esa-sceva/satcom-chunk-collection huggingface dataset:
# Trends in Compliance Monitoring Devices (CMDs) in Ships' Ballast Water Treatment Systems
## 2 A Holistic Approach to Monitoring Threats to the Environment in Ballast Water  
The BWM Convention has set specific standards for treated and compliant ballast water which states that, 'the standards that must be fulfilled in the ballast water discharge: <10 viable organisms m\({}^{-3}\) greater than or equal to 50 \(\upmu\)m in minimum dimension (>50 \(\upmu\)m) and <10 viable (or living organisms according to the language of the USCG) organisms mL\({}^{-1}\) < 50 \(\upmu\)m in minimum dimension and greater than or equal to 10 \(\upmu\)m in minimum dimension (10-50 \(\upmu\)m) [4,14]. This means that there is a specific reportable compliance outcome in all results from the varying types of treatment systems that are available for ballast water applications for ships. It is essential to note the effectiveness of monitoring methods in testing whether international law is being accurately complied with. For example, some assessments use eDNA metabarcoding in their rapid tests and it was found that this type of testing may result in 23% false-positive results when used in testing treated ballast water and thus may create a non-compliant record for that treatment system, when the testing device is the issue. This is why, for example, the scientists who conducted this experiment suggested a combination of eRNA/eDNA metabarcoding to produce a more accurate result [15]. The scientists discovered that the DNA of dead organisms tends to survive longer in the dark and cold ballast water tank environment and thus makes its way to extracted tested water that contains living organisms as well.  
This study focuses on AIS and fouling; however, ballast water management also extends to testing and treating ballast water being discharged from oiled ballast water treatment systems where the concern is the release of contaminants that are petroleum (hydrocarbons)-based and are beyond the regulatory allowance for effluent that can be discharged from ballast water tanks [16]. When monitoring marine environments, other contaminants should not be ignored as they also provide useful monitoring and forecasting tools for environmental protection [16] (pp. 1-2). For example, heavy metals in ballast water, plastics, hydrocarbons and other harmful exposures should continue to be a holistic and multifold concern of all working to protect the marine environment. A study published in 2024 found that ballast water was responsible for introducing not only pathogens but also heavy metals, such as alarming rates of iron, zinc and lead, into marine environments [3,17].
## 3 Results
### Dependence on Epidermis Thickness  
The solution of Equation (9) with boundary conditions Equations (10) and (11) is a linear function. Thus, we have a linear temperature drop in the passive layer from \(T_{d}\) (interface with the dermis) to \(T_{skin}\) (interface with the air). However, as the temperature profile within the passive layer is not measurable and has few practical applications, we can use the overall temperature drop across the passive layer (\(\Delta T_{e}\)) instead. Thus, we can write  
\[T_{d}\ =\ T_{skin}+\Delta T_{e} \tag{18}\]  
where  
\[\Delta T_{e}\ =\ h(T_{skin}-T_{\infty})E/k_{e} \tag{19}\]  
Figure 2: The dependence of the heat loss function, \(q\) [W/m\({}^{2}\)], on the skin temperature \(T_{skin}\) [\({}^{\circ}\)C] in the case of moist skin (\(W=1\)). \(q(T_{skin})\) for was plotted for \(T_{\infty}=22\)\({}^{\circ}\)C (**panel a**) and \(30\)\({}^{\circ}\)C (**panel b**). Relative air humidity was set to 40% (blue line), 70% (red line), and 100% (green line). Skin emissivity \(\varepsilon\) was set to 0.98.  
Thus, if we measure the skin temperature \(T_{skin}\) and know the passive layer thickness, \(E\), we can calculate the temperature drop \(\Delta T_{e}\) and the dermis temperature, \(T_{d}\).  
Alternatively, we can derive the expression for \(T_{skin}\)  
\[T_{skin}\ =\ \frac{T_{d}+T_{\infty}hE/k_{e}}{1+hE/k_{e}} \tag{20}\]  
Then, assuming the constant dermal temperature \(T_{d}\), by measuring \(T_{skin}\), we can estimate the epidermal thickness, \(E\). In particular, the dependence of the \(T_{skin}\) on the epidermal thickness \(E\) can be assessed as  
\[\Delta T_{skin}\ =\ \frac{\partial T_{skin}}{\partial E}\Delta E\ =\ -\frac{(T_{d}-T_{\infty})h/k_{e}}{(1+hE/k_{e})^{2}}\Delta E \tag{21}\]  
We can estimate the expected dependence using realistic numbers for all parameters and variables in Equation (21). For realistic epidermal thicknesses (the 0.1-2 mm range), the primary factor will be \(\Delta T_{skin}/\Delta E\sim-(T_{d}-T_{\infty})h/k_{e}\) which corresponds to the case of a thin epidermis. It limits the \(\Delta T_{skin}/\Delta E\) from above can be as high as 0.765 \({}^{\circ}\)C/mm for a 15 \({}^{\circ}\)C difference between the dermal and ambient temperature for a thin (e.g., 0.1 mm) epidermis. For a thicker (e.g., 2 mm) epidermis, Equation (21) can be 20% smaller: around 0.6 \({}^{\circ}\)C/mm. Thus, a 1.2-1.5 \({}^{\circ}\)C temperature drop over a 2 mm callus accumulation can be expected.  
