esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Effects of Vertical Mass Irregularity on Seismic Behavior of BRBFs and CBFs
## 2 Buildings Studied
### Nonlinear Analysis of the Frames  
In OpenSees, material and element are required to be defined nonlinearly to model beam, column, and bracing steel elements. The present study modeled the behavior of steel materials using \"_Steel02_\" with a yield stress of 2400 kg/cm\({}^{2}\), an initial modulus of elasticity of 200,000 MPa and a strain-hardening ratio of 0.5% in beams and columns. Fiber sections were divided into smaller areas in the beams and columns. The net behavior was obtained by adding together the stress-strain responses  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Regular Buildings**} \\ \hline
10-Story BRBF & 10-Story CBF & 15-Story BRBF & 15-Story CBF \\
1.15 & 1.057 & 1.58 & 1.654 \\ \hline \multicolumn{4}{c}{**Mass Irregularity Buildings**} \\ \hline
10-Story BRBF & 10-Story CBF & 15-Story BRBF & 15-Story CBF \\
1.106 & 1.083 & 1.497 & 1.507 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fundamental periods of structures (sec).  
Figure 2: (a) Plan of structures and (b) topology and grouping details of frames.  
of the materials in these areas. The nonlinear effects of the members were also considered in modeling the beams and columns using a \"_nonlinear beam-column element_\", a widespread distribution of plasticity and seven integration points along each element. Moreover, buckling-restrained braces were modeled using the \"_CrootTruss element_\" with the same responses to compression and tension. In addition, nonlinear geometric effects were incorporated into the frame model using the geometric transformation of P-\(\Delta\). P-\(\Delta\) effects were considered by linking leaning columns with gravity loads to the frame using rigid components.  
To accurately determine the seismic behaviors of structures, nonlinear dynamic analyses were performed by exposing structures to actual ground motions, which were represented as temporal ground acceleration. Ground motions were recorded by determining this acceleration at small time steps. The design demand was then determined as the maximum value of the time history obtained by calculating the structural response at different moments. Moreover, the displacement and forces were obtained by subjecting a mathematical model that directly incorporates the nonlinear features of individual structural elements to earthquake shaking, which is represented as the time history of ground motions. The numerical models could accurately estimate the internal forces and inelastic responses that emerge during an earthquake by including the effect of material nonlinearity [62]. In this study, direct time integration method was used to provide time history analysis. \(\gamma=0.5\), \(\beta=0.25\) as Newmark parameters and 5% damping were considered for the Newmark method used for the time integration scheme. In addition, [PERSON]'s model has been used to apply nonlinear damping [63]. The present research evaluated the results of 96 nonlinear time history analyses.
## 4 Proposed Scheme
### Formation Control Algorithm Based on Reinforcement Learning
#### 4.2.2 Decentralized Formation Maintenance Policy  
The leader-follower method requires the followers to maintain a specific position and direction offset from the leader, so the structure is simple and robust engineering. We consider a chain mode in the leader-follower structure, in which each follower tracks its immediate leader instead of the mode in which all followers track the same leader. The advantage of this method is that the communication pressure of the leader is reduced, and the stability of the formation structure is realized by minimizing the tracking error for each USV.  
We adopt the proposed ADDPG in this paper to train the decentralized formation maintenance policy set \(\mathbf{\mu}^{fm}=\left\{\mu_{\theta_{1}}^{fm},\mu_{\theta_{2}}^{fm},\ldots,\mu_{ \theta_{N}}^{fm}\right\}\) for the decentralized multi-USV system, similar to the training of formation generation policy. For each USV, the state \(s\) contains information about its velocity and position and the relative distance from its immediate leader. The action \(a\) contains the change of velocity. At each time step \(j\) of formation maintenance, each USV \(u_{i}\) obtains its observation \(s_{j}^{i}\), uses its policy \(\mu_{\theta_{i}}^{fm}\) to generate an action \(a_{j}^{i}\) and receive a reward \(r_{j+1}^{i}\) from the environment. After the USV executes the action \(a_{j}^{i}\), the environment state \(s_{j}^{i}\) transfers to the next state \(s_{j+1}^{i}\). The reward function \(r_{3}\) is defined to be the distance difference, which is the error between the current distance \(H^{\prime}_{i}\) and the expected distance \(H_{i}\) between the USV and its immediate leader. The transition experience \(e_{j}^{i}=\left(s_{j}^{i},a_{j}^{i},r_{j+1}^{i},s_{j+1}^{i}\right)\) of the USV \(u_{i}\) is stored in the experience replay buffer \(D_{i}\) and sampled randomly to update the policy. The reward \(r_{3}\) is used to measure the performance of the formation maintenance policy, aiming to reduce the difference between the current formation and the expected formation. All UAVs choose and execute actions asynchronously and are not limited to synchronous operations.  
