esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Vibration Control of a Cantilever Beam with an Eddy Current Tuned Mass Damper  
In this paper, a cantilever beam is used as the primary structure, and an ECTMD is installed on the end of the primary cantilever beam, as shown in Figure 1, to control the vibration of the beam. The ECTMD consists of a small cantilever beam, a permanent magnet and a copper plate fixed on the primary cantilever beam. The small cantilever beam can provide stiffness for the ECTMD and the frequency of the ECTMD can be adjusted by changing the length of the beam. The permanent magnet is placed on the end of the small cantilever beam, and can be regarded as a lumped mass. In this study, the ECTMD or TMD is designed to control the first mode vibration of the primary cantilever beam. When the primary cantilever beam is excited by external forces, the vibration energy can be transferred through the small cantilever beam to the ECTMD.  
To investigate the robustness of the ECTMD, the natural frequency of the cantilever beam is designed to be a variable, while the natural frequencies of the TMD and ECTMD are fixed. When the natural frequency of the cantilever beam is set close to the natural frequency of the ECTMD or the TMD, it is defined as a perfectly tuned situation; the opposite conditions are defined as a detuned situation. To study the detuned influence on the robustness of the ECTMD and TMD, the detuned ratio \(\gamma\) is defined by  
\[\gamma\,=\,\frac{f_{p}-f_{damp}}{f_{damp}}\,\times\,100\%\]where \(f_{p}\) is the fundamental natural frequency of the cantilever beam, and \(f_{damp}\) is the natural frequency of the damper. When the detuned ratio \(\gamma\) is less than zero, this situation is defined as downward-detuned; and if the detuned ratio \(\gamma\) is larger than zero, it is defined as upward-detuned.
# Enhancing Resilience in Digital Twins: ASCON-Based Security Solutions for Industry 4.0
## 3 Proposed Solution
### Payload Encryption Algorithms
For both algorithms (ASCON ([[https://github.com/ascon/ascon_collection](https://github.com/ascon/ascon_collection)]([https://github.com/ascon/ascon_collection](https://github.com/ascon/ascon_collection)) (accessed on 24 May 2023))) and AES-GCM ([[https://github.com/usnistg/Lightweight-Cryptography-Benchmarking/tree/main/implementations/reference_/crypto_aead/aes-gcm/mbedts](https://github.com/usnistg/Lightweight-Cryptography-Benchmarking/tree/main/implementations/reference_/crypto_aead/aes-gcm/mbedts)]([https://github.com/usnistg/Lightweight-Cryptography-Benchmarking/tree/main/implementations/reference_/crypto_aead/aes-gcm/mbedts](https://github.com/usnistg/Lightweight-Cryptography-Benchmarking/tree/main/implementations/reference_/crypto_aead/aes-gcm/mbedts))) (accessed on 24 May 2023)), we selected the optimized reference implementations based on a key length of 128 bits tailored for the ESP32 device chip. However, to enhance the security of our implementation, we incorporated a function to generate a nonce. This aspect is crucial in addressing security attacks, such as replay attacks, which involve the repeated use of encrypted information.
## 4 Detection and Removal of Counterfeit Signals
The corresponding flow chart is shown in Figure 4.  
The whole process of detecting and removing the counterfeit signal is elaborated above. Due to the complexity and variability of deliberate interferences, it is very difficult for one single anti-spoofing method to properly handle all kinds of spoofing. For example, the anti-spoofing method based on the multi-correlator structure can help the tracking receiver to keep the authentic signal locked. However, if spoofing during acquisitionalready exists, this method will be incapable of distinguishing between the authentic and counterfeit signals of the receiver. Therefore, multiple anti-spoofing methods should be adopted in the receiver simultaneously [18]. For instance, if the counterfeit signal corresponding to each satellite comes from a single interference source, the antenna can be moved along any small trajectory. In this case, the relative motion between the receiver and the repeater makes the variation of the carrier frequency of each counterfeit signal exactly the same. If the multi-correlator structure can detect the spoofing at the initial tracking stage, this method can be introduced to help the receiver determine whether the locked signal is the authentic signal. If spoofing with unlocked carrier frequency succeeds in spoofing the target receiver, significant abnormalities in the outputs of phase discriminators will be inevitable [19]. In this case, this method can also be introduced to help the receiver determine whether it is necessary to enter the reacquisition stage. If the carrier frequency difference between the counterfeit signal and its corresponding authentic signal increases to a relatively large value, two distinct correlation peaks will appear at the reacquisition stage. That is, the spoofing degenerates into the simplistic form. In addition, other common anti-spoofing methods can also be used on the receiver to detect the simplistic spoofing earlier or more conveniently and ensure the correctness of the positioning results.
