esa-sceva/satcom-chunk-collection huggingface dataset:
# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 3 Materials and Methods
### Service Module
#### 3.2.2 Positioning Information of Medical Personnel and Equipment
With limited human resources, it can reduce the work pressure on medical staff and medical management staff and improve the efficiency and quality of care. However, if further patient or care recipient safety management is required, for example, when a patient or care recipient enters a dangerous area of the hospital, such as a stairway area, or when a care recipient is found to be leaving the hospital through the entrance area with the driveway outside, the use of more accurate location methods must be considered and addressed. If the positioning information in these areas is not accurate enough, there is a possibility that the patient or care recipient may be injured due to inaccurate positioning information without warning notification. Therefore, this study proposes to use fingerprint positioning [41; 42; 43; 44; 45] in these dangerous areas to improve the accuracy of positioning to avoid danger to patients or care recipients. The main process of fingerprinting positioning can be divided into four steps. (1) The signal strength data of the localization sensors in the area of the environment are collected and uploaded through the Bluetooth Hubs installed in the environment. (2) In order to train the collected signal strength data, the data must be pre-processed into the data format required by the machine learning model. Then, the data collected by the Bluetooth Hubs are combined through the time stamp of the sensor, and the signal strength data originally uploaded in a single entry are now combined into the signal strength data set received by the Hubs at the same time, providing the necessary data for the model. (3) Two machine learning models: K-nearest Neighbor (KNN) algorithm [25; 46; 47] and Support Vector Machine (SVM) [48], are used and compared. (4) Signal entries (data) are divided into the training data and the test data, and the RSSI signal strength is used as the feature value and the cell number as the label.
## 2 Related Work  
CNNs have been considered relatively successful models for designing large vocabulary and continuous speech recognition models, with excellent training speed [2]. Since they have been proved to be an optimal approach for speech recognition, pattern recognition, and many other machine intelligence-related processes, many researchers have approached this newest network.  
The utility and performance of this model can be measured from the research work carried out by various researchers. Multilayer perceptron (MLP), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are the essential algorithms of DNNs. Both CNNs and RNNs are considered suitable for implementing speech and natural language processing. Apart from these, speaker recognition, emotion recognition, speech enhancement, and voice activity detection are essential areas covered by CNNs and RNNs. DNNs use computational models, which are comprised of multiple processing layers. This feature tends to generate the ability to learn data representations with multi-level abstraction compared to single-processing layered models [14]. DNNs have advanced computational power for training in computational models. They also tend to deal with highly complex learning tasks by integrating reinforcement learning [15; 16].  
Some authors developed a speech recognition system using the CNN approach by involving a limited-weight-sharing scheme under CNN architecture. The experimental results showed that the CNN reduced the word error rate by 6-10% compared to the method developed by the conventional technique [11; 17]. In another work, the speech command recognition system used the CNN modeling approach to detect predefined keywords [12]. This system used speech commands from Google's TensorFlow and AIY teams containing 65,000 wave files. The authors used the Vanilla Single-Layer Softmax model, a deep neural network, and a convolutional neural network model. The system with the CNN yielded a 95.1% accuracy level.  
Other authors worked with a CNN using end-to-end acoustic modeling [13]. In their work, different databases were used to carry out the recognition process--TIMIT, WSJ, MP-DE, and MP-F. Their approach resulted in a 1.9%-word error rate, which was relatively lower than the other existing techniques. The overall analysis and contribution of existing works is illustrated in Table 1.  
From the above literature review, it can be concluded that most of the Punjabi language research work has been carried out using ordinary speech signals and HMM, GMM, and ANN modeling techniques. So, there are two aspects to the novelty of the current work: Different input speech signals were considered--the present speech recognition system can also recognize uncommon speech signals. Secondly, the literature survey reveals that CNNs are the most powerful tool and modeling paradigm when applied to speech recognition systems with large datasets [26]. So, the inclusion of a CNN and MFCC was considered for the present system.
