esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### System Design
#### 3.1.1 Participants' Video Capturing System  
This subsystem was built using the Zoom web SDK to work with any video conference conducted on the Zoom platform. By providing a meeting ID and password to this automated system, the user (meeting organizer) can enable the subsystem to join the meeting as a virtual participant, which scans the entire participant list and sends each participant's image after tagging their names. This simple virtual participant subsystem only appears in one of the implementations described in this paper (Figure 3). Another implantation (Figure 2) of this system is a comparatively more complex and less reliable alternative, especially for a bigger audience. However, the second implementation allows it to work with other video conferencing tools such as Microsoft Teams and Skype. In either method, the endpoint of the API request is the cloud API server. The received information is passed to other subsystems by the API service for further analysis. This operation is repeated every 1 min by default (this is a configurable value depending on the audience size; in this study, 500 ms was allocated per participant for capturing the camera image). A higher sampling rate will increase the quality of the analysis of the captured images.
## 2 VR Applications
### 360-Degree Video Streaming over Proposed System  
In order to illustrate the benefits of the proposed system, another important VR application is considered. The 360-degree video streaming technology becomes a demand for many VR applications. The local execution of video processing indicates low performance for high-resolution video applications, e.g., 4 k videos and higher resolutions. To this end, MEC technology should be involved and video computing tasks should be offloaded over an appropriate communication link to the edge-cloud server. Moreover, an efficient data offloading scheme should be introduced for efficient offloading of computing tasks.  
The introduction of heterogeneous distributed edge-cloud servers provides the computing and energy resources to the mobile VR devices and thus, video processing and decoding tasks can be offloaded. The considered MM-MEC system can be used for achieving high efficiency of 360-degree video applications of mobile VR devices. However, this kind of VR application requires a proper communication interface that achieves high spectral efficiency, proper for video applications with the required QoE.  
To achieve high transmission QoS for VR-360-degree video applications, we consider millimeter wave (mmWave) as the communication interface. The IEEE 802.11 ad standard is a multi-Gigabit wireless standard that uses the V band at a frequency of 60 GHz. The use of high wireless bandwidth is efficient for achieving a higher capacity of video-based applications. The main issue with the mmWave compared with the traditional interfaces, e.g., WiFi, is the limited communication range and thus, it is recommended for outdoor applications rather than indoor ones. Due to the recent advances in the antenna design, recent techniques have been developed for adopting mmWave to indoor applications.
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 4 Sensing Algorithms Generation: Target Detection and Localization
### Effective Sensing Channel Estimation  
Although we assume that \(\mathrm{H}_{\mathrm{pre}}(k)\) is known, note that \(\mathrm{H}_{\mathrm{sen}}(k)\) is unknown to the BS, thus precluding the direct computation of (4). The BS can only obtain knowledge about \(\mathrm{H}_{\mathrm{sen}}(k)\) from the received signal \(\mathrm{y}_{l}^{\mathrm{sen}}(k)\). However, as evident from (1), we must first eliminate the transmitted signal's influence. To achieve this, we exploit the fact that the covariance matrix of the transmitted signal is the identity. In particular, we obtain  
\[\hat{\mathrm{H}}_{\mathrm{eff}}(k)=\frac{1}{L}\sum_{l=1}^{L}\big{(}\mathrm{y} _{l}^{\mathrm{sen}}(k)-\mathrm{H}_{\mathrm{pre}}(k)\mathrm{s}_{l}(k)\big{)} \mathrm{s}_{l}(k)^{*}, \tag{5}\]  
which, from (1), is  
\[\hat{\mathrm{H}}_{\mathrm{eff}}(k)=\frac{1}{L}\sum_{l=1}^{L}\big{(}\mathrm{H} _{\mathrm{eff}}(k)\mathrm{s}_{l}(k)+\mathrm{z}_{l}(k)\big{)}\mathrm{s}_{l}(k) ^{*}. \tag{6}\]  
Now, we operate in (6) to obtain  
\[\hat{\mathrm{H}}_{\mathrm{eff}}(k)=\mathrm{H}_{\mathrm{eff}}(k)\frac{1}{L} \sum_{l=1}^{L}\mathrm{s}_{l}(k)\mathrm{s}_{l}(k)^{*}+\frac{1}{L}\sum_{l=1}^{L }\mathrm{z}_{l}(k)\mathrm{s}_{l}(k)^{*}. \tag{7}\]  
The term \(\frac{1}{L}\sum_{l=1}^{L}\mathrm{s}_{l,k}\mathrm{s}_{l,k}^{*}\) is an estimator of the covariance matrix of the transmitted signal, which is, as mentioned before, the identity. Therefore, \(\hat{\mathrm{H}}_{\mathrm{eff}}(k)\in\mathbb{C}^{M\times M}\) can be treated as an estimator of \(\mathrm{H}_{\mathrm{eff}}(k)\).
