esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 3 PL-Based RAIM Availability Assessment
### Deficiency of Classic- and Enhanced-PL-Based RAIM Availability Assessment
#### 3.2.1 Separation  
In this situation, there are three kinds of positional relationships among the \(\xi_{b}-lgP(\text{HMI}|1F)\) curve, the \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve and the \(lgP_{r}(\text{HMI}|1F)\) line, as shown in Figure 2.  
Figure 2: Separation.  
The \(\xi_{b}-lgP(\text{HMI}|1F)\) curve intersects with the \(lgP_{r}(\text{HMI}|1F)\) line, and is higher than the blue dashed \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve, shown as the brown \(P_{1}(\text{HMI}|1F)\) curve in Figure 2. In this situation, RAIM is unavailable and the VPL is larger than the VAL, i.e., \(\text{VPL}\geq\text{VAL}|H_{1}\), meaning a successful detection of \"RAIM being unavailable\".  
The \(\xi_{b}-lgP(\text{HMI}|1F)\) curve is separated from the \(lgP_{r}(\text{HMI}|1F)\) line, and is higher than the blue dashed curve \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve, shown as the black curve \(P_{2}(\text{HMI}|1F)\) in Figure 2. In this situation, RAIM is available but the VPL is larger than the VAL, i.e., \(\text{VPL}\geq\text{VAL}|H_{0}\), meaning a conservative assessment.  
The \(\xi_{b}-lgP(\text{HMI}|1F)\) curve is separated from the \(lgP_{r}(\text{HMI}|1F)\) line, and is lower than the blue dashed \(\xi_{b}-lgP_{a}(\text{HMI}|1F)\) curve, shown as the blue curve in Figure 2. In this situation, RAIM is available, and the VPL is smaller than the VAL, i.e., \(lgP_{r}(\text{HMI}|1F)\) line, the RAIM availability might be optimistically assessed.
# Properties and Mechanism of Hydration of Fly Ash Belite Cement Prepared from Low-Quality Fly Ash
## 5 Conclusions  
This study focused on the hydration properties of FABC clinker predominantly composed of \(\alpha^{\prime}_{L}\)-C\({}_{2}\)S and C\({}_{12}\)A\({}_{7}\), which was prepared from stockpiled FA and lime. Some conclusions and recommendations based on the experimental results were as follows.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Porosity (\%)**} & \multirow{2}{*}{**Most Probable**} & \multicolumn{4}{c}{**Pore Size Distribution (\%)**} & \multirow{2}{*}{**Percentage of harmful**} \\  & & & & & & **Pores \textgreater{}50 nm (\%)** \\ \hline FABC-3d & 41.64 & 58.83 & 12.60 & 32.36 & 46.41 & 8.63 & 55.04 \\ FABC-28d & 34.22 & 25.12 & 43.20 & 50.09 & 1.81 & 4.90 & 6.71 \\ FABC-9d & 33.16 & 20.24 & 56.22 & 36.49 & 2.70 & 4.59 & 7.29 \\ FC-3d & 23.15 & 56.16 & 23.70 & 23.91 & 41.76 & 10.63 & 52.39 \\ RC-28d & 16.60 & 45.10 & 28.61 & 57.13 & 27.12 & 8.6 & 35.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pore structure parameters of different pastes.  
(1) When the dosage of gypsum was 7%, the compressive and flexural strengths of FABC mortars after 3, 28, and 90 days were the highest. Therefore, the optimum dosage of gypsum in FABC was 7%.  
(2) Mostly C-S-H, AFT, mullite, and quartz were found in hydration products with no Ca(OH)\({}_{2}\). This was principally because all the Ca(OH)\({}_{2}\) generated from the hydration of \(\alpha\)'\({}_{\text{L}}\)-C\({}_{2}\)S reacted to form AFT.  
(3) FABC pastes had higher porosity and lower strengths compared with RC pastes at early ages, which was due to a high water requirement and small particle size of clinker. However, the ratio of harmful pores (d \(\geq\) 50 nm) decreased from 55.04% to 6.71% during the hydration period from 3 to 28 days. It was concluded that pores less than 20 nm in diameter were advantageous in improving the strengths and density of fly ash belite cement mortars rapidly at later ages.  
