esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
According to international law, determination of the maritime boundary of a coastal state is referred to as delimitation. It allows for the establishment of four types of borders: territorial sea, contiguous zone, exclusive economic zone and continental shelf [1, 2]. All of these types of borders are determined relative to the so-called baseline [3, 4], which is an internal state border. For this reason, precise baseline determination is an important task for each state because it establishes the area of its sovereignty and jurisdiction.  
The baseline is usually defined in two ways in the literature. In the first case, when the coastline is regular, the baseline is defined as \"the low-water line along the coast as marked on large-scale charts officially recognized by the coastal state\" [5]. In the other definition, the baseline is a straight line and it is applied: \"In localities where the coastline is deeply indented and cut into, or if there is a fringe of islands along the coast in its immediate vicinity\" and \"Where because of the presence of a delta and other natural conditions the coastline is highly unstable\" [5]. In such cases, adopting the baseline is a simple solution, that can be applied in cases where the land border is complex. For the majority of coastal states, the territorial sea is an integral part of the state, and it typically stretches along a belt with a width of 12 nautical miles (22.224 m) from the baseline [5].  
Determination of the baseline is mainly considered in a legal sense, whereby coastal states regulate the issue through domestic legislation. In Poland, the issue is regulated by law [6; 7], but the course of the baseline refers to documents from 1957-1995; therefore, considering the variability in the hydrological conditions, it is probable that the data used in the legal acts are outdated [8].  
Another important issue is the methodology used to perform hydrographic measurements, which enables baseline determination. The baseline in Poland runs at a depth of approximately 1 m, so a special methodology is required for its measurement. The method for its measurement is not defined in the international IHO S-44 standard [9], which also means that the requirements regarding the accuracy of its measurement are not specified. Also, typical hydrographic vessels are frequently unsuitable for making measurements of this type due to their large draft [10]. For these reasons, alternative (precise and time-effective) methods for baseline determination are needed. There are two major trends in the research in this area--the use of GNSS network solutions or the use of unmanned surface vessels (USVs) [11]. Additionally, unmanned aerial vehicles (UAVs) equipped with LiDAR can be used for bathymetric measurements of ultra-shallow waterbodies. However, often this solution cannot be used due to the low water levels in the sounded area [12; 13].  
The aim of this study is to present a methodology for performing territorial sea baseline measurements in selected waterbodies of Poland.
## 4 Conclusions  
A series of numerical simulations were performed to explore the uniaxial compressive strength and failure mechanisms of 3D nonpersistent joints with different inclinations and persistence. The failure modes and stress evolution during loading were presented. The following conclusions were obtained from the research results:  
1. For the strength of the jointed specimens, the numerical results show great agreement with those obtained from the laboratory tests. The peak strength of specimens with \(\theta=45^{\circ}\)-\(75^{\circ}\) is significantly affected by the joint persistence. However, when \(\theta\leq 45^{\circ}\) or \(\theta=90^{\circ}\), the joint persistence plays a small role in the strength of the specimens.
2. Based on microcrack accumulation and evolution, four typical failure processes (shear failure, split failure, mixed failure, and intact failure) are analysed from the micro perspective. As the joint persistence increases, for the specimen belonging to the shear failure mode, the axial strain at which the proportion of tensile and shear failures reaches a stable level decreases continuously. However, for the specimen belonging to split and intact failure, it is basically the same.
3. The stress distribution in rock bridges was monitored and analysed. For the specimen belonging to splitting failure and intact failure, the variation in the stress concentration area is very slight. For the slip fracture mode or mixed fracture mode, slip occurs along the joint plane during specimen failure, and as the joint plane is staggered, the shear stress distribution area also changes accordingly.  
Data curation, H.D.; formal analysis, R.Y., K.L.; investigation, R.C., H.L., and H.D.; methodology, R.Y., R.C., and H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by projects of the National Natural Science Foundation of China (No. 52104110).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 3 Results
### NPH vs. Multiple Conditions (AD vs. Healthy Controls)  
This team conducted a comparative analysis of eight state-of-the-art ML algorithms applied to MRI features from 30 NPH patients and 15 healthy controls [40]. Their systematic performance comparison revealed a clear hierarchy among algorithms: AdaBoost and XGBoost demonstrated the highest accuracy (80.4% and 79.6%, respectively), while MLP showed the lowest (72.0%). This 8.4 percentage point performance gap between algorithms, despite using identical features and validation methodology, highlights the importance of algorithm selection for NPH classification. Notably, ensemble methods (AdaBoost, XGBoost, Random Forest) consistently outperformed single-model approaches, suggesting that combining multiple predictive models may better capture the complex patterns associated with NPH. The relatively modest accuracy across all methods (below 85%) also reflects the inherent difficulty of NPH diagnosis, even with advanced ML techniques.  