# CPM-OFDM Performance over Underwater Acoustic Channels
## 3 System Architecture
### CPM-OFDM Receiver
After tracing the complete signal sequence, all contenders terminate in a common node at the extreme end of the trellis. Hence, the required sequence is the one that is the most likely of them. Since the modulation index \(h\) is the ratio of two relatively prime integers \(p\) and \(q\), VA only keeps track of \(q\) or \(2q\) states at each time interval. If \(m\) represents the decision depth in number of symbols, then VA produces a decision on the entire sequence of \(m\) symbols at the end of \(m\) symbol intervals reducing the complexity of the CPM de-mapper.
## 4 Experimental Results
### Electron Arm  
To test the performance of the electron arm, we shipped the CpVMI spectrometer to the bending-magnet beamline BL02B [27] at the SSRF. This bending-magnet beamline delivers soft X-rays with photon flux around 1 \(\times\) 10\({}^{11}\) photons/s at \(E\)/\(\Delta E\) = 3700 and a tightly focused beam spot size (\(\sim\)200 \(\times\) 75 \(\upmu\)m\({}^{2}\)). Due to the space limitation, we installed the interaction region of the CpVMI spectrometer at 0.8 m away from the focal point of the beamline, where the size of the photon beam is about 2 \(\times\) 3 mm\({}^{2}\). Since the light intensity from a bending-magnet beamline is four orders lower than that from an undulator beamline, we use the effusive molecular beam instead of the supersonic molecular beam to gain the gas intensity by three orders. At the same time, we set the electron arm in the weak focusing mode.  
The test is done by measuring the photoelectrons image of Ar 2p photoionization with the photon energy tuned from 350 eV to 750 eV. Since the highest kinetic energy is 500 eV, we reduce the voltages to half of those in the simulation. Figure 4a shows a raw electrons image recorded at a photon energy of 750 eV, in which there are three distinct circles corresponding to 2p, 2s photoelectrons, and Auger electrons, respectively. However, the image was dominated by the strayed electrons at the center. Since our chamber was located 0.8 m away from the X-ray focus, it is very likely that the photon beam may touch somewhere on the chamber surface, and a huge number of photoelectrons and Auger electrons are created. Some of them may enter the lens and become focused at the center of the detector. These electrons persisted when the Ar gas was switched off. Fortunately, since they are at the center, the analysis of photoelectrons at the border of the image is not affected. Figure 4b shows a slice through the momentum distribution at \(p_{z}=0\) after applying an inverse Abel transformation with maximum entropy approach [28] of the image in Figure 4a. To make the assignment more robust, we repeat the measurement at several photon energies. The corresponding photoelectrons spectrum was shown in Figure 4c. We plotted the \(\Delta E/E\) and \(\Delta E\) as a function of photoelectrons kinetic energy in Figure 4d. We reached a resolution of 1.5% at the electron kinetic energy of 500 eV, and the \(\Delta E\) barely depends on the kinetic energy of the electrons. It agrees well with simulation  
Figure 3: The simulated relative energy resolution \(\Delta E/E\) of the CpVMI spectrometer with the electrons or ions emitted at 90\({}^{\circ}\). (**a**) Relative kinetic energy resolution as a function of electron kinetic energy in the strong focusing mode. (**b**) Relative kinetic energy resolution as a function of ion kinetic energy with the electron arm is set for different kinetic energies.  
in Figure 3a. We listed the performance of several VMI spectrometers that aimed for high kinetic energy in Table 1. Among them, we are the one tested with the highest electron kinetic energy.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 4 Analysis and Comparison
### Security Analysis
#### 4.3.3 Resistance to Attacks  
The new scheme provides a set of session keys to secure data exchange between the base station and sensors. Our proposal, which is based on session and public keys, can effectively resist common network attacks.  
Eavesdropping can be avoided using symmetric encryption, as well as the key update mechanism proposed in this article. Spoofing attacks are avoided in our scheme through mutual authentication based on public-key encryption. Moreover, the authenticity of sensors is achieved via a challenge-response mechanism, and the identity code is preloaded before deployment.  
