esa-sceva/satcom-chunk-collection huggingface dataset:
# Diagnosis and Treatment of MODY: An Updated Mini Review
## 1 Introduction  
Maturity-Onset Diabetes of the Young (MODY) is a monogenic type of diabetes, resultant from single gene mutations [1]. MODY is characterized by mild hyperglycemia, autosomal dominant inheritance, early onset of diabetes (<25 years), insulin resistance, and preservation of endogenous insulin secretion [2; 3; 4]. The genes involved are crucial for the development, function and regulation of beta cells and can cause glucose sensing and insulin secretion disorders [5; 6]. MODY is classified into several subtypes based on the genes involved and clinical phenotypes. Fourteen MODY subtypes have been identified thus far, each caused by a distinct gene mutation (Table 1) [7]. Among the 14 MODY subtypes, mutations in hepatocyte nuclear factor 1-\(\alpha\) (_HNF1A_), glucokinase (_GCCK_), _HNF4A_, and _HNF1B_ are the underlying cause in more than 95% of MODY cases; the other mutations are uncommon in the Caucasian population [2; 8; 9; 10]. These mutations differ in terms of prevalence, clinical features, the severity of diabetes and related complications, and treatment response. Each mutation encodes proteins involved in glucose homeostasis of pancreatic \(\beta\)-cells [11; 12].
## 2 Biological Signal Extraction and Processing
### CSP Filter and Raw Data  
Based on the extracted signals, the output signals consisting of the CSP filter [14] with raw data are compared in an experimental order for signal analysis. The movements of the left and right arms are divided before and after the presentation of the stimuli, and the signals are separated over time. Here, raw EMG is used as evidence to predict the change in output represented by EEG after receiving the muscle stimuli. The common spatial pattern (CSP) filtering method is one of the ways to divide classes by separating the boundaries of uncertain output waveforms, such as brain waves. The covariance results of two classes are used to classify the specificity and characteristics of signals for the distinction of behavior within the existing data by maximizing and minimizing the covariance results [15]. In this study, the data of the subjects collected were converted into (n by m) matrix-type data and then compared with the original signal using CSP filters to distinguish the relevance of the brain wave signals during the arms' operations. Algorithms for data processing and data analysis of the output of the CSP and raw EEG and EMG signals can be found in Figure 7 below.
# Artificial Intelligence in Virtual Telemedicine Triage:
## 4 Results
### Telemedicine Virtual Assistant Performance  
Once the operation of the electronic device had been verified in accordance with the doctor's guidelines, the telemedicine virtual assistant was tested for the medical diagnosis of COVID-19, the common cold, and allergic rhinitis. The identity of the persons is reserved to respect the privacy of the patients with whom the doctor works, the results obtained from the 20 patients treated obtained only two misdiagnoses, the first misdiagnosis was a case of the common cold and COVID-19 that was confused with the common cold.  
In Figure 14, it is observed that the allergic rhinitis class shows three hits with no errors, the common cold class obtained nine hits with one error, and the COVID-19 class had six hits with one error.  
The telemedicine virtual assistant developed in this study combines the information collected by the triage measurement device and the artificial intelligence SVM model to classify patients according to their final diagnosis. The results indicate that the virtual assistant performs well in classifying patients with an average accuracy of 91.3413% in classifying patients with COVID-19, the common cold, and allergic rhinitis.  
The accuracy of the virtual assistant is superior to that of each of the artificial intelligence models separately, suggesting that the combination of information from the triage measurement device and the artificial intelligence model allows for better patient  
Figure 14: Telemedicine virtual assistant performance. (**a**) Rhinitis performance results, (**b**) common cold performance results, (**c**) COVID-19 performance results  
classification. This demonstrates the importance of incorporating the information from the artificial intelligence models into the patient classification process.  
The high performance of the telemedicine virtual assistant has important implications for online healthcare and telemedicine. The ability to accurately classify patients according to their final diagnosis can improve the efficiency of telemedicine services and help patients receive the right care at the right time. In addition, the virtual assistant's ability to accurately detect patients with COVID-19 may be particularly valuable in the early detection and control of the spread of the disease in the community.
