esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Experimental Scenario  
The first data acquisition was performed at the University College London Hospital (UCLH) Proton Centre, which uses a Varian ProBeam gantry to produce the proton beam. For each proton energy, there is an associated cyclotron current \(l_{c}\) that is defined by the system. However, the nozzle proton current \(I_{out}\) that will flow to the patient's body is only a fraction of the cyclotron current:  
\[I_{out}=\alpha l_{c}, \tag{1}\]  
where \(\alpha\) is an empirical value that will be called the beam factor, which depends on the energy and the gantry angle. These values are nominal conversion factors provided by the proton beamline vendors. Figure 2 shows the behaviour of the proton flux at UCLH for every energy that is relevant to this study, bearing in mind that each proton beam exhibits an intrinsically different primary flux due to the current settings. This is expected to have an effect on the detector flux. The detector is set with la ow gain (to be able to record energies up to 700 keV), a bias voltage of \(-\)1000 V and a frame rate \(f=1.6\) kHz. Within the time of a single acquisition frame, the HEXITEC detector can output the location (given by the pixel number in x and y) and energy deposition (given in keV) of every hit. It is additionally important to report that background measurements were taken at the beginning of the experimental data acquisition, where an average of 0.84 \(\pm\) 0.03 counts per acquisition frame was evidenced. Given that the background signal is not significant, no background correction was performed.
###### Abstract  
In response to the challenge of existing wireless communication anti-jamming methods in effectively handling unknown jamming, this paper proposes a multi-parameter control anti-jamming algorithm for wireless communication systems based on the Linear-Quadratic Regulator (LQR). First, the proposed algorithm models the wireless communication system as a linear switched system based on the modulation and coding scheme. Subsequently, a feedback controller design method based on the LQR is introduced. By utilizing the multiple Lyapunov function method combined with linear matrix inequalities, sufficient criteria for the asymptotic stability of the system under unknown jamming conditions are provided. Finally, theoretical analysis and simulation results indicate that the proposed algorithm can rapidly adjust modulation and coding schemes as well as transmission power in complex jamming environments, thereby maintaining bit error rate (BER) stability and enhancing the reliability of the communication system.  
wireless communication; switched system; anti-jamming; linear-quadratic regulator (LQR); multi-Lyapunov function Article
# A Literature Survey on Electrical-Current-Assisted Friction Stir Welding
## 6 EA-FSW of Dissimilar Joints
### EA-FSW of Aluminum to Magnesium  
The aluminum-magnesium joint is also an exciting joint recently considered a reinforced structure in aero structure, automobile, and railway components. The research on FSW of aluminum alloys and magnesium alloys has recently increased. Combining these two alloys leads to the formation of a strong structure. The only research on EA-FSW of aluminum alloy and magnesium was carried out by [PERSON] et al. [40]. They used different electric currents, tool rotational velocities, and traverse velocities of the tool. They indicated that electrical current assistance helps raw materials' fluid-ability and removes the possible defects that may form in the FSW sample. In this case, they stated that the root void forms in the FSW joint, and this root is removed with the increased electrical current. With the increasing electrical current of more than 300 A, minor defects form at the interface of the stir zone and aluminum alloy. Figure 7 presented the cross-section view of samples that welded 0 A, 300 A, and 500 A. The dashed circles show the formed voids in the stir zone. As can be seen, a significant void formed at the root of the joint with 0A electrical current. The size of voids decreased in the 300 A electrical current joint, but they formed in the root of the stir zone. When increasing the electrical current to 500 A, the voids' size significantly decreases and forms at the interface aluminum alloy and stir zone. On the other hand, the formation of IMC at the interface of the base metal would harm the mechanical properties. They stated that in the FSW case, various particles and bands are formed, and when applying the electrical current, the IMC thickness decreased. The IMC in the FSW case had a cloud shape, and in the 300 A EA-FSW case, the IMC shape at the interface of base metals changed to a stream shape. In the 500 A EA-FSW case, the IMC numbers decreased, and sandwich structures formed at the interface. The sandwich structure comprised thin Al-IMC-Mg structures.
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 3 Execution Models for Multicore RTOS
### Implementation of Execution Models
#### 3.2.1 Tdma  
Partition scheduling in VxWorks 653, does not allow the partition start time to be specified. That is, the partitions of each core are executed in parallel simultaneously. For TDMA model, partitions on each core should not be run concurrently. To address this problem, we employed a pseudo-partition as shown in Figure 7 to control the execution order.  
