esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The Daphnet Freezing of Gait Data Set [30], encompass 3D acceleration data derived from sensors on lower extremities, while participants performed three different tests elaborating the activities of walking straight with 180\({}^{\circ}\) turn, random walking with stops and 360\({}^{\circ}\) turns and, simulating activities of daily living, while participants' performance has been rated with the use of H and Y scale. However, only 10 PD patients (who present the FoG symptom) were recorded, there was no control group, ON/OFF medication states were not addressed, and only self-selected pace of walking was examined. Finally, The Smart-Insole Dataset [16], includes data derived from pressure sensor insoles, while participants performed two different sets of tests elaborating different walking speeds, however the performance of the participants has been rated using four items of the MDS-UPDRS, the dataset is small (8 PD patients, 9 elderly and 13 adults are included), while ON/OFF medications states were not addressed. These issues introduce limitations and make the comparative analysis of results very difficult, if not impossible. Moreover, the assessment of PD patients in ON and OFF medication states has been highlighted as an aspect that needs further investigation [31]. Finally, for the accurate automated detection of PD patients' mild-stage motor symptoms, it is important to classify the different severity levels of the disease using as a reference point the quantified metrics that are applied in clinical practice. In such a context, the present work contributes to the challenging task of PD monitoring, by introducing a computational pipeline for identifying relations between the spatial and temporal gait features, PD-related medication states and disease severity levels, as well as for separating PD patients from non-PD groups. To this end, we elaborate on a comprehensive experimental protocol for the assessment of PD motor signs of the lower extremities, as presented in Section 2. The protocol is based on literature findings regarding the most popular tests employed in the domain, and on the description of relevant items of MDS-UPDRS-Part III: Motor Examination, while it encompasses the assessment of different walking speeds. Recordings have been obtained from PD patients, and two control groups, elderly and adults. For PD patients, both, ON and OFF medication states have been examined. Clinical assessment has been performed by neurologists specialized in movement disorders, utilizing the MDS-UPDRS-Part III: Motor Examination. Gait-related spatial and temporal characteristics were extracted from the raw pressure sensor data, and feature analysis techniques have been deployed, as described in Section 3. Furthermore, a set of machine learning algorithms were explored for the classification of the different groups of participants (PD, Elderly, Adults); ON and OFF medication states; and diseases severity levels based on the MDS-UPDRS: Part III ratings, the results of which are presented in Section 4.
## 2 LPWAN Technologies
### Dash7  
DASH7 is an open-source standard for wireless communication. It provides ultra-low-power and mid-range communication connectivity that is suitable for IoT applications. A DASH7 network can establish a communication link up to 5 km. Therefore, DASH7 is technically not an LPWAN technology. It is worth noting that some review papers classify the DASH7 standard as an LPWAN technology [17].  
According to the DASH7 Alliance Protocol (D7 AP) specification, the DASH7 physical layer utilizes Gaussian Frequency-Shift Keying (GFSK) modulation in the unlicensed Sub-1 GHz frequency bands. Moreover, DASH7 signals can be categorized into three channel classes with different parameters. The Lo-Rate class has an operational bandwidth of 25 kHz and a data rate of 9.6 kbps, the Normal class with an operational bandwidth of 200 kHz and a data rate of 55.555 kbps, and finally the Hi-Rate class which has an operational bandwidth of 200 kHz and a data rate of 166.667 kbps [5; 7; 18]. Consequently, the DASH7 physical layer characteristics, i.e., the carrier frequency, the signals' operational bandwidth, and the minimal transmission power, are similar to the aforementioned LPWAN technologies.