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods
### Machine Learning Algorithms
#### 2.4.7 Hyperparameter Optimization  
The various hyperparameters for the different ML techniques were optimized with Bayesian optimization (expected improvement acquisition function) to achieve the best classification accuracy [62]. Table 5 lists the hyperparameters for each ML algorithm. A large number of iterations were performed for each ML algorithm using 10-fold cross-validation techniques.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**ML Algorithm** & **Hyperparameter (s)** & **PS** & **SS-APS** & **TR-APS** & **SB-APS** \\ \hline \multirow{2}{*}{SVM} & Kernel Scale & 0.90 & 0.46 & 30.11 & 0.01 \\  & Box-Constraint & 0.19 & 0.090 & 122.38 & 737.2 \\ \hline \multirow{2}{*}{DT} & Minimum Leaf Size & 1 & 7 & 9 & 8 \\ \cline{2-6}  & Number of Neighbors & 1 & 10 & 7 & 2 \\ \cline{2-6}  & Distance Function & cosine & Spearman & correlation & correlation \\ \hline \multirow{2}{*}{LD} & Delta & 3.86 \(\times\) 10\({}^{-5}\) & 2.1 \(\times\) 10\({}^{-5}\) & 0.01 & 0.0002 \\  & Gamma & 0.005 & 0.0662 & 0.51 & 0.22 \\ \hline \multirow{2}{*}{NB} & Distribution Name & normal & normal & kernel & normal \\  & Width & — & — & 0.0472 & — \\ \hline \multirow{2}{*}{EL} & Learning Rate & 0.9027 & 0.001 & 0.82 & 0.9504 \\  & Number of Learner & 490 & 16 & 222 & 477 \\ \hline \hline \end{tabular}
\end{table}
Table 5: List of optimized hyperparameters for the machine learning (ML) algorithms (PS: physical state classification; APS: acute psychological stress; SS-APS: APS classification in sedentary state; TR-APS: APS classification in treadmill run; SB-APS: APS classification on stationary bike; SVM: support vector machine; DT: decision tree; k-NN: k-nearest neighbor; LD: linear discriminant; NB: naive Bayes; EL: ensemble learning
## 2 Shock Tube Experimental Setup
### Summary  
The experiments conducted aimed to compare various factors, including the position of TNT detonation, the contact between the target and shock tube, the presence of the expansion section, and whether the left side of the driver section was sealed. In the shock tube experiments, the COV value ranged from 0 to 0.18, indicating better reproducibility compared to open-field experiments (0.23 to 0.3).  
1. Effect of TNT detonation position The peak incident pressure was observed twice, when the detonation occurred at the center of the driver section, and the target and shock tube were not in contact. The second peak incident pressure was generated due to a reflection from the driver section. As a result, the peak reflected pressure was also observed twice. Although peak incident pressure was also observed twice when the detonation position was at the left of the driver section, it was the pressure reflected from the target. Consequently, the peak reflected pressure was measured only once. Thus, under sealed conditions, only when the TNT was positioned to the left of the driver section, the pressure characteristics resembled those typically observed in open-field experiments.  
2. Effect of shock tube and target contact on uniform pressure In case 1-1, where the shock tube and target were not in contact, the COV values of the peak reflected pressures at different locations of the target ranged from 0.2 to 0.29. On the other hand, in case 1-2, where the target was in contact with the shock tube, the COV values ranged from 0.067 to 0.15. And in case 3, when the expansion section was used, and the target was in contact with the shock tube, the COV values ranged from 0.024 to 0.11. Therefore, to obtain a more uniform distributed pressure, it is necessary for the shock tube and target to be in contact.  
3. Effect of sealing the driver section The effect of sealing the left side of the driver section can be compared in cases 2 and 3. Case 2 presents the differences when the expansion section is not used, while case 3 examines the differences with the expansion section in place. In both cases, sealing the driver section consistently resulted in higher peak incident and reflected pressures. This is because when the driver section was not sealed, the shock was released through the unsealed side.  