A pseudo-partition is a partition with a single process that executes \"while (1) \(\|\)\". Based on the predefined partition execution time for each core this pseudo-partition allows only a single core at a time access to the memory. Given that ARINC 653 defines the partitions' execution period before runtime, the pseudo-partition's execution period can be set according to the scheduling order. We established a pseudo-partition and heuristically confirmed that the executions on each core did not overlap. This implementation required additional memory space for the six pseudo-partitions to implement TDMA model on a quad-core processor. We used pseudo-partition to control the order of execution between partitions. However, it has the potential to run non-memory jobs instead of pseudo-partitions.  
Figure 6: The architecture of VxWorks 653.
## 2 Comprehensive Attribute Evaluation of Nodes
### Acquired Resource Availability of Nodes  
In opportunistic networks, a node's resources are fixed and non-renewable. In routing, if the node currently carrying a message cannot reach the destination node within the communication range and there is no available neighboring node to relay it, then certain nodes with high priority will frequently perform data transmission, resulting in the early exhaustion of resources, which reduces the lifetime of the network and leads to the phenomenon of invalid routing [12]. Alternatively, when a node has insufficient available resources, it will not be able to continue to relay the message, and the source message that it processed will also be lost. Therefore, resource metrics should be involved as a key factor in the forwarding decision: the richer the node's acquired resource availability, the higher its participation in delivering messages [5]. In this paper, the acquired resource availability of a node is defined as the device capacity (DC), which is expressed as a percentage of the node's available buffer, as shown in Equation (2).  
\[\text{DC}=\frac{B_{r}(t)}{B_{init}}, \tag{2}\]  
where \(B_{r}\) is the remaining buffer size of the node and \(B_{init}\) is the initial buffer size of the node.  
Utilizing DC can effectively evaluate the node's ability to forward messages, use the node's limited resources more rationally, and reduce the loss rate of message transmission.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## Appendix A Doppler-Delay Bartlett Spectrum  
Conventionally, the (empirical) Bartlett spectrum is defined as [46]  
\[\hat{P}(\theta)=\frac{\mathbf{a}(\theta)^{\mathrm{H}}\hat{\mathbf{R}}\mathbf{a}(\theta)}{ \mathbf{a}^{\mathrm{H}}(\theta)\mathbf{a}(\theta)}\,, \tag{1}\]where \(\mathbf{a}(\theta)\) is an array response parameterized on \(\theta\) (often the angle of arrival of a spatial array response), and  
\[\hat{\mathbf{R}}=\frac{1}{N_{x}}\sum_{t=1}^{N_{x}}\mathbf{x}(t)\mathbf{x}^{\mathrm{H}}(t) \tag{10}\]  
is the sample covariance matrix of \(N_{x}\) received signal vectors \(\mathbf{x}\).  
For multiple parameter estimation, Equation (11) can be used with a stacked vector of parameterized array responses. In the case of our Doppler-delay Bartlett beamformer, we thus choose \(\mathbf{a}:=\mathrm{vec}\left(\mathbf{b}(\tau_{\mathrm{H}})\,\mathbf{c}^{\mathrm{T}}(\
u_{ \mathrm{t}})\right)\in\mathbb{C}^{(N_{\mathrm{t}}N_{x}\times 1)}\) and likewise, we stack the received signal matrix into a vector \(\mathbf{x}:=\mathrm{vec}\left(\mathbf{H}_{\bar{n}}\right)\in\mathbb{C}^{(N_{\mathrm{t} }N_{x}\times 1)}\), where we use only \(N_{x}=1\) of such vectors to compute Equation (10). Noting that \(\mathbf{a}^{\mathrm{H}}\mathbf{a}=\|\mathbf{a}\|^{2}\triangleq N_{\mathrm{H}}N_{\
u}\), we formulate the Doppler-delay Bartlett spectrum for a single antenna \(h\) as follows:  
\[\hat{P}(\tau_{\mathrm{H}},\
u_{\mathrm{t}}) =\frac{1}{N_{\mathrm{H}}N_{\
u}}\,\mathrm{vec}\left(\mathbf{b}\mathbf{c} ^{\mathrm{T}}\right)^{\mathrm{H}}\mathrm{vec}\left(\mathbf{H}_{\bar{n}}\right) \mathrm{vec}\left(\mathbf{H}_{\bar{n}}\right)^{\mathrm{H}}\mathrm{vec}\left(\mathbf{ b}\mathbf{c}^{\mathrm{T}}\right)\] \[=\frac{1}{N_{\mathrm{H}}N_{\