# Reliable Internet of Things: Challenges and Future Trends
## 5 Review of Recent Work in Literature on Reliable Data Transmission in IoT
### Security
The result shows that the proposed protocol provides robustness against attacks, lower communication overhead, and lower computation time. In [129], the authors propose a security protocol to detect malicious attacks which are from inside the IoT network. Artificial Intelligence (AI) based distance measurement techniques are used to identify and classify malicious attacksThe results show improved accuracy of detection and reduced computation time. The authors in [130] provide Advanced Encryption Standard (AES) based security algorithm for constrained IoT devices. The proposed protocol reduces the complexity of the standard AES algorithm by using a reduced number of rounds. The mathematical proof of the proposed protocol is also providedThe results indicate reduced encryption time of the proposed protocol. In [131], the authors present a technique to detect data integrity attacks in the Internet of Vehicles (IoVs). In this regard, the isolation forest algorithm finds anomalies in the traffic density information shared by vehicles. After detecting anomalies, verification messages are sent to neighborhood vehicles of potential malicious vehicles to verify if the data transmitted is malicious or correct. Simulation results show that improved accuracy of anomaly detection and reduced false positives.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 2 Study Region Overview  
This study focuses on the coastal city of Macau, located at the lower reaches of the Pearl River. Due to its strategic location and unique environmental features, Macau boasts a vast expanse of mudflats, fostering remarkable biodiversity. This blend of natural landscapes within an urban setting forms a complex wetland system that warrants thorough investigation and strategic conservation efforts. Macau's specific geographic location, between North latitude 22.1068 and 22.2413 degrees and longitudes 113.5281 to 113.5989 degrees East, engenders a combination of climatic and geological factors that support a dynamic aquatic ecosystem. This ecological system, particularly the mudflats, significantly enhances the city's biological diversity [17].  
For the purpose of this study, the BOUL, representing Macau's administrative boundary, has been carefully selected. This delineation seeks to provide accurate demarcation of the city's wetland regions and facilitate a detailed classification of the diverse wetlands within the city's well-defined borders. Observing Macau's wetland distribution, the city encompasses various taxa, from coastal or marine wetlands to several constructed wetlands. The coastal wetlands, influenced by the neighboring marine ecosystem, function as dynamic biomes with diverse flora and fauna [18]. These wetlands offer essential ecological benefits, ranging from serving as natural barriers against severe marine weather, to accommodating a myriad of marine organisms. In contrast, the constructed wetlands, crafted by human intervention, fulfill numerous ecological and socio-economic roles. Created for functions including waste treatment, stormwater management, and habitat restoration, these designed wetlands coexist harmoniously with the urban environment.  
Macau, characterized by a diverse range of wetland types, stands as a suitable focus for this investigation. The city's varied wetland ecosystems, shaped by distinct geographical features and human intervention, highlight its vital ecological significance and emphasize the need for detailed study and conservation.
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 5 Conclusions  
The McMurray test was found to be the most sensitive test for diagnosing medial meniscus lesions, whilst the Thessaly test was the most sensitive for the lateral meniscus. The Apley test was the most specific for both the medial and the lateral meniscus. Therefore, these two tests should be implemented in a daily routine examining knee joints. Joint line palpation was found to be the least sensitive test for the medial and the lateral meniscus. Regarding ACL, the Lachman test showed the best sensitivity, while the lever sign and pivot shift were shown to be the most specific tests for ACL deficiency. As a result, a combination of these tests seems most applicable for detecting ACL insufficiency.  
MRI is more accurate in diagnosing medial and lateral meniscus lesions than physical examination; however, the difference is not statistically significant and MRI should be used as an additional examination to evaluate the extent of meniscal lesion rather than just plain diagnosis. Nevertheless, MRI shows lower accuracy in detecting ACL deficiency than physical examination, a finding that is statistically significant.  
Patients should be referred to MRI to answer specific questions about the morphology of lesions prior to surgery, rather than to confirm diagnosis, which can be established by proper physical examination.  
Conceptualization, methodology, investigation, writing--original draft preparation, resources, P.K.; supervision, A.N. and A.J.; formal analisys, J.J. and R.M.; writing--review & editing, validation, R.K.  
