esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Performance Metric
### System Model  
As illustrated in Figure 1, we consider a single-cell multi-antenna full-duplex TDD system consisting of a full-duplex BS having \(M\) transmit antennas and \(M\) receive antennas and a set of \(N\) half-duplex MSs with a single antenna each, where \(N\geq 2M\). Since full-duplex operation at the BS is assumed, uplink and downlink data transmission can take place simultaneously at the BS. On the other hand, each half-duplex MS can be supported by either uplink or downlink, but not simultaneously, i.e., \(\mathcal{S}^{(d)}\cap\mathcal{S}^{(u)}=\emptyset\), where \(\mathcal{S}^{(d)}\) and \(\mathcal{S}^{(u)}\) denote the sets of downlink and uplink MSs at a given time, and \(\emptyset\) is the empty set. Moreover, we assume that \(\mathcal{S}^{(d)}\) and \(\mathcal{S}^{(u)}\) have the same cardinality of \(M\)i.e., \(\left|\mathcal{S}^{(d)}\right|=\left|\mathcal{S}^{(u)}\right|=M\). We assume that there is no self-interference due to the full-duplex operation at the BS, i.e., self-interference due to the full-duplex operation at the BS is perfectly suppressed.  
Throughout this paper, the operators \(\mathbb{C}\), \(\mathbb{E}[\cdot]\), \(\Pr\{\cdot\}\), and \((\cdot)^{\dagger}\) indicate the field of complex numbers, the statistical expectation, the probability, and the transpose conjugate, respectively. Unless otherwise stated, all logarithms are assumed to be to the base 2. We use the following asymptotic notation: (i) \(f(x)=O(g(x))\) means that there exist constants \(C\) and \(c\) such that \(f(x)\leq Cg(x)\) for all \(x>c\), (ii) \(f(x)=\Omega(g(x))\) if \(g(x)=O(f(x))\), (iii) \(f(x)=\omega(g(x))\) means that \(\lim_{x\rightarrow\infty}\frac{g(x)}{f(x)}=0\), and (iv) \(f(x)=\Theta(g(x))\) if \(f(x)=O(g(x))\) and \(f(x)=\Omega(g(x))\)[31].
## 5 Common Systems
### Data Acquisition
#### 5.3.2 Beam Synchronous DAQ  
The beam synchronous data acquisition system is a new development centered around the idea of data streaming. Load and code complexity on data sources are reduced by immediately streaming raw data out as soon as it becomes available. For a given pulse, data from all configured channels of a source is collected, serialized and sent out in an atomic message. Along with the data of all channels, this message also contains the unique pulse-id of the machine pulse the data belongs to as well as other metadata. As the pulse-id is needed by all systems providing beam synchronous data simultaneously, all these systems need to be connected to the global SwissFEL real-time timing system.  
Load balancing and fail-over is built into the system by design since it uses the ZeroMQ protocol [202] for data transport. Channels to be streamed out by the data sources can be configured dynamically without the need of restarting or interrupting the data collection.  
To prevent data sources from being overwhelmed by client requests, the only client collecting data directly from sources is the Dispatching Layer. Beside data source isolation, this subsystem, as described in the next section, also provides transparent and synchronized access to all incoming data. This layer can also reduce the incoming data streams, e.g., by reducing the frequency, to meet client requirements and capabilities. As not all clients can cope with the incoming data rates and volumes the system also provides a buffering layer in order to provide data for retrieval at a later point in time, namely DataBuffer and ImageBuffer. Although these are two separate subsystems, data is accessible via a common API.  
The subsystems of the beam synchronous data acquisition systems take care of all beam synchronous data, except the primary experimental station pixel detectors (e.g., JUNGFRAU). This data is collected and processed through a system derived from [203], but will be nevertheless synchronized with all the rest of beam synchronous data.
## 5 Results and Discussion
### Spatial Patterns in the Sub Area  
The SCD and BFAST methods were applied to all time series in the sub-area, and maps of the number of detected changes are shown in Figure 8. On the map generated by SCD, the sub-area was dominated with pixels that did not change from 2000 to 2010. Within the changed areas, most pixels have changed once or twice within the 13 years, which likely corresponds to forest clearing for agricultural activities or urban development. In contrast, the map generated by BFAST has more pixels that changed once or twice than pixels with stable land cover. The southeast parts of both maps showed more changes (_i.e._, about 3-9 times per pixel), which were likely associated with forest clearing for industrial mining (bare soil) in the early 2000s, abandonment later and return of vegetation (_i.e._, grassland, shrubland, _etc._) in the late 2000s or early 2010s. With the moderate (250 m) spatial resolution of MODIS NDVI data, it could be clearly seen that neighboring pixels exhibited spatial continuity in change frequencies and possibly similar change dates as well.  