u}}\left(\mathbf{c}\otimes\mathbf{b}\right)^ {\mathrm{H}}\mathrm{vec}\left(\mathbf{H}_{\bar{n}}\right)\mathrm{vec}\left(\mathbf{H} _{\bar{n}}\right)^{\mathrm{H}}\left(\mathbf{c}\otimes\mathbf{b}\right)\] \[=\frac{1}{N_{\mathrm{H}}N_{\
u}}\left(\mathbf{b}^{\mathrm{H}}\mathbf{H}_{ \bar{n}}\mathbf{c}^{*}\right)\left(\mathbf{b}^{\mathrm{H}}\mathbf{H}_{\bar{n}}\mathbf{c}^{*} \right)^{*} \tag{11}\] \[=\frac{\left|\mathbf{b}^{\mathrm{H}}\mathbf{H}_{\bar{n}}\mathbf{c}^{*}\right|^ {2}}{N_{\mathrm{H}}N_{\
u}}\,, \tag{12}\]  
where we use the identity \(\mathrm{vec}(\mathbf{ABC})=\left(\mathbf{C}^{\mathrm{T}}\otimes\mathbf{A}\right)\mathrm{vec }(\mathbf{B})\) in (11) with \(\otimes\) denoting the Kronecker product.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 3 Solar Site Evaluation Using GISs
### Weighted Sum  
These methods, based on the Boolean overlay, can elucidate suitable areas for solar power plant installation, but are limited in that they cannot prioritize suitable areas. To quantify suitability, various MCDA approaches have been developed, and the results have been presented mostly as suitability indices.  
The simplest way to quantify the suitability indices for a solar power plant installation is to normalize and add each factor linearly. At the method, the same or a very simple weight is given. To produce the suitability indices, scaled solar radiation maps can be multiplied times a land capability map or the environmental risk map [31]. There are studies that have analyzed solar power plant suitability indices for various regions using a similar method. The geographical factors such as slope, land use, urban extent, population distribution, and proximity to the power grid, as well as solar  
Figure 2: A representative flow chart of GIS-based MCDA methods for solar site evaluation. (Circle: goals, rectangle: GIS layer, and parallelogram: method).  
radiation are combined to generate a suitability map [32; 33]. In addition, by considering not only several spatial factors, but also social acceptance data collected through surveys regarding the potential public resistance to development, solar energy suitability can be assessed [36]. However, these studies are logically limited in determining scaling and weighting criteria.
## 2 Data Analysis and Features Extraction
### Health Features Correlation Analysis and Processing  
The correlation between HFs and capacity is rigorously quantified using Pearson (\(\rho\)) and Spearman (\(\gamma\)) correlation coefficients to assess their relationship with SOH. The calculation formulas are as follows:  
\[\rho=\frac{\sum_{i=1}^{n}(X_{\mathrm{i-SOH}}-X_{\mathrm{SOH}}^{*})(Y_{\mathrm{i- SOH}}-Y_{\mathrm{SOH}}^{*})}{\sqrt{\sum_{i=1}^{n}(X_{\mathrm{i-SOH}}-X_{\mathrm{SOH}}^{*})^ {2}\sqrt{\sum_{i=1}^{n}(Y_{\mathrm{i-SOH}}-Y_{\mathrm{SOH}}^{*})^{2}}}} \tag{6}\]  
where n is the number of samples; \(X_{\mathrm{SOH}}^{*}\) and \(Y_{\mathrm{SOH}}^{*}\) represents the average value of the samples for the two different SOH groups, respectively, and \(X_{\mathrm{i-SOH}}\) and \(Y_{\mathrm{i-SOH}}\) represents the individual samples. \[\gamma=\frac{\frac{1}{n}\!\sum_{i=1}^{n}\left[R(X_{\mathrm{i-SOH}})-R(X_{ \mathrm{SOH}}^{*})\right]\!\left[R(Y_{\mathrm{i-SOH}})-R(Y_{\mathrm{SOH}}^{*} )\right]}{\sqrt{\frac{1}{n}\!\sum_{i=1}^{n}\left[R(X_{\mathrm{i-SOH}})-R(X_{ \mathrm{SOH}}^{*})\right]^{2}}\sqrt{\frac{1}{n}\!\sum_{i=1}^{n}\left[R(Y_{ \mathrm{i-SOH}})-R(Y_{\mathrm{SOH}}^{*})\right]^{2}}} \tag{7}\]  
where n is the number of samples; \(R(X_{\mathrm{i-SOH}})\) and \(R(Y_{\mathrm{i-SOH}})\) represents the rank of the samples for the two different SOH groups, respectively; \(R(X_{\mathrm{SOH}}^{*})\) and \(R(Y_{\mathrm{SOH}}^{*})\) represents the rank of the average value of the samples for the two different SOH groups. By analyzing the correlation between the SOH of four batteries, the correlation matrix shown in Figure 4 is obtained. In Figure 4, higher correlation coefficients are represented by darker colors. The results indicate that the Pearson and Spearman correlation coefficients between the CS2_38 battery and the others are 0.96, 0.96, 0.99, and 0.96, 0.98, 0.99, respectively, as shown in Figure 4. Due to the higher correlation data for the CS2_38 battery and the availability