esa-sceva/satcom-chunk-collection huggingface dataset:
# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
## 4 Discussion  
### Limitations  
This paper acknowledges certain limitations inherent in the field of DLP. Firstly, the challenge of evidence-based security in cybersecurity [54], particularly in practical contexts, is notable. DLP frameworks and organizational strategies, typically shrouded in confidentiality, pose a barrier to developing an evidence-based approach. This lack of transparent evidence may hinder the continuous evolution and validation of DLP practices. Secondly, while the DLP Maturity Model addresses a broad spectrum of potential threats, it may not fully encompass data leakage by highly-obfuscated cyberattacks, like [55, 56, 57].  
Figure 6: Detailed outcomes as a management dashboard after the self-evaluation on the domain level, inspired by the HTML-based Self-Evaluation Tool [53].  
Such sophisticated threats require a level of detection and response beyond the scope of most existing DLP solutions and might also be seen as part of SIEM or advanced anti-virus tools. This limitation suggests the need for ongoing refinement of DLP strategies to adapt to evolving and increasingly concealed cyber threats.
# Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover
## 1 Introduction
When investigating opportunistic navigation and time keeping for practical purposes, one should not assume the best case (no remote clock error) nor the worst case (maximum permissible clock error according to the specification) but, where possible, employ an interval specific stability metric to inform both the measurement update rate employed and the associated instability parameters expected.
## 5 Regulatory Aspects of Cyclotron-Produced \({}^{68}\)Ga Radiopharmaceuticals
\begin{table}
\begin{tabular}{c l l l} \hline \hline  & **Good Manufacturing Practice (GMP)** & & **Good Preparation Practice (GPP)** \\ \hline \(\bullet\) & Guidance for Industry on Manufacture of & \(\bullet\) & Guidance for Healthcare Establishments \\  & Medicinal Products & & on the Preparation of Medicinal Products \\ \hline \(\bullet\) & Distribution of manufactured & & \\  & radiopharmaceuticals to & \(\bullet\) & Direct supply to patient for in-house use \\  & local/international market & & \\ \hline \(\bullet\) & Manufacture in large-scale of & \(\bullet\) & Preparation in a small scale of \\  & radiopharmaceuticals & & radiopharmaceuticals according to \\ \hline \(\bullet\) & Starting from raw materials to finished & \(\bullet\) & Products are being prepared in dilution or \\  & products & & reconstitution and/or mixing of products \\ \hline \(\bullet\) & High-Risk Preparation & \(\bullet\) & Low-Risk Preparation \\ \hline \(\bullet\) & Distribution of radiopharmaceuticals & \(\bullet\) & Distribution of radiopharmaceuticals \\  & needs to obtain Marketing Authorization & & does not need for Marketing \\  & (MA) & & Authorization (MA) \\ \hline \(\bullet\) & Full quality control test as per monograph & \(\bullet\) & Partial quality control test based on \\  & & manufacturer recommendation or \\  & & in-house method \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Main differences between Good Manufacturing Practices (GMP) and Good Preparation Practices (GPP). is the same as the one generator produced except that cyclotron-produced \({}^{68}\)Ga needs additional steps, such as processing of the solid target and purification steps before it is in the form of ready-to-label [\({}^{68}\)Ga]GaCl\({}_{3}\). These additional steps are critical and will determine the fate of the final product; therefore, it is considered a high-risk preparation that fits the manufacturing element. When considering GMP for cyclotron-based \({}^{68}\)Ga radiopharmaceuticals, a few parameters need to be considered starting from target transfer and processing, chemical preparations, synthesis module software, process validation, quality control testing, and metal testing. These parameters will be discussed briefly in the following subsection.
