esa-sceva/satcom-chunk-collection huggingface dataset:
# Roller-Integrated Acoustic Wave Detection Technique for Rockfill Materials
###### Abstract  
This paper proposes a roller-integrated acoustic wave detection technique for rockfill materials. This technique can be divided into two parts: theoretical analysis and technical implementation. Based on [PERSON]'s problem and an infinite baffle piston radiation acoustic field model, a relationship model between the sound compaction value (SCV) and the dry density of the natural gravel materials (NGM) was established, namely, A-model. During the modeling process, an innovative differential pulse excitation method (DPEM) was used to find the numerical solution of the vertical displacement of the soil surface under harmonic loads. In this research, a continuous compaction control acoustic wave detection system (CAWDS) was developed and utilized along with real-time kinematic global positioning systems (RTK-GPS). The SCV was adopted as a characterization index for the compaction quality of rockfill materials. A case study on a reservoir project in Luoyang, China indicated that the SCV is highly linearly correlated with the number of compaction times, dry density, and compactness of the NGM. This new technique demonstrated several advantages, such as higher accuracy, discreteness, convenience, and suitability for detecting the compactness of the NGM. This technique is an effective tool for compaction quality control of rockfill materials and has a great potential for further applications.  
rockfill materials; sound compaction value; natural gravel materials; differential pulse excitation method; continuous compaction control acoustic wave detection system; real-time kinematic global positioning systems +
Footnote †: journal: Applied Sciences
# Adaptive Versioning in Transactional Memory Systems +
## 3 Model and Preliminaries
### Supporting Durable Transactions in TinySTM  
We implemented durable transactions using TinySTM [26], a widely used lightweight STM implementation, as follows. For eager versioning, we maintain a undo log in persistent memory. When a transaction starts, each variable accessed by the transaction is added to the log before any modification is performed to it. Any update to the variable during the execution of the transaction is written directly to the variable's original address. When the transaction commits, respective log records for the transactions are freed and the memory is made available to use by other transactions. If the transaction aborts, all the changes made by the transaction to the variables are written back with the previous values stored in the respective undo logs. Then the log records are flushed.  
For lazy versioning, when a transaction starts, all the variables accessed by the transaction are loaded to volatile cache and modified. The new (or updated) values written by the transactions are then added to a redo log in persistent memory and also buffered in the volatile cache before the transaction commits. When the transaction commits, the new values are written back to the original memory addresses and the log records are flushed. We attach a timestamp based version number to each transactionl log to make sure that the last committed value is used in the restart process.
# Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals
## 3 Experimental Results  
### Network Training and Test Settings  
We conducted experiments on sign language recognition tasks and performed all experiments on a PC with Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40 GHz CPU and GeForce GTX TitanX GPU with 12 GB of memory. We used momentum stochastic gradient descent (SGDM) with a momentum of 0.9 and an initial learning rate of 0.0001 to train the network to update the weights and biases. The activation function is a rectified linear unit (ReLU). The batch size is set to 16, and the training period is 500. The width and height of the input matrix data are resized to \(224\times 224\). The network weights are initialized using ImageNet's pre-trained model. Our experiment followed the paper of SignFi method training and evaluation scheme, using non-repetitive five-fold cross-validation. The training sample is 80% and the test sample is 20%, which is consistent with the references [1,3]. In order to preserve the percentage of test samples for each category, stratified \(K\)-fold was applied. The dual-output two-stream network contains a lot of batch normalization, so it is necessary to shuffle the training data after each training cycle.