In a study investigated ventriculomegaly and NPH-like features in patients with myotonic dystrophy type 1 [33]. Their binomial LogReg analysis revealed that while age, gender, and CTG repeat numbers did not significantly affect the z-Evans Index, callosal angle and pathological brain atrophy showed significant associations (Adjusted Odds Ratio of 1.0, 95% \(\text{CL}\), \(p<0.01\)). These findings indicate that specific morphological features, rather than demographic or genetic factors, are most closely associated with ventriculomegaly in this patient population. The lack of association between enlarged perivascular spaces and the z-Evans Index suggests that not all radiological findings common to NPH have equal diagnostic value across different patient populations--an important consideration for developing robust AI diagnostic tools.  
Other group measured various morphometric parameters and examined their association with neuropsychological tests in NPH patients [34]. Their LogReg analysis identified significant discriminative factors including Parkinsonism subtype, midbrain anteroposterior diameter, and midbrain tegmentum diameter--highlighting the importance of considering both morphological and clinical characteristics. Their finding that frontal horn length alone achieved 95.0% accuracy for predicting NPH, while combining frontal horn length and callosal angle improved accuracy to 96.3%, demonstrates the potential benefits of feature combinatorics for NPH diagnosis. Interestingly, adding additional parameters (midbrain longitudinal length, Evans ratio, frontal horn ratio, and biparetal diameter) did not substantially improve accuracy, suggesting that feature selection should prioritize quality over quantity. This \"less is more\" principle could guide more efficient and generalizable AI model development for NPH.
## 5 Experiments and Evaluation
### Experimental Setting
#### 5.1.3 Parameter Setting  
In this experiment, we set the following parameters to ensure the reliability of the experiment results. The dimension of representation vector \(H\) is 384, the learning rate \(\eta\) is 0.000003, the iteration period _epoch_ is 60, and mini-batch samples _batch size_ is 1024. In CMF-UL, the migration weight of users' interests in the user-app matrix \(\beta_{1}\) is set to 0.7, and the migration weight of location features in the location-app matrix \(\beta_{2}\) is set to 0.07. The dimension of the representation vector is set to 20.
## 2 System Model  
Health IoT communication systems cover short-range telemetry communication channels such as from in-body to on-body. The on-body device can also collect and transmit high data rates and real-time medical information to doctors and nurses via a gateway [1; 3]. Figure 2a-c illustrates the elements of the CST model, whilst the three primary components of the health IoT WBAN system are shown in Figure 2d with further details given below:  
* _Implant device/sensor_: A biological compatible and miniaturized size implant device that is located inside the human body, either in the tissue/organ region (deep region) or under the skin (near surface) [10].
* _On-body device/sensor_: An on-body (or wearable) device that can be located on either surface or up to 20 mm away from skin [2].  
* _Gateway._ Typically, this has no direct connection to an implanted device or sensor. A smartphone or other personal data device is needed to enable to the collection, processing and transmission of data to doctors and nurses via the internet [3].
# A Comparative Performance Analysis of Four Wind Turbines with Counter-Rotating Electric Generators
## 3 Closed-Form Modeling
### Kinematic Modeling  
The transmission with three inputs (Case A, Figure 1a) is characterized by two independent external speeds; for instance, \(\omega_{R1}\) and \(\omega_{R2}\). For simplicity, it is preferable to use the input speed ratio \(k_{\omega}=-\omega_{R2}/\omega_{R1}\) as an independent variable instead of the speed \(\omega_{R2}\). According to Figure 2a, the following correlations related to the connections between the WSCG components can be added to the kinematic equations in Systems (1) and (2):  
\[\left\{\begin{array}{l}\omega_{R1}=\omega_{2}=\omega_{H_{2}}\\ \omega_{R2}=\omega_{9};\ \ \omega_{R2}=-k_{\omega}\omega_{R1}\\ \omega_{9}=\omega_{H_{1}}\\ \omega_{3}=\omega_{4}=\omega_{7}=\omega_{8}\\ \omega_{7}=\omega_{GS};\ \omega_{8}=\omega_{R3}\\ \omega_{6}=\omega_{GR}\\ \omega_{G}=\omega_{GR}-\omega_{GS}\end{array}\right. \tag{4}\]  
The obtained set of 14 independent equations with 16 unknown parameters allows the description of the 14 dependent kinematic variables (\(\omega_{R2}\), \(\omega_{R3}\), \(\omega_{GR}\), \(\omega_{GS}\), \(\omega_{G}\), \(\omega_{2}\), \(\omega_{3}\), \(\omega_{4}\), \(\omega_{6}\), \(\omega_{7}\), \(\omega_{8}\), \(\omega_{9}\), \(\omega_{H_{1}}\) and \(\omega_{H_{2}}\)), depending on the independent variables \(\omega_{R1}\) and \(k_{\omega}\).  
