esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Performance Analysis
### System-Level Simulation # 1  
For this simulation, the group delay in Path-1 and power variation in Path-2 are extracted as the amplifier is driven into compression with the frequency sweep from 22 to 34 GHz. The group delay is shown in Figure 17a. As evident from the figure, the peak group delay in Path-1 is about 700 ps when the input power to the amplifier is 0 dBm. Also, the group delay contribution from the standalone amplifier is about 250-300 ps which leads to a 400 ps contribution from the integrated passive component block. This 400 ps group delay is comparable to state-of-the-art filters in industry: TDK \(\sim\)250 ps [48], Knowles \(>\)400 ps [49] and \(>\)1000 ps [50]. The RF source power is varied from 0 to 20 dBm with the step size set to 5 dBm. The power variation at the detector port is recorded and it is given by (1):  
\[\begin{split} P_{Det}(\text{dBm})=P_{RF\_in}(\text{dBm})+Gain_{Amp }.(\text{dB})\\ -Loss_{Dip}.(\text{dB})-C(\text{dB})\end{split} \tag{1}\]  
where \(P_{RF\_in}\) is the power level of the RF source, \(Gain_{Amp}\) is the amplifier gain, \(Loss_{Dip}\) is the insertion loss of the band n257 filter in the diplexer and \(C\) is the coupling factor of the coupler. The amplifier is driven into saturation when \(P_{RF\_in}\) reaches 8 dBm, and afterwards, it operates in compression. The power at the detector port saturates as the RF source power is increased, as observed in Figure 17b. A peak power detector can be used to track power variation to form a feedback loop to control the output power of the PA.  
Figure 16: (**a**) PA FEM block with highlighted simulation paths, and (**b**) integrated passive component block variant designed for S\({}_{21}\) measurement only.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 5 Detailed System Description
### User Obtains Ads
#### 5.5.2 Advert Reception through an Agent  
When the Agent encounters the Requester after a collection has been made and after the two users have authenticated each other and logged the meeting, the Requester sends an inquiry about the state of the order to which the Agent can respond by transferring the remaining encrypted ADMs. The Requester decrypts the ADMs and obtains the AD-IDs of his/her requested adverts, the sequence numbers of the Bundles they are located in and the \(DK_{i}^{AD}\) keys that can be used to decrypt them.  
The Requester can now ask for the specific Bundles, locate his/her ads and then decrypt them. Once the process has been deemed successful, the Requester stores the adverts for future viewing and removes the corresponding ARMs and \(K_{i}^{user}\) from his/her queue of pending requests, where they were placed when the request was made. In the event that an Agent fails to deliver after a certain period of time has passed that is determined by the Requester, then the Requester may reinstate the ARMs from his/her queue of pending requests and proceed to forward them to a different Agent.
## 4 Methodology
### Digital Image Processing
#### 4.2.6 Minimum Noise Fraction (MNF)  
Minimum noise fraction (MNF) is an orthogonal transformation that is a very useful algorithm for reducing the inherent dimensionality of multispectral data and reducing noise in images [23]. MNF is essentially two cascaded principal component transformations, where the first transformation is used to separate and rescale the noise in the data, and the second transformation is the standard principal component transformation of the noise-whitened data [56]. MNF transformation is also commonly applied directly to vegetation or lithological mapping [46; 69; 71; 79; 88].
## 3 Results and Discussion
### Limitations of this Study  
* It must be noted here that this study did not investigate the direct effect of impacts on unit load integrity; thus, the severity of the recorded impact events is not suitable to define the possible damage of unit loads in general.
* Determining a complete damage boundary curve for a unique unit load design requires extensive damage analysis based on appropriate test series.
## 3 Proposed Technology Solution  
To address the above-mentioned challenge, we use edge computing for privacy preservation, which utilizes edge computing capabilities to perform context-aware processing and aggregation closer to the data source, minimizing the need to transmit sensitive information over the network [14; 15]. A physical biometrics device, AccesSensor, with an embedded wireless sensor has the capability for fingerprint recognition system input, as seen in Figure 2.  