Producing FABC by low-quality fly ash is believed to be a promising way to resourcefully utilize large amount of fly ash, and the long-term strength development can be found grow rapidly during 3 to 28 days. It can be predicted that FABC has rapid setting and hardening, good resistance to sulfate attack because of low Ca(OH)\({}_{2}\) content, and it is suitable for underground, underwater, or marine engineering, especially for projects requiring rapid construction or repair. Future work will focus on durability and composite cement. The working performance, mechanics performance, and durability of composite cement compounded with FABC and Portland cement will be studied in the future.  
[PERSON] and [PERSON] are the main contributors of this research work. They carried out the main experimental program, analyzed the experimental results, and drafted the research paper; [PERSON] mainly processed experimental data and proofread the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Results and Analysis  
In this study, a total of 129 learning texts were collected from first- and second-year university students over a two-week period, as noted in the data collection methods subsection. The experiments were conducted on a desktop computer equipped with an Intel Core i7-9700 processor (8 cores, 3.0 GHz base frequency), 16 GB of DDR4 RAM, and an NVIDIA GeForce GTX 1660 Ti GPU with 6 GB of VRAM. This setup provided adequate computational power for natural language processing (NLP) tasks, including word-breaking using various toolkits (Jieba, SnowNLP, and NLTK), keyword extraction, and emotion analysis. The data processing pipeline included several stages, and we report approximate times based on the system described above. (a) Speech-to-text conversion:  
Figure 3: Research and analysis process.  
Using the Google Voice Recognition API, converting student discussion audio (averaging 5 min per session) to text took approximately 10-15 s per recording, depending on audio quality and network latency. (b) Word-breaking with Jieba: Processing the 129 learning texts (approximately 645-903 sentences) using the Jieba Chinese word-breaking technology took about 2-3 min in total, including stop-word removal. (c) Keyword extraction and analysis: Generating the keyword matrix and word clouds for the entire dataset required approximately 5-7 min, with manual labeling of high/low-participation groups adding an additional 1-2 h of human effort. (d) Emotion analysis: Calculating emotion scores using the combined Chinese Sentiment Dictionary (7376 positive, 12,646 negative words) and NTUSD (2812 positive, 8276 negative words) took roughly 3-4 min for the full dataset, as the system compared keywords against the dictionaries and computed scores per sentence using Equations (1)-(3).  
The back-end web database used MySQL 8.0 and was hosted locally on the same machine, with a storage capacity of approximately 50 MB for raw text, segmented keywords, and emotion scores. The AI voice recognition system processed real-time audio input via a standard USB microphone (sampling rate: 44.1 kHz). This study analyzed the high-frequency keywords associated with positive and negative emotions in learning experiences. Keywords that appeared more than 10 times were selected and sorted to create the word cloud.  
Model training for the emotion analysis system utilized popular NLP toolkits rather than a machine learning classifier due to the reliance on predefined sentiment dictionaries, which reduced computational overhead while maintaining accuracy for this exploratory study.
## 4 Data Stream Management System in Haskell
### Efficiency Remarks  
The system incorporates the Decisive Plausible Logic tool [27] A Haskell glue module that exports functions requesting proofs [20] is used to make the connection with the other modules. The efficiency is mandatory when one needs to reason on huge data in real time. The efficiency of the proposed solution is based on the following vectors:  
* The implementation of a family of defeasible logic is polynomial [7]. Plausible logic, being a particular case of defeasible reasoning, belongs to this efficiency class. The extensive experiments in [7] have proved that the family of defeasible reasoning tools that we integrated in our framework can deal with knowledge bases up to hundreds of thousands of rules, with a theoretical complexity of O(N log N). The cpu time for proving a conclusion was 0.4 seconds in case of 4200 contradictory rules, 3.83 seconds for 42,000 rules, and 21.15 seconds for 210,000 rules. This cpu time includes the time consumed by the garbage collector in Haskell [7].
* Under the assumption that the domain is static, the translation from description logic axioms into strict rules can be performed offline, before taking real-time decisions. The resulted Description Logic Programs are sub-fragments of Horn logics and their complexity is polynomial, as reported in [8].
