esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Factors Influencing the Tool Wear  
#### 3.2.1 Wear Morphologies of Different Parts of the Drill Bit  
Figure 8 shows the wear morphologies of different parts of the drill bit.  
Figure 8: Wear morphologies of different parts of the drill bit (\(Vc=85\) m/min,\(f=30\) mm/min).  
Figure 7: Entrance and exit delamination factors at different feed rates. (**a**) Effect of feed rate on thrust force at different cutting speeds. (**b**) Entrance delamination factors at different feed rates. (**c**) Exit delamination factors at different feed rates.  
Figure 8a-c shows the wear in the chisel edge. When the drilling process began, first the chisel edge came into contact with the material, but its linear velocity was the lowest. Plastic deformation occurred under the extrusion of the chisel edge. As the machining length increased, the chisel edge was offset in the radial and tangential directions.  
Figure 8d-f shows the wear in the cutting edge. With the increase of the machining length, the cutting edge gradually changed from a straight line to a concave arc. This indicated that different parts of the cutting edge were subjected to different loads. The middle of the cutting edge was the main cutting part and the main channel of chip outflow. It suffered the greatest contact stress, so it wore the most.  
Figure 8g-i shows the wear in the flank face. Due to continuous friction with the glass fiber and resin matrix in the machining area, the flank face wear was significant, and the expansion of the tool wear zone increased the contact area with the material, which further exacerbated the wear. With the increase of machining time, a wear band of different widths was formed on the flank face. It was narrower near the centre of the drill bit and wider near the outer corner.  
According to the wear patterns of the different parts of the drill bit, VB can be selected as the performance index to evaluate the wear.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Experiment of Proposed Novel Derivative-Based PPM Modulation Using VCO and Monostable Multivibrator  
For the novel method of generating PPM without a carrier, a differentiator circuit created using an operational amplifier is employed. The VCO utilizes the IC4046, and the 74 LS123 is used as the monostable multivibrator. The circuit diagram is shown in Figure 23. The experimental results demonstrate a comparison between the traditional method, which uses a sawtooth wave compared with the information signal and feeds to the monostable circuit, and the novel proposed method. The comparison is shown in both the time domain and frequency domain, as illustrated in Figure 24.  
Figure 23: Novel proposed circuit for generating carrierless PPM using VCO.  
Figure 22: Demodulation results using the approach of conversion to a quasi-FM-PWM signal: (**a**) Output of the quasi-FM and PWM demodulation with sine wave and triangle wave input from conventional PPM modulation signal; (**b**) Output of the quasi-FM and PWM demodulation with sine wave and triangle wave input from proposed PPM modulation signal. (CH1) Information signal (upper line); (CH2) Demodulated output signal using the monostable circuit and LPF (lower line).
## 3 Results
### FT-IR Analysis  
The infrared spectrum of the ZrO\({}_{2}\)-CdWO\({}_{4}\) NC (Figure 2) showed a broad-spectrum peak at approximately 3429.5 cm\({}^{-1}\), contributing to the stretching vibrations of the O-H bond [35]. The peaks that appeared at approximately 2919 and 2854.1 cm\({}^{-1}\) are for the stretching vibration of the C-H bond coming from the _Brassica rapa_ leaf extract [36]. A sharp peak at 1633.6 cm\({}^{-1}\) was assigned for the bending vibration of Zr-OH and adsorbed moisture content [33]. The absorption peaks at 1020.7 and 829.3 cm\({}^{-1}\) in the sample are assigned for the -OH group and for the vibration of the Zr-O bond, confirming that ZrO\({}_{2}\) is present in the sample [37]. The sharp peaks at 890.0 cm\({}^{-1}\), 717.6 cm\({}^{-1}\), 587.96 cm\({}^{-1}\), 412.3 cm\({}^{-1}\) and 412 cm\({}^{-1}\) correspond to Cd-O stretching vibrations of Cd-O-W, stretching vibrations of the W-O-W bond in the WO\({}_{4}^{2-}\) group and in-plane deformation of the WO\({}_{4}^{2-}\) group [16; 17; 18; 37; 38; 39; 40].  