## 2 Materials and Methods
### Pilot Study
#### 2.2.2 Functional and Comfort Evaluation  
The present patient was asked to walk at a comfortable walking speed wearing his own shoes in the gait lab in three conditions: wearing the custom PD-AFO, wearing an off-the-shelf polyethylene AFO (\"Codivilla\" spring type), and without AFO (shod). The latter was used as a control to assess the kinematic and kinetic effects of the AFO conditions. Five walking trials were recorded for each condition, the order of which was randomized. The patient attended a familiarization session with the two AFOs for several minutes before undergoing gait analysis using a validated skin-marker-based protocol [20]. Accordingly, reflective markers were attached to relevant body landmarks of the upper trunk, pelvis, and lower limb. Both the affected and the contralateral legs were instrumented with markers. Rotations of the pelvis, hip, knee, and ankle joints in the three anatomical planes were calculated by a joint coordinate system [21], as recommended by the International Society of Biomechanics [22]. Ground reaction forces were sampled at 2000 Hz using two force plates embedded in the floor. Wireless sEMG sensors (Zerowire, Cometa) were placed on the tibialis anterior, gastrocnemius, rectus femoris, and biceps femoris muscles to sample muscle activity at 2000 Hz. All kinematic, kinetic, and sEMG data were normalized to gait cycle duration. Comfort of each condition was assessed via a visual analog scale (VAS) questionnaire. The VAS comprised the following scores: comfort of the footplate, comfort of the calf shell, overall comfort, and perceived support to the pushing phase.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### Evaluation Logic Layer (_EVL_)
#### 4.3.3 Level of Understanding and Detailing  
The abstraction framework aims to explain how a PSS concept achieves an EVC using all relevant knowledge. More precise and reliable causal relationships enhance EVC predictions. Within the PSS abstraction framework, understanding these relationships is defined by the _level of understanding_. Experts assess each relationship's level of understanding based on their ability to identify and describe critical working principles. The level of understanding for each node is categorized into three levels: high, medium, and low.  
* Level of Understanding 3: Meaningful scenario manifestations and their assignments can be defined.
* Level of Understanding 2: Meaningful scenario manifestations can be defined, but the assignment is unclear.
* Level of Understanding 1: Scenario manifestations are only vaguely definable due to a lack of understanding of the causal relationship, making meaningful assignment impossible.  
Detailing of the causal chain leading to an EVC should take place until additional detailing adds no further information due to ambiguity. In Figure 9, the principle of detailing is visualized. For an operational sequence, critical factors are detailed in terms of further causes, highlighting the necessity of detailing relationships until the point where further information would not yield additional insights due to high ambiguity. In cases where a PSS working principle is well understood, e.g., because critical factors are known from a long history of operating machines in the field, further detailing is unnecessary. Conversely, when critical factors with a significant impact and likelihood of occurrence are anticipated, they must be thoroughly explained. When detailing leads to the point where judgment regarding critical factors is lacking due to the absence of knowledge (level of understanding <3), detailing should stop, highlighting critical knowledge gaps among PSS engineers.  
These nodes are then addressed systematically through the knowledge gap layer within the PSS Abstraction framework, as described in the next section.
## 3 Discussion
We propose that Inc-_Nr6a1-1_ act as a scaffold molecule interacting directly with these enzymes. We must underline the fact that these enzymes were copreipitated with antibody against ENO1 irrespective of the presence of Inc-_Nr6a1-1_; however, in _Luc-Nr6a1_-depleted cells, copreipitated enzymes were markedly reduced, indicating that Inc-_Nr6a1-1_ supports formation of a multi-protein complex of glycolytic enzymes and that this results in enhanced catalytic efficiency. Coimmunoprecipitation of this pool of enzymes from UV-crosslinked _Luc-Nr6a1_-depleted cells indicates that other RNAs could jointly contribute to the glycolytic complex formation. In fact, gLINC Inc-RNA was recently reported to act as a backbone for complex formation between PGK1/ENO1/ and PKM2, along with LDHA, promoting glycolytic flux and ATP production [35]; in the same way _NEAT1_ IncRNA forms a scaffold bridge for the assembly of the glycolytic complex PGK1, PGAM and ENO1 promoting a glycolytic state in breast cancer that is associated with more invasive and higher-grade tumors [36]. The contribution of other IncRNAs in the formation of the glycolytic complex would support that the enzymes TPII, PGK1 and PGAM1 are co-immunoprecipitation by ENO1, despite not being present in the Inc-_Nr6a1-1_ interactome. A hallmark of cancer cells is metabolic reprogramming to generate energy through increased glycolysis even in the presence of oxygen, known as the Warburg effect [37; 38]. High levels of _Luc-Nr6a1_ are observed in most of the tumors analyzed (Figure S11), which could be associated with the formation of multienzyme complexes and increased glycolytic activity in cancer cells. Our results show different functions for _Luc-Nr6a1_. On the one hand, by means of its non-polyadenylated transcript, _Luc-Nr6a1_ serves as a miRNA reservoir, giving rise to miRNAs miR-181a2 and miR-181b2; on the other hand, polyadenylated Inc-_Nr6a1-1_ transcript is involved in cell adhesion and glycolytic metabolism. Additional non-overlapping mechanisms for the Inc-_Nr6a1-2_ isoform are not ruled out. Further explorations of the isoform-specific interactome will unveil the full biological activities of this IncRNA.