4. Effect of expansion section In the experiments using the expansion section, the reflected pressure was lower compared to those from the experiments without the expansion section. This is because the volume through which the shock was transmitted increased when the expansion section was used.
## 3 Proposed Scheme
### Communication Round of the Proposed Scheme
#### 3.1.2 Contention Access Period  
During the contention access period, all nodes contend with each other in accessing the medium by applying the CSMA/CA algorithm. In this period, two types of requesting frames are received by the PAN coordinator, such as  
1. Joining request from nonmember nodes.
2. GTS requests from member nodes.  
Nonmember nodes after listening to the beacon frame message send a join request (JR) message to the PAN coordinator to become a member of the WPAN. The _JR_ frame includes the information of the requesting node ID. In response, the WPAN coordinator allocates  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(\sigma_{TD}\) & Data transmitted by all nodes \\ \hline \(\sigma_{\text{{TDL}}}\) & Transmitted data by legitimate nodes only \\ \hline \(\xi_{\text{{ND}}}\) & Total network delay \\ \hline \(\xi_{\text{{NDL}}}\) & Total network delay by legitimate nodes only \\ \hline \(\xi_{\text{{NDA}}}\) & Average network delay \\ \hline \(\xi_{\text{{NDLA}}}\) & Average transmission delay by legitimate nodes only \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Communication session of the proposed MAC.  
the requested node a membership during the next beacon frame message and allocates a unique 8-bit short address. All the member nodes in a WPAN are allocated a unique 8-bit short address for their identification. This limits the size of WPAN to 256 nodes.  
All member nodes that are assigned a short 8-bit address are allowed to send their data during the contention-free period by allocating GTS. Nodes without allocating a short address are not allowed to send their GTS request to the WPAN coordinator. Nodes having data transmit a GTS request to the WPAN coordinator with the number of slots required to send their data. Nodes calculate the number of GTS required (\(GTS_{Req}\)) in transmitting their data with the help of data slot capacity and the amount of data (D) required to be transmitted as follows:  
\[\left\lceil GTS_{Req}=\frac{D}{SD_{b}}\right\rceil \tag{4}\]
## 6 Mathematical Formulation Using Linear Programming
### Minimization of Average Delay  
During the operation of forwarding in the network, sender nodes transmit packets directly or via multi-hops. We define average delay incurred due to direct and multi-hop transmission in \(r\) rounds for \(N\) number of nodes in the network as in Equation (28). In multi-hop transmission, node waits for \(T_{w}\) time as shown in Equation (29),  
\[Min\sum_{i=1}^{N}D_{tot}(i)\quad\forall\,i\,\varepsilon\,N. \tag{28}\]  
\[T_{w}=D_{Proc}+D_{Prop}+T_{hold}, \tag{29}\]  
Figure 6: Feasible regions. (**a**) Feasible region for throughput maximization (GDGOR-IA); (**b**) feasible region for throughput maximization (GRMC-SM).  
\[T_{Prop}=(R_{c}-D(ij))/s, \tag{30}\]  
\[T_{hold}=\sum_{i=1}^{j}D(n_{i},n_{i+1})/s. \tag{31}\]  
Total delay incurred comprises of delay due to direct transmission and multi-hop transmission as in Equation (32),  
\[D_{tot}=D_{DT}+D_{MHT} \tag{32}\]  
For direct transmission to in range sinks, time taken is accumulation of propagation time and processing time. Therefore, delay caused due to that is \(T^{\prime}_{w}\) as in Equation (33):  
\[T^{\prime}_{w}=D_{Proc}+D_{Prop} \tag{33}\]  
\[D_{DT-min}=T^{\prime}_{w}\times H_{n}; \tag{34}\]  
where \(H_{n}=1\) for direct transmission scenario when the sink is in transmission range of source node.  