The FT-IR spectrum of the current ZrO\({}_{2}\)-CdWO\({}_{4}\) NC can be verified with the FT-IR spectrum of the previously reported NC based on CdWO\({}_{4}\) prepared using plant extracts (Table 3). The current nanocomposite showed an almost identical FTIR spectrum to the previous one, confirming the validation of the present analysis. The presence of typical peaks corresponding to the plant extracts (_Brassica rapa_ leaf) as well as the nanomaterial (ZrO\({}_{2}\)-CdWO\({}_{4}\)), which is consistent with the previous study, suggest the preparation of the ZrO\({}_{2}\)-CdWO\({}_{4}\) NC that was chemically functionalized with phytochemicals from the plant extract. These functional groups can aid light absorption as sugg  
Figure 2: FT-IR spectra of the ZrO\({}_{2}\)-CdWO\({}_{4}\) NC.
## 2 Results and Discussion
### Headspace and Volatile Oil Composition of P. pavonica  
A total of 17 compounds were found in the headspace of _P. pavonica_. Aliphatic fatty alcohol octan-1-ol was a dominant constituent (37.73%; 38.60%) along with its oxygenated derivative octanal (9.63%; 7.98%). Another major constituent was dimethyl sulfide (DMS; 18.27%; 26.37%). All three compounds were reported for the first time in this alga (only in the headspace). However, C\({}_{8}\)-hydrocarbons were previously found in various marine algae [32; 33], usually (3Z,5E)-octa-1,3,5-triene (fucoserratene), but octan-1-ol was not found previously in such high headspace abundance. On the other hand, octan-1-ol was found in raw water taken during _Synura uvella_ algal blooms after adsorption on Tenax and thermal desorption [34]. DMS, a highly-volatile organosulfur compound, serves as a chemoattractant for phytoplankton, bacteria, zooplankton, fish, and sea birds [35]. Three sulfur-containing compounds (methylethyl disulfide, diethyl disulfide and benzothiazole) were previously identified in toluene extract of _P. pavonica_ that was subjected to 4-h simultaneous distillation-extraction (SDE) [17], but DMS was not present. Neither was it found after direct SDE of this algae [22]. Therefore, in accordance with our previous paper [32], we can use HS-SPME as the appropriate method for monitoring DMS presence. (_E_)-\(\beta\)-farnesene (7.92%; 6.28%) was a major sesquiterpene, followed by \(\beta\)-bisabolene, \(\alpha\)-farnesene and _cis_- and _trans_-calamene (Table 2). Smaller percentages of dictyopterene A and dictyopterene D\({}^{\prime}\) were found. Benzaldehydeyde was found as a minor headspace constituent, and was also identified after SDE of _P. pavonica_ toluene extract [17]. The chemical profiles of _P.__pavonica_ hydrodistillate and headspace are significantly different containing only three common compounds ((_E_)-\(\beta\)-farnesene, pentadecane and disobutyl phthalate), but with different abundance (Table 2). The major oil compounds were higher aliphatic alcohols: (_Z_)-octadec-9-en-1-ol (25.68%), hexadecan-1-ol (17.29%), and (_Z_,_Z_)-octadec-3,13-dien-1-ol (7.55%) along with minor percentages of tetradecan-1-ol, pentadecanal, hexadecan-2-one, pentadecan-1-ol, (_Z_)-hexadec-11-en-1-ol, (_E_)-hexadec-11-en-1-ol, octadecanal, and (_Z_,_Z_)-octadec-9,12-dien-1-ol. Higher aliphatic esters were found as minor constituents such as: methyl 5,8,11,14-eicosatetraenoate, methyl 5,8,11,14,17-eicosapentaenoate and methyl 8,11,14,17-eicosatetraenoate. Diterpene alcohols _trans_-phytol (6.45%) and pachydictol A (6.03%) were among the relevant compounds in the oil with a minor abundance of isopachydictol A. Several sesquiterpenes were present: _trans_-\(\alpha\)-bergamotene,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.
