esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Multi-Objective Optimization-Based Climate Responsive Design of Residential Building
## 3 Multi-Objective Optimization Platform Set-Up
### Design Parameter Settings of Typical Buildings  
The typical model established by the research is an ordinary two-story residential building (see Figure 11). The specific parameters are shown in Table 3. The design parameters of the building envelope are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Sub-Category** & **Parameter Category** & **Unit** & **Data Range** \\ \hline \multirow{2}{*}{Location} & \multirow{2}{*}{Climate} & Climate & Climatic parameters of & \multirow{2}{*}{-} & According to meteorological \\  & & typical cities & & parameters of typical cities \\ \hline \multirow{8}{*}{Building geometry} & \multirow{8}{*}{Building type} & Number of floors & - & 2.00 \\  & & Net height of each floor & m & 2.70 \\  & & Gross height of each floor & m & 3.10 \\  & & Length (\$/$N direction) & m & 7.9 \\  & & Aspect ratio & - & 2 \\  & & Window to wall ratio (WWR) & - & 0.15 \\  & & orientation & deg & 0 \\ \hline \multirow{8}{*}{Envelope parameters} & \multirow{8}{*}{Envelope} & Volume & m\({}^{3}\) & 774 \\  & & Total surface area & m\({}^{2}\) & 544 \\ \cline{1-1}  & & Total floor area & m\({}^{2}\) & 250 \\ \cline{1-1}  & & Surface to volume ratio & - & 0.7 \\ \hline \multirow{8}{*}{Envelope design parameters} & \multirow{8}{*}{Envelope} & \(U\) value external walls & W/(m\({}^{2}\)K) & \multirow{8}{*}{According to Table 9} \\  & & \(U\) value ground floor & W/(m\({}^{2}\)K) & \\ \cline{1-1}  & & \(U\) value roof & W/(m\({}^{2}\)K) & \\ \cline{1-1}  & & SHGC factor glass & - & \\ \hline \multirow{8}{*}{Building operation parameters} & \multirow{8}{*}{Activities} & Internal gains (lighting, appliances and occupancy, daily average) & W/m\({}^{2}\) & 5 \\ \cline{1-1}  & & Heating set-point temperature & \({}^{\circ}\)C & 20 \\ \cline{1-1}  & & Cooling set-point temperature & \({}^{\circ}\)C & 26 \\ \cline{1-1}  & & Air-change rate (infiltration and natural ventilation) & vol/h & 0.8 \\ \cline{1-1}  & & Schedule & N. & Adjustment according to different cities requirements \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of typical model. Based on the typical model, the optimization mainly focuses on the design parameters of the building envelope. The HVAC system, primary energy, and renewable energy system under the optimization framework are fixed (i.e., not included in the optimization process). The design parameters of the envelope are shown in Tables 5 and 6, and the initial investment cost calculation is shown in Table 7.
# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 2 Related Research
### Study on Defect Detection in OLED Using Deep Learning
These small targets refer to defects that can occur in LCDs, such as pixel damage, spots, panel degradation, and dust accumulation. The deep learning technique resulted in a 91% accuracy rate for defect identification. In another study, the same group used the VGG-16 deep learning algorithm to differentiate between papillary thyroid carcinomas and normal thyroid cells in cytological images [16]. The VGG-16 model was used with either pretrained weights or retrained weights to analyze the images. The experiment results showed an accuracy of 0.976 for fragmented images and 0.95 for patient targeted images, indicating successful differentiation between thyroid carcinoma and normal thyroid cells. In the medical field, a study by [PERSON] et al. focused on automatically detecting and classifying lung regions in CT images to aid medical diagnosis [17]. The experiment used 137 images, and the primary performance metric, the Dice Similarity Coefficient (DSC), yielded an accuracy of 0.986 for lung segmentation. Based on the aforementioned studies, we compared and analyzed deep learning algorithms, including CNN, ResNet-50, and VGG-16, to detect defects in OLED cells. After comparing the performance metrics of the algorithms, we selected the optimal algorithm for image processing and defect discrimination in OLED cells. The aim was to enhance the efficiency of defect detection using this process.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 3 Human Activity Signatures Measured by Using a SISO FMCW Radar System
### Implications on the Performance of an RF-Based Step Counter
We must observe the environment from different perspectives to effectively detect the scatterer's motion. Thus, we need a distributed RF sensing system to effectively determine the TV trajectories of the object under observation. For this reason, we propose to distribute at least two collocated transmitter-receiver antenna pairs in an indoor environment and preprocess the data for each wireless channel link (see Sections 2 and 4). It should be mentioned that for SISO radar-based HAR systems, the classification accuracy will drop as the direction of the human motion relative to the radar's boresight changes from parallel to perpendicular. Furthermore, as we increase the overall system complexity in the context of human activity classification, the performance of a machine learning or deep learning classifier is expected  
Figure 4: The block diagram of the RF-based step counter for a SISO radar system. Figure 5: For a SISO radar, the number of steps detected from the smoothed mean radial velocity \(\bar{\
u}_{11}(t)\) of a human walking activity according to (**a**) Scenario 1, (**b**) Scenario 2, and (**c**) Scenario 3.  
