esa-sceva/satcom-chunk-collection huggingface dataset:
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 3 Survey of Signal Processing Units based on FBGs
### Amplitude and Phase Optical Filters  
The straightforward application of FBGs is that of amplitude and/or phase optical filters. A very popular application of FBGs as an amplitude filter is that of add-and-drop optical multiplexers for DWDM systems [44]. For a finer selection/suppression of channels, the leading and trail edges of the grating spectral response should be as sharp as possible. This has led researchers to seek ways to efficiently suppress the typical lobes that appear in the spectral response of uniformly apodized FBGs (i.e., FBG with a rectangular-like refractive index envelope) [6]. Several proposals were reported based on analysis algorithms, based on particular apodization profiles such as Gaussian, hyperbolic  
Figure 5: Diagram for the principle of operation of an FBG-based optical signal processor in both time and frequency domains. \(e_{i}(t)\) and \(E_{i}(f)\), with \(i=1,2\), stand for optical fields in the time and frequency domain, respectively.  
tangent, etc. [45]. Later, by means of synthesis algorithms, optical filters with rectangular-like amplitude spectral response and linear phase response (i.e., with no dispersion), were attained, which represent an optimized filter spectral response for add-and-drop multiplexers in ultra-dense DWDM systems [40; 46; 47].  
As phase filters, linearly-chirped FBGs have been widely exploited for dispersion compensation. A linear variation of the grating period along its length translates into a linear group delay [48]. Nowadays, commercially available linearly-chirped FBGs modules are implemented to compensate for dispersion along several hundreds of km of G.652 fiber (i.e., conventional single-mode fiber) with very small form factor (e.g., Figure 6 shows the measured reflection spectral characteristic for a commercial linearly-chirped FBG).  
Finally, linearly-chirped FBGs have been also proposed as a real-time Fourier transformer [49], following the time-space duality [50]. Using the temporal analog of spatial Fraunhofer diffraction, the linearly-chirped FBG act as dispersive media in reflection mode, providing at its output a signal, \(y(t)\), proportional to the Fourier transform of the input signal envelope, \(x(t)\), at the angular frequency \(\omega=t/\dot{\Phi}\):  
\[y(t)\propto\left\{\Im[x(t)]\right\}_{\omega=t/\dot{\Phi}} \tag{41}\]  
where \(\ddot{\Phi}\)(s\({}^{2}\)) is the dispersion coefficient for the linearly-chirped FBG (i.e., the linear group delay as a function of the angular frequency). The FBG-based real-time Fourier transformer constitutes a key component for creating time-domain equivalents of well-known spatial optical signal processing systems [51].
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Symbolic Representation of Transition Systems
#### 2.11.3. Symbolic Representation of the Labeling Function  
In order to symbolically represent the labeling function \(L\) and the set of propositional variables \(AP\), first, we need to define the reverse labeling function \(L^{-1}\), mapping each propositional variable \(a\in AP\) to the set of states in which \(a\) holds.  
**Definition 72** (Reverse Labeling).: _The reverse labeling function of a transition system \(\mathcal{TS}\) is the function \(L^{-1}:AP\to 2^{S}\) such that, for each \(a\in AP\),_  
\[L^{-1}(a)=\{s\in S\mid a\in L(s)\} \tag{30}\]  
Each propositional variable \(a\in AP\) is mapped to a set of states by \(L^{-1}(a)\subseteq S\). For each propositional variable \(a\), the set of states \(L^{-1}(a)\) can be represented as its characteristic formula \(\xi_{L^{-1}(a)}\) over \(V\). The labeling function \(L\) is therefore symbolically represented as a set of characteristic formulas \(\xi_{L^{-1}(a)}\) over \(V\), one for each \(a\in AP\). A propositional variable \(a\in AP\) is then symbolically represented by a propositional formula encoding the set of states \(L^{-1}(a)\) for which \(a\) holds.  
**Definition 73** (Characteristic Formula of Atomic Propositions).: _The characteristic formula of an atomic proposition represented by the propositional variable \(a\in AP\) is the propositional formula \(\xi_{a}=\xi_{L^{-1}(a)}\)._  
Note that \(\xi_{a}\) is satisfied only by those states \(s\in L^{-1}(a)\).  