Figure 7: Examples of detected changes for three 16-day NDVI time series (smoothed) extracted from three MODIS pixels at 250 m resolution in southeast Ohio (tile h11v05). The corresponding land-cover changes are forest \(\rightarrow\) bare soil \(\rightarrow\) grassland (**a**,**b**) and bare soil \(\rightarrow\) grassland (**c**).
## 2 Materials and Methods
### Experiment Setup  
In terms of hardware and software specifications, the experiments were conducted on a Dell G15-5515 laptop with an AMD Ryzen(tm) 7 5800H Mobile (20 MB total cache, 8 cores, 16 threads), with an operating system Windows 11 Home Edition. The available laptop consists of 16 GB RAM with 2 x 8 GB, DDR4, 3200 MHz, and an NVIDIA GeForce RTX(tm) 3060, 6 GB GDDR6, 3 DP card. Regarding the coding process, we employed Python 3.9.0, utilizing TensorFlow 2.10.1 and Keras 2.10. To handle our dataset, the OpenCV library was employed, and for dataset splitting and result computation, scikit-learn was utilized. An investigation was applied through a series of experiments to conclude the suggested architectures. This involved evaluating various architectures for FCM and PSO parameters.  
Commonly employed performance metrics were utilized in this study, like accuracy, loss, sensitivity, specificity, and precision, to assess the effectiveness of the proposed DeepFCM architecture. Accuracy denotes the ratio of correctly classified instances to the total number of instances. Loss indicates the error between predicted and actual values. Sensitivity and specificity indicate the true positives (TP) and true negatives (TN) accordingly. Precision is the ratio of the number of true positives to the total number of positive predictions. These metrics offer a well-rounded assessment of the model's capabilities, addressing both the classification accuracy and its ability to correctly identify CAD-positive and CAD-negative cases [2; 19].  
Regarding ensuring our model's robustness, k-fold cross-validation was performed, where k represents the number of partitions into which the dataset is divided [47]. In our case, we divided the dataset into 10 partitions, of which 9 were utilized as training and 1 as testing. This process was repeated until each partition had been utilized for testing. With the application of k-fold cross-validation, overfitting can be avoided and generalization can be ensured [47]. To reduce redundant training iterations, early stopping was applied. Early stopping is a regularization technique that is utilized in the CNN training process to prevent overfitting and improve generalization error and overall accuracy as well. Early stopping enhances the training process and minimizes computation time. Moreover, it inspects the generalization error that is calculated during the training process and stops the training [48].
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
### Problem Formulation  
In this paper, we propose a novel IRS-equipped WSN system. Here, the novel IRS-equipped WSN can mitigate the issue of low battery lifetime in sensors. Instead of periodically replacing/recharging batteries embedded in the sensors, the IRS minimizes battery usage by relying on an outer power source for transmission while limiting the embedded energy source to local sensing and processing. A use case for this could be environment monitoring covering a large region with many nodes; in such a scenario, placing the sensor nodes is easy (i.e., by flying drones), while reaching the sensors to replace the batteries is an expensive and complex process. Another use case is in the event of limited transmission of economical off-the-shelf sensor nodes, in which case the transmission range could be increased without incurring any increase in the cost of the off-the-shelf sensors. In this work, we investigate the workings of this energy-efficient variant of a typical WSN.  
First, we describe a scheme that caters to a system with blind operation of the IRSs in the sensor nodes. The rate of a BPSK input to a Gaussian channel is illustrated, and the system outage probability is defined for different scheduling schemes. The fairness/performance trade-off for different scheduling schemes in the system is explored via simulations. Finally, multi-sensor diversity in the system is exploited and the effect of increasing the number of ISNs is investigated. Our main contributions can be summarized as follows:  
* We design a scheme for an IRS-equipped WSN with proper channel modelling and system parameterization.
* We analyze the performance of ssuch ISNs in terms of scheduling protocols, system outage probability, and rate analysis.