## 3 Results and Discussions
### The Closed-Microchannel Structure
that is capable of producing homogeneous shear stress within the centre of the main channel even with a greater gap spacing micropillar (50 \(\upmu\)m). Additionally, the pressure was also evaluated in this study as another parameter to simulate within the side and main microchannels. As shown in Figure 9d, the pressure in the main microchannel is lower than in the side microchannels for two reasons: first, the width of the main microchannel (350 \(\upmu\)m) is bigger than the side microchannels (250 \(\upmu\)m), and second, the flow rates in the main microchannel are lower than in the side microchannels for the second and third simulation experiments (Figure 8). In fact, based on Equation (6), the width and pressure are inversely proportional, while the pressure and flow rate are directly proportional. Therefore, as the side microchannels have a smaller width and a higher flow rate than the main microchannel, the resulting pressure in the side microchannels will be higher. Moreover, in vivo and in vitro studies have shown that haemodynamic forces (i.e., shear stress, velocity, flow rate, pressure) can significantly influence the gene expression and phenotype of vascular cells that induce functional changes in response to mechanical stress [60]. In addition, when the cells are exposed to microenvironmental signals such as cell-cell interactions, cell-extracellular matrix, and physical forces, this can result in the activation of cell behaviour. For instance, this type of geometry allows the injection of extracellular matrix into the main microchannel so that the cells located in the side microchannels can interact with it. In specially designed microdevices, the essential signals in cellular microenvironments can be controlled precisely compared to macroscale devices, and this can allow more accurate modelling of physical situations for both drug development and fundamental research [61].
## 3 Methodology
### Path Planning Model
#### 3.3.2 Variable Definitions  
\(C\) represents the number of user-recorded walking trajectories;  
\(C^{\prime}\) represents the number of filtered hotspot chains;  
N\({}_{\rm c}\) represents the total number of position information in the c user's walking trajectory, where c = \(1,\cdots,\) C;  
\(M\) is repressed as the judgment standard of the user activity hotspot;  
w\({}_{\rm ci}\) represents the location information recorded by the user in track i, in walking trajectory c;  
Figure 4: Parcel locker selection. (**a**) Regular hotspot selection; (**b**) PLC selection. The blue line indicates the distance between the regular activity hotspot and the recipient point, while the black line represents the distance between the parcel locker and the regular activity hotspot as well as the recipient point.  
\(\text{t}_{\text{ci}}\) represents the time information recorded by the user in article i of the article c, walking track;  
\(L\) represents the spatial distance threshold between points (the threshold is set to 50 m);  
\(\gamma\) is the distance constraint indicating whether a hotspot is a receiving point;  
\(\text{L}(\text{w}_{1},\text{w}_{2})\) represents the spherical distance between position points \(\text{w}_{1}\) and \(\text{w}_{2}\);  
\(\text{T}_{\text{d}}(\text{w}_{1},\text{w}_{2})\) represents the temporal distance between position points \(\text{w}_{1}\) and \(\text{w}_{2}\);  
\(\overline{\text{w}}(\text{c},\text{i},\text{n})\) represents the average position information of the user's c trace from i to N.  
\(\text{(i}+\text{n}-1)\);  
\(\text{X}_{\text{Z}}\) indicates the receiving point location information completed by the user;  
\(R\) represents the number of PLCs;  
\(K\) represents the number of user activity hotspots;  
\(\text{K}_{\text{s}}\) indicates the number of active hotspots after excluding the receive point hotspot;  
\(\text{w}_{\text{r}}\) represents the location information of the r PLC, where r = 1...R;  
\(\text{X}_{\text{ck}}\) represents the location information of the k activity hotspot of the user's article c, walking track, where c = 1...C, k = 1...K;  
\(\text{Y}_{\text{ck}}\) represents the entry time information of the k activity hotspot of the user's article c walking trajectory, where c = 1...C, k = 1...K;  
\(\text{T}_{\text{ck}}\) represents the residence time information of the k activity hotspot of the user's article c walking track, where c = 1...C, k = 1...K;  
\(\text{RT}_{\text{W}}\) represents the ratio of the express weight to the maximum load of the PLC;  
weight;  
\(\text{RT}_{\text{V}}\) represents the ratio of the express volume to the maximum capacity of PLCs;  
\(\text{RT}_{\text{WW}}\) represents the express delivery weight-volume-urgency ratio;  
\(\alpha\) represents the urgency parameter for parcel delivery;  
\(\text{H}_{\text{c}k^{\prime}}\) is the decision variable indicating whether or not an activity hotspot is selected;  
\(\text{H}_{\text{r}}\) is the decision variable indicating whether to choose a PLC.
## 3 Methods
### Gru  
To address the gradient explosion and gradient vanishing problems in RNN, GRU applies the reset gate and update gate to control how the information from the past and current input change the current state simultaneously.  