**Example 8**.: _Given the hardware design and its transition system described in Example 1, the atomic propositions \(\{a_{0},a_{1}\}\) will be encoded as follows by the reverse labeling function \(L^{-1}\):_  
\[L^{-1}=\{(a_{0},\{s_{1},s_{3}\}),(a_{1},\{s_{2},s_{3}\})\} \tag{31}\]  
\[a_{0}=\xi_{s_{1}}\vee\xi_{s_{3}}=(v_{0}\wedge\
eg v_{1})\vee(v_{0}\wedge v_{1 })=v_{0} \tag{32}\]  
\[a_{1}=\xi_{s_{2}}\vee\xi_{s_{3}}=(\
eg v_{0}\wedge v_{1})\vee(v_{0}\wedge v_{1 })=v_{1} \tag{33}\]
# Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory
## 4 Experimental Results
### Tail Latency  
We performed tail latency evaluation for the range index structures for disaggregated memory, as shown in Figures 9 and 10. Overall, RF-tree shows lower latency than other range index structures in medium latency (50%) and 99 th latency. While RF-tree might experience high tail latency at 99.9 and 99.99 th, this can be further optimized by adopting optimization techniques in Sherman (e.g., batching RDMA operations and leveraging on-chip memory). We leave this for future work considering that a high tail latency in Write-intesive workloads represents the worst-case scenario and that RF-tree has a performance benefit for a Read-intensive workload.  
Figure 8: Performance breakdown of range index structures for disaggregated memory with YCSB workload. In this experiment, B stands for Blink-tree, S stands for Sherman, and R stands for RF-tree. For workload, LA stands for load A, and WA-WE stand for workload A–E.  
Figure 7: Throughput of range index structures for disaggregated memory (uniform distribution).  
**Zipfian distribution.** In load A, RF-tree shows higher latency at the 99.99 percentile, as it frequently reads the search layer from the memory server. Note that the load A workload includes insert operation only and starts from scratch. In that case, the search layer is updated frequently, and thus, it worsens the performance of RF-tree.  
In workloads A-B, Sherman shows lower tail latency than RF-tree, which translates to Sherman's optimization techniques for RDMA Write/CAS operation. The workloads include Write operations, and there are contentions between clients with the Zipfian workload, so RDMA CAS overhead was increased. Moreover, retrying the search layer Read operation incurs additional CRC overhead, so the tail latency is larger. In workloads C-E, RF-tree shows comparable performance to Sherman, as it efficiently leverages the SLC.  
**Uniform distribution.** In the tail latency evaluation with uniform distribution, RF-tree exhibits lower latency than Sherman in most workloads. This is because the effect of Sherman's optimization techniques is not significant where there is low contention between clients.  
Figure 10: Tail latency of range index structures for disaggregated memory with YCSB workload (Uniform Distribution).  
Figure 9: Tail latency of range index structures for disaggregated memory with YCSB workload (Zipfian Distribution).
# Image-Based Crack Detection Using Total Variation Strain DVC Regularization
## Appendix B Optimization of the Parametric Cost Function  
The cost function is defined by two terms. \(C_{S}\) is convex and differentiable and \(C_{R}^{TV}\) is convex but not differentiable at the origin. Since the derivative of \(C_{R}^{TV}\) is not defined at the origin, gradient-descent-based optimizers might give poor-quality results.  
The sub-gradient method is one of the techniques for non-differentiable function optimization [51]. The sub-gradient, or sub-derivative, approximates the derivative of convex functions in the points where the derivative is not defined. The sub-gradient of the \(11-norm\) is the \(sign\) function.  
According to the underlying principle of the sub-gradient method, the directional derivative always exists for a convex function [37].  