\[r_{3}=-\big{|}H^{\prime}_{i}-H_{i}\big{|} \tag{13}\]
# Absolute Capacitance Measurement by Direct Digital Fitting of Proportional Coefficient
## 2 Measurement Principle and Simulation
### Direct Fit Proportional Coefficient Capacitance Measurements
#### 2.2.2 Discrete Sampling of Input and Output Signals  
As can be obtained from Equation (10), to solve the measured capacitance \(C\), it is necessary to obtain \(V_{i}(t)\) and \(V_{o}(t)\), and analog signals can be converted into discrete digital signals by ADC sampling. In the sampling need to choose the appropriate sampling frequency, the higher the sample frequency, the higher the degree of reduction of the signal, the more accurate measurement results.  
The internal 12-bit Digital-to-Analog Converter (DAC) module of the STM32F4 forms the input square wave excitation signal \(V_{i}\) and the analog voltage output signal with RC time constant \(V_{o}\), and the two internal 12-bit ADCs modules complete the signal acquisition. Sampling the signals \(V_{i}\), \(V_{o}\) with ADCs can convert the analog signals into discrete digital signals:  
\[V_{i}(t)\to V_{i}(n) \tag{11}\]  
\[V_{o}(t)\to V_{o}(n) \tag{12}\]  
When the input signal \(V_{i}\) is a square wave signal, the capacitor is charged and discharged. Under different capacitance values of the capacitor, the resulting square wave response signal is sampled by the ADC and plotted as the image shown in Figure 3, at which time the sampling frequency is 560 kHz.  
The input square wave signal as well as the output intermediate node signal are A/D acquired using ADC sampling in the microcontroller, which samples the input and output signals with a time interval of \(\Delta\), which converts the input and output signals into digital quantities to facilitate the computation of the signals. The differentiation of the output signal can be replaced by the difference between consecutive sampling points of the output signal, the sampling interval of the square wave response signal is used to approximate the time derivative, and \(k\) denotes the ratio of the difference in the output signal to the difference between the input and output signals, and the capacitances calculated when the reference resistance \(R\) is known. The relationship between the capacitance \(C\) and the scaling factor \(k\) is shown in Equation (13).  
\[k=\frac{\Delta}{CR} \tag{13}\]  
In the traditional time constant method, it is necessary to precisely measure the time interval from the start to the end of the charging or discharging process. However, noise interference at the beginning and end points can easily affect the measurement of the charging/discharging time, leading to inaccuracies in capacitance measurement. The method proposed in this paper calculates the entire set of sampled input and output signal data points, thereby avoiding the susceptibility to noise that is inherent in the traditional time constant approach.
