esa-sceva/satcom-chunk-collection huggingface dataset:
# User-BS Selection Strategy Optimization with RSSI-Based Reliability in 5G Wireless Networks
## 2 Related Work  
### User-BS Selection Strategy  
User-BS selection, aiming to associate a user with a particular serving BS, is a critical procedure in wireless networks that substantially affects network performance [13]. In traditional LTE systems, the radio admission control entity is located in the radio resource control layer of the protocol stack, which decides whether a new radio-bearer admission request is admitted or rejected [14]. The distance-based user-BS selection strategy where a user chooses to associate with the nearest BS is the most prevalent. Five metrics are commonly used in user-BS selection, namely, outage/coverage probability, spectral efficiency, energy efficiency, QoS, and fairness [15]. In actual situations, one or a combination of several indicators are used. The new technologies and standards of 5G networks inevitably render ineffective the above rudimentary user-BS selection strategy and metrics, and more effective user-BS selection algorithms are needed for addressing the unique features of emerging 5G wireless networks.  
Utility is widely used in the modeling of user association problems. In order to make decisions, utility quantifies the satisfaction that a particular service provides to decision makers [16]. According to the used metrics, utility considered in user association may consist of, for example, spectral efficiency [17], energy efficiency [18; 19], QoS [20]. Logarithms, exponentials, and sigmoidal utility functions are used to model these properties. For studies that do not specifically discuss the selection of utility functions, it can be assumed that they use linear utility functions, that is, utility is spectral efficiency, energy efficiency, or QoS itself [21]. Game theory [22], combinatorial optimization [23], and random geometry [24] are commonly used models to solve user-BS selection strategy problems. Details are shown in Table 1.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 5 Numerical Results  
In this section, we investigate the performance of the regular LT codes and the proposed scheme. First, we show the results of optimizing the regular LT codes. Then, we compare the results of regular LT codes and the proposed scheme in terms of two performance measures--specifically, DSP and BER. In addition, the three channels in the Y-network are modeled as BECs. Namely, the first erasure channel is between the first source and the relay with an erasure probability of \(\varepsilon_{1}\), the second erasure channel is between the second source and the relay with an erasure probability of \(\varepsilon_{2}\), and the third erasure channel is between the relay and the destination with an erasure probability of \(\varepsilon_{3}\). Note that all of the performance measurements of the proposed scheme and the conventional schemes are implemented and evaluated using the MATLAB R2015a platform.
# A Real-Time Numerical Decoupling Method for Multi-DoF Magnetic Levitation Rotary Table
## 3 Real-Time Decoupling Unit Implemented on FPGA
### Current Decoupling Unit Implementation
Therefore, the total delay of the decoupling unit is given by  
\[T_{\text{delay}}=T_{\text{clk}}\times\left(n_{\text{wm\_latency}}+n_{\text{ MO\_latency}}\right), \tag{15}\]  
Figure 4: Flowchart, design structure, and timing sequence diagram of the current decoupling unit on the field programmable gate array (FPGA) platform. POV—position and orientation vector. where \(T_{\text{clk}}\) means the clock period of the FPGA. As this module is required to be inserted in the controller for the maglev rotary actuator, the \(T_{\text{delay}}\) should be less than the sampling cycle of the control system. The state machine given in Figure 5 introduces the layout in detail. State **S1** reads POV \({}^{s}\)**p** and starts the counter. Then, State **S2**, presenting the pipelined program structure, begins. In these states, **S2\({}_{1}\)\(\sim\)S2\({}_{5}\)**, both the **FT** IP core and **SUM** IP core are invoked and idle with a certain timing sequence. In State **S2\({}_{6}\)**, counter \(n\) needs to be cleared for the next coil computation, the **SUM** IP core should reset, and results of **SUM** IP core are stored in the bipolar RAM. Inversely, the **FT** IP core cannot be reset like the **SUM** IP core because its latency is very long. If the **FT** IP core has finished the force and torque calculation for the last coil, the state machine switches from **S2\({}_{4}\)** to **S2\({}_{7}\)** and waits for the accomplishment of the summing operation. After that, State **S3** reads the required wrench vector and invokes the **MO** IP core. With the current vector solved in State **S4**, the program goes to **S5**, waiting for the next time step. With reconfigurable characteristics, the RTL structure for the decoupling unit can be implemented on the arbitrary FPGA platform with enough computation resources.