## 2 Basic Theory
### Common Approach of TDG Methods
#### 2.1.1 Differences Between the Conventional TDG and the Optimized TDG  
In conventional TDG method, with Equations (7) and (8), the high-order temporal derivatives can be expressed as [12]:  
\[\frac{\partial^{2}\mathbf{u}}{\partial t^{2}}=\frac{\partial}{\partial t}\bigg{(} \frac{\partial\mathbf{u}}{\partial t}\bigg{)}=\frac{\partial}{\partial t}\bigg{(} -\frac{\partial f}{\partial x}\bigg{)}=-\frac{\partial}{\partial x}\bigg{(} \frac{\partial f}{\partial t}\bigg{)}=-\frac{\partial}{\partial x}\bigg{(} \mathbf{A}\frac{\partial\mathbf{u}}{\partial t}\bigg{)}=\frac{\partial}{\partial x }\bigg{(}\mathbf{A}\frac{\partial\mathbf{f}}{\partial x}\bigg{)}, \tag{9}\]  
Similarly, for the third temporal derivative:  
\[ \begin{array}{l}\frac{\partial^{2}\mathbf{u}}{\partial t^{2}}=\frac{\partial}{ \partial t}\bigg{(}\frac{\partial^{2}\mathbf{u}}{\partial t^{2}}\bigg{)}=\frac{ \partial}{\partial t}\frac{\partial}{\partial x}\bigg{(}\mathbf{A}\frac{ \partial\mathbf{f}}{\partial x}\bigg{)}=\frac{\partial}{\partial x}\bigg{(} \frac{\partial\mathbf{A}}{\partial t}\frac{\partial\mathbf{f}}{\partial x}+ \mathbf{A}\frac{\partial}{\partial t}\frac{\partial\mathbf{f}}{\partial x}\bigg{)},\\ =\frac{\partial}{\partial x}\bigg{(}\frac{\partial\mathbf{u}}{\partial t}\frac{ \partial\mathbf{A}}{\partial t}\frac{\partial\mathbf{f}}{\partial x}+\mathbf{A}\frac {\partial}{\partial x}\frac{\partial\mathbf{f}}{\partial t}\bigg{)}=-\frac{ \partial}{\partial x}\bigg{(}\frac{\partial\mathbf{A}}{\partial t}\bigg{(}\frac{ \partial\mathbf{f}}{\partial x}\bigg{)}^{2}+\mathbf{A}\frac{\partial}{\partial x} \bigg{(}\mathbf{A}\frac{\partial\mathbf{f}}{\partial x}\bigg{)}\bigg{)}.\end{array}  \tag{10}\]  
At the time \(=n\), the estimated value of the second temporal derivatives can be defined as:  
\[\overline{\mathbf{u}}_{tt}^{n}=\left.\frac{\partial}{\partial x}\bigg{(}\mathbf{A }\frac{\partial\mathbf{f}}{\partial x}\bigg{)}\right|_{t=n}, \tag{11}\]  
Similarly, for the third temporal derivative:  
\[\overline{\mathbf{u}}_{ttt}^{n}=\left.-\frac{\partial}{\partial x}\Bigg{(}\frac{ \partial\mathbf{A}}{\partial\mathbf{u}}\bigg{(}\frac{\partial\mathbf{f}}{\partial x} \bigg{)}^{2}+\mathbf{A}\frac{\partial}{\partial x}\bigg{(}\mathbf{A}\frac{ \partial\mathbf{f}}{\partial x}\bigg{)}\Bigg{)}\right|_{t=n}. \tag{12}\]  
Then, Equations (11) and (12) are substituted into Equation (6), and the spatial integration to obtain the solution at the next time is performed. This conventional TDG procedure elegantly combines spatial discretization (via DG) and temporal discretization (via Taylor expansions) to achieve high-order accuracy. However, a major limitation arises from the complexity of computing \(\partial\mathbf{A}/\partial\mathbf{u}\). Even for relatively simple conservation laws, such as the Euler equations, \(\partial\mathbf{A}/\partial\mathbf{u}\) forms a third-order tensor that significantly increases computational overhead and complexity.
