esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Concept
### Self-Organized Device Network  
Figure 1 illustrates the concept of a self-organized device network in an indoor unit space. This collaborative network includes three types of devices (wearable, tag, and stationary). The tag device is easily attached to home appliances and can measure their usage states (e.g., opening and closing a refrigerator door, opening and locking a faucet, and turning on and off a gas stove). The tag device normally disables its internal sensors for low-power consumption. The stationary device in each unit space maintains connections with multiple tag devices and can activate the tag devices when a user enters that space. By using indoor localization as described in our previous study [24], a user's wearable device can recognize its current indoor location and whether it enters or exits a unit space wherever a stationary device is installed. When the wearable device passes through the door, it notifies the stationary device by sending a user entry packet that includes its ID information. Then, the stationary device detects which family member has entered the zone and sends activation messages to the connected tag devices. When the user enters, the tag device (which was in low-power mode) enables its internal sensors and prepares to measure appliance usage. Thereafter, the state changes of home appliances are measured by the tag devices, collected by the stationary device, and recorded in the wearable device. This autonomous process for measuring a user's ADL data is performed through inter-device collaboration without any user intervention.
## 2 General Sensor Specifications
Eight of the eleven channels are sensitive in the visible spectral range between 390 nm to 750 nm [23], which matches closely to the PAR spectrum, as shown in Figure 1. It is worth mentioning that due to the inbuilt optical filters and analog-to-digital converter (ADC), additional analog circuitry is not needed which makes the integration very easy and convenient at a sensor cost below 10 Euros. For each channel, the AS7341 returns a count indicating the amount of light that was collected during one measurement. The maximum count number is dependent on the user-modifiable integration time for one measurement interval and, therefore, determines the achievable resolution. A third parameter, the gain of the ADC, controls the sensitivity of each channel. The data can be read out digitally via a common IC protocol. The sensor footprint of 3 mm by 2 mm and a low current consumption of only 210 uA at 1.8 V (during a measurement) are beneficial for being operated in a compact housing and for a long-term operation powered by solar cells or batteries. In addition, the sensor can be powered down between measurements, resulting in a standby current draw of only 0.7 uA at 1.8 V. For the experiments, the sensor has been embedded in a 3D printed housing shown in Figure 2, which is inspired by typical PAR quantum sensors, such as the LI-190. An acrylic light diffuser plate is chosen to uniformly illuminate the used sensor chip. The housing top with the diffuser is shaped in a way to perform a cosine correction for the incident light. A glass dome is fitted on top of the diffuser to avoid any dirt contaminating the light path.
## 3 Multisolution Selection of Joint Angle Based on the Key Order of the Joint
### Key Order of the Joint  
When the multijoint industrial robot is running according to the specified path, the rotation or movement of different joints may have different effects on the end position, which may affect the positioning accuracy of the robot. The appropriate solutions can be selected based on the influences of the joints on the robot, which can evaluate the key degree of the joints.  
Sensitivity is introduced to evaluate the influence of each joint on the end position. Generally, sensitivity refers to the ratio of independent variable change to dependent variable change in a system. For sensitivity analysis of the joint in this paper, the angles of each joint of the multijoint industrial robot were regarded as independent variables of the system, and the dependent variable was the displacement of the end of the robot in a single direction. The sensitivity component of the hth joint in one direction can be obtained by calculating the partial derivative of the angle of the hth joint with respect to the positon of the robot end-effector in this direction, that is,  
\[S_{k}=\frac{\partial P_{k}}{\partial\theta_{h}}\ \,\ \ \ h\in[1,m],k=x,y,z\]  
where \(P_{k}\) is the position of the robot end-effector in the k direction; \(\theta_{h}\) is the joint angle of the hth joint, and \(m\) is the number of joints of the robot.  
Further, in order to understand the effect of rotation or movement of a single joint on the end position of the robot, the sensitivity of a joint to the end position can be expressed by \(S_{\theta_{h}}\), that is,  
\[S_{\theta_{h}}=\sqrt{S_{x}^{2}+S_{y}^{2}+S_{z}^{2}}=\sqrt{\left(\frac{ \partial P_{x}}{\partial\theta_{h}}\right)^{2}+\left(\frac{\partial P_{y}}{ \partial\theta_{h}}\right)^{2}+\left(\frac{\partial P_{z}}{\partial\theta_{h}} \right)^{2}}\]  
where \(P=(P_{x},P_{y},P_{z})\) is the path point of the robot. The sensitivity of each joint can be solved by Jacobian matrix. The Jacobian matrix \(J\) can be obtained from Equation (15).  