## 4 Discussion  
### The Relationship between the Change in the TIR and Stress  
According to [PERSON]'s law [55], there is a thermoelastic effect in response to the elastic stress condition for solid material. The temperature increases as the stress increases and vice versa. The temperature change is proportional to the sum of the principal stress:  
\[\Delta T=-\frac{\alpha T}{\rho\mathrm{C}_{p}}\Delta\sigma \tag{6}\]  
where \(\Delta T\) is the temperature change (K), \(\Delta\sigma\) is the change in the sum of the principal stress (MPa), \(\alpha\) is the thermal expansion coefficient for solid material (\({}^{\circ}\)C\({}^{-1}\)), \(\rho\) is the density (kg/m\({}^{3}\)), \(C_{p}\) is the specific heat capacity (J/kg\({}^{\circ}\)C), and \(T\) is the absolute temperature of a unit object (K). According to the literature [47; 54], the values of thermal parameters for sandstone are as follows: \(\alpha\) = 7.8 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\), \(\rho\) = 2230 kg/m\({}^{3}\), and \(C_{p}\) = 710 J/kg\({}^{\circ}\)C. Those for granite are: \(\alpha\) = 7.5 \(\times\) 10\({}^{-6}\)\({}^{\circ}\)C\({}^{-1}\), \(\rho\) = 2640 kg/m\({}^{3}\), and \(C_{p}\) = 820 J/kg\({}^{\circ}\)C. According to Equation (6), when the initial temperature of the granite was 20.3 \({}^{\circ}\)C, and the main stress variation was 60 MPa, the temperature increase can be calculated as 0.061 K. For sandstone, when the main stress variation was 35 MPa, the temperature increased by 0.051 K. The increasing rate of \(\Delta\mathrm{AIRT}_{\mathrm{mid}}\) for sandstone is higher than that for granite. Figures 4a and 6a show that the amplitude of ambient temperature change was about 0.2 K, and that the amplitude of \(\Delta\mathrm{AIRT}_{\mathrm{mid}}\) was only 15-40% of the ambient temperature. It was relatively weak compared to the ambient temperature.  
Equation (6) shows that the TIR intensity variation is inferred to be linearly correlated with the change in stress. The experimental results showed that the \(\Delta\mathrm{AIRT}_{\mathrm{mid}}\) changed consistently with the change in stress with the average correlation coefficient larger than 0.9. As there was no cyclic component with the same period of stress change during the loading process, it can be inferred that the \(\Delta\mathrm{AIRT}_{\mathrm{mid}}\) was caused by the stress change.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 2 Materials and Methods
### Enzyme Assay  
The enzyme activity was determined according to [PERSON] et al. [31], with slight modification. Quantitation of reducing sugar released from CMC and xylan was carried out by the 3,5-dinitrosalicylic acid (DNS) method [32], using D-glucose and D-xylose as standard for cellulase and xylanase respectively.  
_Cellulose assay._ Cellulose activity was determined by a reaction volume comprising 1 mL of crude enzyme and 1 mL of 1% (\(w\)/\(v\)) CMC dissolved in 50 mM phosphate buffer (pH 7.0), which was incubated in a water bath (Lasec, Cape Town, South Africa) at 50 \({}^{\circ}\)C for 30 min.  
_Xylanase assay._ Xylanase activity was carried out by mixing 1 mL suitably diluted enzyme with 1 mL of 1% (\(w\)/\(v\)) beechwood xylan, dissolved in 50 mM phosphate buffer (pH 7.0). The reaction mixture was incubated for 10 min at 50 \({}^{\circ}\)C.  
Reaction tubes were incubated with controls and sugar standards. All reactions were terminated by addition of 2 mL 3,5-dinitrosalicylic acid reagent, placed in boiling water bath for 10 min and cooled in ice water. The absorbance was read at 540 nm. One unit of enzyme was defined as the amount of enzyme per milliliter needed to liberate 1 \(\upmu\)mol of reducing sugar from a substrate per minute under the stated assay conditions.