* We investigate dual IRS gains attained from IRS opportunistic beamforming and IRS information encoding using both continuous phases and practical discrete phases.
* Using simulations, we demonstrate how multi-sensor diversity can be exploited to overcome the blind operation of ISNs.
## 5 Performance Evaluation
### Mobility Patterns
#### 5.4.3 Gauss Markov Model (GMM)  
GMM is a memory-based mobility pattern in which the temporal dependency of the nodes can be defined using a parameter \(\alpha\) (between 0 and 1). Higher values of \(\alpha\) imply higher dependency and less harsh turns and speed changes. This leads to more realistic mobility patterns and configurable randomness.
## 4 Result  
The propagation channel characteristics for the five crossings are presented in Figures 5-9. The measured path loss values from the omnidirectional antenna with the 3 GPP models are shown in Figure 5. The box plots of Figures 6-9 illustrate the distributions of the path loss exponent, Ricean K-factor, RMS delay spread, and the Doppler spread over the measurement distance of 400 m (200 m before and after each crossing) for the omnidirectional and bidirectional antenna configurations. The boxplot shows the distribution of the data including outliers, which are 1.5 IQR away from the top or bottom of the box plot and marked as '+'.  
Figure 4: Antenna beam pattern (**a**) omni h-plane, (**b**) omni e-plane, (**c**) bi h-plane, (**d**) bi e-plane.  
Figure 5: Path loss comparison between 3 GPP path loss models and measured values for crossing #1–5 with an omnidirectional antenna.  
Figure 8: RMS delay spreads for different crossings with (**a**) omnidirectional, (**b**) bidirectional antennas.  
Figure 6: Path loss exponents for different crossings with (**a**) omnidirectional, (**b**) bidirectional antennas.  
Figure 7: K-factors for different crossings with (**a**) omnidirectional, (**b**) bidirectional antennas.
# High-Efficiency Quantum Dot Lasers as Comb Sources for DWDM Applications
## 3 Performance of CPMLLs  
### LIV Performance  
Devices were tested continuous-wave on a temperature-controlled copper heatsink at 20 \({}^{\circ}\)C. An integration sphere was used to measure light output, and voltage was measured in a four-wire configuration. The light-current and single-sided WPE curves for the 60 GHz 3 rd harmonic CPMLLs are shown in Figure 3a,b. The total SA length, as a fraction of the cavity length (2048 \(\upmu\)m), is labeled on the respective curves. The performance of the devices are shown under two conditions; the first is with the SA sections connected in parallel with the gain section (labeled forward-biased), and the second is with the SA sections reverse-biased at 2 V (labeled 2 V). The forward-biased performance mimics the expected performance of Fabry-Perot lasers of the same design (i.e., lasers with no SA section). There was no significant difference in the light output or voltage between the three devices. When the SA was reverse-biased, the decrease in output power due to the SA was clear. The small penalty in efficiency imposed by the SA created equal frequency spacing between comb lines. The decrease in output power as a percentage of forward-biased power is shown in Figure 3c. When considering devices with shorter SA sections, which will later be shown to be optimal for comb generation, the decrease in output power and WPE is only in the 10-20% range.
# Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis
## 3 Materials and Methods
### Algorithm Description
The coordinates and integration domain transformation in the quadrature are then introduced using the Jacobian matrix, which contains the partial derivatives of the interpolation functions \(N_{i}\) that are differentiated with respect to the parent system variables \((\xi,\eta)\)[76]. \[ \begin{split} I&=\int_{\Omega}\bar{H}_{i}(\mathbf{x})p _{n}(\mathbf{x})\,\mathrm{d}\bar{\Omega}=\int_{\Omega}\bar{H}_{i}(\xi)p_{n}(\xi) \,|\,\mathbf{J}|\mathrm{d}\Omega=\\ &=\sum_{j=1}^{gp}w_{j}\bar{H}_{i}(\xi_{j},\eta_{j})p_{n}(\xi_{j}, \eta_{j})\,|\,\mathbf{J}(\xi_{j},\eta_{j})|\end{split}  \tag{25}\]  
where \(|\,\mathbf{J}|\) is the Jacobian matrix determinant. The integral in Equation (22) is calculated in (25), applying the standard scheme of the Gauss-Legendre numerical quadrature [77]. In (25), \(gp\) stands for the number of Gauss-Legendre quadrature points and \(w_{j}\) stands for each point weight. It needs to be emphasised that the proposed algorithm is intended for integration over the entire domain \(\bar{\Omega}\) yielding the result of the integral over the subdomains \(\bar{\Omega}_{A}\), \(\bar{\Omega}_{B}\), \(\bar{\Omega}_{\mathrm{C}}\) and \(\bar{\Omega}_{D}\). Thus, the discontinuities equations have to be accurately defined, so that the unit vectors \(\mathbf{n}_{i}{}^{+}\) point inwards. Additionally, it has to be noted that the discontinuities do not necessarily have to intersect \(\bar{\Omega}\) or one another (the proposed algorithm can handle all possible scenarios). The composition and the degree of the polynomials that can be precisely integrated using the suggested technique rely on specific requirements necessary to find the equivalent polynomial, as can be deduced from Refs. [13; 19].