\[J=\left[\begin{array}{cccc}\frac{\partial P_{x}}{\partial\theta_{1}}&\frac {\partial P_{x}}{\partial\theta_{2}}&\ldots&\frac{\partial P_{x}}{\partial \theta_{m}}\\ \frac{\partial P_{y}}{\partial\theta_{1}}&\frac{\partial P_{y}}{\partial\theta _{2}}&\ldots&\frac{\partial P_{y}}{\partial\theta_{m}}\\ \frac{\partial P_{z}}{\partial\theta_{1}}&\frac{\partial P_{z}}{\partial\theta _{2}}&\ldots&\frac{\partial P_{z}}{\partial\theta_{m}}\end{array}\right]\]  
The sensitivity can be used to evaluate the degrees of influence of the joints on the precision of the robot, so the sensitivity can be used to present the key degree of a joint. According to the sensitivity of all joints, joints are sorted from high key degree to low key degree. The sequence of each joint after sorting is the key order of the joint, which is from one to the number of the joints in the robot. For example, if the sequence of the hth joint after sorting is j, then the key order of the hth joint \(d_{h}\) is j, and the hth joint also is jth key joint.
# Changes in the Building Stock of Da Nang between 2015 and 2017
## 2 Methods
### Satellite Data  
Two image products from the Pleiades satellite constellation acquired on 24.10.2015 and 13.08.2017 were used in this study. For this period, the United Nations estimated a growth of 3.5% for the city of Da Nang [5]. The sensor acquires four multispectral bands (blue, green, red, and infrared) at 2.8 m and one panchromatic band at 0.7 m spatial resolution (resampled to 50 cm by the data provider). Both products were pan-sharpened to a final spatial resolution of 50 cm and radiometrically and geometrically calibrated as described by [PERSON] et al. [4]. The two images were acquired from slightly different off-nadir angles, which resulted in the geometric shift of high buildings towards the sensor. Furthermore, the different acquisition dates within the year lead to differences in illumination and the shadows that are cast from large objects. Lastly, there are phenolic differences between October and August, so urban greenspaces or trees can produce unequal patterns in both images. All these slight distortions can make the visual identification of changes challenging, especially in densely built-up areas.  
Figure 3 gives an example on the differences between both images for a small selected area in the An Hai Bac ward of the Son Tra district in the eastern part of Da Nang. Although the building in the center has not changed between both image acquisitions, the red color of the roof looks different because of sunlight illumination. Moreover, the shadows at the northern edges of tall buildings are only present in the image from October 2015. These seasonal differences were reduced during the radiometric calibration, but could not entirely be removed. Accordingly, these radiometric differences can potentially increase the chance of falsely assigned changes at the building level during the latter visual interpretation of changes at the building level. The figure also demonstrates that actual changes in the morphologic structure of the city, such as the creation of paved roads (in the northern and western part of the image), or the construction of new buildings (two bright lines in the center north) or other infrastructure (blue sports court in the center) can clearly be identified by visual comparison.  
Figure 2: Study area, extent of the analysis, and field reference data collection.  
However, slight differences had to be kept in mind by the interpreters in order to avoid errors of commission during the identification of changes between 2015 and 2017.  
As a qualitative historic reference, an image acquired on 15.08.1977 by the Keyhole-9 (KH-9) mission \"Hexagon\" [7] was used in this study. Its spatial resolution of 7 m allowed us to identify the historic city center (see Section 3.2) of Da Nang.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 4 Evaluation and Analysis
### Extension of the Opportunistic Networking Environment (ONE) Simulator  
Configuring a simulation scenario in the ONE simulator involves defining motion traces, packet generation events, and hardware-related simulation parameters. The ONE simulator also supports external motion traces for the simulated nodes and accepts time series of node locations (t, id, x, y) as an input, where t is the time of the location, id is the node identifier, and (x, y) are the node coordinates.  
Our flying fox dataset is relatively sparse in recorded contacts between animals. We therefore model the behaviour and motion patterns of the animals based on empirical traces. Specifically, we classify the animal behaviours into one of three states: resting, foraging, and travelling between foraging sites. The animals are stationary in the resting state, follow random movement within the foraging area in the foraging state and follow a randomized shortest path to travel between foraging areas. Parameters of the random motion were obtained empirically from existing flying fox movement traces. The behaviour and motion model generates a single motion trajectory for one day in the life of a flying fox, which is used in the simulator.  
The energy consumption model in the ONE simulator by [PERSON] et al. [28] was insufficient for our purposes, as it does not support energy harvesting. We have therefore extended the model to allow for periodic harvesting of energy at predefined time intervals (such as daylight periods for solar harvesting) and introduced a parameter that defines the capacity of the energy storage.