Attacks like modification, reply, and insertion can be resisted via symmetric encryption and message authentication code added to each message. Only those authenticated nodes can send or modify data packets on the network.  
Attackers obtain the secret information by capturing nodes or other physical means. We define resilience against node capture as the probability \(F(x)\) that attackers obtain the key from the uncaptured node according to a certain number of captured nodes \(x\). Thus, we find  
\[F(x)=\frac{\text{number of compromised links between uncaptured nodes}}{\text{number of uncompromised links}} \tag{8}\]  
Resilience against sensor capture is first evaluated. Unlike the random key pre-distribution schemes in [10; 11; 42], sensors only need to pre-load a public key in our approach, which saves the memory of the sensor node. Due to the periodical key update applied, it is too hard for attackers to find the constantly updated session key, despite physically capturing a sensor in our proposal. Thus, the probability of resilience against node capture is \(F(x_{s})=0\), where \(x_{s}\) represents the number of captured sensor nodes. As shown in Figure 8, the resilience performance worsens with the increasing number of captured nodes for random key pre-distribution schemes, because of the storage of a large number of session keys. Since the sensors store matrixes instead of keys, the resilience performance of [PERSON]'s scheme [31] is better than that of the AP scheme [30]. Simulation results indicate that threat of sensor capture is perfectly eliminated via our scheme.  
Figure 7: Secure connectivity versus key pool size \(P\).  
Finally, Table 2 presents several typical schemes of key management in WSN that emerged recent years. In our scheme, we provide a simple and feasible mutual authentication mechanism comparable to [30; 34; 40]. [PERSON], in [32], used an asymmetric encryption algorithm with more computation overhead than in [34] and our proposal. Furthermore, our scheme outperforms other schemes in terms of resilience against node capture and resistance to eavesdropping.
## 1 Introduction  
Oxides of iron (Fe), manganese (Mn), and aluminium (Al) are effective sorbents for trace elements [1, 2]. Therefore, they play an important role as the sinks for potentially toxic heavy metals and metalloids in various environmental compartments such as soils, sediments, and aerosols, etc. [3, 4, 5, 6]. In soils that are contaminated by potentially toxic elements, the presence of soil-borne Fe, Mn, and Al oxides immobilize pollutants entering soils from external sources, and consequently reduce their bioavailability. This represents a major chemical mechanism through which the toxic effects of trace elements entering a soil system can be attenuated [7]. However, upon changes in environmental conditions, the trace elements bound to these oxides can be released. Soil acidification is an important driver for dissolution of metal oxides [8, 9, 10]. For manganese and iron that have variable valence, a drop in redox potential could also destabilize Fe and Mn oxides [11, 12].  
In vegetated soils, low-molecular-weight organic acids (LMWOAs) contained in root exudates of plants play an important role in mobilizing trace elements bound to Fe, Mn, and Al oxides in rhizosphere soils [13; 14; 15; 16]. While localized acidification in rhizospheric soils due to secretion of organic acids from plant roots and rhizobacteria solubilizes Fe, Mn, and Al oxides, complexation of Fe, Mn, and Al with organic ligands is likely to play a more important role in the dissolution of Al, Mn, and Fe oxides [3; 17; 18; 19]. In addition, certain types of organic acids such as oxalic acid also have a strong capacity to cause reductive dissolution of iron oxides [20; 21].  
In soil systems, Al, Mn, and Fe oxides, together with other soil minerals, compete for available LMWOAs, resulting in consumption of free organic ligands, and acid neutralization, which causes the increase in solution pH [7; 22]. This could, in turn, lead to re-immobilization of the mobilized Al, Mn, and Fe [7; 23], and consequently re-immobilization of the previously released trace elements in the soil solutions. So far, there have not been detailed investigations on (a) competitive dissolution among Al, Mn, and Fe oxides upon attack by LMWOAs, (b) temporal variation in LMWOAs-mobilized Al, Mn, and Fe, and (c) effects of (b) on trace elements in soil solutions. The aim of this study was to close the above knowledge gaps.
## 3 Results and Discussion
### Experimental Input-Output Data Collection and Analysis  
BBD-based experimental matrices are designed for four input variables (M:O, CC, Rt, and RT) and measure the output--biodiesel yield (see Table 3). Each experiment was repeated thrice, and average values corresponding to yield were recorded. It was observed from the total 29 transesterification experimental sets that the maximum and minimum deviation from the average biodiesel yield was found to be \(\pm\) 0.8 and \(\pm\) 0.2, respectively (see Table 3).
