esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Ar/CF4 Mixed Gas Flow Rate on Silicon Etching Using Surface Discharge Plasma
## 1 Introduction  
In recent years, significant strides have been made in technology concerning the generation of glow discharge characterized by high spatial uniformity under atmospheric pressure conditions [1, 2, 3]. This technological breakthrough has practical applications as a surface treatment method for various materials, including semiconductors [4, 5, 6]. Etching stands out as one of the most crucial techniques for fabricating microelectronics [7, 8]. Additionally, massless etching technology for microelectronics is desirable due to its potential for low-cost fabrication. Traditionally, generating non-equilibrium plasma for atmospheric pressure pattern processing required conditions below atmospheric pressure. Furthermore, plasma processing within a pattern involved the sequential application and removal of a resist mask, which complicated the process. The authors explored the utilization plasma characteristics for spatial confinement under atmospheric pressure. Surface dischargeplasma, known for its adaptable spatial distribution, enables the realization of maskless plasma pattern treatments [9, 10]. The proposed surface discharge plasma technique allows the creation of non-equilibrium plasma within atmospheric pressure confines. Moreover, by confining the plasma within specific regions, patterned plasma etching can be carried out without relying on masking procedures. This advancement holds promise for applications in electrode grooving technology, particularly in the context of single-crystal silicon solar cells featuring buried contacts and selective emitter structure configurations [11, 12, 13]. Furthermore, the authors investigated the influence of argon (Ar) and carbon tetrafluoride (CF4) mixed gas flow rates on the formation of electrode grooves atop single-crystalline silicon substrates using surface discharge plasma [10]. However, these single-crystalline silicon substrates are additionally endowed with an anti-reflection film (Si\({}_{x}\)N\({}_{\gamma}\)) and a textured structure achieved via wet processing [14, 15, 16]. The effect of the gas flow rate on the etching of silicon materials by surface discharge plasma etching must be evaluated using a silicon substrate with a flat surface. Optimizing the conditions for generating plasma and understanding the etching characteristics of silicon materials are crucial for verifying the potential for further applications of surface discharge plasma, not only in solar cell electrode groove fabrication but also in semiconductor manufacturing technology.  
This study investigates the etching characteristics of flat-surface single-crystalline silicon substrates using surface discharge plasma generated under various Ar and CF4 mixed gas flow rate conditions. Consequently, we elucidate the influence of the Ar and CF4 mixed gas flow rate on the etching characteristics of single-crystalline silicon substrates.
## 4 Experiments and Analysis
### Analysis of Detection Effects of Various Loss Functions  
To demonstrate the advantage of integrating Inner-MPDIoU, this study conducted comparative experiments on the YOLOv8 framework, utilizing various prevalent loss functions while ensuring uniformity in the training parameters and conditions. Table 2 presents the [EMAIL_ADDRESS] and F1 scores for different loss functions. The experimental data indicate that when Inner-MPDIoU is employed as the bounding box regression loss function, the model  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Types** \\ \hline Operating system & Ubuntu \\ CPU & 18 vCPU AMD EPYC 9754 128-Core Processor \\ GPU & RTX 4090 (24 GB) \\ Language & Python 3.8 \\ Accelerating environment & CUDA11.8 \\ framework & torch2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental configuration parameters.  
achieves optimal detection performance. MPDIoU's direct minimization of the distances between the predicted and truth bounding boxes' top-left and bottom-right points enhances the model's precision in localizing eggplant diseases. Furthermore, by incorporating a scaling factor and leveraging inner boxes for loss computation, Inner-MPDIoU accelerates the model's convergence, resulting in an mAP of 76.6%, a 2.5% improvement over the original CIoU loss function. The F1 score increases from 71.2% to 74%, marking a 2.8% enhancement. Consequently, we contend that adopting Inner-MPDIoU as the loss function for the YOLOV8-E model represents the optimal choice.
## 2 Materials and Methods
### Study Protocol  
This study comprised two investigations: (1) the reliability of knee ROM measurements and the time spent completing the 5 TSST using the MSD and (2) the feasibility and user experience of using the MSD for the post-TKA in-home rehabilitation. The Ethics Committee of Taipei Medical University approved the entire protocol and instrumentation (TMU-JIRB N201802034). All participants consented to participate in the study and the publication of the data.  