* Only few general ontologies and few medical ontologies have this size. Most of the domain ontologies do no reach the comparable size of 4200 axioms. Depending on the time constraints for the given problem, a smaller ontology can be imported instead of a refined one.
* The plausible rules and preferences added by the human expert also do not go up to thousands of rules. Given the experience from the expert systems domain, few of the existing commercial expert systems do contain thousands of rules [28].
* The possibility to select the current inference algorithm among \(\{\mu,\alpha,\pi,\beta,\gamma\}\) can also be exploited to adjust the reasoning task to the complexity of the current real-time decision.
# Integrated Photonics on Glass: A Review of the Ion-Exchange Technology Achivements
## 2 Ion-Exchanged Waveguides
### Waveguide's Performances
#### 2.3.2 Active Glasses  
The possibility of performing ion-exchange on rare-earth doped glasses was identified quite early. However, it was only in the 1990s with the development of WDM telecommunication that a lot of work was carried-out on the realization of efficient optical amplifiers and lasers. Because the solubility of rare earths into silicate glasses is quite low, which entails quenching due to clustering and reduces the amplifier efficiency, phosphate glasses rapidly emerged as the most efficient solution for obtaining high gain with compact devices. Among phosphate glasses, two specific references set the state of the art: they were the IOG 1 by Schott [41] and a proprietary glass referred as P1 by TeemPhotonics [42]. These two glasses succeeded in obtaining a high doping level without rare-earth clustering while being chemically resistant enough to withstand clean room processes and ion-exchange. The competition in the field of rare earth doped waveguides having been very hard, the characteristics of the different waveguides obtained in these glasses are difficult to find in the literature since the emphasis was mostly put on the active device performances, as will be detailed later.
## 2 Wireless Body Sensor Networks and QoS
### QoS in Wireless Body Sensor Networks
#### 2.2.2 QoS Requirements from the Users' Perspective  
For the user or application perspective of QoS, we have considered a case study that includes three medical application areas, including asthma and allergy, cardiovascular and diabetes. Medical specialists and nurses were selected from the three respective departments of the selected hospital and the health center. Based on their responses, we have summarized the sensor level priorities of the four chosen  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Delivery Model** & **Real-Time Delivery** & **Reliable Delivery** & **Energy Efficiency** & **Adaptability** \\ \hline Event driven & ✓ & ✓ & ✓ & ✓ \\ Query driven & ✓ & ✓ & ✓ & ✓ \\ Continuous & - & - & ✓ & ✓ \\ Hybrid & ✓ & ✓ & ✓ & ✓ \\ \hline \end{tabular}
\end{table}
Table 1: Key QoS requirements and metrics for different delivery models.  
Figure 2: Wearable health monitoring system architecture: (**a**) Flat architecture of BAN, (**b**) Hierarchical architecture of BAN.  
applications in Table 2. These sensor priorities are based on application or user's (patient's) demand. Along with the literature [2,6,11], respondents in the case study agreed that reliable and real-time data delivery are the two major QoS requirements in WBSNs or BANs. Table 3 summarizes the key QoS requirements in WBSNs and their related performance metrics.
## 4 Experiments and Results
### Results with the ACDC Dataset  
This section presents the results obtained from the experiments with the ACDC dataset. As mentioned earlier, a set of 100 exams from the ACDC dataset was used to develop the proposed method. Three subsets were created at random: training (60 exams), validation (20 exams), and testing (20 exams). These subsets were kept the same during the experiments. The results of each substage of the method are shown in the following subsections.
# Acceleration of Service Life Testing by Using Weibull Distribution on Fiber Optical Connectors
## 3 Validation of the Procedure
### Numerical Validation  
A numerical comparison of the failure times between the service life tests and an accelerated vibration test were performed, as outlined in Figure 2. The two fiber optic connectors that implied a connection were submitted to the GR-326 [14] service life program testing in the order and sequence shown in Figure 2. As per GMW 3172 in Appendix D [26], six samples were tested with no fiber damage in 864 h of service life testing of the temperature profiles. In Figure 4, the orange line shows the temperature profiles applied, and the lines at the bottom represent the insertion loss measurements of the six samples that were stable. The insertion loss requirement limit was 0.4 (dB).  