The other three cases (B, C and D) can be modeled kinematically based on the relations previously obtained for the archetype model, in which the following particularizations occur:  
* Case B: C1 = 1, C2 = 0 and \(\omega_{R2}=0\); implicitly: \(\omega_{9}=\omega_{H1}=0\) (Figure 2b);
* Case C: C3 = 0 and \(\omega_{R3}=0\); implicitly: \(\omega_{8}=0\) and \(\omega_{7}=\omega_{4}=\omega_{3}\) (\(\
eq 0\)) (Figure 2c);
* Case D: C1 = 1, C2 = C3 = 0 and \(\omega_{R2}=0=\omega_{R3}=0\); implicitly: \(\omega_{9}=\omega_{H1}=0\), \(\omega_{8}=0\) and \(\omega_{7}=\omega_{4}=\omega_{3}\) (\(\
eq 0\)) (Figure 2d). The transmission amplification ratio \(i_{a}\) can be obtained with the relation: \[i_{a}=\frac{\omega_{G}}{\omega_{R1}}\] (5)  
The kinematic correlations of the speed increasers from Figure 1, systematized in Table 1, can be obtained by solving the set of kinematic Equations (1), (2), (4) and (5) and considering the previous particularizations. It can be observed that, except for the speed \(\omega_{R3}\), the relations are identical for Cases A and C (2-DOF variants) and B and D (1-DOF variants), respectively.
# Blockchain for Doping Control Applications in Sports: A Conceptual Approach
## 2 Background
### Analysis of the Current AD Ecosystem
**Non-Digitalized Processes:** Some important processes are still relying on paperwork or semi-digitalized processes. Relevant examples are the collection and the chain of custody (CoC) of doping control samples, the TUE request, and the Athletes' Whereabouts [56; 57; 63; 4]. Non-digitalized processes are more easily circumvented or frauded, with no evident signs being left behind. One of the most notorious scandals occurred in the 2014 Sochi Winter Olympic Games. A state-sponsored, audacious, and large-scale fraud scheme for swapping compromised urine samples and tampering with positive results was established, and only revealed later thanks to one of the participants who decided to blow the whistle on the scheme [7; 68; 69]. To make these processes safer, the adoption of digital technologies, such as biometry for identity assurance, electronic forms, IoT sensors, and other forms of digitalization, can play an important role. **Insufficient Anonymization and Privacy:** To perform their tasks and following a selective security criterion, organizational staff have privileged access to records [5]. Although participants of the AD program assume the responsibility to comply with the International Standard for the Protection of Privacy and Personal Information (SPPPI) [4; 58], trust is totally on the individuals, making it difficult to trace any eventual leakage of personal data. Another example is the samples' CoC, as each bottle is anonymized, meaning the athletes' identities are not informed to the laboratories. However, a bottle's apparent identification number is informed to the athlete; then, if corrupt members within the organizations are inclined to trade in favor of athletes willing to clean their samples, bottle swapping schemes can be established, as already mentioned in the Sochi Winter Olympics case [68]. The same is valid for the TUE request; once the submission is undertaken, the identity of the requestor will become known at different checkpoints in the process by internal staff within the involved organizations [7; 68; 69]. The adoption of digital technologies for anonymization of physical or digital assets can be helpful to avoid privacy leaking issues. Figure 1 exemplifies two of the current processes within the AD ecosystem, doping control and TUE request, highlighting some of their vulnerabilities. Figure 1: Examples of the current doping control and TUE request processes and their vulnerabilities.