Figure 3: Sets of home-based knee exercises displayed on the mobile app and demonstrated by participants: (**a**) straight leg raise in bed; (**b**) knee flexion in bed; (**c**) sitting knee stretch; (**d**) sitting knee flexion; (**e**) sit-to-stand; (**f**) semi-squat on wall; (**g**) standing knee flexion; (**h**) standing hip extension; (**i**) standing hip abduction; (**j**) forward step-ups.  
2.1 First Investigation: Evaluating the Reliability of Knee ROM Measurements and the Measurements of Time Spent for Completing 5 TSST Using the MSD  
A total of 12 healthy participants, aged 20-70 years and without reported discomorts or limited ROM of the knee within the preceding year, were prospectively enrolled to determine knee angular motion and time spent for completing 5 TSST.
# Classroom Expression Recognition Based on Deep Learning
## 3 Theoretical Framework of Facial Expression Recognition  
### Research Methods  
This study aims to develop a classroom concentration analysis system based on deep learning. The system automatically identifies students' head-up rates and facial expressions from classroom images and analyzes classroom concentration using a concentration modelto determine the current level of student engagement. The system architecture is shown in Figure 3. The primary research components of this paper include the following:  
Classroom Expression Dataset (CED): This paper constructs a dataset tailored for the analysis of student facial expressions in classroom settings. Using video recordings from 30 real-world university classrooms, the dataset comprises 12,174 images spanning five categories: resistance, confusion, understanding, fatigue, and neutrality. Various data augmentation methods are employed to enhance the dataset's diversity.  
Deep Learning Expression Recognition Algorithm: The paper proposes a Multi-Attention Fusion Network for Expression Recognition (MAF-ER), integrating spatial attention, channel attention, and self-attention mechanisms. The network consists of four key components: a feature extraction network, channel attention mechanism, spatial attention mechanism, and self-attention mechanism. The feature extraction network captures facial expression features, while the attention mechanisms enable the neural network to focus on significant facial expression details. The self-attention mechanism reduces ambiguity from uncertain samples, improving the model's representational capacity.  
Application to Intelligent Classroom Evaluation: This study serves as an effective tool for classroom emotion recognition, enhancing the educational experience for students and providing teachers with new insights into student observation and analysis, thus advancing classroom instruction toward the era of smart education.
## 3 Methodology
### Feature Extraction Methods
#### 3.4.4 Discrete Wavelet Transform (DWT)  
In DWT, data are decomposed into low pass \(L\) and high pass \(H\) components, yielding two sub-bands of \(L/H=l_{1},l_{2},\ldots,l_{\lfloor}N/2),h_{1},h_{2},\ldots,h_{2}\). For a bidimensional image, decomposition is performed in the vertical and horizontal directions, resulting in four sub-bands. The formed sub-bands are Approximation, Horizontal, Vertical, and Diagonal details and are due to the combination of \(LL\), \(LH\), \(HL\), and \(HH\) filters, respectively. Statistical parameters can be computed from each sub-band to have a reduced feature set [43; 47]. Methods such as the Line Measure Technique (LMT) [49] are also used to reduce the feature set. The mean, energy, standard deviation, and entropy of each sub-band were used as the extracted features in this work, summing to a total of 16 features.
# MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
## 3 Proposed Design
### Index Model
#### 3.2.2.1 Indexed Store Algorithm (ISA)
** The Indexed Retrieve Algorithm (IRA)  
\(T\): all entries that meet any one of the search conditions  
\(L\): intermediate results according to the \(T\) and \(OP\)  
**Input:**  
\(D\): the query conditions  
\(OP\): the logical relationship which means it matches all conditions or one condition  
**Output:**  
\(F\): the key-value entries set  
**Procedure:**  
(1): set _flag_ = False  
(2): get all columns in \(D\) as _colList_  
(3): get total predefined indexes in index table as _indexList_  
(4): get indexes columns list: _idxcol_ = _colList_ \(\cap\)_indexList_  
(5): **if**_idxcol_size_ < _colList_._size_ **then**  
(6): set _flag_ = True  
(7): **end if**  
(8): **for each**_idxcol_\(i\) _idxcol_ **do**  
(9): determine the index column of _idxcol_\(i\)  
(10): generate a new rowkey of _idxcol_\(i\)  
(11): set startkey and stopkey of scanner on the basis of changed rowkey  
(12): get results by changed rowkey and add them into \(T_{i}\)  
(13): **end for**  
(14): if \(OP\) = _Equal_All_ **then**  
(15): \(L=\overset{idxcol\_size}{\underset{i=1}{\overset{i=1}{\cup}}}T_{i}\)  
(16): if _flag_ = True **then**  
(17): filter \(L\) by using unindexed columns in \(D\) and refresh \(L\)  
(18): **end if**  
(19): **else if**\(OP\) = _Equal_One_ **then**  
(20): \(L=\overset{idxcol\_size}{\underset{i=1}{\overset{i=1}{\cup}}}T_{i}\)  
(21): if _flag_ = True **then**  
scan table to fiter data on unindexed columns in \(D\) and append results to \(L\)  
(22): **end if**  
(23): **end if**  
(24): \(F\) = use each key in \(L\) as rowkey to get exact rows  
(25): **return**\(F\)  
The columns in query condition must first be checked on the server side. For example, server gets _idxcol_, which is intersected with _colList_ (total qualifiers in query condition \(D\)) and _indexList_ (predefined indexes in index table) in the first step. If the size of _idxcol_ is smaller than _colList_, it suggests that query condition contains unindexed columns. Thus, we set the flag _True_. After that, we use the _IndexRegionObserver_, an enhanced subclass instance of the coprocessor method above to traverse the _idxcol_ to decide which index these columns belong to. In each iteration, there is a match between index and column and it will return a new key that contains an index name and the field value. Next, _IndexRegionObserver_ creates a scanner prefixed with the key that determines the start and stop locations on each region and equally retrieves the region.