Equation (19) for \(\Delta T_{e}\) can be rewritten in terms of \(T_{d}\):  
\[\Delta T_{e}\ =\ \frac{(T_{d}-T_{\infty})hE/k_{e}}{1+hE/k_{e}} \tag{22}\]  
In Figure 3, one can see the dependence of the epidermal temperature drop, \(\Delta T_{e}\), as a function of the passive layer thickness \(E\) for several dermal temperatures/ambient temperature gradients: (\(T_{d}-T_{\infty}\)) 15 \({}^{\circ}\)C (solid red) and 10 \({}^{\circ}\)C (dashed blue).
## 2 Methodology: Image Processing and Pattern Detection
### Pattern Detection  
Pattern detection is the study of how computer programs can observe a context, learn, and classify patterns of interest, allowing to make intelligent decisions [10]. A pattern detection system consists of procedures that partition the universe of classes in such a way as to be able to assign elements to classes depending on a set of characteristics of each studied element (the characteristics pattern). On the one hand, when patterns are unknown a priori, the process is called _pattern recognition_; on the other hand, when the patterns are known, the process is called _pattern matching_. The pattern detection process usually consists of three stages: segmentation, features selection, and extraction and classification. The main details of each stage are commented next:  
1. _Segmentation_ in pattern detection systems is similar to the segmentation applied in image processing systems. The goal of the segmentation stage is to simplify the input, resulting in a set of information that is easier to process.
2. _Feature extraction_ takes as input the result of the segmentation stage. The input is processed to extract relevant information about specific objects, remove redundant/irrelevant information, to reduce the dimension of the problem. Quantitative (e.g., speed, distance) and qualitative (e.g., occupation, sex) features are extracted and used to build a vector of features. The goal is to select a subset of features (from the original set) in order to optimize a predefined target function. Feature selection can be done by applying statistical techniques. This procedure usually requiresa deep knowledge of the problem. Selection feature methods consist of three components: at least one evaluation criterion, a procedure or search algorithm and a stop criterion [11].
3. _Classification_ processes features a vector to assign features to specific classes, according to predefined metrics. Classification methods (i.e., _classifiers_) depend on the nature of the problem and, in general, their performance also depends on the quality and number of extracted features. There are two main groups of classifiers: _supervised_, and _unsupervised_[12]. Supervised classifiers are based on a set of elements, known as _training data_, for which the class they belong is previously known by the classifier [13]. Some typical supervised methods are Bayesian, Support Vector Machine (SVM), _k_-nearest neighbors (_k_-NN) and neural networks, among others [14]. Unsupervised classifiers tries to discover the classes of a given problem from a set of elements of which the classes are unknown [13]. The number of classes to be discovered by an unsupervised classifier can be known in advance or not, depending exclusively on the datasets handled. Some typical unsupervised methods are Simple Link, ISODATA and _k_-means, among others [14].
## 3 The Implementation of the Novel ARPD-RTK/INS Algorithm
### INS Derivation  
In the derivation stage, the transition model has been described in Section 2.1. Besides updating the state vectors, their corresponding error propagation also needs to be calculated. The state error vectors of the ARPD-RTK/INS algorithm contain the following 15 states.  