The research was financed in the framework of the project Lublin University of Technology--Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19).  
The authors declare no conflict of interest.
## 5 Active Coating and its Control Algorithm
### The Scattering Suppression System and Algorithm
#### 6.2.2 Vector Microphone  
To ensure the stability of active system we form a vector microphone (with selective directional pattern) with its direction of maximum sensitivity coinciding with the vector \(\mathbf{w}_{1}\) of the first incident wave, using the microphones of the bearing group, so we need two microphones, spaced on the axis \(\mathbf{w}_{1}\) at some distance \(h_{1}\) from each other (Figure 12). At first we take the title microphone with output signal \(P_{\widetilde{m}(1,0)}(t)\). The signal \(\overline{P}_{\widetilde{m}(1,0)}(t)\) of the 2-nd microphone, which is spaced in some point \(\mathbf{R}_{\widetilde{m}(1,0)}+h_{1}\mathbf{w}_{1}\) (so far we only know that \(h_{1}\)\(>\)0) will form a combination \(\overline{P}_{\widetilde{m}(1,0)}(t)=\mu(1,1)P_{\widetilde{m}(1,1)}(t)+\mu(1,2 )P_{\widetilde{m}(1,2)}(t)+\mu(1,3)P_{\widetilde{m}(1,3)}(t)\) of signals of benchmark microphones \(\mathbf{\hat{\star}}\), where:  
\[\mu(1,1)+\mu(1,2)+\mu(1,3)=1,\,(\mu(1,1),\mu(1,2)\mu(1,3)>0). \tag{23}\]  
Ensuring the equation:  
\[\mu_{(1,1)}\mathbf{R}_{\widetilde{m}(1,1)}+\mu_{(1,2)}\mathbf{R}_{\widetilde {m}(1,2)}+\mu_{(1,3)}\mathbf{R}_{\widetilde{m}(1,3)}=\mathbf{R}_{\widetilde{m} (1,0)}+h_{1}\mathbf{w}_{1} \tag{24}\]  
(_i.e. center of masses_\(\mu(1,1),\mu(1,2),\mu(1,3)\), spaced at points \(\mathbf{R}_{\widetilde{m}(1,1)}\), \(\mathbf{R}_{\widetilde{m}(1,2)}\), \(\mathbf{R}_{\widetilde{m}(1,3)}\) coincides with the point \(\mathbf{R}_{\widetilde{m}(1,0)}+h_{1}\mathbf{w}_{1}\) ) we obtain concrete weight coefficients \(\mu(1,1)\mu(1,2)\mu(1,3)\) and distance \(h_{1}\). Taking into account the smoothness of the incident wave we form the signal \(\overline{P}_{\widetilde{m}(1,0)}(t)\) of the virtual microphone which is spaced at the point \(\overline{\mathbf{R}}_{\widetilde{m}(1,0)}=\mathbf{R}_{\widetilde{m}(1,0)}+h _{1}\mathbf{w}_{1}\). Now we obtain the output signal \(U(t)=P_{\widetilde{m}(1,0)}(t-\tau_{1})-\overline{P}_{\widetilde{m}(1,0)}(t)\) (where \(\tau_{1}h_{1}/c_{w}\)) of the vector microphone. For the wave running in direction \(+\mathbf{w}_{1}\) (or \(9=0\), _i.e._ the first incident wave) the signal \(U(t)\) represents time derivative of sound pressure of incident wave in the point \(\mathbf{R}_{\widetilde{m}(1,0)}\) with delay \(\tau_{1}/2\)  
Figure 12: Forming of virtual vector microphone (b) with cardio directional pattern of sensitivity by means the combination (a) of signals of microphones group of bearing of the \(i\)-th incident wave. The mass center of points \(\mathbf{R}_{\widetilde{m}(1,1)}\), \(\mathbf{R}_{\widetilde{m}(1,2)}\), \(\mathbf{R}_{\widetilde{m}(1,3)}\) (with weights \(\mu(1,1)\), \(\mu(1,2)\), \(\mu(1,3)\) ) must be laying on the axis, coming from the point \(\mathbf{R}_{\widetilde{m}(i,0)}\) in direction of the vector \(\mathbf{w}_{i}\).  