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 3 Description of Data
### Integration of Weather and Outage Data  
Since local weather data is collected by a different institute than outage data, AWS data is required to be mapped with outage data to analyze the relationship between them. Furthermore, the KMA provides a detailed regional short-term weather (STW) forecast every 3 h at a dense grid point by gridding the whole country into \(5\times 5\) km intervals. The grid state prediction would be performed using STWs; therefore, the available datasets, with different data frames and time periods, requiredintegration into 3-h segments to develop the power grid state prediction model. This data frame is developed to utilize weather forecast data. It allows the proposed method to be extended to other disruptive weather events.
## 2 Materials and Methods
### Procedure  
The data collection procedure was carried out between August and September 2024 and included the following steps:  
* Introduction and Consent:
* Students were welcomed, and the study context and objectives were explained.
* Participants read an informed consent form, and those who agreed to participate provided their consent.
* Questionnaire Administration:
* The questionnaire was administered via Google Forms, allowing for efficient and anonymous data collection.
* Students completed the questionnaire using their personal devices (mobile phones or tablets) in the classroom, ensuring consistent technological conditions.
* The questionnaire included the following sections: 1. Demographics: Collected data such as age, gender, educational institution, university program, year of study, and prior experience with ChatGPT to contextualize responses. 2. Case Analysis: Presented a common scenario for all participants: preparing for a job interview in their field of study. 1. Situation: You have a job interview for an internship at a company in your field of study. You need to be prepared for the possible questions you will be asked and how to answer them effectively. 2. Instructions: Use ChatGPT to generate examples of common job interview questions and tips on how to answer them. 3. Satisfaction Assessment: Measured the level of satisfaction with the responses generated by ChatGPT.
* Prompt Interaction:Students wrote their prompt in the questionnaire, copied it into the free version of ChatGPT (3.5), and pasted the AI's response back into the form.
2. Students evaluated their satisfaction with the response: 1. If satisfied, the test concluded. 2. If dissatisfied, they rewrote the prompt (up to a maximum of 7 iterations) until they were satisfied.
* Ethical Considerations: 1. Participation was voluntary, with students free to withdraw at any time without consequences. 2. Anonymity and confidentiality were guaranteed, with all data used exclusively for academic purposes.
* Data Analysis: 1. After data collection, responses were analyzed using the Content Assessment Instrument, validated by expert judgment. 2. A linguist with expertise in morphosyntax evaluated each response individually, and the results were tabulated for further analysis.  
The entire activity lasted approximately 35 min, providing a structured and controlled environment for data collection and analysis.
# An Improved Product Code-Based Data Hiding Scheme
## Product Code-Based Data Hiding
### Optimal Solution  
In Section 2, suppose the toggle syndrome \(\mathbf{ts}\) = (011) and the toggle sequence \(\mathbf{t}\) = (0110000) is constructed by directly appending the four information bits into \(\mathbf{ts}\), then the found toggle sequence \(\mathbf{t}\) is not the coset leader \(\mathbf{e}\) = (0000100). Further, in Table 1, we know the coset leader \(\mathbf{e}\) is equal to the toggle sequence \(\mathbf{t}\) plus the codeword (0110100), which is closest to \(\mathbf{t}\). Hence, the toggle array \(\mathbf{T}\) found in _Step 3_ of Section 3.1.1 and illustrated in the form of Figure 4b is not a good solution. Usually, it is not a coset leader. Suppose we are allowed to sequentially add up to \(2^{16}\) code arrays of the (49, 16) product code into the toggle array \(\mathbf{T}\). Then, among those \(2^{16}\) resultant arrays, the one with the minimum Hamming weight is the coset leader, where the Hamming weight of a binary array is defined as the number of \(1\)s in the array. Here, the found coset leader for a 2-D linear code is termed the optimal toggle array.  