The AccesSensor acquires fingerprint images using biometric sensors [16] as displayed in Figure 3 Preprocess the fingerprint images to enhance quality and remove noise. This involves the filtering technique.  
Figure 2: AccesSensor User Validation.  
Extract discriminative features from the preprocessed fingerprint images. Common features include ridge orientation and patterns to gather data such as the Finger ID = f.ID [17]. This is then stored in the system's database within the edge network server. Based on demand, the system deploys the Gabor filtering and feature point detection techniques to extract relevant features from the fingerprint images [18]. The system uses the minutiae-based technique, where the features of the minutiae points (ridge endings and bifurcations) are detected. Let \(I(x,y)\) be the preprocessed fingerprint image, where \((x,y)\) are pixel coordinates. The equation for feature extraction using the minutiae-based approach can be represented as:  
\[F=1,2,\ldots,n,F=(f1,f2,\ldots,fn) \tag{1}\]  
where \(F\) is the set of extracted features, and \(fi\) = the feature descriptor inputted for a region in the fingerprint image Extracting discriminative features from preprocessed fingerprint images involves various techniques tailored to each specific feature [19]. There are some methods commonly used to extract, which include ridge orientation, ridge frequency, minutiae points, and texture descriptors (Gabor Filters). In this work, the Gabor filters are used to extract texture features by convolving the fingerprint image with a set of Gabor filter kernels at different orientations and frequencies, capturing texture variations at multiple scales [20].
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 3 Satellite Development Lessons  
Building a satellite requires intelligent decisions during the initial stages of the project. The management team challenged the idea of adherence to a CubeSat standard because, in a long-term vision of reaching capabilities for the MAT-type a mission [42], the CubeSat-type satellite is not required as the whole rocket can be used to launch tens or hundreds of spacecraft simultaneously using arbitrary design spacecraft. The management team learned that for technology demonstration purposes, the CubeSat standard would be the best design choice for the project. This helped us focus on the satellite design choices that must match the mission requirements and the team's capabilities, which can change  
Figure 14: In-house developed circularly polarized S-band patch antenna.  
over time. In addition, existing and planned piggyback opportunities for CubeSat-based missions would allow the team to save funding.  
In 2015, after surveying the available COTS satellite platforms, the team decided to take on a serious engineering challenge to develop the universal, compact, and integrated satellite bus platform in a two to three-year timeline. This decision was made because available COTS satellite solutions could not satisfy the mission requirements. The ambitious goal included developing a satellite structure with deployable panels, compact avionics electronics module, and side panels (see Sections 2.3.1-2.3.3) for interplanetary spacecraft prototype. Although we could not make a fully universal platform because payloads and procured COTS systems (see Section 2.3.6) required a custom interface development, designing the whole satellite from the ground up was a fantastic learning opportunity for every student who joined the project.
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
###### Abstract  
In this connected world, communication in all kinds of complex environments is crucial. As a result, indoor satellite communication could enable many new applications and use cases. In this study, we explore the potential of Low Earth Orbit (LEO) satellites to provide indoor coverage. This is done by evaluating the signal strength of Orbcomm LEO satellite signals in multiple indoor environments within a suburban home. Starting from IQ samples, we developed an algorithm to calculate the Carrier-to-Noise Density Ratio (C/N0) as a key performance metric to compare environments when the Carrier-To-Noise Ratio (CNR) is above 0 dB. By utilizing a Software Defined Radio (SDR) in combination with this algorithm, we were able to evaluate the signal strength differences between environments. We found that the LEO satellite signals penetrated into every environment including the basement. The signals were even received with high signal strength in the static, reaching values above 55 dB-Hz. Moreover, the signals were well received in every above-ground environment. Unsurprisingly, the satellite signals were received the weakest in the basement and only for a short duration of time.  
LEO; Orbcomm; signal strength; indoor satellite communication; LEO-PNT; satellite communication +
Footnote †: Corresponding: [EMAIL_ADDRESS]  
Footnote †: Presented at the European Navigation Conference 2024, Noordwijk, The Netherlands, 22-24 May 2024.  