Therefore, for the optimization of the cost function (4) we used the sub-gradient techniques implemented by L1 General which is a pre-existing MATLAB package [38; 39]. The sub-gradient method iterates:  
\[\mathbf{z}(\Phi)^{(k+1)}=\mathbf{z}(\Phi)^{(k)}-\alpha\mathbf{d}^{(k)} \tag{10}\]  
where \(\mathbf{d}^{(k)}\) is the search direction and \(\alpha\) is the step length. Since the line search has no procedure to set variables to zero, ref. [37] defined a projection to set \(\mathbf{z}_{k+1}(\Phi)\) to zero if its sign differs from the sign of \(\mathbf{z}_{k}(\Phi)\). The projection is defined as follows:  
\[P_{O}(\mathbf{z}(\Phi)+\mathbf{d})_{i}\triangleq\begin{cases}0&\text{if }z_{i}( \phi)(z_{i}(\phi)+d_{i})<0\\ z_{i}(\phi)+d_{i}&\text{otherwise}\end{cases} \tag{11}\]  
this projection also ensures that the line search does not cross the non-differentiability point. To force \(\mathbf{d}\) to be a descent direction and, therefore, to agree with the steepest descent direction \(\
abla f(\mathbf{z}_{k}(\Phi))\), ref. [37] defined the following projection:  
\[P_{S}(\mathbf{d})_{i}\triangleq\begin{cases}d_{i}&\text{if }d_{i}(\
abla_{i})>0 \\ 0&\text{otherwise}\end{cases} \tag{12}\]  
For \(k=0\), \(\mathbf{d}\leftarrow-min\{1,1/||\mathbf{g}||_{1}\}\), for \(k>0\) then \(\mathbf{d}\leftarrow-H^{-1}\mathbf{g}\), where the Hessian matrix \(H\) is defined by using the quasi-Newton approximation \(I\sigma^{k}\), where \(\sigma^{k}\approx\frac{(\mathbf{r}^{k})^{T}\mathbf{y}^{k}}{(\mathbf{y}^{k})^{ T}\mathbf{z}^{k}}\), \(\mathbf{s}^{k}=\mathbf{z}^{(k+1)}(\Phi)-\mathbf{z}^{k}(\Phi)\), \(\mathbf{y}^{k}=\mathbf{g}^{(k+1)}-\mathbf{g}^{k}\) and \(I\) is the identity matrix. \(\mathbf{g}\) is the sub-gradient of the cost function defined, in this case, by the sum of the gradient of \(C_{S}\) and the sub-gradient of \(C_{R}^{TV}\) that is the \(sign\) function. With respect to a variable \(i\),  
\[\delta_{i}f(\mathbf{z}(\Phi))=\
abla_{i}C_{S}(\mathbf{z}(\Phi))+\lambda\,sign (\epsilon(z_{i}(\phi))) \tag{13}\]  
For more details about the optimization algorithm refer to Algorithm 1:
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 2 Precoding Schemes
### Non-Linear Precoding Schemes  
Linear precoding has relatively low complexity; however, it has been shown that the sum rate of channel inversion is limited when increasing the number of users. Furthermore, linear precoding does not perform well in scenarios with high UE density and correlated channels.  
Non-linear precoding schemes are valid alternatives, which instead of spatially separating the users, they eliminate the interference through non-linear operations. The non-linear concept was introduced in Dirty Paper Coding (DPC) [14]. It can achieve the maximum sum rate of the system when the full Channel State Information (CSI) is available at the transmitter side. The DPC's pre-subtraction of the non-causally known interference has a high complexity, and as a result, sub-optimum non-linear precoding schemes such as Tomlinson-Harashima Precoding (THP) and Vector Perturbation (VP) were proposed as alternatives with lower complexity [10].
## 4 Discussion
### RQ2: Which Stimuli May Be Used to Elicit Emotions?  
Various methods for emotion elicitation were identified. Most studies used shared databases (e.g., IADS, IAPS) as the source of stimuli to elicit emotions. Those databases have the advantage of representing standardized and internationally known methods, and normative ratings have been extensively provided. On the other hand, a large panel of stimulus types has been outlined, with pictures and films being the most frequent. Contrary to pictures, films are dynamic, and thus more ecological as they approximate real-life environments. Therefore, films are expected to elicit higher emotional experiences than pictures. Very few of the reviewed studies considered the type of stimulus to discuss the observed behavioral and physiological responses to emotional elicitations. Besides, none of them was interested in exploring the effectiveness of different stimulus types regarding emotional and cognitive processes. The mentioned proposal might be very promising, as far as films imply more complex and multimodal emotional processing. However, as films use narrative and dramaturgic structures to tell emotional meanings, the time of exposure may need to be longer to reach conveying of the target emotion. Besides, longer exposure might cause time variations of emotional intensity from the start to the end of the video clip [73]. If such is the case, then temporal dynamics must be included in the analysis of behavioral and physiological correlates. Hence, the time of exposure must be considered rigorously according to the nature of the stimuli.