\[\delta E=\left[\begin{array}{c}\delta\mathbf{\psi}_{eb}^{e}\\ \delta\mathbf{v}_{eb}^{e}\\ \delta\mathbf{r}_{eb}^{e}\\ \mathbf{b}_{a}\\ \mathbf{b}_{g}\end{array}\right] \tag{12}\]  
where \(\delta\mathbf{\psi}_{eb}^{e}\) denotes the error of the Euler angle from the ECEF frame to the body frame, and \(\delta\mathbf{v}_{eb}^{e}\) and \(\delta\mathbf{r}_{eb}^{e}\) denote the errors of velocity and position, respectively. They describe a kinematic property of the body frame with respect to the ECEF frame, which is expressed in ECEF frame axes. Then, the error propagation in the ECEF frame over the time interval \(\tau_{s}\) can be obtained by the time derivative of each state error as follows [17]:  
\[\mathcal{G}_{INS}^{e}\approx\left[\begin{array}{cccc}\mathbf{I}_{3\times 3}-\mathbf{ \Omega}_{ie}^{e}\tau_{s}&\mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}&\mathbf{0}_{3 \times 3}&\mathbf{C}_{b}^{e}\tau_{s}\\ \mathbf{F}_{21}^{e}\tau_{s}&\mathbf{I}_{3\times 3}-2\Omega_{ie}^{e}\tau_{s}&\mathbf{F}_{2 3}^{e}\tau_{s}&\mathbf{C}_{b}^{e}\tau_{s}&\mathbf{0}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{I}_{3\times 3}\tau_{s}&\mathbf{I}_{3\times 3}&\mathbf{0}_{3 \times 3}&\mathbf{0}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}&\mathbf{I}_{3\times 3} &\mathbf{0}_{3\times 3}\end{array}\right] \tag{13}\]  
\[\mathbf{F}_{21}^{e}=\left[-\left(\mathbf{C}_{b}^{e}\mathbf{f}_{ib}^{e}\right)\wedge\right] \tag{14}\]  
\[\mathbf{F}_{23}^{e}=-\frac{2\gamma_{ib}^{e}}{r_{e5}^{e}}\frac{r_{eb}^{e}\,^{T}}{ |\mathbf{r}_{eb}^{e}|} \tag{15}\]  
The function \([x\wedge]\) represents the skew-symmetric matrix of \(x\). \(\gamma_{ib}^{e}\) denotes the acceleration due to the gravitational force, and \(r_{e5}^{e}\) is the geocentric radius at the Earth's surface. The remaining notations are identical with the ones mentioned above.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description
### Description of the Protocol Phases
#### 3.3.2 Phase 1--Key Generation  
The input variables used may change depending on the scheme used. Generally, the keys require primes \(p\) and \(q\) and a generator \(g\). These values are agreed upon in phase 0.  
The first key to be generated is the symmetric key. It is based on asymmetric cryptography, where the KEM will act as the key agreement protocol. It is performed between A and B using the KeyGen algorithm executed by the server to generate a key pair \((P_{k},S_{k})\). \(P_{k}\) is sent to B as input to \(Encaps(P_{k})\), which returns the values \(K_{B}\) and \(ct_{B}\). \(ct_{B}\) is the input to the hash function along with the identifier of B: \(ID_{B}\), such that \(H(ID_{B},ct_{B})\to h_{ct_{B}}\). The output \(h_{ct_{B}}\) of this function and the value \(ct_{B}\) are sent back to entity A. In this way, A can verify that the received value is integer; subsequently, the \(Decaps(S_{k},ct_{B})\) algorithm is executed to obtain \(K_{B}\). Once both entities know \(K_{B}\), using a Key Derivation Function (KDF), the symmetric key is derived by applying the function \(KDF(K_{B},Label,Context,L)\), where \(Label\) contains the purpose of using KDF, \(Context\) is a binary string with \(K_{B}\) information, and \(L\) is the length of the output key. All the above values must be equal in both entities to have the symmetric key \(K_{s}\). After this, the server must generate a new key pair \((P_{KA},S_{KA})\) using \(KeyGen\) from the digital signature scheme to obtain the key that will sign subsequent phases. When the public key (\(P_{KA}\)) is obtained, it is sent to B, and D. Figure 6 shows the diagram referring to phase 1 of the protocol.  
Figure 5: Phase 0: Interaction diagram with keyless authentication using accelerometer and gyroscope data.  
Figure 6: Phase 1: Symmetric key generation between A and B. Asymmetric keys generation from A for subsequent distribution to B and D.