## 4 Future Directions  
Based on the results obtained from the current research study conducted in Sections 2 and 3, the future direction concerning the energy optimization issues in UIoT networks is discussed below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Years** & **Main Clause** & **Subclause** & **Paper Count** & **Reference Number** \\ \hline \multirow{6}{*}{2010–2022} & \multirow{6}{*}{Underwater wireless power transfer approach} & Underwater acoustic wireless power transfer (UA-WPT) & 4 & [158; 159; 160; 161] \\ \cline{3-5}  & & & Consideration for underwater optical wireless power transfer (UO-WPT) based on the analysis of O-WPT & \(11\) & [162; 163; 164; 165; 166; 167; 168; 169; 170; 171; 172] \\ \cline{3-5}  & & Underwater inductive wireless power transfer (UI-WPT) & 13 & [173; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183; 184; 185] \\ \hline \multirow{2}{*}{2010–2022} & \multirow{2}{*}{Underwater wireless power transfer approach} & Power transfer to & & \\  & transfer approach & AUVs/UUVs/ROVs & 10 & [202; 203; 204; 205; 206; 207; 208; 209; 210; 211] \\ \hline \multirow{2}{*}{2010–2022} & \multirow{2}{*}{Auto-recharge/ battery-free system} & Automatic recharge of sensor nodes using water particles, self-recharging, and battery-less sensor nodes & \multirow{2}{*}{4} & [21; 36; 37; 38] \\ \hline \multirow{2}{*}{2010–2022} & \multirow{2}{*}{Battery swapping approach} & Replacing the battery of AUVs with another battery & 2 & [23; 24] \\ \hline \multirow{2}{*}{2010–2022} & \multirow{2}{*}{AI and ML approach} & Deep learning, reinforcement learning, and lightweight AI mechanism & 6 & [27; 211; 212; 213; 214; 215] \\ \hline \multirow{2}{*}{2010–2022} & \multirow{2}{*}{Battery management approach} & To control and optimize the performance of battery modules & 10 & [120; 216; 217; 218; 219; 220; 221; 222; 223; 224] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of various energy optimization techniques in UIoT.  
Figure 10: Analysis of various energy optimization techniques in UIoT networks.
# Selected Aspects of Designing Modular PEMFC Stacks as Power Sources for Unmanned Aerial Vehicles
## 2 Experimental Parts
### Description of Designed and Assembled Modular PEMFC Stacks
#### 2.1.2 Second Power Source: A 500-W PEMFC Stack with Three Modules Connected in Parallel  
As shown in Figure 2a-c, the other PEMFC stack consisted of three fuel cell modules (1-3), each module consisting of 44 single MEAs connected in series. The MEAs consisted of Nafion 112 membranes, catalyst layers with a total Pt loading of 0.4 mg cm\({}^{2}\) on anode and cathode sides and SGL carbon with microporous layer as gas diffusion layers (GDL) were commercially supplied by Fuel Cell Store. The dimensions of the graphite bipolar plates used for the assembling PEMFC stack were 100 mm \(\times\) 30 mm \(\times\) 1.2 mm. The active area of each MEA was \(\sim\)25 cm\({}^{2}\). Copper-coated glass/epoxy laminate separation plates were placed between the modules. The copper layer was protected from corrosion by a layer of gold. Hydrogen under 0.5 bar of pressure was supplied successively to all MEAs in each module through an internal insulated duct. To supply the cooling air and oxygen flow required for the cathode reaction, five axial fans were used. The rotational speed of the fans was monitored by the PEMFC stack central microcontroller unit (MCU), which calculated the control signal based on the temperature measurements of the individual modules from the stack and management of the performance of the complete unit. The elaborated microcontroller unit managed the purge valves and the operation of the short-circuit units (SCUs) and monitored the levels of voltage and current of individual modules 1-3. The modular 500-W fuel cell stack was connected in parallel to a lithium polymer battery (5-9 s) through a charge\(-\)discharge process control board.  
Figure 1: (**a**) The 2-kW polymer membrane fuel cell (PEMFC) stack consisting of two fuel cell modules, (1) and (2) connected in parallel with a potential connection to an electrochemical battery. (**b**) The photo of 2-kW PEMFC stack consisting of two fuel cell modules.
# Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation
## 5 Channel Distillation
To address this, we have developed an improved version of knowledge distillation (KD), termed guided knowledge distillation (GKD). Our approach ensures that only the positive prediction distributions from the teacher are transferred to the student, while the negative predictions are disregarded. More specifically, our method involves backpropagating the KD loss only for samples correctly predicted by the teacher network, ignoring the errors. The formula for GKD is defined as follows:  
\[GKD(s,t)=\frac{\sum_{i=1}^{n}I\big{(}p_{t}^{i},y_{i}\big{)}KL(p_{s}^{i},p_{t}^ {i})}{\sum_{i=1}^{n}I(p_{t}^{i},y_{i})} \tag{14}\]  
In this formula, \(I\) represents an indicator function that equals 1 when the teacher's output matches the true label, denoted as \(I\big{(}p_{t}^{i},y_{i}\big{)}\), and 0 otherwise, \(I\big{(}p_{t}^{i},y_{i}\big{)}\). For example, consider a batch containing \(n\) samples, where the teacher model correctly predicts \(n_{1}\) samples. In this case, the GKD (guided knowledge distillation) loss is computed only for these \(n_{1}\) correctly predicted samples. After enhancing the network model as described above, channel pruning was applied, followed by channel distillation to obtain our final OD-YOLOV7-tiny network model. The entire process is illustrated in Figure 13.
# Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training
## 4 Discussion
### Limitations of the Study  
This study has limitations due to external factors already mentioned in the database description that may have affected the temporal trends of the data. The anomaly related to the 2005 concentrated inspection campaign focused on the GMDSS cannot be evaluated because we do not have data from previous years. Regarding the years impacted by the COVID-19 pandemic, there has been a measurable effect on both inspection rates and reported deficiencies [68]. Once the 2023 GMDSS inspection data becomes available, statistical analysis can be performed to estimate the extent of the true impact of the pandemic.
## 1 Introduction
This is because the detection accuracy of the conventional machine learning methods may not be enough to satisfy the common BER requirements of the communications. Therefore, we propose a biomimetic communication scheme: the transmitter modulates the whistles with the larger distances and DoM based on the link information among adjacent whistles, and the receiver demodulates and detects the distorted whistles using a DAG-net based LSTM with additional link information among whistles. The proposed modulation and demodulation method are described in the following sections.
# A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
## 1 Introduction
In [17], the CNN network needs certain size image input, so that the certain length image representation was generated after that any size image passed through pooling layer in SPP-net, which improves the speed of R-CNN for detection above 24 times. In [18], a separate CNN model is utilized in You Only Look Once(YOLO) algorithm to obtain end-to-end target detection function. The initial image is firstly normalized to the size of 448 \(\times\) 448, and then it is transmitted to CNN network for feature extraction. In the end, the network prediction consequence are dealt with to detect object categories. According to the comparison with R-CNN model, YOLO is a centralized framework with faster image processing speed with the training procedure is the format of end-to-end meanwhile. [PERSON] et al. [19] proposed YOLOv3 algorithm, which has no pooling layer and full connection layer in the entire YOLOv3 model. In the procedure of forward propagation, the size alteration of tensor is implemented by transforming the stride of convolution kernel. It has an obvious architecture and excellent real-time property. Two methods are proposed in [20] to obtain better helmet detection performance, utilizing haar trait and circle hough transform for face detection separately. [PERSON] et al. [21] presented an architecture for helmet detection of Motorcyclists on the moving autocycles. In the presented method, YOLOv3 model was employed for the detection of motorcycle riders at first stage, while an algorithm structure on the basis of CNN was brought forward for SHW detection of Motorcyclists. [PERSON] et al. [22] intended to establish an intelligent system to determine whether a cyclist is wearing a helmet, which was applied as the basis of the law enforcement to impose a fine on the offender. [PERSON] et al. [23] adopted YOLOv3 model to fine-tune the datasets for electric power construction scene, with accuracy of 90 %. To summarize, the previous methods are effective for target detection with low speed requirement and with high accuracy in an ideal environment. However, the accuracy and speed cannot meet the requirements in the actual engineering environment. In this paper, YOLOv3 is improved to handle various situations due to its strong robustness in terms of real-time detection performance. In this paper, an innovative method based on CSYOLOv3 was put forward for SHW detection of video surveillance on construction site. With the purpose of evaluating the feasibility and stability of the newly raised method, various visual conditions of construction sites were considered for experiment. The overall structure of the paper is as follows. Section 2 presents the overall network structure of the original YOLOv3. In Section 3, the novel algorithm of CSYOLOv3 is illustrated in detail. Section 4 presents the experimental data and curves. Finally, the conclusion of the raised method will be expressed in Section 5.