## 5 Applications and Services
### Services
#### 5.1.2 Internet of Vehicles (IoVs)  
Machine-type communications (MTCs) aim to establish a wireless network with low latency, high reliability, energy efficiency, and scalability to support the upcoming IoT applications [99]. The most promising application of UAV-enabled, RIS-assisted wireless communication is for intelligent vehicles and IoVs [136; 140]. Enabling reliable V2X connectivity with low-latency communication is a big step toward realizing autonomous driving in smart cities [141; 32; 142].  
The multi-UAV trajectory design has been studied for providing a quick wireless connection for IoVs [100]. The proposed techniques use SCA to handle nonconvexity and decompose the problem into three communication scheduling, power allocation and UAV trajectory optimization sub-problems. A conformal RIS array is mounted on ground vehicles as a relay [107]. To handle dynamic beamforming and complexities associated with adaptively controlling intelligent reflecting surfaces, DRL techniques have been used.
## 5 Proposed Source Authentication Schemes
### Bloom Filter Based Source Authentication (BFBSA)  
BFBSA takes advantage of a Bloom filter to verify the authenticity of packets. Like SPA, the BS splits a new code image into pages which are further divided into fixed-size packets called basic packets. It is important to note that basic packets in BFBSA is the same as packets while basic packets in SPA consist of packets and a hash value on the next basic packet. The BS then makes a \(m\)-bit length Bloom filter by applying \(k\) different hash functions to all basic packets as follows:  
\[BF[H_{n}(Pkt_{i,j})]\ =\ \left\{\begin{array}{ll}1,&n\in[1,k],\ i\in[1,x],\ j\in[1,y]\\ 0,&\text{otherwise}\end{array}\right. \tag{7}\]  
where \(BF[]\) is a \(m\)-bit array indicating a Bloom filter and \(H_{n}\) is a hash function which maps packets into an integer with a range of 0 to \(m\)-1.  
When the Bloom filter is built, the BS broadcasts the entire Bloom filter to all sensor nodes together with a hash value on the first basic page (\(h_{1}\)) after signing with a BS's private key. It is important to note that \(h_{1}\) is used to verify authenticity and integrity of the page for defending against malicious packets due to false positive property of the Bloom filter. After each sensor node verifies a digital signature of the Bloom filter with a BS's public key, it stores the Bloom filter to authenticate the subsequent packets.  
The BS now sends a new code image to the neighbor nodes with variable-size packets, depending on the link quality as follows:  
\[BS\rightarrow*:\ [header||Pkt_{i,j}||Pkt_{i,j+1}||\cdots||Pkt_{i,j+n}] \tag{8}\]  
where \(n\) depends on the link quality, and the last packet can be fragmented. It is important to note that unlike previous schemes, there is no overhead for each packet such as MACs and hash values. Upon receiving a packet, the receiver node splits it into basic packets, each of which is then verified using the Bloom filter with \(k\) different hash functions. The basic packet is considered authentic if it satisfies the following equation:  
\[BF[H_{n}(Pkt_{i,j})]\ ==\ 1,\qquad\forall n\in[1,k] \tag{9}\]  
If it fails to pass the test, it definitely is not a valid packet. However, the authenticated packets might be forged packets due to false positive. To overcome this problem, we must adjust \(k\) and \(m\) appropriately which will be investigated in detail in Section 6. In addition, we add a hash value on the next page to the end of the last packet in each page. After receiving all packets in the page, the receiving node can verify the authenticity of the whole page with the hash value. This entire process is illustrated in Figure 7.  