# Measuring the Positions of the Solder Pins of Electrical Connectors from the Side
## 1 Introduction
The in-plane accuracy of this system was 0.066 mm, and the depth accuracy was 0.084 mm. [PERSON] et al. [15] also adopted two cameras to inspect connectors. They developed an algorithm based on deep neural networks and pattern matching so that the vision system could be used on multiple types of connectors. They demonstrated that the algorithm could be used to inspect 33 different types of connectors successfully. Other methods that can be used to measure the positions of the pins include 3D laser triangulation [16] and a structured light pattern projector. A comparison of these methods can be found in [17]. Recently, augmented reality and deep learning [18; 19; 20] were introduced into the industry for inspecting connectors, but they are mainly for small pin detection, pin sequencing, and mismatched pin visualization. If machine vision, or automated optical inspection, can be used to measure the positions of the connector pins and the measurement difficulties reduced, it will significantly help the connector industry in terms of inspection. Furthermore, the camera is usually placed on top of the pins when using machine vision to measure the pins' positions. However, the area of the pin tip is small, and the surface is uneven. Thus, it is prone to irregular light source reflections that interfere with the image, causing measurement errors [5]. Nevertheless, this problem may be avoided if the image is not taken from the end face of the solder pins. Therefore, this research aimed to establish a machine vision inspection system, which measures from the side of the connector pins and calculates the positions of the pins by capturing images in two different view directions. The results of this research can be applied to the connector and the electronics industries to provide innovative methods for pin inspection.
# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
## 4 Proposed Indoor Positioning
### Fingerprinting Scheme  
The fingerprinting scheme is a method of constructing a database by measuring RSSI values at a specific location in the offline step. In the case of a real environment, the RSSI value from the AP must be collected at a certain location. In recent years, as indoor environments have become wider and more complex, i.e., large department stores, skyscrapers, and airports--big data technology that can store a large number of RSS samples has been needed when constructing fingerprinting databases. Hence, if a large number of SPs are used, problems arise in terms of time to measure the RSSI value for each SP and cost when managing the measured data. Conversely, if a small number of SPs are used, the error in positioning accuracy increases. Therefore, in a real environment, the two aspects should be considered, and an appropriate number of SPs suitable for the size of the positioning environment should be used. Due to this problem, in this paper, we perform a fingerprinting scheme based on simulation. To conduct this, we initially place the SP at a certain location. After that, each AP calculates the RSSI value for each SP based on (1) and builds the fingerprint database \(H^{RSSI}\). The established fingerprinting database \(H^{RSSI}\) can be expressed as (3) below.  
\[H^{RSSI}=\left[\begin{array}{cccc}h_{1}^{1}&\cdots&h_{1}^{m}&\cdots&h_{1}^{M} \\ \vdots&\vdots&\vdots&\vdots\\ h_{n}^{1}&\cdots&h_{n}^{m}&\cdots&h_{n}^{M}\\ \vdots&\vdots&&\vdots\\ h_{N}^{1}&\cdots&h_{N}^{m}&\cdots&h_{N}^{M}\\ \end{array}\right] \tag{3}\]  
where \(h_{n}^{m}\) represents an RSSI value between the \(m\)-th AP and the \(n\)-th SP. Thereafter, the \(H^{RSSI}\) value is used to estimate the actual user's position in WFM.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 5 Results
### Performance Improvement via Proposed Cycle Slip Detection Strategy for a UAV  
Compared to the GNSS measurements received from a stationary GNSS receiver, we found that the code and carrier phase measurements of a UAV contain a relatively large level of noise. Figure 15a compares experimental distributions of the TDDFC metrics from the static GNSS receivers and the flight test data. Therefore, using the well-known carrier phase measurement noise model derived from static ground receivers in [36] may lead to a significant number of false alarms in detecting cycle slips. The effect of tight thresholds not only reduces the average number of carrier phase measurements, but frequent exclusions and re-introduction of satellites into the KF lead to poor convergence performance overall.  
Figure 14: Box plots comparing (**a**) the percentage of ratio test passes and (**b**) 3D RMSE with and without GNSS measurements time synchronizations based on the 45 flight test data sets.  
Figure 15b shows the minimum, maximum, and average number of carrier phase measurements remaining after the proposed cycle slip detectors with threshold in [36] and from Table 3, respectively. It can be clearly seen that with the TDDFC threshold in [36], a large amount of GNSS data from moving UAVs would be declared as a cycle-slipped carrier phase and excluded accordingly.