to further degrade for a SISO radar system. For instance, the system complexity is increased by classifying more than two kinds of activities such as falling, walking, sitting on a chair, and standing from a chair. We can now safely assert that a monostatic SISO radar system is not an apposite choice for direction-independent human activity detection.
# Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
## 2 Background
### Internet of Things Security  
The IoT is a market that is populated with a myriad of device types. The purpose of the IoT is to interconnect physical objects seamlessly to the information network. IoT thrives on heterogeneity which enables the different devices to connect. As IoT devices are often manufactured using different hardware, specific protocols and platforms must be created for these devices [23]. On the other hand, heterogeneity limits security advancements because there is no universal international standard. Furthermore, because of the lack of security consideration, there is some reluctance to deploy IoT devices, as both businesses and independent parties are unwilling to risk their security [3].  
As IoT attacks are increasing in popularity, and the significance of data is high (e.g., within the health sector or critical infrastructure), the incentive to improve IoT security is increasing. However, through a profit-driven business model in a competitive market, IoT security is often considered an afterthought. Data theft is regarded as a high impact risk; however, sometimes, IoT data could be temperature monitoring, which is considered trivial unless the integrity is manipulated and fails to alert deviations. Until now, the most challenging attacks in the IoT field have been botnet attacks [6; 14]. A botnet consists of several zombie devices which an attacker is controlling. A large scale botnet enables a full-scale Denial of Service (DoS) attack to be carried out--such as Mirai botnet, or Bashlite [6]. Moreover, botnets can be used for information gathering attacks such as port-scanning and port-probing to gather information about running services on their targets [6].  
Since the IoT network poses many challenges, and there are newly introduced network varieties, traditional NIDS can no longer act independently. The importance to protect the network lies in the pursuit of privacy. Data privacy seeks to accomplish two things. Firstly to maintain data confidentiality, and secondly, to uphold anonymity and to make it impossible to correlate data relations between the data and their owner [27].
_J. Low Power Electron. Appl._**2011**, \(1\), 109-130; doi:10.3390/jlpea1010109  
**Journal of Low Power Electronics and Applications**  
**ISSN 2079-9268**  
www.mdpi.com/journal/jlpea/  
_Review_  
**Energy Efficient Design for Body Sensor Nodes**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON]. 1, [PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 1**  
The Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22908, USA; E-Mails: [EMAIL_ADDRESS] (Y.S.);  
[EMAIL_ADDRESS] (A.T.B.); hcp7 [EMAIL_ADDRESS] (H.C.P.); [EMAIL_ADDRESS] (S.A.R.);  
[EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (B.H.C.)  
Wireless Health Interactive, LLC, Vienna, VA 22182, USA; E-Mail: [EMAIL_ADDRESS]  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-434-466-3957; Fax: +1-434-924-8818.  