Figure 4: (a) Toggle syndrome \(\mathbf{TS}\) (b) Toggle array \(\mathbf{T}\) (c) Stego array \(\mathbf{Y}\).  
Figure 3: (a) \(\mathbf{X}_{\text{P}}\) part of the cover array \(\mathbf{X}\) (b) Secret message block \(\mathbf{M}\).  
Since table lookup is impractical due to the issue that the size of the standard array for the (49, 16) product code is rather large, and finding the coset leader of the coset to which \(\mathbf{T}\) belongs is a time-consuming process, so finding the sub-optimal approach is necessary.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 2 Literature Review
### Reconstructed Speech with Unconstrained Datasets  
Recent research has aimed to improve reconstructed speech quality using datasets containing more speech from individual speakers without any constraints, such as Lip2 Wav [29], AVSpeech [31], and UTKFace [31]. [PERSON] et al. [16] introduced an adapted autoregressive sequence-to-sequence model, Lip2 Wav, derived from Tacotron 2, to produce mel-spectrograms based on video frames. Their emphasis was on precise lip-to-speech mappings for individual speakers within the unconstrained Lip2 Wav dataset, encompassing 120 h of talking face videos from five speakers. Through comparisons with prior research [21; 23] in the same dataset, their approach resulted in a substantial fourfold increase in overall speech intelligibility.  
Subsequent studies aimed to enhance the Lip2 Wav model. [PERSON] et al. [29] introduced a flow-based non-autoregressive lip-to-speech model named GlowLTS, designed to bypass autoregressive constraints and facilitate faster inference. [PERSON] et al. [30] used framed frame sequences as feature distributions using transformers within an autoencoder context. Their approach yielded marginal improvements, raising the Short-time Objective Intelligibility measure (STOI) measurements from 0.377 to 0.394 and 0.490, respectively, in the Lip2 Wav dataset for Chemistry Lectures. In addition, [PERSON] et al. [31] presented the Lip2 Speech method, with key design choices to achieve accurate lip-to-speech synthesis with the unconstrained datasets AVSpeech, LRW and UTKFace. They achieved STOI measurements of 1.38 with the LRW dataset and 3.55 MOS score compared to the groundtruth of 4.56 with the LRW dataset.  
These strategies offer viable solutions for unconstrained lip-to-speech synthesis, aiming to enhance speech quality and intelligibility through large datasets. Nonetheless, their reliance on a two-stage pipeline involving the Griffin-Lim algorithm constrains audio quality and inference speed. Additionally, the speaker-specific training approach necessitates separate models for each speaker, entailing speaker dependence and restricting broader applicability.
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 5 Numerical Experiments  
### Prediction Accuracies Analysis  
The observations and predictions of the IGU and GBU clock products from 1 January 2017 to 30 January 2017 are downloaded to test the improved clock model. For comparing the accuracies of the improved model with ISU and GBU products, four schemes are designed and analyzed as follows:  
Scheme 1: Prediction clock offsets using the improved model with no BP corrections (no-BP);  
Scheme 2: Prediction clock offsets using the improved model with BP corrections (with-BP);  
Scheme 3: Prediction clock offsets from the ISU product (ISU-P) and GBU product (GBU-P); and  
Scheme 4: Prediction clock offsets only using the BP algorithm as reference [26].  
By comparing with Scheme 1 and Scheme 2, the available of BP corrections can be proved. By comparing with Scheme 2 and Scheme 3, the improvement of the proposed model can be achieved. By comparing with Scheme 2 and Scheme 4, the enhanced level of the proposed model referred to the previous BP algorithm [26] can be verified.  
The residuals of the BDS clock offset predictions obtained by the four schemes are shown in Figures 11 and 12, respectively.  
The average accuracy statistics of BDS clock offset predictions from the different schemes for 30 days are shown in Table 3 and Figure 13.  
Figure 11: GBU-BDS clock offset prediction residuals. (**a**) The residuals of the GBU forecasting model with no-BP; (**b**) the residuals of the GBU forecasting model with-BP; (**c**) the residual of the GBU forecasting model; and (**d**) the residuals of the GBU forecasting model with only BP.  