of more cycle data, the CS2_38 battery is selected as the representative sample for further data analysis and model experimentation in this study [32]. Due to the varying trends of each HFs, it is challenging to directly determine their correlation with capacity. Therefore, the HFs were normalized, and the correlation coefficients between each HF and SOH are presented in Table 1, with the results shown in Figure 5. It can be observed that HF1-HF6 exhibits a clear correlation with the battery's HFs, while it is difficult to ascertain whether HF7 and HF8 are correlated with the battery's HFs. Figure 3: Second-order curve of battery voltage versus time. To ensure that the extracted HFs are applicable to different batteries and operating conditions, the HFs with Pearson and Spearman correlation coefficients exceeding 0.7 were chosen for inclusion in this study. These features are HF1, HF2, HF3, HF4, HF5, and HF6, as shown in Figure 6, which presents the heatmap of the correlation between the six HFs and the SOH of the four batteries [32].
## 2 UAM Vehicle and Airspace Environment Analysis  
### Characteristics Analysis by UAM Vehicle Type  
UAM vehicles are classified into various types according to their operational purposes and characteristics. Most of them are being developed as vertical takeoff and landing (VTOL) vehicles that can be operated in urban environments. In addition, UAM vehicles are being developed in various configurations, and for this reason, their flight performances and procedures vary. For example, multicopter-type vehicles do not need transition modes. In addition, compared to winged-type aircraft, the flight speed is relatively slow and the turn rate is high. Conversely, for winged-type VTOL aircraft, transitions are required to switch from vertical takeoff and landing modes to cruise mode. At the same time, compared to multicopter vehicles, the flight speed is relatively high and the turn rate is low [3]. Table 1 shows the the classification and definition of vehicles according to their configurations.  
Wingless-type vehicles are classified as multicopter types. These vehicles do not have fixed wings and are equipped with multiple propulsion units (motors and propellers). Winged-type vehicles with fixed wings are classified as the lift-thrust type and vectored thrust type. First, the lift-thrust-type vehicle is one in which the propulsion part for vertical takeoff and landing and the propulsion part for cruise mode operate independently. Due to its simple mechanical structure, it is lighter and cheaper than a vectored thrust-type vehicle. However, due to the propeller used for vertical takeoff and landing, it receives  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Types**} & \multicolumn{2}{c}{**Features**} & \multicolumn{1}{c}{**Configurations**} \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Wingless Type \\ \end{tabular} } & \multirow{3}{*}{Multicopter} & \(\bullet\) & Equipped with multiple motors. \\  & & \(\bullet\) & Distributes the thrust to fly. \\  & & \(\bullet\) & Need to deploy a parachute in case \\  & & & of an emergency landing. \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Winged \\ Type \\ \end{tabular} } & \multirow{6}{*}{Lift-Thrust Type} & \(\bullet\) & Lift and thrust propulsion units \\  & & \(\bullet\) & operate independently. \\  & & \(\bullet\) & Transition phases are required \\  & & \(\bullet\) & during takeoff and landing. \\  & & & Need space for forced landing in \\  & & & case of a critical emergency. \\ \hline \hline \end{tabular}
\end{table}
Table 1: UAM vehicle types [4].  
Figure 1: FAA standard holding pattern [2].  
more drag. The vectored thrust-type vehicle includes tilt-prop and tilt-wing-type vehicles. It has complicated mechanical structures, making it the heaviest and most expensive, but it has the best flight performance because it is the fastest and can cover the longest distances [4].  
Considering the holding of the vehicle, the wingless-type vehicle has good turning maneuverability due to its low speed and good turn rate. However, the winged-type vehicle has a high speed and a poor turn rate, so the turning maneuverability is not good.