_Received: 24 November 2010; in revised form: 21 March 2011 / Accepted: 7 April 2011 / Published: 11 April 2011_  
**Abstract:** This paper describes the hardware requirements and design constraints that derive from unique features of body sensor networks (BSNs). Based on the BSN requirements, we examine the tradeoff between custom hardware and commercial off the shelf (COTS) designs for BSNs. The broad range of BSN applications includes situations where either custom chips or COTS design is optimal. For both types of nodes, we survey key techniques to improve energy efficiency in BSNs and identify general approaches to energy efficiency in this space.  
**Keywords:** body sensor networks; sub-threshold circuits; wearable computing; energy efficient design
## 4 Results
### Impact of the Local Damping Ratio  
The local damping in this study is set to 0.005, which is obtained from many repeated trials. As mentioned in Section 3.2, local damping is used to reduce unnecessary numerical vibrations. If the local damping is too large, the rock slide will remain stable under all types of seismic waves. If the local damping is too small, the rock slide will float in the air, which is obviously unrealistic.  
In the local damping ratio sensitivity analysis, a three-directional seismic wave is adopted. The value of damping ratio is conducted with 0.001, 0.005, and 0.01. The stability of the perilous rock mass increases with the growing value of local damping ratio (Figure 20). When the value of the local damping is 0.001, the rock slide will float in the air, and the 3 DEC computations will diverge (Figure 20a). When the value of the local damping is 0.01, the rock slide is not completely failing (Figure 20b).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cases** & **Item** & **Farthest Distance** & **Farthest Distance** & **Main Deposition Area** \\  & (X Direction) & (Z Direction) & (X Direction) \\ \hline
1 EW & 450 m & \(-\)67–60 m & 160–240 m \\
2 NS & 340 m & \(-\)89–60 m & 172–223 m \\
3 UD & 360 m & \(-\)60–57 m & 176–221 m \\
4 EW\_NS\_UD & 350 m & \(-\)74–60 m & 155–235 m \\ \hline \end{tabular}
\end{table}
Table 2: Deposition of high-steep rock slide in four cases.  
Figure 19: Deposition of high-steep rock slide in four cases (the three red circles in the figure represent the most distal, leftmost, and rightmost blocks, respectively, and the white circles are the main rock slide deposition). (**a**) Case 1: EW seismic wave; (**b**) case 2: NS seismic wave; (**c**) case 3: UD seismic wave; and (**d**) case 4: EW\_NS\_UD seismic wave.
# Real-Time Feedback in Swimming Exercises Using Embedded Devices in Water
## 2 Materials and Methods
### Reading  
Sensor data are read by slave-type devices: Node_1, Node_2, and Node_3. The MPU6050 sensor provides measurements of both acceleration and gyroscopic rotation (angular velocity).  
For the initial testing phase, only acceleration data along the X, Y, and Z axes were utilized. This approach was chosen to establish the core system functionality, which integrates all essential components. Consequently, it was more practical to validate three data streams (acceleration) rather than six (acceleration and rotation). A library for the MPU6050 sensor was used, which by default applies the most sensitive configuration settings (-0.000061 g, with 1 g \(\approx\) 9.81 m/s\({}^{2}\)). These settings were retained for this project, with the accelerometer configured to a \(\pm\)2 g range, yielding a data range of \(-\)32,768 to 32,768 for each axis (using int16 representation). This configuration was achieved by setting the AFS_SEL register to 0 \(\times\) 00.  
Similarly, the default configuration was used for the gyroscope, offering the highest sensitivity (-0.00763 \({}^{\circ}\)/s). This was configured by writing the value 0 \(\times\) 00 to the GYRO_CONFIG register [15].  
Figure 3: Device waterproofing using plastic bags enclosures.  
The MPU6050 sensor was selected due to its combination of characteristics well-suited to the requirements of the implemented system. These include its integrated 3-axis gyroscope and 3-axis accelerometer, low power consumption, compact form factor, and widespread availability.  
The power consumption values presented in Table 1 are derived from the respective sensor datasheets and reflect typical active-mode current under standard operating conditions. It should be noted that actual power consumption may vary depending on sensor configuration, sampling rates, and specific usage scenarios.  
The estimated read time per sample was calculated by dividing the number of data bytes to be read by the communication protocol's data transfer speed.  