This scheme incurs storage and communication overhead for the Bloom filter but has no communication overhead in each packet. In addition, this scheme can support fully variable-size packets.
# Apparatus and Method of Defect Detection for Resin Films
## 2 Materials and Methods
### 2K Resolution Camera
#### 2.2.1 System Overview  
Figure 9 depicts the sketch map of the 2K-resolution camera. The key component of the system is the VS-LDA series lens from vs. technology corporation [35], which has a 2K resolution (\(5472\times 3648\) pixels) and has been fixed at a shooting distance of 12.5 cm on our machine. This series lens is best known for its low distortion, even when using extension tubes and is designed to support a wide range of magnification, wide angle (_WD_), and depth of field. The resolution of the camera can reach 762 dpi (30 pixels/mm), which satisfies more than 25-\(\upmu\)m defect detection requirement. A light regulator was used to control light sources to ensure the same light condition was used throughout the study. This minimized the influence of light on the data. A dust removal fan was used to filter dust on plastic resin films.
## 4 Case Study
### Application of the Models
The resulting HIs are 0.04 and 0.45, as shown in Figure 16c,d, for HD and TB, respectively. Comparing the two HIs, in view of the maintenance strategy, Figure 16c in which the HD is replaced is advised, since it restores the system performance more fully. Figure 16b shows the contribution ratio of the HD, TB, and their interaction effect on the system health degradation, in which the areas by the HD and TB are given by the restored HI by replacing each component. For the HD and TB, the HIs are restored from 0.65 to 0.04 and 0.45, hence, the restored HIs are 0.61 and 0.20, respectively. Note, however, that the sum of these two is 0.49, which does not agree with the HI of the current system due to  
Figure 16: (**a**) Current state of health of components and system; (**b**) contribution of system performance degradation; (**c**) state of health after replacement of HD; and (**d**) state of health after replacement of TB. Figure 15: Error histogram for HI by ANN regression (**a**) for HD with RMSE 0.0911 and (**b**) for TB with RMSE 0.1955.  
the interaction effect, which is 0.16. By normalization, these values are transferred into the ratio, as shown in the figure. Based on this information, the equipment engineer can diagnose the current health of the system and make appropriate decisions in terms of which component to replace early, before failure. Although this is illustrated via virtual data, the procedure is the same when the signals are acquired in the real operation of a new robot: At first, using the motor control signal, extract five significant feature values. Second, apply the five feature values to the ANN model to estimate the fault severities of the two components, the HD and the TB. Third, apply the two severity values to the GPR model to estimate the current system performance, which will look like the performance illustrated in Figure 16a. Finally, perform the what-if analysis as in Figure 16c,d by restoring the health of either component and make maintenance decisions based on the results.
## 3 Results and Discussion
### Characterization of the Materials
Figure 2: SEM (**a**,**b**) and EDS (**c**) of MIBA. \[\text{Ca(OH)}_{2}+2\text{HCl}\rightarrow\text{CaCl}_{2}+2\text{H}_{2}\text{O} \tag{2}\]  
Thus, through Equation (3), it is possible to calculate the mg Ca(OH)\({}_{2}\)/g of by-product [26]. \[\frac{\text{mg Ca(OH)}_{2}}{\text{g by }-\text{product}}=\frac{28\cdot(\text{v}_{3}- \text{v}_{2})\cdot\text{F}_{\text{c}}}{\text{m}_{2}}\times 1.32 \tag{3}\]  
where m\({}_{2}\) is the mass of the pozzolanic material expressed in grams, v\({}_{2}\), the volume of HCl consumed by the sample in mL; v\({}_{3}\), the volume of HCl consumed by the blank in ml; Fc, the correction factor for a 0.1 M solution (ratio between the theoretical and experimental volumes of consumed HCl); and 1.32 is the molecular relationship between Ca(OH)\({}_{2}\)/CaO. Through the Chapelle test, values of 566 mg and 445 mg of Ca(OH)\({}_{2}\) were obtained per g of residue for FA and MIBA, respectively, which are higher than the minimum limit of 436 mg Ca(OH)\({}_{2}\)/g of addition for the classification of a mineral addition as a pozzolanic material. According to these results, both by-products can be considered as pozzolanic.