# Assessment of the Quality of Video Sequences Performed by Viewers at Home and in the Laboratory
## 1 Introduction
They emphasize a good distortion representation, which is crucial for the performance of NR-IQA or blind image quality assessment (BIQA). In [34], the relationship between different distortion levels and their types is analyzed. The authors proposed a new approach, named 'GraphIQA', which presents a distortion graph representation-based deep learning BIQA. General-purpose BIQA models suffer from catastrophic forgetting, which refers to the tendency of a neural network to 'forget'. A solution to this problem may be the lifelong blind image quality assessment (LIQA) approach, which not only learns new distortions, but can also mitigate the catastrophic forgetting of identified distortions [35]. The main purpose of our work was to assess the influence of the environment on the video quality experienced by the user and to find correlations with the results of the objective quality assessment. The objective evaluation was based on the full reference (FR) method, where not only the distorted video, but also the reference footage was available. The goals of the research were to:  
1. Conduct a comparative analysis of the video quality assessment results obtained under laboratory and real home (not lab-emulated) conditions;
2. Find correlations between objective results and subjective assessment scores, taking into account the influence of the test environment. The results of the research should answer the question of whether laboratory tests can replace the video quality assessment conducted in users' homes and reduce testing costs. Furthermore, the research should show which type of subjective quality assessment is more closely correlated with objective quality assessment methods and which metric is worth using. The video quality assessment was made taking into account:  
1. H.264, H.265, and VP9 encodings [36; 37; 38];
2. The bit rate (from 300 kbps to 6000 kbps);
3. Resolutions (640 \(\times\) 360--inth high definition (nHD), 858 \(\times\) 480--standard definition (SD), 1280 \(\times\) 720--high definition (HD), and 1920 \(\times\) 1080--full high definition (Full HD)). The paper is organized as follows. After the introduction, Section 2 describes the video test sample preparation procedure and the methods used in the research. In the next section, the results of the subjective and objective quality assessment are presented and discussed. At the end, the results are summarized and the conclusions drawn.
## 3 Numerical Analysis Results
### Nut Type Effect on SCF  
Two different nut types--standard and round--were studied for the reduction of the SCF, as shown in Figure 9. For the blade root joint analysis, smeared material properties for blade root laminates and steel properties for bolt and nut components were used. For the reliable and intensive analysis of a bolted joint, 2D model and 3D model were constructed as shown in Figure 10, and results from both the three-dimensional linear FE analysis and the two-dimensional liner FE analysis are shown in Tables 5 and 6.  
The values of stresses and SCFs resulting from 3D and 2D FE models are reasonably similar. The values show that using the round nut type to connect bolts and inner pitch bearing will reduce SCF by 18.5% on average compared to the standard nut type. The effect of the number of engaged threads (or nut height) on SCF will be explained in the following section.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 3 Enhancements and Improvements to the Original Protocol
### Destination Addressing  
Given the all-to-all nature of the MQTT publish/subscribe model, where all messages sent within a specified topic are relayed to all subscribers; a system with multiple C2 servers subscribing to a channel used for a specific device also has the problem of identifying to which of the potential recipients a message is addressed. To a great extent, this is not an issue to be solved by the protocol, but rather is a system implementation issue of managing complex C2 relationships. There are a number of approaches that could be adopted by a C2 system to address the potential ambiguities of having multiple C2 reporting lines; all well understood within the military domain (e.g., segregation by message type, transfer of active command, a superior/subordinate hierarchical approach); or control could simply be handed over from one server to another entirely, eliminating the situation where a device was simultaneously connected to multiple C2 servers (or more formally, ensuring that only a single C2 server was subscribed to a given device topic at any once time). In this context, the method of using a sender ID is more than adequate.  
One of the advantages of using the MQTTs publish/subscribe model is that messages can easily be sent to more than one receiver at once. For example, a C2 server could establish a group of devices (either by pre-arrangement or in the future, dynamically via a new SRUP_MESSAGE_TYPE_GROUP_ADD message type). Such a group could then be used to pass instructions to all devices in that group with a single C2 message. This too can be implemented without the requirement for destination addressing beyond the use of the MQTT topic.  
Whilst it is possible for the sender ID in the base message to be mirrored by the further addition of a destination ID value (and noting that such an approach would be consistent with lower-level networking protocols such as TCP), unlike lower-level protocols, this destination addressing is not required for routing (at either a highly abstracted logical level or at the network transport layer level), since the centralized MQTT broker and the publish/subscribe model take care of both of these for us. Furthermore, because SRUP imposes a centralized C2 model, as previously discussed, there is no scope for direct device-to-device messaging.  
It should however be noted that to some extent, SRUP does use the concept of a destination ID, as the update initiate message has a target ID field. This is the exception, however, since it forces a one-to-one mapping between devices and update initiation messages. There is a requirement to be able to categorically ensure that the device-specific payload delivery can be maintained (a requirement brought out in conversations with potential users of the protocol, in some military and safety-critical applications).