## 2 Materials and Methods
### Instruments and Tasks  
The ice-hockey players performed the analyses by sliding upon a slide vibration board (SVB; Vislide, Viequipment, Movilani System SCP, Sant Joan Despi, Barcelona, Spain). The characteristics of the SVB were as follows: frequencies (20, 25 and 30 Hz), amplitude (1.7 mm [peak-to-peak displacement]), total size (2.27 \(\times\) 0.74 \(\times\) 0.24 m) and sliding surface size (2.00 \(\times\) 0.59 m). The SVB is a synchronously vibrating platform. Players wore a pair of nylon socks over their shoes to be able to slide on the polyethylene surface [30]. The acceleration of the ice-hockey players was registered using an IMU (WIMU, Realtrack Systems, Almeria, Spain) with a 3D accelerometer (range: \(\pm\) 100 G; sampling frequency: 1000 Hz). The rhythm of the skating exercise on the SVB was controlled using a metronome (Korg KDM-3, Tokyo, Japan) [31].  
The task consisted of sliding from side to side upon the SVB according to the players' own technique for 30 s. The players performed the four tasks with different conditions in randomised order, constrained or not by the combinations of vibration and a stick. Carrying a stick meant successfully driving a puck with the stick while performing the task on the SVB. The conditions analysed are described below:  
* No vibration and no stick (NVNS): the SB skating exercise was performed without WBV and without the stick implement.
* No vibration with a stick (NVS): the SB skating exercise was performed without WBV and with the stick implement driving a puck.
* Vibration without a stick (VNS): the SB skating exercise was performed with WBV and without the stick implement.
* Vibration with a stick (VS): the SB skating exercise was performed with WBV and with the stick implement driving a puck.  
To determine the influence of the lateral dominance of the lower limbs, the dominant and non-dominant leg were analysed separately. Lateral dominance was determined by interviewing the athlete.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 4 Simulation Environment  
### Assumption and Simulation Constraints  
To keep the focus on specific aspects such as ore detection, collection efficiency, navigation precision, and communication reliability of the swarm robots model application in the mining environment, a number of assumptions and conditions have been set for the simulation environment and the execution of the simulations. To make the evaluation of ore exploration, collection, and transportation more realistic, the simulation was conducted after pre-stepping terrain conditions. In this setup, the robots navigate a flat and smooth surface, similar to the terrain at the Pilbara iron ore mine following the pre-stripping process, which is typically performed before mining excavation begins. To address the need for continuous autonomy in large-scale mining, robots have been equipped a battery with 8 h life To ensure a stable communication network for simulation, it was assumed that issues such as latency and interference are not present. The simulation assumes static environmental conditions, no sensor errors, and no physical challenges such as dragging or ore breakage. The simulation was carried out for 15 robots due to ROS simulation environment limitations resulting in system instability at higher numbers. The performances of the LiDAR, IMU, and wheel encoders were optimized to achieve 100% accuracy. For the predetermined ore locations as well, the robots would still simulate autonomous navigation and adaptation. Demonstrating that the robots can perform this task flawlessly in the simulation is important because dynamic exploration is essential for real-world mining. Focusing on mining-related specific aspects in this case study provides insights into the performance of swarm robots in challenging mining environments.
## 3 Results and Discussion
### Tensile Strength
Figure 8: SEM of 10% NaOH treated rubber surface morphology. Figure 7: SEM of 7% NaOH treated rubber surface morphology. In the tensile fracture micrograph in Figure 11, stronger rubber-polyester adhesion for rubber that treated with 7% NaOH can be observed. The strong rubber-polyester adhesion provide composite with higher tensile strength. The SEM showed the rubber crumb was strongly embedded in the matrix  
Figure 10: SEM of 4% NaOH treated rubber surface morphology. Figure 9: SEM of 1% NaOH treated rubber surface morphology. even after fracture, and it is evident that the crumb was firmly adhered to the matrix. Figure 12 showed the rubber pull-out from the matrix, which demonstrated a poor interfacial adhesion between the rubber treated with 4% NaOH and the polyester matrix. Figure 11: Tensile fracture micrograph of composites with 7% NaOH treated rubber crumbs. Figure 12: Tensile fracture micrograph of composites with 4% NaOH treated rubber crumbs. Apart from tensile strength, the modulus of elasticity of the composite also increased with an increased concentration of NaOH treatment, as shown in Figure 13. The improved bonding between the rubber matrix and the polyester matrix has a positive effect on their interfacial adhesion and prevents the failure of the composite from accelerating [23]. Furthermore, the ability of the rubber crumbs to adhere well in the composites enabled rubber to transfer the applied load evenly throughout the composites. It preventing from composite failure, thus providing the composite with better elastic modulus [41; 50]. However, treatment with NaOH, to a certain extent, caused damaged to the rubber structure and elasticity, which decreases the composite modulus elasticity.