## 2 Materials and Methods
### WSA Firmware
**After transmission:** After transmitting three 10-ms time frames, the number of which depends on the configuration of the synchronization interval, the delay timer is reconfigured to turn on the receiver just before the next synchronization packet is expected so that the remote device can continue communicating with the master device. 8. **Instructions (USB, radio):** WSA devices support interrupt-based commands via the USB port and via radio waves. The device is configured via the USB port with a user-defined command set through a graphical user interface or an advanced serial communications program. The commands include reading and writing variables to flash, waking up from power-save mode, starting and stopping the sensor data's acquisition, and various other commands for diagnostics. Some of the commands can be sent over radio waves, for example, to start and stop the sensor data's acquisition and communications. 9. **Energy-efficient communication:** Since the DWM1000 radio receiver consumes the most power, it is important to turn it off as soon as possible when not in use and turn it on as late as possible when reception is expected. 10. **Power on:** The device is turned on by a switch that connects the battery and the node to the circuit. Upon power-up, the low-level hardware of the microprocessor and the user-defined registers stored in the flash are initialized. The user-defined flash register contains the pre-configured device address, timer parameters (pre-scaler and period), various radio parameters such as radio channel and output power gain, the scaling of the inertial sensor and the operating mode of the device (master or slave). 11. **Power-save mode:** After the initialization, the remote-control unit enters the \"listening mode\" in which the radio receiver is on and the unit waits for remote commands. If no command is received within 10 s, the device enters the power-saving mode. In the energy-saving mode, the device switches the radio receiver on and off alternately for 50 us, every 3 s and 133 us. During the times when the receiver is on, the device waits for the wake-up command. If the wake-up command is not received, the device remains in energy-saving mode. 12. **Firmware details:** The firmware was written in C language and supplied with the STM32 HAL drivers. The initial configuration of the microprocessor is based on STM32 CubeMX version 5.0.1 and the library for STM32L4 version 1.11.0. The radio module DWM1000 uses the API driver version 4.00.06 from Decawave.
# Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm
## 1 Introduction
In addition to theoretical considerations describing the proposed error model, the paper also contains tips regarding the practical application of this model. For the purpose of this work, an exemplary measurement chain was created, for which the parameters of the indicated error model were identified and the uncertainty budget of the input quantities of the WT algorithm was determined. The main aim of the work is to indicate how to quantitatively determine the metrological parameters of a measurement chain containing a block that performs WT. This action is intended to enable a quantitative description of the measure of inaccuracy in the output quantities of the analyzed measurement chain. The presented analysis then makes it possible to indicate which source of error is the most important, and therefore gives the designer of the measurement chain the opportunity to improve the metrological properties of this path. The considerations presented in this work are limited only to the identification of error sources and the identification of the parameters of the error model associated with them, which is necessary to determine the expanded uncertainty values of the output quantities of this chain. The article is divided into six sections. The Section 1 is an introduction to the work and contains its most important assumptions. The Section 2 focuses on the definition and quantitative description of the properties of error signals. The Section 3 describes the error model for the measurement chain and presents the most important relationships between its subsequent fragments. The Section 4 contains an example of the application of the discussed analysis method using a measurement chain built for this purpose. The Section 5 discusses the results of the measurement experiment and indicates the reasons for the discrepancies between the results obtained using the discussed analysis method. The Section 6 contains the most important conclusions from the work.
## Efficient Aggregation of Multiple Classes of Information in Wireless Sensor Networks  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Department of Computer Science, University of California, Davis, CA, USA;  
E-Mails: [EMAIL_ADDRESS] (H.P.L.); [EMAIL_ADDRESS] (B.M.)  
\({}^{2}\) School of Electronic Information, Wuhan University, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Qualcomm Inc., San Diego, CA, USA; E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +(530)-754-9251.  
_Received: 22 July 2009; in revised form: 8 September 2009 / Accepted: 27 September 2009 / Published: 14 October 2009_  
**Abstract:** Congestion in a Wireless Sensor Network (WSN) can lead to buffer overflow, resource waste and delay or loss of critical information from the sensors. In this paper, we propose the Priority-based Coverage-aware Congestion Control (_PCC_) algorithm which is distributed, priority-distinct, and fair. _PCC_ provides higher priority to packets with event information in which the sink is more interested. _PCC_ employs a queue scheduler that can selectively drop any packet in the queue. _PCC_ gives fair chance to all sensors to send packets to the sink, irrespective of their specific locations, and therefore enhances the coverage fidelity of the WSN. Based on a detailed simulation analysis, we show that _PCC_ can efficiently relieve congestion and significantly improve the system performance based on multiple metrics such as event throughput and coverage fidelity. We generalize _PCC_ to address data collection in a WSN in which the sensor nodes have multiple sensing devices and can generate multiple types of information. We propose a _Pricing System_ that can under congestion effectively collect different types of data generated by the sensor nodes according to values that are placed on different information by the sink. Simulation analysis show that our _Pricing System_ can achieve higher event throughput for packets with higher priority and achieve fairness among different categories. Moreover, given a fixed system capacity, our proposed _Pricing System_ can collect more information of the type valued by the sink.  