## 5 Experiments and Results
### Quality Assessment Metrics
#### 5.2.3 Mean Squared Error (MSE)  
The MSE is a standard metric for evaluating the average of the squared differences between actual and predicted values:  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}(Y_{i}-\hat{Y}_{i})^{2}. \tag{12}\]  
This metric is sensitive to large errors, providing a measure of prediction accuracy.
## 1 Introduction
### Background of PunCar Action  
\"PunCar Action!\" is the first digital divide project in Asia that maintains records in real-time and utilizes Web 2.0 technology services. The project was implemented by a non-profit organization, the Association of Digital Culture, Taiwan (ADCT), and targets rural communities and social welfare organizations. The first ICT education course was offered in May 2008 [16]. The founders of PunCar Action, [PERSON] and [PERSON], are famous bloggers who grew up in the metropolitan area of Taipei. They have a deep knowledge of digital technology as well as tremendous ICT skills and a passion to share their knowledge. Two concepts drive the program, which the creators describe as follows: \"let Otaku (a Japanese term used to refer to young people with obsessive interests, particularly anime, manga, or video games) teach residents of rural communities in the use of computers and the internet,\" and \"move the computer classroom to the front door of your home\". They borrowed money to buy a van (in Chinese, van is literally translated as bread car, and Pun is the Taiwanese word for bread, hence, the name PunCar), and installed information equipment inside. The PunCar carried volunteer lecturers on a tour around Taiwan for the provision of digital education (see Table 1). They sought to disseminate knowledge about information technology, explain the current status of applications, and help to digitalize and promote the features of rural communities.  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Implementation unit** & **ADCT, Association of digital culture Taiwan (NPO)** \\ \hline Starting date & May 17, 2008 \\ Total lectures & 628 \\ Total teaching hours & 2884 h \\ Average number of students per lecture & 15 \\ Number of volunteer lecturers & 88 \\ Average number of lectures at each community & 6.5 \\ Total number of students & 12,056 \\ Average age of students & 53.2 years old \\ PunCar Action Van & Replaced four vans \\ Total mileage & 316,000 Km \\ \hline \hline \end{tabular}
\end{table}
Table 1: PunCar Action tour record (up to the end of 2012).  
PunCar Action directly addressed the digital divide by sending people and resources to remote areas. Volunteers developed flexible participatory courses in order to disseminate knowledge about internet tools and also combined life applications with community features. PunCar Action focused on the quality of offered courses while the government focused on quantity. PunCar Action represents a groundbreaking idea for the youth of Taiwan, and provides a new approach to achieving e-inclusion in rural communities. The project won the \"digital community\" award at the 2010 ARS Electronica in Linz, Austria [17].
## 6 Conclusions and Future Work  
ACO-based routing has been used widely in WSNs. To improve convergence performance and save energy consumption in basic ACO routing methods, quantum computing mechanisms were introduced in the QACMOR method. This paper studied two performance metrics: convergence rate and network lifetime, with reference to the features of industrial continuous steel casting production. Simulation results indicated that the algorithm proposed can rapidly obtain the optimal path with a fast convergence rate, and prolong the network lifetime. A WSN, based on the proposed QACMOR algorithm, was also deployed in an MRO system for a steel manufacturing enterprise. Physical WSN deployment and experiments showed that the proposed QACMOR algorithm is reliable in such applications, after consideration of packet loss based on our previous work [21; 30]. In future work, focus and attention should be given to the potential synergies between WSNs and other existing and emerging technologies, such as Cloud Computing and Big Data, so as to improve their overall performance and efficiency.  
**Author Contributions:** [PERSON] was responsible for methodology, validation and original draft writing, and [PERSON] guided the whole process, and also reviewed and edited the manuscript, and [PERSON] worked in software and experimental data collection.  
**Funding:** This research was funded by the Scientific Research Projects of the National Natural Science Foundation of China (Grant no. 61573257), Hangzhou Municipal Science and Technology Bureau of Social Development and Scientific Research Projects (No. 20150533B16), and by the Scientific Research Project of Zhejiang Education Department (No. Y201432791).  
**Acknowledgments:** The research work is partially supported by Hangzhou Key Laboratory for IoT Technology & Application, and Zhejiang Engineering Laboratory Intelligent Plant Factory.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Comparison of network lifetimes in a continuous steel casting line.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Value** \\ \hline Number of nodes & 60 \\ Network range & 300 m \(\times\) 280 m \\ Initial energy & 0.5 j \\ \(C_{1}\) & 0.5 \\ \(C_{2}\) & 0.1 \\ \(C_{3}\) & 0.1 \\ \(C_{4}\) & 0.1 \\ \(t_{max}\) & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter-setting in case study.