words we assume that the microphone system and system of incident waves always permit us to form bearing groups without intersection of microphones for targeting each incident wave.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 3 The Proposed Approach
### Hhs
#### 3.2.2 Fitness Function  
In the following, first, the low-level heuristics of the HHS algorithm are presented, which include GA, PSO, ACO, and SA, and then the proposed algorithm is explained. These algorithms use the fitness function to evaluate the proper allocation of PEs to modules. The fitness function [11] can be defined by \(Fitness=\frac{1}{w_{1}*\sum_{i=1}^{N}U_{i}+w_{2}*BW}\), where \(BW\) denotes the bandwidth in a module. \(w_{1}\) and \(w_{2}\) are weights of \(TUC\) and \(BW\), respectively, and their values are equal 0.5, \(U_{i}\) is the utilization of \(ith\) tuple, and \(N\) is the total number of tuples. The value of \(BW\) for each module is set in the start of the simulation. To find the best scheduling, the fitness value must be minimized.
## 2 Methods
### Preprocessing
#### 2.1.2 Fast Fourier Transform  
Fast Fourier Transform (FFT) is commonly used for calculating the Power Spectral Density (PSD), which is the power distribution of a signal in the frequency domain. The calculation process is as follows: For a discrete-time signal \(x[n]\), its FFT is denoted as \(X[k]\).  
\[X[k]=\sum_{n=0}^{N-1}x[n]e^{-2\pi\frac{ln}{N}} \tag{2}\]  
The power spectrum \(P[k]\) is usually calculated as the amplitude squared of the FFT result:  
\[P[k]=\left|X[k]\right|^{2} \tag{3}\]  
PSD is the normalized form of the power spectrum, which is usually normalized taking into account the signal length \(N\) and the sampling frequency \(f_{s}\).  
\[\text{PSD}(f)=\frac{1}{Nf_{s}}\left|X[k]\right|^{2} \tag{4}\]  
The EEG signal itself contains features in both time and frequency domains. However, it cannot effectively solve the problem of information correspondence in these two domains. While some researchers focus only on time- or frequency-domain signals, which may overlook important information, others work on combining various features to improve classification performance. For this limitation, the FFT was extended to various time-frequency domain analysis methods, such as the Short-Time Fourier Transform (STFT) and the CWT, which were used in subsequent studies.
## 3 Proposed TIGSO-EDS System Architecture
The neighborhood range is calculated using Equation (12):  
\[r_{d}^{l}(t+1)=\min\big{\{}r_{s},\max\{0,r_{d}^{l}(t)+\beta(n_{t}-\mid N_{i}( t)\mid)\}\big{\}} \tag{12}\]  
where the following definitions apply:  
* \(r_{s}\) is a sensor range which is a constant that limits the size of the neighborhood range;
* \(n+t\) the number of desired neighbors;
* \(\mid N_{i}(t)\mid\) is several neighbors of the node \(n\) at \(t\) time, and \(\beta\) is a model constant. In GSO, the specifications used are \(\gamma=0.6,\rho=0.4,\beta=0.08,n_{t}=5,s=0.03\), and \(l_{k}=5\). The glowworm identifies its neighbor's movement and computes it by exploiting an adaptive neighborhood, which is bounded above by its sensor range. Each glowworm selects a neighbor using a probabilistic method, and it has a luciferin value superior to its other value, which moves toward it. The movements are based on the local information, and the selected neighbor interactions enable the glowworm swarms to divide into separate subgroups which converge on the several optima for a multi-modal function. Here, VIKOR is used to determine the fitness evaluation. The overview of the hybrid integration of the VIKOR GSO model for clustering in the proposed work is depicted in Figure 3.  
After clustering, information is gathered by the CH and later aggregated and encrypted before being forwarded to the BS. At this point, a new hybrid encryption algorithm [34] is used to ensure security and develop useful security mechanisms. In the authenticating protocols, the sensor nodes initiate the key establishment process to communicate with a trusted third party, which is typically the base station, and obtain a session key. Usually, this communication is multi-hop with other nodes working as routers, particularly when large sensor networks are considered.