# Research on Lateral Deformation Control Criteria of Metro Shield Tunnels with Excessive Ellipticity
## 3 Finite-Element Analysis
### Loading Results for Bolts and Reinforcement  
The Guangzhou subway tunnel, as discussed in this study, employs 5.8-grade high-strength bolts with a yield strength of approximately 400 MPa. Figure 15 shows that when the ellipticity approaches 30%, the bolts at the joints with the most significant deformation begin to yield. After the bolts at the top joints yield, the connecting bolts at the waist-part joints also enter the yielding stage as deformation increases. Through loading simulation, for soft soil regions, tunnel segment deformation primarily arises from joint deformation. The connecting bolts might yield tension due to the excessive opening of the tunnel segment joints. Conversely, in hard rock areas with significant stratigraphic restriction, tunnel segment joint deformation is minimal. Deformation mainly results from the softening and cracking of the concrete tunnel segment, and the bolts are less likely to experience substantial stresses. The discrepancy in bolt stress under varied surrounding rock conditions might not be stark, but it does illustrate the deformation characteristics of the shield tunnel structure.  
Regarding the impact of the reinforcement ratio on the shield tunnel's lateral deformation, numerical models indicate that the tunnel segment's lateral deformation primarily stems from joint deformation. This causes the tunnel segment to exhibit a \"transverse duck-egg\" deformation pattern. Within a reasonable reinforcement ratio, increasing this ratio enhances the ultimate load-carrying capacity and crack resistance of the tunnel segment. However, it minimally affects the local mechanical behavior of the joints and the tunnel segment's overall deformation stiffness. The reinforcement ratio's influence on the entire ring's bearing capacity is less pronounced than its impact on the tunnel segment's bearing performance. In general, the bolts and tunnel segment reinforcement affect the entire ring of segment's ultimate load-carrying capacity but minimally influence the overall deformation pattern of the tunnel segment and the establishment of generalized lateral deformation limits for tunnels.  
Figure 14: Damage cloud (perspective view) of tunnel segments at an ellipticity of 25%.
## 1 Introduction  
Among the main polysaccharides constituting brown algae, alginate represents 40% of their dry weight and it is mainly studied for its gelling properties [1]. The existence of alginate in the form of alginic acid substituted with mineral salts, notably calcium, magnesium and sodium, gives the brown algae a strong and flexible appearance [2]. At the molecular level, alginate is a linear anionic copolymer formed from two monomers--\(\alpha\)-L-gluturonic acid (G) and \(\beta\)-D-mannuronic acid (M)--linked together by (\(\beta\)-1 \(\rightarrow\) 4) link and organized as homo and/or heteropolymeric blocks (FMM/FGG and FGG blocks, respectively) [3]. About 30,000 tons of alginate are produced each year [4]. Therefore, alginate could be accounted as a copious resource for the development of biomaterials [5].  
Due to its high viscosity, commercial applications of alginate are often limited in the food industry. Reducing the molecular weight of alginate is a good way to lower its viscosity and improve biological properties through the generation of low molecular weight (LMW) of alginate and oligo-alignates. Generally, and based on the literature, the LMW is represented by the degree of polymerization (DP) from 11 to 30, while the oligosaccharides are oligomers with a DP from 2 to 10 [6]. Several strategies have been developed to produce oligo-alignates, mainly enzymatic depolymerization by alginate-lyases [7] and acid depolymerization [8]. Numerous studies published over the past decades have described the potential applications of oligo-alignates in therapeutics and in biotechnology [9; 10; 11; 12]. It has been proven that oligo-alignates generated by enzymatic hydrolysis increase the proportion of _lactobacillus_ and _bifidobacteria_ in rats fed with alginate oligosaccharides (AOS) [13]. In addition, these oligo-alignates induce the secretion of cytokines from RAW264.7 cells of mouse macrophage cell line [10]. Previous studies have mentioned the elicitor effect of oligo-alignates, by the induction of the oxidative burst in _Laminaria digitata_, by causing an inhibitory effect against potentially harmful microorganisms [14]. In addition, oligo-alignates produced by gamma irradiation are growth promoters on _Amaranthus curentus_[15]. On that account, this present work focuses on the production of oligo-alignates from alginate of Moroccan brown seaweed _Bifurcaria bifurcata_ which has not been studied to date. Therefore, the aim of this work is to produce in good yields, the low molecular weight of alginate (Mw \(\leq\) 5.5 kDa) using for the first time a green chemistry process with hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), as well as study their structural characterization using different spectroscopic and chromatographic technics.