## 5 Simulation Verification
### Dynamic Performance Comparison  
According to [13], reducing the value of \(k_{pcdc}\) can improve the output impedance of the DC-voltage-controlled converter, thereby reinforcing the stability of the system. However, this condition influences the dynamic performance of the control. This paper compares the dynamic performance of the two control strategies. Similarly, the DC cable length is 50 km, and the active power instruction changes from 500 MW to \(-\)500 MW at 2 s. As shown in Figure 16a, the blue line represents the DC voltage under the traditional control strategy [13]. When \(k_{pcdc}\) = 5 and \(k_{icdc}\) = 100, the red line represents the DC voltage under the proposed control strategy when \(k_{pcdc}\) = 15, \(k_{icdc}\) = 100, \(k_{i}\) = 10 and \(R_{eq}\) = 2 \(\Omega\), and the yellow line represents the DC voltage under the proposed control strategy when \(k_{pcdc}\) = 15, \(k_{icdc}\) = 10, \(k_{i}\) = 10, and \(L_{eq}\) = 0.02 \(\Omega\). The DC voltage in the blue line drops by approximately 9% when the instruction value is changed and gradually increases to the instruction value. The DC voltage in the red line drops by only 2% when the instruction value is changed and increases the instruction value by more than 2%. The DC voltage in the yellow line drops by only 4% when the instruction value is changed and gradually increases to the instruction value. The dynamic performance of the proposed control strategy is better than that of the traditional control method.  
Figure 16b compares system DC voltages under the proposed control strategy with different virtual impedance parameters when \(k_{pcdc}\) = 15, \(k_{icdc}\) = 100 and \(k_{i}\) = 10. The blue line represents \(R_{eq}\) = 0.5, \(L_{eq}\) = 0.02 \(\Omega\), the red line represents \(R_{eq}\) = 2 \(\Omega\), \(L_{eq}\) = 0.02 \(\Omega\), and the yellow line represents \(R_{eq}\) = 2 \(\Omega\), \(L_{eq}\) = 0.002 \(\Omega\). From the comparison of these three lines, it can be seen that the speed of DC voltage regulation mainly depends on \(R_{eq}\). As can be seen from Figure 9a, increasing \(R_{eq}\) can reduce the amplitude response of \(Z_{dc1}\) in the low-frequency band. However, increasing \(L_{eq}\) has little effect on the amplitude response of \(Z_{dc1}\) in the low-frequency band, as in Figure 9b.  
Figure 15: DC-link voltage and active power of the VSC–HVDC system under different DC side capacity.
## 2 Control Strategies
\[\beta_{des}=\frac{b-\frac{amV^{2}}{S_{tar}(e+b)}}{a+b+\frac{mV^{2}(BC_{tar}-A_ {sf})}{2C_{sf}/C_{ar}(a+b)}}\delta=K_{\beta}\cdot\delta \tag{7}\]  
\[\dot{\gamma}_{des}=r_{des}=\frac{\dot{x}}{R}=\frac{\dot{x}}{a+b+\frac{mt^{2}( BC_{tar}-aC_{sf})}{2C_{ar}C_{sf}(a+b)}}\delta=K_{r}\cdot\delta \tag{8}\]  
\[\delta=\frac{a+b}{R}+K_{V}ay \tag{9}\]  
\[K_{V}=\frac{b\cdot m}{2C_{sf}(a+b)}-\frac{a\cdot m}{2C_{ar}(a+b)} \tag{10}\]  
However, desired states (7) and (8) cannot always be obtained, for example, in poor tire-road friction conditions. For these reasons, target states must be bounded, considering safety factors. If the lateral acceleration at the vehicle Centre of Gravity (CoG) is defined by (11), it should be upper bounded by the friction coefficient \(\mu\). \[a_{y}=\dot{x}r+\ddot{y} \tag{11}\]  
Since (12) is valid, lateral acceleration can be expressed in function of \(\beta\) and \(r\) from (13). \[\dot{y}=\dot{x}tan(\beta) \tag{12}\]  
\[a_{y}=\dot{x}r+\dot{x}tan(\dot{\beta})+\frac{\dot{x}\dot{\beta}}{\sqrt{1+tan( \dot{\beta})^{2}}}\leq\mu g \tag{13}\]  
Assuming that \(\beta\) and its derivative are small, the second and the third term of (13) are negligible, while the first one dominates. Considering a safety factor of 0.85, the target yaw rate can be bounded by (14), as described in (15). In this way, it is supposed that the second and third elements of (13) contribute only 15% to the total lateral acceleration. \[|r_{bound}|\leq 0.85\frac{\mu g}{\dot{x}} \tag{14}\]  