\[D_{MHT-min}=H_{n-min}\times T_{w}; \tag{35}\]  
\[D_{MHT-max}=H_{n-max}\times T_{w}; \tag{36}\]  
The objective function in Equation (28) is formulated under following constraints C1, C2, C3:  
at C1: \(0<D^{ij}_{max}\leq R_{c}\)  
at C2: \(0<T_{w}\)  
at C3: \(H_{n-min}\leq H_{n-max}\)  
**Graphical analysis**: Let's consider, if source node be in the transmission range of sink and it relays data directly. During this, delay caused is represented via \(D_{DT}\). On the other hand, when sink cannot be accessed directly by the sender node, then packet is transmitted through multiple hops. By assuming that minimum delay is caused on one-hop transmission and maximum delay occurs when data is delivered through multiple hops. We can compute maximum and minimum delays caused in both direct transmission scenario and multi-hop scenario; as, shown in Figure 7.  
\(1.35\leq D_{DT}+D_{MHT}\leq 3.45\)  
\(0.45\leq D_{DT}\leq 0.6\)  
\(0.9\leq D_{MHT}\leq 2.85\)  
Each vertex of the region is shown as:  
at \(P1:0.45+0.9=1.35\) s  
at \(P2:0.45+2.85=3.30\) s  
at \(P3:0.6+0.9=1.5\) s  
at \(P4:0.6+0.285=0.885\) s  
Each vertex of the region is shown as:  
\(1.72\leq D_{DT}+D_{MHT}\leq 3.71\)  
\(0.50\leq D_{DT}\leq 0.65\)  
\(1.22\leq D_{MHT}\leq 3.06\)  
Each vertex of the region is shown as:  
at \(P1:0.5+1.22=1.72\) s  
at \(P2:0.5+3.06=3.56\) s  
at \(P3:0.65+1.22=1.87\) s  
at \(P4:0.65+3.06=3.71\) s
## 5 Discussion
### Gas Sources  
There are still uncertainties about the origins of the gas in the Kuqa Foreland Basin. It has been proposed that the gas condensate is gas cracked from oil or from kerogen due to the heating of Paleozoic source rocks in Kela 2 Gas Field and Kokyar Gas Field [106; 107]. However, further investigations have demonstrated that the Middle and Lower Jurassic coal measures are the main sources of the gas condensate in Dabei Gas Field, Di'na2 Gas Field, and Dbei Gas Field [48; 108; 109]. It has also been suggested that the gas comes from a mixing of coal-derived gas with petroliferous gas [48; 110]. The modeling results show that gas from oil cracking is not the main source of the gas (Figure 16A). The condensate gas accumulated in the Dbei Gas Field is dominated by humic gas, with minor mixing of sapropelic gas (Figure 9), and derived mainly from the Jurassic coaly source rocks.
## 2 Materials and Methods
### Results of the First Detailed Landcover Map Update Process  
In the first process, misclassified and redclassified items were updated according to the criteria and temporal changes, respectively. Then, detailed classification of the grassland and barren land areas existing within urban dryland (the broad classification) prevailing in new urban areas was updated. Figure 3 shows the results of the first and second updates for an apartment complex in Sejong City. The land-use classes in the existing landcover map consisted of \"multi-family dwellings\" (112); \"commercial office and buildings\" (131); \"roads\" (154); \"other grass\" (423); and \"other barren\" (623). The first update resulted in the addition of \"cultural, sports, and recreational facilities\" (141) and correction of \"other grass\" and \"other barren\" areas to roads and cultural, sports, and recreational facilities.  
Furthermore, most items were changed in the high-resolution (large-scale) landcover map during the first update, most notably areas of the 14 detailed classifications for \"Used Area\". Increased categories included \"single-family dwellings\" (0.14%); \"multi-family dwellings\" (0.13%); \"industrial facilities\" (0.13%); \"commercial and office building\" (0.15%); \"cultural, sports, and recreational facilities\" (0.09%); \"railways\" 0.01%; \"roads\" (1.67%); \"educational and administrative facilities\" (0.01%); and \"other public facilities\" (0.14%); these totaled 2.46%, with \"roads\" showing the highest increase rate. Unclassified shopping areas and parks within residential areas (e.g., apartment complexes) were added, along with boundary corrections. The post-2015 temporal land-use changes were not reflected in the high-resolution landcover maps of Sejong City, and the areas classified as \"other barren\" primarily arose during urbanization. The greenery areas within apartment complexes were corrected to \"other grass\" in the detailed classification of impervious surface areas.