# Acoustic and Dynamic Response of Unbaffled Plates of Arbitrary Shape
## 2 Models and Methods
### Calculation of the Dynamic Response of the Structure Immersed in the Diffuse Field  
A diffuse field can be modeled by superpositioning plane waves traveling in different directions ([PERSON] et al. (2014); [PERSON] et al. (2014); [PERSON] and [PERSON], (2012); [PERSON] (2018). Let us assume that the elemental power spectral density of the incident wave in the \(ij\) direction, defined by angles \(\theta\) and \(\phi\) (Figure 4), is \(W_{p_{ij}}(\omega)\) (in Pa\({}^{2}\)/Hz). If the different directions of the incident waves are uncorrelated, the total power spectral density \(\hat{W}_{\text{p}}\) will be the sum of all the elemental densities. As the acoustic requirement of a launcher is expressed in N dB per octave for a specific bandwidth (ESA PSS-03-204, (2017)), the total power spectral density \(W_{p}\) is constant over such a bandwidth, which results in the following expression:  
\[N=10\log\frac{P^{2}}{P_{r}^{2}}=10\log\frac{\hat{W}_{p}\Delta f}{P_{r}^{2}} \tag{24}\]  
where \(Pr=2\times 10^{-5}\) Pa is the reference pressure. From Equation (24), the power spectral density equation can be written as follows:  
\[\hat{W}_{p}=\frac{P_{r}^{2}}{\Delta f}10^{N/_{10}} \tag{25}\]  
Figure 4: Geometrical representation of the unbaffled plate submitted to plane wave.  
The space field is divided into a finite number of directions \(\theta_{i}\) and \(\varphi_{j}\). There is a solid angle \(\mathrm{a}_{ij}\) associated with each direction. It is assumed that the power spectral density in the \(ij\) direction is \(a_{ij}\cdot\hat{W}_{p}(\omega)\), where \(\hat{W}_{p}\) is the total power spectral density.  
When all problem parameters have been defined, the structural response to the diffuse field is obtained as follows.  
Let \(H_{rij}(\omega)\) be the transfer function between the \(r\) degree of freedom of the structure and the plane wave in the \(ij\) direction. The power spectral density induced by all plane waves in the solid angle corresponding to the \(ij\) direction for the \(r\)th degree of freedom is obtained using Equation (26):  
\[\hat{W}_{r,ij}=\big{|}H_{r,ij}(\omega)\big{|}^{2}\hat{W}_{pij}=\big{|}H_{r,ij} (\omega)\big{|}^{2}a_{ij}\hat{W}_{p} \tag{26}\]  
As all directions are uncorrelated, it can be demonstrated that for a random field, the structural response is obtained as the sum of the elemental spectral densities ([PERSON] and [PERSON], [35]):  
\[\hat{W}_{r}(\omega)=\hat{W}_{p}(\omega)\sum_{i,j}\big{|}H_{r,ij}(\omega)\big{|} ^{2}a_{ij} \tag{27}\]  
Thus, the relationship between the power spectral density of the \(r\) degree of freedom of the structure, the acoustic power spectral density \(W_{p}\), and the transference function between the \(r\) degree of freedom and the plane waves consisting of the diffuse field is established.
## 3 Data Integrity Ontology in SPuMoNI  
### SPuMoNI RegTech Architecture  
Industrial system validation processes involve specific rules and related guidance documents, thus defining a regulatory compliance framework of a specific industrial environment. The SPuMoNI system architecture has been designed to support dynamic data quality assessment, regulatory compliance and auditability of pharmaceutical manufacturing processes and data. Within this framework, the RegTech of the SPuMoNI system, whose architecture is depicted in Figure 1, supports required regulatory validation processes, meeting dynamically changing regulatory compliance requirements. Since the RegTech architecture has been particularly designed with the aim to check ALCOA data integrity rule compliance, an ALCOA assessment module has been included in the RegTech architecture. This ALCOA assessment module is the core module of RegTech architecture and it is responsible for interpreting ALCOA principle rules, for performing ALCOA compliance evaluation, and for producing reports. This module, as seen in Figure 1, is responsible for extracting heterogeneous raw data from corresponding system sources and running validation checks to yield the final positive or negative decision of ALCOA compliance, or non-compliance, respectively.
## 3 Results and Discussion
### Macroscopic and Microscopic Investigation of Complex Coacrerate Formation
#### 3.2.2 Microscopic Aspect of Coacervates  
The preparation of the microcapsules was tracked using optical microscopy imaging, captured at different magnification levels (40\(\times\), 100\(\times\), 400\(\times\), and 1000\(\times\)). Samples for microscopic investigations were taken during the third washing cycle from the microcapsule dispersion. From the lower magnifications (40\(\times\), 100\(\times\)), it could be observed that polydisperse circular-shaped microcapsules were formed with diameters in the range of 14-132 \(\upmu\)m. On images recorded at higher magnifications (400\(\times\), 1000\(\times\)), it could be noticed that microcapsules have a polynucleated core, which is formed from essential oil droplets accumulated in the center of the microcapsules, and there is a narrow droplet-free layer surrounding the interior wall of the microcapsules. The optical microscopic investigations of microcapsules are presented in Figure 5.  