## 3 System Model
After phase distortion cancellation, the upper bound of the instantaneous SNR attained at UE is as follows:  
\[\varepsilon_{max}\ =\ \frac{\left|\sum_{i=1}^{N}\beta_{i}\alpha_{i}^{s}\right|^{2}E _{s}}{N_{o}} \tag{7}\]  
The optimal maximum likelihood (ML) detector can be employed for the detection of the transmitted symbol from the received signal at the UE as follows:  
\[\hat{x}=\underset{x}{\text{argmin}}\left|r-\left(\sum_{i=1}^{N}g_{i}\hat{n}_{i }^{s}e^{i\phi_{i}}\right)x\right|^{2} \tag{8}\]  
The ML detector requires perfect CSI at the UE to detect the transmitted symbol accurately. Although the RIS possesses an ideal CSI, its operation resembles an ON-OFF switch. In practice, it cannot produce all possible phase angles. The practical implementation limits each RIS element's phase shift to a finite number of discrete phase resolutions. Let \(P=2^{b}\) be the total number of phase shift resolutions the RIS can produce, where \(b\) is the number of bits required to represent the number of phase shift levels. The discrete phase shift values can be acquired via uniform quantization from the interval \(\phi_{i}^{d}\epsilon[0,2\pi)\)[54, 55]:  
Figure 1:0  
For a one-bit discrete phase shifter \((b=1)\), the corresponding two discrete phase levels are 0 and \(\pi\). For the two-bit discrete phase shifter \((b=2)\), the corresponding four discrete levels are 0, \(\frac{\pi}{2}\),\(\pi\),\(\frac{3\pi}{2}\). The discrete reflection phase shifter produces a quantized phase corresponding to the total phase shift of the dual-hop channel, which is given as follows [13, 56]:  
Figure 1:1  
where \(q_{b}\) is the quantized phase levels. The specific phase angle that needs to be generated should be selected based on the proximity to the angles it can produce. The instantaneous SNR at the UE due to the discrete reflection RIS is given as follows:  
Figure 1:2  
Due to the residual phase, there will be a degradation in SNR at UE compared to infinite resolution RIS, i.e., \((\epsilon^{\prime}<\epsilon)\)[56]. The ML detector for discrete RIS is similar to (8), replacing \(\phi_{i}\) with \(\phi_{i}^{d}\).
## 5 Errors Compensation
### Compensation Examples
Figure 14: Compensation process for aperture field phase errors caused by feed offset errors: (**a**) aperture field phase errors caused by surface errors of the antenna; (**b**) aperture field phase errors induced after incorporating feed offset errors; (**c**) aperture field phase errors obtained from polynomial fitting; (**d**) aperture field phase errors obtained from fitting using the proposed method; (**e**) aperture field phase errors compensated using polynomial fitting; (**f**) aperture field phase errors compensated using the proposed method. Figure 15: The scanning error compensation schematic diagram: (**a**) the distribution of surface shape measurement error under the combined action of the scanning error and the antenna surface error; (**b**) the distribution of surface shape measurement error after the scanning error compensation.
## 3 Results and Discussion
### Sedimentary Model  
The Chang 9 reservoir of the Yanchang Formation in the Ordos Basin can be considered to be part of the slow rising period of the lake basin as a whole. The sedimentary evolution of Chang 92 and Chang 91 is highly consistent with the overall sedimentary evolution law of the Chang 9 period in the Ordos Basin. The provenance supply system of the Chang 9 sedimentary period is mainly Qinling ancient land in the south and Longxi ancient land in the southwest. From a regional perspective, the Chang 9 period in the study area was a significant period of delta formation. The water body in the Chang 9 sedimentary period is relatively shallow, and a shallow sedimentary environment was developed. The central area of the lake basin is located in the Huachi area. The sedimentary microfacies map and sedimentary model map indicate that during the early Chang 9 period, the lake transgression expanded, the shallow lake area continued to expand, the area of the delta front continued to decrease, the thickness of the sand body decreased and the thickness of the sand layer was between 5 and 15 m. During the middle and late stages of the Chang 9 period, the lake basin continued to expand at a slow rate. The provenance of the lake basin was primarily sourced from the southwest, and the provenance supply was sufficient. The delta front area was relatively large, and numerous underwater distributary channel microfacies were developed in the area. The sand body distribution was stable, and the sand layer thickness was between 15 and 30 m. A schematic diagram of the sedimentary environment is presented in Figure 12.  
The sedimentary environment of the Chang 9 oil layer formation in the Ordos Basin exhibits similarities to and differences from sedimentary environments in other regions, both within and outside of China. Domestically, similar environments may be present in areas such as the Tarim Basin, Badain Jaran Desert and Ebo Liang, influenced by geological structures and continental lakes. Internationally, analogous environments can be observed in regions such as the Altai Mountains of Central Asia and the Baikal Basin in Siberia, char  
Figure 11: Sample diagram of vertical structure style of single sand body with Chang 9.  
acterized by comparable geological backgrounds and climate conditions [28; 29; 30; 31]. Despite these parallels, variations exist in sediment sources, deposition processes and paleoclimatic conditions among different regions. By comparing with these areas, a better understanding of the sedimentary environment characteristics of the Yanchang Formation in the Ordos Basin can be obtained, contributing to a more insightful exploration of oil and gas resources.