Figure 2 shows the GRU structure. At time step \(t\), GRU receives the input \(x_{t}\) and updates the hidden state \(h_{t}\). The reset gate \(r_{t}\) takes input \(x_{t}\) and calculates the candidate hidden state \(h_{t}\). Update  
gate \(z_{t}\) chooses information from the current candidate hidden state \(\tilde{h}_{t}\) and the last time step hidden state \(h_{t-1}\) and outputs \(h_{t}\). The update functions are as follows:  
\[z_{t} =\sigma\left(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}\right), \tag{4}\] \[r_{t} =\sigma\left(W_{r}x_{t}+U_{r}h_{t-1}+b_{r}\right),\] (5) \[\tilde{h}_{t} =\tanh\left(W_{h}x_{t}+U_{h}\left(r_{t}\odot h_{t-1}\right)+b_{h }\right),\] (6) \[h_{t} =\left(1-z_{t}\right)\odot h_{t-1}+z_{t}\odot\tilde{h}_{t}. \tag{7}\]  
\(\odot\) indicates element-wise multiplication.
# Preparation of Magnetic Polymers for the Elimination of 3-Isobutyl-2-methoxypyrazine from Wine
## 1 Introduction
Magnetic particles coated with MIPs have been used to extract resveratrol from red wine [25], Sudan dyes from chili powder [27], bisphenol A in milk [28], and vanillin in food samples [29]. In this study, attempts were made to produce synthetic MMIPs for IBMP extraction from wine, with a comparison to magnetic non-imprinted polymers (MNIPs), and to non-magnetic counterparts for what appears to be the first time. In addition, microwave-induced polymerisation versus conventional thermal synthesis was evaluated. Physical characterisation and adsorption analysis were carried out to evaluate the different polymers.
## 2 System Architecture  
This study focuses on an A-HMI whose architecture concept is depicted in Figure 1. It is functionally characterized by the following key features:  
* Multi-modal HMIs, i.e., I/O devices shared by different ADAS and IVIS, such as Liquid Crystal Displays (LCD), Head-Up Displays (HUD), speech I/O, haptic I/O.
* An Information Management System (IMS), the control authority of the A-HMI, i.e., a centralized intelligence that performs information prioritization and scheduling.
* A gateway to connect nomadic devices to the in-vehicle system for sharing data, applications, and I/O devices. Thus, the functionality of the nomadic devices can be used by the in-vehicle system and vice versa.
* A system for real-time monitoring of driver and driving situation to assess information about Driver, Vehicle, and Environment (DVE status recognition and monitoring).  
Scenarios that the A-HMI should address are defined using two parameters [47]: (i) actions; (ii) Driver-Vehicle-Environment (DVE) conditions. The actions represent an event by an application, i.e., component offering a specific functionality to the user (such as navigation, phone, lane departure warning, forward collision warning, etc.), classified into priority classes on the basis of their importance for the driver. In contrast, the DVE conditions are discrete values representing the current state of driver, vehicle, and environment perceived by a DVE state recognition and monitoring system, which give a description regarding the driver's ability and availability to drive the vehicle. It is precisely the perception of the current driving scenario and its impact on the driver which enable the adaptive functions of A-HMI according to the driving conditions. Indeed, the DVE state, together with the applications and their priority classes as well, are considered in the problem-solving process of the IMS control framework to assign priorities to each output request from the applications, to schedule their presentation, and to select modality, channel, and layout for the outputs. The DVE state recognition and monitoring system and the IMS, which are the composing modules of the A-HMI, are detailed in the following.
## 5 Experimental Section
### Impact Factors Analysis
#### 5.3.2 Data Gathering Period  
Another impact factor is the length of data gathering period \(\Upsilon\), which is the period when \(MC\) are within the network. From Figure 9 we could see that the lines are relatively flat, showing that \(\Upsilon\) has small impact on the number of total message transmissions. Larger \(\Upsilon\) denotes larger contact chances between the mobile collector and in-network nodes and hence more message transmissions between them. The number of message transmissions are expected to goes up with \(\Upsilon\). When \(\Upsilon\) increase from 600 to 7200 s, the number of total transmissions goes up from 1.66E+5 to 1.79E+5 for ADG, and from 1.45E+5 to 20.5E+5 for PDA. As the number of messages are mainly composed of in-network node-to-node transmissions, the impact of the increased message transmissions between \(MC\) and in-network nodes is relatively small on the overall messages transmissions.  
Figure 8: Performance of ADG with 2 \(MC\).  
Figure 7: Number of nodes _vs._ data coverage.  
