in the text.
## 3 EM Techniques for CH Diagnosis
### Neutron Activation Analysis (NAA) in CH Applications  
This technique is based on the detection of gamma rays produced by the nuclei of the object under investigation. The term \"activation\" is used because the radiation literally activates a series of nuclear transitions as the target nucleus attempts to reach a more stable configuration. These transitions are responsible for the emission of gamma rays. Since the gamma rays emitted are characteristic of each element, their detection allows for the identification of the constituent materials of the object under examination. Activation may be induced by thermal neutrons, charged particles, and photons [89]. The most relevant parameters for this technique and their meanings are reported in Table 10.  
Particularly relevant to understanding the potentialities and limits of NAA in CH applications is the detection limit. It has a strong impact on the type of facility/instrumentation needed to perform the analysis and on the types of elements that can be detected. This parameter, in fact, is inversely proportional to the:  
* neutron flux;
* capture cross-section;
* number of target atoms.  
This means that high fluxes are required, and elements need to have a mass number within a certain range to be detectable. In Table 11, the main advantages and limitations of this technique, relevant for CH applications, are reported.  
Practical Application of NAA to CH  
An example of the NAA technique applied to CH is very difficult to find. As noted in Table 7, the need for a nuclear reactor is very limiting, while the use of a radioisotopic neutron source isn't attractive compared with other techniques because the fluxes it can provide become so low that the detection limit increases prohibitively.