###### Abstract  
In this connectioned world, communication in all kinds of complex environments is crucial. As a result, indoor satellite communication could enable many new applications and use cases. In this study, we explore the potential of Low Earth Orbit (LEO) satellites to provide indoor coverage. This is done by evaluating the signal strength of Orbcomm LEO satellite signals in multiple indoor environments within a suburban home. Starting from IQ samples, we developed an algorithm to calculate the Carrier-to-Noise Density Ratio (C/N0) as a key performance metric to compare environments when the Carrier-To-Noise Ratio (CNR) is above 0 dB. By utilizing a Software Defined Radio (SDR) in combination with this algorithm, we were able to evaluate the signal strength differences between environments. We found that the LEO satellite signals penetrated into every environment including the basement. The signals were even received with high signal strength in the static, reaching values above 55 dB-Hz. Moreover, the signals were well received in every above-ground environment. Unsurprisingly, the satellite signals were received the weakest in the basement and only for a short duration of time.  
LEO; Orbcomm; signal strength; indoor satellite communication; LEO-PNT; satellite communication
# Modeling the Submergence Depth of Oil Well States and Its Applications
## 2 Materials and Methods  
### Submergence Depth Model Construction  
When an oil well is pumping, it is either in a full pumping state or a nonfull pumping state. For a given pumping speed, when the submergence depth is greater than or equal to the stroke height of the oil well pump, it is in the full-pumping state; otherwise, it is in the nonfull-pumping state. Whether the oil well is in a full-pumping state or a nonfull-pumping state, it meets the following fluid balance equation:  
\[Q_{In}-Q_{Out}=Q_{Dif}, \tag{1}\]  
where \(Q_{In}\), \(Q_{Out}\) and \(Q_{Dif}\) represent the input, output, and remaining quantities, respectively. For oil well pumping, these quantities represent the permeability of the reservoir (m\({}^{3}\)/s), the pumping capacity (m\({}^{3}\)/s), and the remaining capacity of the oil well (m\({}^{3}\)/s), respectively.  
In fact, the fluid balance equation is also applicable for describing the balance state of other substances with similar balance properties, such as liquids whose viscosities are lower than a certain value, gases, and electric charges. In the following, the balance equation is used to construct the models of the submergence depth of oil wells in the full-pumping state and the nonfull-pumping state.
## 6 Challenges and Opportunities for EO Capacity Building in Africa
Although African institutions such as SANSA and RCMRD have previously contributed lectures to CEOS WGCaPD webinars, the potential of online distance learning has not been fully explored and exploited by African organizations. As such, emerging space agencies in Africa are missing out on leveraging each other's know-how, as well as those of well-established international organizations, in augmenting EO skills gaps in the continent. Perhaps one of the major limitations for the wide adoption of online distance learning, as well as adopting cloud platforms such as GEE, is the ICT challenges, particularly slow internet speed. However, the availability of policy frameworks such as the Reference Framework for Harmonization of Telecom/ICT Policies and Regulations in Africa indicates that there is vision, interest and determination to improve the current situation. In addition, African Space Strategy recognizes the contribution of ICT in promoting effective governance, influencing policy and regulatory frameworks. Therefore, African emerging space agencies should promote and expose users to existing platforms and also develop their own capacities in preparation for future possibilities for offering online distance education, as ICT infrastructure improves and the difference between face-to-face and online education is increasingly becoming blurred.
## 2 Results and Discussion
### Generation of Water Window IAPs from an MIR Waveform Synthesizer  
For IAP generation by a single-color pump, a driving laser with a few-cycle duration is generally required. In contrast, a driving laser with much longer duration (multicycle) can be employed to generate a significantly stronger IAP in a waveform synthesizer [33, 34, 35, 36], where the central peak of the pulse envelope is close to that of a subcycle pulse but with a modified/optimized electric field waveform. In this section, we simulate the generation of IAPs in the water window region using a multicycle MIR waveform synthesizer in a gas cell.
# A Multi-Objective Evaluation Method for Smart Highway Operation and Management
## 2 Literature Review
### Methods Available for the Establishment of Evaluation Systems  
Establishing an evaluation framework for smart highways has become a crucial issue in the research and exploration of smart highways. The evaluation system for smart highways exhibits characteristics of diversification, and its evaluation methods fall under the research methods of multi-objective decision theory.  