## 1 Introduction
### Motivation and Objectives  
The SDWSN environment suffers from various issues such as energy efficiency, security, and connectivity, respectively. The existing works fail to provide combined solutions for the routing--scheduling issues in the SDWSN environment and security. Some of the major problems of the SDWSN environment regarding routing and scheduling are provided below:  
* Fewer Energy Efficiency and Connectivity Issues--All the existing works tend to increase their energy efficiency and connectivity at the node level, leveraging the communication management among the sink nodes and wireless sensor nodes (i.e., random placement of sink nodes in the SDWSN environment), which affects the connectivity and energy efficiency of the SDWSN environment.
* Improper Scheduling--The scheduling undergone by the existing works is merely based on the control messages and faults. However, with these metrics, the scheduling was not done in an effective manner. Furthermore, the existing works are limited to single-tier routing (i.e., schedules all the nodes in the SDWSN environment on a single round), which leads to increased interference issues.
* Poor Routing Security Measures--The existing works only consider trust-based routing as a security practice. However, they lack providing security to the routing data, which leads to spoofing attacks. In addition, the routing path and detection surface are kept static, which also leads to reconnaissance attacks.  
The main aim and scope of this research are to ensure energy efficiency, connectivity, and security to the SDWSN environment using intellectual techniques. In addition, this work also addresses and resolves the important existing issues in the SDWSN environment. The foremost objective of this research is to model an energy-efficient and secure SDWSN environment for enabling secure routing and interference-aware scheduling. In addition, this research also coins an effective problem statement by leveraging the issues in the prior works. Some of the supplementary objectives of this work are provided below:* To reduce the connectivity issues in the proposed environment by performing connectivity-based network construction, which also improves the energy efficiency.
* To resolve the issue of energy consumption by performing clustering of the underlying wireless sensor nodes-based density, which also manages the nodes' mobility in the SDWSN environment.
* To reduce the interference in the SDWSN environment by performing scheduling of the wireless sensor nodes into three tiers that also improve the sensor node lifetime in the network.
* To mitigate the cyber vulnerabilities in the SDWSN environment by performing secure routing in two ways, which also reduces the link failure issues.
###### Abstract  
Optical links are rapidly becoming pervasive in the readout chains of particle physics detector systems. Silicon photonics (SiPh) stands as an attractive candidate to sustain the radiation levels foreseen in the next-generation experiments, while guaranteeing, at the same time, multi-Gb/s and energy-efficient data transmission. Integrated electronic drivers are needed to enable SiPh modulators' deployment in compact on-detector front-end modules. A current-mode logic-based driver harnessing a pseudo-differential output stage is proposed in this work to drive different types of SiPh devices by means of the same circuit topology. The proposed driver, realized in a 65 nm bulk technology and already tested to behave properly up to an 8 MGy total ionizing dose, is hybridly integrated in this work with a lumped-element Mach-Zehnder modulator (MZM) and a ring modulator (RM), both fabricated in a 130 nm silicon-on-insulator (SOI) process. Bit-error-rate (BER) performances confirm the applicability of the selected architecture to either differential and single-ended loads. A 5 Gb/s data rate, in line with the current high energy physics requirements, is achieved in the RM case, while a packaging-related performance degradation is captured in the MZM-based system, confirming the importance of interconnection modeling.  
silicon photonics; optical communications; Mach-Zehnder modulator; ring modulator; current-mode logic; CMOS; pseudo-differential; radiation hardness; high energy physics; BER characterization +
Footnote †: This paper is an extended version of our paper published in ApplePies 2019: Applications in Electronics Pervading Industry, Environment and Society, Pisa, Italy, 11–13 September 2019; pp. 11–18.