Next, the service life vibration test with the following experimental setup was performed. The samples were vibrated for 2 h on each of the third principal axes at an amplitude of 1.5 mm with a frequency between 10 and 55 Hz, and the insertion loss was measured during the test using Agilent N7745A equipment with an LED source at 1310 and 1550 nm, with the connections shown in Figure 1. The insertion loss failure times induced by the vibration load are shown in Figure 5 and Table 1.  
Figure 4: GR-326 service life insertion loss measurements (**a**) at 1310 nm; (**b**) at 1550 nm.  
Figure 3: Single fiber alignment by two connectors.  
Four of six samples (1-4) failed the service life vibration test. The two remaining samples (5-6) that met the requirement were still used, but under vibration ALT analysis that is presented in the next section. However, the failure times of samples 5-6 are included in Table 1 due to the purpose to calculate the Weibull \(\beta\) and \(\eta\) parameters of the service life testing program.
## 2 Description of the Project and Ground Conditions
### Field Test Conditions
#### 2.2.2. _Dynamic Compaction Vibration Monitoring_  
2.2.1. Equipment for Vibration Measurement  
The main instruments used in this test include high-precision acceleration sensors (unidirectional and triaxial), a 12-channel constant current adapter CT5204, a data acquisition unit, and an analysis system DAQami. The vibration measurement system consists of sensors, vibration meters, computers, cables, etc. Figure 4 shows the monitoring system and sensors. The parameters of the vibration meter are shown in Table 2. The frequency range of the sensor is wide, spanning from 1 Hz to as high as 100,000 Hz. It is powered by direct current (DC) with a voltage of \(\pm\)24 V and a constant current of 2 mA. The sensor exhibits a very high accuracy, less than 1%. Additionally, it operates on alternating current (AC) with a stable voltage of 220 V.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 3 The Proposed Approach
### New Program Structure Based on Information Transmission
#### 3.2.1 New Function and Terminal Set
When the function _Transmit_F_ receives input originating from the feature extraction layer, it checks whether the third element of the tuple is a feature vector. If it is, the extracted features are returned directly in vector form. If it is not a feature vector, the decision to pass the newly generated manual features from the feature construction layer to subsequent layers is made based on the parameter \(p\). Furthermore, considering the program structure uses strongly typed GP, the transmission layer also acts as a transitional layer for input-output type conversion, ensuring that the feature concatenation layer can be defined as a flexible layer. Functions of the feature concatenation layer: The primary function of the \(Concat2\) function in the feature concatenation layer is to concatenate the feature data inputs from two transmission layers and output them. Due to the specific design of the transmission layers, the input data may include handcrafted features generated by the feature construction layer. Based on this, the \(Concat2\) function integrates all input features, forming a feature vector as the output. As a flexible layer, it can combine various features from the same region or similar features from different regions. Functions of the classification layer: The function of the \(SVM\) in the classification layer is to receive the learned features and input them into the Support Vector Machine (SVM) classifier for categorization, thereby outputting the predicted category labels.
## 6 Implementation Scenarios  
For ease of explanation, the downlink communication network in Figure 2 is explained first for the three performance evaluation scenarios: (1) zero to a few obstacles, (2) few to moderate obstacles, and (3) moderate to many obstacles. The communication network consists of four users referred to as U\({}_{1}\), U\({}_{2}\), U\({}_{3}\), and U\({}_{4}\); one BS is referred to as BS\({}_{1}\), and one HRIS located on top of a building is referred to as B\({}_{1}\). As the network experiences different obstacle densities, users' locations are set in accordance to represent the three scenarios; U\({}_{3}\) is considered the first scenario, U\({}_{4}\) is considered the second scenario, and U\({}_{1}\) and U\({}_{2}\) are considered the third scenario. It is important to note that the actual performance test results used Table 3 parameters and were compared against the conventional T/R RIS and BS handover.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**User Density (km2)** & **THz Frequency** \\ \hline \(3\times 10^{5}\) & 300 GHz \\ \hline \(9\times 10^{6}\) & 400 GHz \\ \hline \(16\times 10^{6}\) & 500 GHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation test parameters.