## 3 Methods
### Processing Steps for \(T_{b}\) Data
\(M\) and \(C\) can be expressed as follows:  
\[T_{b}=(1-w)T_{b,l}+wT_{b,w} \tag{1}\]  
\[M=T_{b,m}=T_{m}((1-w)\varepsilon_{l}+w\varepsilon_{w}) \tag{2}\]  
\[C=T_{b,c}=T_{b,l}-T_{c}\varepsilon_{l} \tag{3}\]  
where \(w\) stands for the surface water fraction of the pixel; \(T_{b,l}\) is the \(T_{b}\) of land; \(T_{b,w}\) is the \(T_{b}\) of water; \(T_{b,m}\) is \(M\); \(T_{m}\) is the physical temperature of the pixel \(M\); \(\varepsilon_{l}\) is the emissivity of the land part of the pixel \(M\); \(T_{b,c}\) is \(C\); \(T_{c}\) is the physical temperature of the pixel \(C\); \(\varepsilon_{w}\) is the emissivity of water. For pixels \(M\) and \(C\) at a certain region, an assumption is made that the physical temperatures of them are the same and the emissivity of the land in both pixels can be considered as equal. Therefore, the ratio of \(M\) and \(C\) can be expressed as:  
\[T_{m}=T_{c} \tag{4}\]  
Figure 2: Methodological workflow to assess hydrological drought based on passive microwave remote sensing. \[\epsilon_{l,m}\approx\epsilon_{l,c}\approx\epsilon_{l} \tag{5}\]  
\[\frac{M}{C}=\frac{T_{b,m}}{T_{b,c}}=\frac{T_{m}((1-w)\epsilon_{l}+we_{w})}{T_{c} \epsilon_{l}}\approx 1-w+w\frac{\epsilon_{w}}{\epsilon_{l}}=f(w) \tag{6}\]  
Previous studies have shown that \(M/C\) reflects the variability of the water fraction in the pixel \(M\) and can be used as a monitoring signal for runoff variability. Since the emissivity of water bodies tends to be around 0.5, while the emission rate of land (e.g., rock minerals) is between 0.75 and 0.95 [49], the ratio \(M/C\) is taken in the range of 0 to 1. Moreover, the larger the water fraction \(w\), the smaller the value of \(M/C\) and correspondingly the larger the runoff of the river within the pixel \(M\).
# Strength Enhancement in Fused Filament Fabrication via the Isotropy Toolpath
## 5 Conclusions and Discussion  
In this paper, an in-plane isotropy toolpath is developed for the extrusion-based FFF process. The path is designed to minimize longitudinal filament accumulation, thus reducing the in-plane anisotropy that unbalances the strength in the XY plane. Tensile tests with varied layer thicknesses confirm the increase of the tensile strength. This result indicates that the rotational angle can optimize the tensile strength using a traditional linear toolpath, while the contour toolpath can achieve the maximum tensile strength for the build. Compared with those specimens with traditional toolpath strategies, the tensile strength can be improved by at least 20%. Furthermore, the proposed toolpath behaves with a stronger mechanical strength and with a higher modulus; the better ductile property also indicates the specimens with the proposed toolpath have lower voids fraction ratios.  
This paper presents a toolpath that minimizes the anisotropy material distribution in the XY plane and can improve the mechanical strength of the build by randomized the material distribution in the planar layer while maintaining the continuous deposition manner. The effectiveness of the proposed fabrication strategy is shown by analyzing the tensile strength of different toolpaths under the same printing condition. However, the layer-by-layer material deposition manner still introduces the bonding between the layers, which may cause anisotropy material property in the 3D space. Therefore, the toolpath strategy in the 3D space that can minimize the bonding effects needs to be discussed in the future to create a part with isotropic material property throughout the whole geometry.  
Figure 11: Microscope of the fracture of specimen 6 (**left**) & 10 (**right**).  
In addition, the impact from the process parameters, such as printing speed and layer thickness, will be considered in discussing the isotropy toolpath in the future.  
Conceptualization, X.X. and B.-M.R.; methodology, X.X.; software, X.X.; validation, X.X., and B.-M.R.; formal analysis, X.X., B.-M.R.; investigation, X.X., F.Z.; resources, X.X.; data curation, B.-M.R.; writing--original draft preparation, X.X., B.-M.R.; writing--review and editing, X.X., B.-M.R., F.Z.; visualization, X.X., B.-M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.