The MPU6050 sensor was selected due to its advantageous combination of features, including integrated accelerometer and gyroscope, which sufficiently meet the requirements of this study. Its power consumption is negligible for the intended application, and the accuracy is adequate given the relatively simple motion patterns and sampling rates involved. Additionally, the sensor's wide availability and low cost facilitate testing, enabling the use of multiple units with necessary soldering and connection points.  
A comparative analysis of several commonly used motion tracking sensors is presented in Table 1 to justify this choice:
# Crack Initiation and Propagation Fatigue Life of Ultra High-Strength Steel Butt Joints
## 4 Fatigue Assessment
### Assessment of Crack Propagation Life by Fracture Mechanics
#### 4.3.1 Crack Propagation Analysis  
If the crack initiation phase of a component is significantly exceeded, the fatigue assessment of a structure cannot be continued with stress-based approaches. Thereby, the fatigue action at a crack tip is described by the stress intensity factor (SIF) range \(\Delta K\) (see Equation (7)). The crack propagation rate for each load cycle is then calculated by crack propagation laws, such as the Paris law (see Equation (4) and [56]).  
\[\Delta K=\Delta\sigma\cdot\sqrt{n\cdot a}\cdot Y(a,c) \tag{7}\]  
The stress intensity factor strongly depends on crack configuration and geometrical shapes which are considered by a correction function _Y(a,c)_. For standardized crack configurations and load cases, parametric formulae have been published (see, e.g., [66; 67]).
## 1 Introduction
Their focus is often restricted to the stance phase of gait, omitting data from the swing phase and the complete gait cycle, and require multiple trials to analyze both feet, increasing variability in measurements. Furthermore, they can alter natural movement patterns as participants must adjust their steps to align with the plate. By integrating sensor matrices under the running belt of treadmills, also known as instrumented treadmills, clinicians and researchers have been given the opportunity to analyze gait and movement in a manner biomechanically comparable to overground walking [30]. This approach offers detailed spatial, temporal, and dynamic data over complete gait cycles, enabling the identification of inefficiencies or asymmetries by allowing the simultaneous measurement of both steps without requiring users to adjust their natural gait. Additionally, by controlling and standardizing test condition variables such as the speed and slope of gait, this method is particularly beneficial for individuals with significant joint misalignments, such as foot hyperpronation. Locomotion on inclined surfaces at varying speeds may impose different demands on the musculoskeletal system compared to level walking, requiring postural adjustments and increased involvement of lower body muscles [31; 32]. Yet, studies utilizing instrumented treadmills for gait analysis in individuals with joint misalignments, such as foot hyperpronation, are lacking, and those that do exist often conduct gait analysis on conventional treadmills, relying on pressure-sensing insoles [33] or optoelectric systems [34] for data collection. Therefore, the aim of the current study was to analyze gait in individuals with foot hyperporation, utilizing the benefits provided by an instrumented treadmill. By examining these characteristics, this research seeks to advance clinical and sports science practices, providing valuable insights into optimizing performance, preventing injuries, and enhancing rehabilitation.
## 2 Shaking Table Test Condition  
### Similarity Ratio  
A small radius curved bridge with a radius of 40 m in Beijing was taken as a prototype structure, and this bridge was reported in detail in reference [20]. The similarity relationship of the model structure was deduced from the Buckingham \(\pi\) theorem [21]. The size of the shaking table used in this study was 5 m \(\times\) 5 m, the bearing capacity was 45 t, and the maximum allowable acceleration was 1.2 g. Considering the size and bearing capacity of the shaking table, the geometry similarity ratio of the model structure was 1/16. The acceleration similarity ratio was 1.0. Micro-concrete was used to make the test model, and the similarity ratio of elastic modulus was 1/4.  
The similarity relationship of the model soil was determined by the Froude value from the Dimensional analysis theory [22], which reflects the criterion of gravitational force similarity.  
\[\mathrm{Froude}=\mathrm{v}_{\mathrm{s}}^{2}/(\mathrm{lg}) \tag{1}\]  
where \(\mathrm{v}_{\mathrm{s}}\) is the shear-wave velocity, 1 is the geometrical dimension, and g is the acceleration of gravity. Froude value of model soil remained the same as the prototype required the criterion of gravitational force similarity. The geometry similarity ratio of the model soil to the prototype soil is defined as follows:  
\[S_{l}=\mathrm{v}_{\mathrm{sm}}^{2}/\mathrm{v}_{\mathrm{sp}}^{2} \tag{2}\]where \(\rm{v_{sm}}\) and \(\rm{v_{sp}}\) are the shear-wave velocity of the model soil and the prototype soil, respectively.  