Figure 12: ISU-BDS measured clock offset prediction residuals. (**a**) The residuals of the ISU forecasting model with no-BP; (**b**) the residuals of the ISU forecasting model with-BP; (**c**) the residual of the ISU forecasting model; and (**d**) the residuals of the ISU forecasting model only with BP.  
More analysis results of the above figures and tables will be discussed in Section 6.
## 4 Experimental Results and Security Analysis
### Security Analysis
#### 4.2.2 Chosen Ciphertext Attack  
A chosen ciphertext attack is an attack related to cryptanalysis in which the crypt-analyst gathers information, at least in part, by choosing a ciphertext and obtaining its decryption under an unknown key [32]. In this attack, an attacker has to obtain the ciphertext and its corresponding plaintext. From these pieces of information, the attacker can attempt to recover the hidden secret key used for decryption.  
In the proposed improved schemes, including the CMLs that use the chaotic system, there are four initial values and four system parameters. In the compound schemes with four stages, including the CML that combines two different chaotic systems, there are eight initial values and eight system parameters. All of system parameters and initial values are produced by the input of 128-bit secret key. After obtaining the keystream sequence, the attacker will find it hard to crack the 128-bit secret keys and will face the problem of chaos.  
Figure 16: The linear complexity of the proposed improved CMLs. (**a**) The improved CML based on logistic map; (**b**) The improved CML based on tent map; (**c**) The improved compound CML, combining both the logistic map and tent map in a single stage; (**d**) The improved compound CML, combining the logistic map and tent map in an alternate stage.
## 4 Discussion  
### Objective 1--Part 1: Data Collection Parameters  
Before collecting any data, the various parameters affecting quality image collection with UAS were defined by reviewing similar studies within the literature and recalling UAS survey experience prior to engaging in this research study. Unfortunately, as [PERSON] et al. (2015) points out, each study contained a unique set of parameters for various parts of the collection, processing, or classification steps--thus, standards for this UAS application were non-existent. This led to the iterative testing of atmospheric, sensor, and mission planning parameters, in conjunction with diligent note-taking, to infer how these variables affected the quality of imagery collected by the UAS. Fortunately, a number of suitable prairie burn and timber harvest sites were identified, which allowed for this type of iterative testing without compromising the production of final land cover classifications for either type of disturbance.  
The results show that prairie burn sites were less sensitive to atmospheric conditions, such as sun angle, brightness/cloud cover, and wind (Tables 12 and 13). This was likely due to the low height and visual simplicity of the vegetation being imaged. Conversely, timber harvest sites were more sensitive to all three atmospheric conditions, likely due in part to the tall height and visual complexity of mature forest canopies. Therefore, the best resulting images were collected as close to solar noon as possible, with overcast skies and diffuse lighting, and low wind (Table 13). Furthermore, the sensor settings were mostly the same, with the exception of shutter speed. However, this was mostly dependent on lighting and wind (Tables 12 and 13). Finally, the flight altitude and the number of images collected outside the study area differed between the two land covers. Ensuring sufficient distance between the sensor and vegetation was easily done at 121 m above ground level (AGL) for prairie sites but given the height of mature trees at timber harvest sites (often between 24 and 30 m), the flight altitude was increased to 121 m _above forest canopy_ (APC). Additionally, while prairie burn sites required just 1 image outside the study area to reduce outer image distortion in resulting orthomosaics, timber harvest sites required 2 or more, depending on the adjacent vegetation. This was likely due to the presence of ground within the datasets, where pra prariies had ample visible ground within and surrounding the study area, timber harvest sites only had visible ground present along plot perimeters, if at all.  
Overall, quality images were gathered with the respective data collection parameters, despite logistical challenges that prevented ideal data collection scenarios, especially for timber harvest sites.
## 2 Antennas with a Single Reconfigure Feature  
Frequency switching, bandwidth switching, radiation pattern switching, and polarization switching are the different operations which can be performed by the reconfigurable antennas through changing the antenna size and shape of the radiators using active elements. In this section, different types of reconfigurable antennas with a single reconfigurable function are studied.