# Hydraulic Analysis of a Passive Wedge Wire Water Intake Screen for Ichthyofauna Protection
## 2 Materials and Methods
### Wedge Wire Intake Screen Model  
The model of the wedge wire intake screen was made of stainless steel. The outer part of the screen consisted of a wedge wire slotted pipe 150 mm long and 150 mm in diameter. This pipe was made of surface profiles spirally coiled and permanently welded to the crossbars placed inside parallel to the axis of the pipe. Water flowed into the screen through the wall surface of the slotted pipe, hence the wedge wire profile was wider on the outside of the pipe and narrower on the inside. The use of this type of wire minimizes clogging of the slots and facilitates cleaning. Solid elements can be retained on the outer surface of the screen, where they can easily be flushed out by the flowing water stream, if any, and contaminants of smaller sizes, thanks to the profile of the slot widened on the inner side of the screen, will not be wedged but drawn in through the vacuum created there. The clearance of the slots on the outer side of the screen was 1.5 mm. The width of the wedge wire profile in its wider part was also 1.5 mm. Figure 6 shows a section of the screen model along with the enlarged wedge wire profile.  
During the tests, the effect of two different deflectors installed separately inside the screen was also analyzed. Both deflectors were cylindrical in shape with an outer diameter of 50 mm and an inner diameter of 46 mm. On the perimeter of the first one, openings were equal with diameters of 6 mm, as shown in Figure 7a, and for the second one, the diameters varied from 3 mm on the outflow side to 10 mm on the opposite side, as shown in Figure 7b. The following openings diameters were assumed: 3 mm, 3 mm, 3 mm, 4 mm, 4 mm, 4 mm, 4 mm, 8 mm, 10 mm and 10 mm. The location of the deflector inside the screen is shown in Figure 6, and the location of the openings on the perimeter of both of them is shown in Figure 7. The summed areas of the openings were similar to each other and were, in the case of uniform perforation, 2827 mm\({}^{2}\) and, in the case of non-uniform perforation, 2788 mm\({}^{2}\), corresponding to a perforation degree of 12 percent and 11.8 percent. The variation of openings in the deflector shown in Figure 7b was adopted based on the studies presented in the paper [13; 38; 39].  
Figure 5: Diagram of the hydraulic trough in which the laboratory bench shown in Figures 3 and 4 is installed.  
Figure 6: A cross-section of the screen model along with an enlarged section of the wedge wire profile.
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 5 Fifth-Generation Performance with Multiservice
### Assumed Service Categories  
In this analysis, three types of services are considered [39; 40]:  
1. Web browsing via a new radio (WBo5G): This service allows users to browse, find, and download web content from the internet at speeds exceeding one gigabyte. Because most internet activities involve web browsing, 60% of the available services are for this type of service.
2. Video Streaming-over-5G (STRo5G): This service provides real-time video streaming, video on demand (VoD), and virtual and augmented reality video experiences. To ensure minimal latency for multimedia services, 30% of all multimedia services are designated for this type of service.
3. Voice-over-5G (Vo5G): Also known as voice over new radio, this provides high-definition (HD) voice communication for smartphones. It is infrequently used and is mainly reserved for emergency calls; thus, it constitutes the remaining 10% of all available services. Regarding performance measures, the analysis yields the following performance parameters via the Continuous Time Markov Chain (CTMC) solver in the MOSEL-2 language [39; 41]:  
1. Blocking Probability: The likelihood of call rejection if the call's bit rate falls below the minimum threshold bit rate assumed in the cell.
2. Loss Probability: The likelihood of a call drop after admission when the user's bit rate falls below the threshold value due to interference or channel quality degradation.  
Figure 4: Virtual zones of 5G cell.  
Utilization: Average total bit rate relative to maximum cell capacity.
* Cell Delay: The time taken for a message to successfully reach its destination compared to the time the first bit is sent from the source.
* Average Bitrate of the Cell: The mean total nitrate in the cell relative to the average session duration.
* Aggregate Bit Rate of the Cell: The average total bit rate across all services in the cell.
* Aggregate Bit Rate per Service: The aggregate bit rate is calculated as the average number of jobs in each service multiplied by the average bit rate required for that service's area.
* Throughput: Calculated as the mean total bit rate in all zones for all services of the cell.