Before the shaking table tests, sthe hear-wave velocity of the model soil was measured, and the value was about one half of that of the prototype soil. Therefore, the geometry similarity ratio of model soil was \(1/4\), and other similarity ratios could be derived. The similarity relations of the model structure and model soil are shown in Table 1.  
To analyze the influence of duration compression ratio of input motions, three duration compression ratios were adopted, including: (1) \(1/4\) provided by the time similarity ratio of model structure; (2) \(1/2\) provided by the time similarity ratio of model soil; (3) \(1/1\) without considering the similarity relationships.
# Smart Manufacturing Scheduling Approaches--Systematic Review and Future Directions
## 4 Main Findings
### Manufacturing Scheduling Requirements
#### 4.1.3 Processing Times Variation  
Most of the time, in the literature, task processing times are considered to be static, i.e., they are known in advance and do not change along the way. However, in real manufacturing systems, due to the most diverse situations, they may vary, mostly to increase over time. This situation can happen due to resource deterioration, a fault in the setup, or because of the surrounding conditions [26; 41]. However, processing times may also be reduced, for example, by assigning more workforce to a task, which means that those time variations can sometimes be controlled [27]. This means that neglecting processing times may reflect in the actual production process.
## 4 Results
### Printing Trajectories  
The algorithm discussed in Section 3.1 was applied to project three different printing trajectories (Hilbert, re-entrant, and hexagonal). The printing trajectories and the algorithm for conformal printing were calculated in Matlab(R2018a). The algorithm's inputs were the STL of the target structure, the main points of each printing trajectory, and the projection vector \([0,0,-1]\), such as the -z direction. As output, the algorithm gives the new location of each point projected on the target surface and the G-code for conformal 3D printing. The printer and printing material used were the same as those for the target structure.
# Percolation Distribution in Small-World Quantum Networks
## 1 Introduction
Therefore, if we preprocess the network using entanglement swapping before the percolation occurs and lower the percolation threshold of the quantum network on purpose, we can establish the quantum communication network more efficiently. This is called Quantum Entanglement Percolation (QEP). After QEP, the change in the network structure is expressed as the change in the degree of nodes. Preprocessing in small-world quantum networks can have problems: The nodes have different degrees and neighboring nodes have influence on each other because they share an edge, and the connected edges cannot be exchanged again once entanglement swapping is performed. Moreover, a reliable connection composed of maximally entangled state can be difficult for real application scenario, so the ConPT [7] method is introduced and \"sponge-crossing\" probability is used to evaluate the percolation process instead of traditional size of clusters. In this paper, q-pswap is proposed to overcome these problems, and a central node is added when establishing the network in order to deal with the appearance of isolated nodes after entanglement swapping. Furthermore, the effects of ConPT methods are investigated so that the robustness of the network can be improved and the percolation threshold of the network can be reduced.
## 3 Results and Discussion
### Validation of the Land-Use Maps Using the Ground Truth Data
According to our experience, land-use classification results are always dependent on the class division, the quality of the possible training data, test data and the characteristics of the study area including the size of the land-use units in comparison to EO data resolution. Therefore, qualitative comparison to other studies is not straightforward and more attention should be paid on the general trends in the historical changes. In a study by [PERSON] _et al._[20], land cover was classified in Tra Vinh and Ca Mau provinces from SPOT imagery with an average accuracy nearing 80%, similarly to our results. In [PERSON] and [PERSON]'s research [28], ENVISAT WSM temporal backscatter change was used to derive rice area and was compared to statistical planted area information for 13 provinces in the Mekong delta region, and an R\({}^{2}\) value of 0.92 was reported. They estimated the rice acreage at different seasons, however here we used the number of crops per year, and thus the results are not directly comparable.