## 2 Materials and Methods
### Data Adjustment Techniques
#### 2.2.2 Artificial Neural Networks  
An ANN is developed to replicate the interaction between the two influencing factors in the _Trichoderma harzianum_ production, the culture time, and the humidity percentage used. The ANN input layer is composed of two neurons, which represent the two independent variables. The output layer just contains one neuron, which represents the amount of conidiospores produced, as a response variable, similar to the RSM model. The data are analyzed with the ANNs toolbox from Matlab(r) 8.1. The hyperbolic tangent sigmoid transfer function is used as an activation function for each hidden layer, and the linear transfer function is applied in the output layer.  
The experimental response values are used to identify the most appropriate ANN model to recreate this biotechnological process. Furthermore, one objective is to identify the combination between the input values to obtain the maximum amount of _Trichoderma harzianum_ spores. Thus, a Matlab(r) script is implemented to find the ANN with the lowest quadratic error. The script can generate ANN models, starting with a 3-layer model, where the first layer (the input layer) contains 2 neurons (culture time and humidity percentage); the midterm layer is the hidden layer, and its number of neurons is changed from one to ten neurons; and the output layer consists of only one neuron, representing the amount of the conidiospores.  
A random selection from around 70% of the experiments (26 samples) is chosen to train the ANN. A total of 15% of the experiments (5 samples) are used to validate the model.  
Lastly, the other 15% (5 samples) are used for the test phase [28]. The training set achieves an R\({}^{2}\) very close to 1; however, the test and validation sets obtain an R\({}^{2}\) smaller because these records were not used in the training phase. Finally, considering the whole dataset, the R\({}^{2}\) obtained is 0.9952.  
The ANNs are trained with the retropropagation Levenberg-Marquardt algorithm [29]. Each ANN contains from 1 to 4 hidden layers, and each hidden layer contains from 1 to 10 neurons.  
Due to the combination of the number of neurons in one to four hidden layers, a total of 10,000 ANNs were trained. In the search for the ANN with the best performance, at every iteration, the new ANN was compared with the best ANN found until that moment, keeping the best one for the next comparisons. During the 10,000 iterations, 33 times the best ANN was updated. The behavior of \(R^{2}\) (Figure 1a), _RMSE_ (Figure 1b), and _ADD_ (Figure 1c) for these 33 ANNs denotes how the \(R^{2}\) is moving to one and the _RMSE_ and the _AAD_ are approaching zero.  
The network that performs better has an input layer with 2 neurons, an output layer with one neuron, and 4 hidden layers, with 9, 5, 3, and 9 neurons, respectively, with a determination coefficient \(R^{2}=0.9952\) (Figure 2).
## Materials and Methods
### Antibodies and Sera  
Murine IgG monoclonal antibodies (MAbs) against RSV-G (1227, 1197, 104-5, 109), RSV-F (1243), PIV3-F (b108), or PIV3-HN (170) were obtained from a WHO Reagent Bank. Murine MAb's 131-2G (anti-RSV-G), 130-12H (anti-RSV-N), and anti-FLAG were purchased from Millipore-Sigma (St. Louis, MO, USA).  