## 3 Results and Discussion
### Spatial Pattern of Terrestrial NPP in China
Especially in Northwest China and Southwest China, the total NPP over the period of 2001-2016 increased by 20.00% and 16.05%, respectively, compared with that during 1961-1980. However, the total NPP in Northeast China and North China had no significant trend. As shown in Figure 6, the temperatures increased in six regions of China over the periods of 1961-1980, 1981-2000, and 2001-2016, while precipitation showed distinguishable differences. Generally, an increase (decrease) in precipitation caused an increase (decrease) in total NPP in Northwest China, East China, and North China, but a decrease in precipitation in South-central China, which located in humid areas, didn't cause a decrease in total NPP. Temperature played an important role in the increasing NPP in high altitude regions such as Southwest China. Distinguishable difference in total NPP trends in six geographical regions led to the interannual variability (IAV) of terrestrial NPP in China from 1961 to 2016. An index [9] was used to assess the contribution of individual regions to the national total NPP IAV. \[f_{i}=\frac{\sum\limits_{t}\frac{NPP_{i,t}|NPP_{i}|}{NPP_{i}}}{\sum\limits_{t} |NPP_{i}|} \tag{2}\]  
where NPP\({}_{i,t}\) is the total NPP anomaly (departure from the average NPP from 1961 to 2016 in this study) for region i at time t (in years in this study), NPP\({}_{t}\) is the national total NPP anomaly, and \(|\)NPP\({}_{t}|\)  
Figure 5: Of total annual NPP and average NPP in six geographical regions of China from 1961 to 2016. Grey bars are averages of regional total NPP (Pg Cyr\({}^{-1}\)) in the periods of 1961–1980, 1981–2000, and 2001–2016, and the blue bars are the average total NPP of the regions between 1961 and 2016. The black lines are the average NPP (g C m\({}^{-2}\) yr\({}^{-1}\)) in the periods of 1961–1980, 1981–2000, 2001–2016, and 1961–2016. NW: Northwest China (Xinjiang, Qinghai, Gansu, Ningxia, and Shaanxi); NC: North China (Inner Mongolia, Shanxi, Hebei, Beijing, and Tianjing); NE: Northeast China (Heilongjiang, Jining, Liaoning); SW: Southwest China (Tibet, Sichuan, Chongqing, Yunnan, and Guizhou); CS: South-central China (Henan, Hubei, Hunan, Guangxi, Guangdong, Hainan, HongKong, and Macao); EC: East China (Shandong, Anhui, Shanghai, Jiangsu, Zhejiang, Jiangxi, Fujian, and Taiwan). is the absolute national total NPP anomaly. \(f_{\rm i}\) values are in the range of 0-1; a higher \(f_{\rm i}\) indicates a larger contribution to the national total NPP anomaly. NPP anomalies of six geographical regions are shown in Figure 7. Southwest China was found to make the largest contribution (30.32%) to the national total NPP IAV, followed by South-central China (18.81%); the two regions contributed nearly 50% to the national total NPP IAV. Northwest China (13.98%), East China (13.50%), Northeast China (11.96%), and North China (11.43%) played a similar role in regulating national total NPP IAV.
## 4 Experiment and Results
### Dataset
#### 4.1.1 Dataset Description  
One key challenge when analyzing an author's individual publication information was that, to the best of our knowledge, there has not been any prior work that examines such information about the author as an individual unit, which can be referred to. This research therefore focuses on individual authors in terms of their publication activity, research areas, and affiliations; hence, our work is based on a new dataset that is comprised of the authors' publication details, including the range from their first publication to their last one. After collecting the required data for this research using the PoP tool in a CSV form, it is then further enriched by adding keywords and affiliations. The collected data shows that there are a few records with three different languages other than English, which are Arabic, French, and Chinese. However, since this research work is carried out in English, only English publications were considered during the data analysis. The key attributes of this dataset are illustrated in Table 2. Moreover, the collected data includes 120 authors and 3898 publication records for 12 universities from up until 23 February, 2021. The statistics of the gathered data are shown in Table 3.
## 2 The Round-Off Noise Model
### The Round-Off Noise Model  
The study is based on the following assumptions. Each multiply and accumulate action in a stage is modeled as an infinite precision multiplier, followed by a summation node. After the summation node, rounding is performed and consequently round-off noise is added to the system. In the present paper, it is assumed that the rounding process uses conventional rounding and saturation. For the real pole approximation, the conjugate pole pair cascade approximation and their respective linear phase approximations, each stage has one single noise source at the output of the stage. It is assumed that  
* every sample of the noise source is uncorrelated with the previous sample,
* all noise sources are uncorrelated,
* the noise sources are uncorrelated with the input signal,
* every noise source is a time discrete stationary zero mean white random process with output variance \(q^{2}/12\). Here, \(q\) is the smallest quantization step (\(q=2^{-b}\) where \(b\) is the number of bits (without the sign bit) used to quantize the signal).