## 3 Results and Discussion
### Oedometer Test Results
Variability in the Solid Particle Density Caused by the Decomposition Process and Its Influence on the Final Void Ratio and Dry Density Values  
During the anaerobic decomposition phase, the specimen's dry mass and height decreased by 0.084 kg and 1.89 mm, respectively. With respect to the initial value, the specimen's dry mass decreased by 20%. Evidently, the loss of the organic particles reduced the dry mass and opened additional void space, while at the same time, the compaction of the sample caused by the overburdened pressure took place. Moreover, the solid particle density of the tested specimens, which was measured with the air pycnometer, also increased to 2053.7 kg/m\({}^{3}\) (or 11%) with respect to the initial solid particle density value. Both the decrease in the dry mass and the increase in the solid particle density can be solely attributed to the loss of biodegradable organic matter. It is worth noting that other researchers [3; 8] also confirmed that the solid particle density of organic waste materials increased with the progression of the decomposition process. Figure 5: Coupled air pycnometer and oedometer test results. Except for decomposition, the solid particle density of waste materials also increased with the increase in the applied vertical stress [25; 26]. However, for the MBT waste material considered within this research, the compaction process increased the solid particle density only by a negligible amount [15]. Therefore, the increase in the solid particle density in the present research was solely attributed to the decomposition process. As can be seen from Figure 5, the void ratio and corresponding dry density at the beginning of the anaerobic decomposition phase were 1.79 and 641.4 kg/m\({}^{3}\), respectively. At the end of the anaerobic decomposition phase, the recorded dry density and corresponding void ratio were 540 kg/m\({}^{3}\) and 2.80, respectively. Therefore, with respect to the initial values, the final dry density decreased by 16%, while the final void ratio increased by 36%. From the definition of dry density, it becomes obvious that the loss of mass caused by the degradation process had a greater impact on the final dry density than did the simultaneous decrease in the sample volume due to the applied vertical stress. Clearly, the amount of settlement caused by the decomposition process was not sufficient to reduce the sample volume to such an extent that it could efficiently compensate for mass loss. Consequently, the final void ratio and final dry density did not exceed their values from the beginning of the decomposition process, as might be anticipated when considering only the short-term oedometric test results. Although additional settlements caused by the decomposition process would act beneficially to the total landfill capacity, it would also decrease the dry density values and produce a more porous material.
###### Abstract  
Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60\({}^{\circ}\) corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field.  
deep learning; electromagnetic characteristics; multi-parameter optimization; neural network +
Footnote †: journal: Applied sciences  
[PERSON], [PERSON], [PERSON], H. A Multi-Parameter Optimization Method for Electromagnetic Characteristics Fitting Based on Deep Learning. _Appl. Sci._**2024**, _14_, 10652. [[https://doi.org/10.3309/app142210652](https://doi.org/10.3309/app142210652)]([https://doi.org/10.3309/app142210652](https://doi.org/10.3309/app142210652))
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 3 Experiment and Results
### Experimental Settings  
We divide the images of two datasets into the train set, the validation set and the test set according to the defect's ratio of 4:1:1. The training set is doubled with random flips. The batch size is 4 for the denim dataset and 32 for the plain fabric dataset because of their difference image size. The optimizer is stochastic gradient descent optimizer (SGD); the momentum and weight decay are, respectively, set as 0.9 and 0.0001. The defect probability threshold \(\Delta L\) is set as 0.5. The initial learning rate of the network is 0.005 and trained 20 epochs in total. The learning rate is reduced to 0.0005 and 0.00005 at epoch 8 and 11.  
The training loss value curves and the accuracy curve of PRAN-Net for the denim dataset are shown in Figure 10. It can be seen that as the number of epochs increases, the curves of the train loss value tend to convergence. When the epoch is greater than 12, the total loss is steady at 0.04, and the corresponding accuracy reaches the maximum and tends to be stable.
# Two-Stage Beamforming Technique for GNSS Applications +
## 4 Conclusions  
In this paper, we have provided a robust beamforming technique using array antennas. The proposed solution constitutes two stages, the first stage exploits an array antenna system to decompose the received signals into direct and reflected signals based on the SAGE algorithm. The second stage, on the other hand, exploits the MVDR algorithm (with the decomposed and de-correlated signals) to produce the weight vector that steers the main beam towards the satellite's direction and the nulls towards the multipath effect.  
Experimental analysis was conducted in an open-sky static environment using a four-RF-port Vector Signal Transceiver (VST) system, manufactured by National Instruments (NI) as part of the ESA NAVISP program. The experimental results show a significant improvement in the estimated CN0 values, using the proposed solution, compared to a single-antenna system. Furthermore, in the experimental conditions, the traditional MVDR beamformer could not track the GNSS signals; this can be attributed to the fact that the MVDR beamforming capability deteriorates with the presence of multipath effect and the low sample size of the received signals that are used to construct the array antenna covariance matrix.  
**Author Contributions:** Conceptualization, N.B.; methodology, N.B.; software, N.B.; validation, N.B., S.C. and S.B.; formal analysis, N.B.; investigation, N.B.; resources, N.B.; data curation, N.B.; writing--original draft preparation, N.B.; writing--review and editing, N.B., S.C., S.B., R.F. and P.C.; visualization, N.B.; supervision, N.B.; project administration, R.F.; funding acquisition, R.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the European Space Agency (ESA) Navigation Innovation and Support Program (NAVISP).  
**Institutional Review Board Statement:** Not applicable.  
Figure 5: Obtained positioning estimation error; the error is presented in terms of deviation from true 3D position in East (E), North (N), and Up (U).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article. The content of the present article reflects solely the authors' view and by no means represents the official ESA view.  
**Conflicts of Interest:** Author [PERSON], [PERSON] and [PERSON] were employed by the company National Instruments. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.