In the process of addressing the issue of weight allocation for multi-objective decision-making, two reliable methods are available [30; 31]. The first category is subjective weighting methods, such as the Analytic Hierarchy Process (AHP) [32; 33], expert scoring method [33; 34], and Fuzzy Analytic Hierarchy Process [35]. The second category is objective weighting methods, including the critic method [36] and entropy weight method [37]. The studies mentioned above have formed the research methods and foundation for the evaluation system of smart highways [38].  
It has been demonstrated that the AHP is suitable for multiple domains such as smart highways and smart transportation. However, it also highlights the issue of the subjective factors of judging experts having a significant impact on the evaluation conclusions, leading to a certain degree of distortion in the final results. According to multi-criteria decision theory [39], the subjective weighting process suffers from issues such as confusing subjective logic and the evaluation attitude of the decision-maker [40]. Objective weighting methods rely entirely on measured data, disregarding the decision-maker's professional experience and knowledge. Therefore, combining and integrating subjective and objective weights is necessary [41]. The use of optimization algorithms to determine subjective weights from experts and objective attribute weights is becoming increasingly widespread [42; 43; 44; 45].  
These research advancements have propelled the evolution of the smart highway evaluation system. However, previous evaluation indicators have primarily focused on roads, traffic operations, the environment, and benefits, neglecting the inclusion of traffic engineering facilities. Secondly, deriving precise indicator weights and formulating a comprehensive evaluation method tailored for assessing smart highways in diverse regions poses a significant challenge. Furthermore, within comprehensive evaluation methodologies, the selection of weights can be either subjective or objective. Consequently, this paper enhances and refines the smart highway evaluation model and methodologies, introducing a more comprehensive evaluation system.
# An Image-Based Fall Detection System for the Elderly
## 2 Related Works
### Wearable-Device-Based Methods  
[PERSON] et al. [10] provided a review of the approaches that have been proposed for fall assessment, fall prevention, and fall detection that use wearable devices. According to their survey, most of the wearable-device-based methods adopted triaxial accelerometers to collect fall signals. Besides this, the review shows that the most-used segment for the wearable device sensor is the trunk of the body, since it is tightly coupled to walking. For example, [PERSON] et al. [2] used triaxial accelerometers distributed over a body to collect acceleration signals. When the acceleration exceeded a pre-defined threshold, the system determined that it was the result of a fall accident. Their method could also determine the injury level by comparing the acceleration at impact with normal acceleration. Similarly, [PERSON] et al. [11], [PERSON] et al. [12], and [PERSON] et al. [13] used machine learning technologies to analyze signals that were received from triaxial accelerometers that were distributed over a body. In addition, [PERSON] et al. [14] detected falls by using not only acceleration information but also angular velocity information. The accuracy of wearable-device-based methods can be improved by obtainingsignals from different kinds of sensors. For example, [PERSON] et al. [15] used location sensors to locate the user. When the user lay on the ground, the system triggered an alarm. [PERSON] [16] and [PERSON] et al. [17] used not only triaxial accelerometers but also gyroscope, magnetometer, and barometer sensors to recognize the posture of users. [PERSON] et al. [18] developed a method to identify users at risk of falls and maintain daily tracking. Similarly, [PERSON] et al. [3] used the accelerometers and gyroscopes in smartphones to detect falls and track users' pathology during rehabilitation tasks. However, the elderly usually forget to wear wearable devices and smartphones. Thus, the existing wearable-device-based methods are not practical for long-term use.