# Dual-Arm Peg-in-Hole Assembly Using DNN with Double Force/Torque Sensor
## 3 Methodology
### Error Recovery Sequence  
Since the DNN models present misclassifications in a real scenario, the assembly sequence is strengthened by adding logic constraints in every transition between steps. Two repetition cycles are added to the sequence. The first is represented as step S7, which repeats an orientation selection in case that the first DNN model outputs a wrong class, causing different contact states conditions. This can be identified and then triggers transition T6 to start this correction cycle.  
For the case of the final insertion step S5, in case the insertion forces exceed the defined limits, the logic conditions will trigger T8 to start the adjustment cycle S8, where the second DNN model outputs a robot action that moves the peg to a center position.  
This is one of the strengths of a discrete event system, that any step can be adjusted without modifying the whole control strategy. This new sequence program would lead the system to increase the rate of successful assemblies. Figure 4 shows the petri-net that represents the recovery sequence.  
The rule for trying another orientation after the first contact step is defined as follows:  
\[o_{n+1}=\begin{cases}o_{n}+1&f>f_{min}.o_{axis}<o_{max},try=1\\ o_{n}-1&f>f_{min}.o_{axis}<o_{max},try=2\end{cases} \tag{14}\]  
Figure 4: Modified sequence considering recovery steps.  
The rule for aligning the peg during the insertion step uses a DNN trained with basic contact states in order to output a reaction movement according to the predicted F/T pattern. The logic rule is described in the Table 1.  
The first four patterns consider \(X\) and \(Z\) axis movements for error correction and for the pattern # 5; no error correction is commanded in the X-Z plane but there is a movement in the Y axis to continue insertion.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 4 Performance of fs-LIMS Instruments in the Element and Isotope Analysis
### Chemometrics and Analysis Protocols
Furthermore, correlation of isotopes of an element is used to identify and reject the data points that, for example, do not belong to the species of interest or are affected by detection effects, such as detection saturation, spectral distortion or insufficient S/N. The isotope intensity correlation method was successfully applied in LA-ICP-MS earlier, yielding an improved isotope accuracy and precision compared to more traditional methods [1,153]. A similar method of the atomic intensity correlation based on the depth profiling data was developed very recently for in situ calibrations of atomic abundances owing to insufficient atomic ionisation efficiency (matrix effects) [34]. The method can be applied to a highly heterogeneous sample where simple oxides or sulphides are present. The ablation of such molecular compounds, if sampling resolution is sufficient, results in simultaneous detection of atoms combined in the compound. These compounds can be present at various depths in a heterogeneous sample. Nevertheless, with depth profiling data, one can correlate atomic intensities and perform a linear correlation analysis. From the slope of this correlation line, the atomic abundance ratio is derived and can be compared with the expected ratio for a given compound allowing derivation of RSC factors for the constituting elements. With the intensity (isotope, atomic) correlation methods, the quantitative element and isotope analysis can be improved significantly, which is important for heterogeneous samples where the RSCs change with the local composition. Additional studies have to be conducted to understand the possible limitations of this method and if the accuracy and precision can be further improved.
# Extended L-Band InAs/InP Quantum-Dash Laser in Millimeter-Wave Applications
## 3 Results and Discussion  
In this work, a single-mode, external-cavity-based CL (Agilent 8164B-81600B) with an optical linewidth of 50-100 kHz was utilized to realize a 33-GHz spaced DWL (CL-DWL) for the performance comparison with a QD-DWL emitting at an identical wavelength (i.e., 1610.85 nm). This was accomplished by replacing the components before CP-2 with the CL (as shown in Figure 1) and then transmitting the resulting 2-Gbits/s-QPSK-modulated 28-GHz MMW beat tone in the WL and hybrid-WL channels. In the literature, C-band QD-LDs have been engaged to generate MMW signals ranging from 40 GHz to 146 GHz; however, the 28-GHz frequency, which was investigated in this work, has not been reported yet, to the authors' knowledge. The transmission performance of the QD-DWL 28-GHz MMW beat tone has been characterized in two-channel configurations: a WL channel with a variable distance of 0-6 m that was the distance between the horn antennas and a hybrid-WL channel with a 20-km SMF and a variable 0-6 m WL sub-link. In each case, the channels were compared with the back-to-back (BtB) configuration (i.e., WL link fixed to 0 m). Furthermore, the system's performance was analyzed by measuring the bit error rate (BER) estimated from the error-vector magnitude (EVM) and SNR degradation at different received optical power values. Besides, a forward-error-correction (FEC) BER limit of 3.8 \(\times\) 10\({}^{-3}\), according to the ITU-T G.975.1 recommendation, was employed in this work to ascertain successful transmission. At this BER, the FEC criterion ensured sufficient average SNR and EVM for coherent reception, which were 8.5 dB and 37%, respectively [28; 29]. The generated MMW beat tones of the CL-DWL and the QD-DWL exhibited narrow linewidths and low phase noises near \(-\)\(-\)120 dBc/Hz; however, the former source demonstrated a slightly higher phase noise characteristic.