## 6 Experiments and Results
### Experimental Setup, Tools, and Evolutionary Parameters
parameter S5 = 3'b100;  <sequential> ::= always @(posedge clk)  begin  if(!rst) begin  <state_block>  end  <last_else>  end  <state_block> ::= if (state == <state>)  begin  <conditional>  end  else if (state == <state>)  begin  <conditional>  end . <last_else> <conditional> ::= if (<var> == <val>)  begin  state = <state>;  out = <val>;  rtrn = <val>;  end  else if (<var> == <val>)  begin  state = <state>;  out = <val>;  rtrn = <val>;  end  else begin  state = <state>;  out = <val>;  rtrn = <val>;  end  <last_else> ::= else begin  state = S0;  out = 0;  rtrn = 0;  end  <state> ::= S0 | S1 | S2 | S3 | S4 | S5 <var> ::= x0 | x1 <val> ::= 0 | 1  
Figure 5: BNF grammar with full structure of the HDL code for FSM to evolve the current state, input variables and their values, outputs’ values, and the next state of the MSD. The overall fitness score \(F\) for an individual is calculated by summing the scores across all 64 3-bit vectors and is defined as:  
\[F=\sum_{i=1}^{N}s_{i} \tag{1}\]  
where the following are used:  
* \(N=64\) is the total number of 3-bit vectors;
* \(s_{i}=1\) if the individual's output for vector \(i\) matches the expected output;
* \(s_{i}=0\) otherwise.
## 2 Materials and Methods
### Patient Post-ERCP Follow-Up  
All patients had a minimum of 3 days of hospital admission, and a minimum of 24 h of in-hospital follow-up after each procedure. Clinical and biological workups were performed for all patients at 24 h after the ERCP procedures. The 24-h biological panels for all patients included complete blood count, serum lipase (mg/dL), CRP (mg/dL), and serum creatinine (mg/dL); nartium, potassium, chlorine, and bicarbonate levels (mmol/L); serum lactate (mg/dL), total and direct bilirubin (mg/dL), and glycaemia (mg/dL). When perforations were suspected, 24-h post-ERCP computed tomography scans were carried out.  
The presence of post-ERCP SIRS and the qSOFA were monitored. The definition of SIRS included the presence of two or more of the following items at 24 h after the ERCP [13; 14]: respiratory rate > 20/min; peripheral body temperature > 38 \({}^{\circ}\)C or <36 \({}^{\circ}\)C; heart rate > 90/min; and white blood cells > 12,000/mm\({}^{3}\), or <4000/mm\({}^{3}\), or >10% immature peripheral white cells. Calculation of qSOFA required the presence of the following parameters at 24 h post-ERCP [15]: systolic blood pressure \(\leq\) 100 mmHg, respiratory rate > 22/min, or an altered mental status (Glasgow comma scale < 13). In patients with more than 24 h of post-ERCP hospitalization, monitored parameters included complete blood count and CRP levels (mg/dL) at the patient's release.  
The survival rate was evaluated for all patients at 15 d and 60 d after the procedures. Consequently, given the follow-up protocol, the following parameters were monitored and included in the study: incidence of post-ERCP complications (24 to 48 h after ERCP), total hospitalization length (d), number and type of organic dysfunctions occurring within 24 to 48 h after ERCP, 15-day interval mortality, and 60-day interval mortality.  
Organic dysfunctions have been defined for respiratory, hematological, hepatic, cardiovascular, neurological, and renal systems. Since arterial gas sampling is not routinely collected in our gastroenterology and endoscopy unit, oxygen arterial pressure (PaO\({}_{2}\)) could not be monitored. Thus, oxygen saturation was measured (SpO\({}_{2}\)) by pulse oximeter readings to calculate the SpO\({}_{2}\)/FiO\({}_{2}\) ratio, which was used to define respiratory dysfunction. Organic dysfunctions were therefore defined as follows: (a) respiratory system SpO\({}_{2}\)/FiO\({}_{2}\) below 500; (b) renal system: rise in serum creatinine \(\geq\)0.3 mg/dL within 48 h, or rise in serum creatinine \(\geq\)1.5 times baseline 48 h after ERCP, or urine output <0.5 mL/kg/h for six hours; (c) nervous system a decrease in Glasgow comma scale below or equal 13 after 24 h post-ERCP; (d) cardiovascular system: mean arterial pressure below 70 mmHg or new need of any dose of vasopressor infusion; and (e) hematology: platelet decrease below 150,000/mL after ERCP. Liver dysfunction was not monitored as such a parameter could be highly influenced by pre-existent bile duct obstruction, which serves as main indication for ERCP procedures.