The particle size characteristics are presented in Table 5.  
Figure 4: Narrow essential oil microcapsule sediment.  
Figure 5: Optical microscopic images of yarrow essential oil containing microcapsules at different magnifications.  
These morphological characteristics were also observed by [PERSON] et al. when they produced microcapsules of ascorbic acid through complex coacervation using gelatin and gum arabic [55]. According to [PERSON] and [PERSON], the diameters of moist particles ranged between 43.7 \(\pm\) 3.4 and 96.4 \(\pm\) 10.3 \(\upmu\)m when these authors produced microcapsules of paprika oleoresin by complex coacervation using similar encapsulating agents [56].
## 5 Case Studies
### Validation by Physical Machining Experiment  
The proposed method is also verified by physically machining an aluminum alloy aero-engine casing part on a DMG 80P machine tool. In the experiment, the machining parameters are set as follows: spindle speed 6000 rpm, cutting depth 1.25 mm, cutting width 8 mm, feed rate 1000 mm/min, and tool diameter 20 mm. In order to monitor the radial deformation force of the part, a fixture with both clamping force control and monitoring function is designed and fabricated as shown in Figure 10(a). The fixture consists of a flat positioning element, a plurality of axial clamping element, and the auxiliary supports arranged along the circumference.  
The part is machined while the change in clamping force being monitored and recorded, as shown in Figure 10(b). The pre-support force is exerted according to the proposed model, and the deformation forces are monitored/recorded at certain machining steps of the in-process workpiece, based on which the corresp  
Figure 11: The physical machining environment and machining process. (**a**) The physical machining environment; (**b**) Machining process.  
Figure 10: Clamping deformation and machining deformation at each machining step.  
is calculated. Figure 12 shows the deformation forces monitored in the experiment. It is necessary to mention that, if the thickness of the part is too large, it will be inaccurate to calculate the deformation of the measurement points which will be outside the part based on the calculated deformation forces at the clamping points. The amounts (in mm) of deformation respectively obtained by our estimation model and physical measurement are both listed in Table 2, which show an error range of 0.001-0.008 mm.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 4 Experimental Implementation
### Network Setup  
The ESP-NOW protocol is used to establish a multi-hop network in which each sensor acts as both a data collection point and a relay node. The placement of the nodes is optimized using a REM, ensuring robust coverage across the industrial site. The implementation in the multi-hop network involves configuring ESP32 microcontrollers as source, relay, and gateway nodes, each with unique roles in data transmission:  
* Source Node Configuration: The source node is configured to connect with a local Wi-Fi access point and subscribe to fire detection messages via MQTT. Data updates from the MQTT broker are stored in an internal data structure, including fire status and packet counts. The source node's ESP-NOW settings are configured to scan the Wi-Fi channel and establish peer connections with the next relay node, ensuring uninterrupted data flow. This setup enables the source node to act as a bridge between MQTT data and ESP-NOW's peer-to-peer network.
* Relay Node Operations: Each relay node processes data and filters out redundant packets, forwarding only updated information to the next node. ESP-NOW's OnDataRecv callback is triggered upon data receipt, while the OnDataSent callback confirms successful transmission or re-establishes connections if necessary. These relay nodes, configured in succession, maintain communication coverage over long distances and navigate around obstacles without requiring additional access points.
* Gateway Functions: The gateway connects to the local Wi-Fi network, establishes the MQTT link, and synchronizes all nodes on a unified Wi-Fi channel, maintaining  
Figure 4: Devices used for the REM.  
stable and cohesive communications. By bridging ESP-NOW data to the MQTT broker, it transmits fire alerts to the server for remote visualization and monitoring. This centralization at the gateway allows data aggregation and seamless flow to the server, ensuring reliable and responsive monitoring.