## 2 Basic Theory for Injury Analysis and Evaluation
### Assessment of Injury Severity  
Several studies, including the AEP-55 issued by the North Atlantic Treaty Organization, have explored the injury site and degree of injury by analyzing human injuries using MADYMO [7; 8; 9]. The AIS is an anatomical-based coding system established by the Association for the Advancement of Automotive Medicine that classifies and describes the severity of injuries [10; 11]. AIS represents the threat to life associated with the injury rather than a comprehensive assessment of its severity [12]. AIS is one of the most common anatomic scales for traumatic injuries and is an up-to-date medical term that provides an internationally recognized tool for assessing injury severity [13]. The Abbreviated Injury Scale (AIS) is an anatomical-based coding system created by the Association for the Advancement of Automotive Medicine to classify and describe the severity of injuries. It represents the threat to life associated with the injury rather than the comprehensive assessment of the severity of the injury. The cervical/thoracic spine is the part most closely related to life in car accidents, and the AIS grade is mainly classified according to the degree of damage to the cervical/thoracic spine. In addition, the spinal cord is also included. The injury grade of AIS is mainly related to cervical/thoracic spine: damage to the spinal cord. Accordingly, it is a global severity scoring system that classifies individual injuriesby body part according to their relative severity based on a six-point scale (1 = minor, 6 = maximum). The standard table of human injuries presents the injury standards for six parts of the human body, including the head, neck, thorax, spine, femur, tibia, and non-auditory pressure-induced injuries.  
into the DB program. First, considering the scenario where the elevator wire rope was cut, and the elevator fell freely from the 5 th floor at a speed of 14 m/s, a fall and injury analysis was performed, assuming that one individual was lying down. According to the analysis results, despite the fall from the fifth floor, i.e., the lowest floor, the maximum impact force on the human body, as shown in Figure 3, was 70,000 kg m/s\({}^{2}\). This impact value results in the rupture of all the parts and joints of the human body. Based on the above results, we established that comparing the amount of impact and degree of injury according to the occupant's posture and the use of air mats was not meaningful for floors above the 5 th floor, as presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Floor** & **Height (m)** & **Fall Velocity (m/s)** \\ \hline
1 & 20 & 47.5 & 30.5 \\
2 & 15 & 35.0 & 24.2 \\
3 & 10 & 22.5 & 21.0 \\
4 & 5 & 10 & 14.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The fall height and fall speed for four levels.  
Figure 3: _Cont._
## 3 Results and Discussion
### DCF and PARX Adsorption Assays
#### 3.2.4 Temperature Effect  
The influence of temperature on the uptake capacities of DCF and PARX by the ACK and CC samples were studied in the range 298-323 K (25-50 \({}^{\circ}\)C). The initial concentrations of each adsorbate were 100 mg L\({}^{-1}\), and the equilibrium times were the ones chosen in the previous assays. The results present in Figure 9 show that the adsorption of DCF and PARX onto ACK carbon decreases with an increase in the temperature, as well as the adsorption of PARX onto CC carbon. These findings indicate that lower temperatures facilitate adsorption, meaning that an exothermic process is prevalent in those cases. In contrast, the uptake of DCF with CC carbon increases with the temperature, suggesting the prevalence of an endothermic process.  
The formation of an adsorption 'bond' between sorbate and sorption sites is exothermic by nature [55]. Thus, an endothermic process indicates that the energy released from interactions between CC surface sites and diclofenac molecules is lower than the energy involved in the desolvation of CC surface sites and diclofenac molecules, as well as in the solute-solute interactions on the adsorbent surface [55; 56].
## 4 Discussion
### Assessment of the LAI Empirical Relationship with Other Datasets  
Our analysis shows that the simple model for estimating annual GPP from LAI has comparable accuracy--at global scales--to the MOD17 GPP product (Figure 6). LAI-based estimates are also consistent with the expected influence of climate on GPP (Figure 7), with estimates increasing with precipitation and temperature and average DTR. High DTR is indicative of dry conditions, while lower DTR is associated with low radiation. Both conditions are unfavorable for vegetation photosynthetic activity, as are extreme cold and low precipitation [78]. The fact that our GPP estimates broadly follow these patterns lends additional confidence that the simple approach does not generate spurious predictions. Our estimates of GPP for both tropical (2,332.9 g C/m\({}^{2}\)/year) and non-tropical (755.6 g C/m\({}^{2}\)/year) forest are also comparable to the ensemble median of 6 models by [PERSON] _et al._[2], further supporting that our results are reasonable.  
At finer temporal resolutions, or for interannual variability, important differences may exist between the LAI-based GPP and MOD17, and we do not argue the two approaches are necessarily interchangeable. Indeed, in spite of errors and/or biases in input meteorology, upstream MODIS products, and structural issues with the algorithm itself [31, 79], MOD17 is more highly correlated to the short-term measured GPP than any of the single MODIS products. But for research focused on broad-scale patterns of annual GPP variability, the simple model does appear to provide a useful alternative, and one that is consistent with theoretical principles developed decades ago.