## 5 Conclusions  
This paper presents a novel magnetic induction-based MIMO model for underwater communications. We introduced the AWOA, an advanced whale optimization algorithm for signal detection in the MI-MIMO system, and compared its performance and complexity with conventional approaches such as ZF, MMSE, and ML. Our simulation Experiments revealed that the AWOA provided suboptimal results with a bit error rate that was similar to the ML algorithm while maintaining a complexity level comparable to the MMSE strategy. These results demonstrate the effectiveness of the AWOA in improving the performance of underwater MI-MIMO systems. Overall, the proposed model and algorithm provide potential solutions to the limitations of traditional aquatic communication systems, including issues related to attenuation, multi-path fading, and noise interference.  
Figure 14: Comparison of execution time.  
Figure 13: The comparison of FLOPs.  
However, the AWOA still uses the search mechanism of the WOA, which may need to exploit the search space fully. In addition, the performance of the AWOA heavily relies on parameter selection, which is currently based on user experience and is difficult to set comprehensively and accurately in large-scale underwater MIMO systems. Our future research can introduce new search mechanisms, such as mutation and crossover, to improve the global search performance of the AWOA. We can also use a dynamic parameter tuning strategy to adjust algorithm parameters and adaptively improve algorithm performance and stability. Finally, developing an underwater MI-MIMO application system based on the improved WOA is an exciting and challenging task.  
Conceptualization, formal analysis, investigation, methodology, project administration, resources and validation, G.G.; data curation, funding acquisition, investigation, methodology, supervision, validation, writing--review and editing.J.W.; data curation, resources, software, supervision, validation, visualization, writing--original draft preparation, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Key Scientific and Technological Project of Henan Province (23210211128, 222102320181, 212102310087), the Innovation and Entrepreneurship Training Program of National College Students in China (202110467001), the Major Special Project of Xinxiang City (212 ZD003), and the Key Scientific Research Projects of Colleges and Universities in Henan Province (23B520003, 21A520001). The authors approved the version of the manuscript to be published. They agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.  
The data used to support this study are available from the corresponding author upon request.  
The author declares that they have no conflict of interest.
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### PUF Metrics
#### 4.3.3 Bit-Error Rate  
The BER is a metric used to assess the reliability of a PUF. It involves generating a sequence of responses on the client device by applying a set of challenges and comparing it to the one generated on the server side using the same set of challenges. The server utilizes enrollment data to generate responses, while the client generates real-time responses using the PUF. The BER is determined by calculating the number of discrepancies between the two response sequences and dividing it by the size of the response sequence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(HD_{inter}\) & 0 \({}^{\circ}\)C & 23 \({}^{\circ}\)C & 80 \({}^{\circ}\)C \\ \hline Filtered & 0.4966 & 0.4987 & 0.4971 \\ Unfiltered & 0.4982 & 0.4980 & 0.4986 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average \(HD_{inter}\) over 5 different SRAM XOR PUF pairings. With very little variation with temperature and filtering, the \(HD_{inter}\) is not heavily affected by temperature or filtering.  
In essence, the BER is analogous to the intra-chip Hamming Distance (\(HD_{intra}\)) in that they both measure the reliability of a PUF. The BER compares the response of a PUF to the response generated from the enrollment data. However, the \(HD_{intra}\) describes the average expected difference between two PUF responses obtained using the same PUF and the same challenge.  
To evaluate the BER of the proposed design, enrollments were conducted at three different temperatures: 0 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. The SRAM XOR PUF was subsequently tested at each temperature using the enrollments obtained at all three temperature settings.  
In each test, a key consisting of one million bits was generated by utilizing the physical SRAM devices on the client side and the enrollment information on the server side. To assess the quality of the generated keys, the Hamming Distance between the two keys was calculated. This distance measurement was then divided by the total size of the key to determine the BER. The results of the BER tests can be found in Table 4.  
The BER results show low error when using the appropriate enrollment, maintaining a BER below 1% when using the ideal enrollments. The BER does get worse when non-matching enrollments are used, reaching as high as 10%.
## 7 Simulation Results and Discussion
### Observations of the Research
#### 7.3.2 Performance Analysis of GRMC-SM by Varying Number of Sinks  
To investigate the fraction of isolated nodes and their effect on PDR, we have conducted an analysis by varying sonobuoys from 9-64 sonobuoys. Void regions in the network are significantly reduced in GRMC-SM due to three dimensional deployment of sinks in the network region. Worst scenario is when number of sonobuoys are 9 and performance gets better with the increased number of sonobuoys. Because of increase in sonobuoyoys number, the void regions and connectivity holes in the network are avoided. Hence, other performance parameters improve along with fraction of void nodes as shown in Figure 14. Considering the fact that only 5% nodes are in void region in case of 64 sonobuoys deployed in the network, we observe PDR gets higher in this scenario while compared with other scenarios. Average delay reduces due to more direct transmissions in 64 sonobuoys in the network. Anyhow, there are few costs associated with multi-sink architecture, specifically, when sinks are deployed in three dimensional field.  
Figure 14: _Cont._