keywords: wireless sensor networks; congestion control; fairness; performance analysis +
Footnote †: journal: Journal of Computer Science
## 3 Results and Discussion
### Water Efficiency  
In both the LEED-CI v3 and v4 certifications (Table 4), the Chinese Silver and Gold projects performed to a higher standard compared to the U.S. Silver and Gold projects. In LEED-CIv3, the Chinese and US Gold projects performed better than their Silver projects, whereas, in LEED-CIv4, the Chinese and U.S. projects performed similarly in both the Silver and Gold certifications.  
It should be highlighted that almost all of the Chinese LEED-CI v3 and v4 Gold projects achieved scores of 11 or 12, i.e., maximum points (Table 4). Such results were expected, because the projects analyzed in this study were located in North China (Beijing) and East China (Shanghai), as well as in South Central China (Guangzhou and Shenzhen) and South West China (Chengdu), which experience extreme or moderate water stress [32]. Moderate and extreme water stress was defined by [PERSON] et al. [33] as 40-100% of the water requirements relative to the renewable fresh water.  
As was noted previously, most of the evaluated projects in China were located in Shanghai and most of the evaluated projects in the U.S. were located in California. Thus, it should be added that Shanghai is a huge consumer of goods produced (with high water consumption) in other provinces. Thus, Shanghai has 79% virtual water, which arrives from other provinces due to these water-consuming goods [33]. Overall, this megacity has a water shortage problem and water saving practices are still urgently required in its construction sector.  
With respect to California, this state should save water according to California's Code of Regulations (Title 24, Section 4.3 Water efficiency and conservation [31]), which is equivalent to LEED-CI v3 and v4 WE prerequisite requirements to reduce potable water by 20% from the calculated baseline [34]. According to the results of the WE category (Table 4), in the US, only 35% (6 points) of water saving was achieved, which is an achievement only slightly above the requirements of California's Code of Regulations. This means that low requirements of local water codes, such as the considered example of California's Code of Regulations, may serve as an obstacle to the high achievement of water-related credits in the US.
## 3 Dataset Description  
In order to build an effective model, as well as for subsequent evaluation, it is necessary to have a large amount of data. This section describes the datasets used for the experiments.  
The dataset _malware-analysis-datasets-api-call-sequences_[3,4] contains 42,797 malware API call sequences and 1079 goodware API call sequences. Each API call sequence consists of the first 100 consecutive non-repeating calls associated with the parent process, extracted from the \"calls\" elements of the reports obtained from Cuckoo Sandbox. The _APIMDS_ dataset [5] instead consists of 23,080 malware samples randomly chosen from two other datasets: the Malicia project [23] and Virus Total [24]. A detailed description of the organization of the dataset can be found in Table 1, which shows the multitude of malware types recognizable using the analyzed approaches. However, this study focuses on binary classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Subcategory** & **Ratio (\%)** \\ \hline Backdoor & - & 3.37 \\ \hline \multirow{4}{*}{Worm} & Worm & 3.32 \\  & Email-Worm & 0.55 \\  & Net-Worm & 0.79 \\  & P2P-Worm & 0.30 \\ \hline Packed & - & 5.57 \\ \hline \multirow{4}{*}{PUP} & Advance & 13.63 \\  & Downloaded & 2.94 \\  & WebToolbar & 1.22 \\ \hline \multirow{4}{*}{Trojan} & Trojan (Generic) & 29.3 \\  & Trojan-Banker & 0.14 \\  & Trojan-Clicker & 0.12 \\  & Trojan-Downloader & 2.29 \\  & Trojan-Dropper & 1.91 \\  & Trojan-FakeAV & 18.8 \\  & Trojan-GameThief & 0.63 \\  & Trojan-PSW & 3.79 \\  & Trojan-Ransomware & 2.58 \\  & Trojan-Spy & 3.12 \\ \hline Misc. & - & 5.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: APIMDS dataset structure [5].