## 3 The Proposed Circuits
### The XOR Gate Design  
The XOR gate is one of the most momentous and main logic gates. Moreover, the XOR gate is the fundamental logic gate, plays an important role in digital circuit design, and is the core of several reversible gates. The proposed XOR gate in this research has the following advantages over the previous best design:  
- Reduced number of cells consumed;  
- Reduced consumption area;  
- Reduced cost function;  
- Less delay;  
- No use of NOT gates;  
- No use of crossovers;  
- No use of rotary cells (45-degree cells).  
Figure 7 shows the implementation of the proposed XOR gate design, and Figure 8 shows the simulator output of this gate. Equation (1) shows the logical relationship of this gate:  
\[\text{A}\oplus\text{B}=\text{A}.\text{B}^{\prime}+\text{A}^{\prime}.\text{B} \tag{1}\]  
To design a large circuit using smaller units, the area and delay of the small units are crucial because these smaller units are used multiple times. Hence, our main goal is to design an efficient XOR block. The proposed structure for a two-input XOR gate (with a 0.25 clock cycle delay) can be effectively utilized as a building block in designing multi-input XOR gates. Similarly, a three-input XOR gate can be designed using only two two-input XOR gates with a 0.5 clock cycle. A four-input XOR gate can be designed using only three cascaded two-input XOR gates with a 0.75 clock cycle. In the same way, an ultra-high-speed five-input XOR can be effectively designed using four cascaded gates working in one clock cycle as shown in Figures 9 and 10, which show, respectively, a block diagram and implementation. Following the proposed five-input XOR gate, we also provided cost function, complexity and delay diagrams in Section 4 to illustrate the application of the cost function for L, K, P = 2. Equation (2) below describes this important criterion. As can be seen in cost function diagram in Section 4, in evaluating the comparison of the cost function parameter [18] for QCA XOR, the highest value is related to the designs [19; 20], and the lowest value is related to our proposed design.  
\[\text{Cost Function}=(\text{M}^{\text{K}}+\text{I}+\text{C}^{\text{L}})\times \text{T}^{\text{P}} \tag{2}\]  
Figure 7: The proposed XOR gate.  
Figure 8: Simulation results for the proposed XOR gate.  
Figure 9: Logical diagram of the proposed 5-input XOR gate.
# Towards Explainable Deep Neural Networks for the Automatic Detection of Diabetic Retinopathy
###### Abstract  
Diabetic Retinopathy (DR) is a common complication associated with diabetes, causing irreversible vision loss. Early detection of DR can be very helpful for clinical treatment. Ophthalmologists' manual approach to DR diagnoses is expensive and time-consuming; thus, automatic detection of DR is becoming vital, especially with the increasing number of diabetes patients worldwide. Deep learning methods for analyzing medical images have recently become prevalent, achieving state-of-the-art results. Consequently, the need for interpretable deep learning has increased. Although it was demonstrated that the representation depth is beneficial for classification accuracy for DR diagnoses, model explainability is rarely analyzed. In this paper, we evaluated three state-of-the-art deep learning models to accelerate DR detection using the fundus images dataset. We have also proposed a novel explainability metric to leverage domain-based knowledge and validate the reasoning of a deep learning model's decisions. We conducted two experiments to classify fundus images into normal and abnormal cases and to categorize the images according to the DR severity. The results show the superiority of the VGG-16 model in terms of accuracy, precision, and recall for both binary and DR five-stage classification. Although the achieved accuracy of all evaluated models demonstrates their capability to capture some lesion patterns in the relevant DR cases, the evaluation of the models in terms of their explainability using the Grad-CAM-based color visualization approach shows that the models are not necessarily able to detect DR related lesions to make the classification decision. Thus, more investigations are needed to improve the deep learning model's explainability for medical diagnosis.  
Keywords:explainable deep networks; diabetic retinopathy; deep learning; Grad-CAM; convolutional neural networks; ResNet; DenseNet +
Footnote †: journal: Medical Imaging