\[ \begin{cases}r_{tar}=r_{des}&if|r_{des}|\leq r_{bound}\\ r_{tar}=r_{bound}\cdot sign(r_{des})&if|r_{des}|>r_{bound}\end{cases}  \tag{15}\]  
Limiting the target side-slip angle is also very important because higher values of \(\beta\) lead the tires to lose their linear behavior, approaching the limit of adhesion. Thus, it is upper bounded by the empirical relation (16), as is visible in (17). \[|\beta_{bound}|\leq r_{tar}\bigg{(}\frac{b}{V_{x}}-\frac{m\cdot a\cdot V_{x}}{ C_{yr}\cdot(a+b)}\bigg{)} \tag{16}\]\[ \begin{cases}\beta_{tar}=\beta_{des}&if|\beta_{des}|\leq\beta_{bound}\\ \beta_{tar}=\beta_{bound}\cdot sign(\beta_{des})&if|\beta_{des}|>\beta_{bound} \end{cases}  \tag{17}\]
## 3 Experimental TES System: Design, Construction, and Performance Evaluation of a Prototype  
### Development of a TES Test Bench  
A prototype of a TES system has been built in the LaTEP laboratory and is presented in Figure 5. It is composed of a 0.1 m\({}^{3}\) cylindrical tank (0.4 m diameter and 0.8 m length) made of steel and filled with spherical nodules that contain the SA-PA mixture studied in the previous paragraph. Water is used as the HTF. The tank can rotate around a horizontal axis so the test can be performed in vertical or horizontal positions.  
For efficient storage, it is important to respect the natural thermal stratification of the HTF inside the tank during the charge and discharge modes. During the phase change, additional natural convection movements of the HTF occur near the nodule due to PCM latent heat. When the forced circulation of the HTF (due to the pump) is in the same direction as these natural convection movements, then the storage (or discharge) process is efficient. Thus, when the tank is in a vertical position, the HTF flows through the tank from top to bottom for the charge (heating) mode and from bottom to top for the discharge (cooling) mode. When the tank is in a horizontal position, a specific diffuser allows the HTF to flow vertically through the tank.  
The spherical capsules have an external diameter of 98 mm and are blow-molded from a blend of polyolefins with an average thickness of 2 mm. They are partially filled with the SA-PA mixture, leaving an empty volume, making thermal expansion possible when the PCM melts. To measure the spatial-temporal evolution of the temperature, several T-type thermocouples (wire diameter 0.5 mm) are located at different places inside the tank. They are set on bars fixed on the vertical axial bar. Note that these thermocouples measure the local temperature of the HTF. Moreover, a thermocouple is fixed on the external surfaces of eight capsules by means of a steel ring. These thermocouples allow one to detect the instant and the duration of the crystallization. In this experimental configuration, the tank contains 84 capsules totaling 32.5 kg of SA-PA mixture, but can receive up to 121 capsules by removing part of the instrumentation. As the thermocouples are connected to a data  
Figure 4: SA–PA mixture’s properties’ evolution over constant temperature exposure and in contact with polyolefin sample: latent heat of fusion (**top**) and onset temperature (**bottom**).  
acquisition system (CompactDAQ cDAQ-9188--National Instruments), it is possible to determine the temperature with a precision of 0.5 K at different points inside the tank, located in relation to radius and the height, versus time. The properties and geometric data are referenced in Tables 3 and 4. The tank is insulated with glass wool. The HTF flow rate is measured by a Coriolis flowmeter (CMF100 from MICRO MOTION ELITE, 1% error).  
Figure 5: Laboratory-scale latent TES prototype.