The data coverage in all the schemes goes up with \(\Upsilon\), as illustrated in Figure 10. The PDA, PROPHET, Epidemic achieve their best performance at 78.5%, 66.3%, and 62.2% when the period is 7200 s; and their data coverage is less than 15% when the data gathering is limited and short, e.g., 600 s. These schemes are designed for general purpose routing and data gathering when the data gathering period is unlimited. So \(MC\) might not have enough time to contact with the in-network nodes and gathers the data from them when the data gathering period is small. However, the data coverage goes up relatively sharp from16% to 40% and from 20% to 62% for the PROXY and ADG schemes respectively when \(\Upsilon\) increase from 600 to 1200 s, and then increases to near 71% and 93% respectively when the data gathering period is about 7200 s. Both schemes use proxy nodes as the intermediate storage. However, the proxy nodes at PROXY might still be moving when the \(MC\) is scheduled to collect the data from them, which makes the data transmission from some proxy nodes and \(MC\) unfeasible. Instead, at the ADG scheme the proxy node is scheduled to be visited during its _key stationary duration_, so message transmissions from the  
Figure 10: Data gathering duration (\(\Upsilon\)) _vs._ data coverage.  
Figure 9: Data gathering duration (\(\Upsilon\)) _vs._ total message transmissions.  
proxy node to the \(MC\) is stable and efficient. Given a fixed time slot for each proxy node, longer data gathering period means \(MC\) could visit more proxy nodes and hence gather more data from them. So when \(\Upsilon\) is large, e.g., more than 6000 s, \(MC\) would have enough time to visit each proxy node several times and pick the data up, so the data coverage goes higher. Yet in real network deployment, the data gathering period \(\Upsilon\) is limited because the speedy \(MC\) has a large energy consumption rate, and it should replenish its energy after a trip.
# On the Need for a General REST-Security Framework
## 5 Related Work Analysis
### Mutual Authentication Protocol for HTTP  
The experimental RFC mutual authentication protocol for HTTP [38] is an approach for authenticating requests and responses without sending the user's password in plain-text. To transfer the password in a confidential manner, the client as well as the server each generate a key exchange value. The generated exchange value of the client is sent via a request to server and the generated exchange value of the server is returned to client by a response.  
Based on these client- and server-generated key exchange values, a session secret is calculated. The client as well as the server use this session secret to create a verification value. Included within the authorization request header of the request or the authentication-Info response header, the verification value serves as a parameter for the server and the client respectively for validating the authenticity of the communication partner's received messages.  
The procedure for computing the verification value, the key exchange value and the session secret is not specified in [38]. A description of algorithms for computing the credentials is provided in a separate specification [39]. Here, the key exchange values are randomly generated. The session secret is a SHA-256 or SHA-512 hash calculated from the key exchange values of the client and the server as well as the user's password. Alternatively, the session secret can be calculated via elliptic curve digital signatures, which integrates the key exchange values and the password in the computation process as well. Both verification values are hashes or digital signatures based on the key exchange values and the session secret.  
As the name implies, this approach provides a mutual authentication protocol for clients and servers to verify the authenticity of requests and responses. However, only authenticity can be ensured by this specification. Similar to HOBA and HTTP SCRAM, neither the client nor the server can validate whether other headers or the message body has been manipulated.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Scenarios
#### 6.2.1 NPA Execution Example  
In this subsection, we present the derived topologies after the execution of the Node Placement Algorithm from Section 4. Recall that there are two variations, the Dynamic MobileCC and the Direct MobileCC.  
Figure 5a presents the topology after the sink calls the Dynamic MobileCC algorithm. In this scenario, two mobile nodes are employed, one for each congestion occurrence. In Figure 5b, we present the topology after the sink calls the Direct MobileCC algorithm. In this case, two alternative mobile node paths are created. The first path consists of two mobile nodes and the other one of four mobile nodes. The different number of mobile nodes used for each path is related to the distance of the congested node from the sink. Cumulatively, this algorithm employs six mobile nodes for the creation of two disjoint paths to solve the congestion problem.  
This simple experiment demonstrates that both Dynamic and Direct MobileCC algorithms can solve the problem locally. Both algorithms must employ at least one mobile  
Figure 5: Execution example of the NPA variations. (**a**) Dynamic MobileCC Execution of the Example, (**b**) Direct Path MobileCC Execution of the Example.  
node for each congestion occurrence in the network. This example indicates that Direct MobileCC needs more mobile nodes than Dynamic, which is expected since the former implements a full path of mobile nodes from the congested point to the sink.