## 2 Predictive Model
### Regression-Based Methods
[77] provide theoretical support for the prediction of faults in highway electromechanical equipment through a panel data model-based multi-factor predictive model. This model is characterized by a two-dimensional multivariate regression analysis based on and individuals and time. Emphasizing the intelligent diagnosis of faults, the classification and regression tree (CART) is used by [PERSON] et al. [78] as a back-end classifier to diagnose synchronous generators. The statistical characteristics of the frequency domain are extracted from the current signals of the in-test generators. According to the work presented by [PERSON] et al. [79], the hidden patterns and nuances of differences between healthy performance firms and several fault signatures using time-series DM for the diagnosis of eccentricities and bar/end-ring connector breakages in polyphase induction motors can be identified. In a more general scenario, [PERSON] and [PERSON] [80] propose a method of failure prediction related to quality by constructing a total principal component regression model, which can divide the space of the variables into two subspaces, and only one of them will be related to the quality fault. Table 2 summarizes the above-reviewed information, where the effectiveness percentage of each method is also presented; from this information, it is evident that the SVMs are one of the most used methods for fault detection. Many authors agree that SVMs are more robust than other algorithms and satisfy the minimization of structural risk; yet, its effectiveness relies on the features and preparation of data. In addition, they have a high correct identification relationship according to the reported effectiveness percentages. In several works, SVMs have presented a better performance than NNs. These works highlight that SVMs reach the global optimum in a more direct way, are less prone to overfitting, present a smaller computational model, etc. Similar to other algorithms such as NNs, once the training stage has been carried out, the computational time to perform a SVM-based diagnosis is relatively short, making it a suitable for online and continuous diagnosis of electric equipment. Although in several works SVMs have presented a low computation cost/time, it cannot be suitably compared if aspects such as effectiveness reached, overfitting issues, robustness, number of hidden layers and neurons per layer, number of nodes, model complexity, activation and kernel functions used, and training algorithms, among others, are not taken into account.
# BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
## 4 CATE with Censored Data
In other words, predictions corresponding to a patient characterized by vector \(\mathbf{z}\) in survival analysis provided by a survival machine learning model are represented in the form of functions of time, for instance, in the form of SF \(S(t\mid\mathbf{z})\). This implies that the CATE \(\tau(\mathbf{x})\) should be reformulated by taking into account the above peculiarity. It is assumed that SFs as well as hazard functions for control and treatment patients, estimated by using datasets \(D_{0}\) and \(D_{1}\), will have indices 0 and 1, respectively. The following definitions of the CATE in the case of censored data can be found in [58]:  
1. Difference in expected lifetimes: \[\tau(\mathbf{z})=\int_{0}^{t_{\max}}(S_{1}(t\mid\mathbf{z})-S_{0}(t\mid \mathbf{z}))\mathrm{d}t=\mathbb{E}\{T_{1}-T_{0}\mid X=\mathbf{z}\};\] (10)
2. Difference in SFs: \[\tau(t,\mathbf{z})=S_{1}(t\mid\mathbf{z})-S_{0}(t\mid\mathbf{z});\] (11)
3. Hazard ratio: \[\tau(t,\mathbf{z})=\lambda_{1}(t\mid\mathbf{z})/\lambda_{0}(t\mid\mathbf{z}).\] (12)  
We will the first integral definition of the CATE. Let \(0=t_{0}<t_{1}<\ldots<t_{n}\) be the distinct times to an event of interest, which are obtained from the set \(\{f_{1},\ldots,f_{c}\}\cup\{h_{1},\ldots,h_{s}\}\). The SF provided by a survival machine learning model is a step function, i.e., it can be represented as \(S(t\mid\mathbf{z})=\sum_{j=1}^{n}S^{(j)}(\mathbf{z})\cdot\chi_{j}(t)\), where \(\chi_{j}(t)\) is the indicator function, taking a value of 1 if \(t\in[t_{j-1},t_{j}]\); \(S^{(j)}(\mathbf{z})\) is the value of the SF in interval \([t_{j-1},t_{j}]\). Hence, the following holds:  
\[\tau(\mathbf{z}) =\int_{0}^{t_{\max}}(S_{1}(t\mid\mathbf{z})-S_{0}(t\mid\mathbf{z})) \mathrm{d}t\] \[=\sum_{j=1}^{n}\Bigl{(}S_{1}^{(j)}(\mathbf{z})-S_{0}^{(j)}( \mathbf{z})\Bigr{)}(t_{j}-t_{j-1}). \tag{13}\]