## 1 Introduction
Third, it is difficult to measure how much traffic volume is actually used by the user, hence traffic pricing could be problematic. As an improvement to proactive caching, we recently proposed a new protocol of UiL content delivery in [20]. Figure 1 illustrates the four steps of the proposed protocol. (1) The base station (BS) collects relevant physical layer information and send it to the central server to indicate the capacity of each radio link; (2) Taking the physical layer information as a key reference, the central server generates a personalized list of content recommendation for each user; (3) Users browse the recommended list and request interesting content from the list; (4) Content is delivered from the central server to the user. The novelty of our protocol lies in that content recommendation is used as a traffic shaping technique to avoid traffic congestion and excessive delay. Unlike the proactive caching protocols, which is a \"push-type\" protocol, our protocol is a \"pull-type\" protocol in a sense that it only delivers a file after the actual user request occurs. As a result, all traffic in the proposed system is on-demand traffic requested by the user, so that the problem of invalid traffic no longer exists. Moreover, in comparison with the conventional hypertext transport protocol (http), which is also a pull-type protocol, our protocol is more advanced in that it exploits the semantic level information to jointly optimize the overall user experience and reduces the risk of network congestion. Our previous work in [20  
model of outage delays. Third, all users are treated equally in terms of QoS requirements, which means that the delay requirements are identical for all users. In practice, it is desirable to have the flexibly to customize the QoS for different users. This paper contributes in improving our previous work in [20] by addressing the above-mentioned three limitations. First, the synchronous user behavior model is extended to an asynchronous model, which better captures the user behaviors in reality. Second, we use outage delay to bound the delay performance of the content delivery system. The outage delay constraint is a generalization from the fixed delay constraint. Third, our algorithm in this paper can support customized user QoS by allowing different delay constraints for different users. Hence, it allows more flexibility to manage the overall QoE in practice. In summary, our work generalizes the assumptions of homogeneous user behavior, delay constraint, and user QoS requirement to the heterogeneous cases. The rest of this paper is organized as follows. Section 2 introduces the system model and formulates the problem of UiL content delivery. Derivations of several theoretical performance bounds are presented in Section 3. A heuristic algorithm to solve the problem is proposed in Section 4, followed by simulation results in Section 5. Finally, conclusions are drawn in Section 6.
# Matching of Energy Provisions in Multihop Wireless Infra-Structures
## 5 Energy Differentiation Factor for Energy Provision in WMI Networks
### Zone Model of Differentiated Energy Consumption in 2D Topology
Let \(ZTX_{local}(i)\) be the number of packets originating at \(BSs\) in zone(\(i\)). According to the packet flow feature in the network, The \(TX\)-\(TX_{local}\) relation among zones can be approximately expressed by:  
\[ZTX(k)=\sum_{i=k}^{H}ZTX_{local}(i) \tag{12}\]  
The \(TX\)-\(RX\) relation among zones can be given by:  
\[ZRX(k)= \begin{cases}ZTX(k+1)&0<k<H\\ 0&k=H\end{cases}  \tag{13}\]  
The overhearing in WMI with 2D topology is more complex than in the line-topology WMIs. We assume that each BS in a zone(\(i\)) may overhear packets from the upstream zone(\(i-1\)), the zone(\(i\)), and the down-stream zone(\(i+1\)). To simplify the analysis, we define a \(OH\) factor \(K_{OH}\) such that the \(TX\)-\(OH\) relation can be described consistently with that in line-topology WMI. Let \(K1_{OH}\), \(K2_{OH}\), and \(K3_{OH}\) stand for the average numbers of neighbors in upstream zone, same zone, downstream zones of a BS, excluding the \(RX\) neighbors. Then a general description of \(ZOH(k)\) can be given by:  
\[ZOH_{Full}(k)= \begin{cases}ZTX(k-1)K1_{OH}+ZTX(k)K2_{OH}+ZTX(k+1)K3_{OH}&2<k\leq H \\ ZTX(k)K2_{OH}+ZTX(k+1)K3_{OH}&k=1\\ ZTX(k-1)K1_{OH}+ZTX(k)K2_{OH}&k=H\end{cases}  \tag{14}\]  
To provide a more simplified basic \(TX\)-\(OH\) relation among zones, we express the \(TX\)-\(OH\) relation in terms of an integrated factor \(K_{OH}\) by:  
\[ZOH(k)= \begin{cases}ZTX(k-1)K_{OH}&2<k\leq H\\ 0&k=1\end{cases}  \tag{15}\]  
where \(K_{OH}\) is a constant denoting the approximate ratio of packet overhearing to the packet overhearing from the upstream zone. Hence, given \(ZTX_{local}\) in a grid WMI, Equation (9) can be obtained by substituting \(ZTX(k)\), \(ZRX(k)\), and \(ZOH(k)\) based on Equations (12), (14) and (15). In addition, we can calculate Equation (10) based on Equation (9). Consequently \(Diff(k)\) can be obtained according to Equation (11). We can then express the energy provision at a BS in zone(\(k\)) as:  
\[E0_{BS}(k)=\frac{Diff(k)}{\sum_{i=1}^{H}Diff(i)}E_{0-all}=\frac{Total_{B}(k)}{ \sum_{i=1}^{H}(i)Total_{B}(k)}E_{0-all} \tag{16}\]The zone analysis model can be used in a WMI network with a given topology. Based on the zone model, the network is divided into several zones according to the topological distance to the GW. Then energy resources, such as backup batteries, and solar-battery sets are assigned to the BSs according to the zone they resides. BSs in the same zone will be assigned with the same amounts of energy resources. BS in the different zones will be assigned with the different amount of resources according to the analysis of differentiation of energy consumption analyses.
## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Ground-Observed Air Temperature  
Ground-based air temperature data, including daily maximum air temperature (\(T_{\text{max}}\)) and daily minimum air temperature (\(T_{\text{min}}\)), from 1981 to 2019, were acquired from the meteorological stations operated by the Thai meteorological department (TMD). These observations are measured at 2.0 m above the ground according to the world meteorological organization (WMO) standard [67]. In this research, temperature data from 10 meteorological stations are utilized as depicted by the dotted points in Figure 1a. These stations are classified into urban, peri-urban, and rural categories based on their geographical locations as outlined in Table 1. As there is no ground station observed in Sara Buri province, which represents the rural area, this study designates the meteorological stations in Ayutthaya, Lopburi, and Nakhon Ratchasima to serve as proxies for this inactive area.
# Investigation of Using CAPTCHA Keystroke Dynamics to Enhance the Prevention of Phishing Attacks
## 4 Methodology
### Finding the Distance and Classification Methods  
To determine how much a user's test data matched their profile, a Euclidean distance measure was used. Thus, the system measured the distance between two vectors based on a Euclidean distance equation in three-dimensional space [34]:  
\[d(x,\ y)=\sqrt{\left(x_{1}-y_{1}\right)^{2}+\left(x_{2}-y_{2}\right)^{2}+\left( x_{3}-y_{3}\right)^{2}} \tag{1}\]  
where \(x\) and \(y\) are two timing vectors. In this study, the user login was \(x\) and the user profile vector was \(y\). Moreover, \(d(x,y)\geq 0\)[34]. Algorithm 1 indicates how Euclidean distance was computed in the proposed work.  
```
1:begin
2:Compute the different values between two timing vectors.
3:Calculate Square value
4:Sum the values of step 3
5:Take the square root
6:end
```  
**Algorithm 1**Euclidean distance (ED)  
To identify the users, standard deviation (SD) was applied as a threshold for Euclidean distance in an approach inspired by the work in Ref. [33]. In the proposed system, Euclidean distance was compared to the SD. If the Euclidean distance was less than the half rows of SD values stored in the database, it was considered as a similarity threshold, with the new time vector belonging to the same user as the profile being compared. Hence, the new vector would be stored in the database. Otherwise, the system would give the user six attempts. If this number of attempts was exceeded, the user would be classified as a phisher.  
\[\text{SD}\ =\ \sqrt{\frac{\sum_{i=1}^{N}\left(x_{i}-\overline{x}\right)^{2}}{N-1}} \tag{2}\]  
Figure 3: Example of extracting time features of keystroke dynamics.  
where \(x_{i}=\{x_{1},x_{2},\ldots,x_{i}\}\) represents the values of the time features, these being the mean values for hold, UD, and DD times. Meanwhile, \(\overline{X}\) represents the mean value of all features used, and N represents the number of features [35]. Algorithm 2 illustrates how to compute the SD in the proposed work.  
```
1:begin
2:Compute the mean values for each feature
3:Calculate Square value
4:Sum the value of step 3
5:Divide by 2
6:Take the square root
7:end
```  
**Algorithm 2** Standard Deviation (SD)