## 2 Navigation and Localization
### Geophysical Navigation
#### 2.3.1 Gravity Navigation  
As mentioned in Section 2.1, the earth's gravitational field is far from being uniform and, for an INS, the effects of a change in the local gravitational field are indistinguishable from accelerations of the vehicle. One alternative is complementing the INS with gravity navigation. At the same time that an INS estimates the position of the vehicle, a gravity sensor--gravimeter or gradiometer--measures the gravity and gravity gradient where the AUV is located. A gravimeter measures gravity anomaly or the deviation in the magnitude of the gravity vector relative to a nominal earth model. A gradiometer is a pair of accelerometers with parallel input axes on a fixed baseline that measures gravity gradients or the rate of change of gravity with respect to linear displacement [29]. The difference in the accelerometer's output excludes the linear vehicle acceleration but contains the gradient of gravitation across the baseline. Based on the position and the measurements from the sensor, the database searches for the best fit of gravity and gravity gradient, and then the optimal matching position will be used to correct the position error of the INS. [PERSON] et al. [58] proposed a matching algorithm for gravity aided navigation, combining an ICCP algorithm with a Point Mass Filter (PMF) algorithm. The algorithm involved a two-step matching process. First, the PMF based on vehicle position variable can obtain in real-time an instructional position given in a large initial position error. Then, the ICCP algorithm can be employed for further matching. In order to verify the validation of the proposed matching algorithm, a numerical simulation was performed with a 12 h sailing period, where the speed of the underwater vehicle was set to 10 nmi/h. Simulation tests indicated that compared with the conventional ICCP algorithm, the proposed algorithm can achieve better results in terms of latitude and longitude positioning errors, which were reduced up to 56% and up to 65% when compared with the INS standalone.
## 4 Experimental Results
### Influence of the Structure of Recurrent Network on DRKMSVM  
In this subsection, we evaluate the influence of different recurrent structure on the performance of DRKMSVM. The experiments involve the BRNN with LSTM hidden layer neurons, denoted as DRKMSVM-LSTM, and the unidirectional RNN with GRU hidden layer neurons, denoted as Uni-DRKMSVM. All the experiments share the same parameters mentioned above in Table 3. The only difference is the recurrent structure, that is BRNN for DRKMSVM and RNN of Uni-DRKMSVM. Experimental results are shown in Figure 7 and Table 8.  
From Figure 7 and Table 8, we can see that in terms of four classification indexes, the performance of DRKMSVM exceeded those of DRKMSVM-LSTM and Uni-DRKMSVM on five datasets, which means that the proposed DRKMSVM which uses BRNN structure with GRU neurons is more competent than unidirectional recurrent network and BRNN with LSTM neurons in terms of improving the performance of text classification. This is because in the process of interpreting language, human beings tend to take context into consideration, and bidirectional recurrent network can effectively obtain both the previous and the future features and thus achieve better effects than unidirectional recurrent network.
## 2 Materials and Methods
### Statistical Analysis  
All experiments were performed in triplicate. GC-IMS assisted analysis software includes a VOCal with four plug-ins (reporter module, gallery plot module, dynamic PCA module, fingerprint similarity analysis module) and a GC \(\times\) IMS library search function to analyze samples from different perspectives. IMS data were acquired and processed using VOCal processing software. Principal component analysis (PCA) was a multivariate statistical analysis technique to determine principal component factors instead of complex variables in the original samples. The PCA was performed through the normalization of the signal intensities to a range from 0 to 100 by VOCal. Fingerprint similarity analysis (FSA) is a measurement performed by VOCal that will be selected based on the Euclidian distance of the evaluation area intensities, containing all of the signal intensities. The x-shift represents the relative distance. The idea of FSA is a visual grouping/clustering based on similarities and highlights potential grouping/clustering. The partial least squares regression (PLSR) analysis was performed by SIMCA 14.1 software (Umea, Sweden). Analysis of variance (ANOVA) was used to evaluate the differences between samples by the SPSS Statistic 22.0 software (SPSS Inc., Chicago, IL, USA), and \(p<0.05\) indicated statistically significant differences. Data were reported as the mean \(\pm\) standard deviation.