## 2 Description of IFGD Process and Modeling Problem
### Description of IFGD Process and Modeling Problem  
The IFGD process under study is an inherently non-linear dynamic process, and dynamics involved in it is quite complicated even though the overall reaction shown in (1) is simple. The distinct nonlinearity in FGD process stems from the dynamics of thermodynamic relations, chemical reactions, etc. Furthermore, long time delay is often involved in a lot of industrial processes, due to mass transportation, energy exchange and so on [19; 20]. As for the IFGD process under study, the time delay mainly arises from slurry flow through pipes. Empirically, it is found that the time delay in our process is approximately half an hour, which makes FGD modeling really a challenging problem in the field of system identification. In an IFGD process, both desulfurization efficiency and gypsum quality are critically dependent upon the pH of absorber slurry; on the other hand, the slurry flow has a direct influence on the pH value. In this sense, the input and output of the process are chosen as slurry flow (m\({}^{3}\)/h) and pH in absorber slurry, respectively. Let the IFGD process be described by the following single-input single-output (SISO) discrete-time nonlinear dynamical system,  
\[y(k+1)=\Phi[x(k),\cdots,x(k-n+1)]=\Phi[y(k),\cdots,y(k-n+1),u(k),\cdots,u(k-n+1),\theta] \tag{3}\]  
where, \(x(k)\triangleq[u(k),y(k)]\), \(k\in\mathbb{Z}^{+}\) is the time instant, \(y(k)\) is the system output and the input is \(u(k)\in\Re\). \(\Phi(\cdot)\) is some continuous and smooth nonlinearity, \(n\in\mathbb{Z}^{+}\) is the system order, and it is assumed that the system (2) is bounded input bounded output (BIBO) stability. Given \(N\) observed input/output data pairs \(\{u(t),y(t)\}_{t=1}^{N}\), we use a neural model to define a continuous nonlinear mapping \(NN:\mathbb{R}^{m_{0}}\rightarrow\mathbb{R}^{m_{L}}\), where, \(m_{0}\) and \(m_{L}\) denote the network input and output, respectively. Our objective is to properly choice the structure and parameters of network, such that the approximation error \(\|\Phi(\cdot)-NN(\cdot)\|\) can be made less than any specified positive constant \(\varepsilon>0\) over a compact set. In comparison with other processes, the IFGD process has the following notable characteristics: (1) long-term dependencies between time steps due to high time delay and (2) non-stationary property arising from varying load conditions. All the above facts call for a type of powerful modeling technique to deal with the above challenges. In the following section, the proposed BEMD-TCN approach will be described in detail.
## 3 Results and Discussion
### Shear Test  
The shear strength is an important criterion for the quality of a brazed joint. Four brazed joint specimens were prepared for the shear test. The average shear strength of the brazed joints obtained was determined to be 47.3 MPa. The standard deviation of the shear strength was 4.35 MPa, indicating that the shear strength of the brazed joint with the electroplated Ni-11P filler alloy is relatively stable. [PERSON] et al. [18] studied the joint strength of SUS304 brazed with BNi-2 filler metal for use in a heat exchanger. The shear strength of the brazed joint was approximately 100 MPa at 1040 \({}^{\circ}\)C. [PERSON] et al. [19] reported that the shear strength of the brazed joint with NiCrP filler metal increased from 36 MPa at 980 \({}^{\circ}\)C to 137 MPa at 1040 \({}^{\circ}\)C, i.e., the shear strength increases with the brazing temperature. Although the shear strength obtained in this study is low, the brazed joint has excellent quality in terms of the absence of voids and flaws. The shear strength obtained in this study seems to correspond to that of the Ni-11P alloy and it has a meaningful guiding role in practical terms.  
The fracture morphology of the brazed joint was observed by SEM. The result is shown in Figure 7(a). The bright gray phases and the brazed filler zone were observed on the fractured surface, indicating that failure occurred in the brazed filler zone. Figure 7(b) shows an optical micrograph of the cross section of the fractured joint. A similar fracture mode was observed. As mentioned above, the P-containing phases were mainly concentrated in the brazed filler zone. These phases are brittle compounds and thus they appear to reduce the strength of the brazed joints.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Point**} & \multicolumn{4}{c}{**Element Composition (mol\%)**} & \multirow{2}{*}{**Possible Phase**} \\ \cline{2-2} \cline{4-6}  & **Ni** & **P** & **Fe** & **Cr** \\ \hline A & 41.3 & 2.0 & 48.1 & 8.6 & Fe-Ni-Cr solid solution \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of point A in brazed joint.  
Figure 8: Fracture mode observation of brazed joint.