## 4 The Energy Balance Problem
### The General Solution
The full expression for \(E[f_{i}]\) can be expressed by substituting \(i\) with \(n-i\), thus  
\[E\left[\frac{\sum_{k=1}^{h_{i}}\epsilon_{ik}}{S_{i}}\right]=E\left[\frac{\sum_{k =1}^{h_{j}}\epsilon_{jk}}{S_{j}}\right]\quad\forall i,j\in\{1,\ldots n\} \tag{5}\]2  
where \(\prod_{i}^{i-1}a_{i}=1\). We note that all the parameters of the recurrency solution above are expressed as a function of \(E[f_{1}]\) and \(i\). So as to compute them, we firstly compute the value of \(E[f_{1}]\). Then we can compute all the other parameters by replacing the already computed \(E[f_{1}]\). Now, the calculation of the probabilities \(p_{i}\) is quite easy. **Theorem 4**: The energy balance property is achieved if any ring sector (say \(T_{i}\)) propagates each message it handles with probability \(p_{i}\) to the next ring sector, \(T_{i-1}\), and with probability \(1-p_{i}\) it propagates the message directly to the sink. The value of each \(p_{i}\) is given by the following relation  
\[E\left[\frac{\sum_{k=1}^{h_{i}}\epsilon_{ik}}{S_{i}}\right]=E\left[\frac{\sum_{k =1}^{h_{j}}\epsilon_{jk}}{S_{j}}\right]\quad\forall i,j\in\{1,\ldots n\} \tag{5}\]3  
where the values of \(E[f_{i}]\) and \(E[g_{i}]\) are obtained from lemma 3 and lemma 12, respectively. **Proof:** From equation 11 we know that \(E[f_{i-1}]=p_{i}E[h_{i}]\) and also by lemma 10 we know that \(E[h_{i}]=E[g_{i}]+E[f_{i}]\). \(\diamond\)  
**Remark. ** Note that, interestingly, \(p_{i}\)'s are _independent_ of the number \(N\) of the events that occur in the network, since \(p_{i}\)'s depend only on \(i\) and the number of ring sectors \(n\) (which is broadcast to sectors by the sink). Thus the protocol assumes only local information. We note that the analysis above allows the exact derivation of probabilities \(p_{i}\)'s as a function of \(i\) and \(n\) which (although complicated and not obviously leading to a closed form) can be easily calculated by the sensors in the network by carrying out very simple calculations. The authors of [16] also prove the correctness of the protocol:  
**Theorem 5**: Given that the energy is each time on the average the same in all network sensors, each message will finally get to the sink.
# An Overview of Emergency Communication Networks
## 4 Discussion
* Access, switching, and fusion: the hybrid network is composed of multiple sub-nets, and different communication networks differ in terms of their nodes, links, functions, and other attributes. Therefore, an efficient and robust network structure is required to cope with the access, switching, and fusion among heterogeneous networks. With the rapid development of network technology and information technology, the IoT, cloud computing, and big data have penetrated into the daily applications of humans. The emergency wireless private network is evolving toward the PDT narrowband digital cluster and B-TrunC broadband cluster private networks. The development of satellite mobile communication will be in the direction of large capacity, high power, high speed, and low cost. The integration of wireless ad hoc network technology, 5G communication technology, satellite communication technology, and other private network technology will also be the focus of future development. The construction of the space-air-ground-sea  
Figure 13: Collaborative network scenario. integrated emergency communication network has gradually entered the 21 st century, and the rapid development of wireless, broadband, ubiquitous, fusion, and three-dimensional network technologies has enabled the construction of emergency communication platforms characterized by information sharing, interconnection, unified command, and coordination. This network has a layered heterogeneous architecture, which will realize multi-platform emergency resource planning and collaborative services, and is the main development trend in emergency networks.