### Requirements for a New Conceptual Interoperability Framework  
Based on the related frameworks, discussed in Section 1.2 and other existing research, several key requirements for an efficient conceptual interoperability framework can be put forward, as follows:  
1. Modern and Scalable Technology: Interoperability frameworks should utilize modern and scalable technologies to ensure that they can manage a large volume of data and participants without compromising performance. This includes avoiding single points of failure, as these can lead to significant downtime [32; 33].
2. Data Privacy and Security: Ensuring the privacy and security of patient data is paramount. The framework must have robust mechanisms for access control, data obfuscation, encryption, and compliance with data protection regulations such as the UK DPA (GDPR) and the US Health Insurance Portability and Accountability Act (HIPAA) [34].
3. Standardized Terminology: The consistent use of standardized healthcare terminology is crucial for successful data sharing and interpretation. Addressing the challenges of inconsistent healthcare terminology is essential for achieving semantic interoperability.
4. Flexibility and Adaptability: The framework should be adaptable to different healthcare settings and EHR systems. It should support various data modalities and allow for customization to accommodate diverse clinical applications as well as diverse input and output formats [35].
5. Support for Right of Erasure: Compliance with data protection regulations like GDPR and HIPAA, including the right of erasure, should be part of the framework. It should provide users with control over the data that have been captured and processed [36].
6. Progressive Resistance against Data Breaches: The framework should implement measures like dynamic data-masking techniques and transparent database encryption to actively resist data breaches [36].
7. Reusability of APIs: An API-led integration approach should focus on creating reusable APIs. This contributes to efficient application development and data exchange among healthcare organizations [37; 38].  
* Real-Time Capabilities: Frameworks should prioritize real-time data exchange capabilities to support immediate decision-making processes and provide up-to-date information [37].
* Compatibility: A well-structured architecture with orchestration capabilities should be integral to the framework, ensuring that different components can work harmoniously despite their differences. This will also ensure that legacy systems can be integrated into the conceptual framework [21].
* Practical Applicability beyond the Original Context: The framework should be designed with the ability to be implemented beyond its original context or region. It should address various technical, semantic, and regulatory concerns [38].  
In conclusion, the above requirements should be incorporated into any new interoperability framework to address the limitations observed in the existing frameworks, which are critiqued in the above. The next section proposes a new framework that could better serve the needs of a wide range of healthcare systems.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 3 Damping Extraction Methods
### Wavelet-Based Damping Extraction
#### 3.2.3 Damping Extraction Using Wavelet  
As presented in [43], the damping can be computed from the natural logarithm of the absolute value of wavelet transform in a fixed scale \(s=s_{0}\):  
\[\ln\left|\mathcal{W}_{\Psi,x}(s_{0},\tau)\right|\approx-\zeta\omega_{n}\tau+c, \tag{31}\]  
where \(c\) is a constant. Therefore, \(\ln\left|\mathcal{W}_{x}(s_{0},\tau)\right|\) is linearly related to time \(\tau\), and computing the slope of this line leads to evaluating the damping coefficient since \(-\zeta\omega_{n}=-\lambda\). One can find the damping ratio by knowing the slope of this line and the natural frequency \(\omega_{n}\)
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 3 Comparison with Analytical Methods
### Rain Attenuation Models for Terrestrial Radio Links
#### 3.1.2 Lin Model  
Additionally, the methodology proposed by [PERSON] in [12] takes advantage of the concept of the path reduction factor to estimate rain attenuation statistics on terrestrial links. According to this model, the rainfall attenuation exceeded for a percentage \(P\) of the yearly time can be calculated as:  
\[A(P)=kR(P)^{\alpha}Lr, \tag{6}\]  
where \(L\) is the path length, \(r\) is the path reduction factor, \(k\) and \(\alpha\) are the coefficients in Table 1, and \(R(P)\) is the rain rate exceeded for the same percentage \(P\) of the time. The factor \(r\) takes the following simple expression:  
\[r=\frac{1}{1+L/L(R)}, \tag{7}\]  
where:  
\[L(R)=\frac{2623}{R(P)-6.2}. \tag{8}\]