Four-to six-week-old female BALB/c mice were infected intranasally with 0.1 mL inoculum containing approximately \(10^{6}\) TCID\({}_{50}\) of RSV-A2 or RSV-B1 on days 0 and 42. Sera were collected before infection and on days 35 and 77. Pre-immune mouse sera served as the negative control. Animal experiments were approved by the U.S.A. FDA Institutional Animal Care and Use Committee (IACUC) (protocol #2010-24).  
Rabbits were immunized with UV-inactivated, sucrose-gradient purified viruses (RSV-A2, RSV-18537) or proteins (RSV-F\({}_{\rm A}\), RSV-G\({}_{\rm A}\)) as previously described [25]. Another group of rabbits were immunized with UV-inactivated viruses including MV and hMPV, in a similar manner. Rabbits immunized with purified UV-RSV-18537 were also boosted once with 0.12 mg of purified RSV-G\({}_{\rm B}\) protein. Rabbits were also immunized intramuscularly with 400 \(\mu\)g Keyhole limpet hemocyanin (KLH)-conjugated synthetic linear peptides: G\({}_{\rm A231-250}\) (RSV-A amino acids T\({}_{\rm 231}\)-S\({}_{\rm 250}\)), G\({}_{\rm A262-291}\) (RSV-A amino acids M\({}_{\rm 262}\)-S\({}_{\rm 291}\)), LHWT-G\({}_{\rm A}\) or LHWT-G\({}_{\rm B}\) (CX3 CR1 binding domain consisting of V\({}_{\rm 171}\)-P\({}_{\rm 202}\) for both RSV-A and -B strains) [26]. Immunoprecipitation assays evaluating reactivity of rabbit antisera with luciferase-tagged RSV-G proteins used rabbit anti-FLAG IgG (Sigma-Aldrich, St. Louis, MO, USA) and pre-immune rabbit sera as positive and negative controls, respectively. Animal experiments were approved by the U.S.A. FDA IACUC (protocol #2001-23).  
Anonymous human serum samples (6 mo to 14 yrs) were obtained with informed consent from Institutional Review Board (IRB)-approved studies and tested with approval by the FDA Research involving Human Subjects Committee (FDA RIHSC #11-030B). Thirty-six (of 100) samples obtained at 9 months were previously identified as zero-negative for both anti-RSV neutralizing and anti-RSV-N IgG antibodies [25]. Of these 36, 12 had paired samples obtained at 15-18 months of age; these 12 paired serum samples were further evaluated herein. Human reference immune globulin to RSV (BEI NR-21973) was diluted to 1% IgG concentration in DPBS, unless otherwise stated. Human sera depleted of IgG, IgM and IgA (Ig\(\Delta\)) was obtained from SunnyLab (Sittingbourne, UK). Aliquots of all reference sera were stored at \(-20\,^{\circ}\)C.
## 6 Result and Analysis
### Cross Validation  
To validate our findings, we utilized the Combine_All dataset. Initially, we segmented our dataset into 5 folds by ensuring that it did not contain any records from the training set. We then applied our comparative framework to iteratively train each algorithm on \(k-1\) folds while using the remaining holdout fold as the testing set. All the transformer models performed the best, with 90% or more ROC scores at each fold. In contrast, NB performed the worst, with ROC score less than 60% (Figure 10). Our \(k\)-fold results showed that our findings in Figure 8 were consistent with comparative ROC values with the cross-validation runs.  
Figure 10: ROC/AUC results across \(k\)-fold cross-validation.  
Figure 9: Qualitative ROC/AUC results for synthetic data (SMOTE) across (**a**) agency, (**b**) employee and (**c**) customer for state-of-art methods.  
In Figure 11, we provided the average ensemble count (average of predicted labels for all models) of all the three classes corresponding to conventional ML, DL-based, and transformers. Additionally, we also reported a confusion matrix to verify the true-positive (TP), true-negative (TN), false-negative (FN), and false-positive (FP) classes for each fold. For example, the average ensemble count was the actual MIR value versus the predicted value for each fold (from 1 to 5), i.e., the true-positive (TP). The confusion matrix for ML, DL, and Transformers are listed in Tables 5 and 6. For example, in \(k\)-fold, there were 1731 total MIR, whereas the average ML (i.e., the average of the predicted label of all the ML models) predicted 1172 TP MIRs, as well as 1468 TP MIRs for the DL-based and 1609 TP MIRs for the transformers. Similarly, for TNs, NB predicted the 64,722 TNs for ML. For DL, Bi-LSTM predicted 64,600 TNs, and for the transformers, BERT reported the highest 64,605 TNs values. For conventional ML, the average predicted value for all folds was 1153, with SVM having the highest share with 1499, which was lower than the predicted labels of 1731. The DL models showed better ensemble results with 1394 predicted labels for all folds, with CNN having the highest number of 1673 predicted labels. Transformers outperformed the conventional ML techniques with 1584 average predicted ensemble labels. RoBERTa performed best with 1693. Overall, the transformers models performed the best for the Combine_All datasets (Refer Figure 8).  
Similar to the average ensemble count, RoBERTa correctly predicted 1462 TPs and 64,558 TNs, whereas NB performed worst by identifying only 270 TPs and 64,722 TNs.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 5 Constraints of IPCB Geometry Configuration
### Airspace Constraint  
Airspace is the space in which an IPCB is approved to fly at a certain time. Within the approved airspace, an IPB can fly with its gondola, and its payloads, such as signal generators and transmitters, can run normally. Once an IPB flies out of the approved airspace, its gondola will be cut off from its balloon, and the payloads in the gondola will be switched off, making it impossible for the IPB to emit navigation signals. So, the longitudes and latitudes of each IPB in the IPCB should vary depending on the extent of the approved airspace.
# Estimating Interception Density in the BB84 Protocol: A Study with a Noisy Quantum Simulator
## 4 BB84 Simulation Model
### Key Checking  
Figure 3 illustrates the flow chart of the key-checking procedure. This procedure determines whether [PERSON] and [PERSON] share a secure secret or whether it is necessary to restart the BB84 protocol due to [PERSON] observing bit errors on the received key, which may be caused by [PERSON]'s interference and/or system noise. This is achieved by [PERSON] publicly sharing the  
Figure 2: BB84 key generation.  
first \(m=k/2\) bits of the key with [PERSON]. Subsequently, both parties compare these bits to ascertain any discrepancies. In such an event, the algorithm fails and necessitates a restart. Otherwise, if all the shared bits are identical, probably, the remaining, unshared bits will also be so. Subsequently, [PERSON] and [PERSON] discard the shared bits, as they have become public and cannot be utilized for the private key generation. The remaining bits then form the actual shared secret key, which can be used by both participants.  
It should be noted that QS does not perform information reconciliation and privacy amplification techniques because, firstly, they are not strictly part of the protocol and, secondly, the described implementation requires a perfect match (i.e., zero error rate) between the cases of [PERSON] and [PERSON] compared key bits, rendering these techniques less indispensable. In a real-world scenario, instead, both are typically employed, as commented upon in Section 3.2.
## 4 Network Model  
The network model is described in Figure 4, where mmWave nodes operating at 26 GHz with multiple RRUs connect a finite number of MTC devices, with different sensor types. Four networks have been considered, each with specific device requirements. These network ranges from net-1 with  
Figure 3: The signal to interference plus noise ratio (SINR) mapping with and without Remote Radio Units (RRUs).  
the least demanding MTC devices to net-4, with the most stringent requirements. The MTC devices are either fixed, such as smart meters or mobile such as health monitoring sensors; which have been modelled with three different speeds, to evaluate their performance, namely (0, 30 and 100) kmph, as such sensors are expected to be worn by the mobile user.  
Orthogonal Frequency Division Multiplexing (OFDM) is used as the waveform in this work due to its high performance in dealing with multipath signals, lower computational complexity and direct compatibility with Multiple-Input Multiple-Output (MIMO) system. The system bandwidth is divided into small divisions called Resource Blocks (\(RB\)). Each \(RB\) is allocated to a single MTC sensor for single Transmission Time Interval (TTI), each TTI equals to 1 ms [25].  
In Reference [3] the authors have studied the requirements of real-time fixed smart metres, they have found that data transaction by these smart metres occur every Ts = 120 s, where smart metres transfer data by accessing the network for Ta = 430 ms and transmit their payload at a rate of Rb = 60 kbps. This is the most stringent fixed sensor requirement, as other sensors are less stringent. For instance, some weather sensors access the network to upload data at periods of five minutes. Additionally, some health sensors require uploading data with very low rate (heart beat Rb = 640 bps, body temperature at 2.4 bps and blood pressure at rate of 1.92 kbps [19]). However, EEG has the highest demands of 98 kbps as shown in Table 1 and therefore, a margin of 100 kbps is used in the setting of our algorithm.