esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stochastic Load Conditions  
### Wind Environments  
The wind information at the bridge site is necessary for fatigue damage evaluation. The wind records of 10 min mean wind speed and direction from the anemometer installed on the bridge tower from the period between 1 March 2008 and 31 December 2010 are used in the present study for wind data analysis [25]. The wind velocities were collected from the anemometer, with a height of150 m above the ground level, and were further transformed to the wind velocity at the bridge deck level via the wind profile power law with the exponent being 0.2. The wind records were carefully checked to exclude any abnormal records. All the wind records were classified into 16 sectors of the compass with an interval of \(\Delta\theta=22.5^{\circ}\), according to the wind directions. In each sector, the mean wind speed was further divided into 7 ranges from 0 to 21 m/s with an interval of \(\Delta U=3\) m/s. As a result, a total of 112 cells were used, in which each cell recorded the relative frequency associated with the corresponding wind speed and wind direction, as tabulated in Table 1. In Table 1, the last row displays the relative frequency of mean wind speed without considering the wind direction, while the last column lists the relative frequency of wind direction without considering the wind speed. For better illustration, the wind data are also displayed as the wind rose map, as shown in Figure 2. The bridge alignment, normal to the gorge axis and directed from the ESE (112.5\({}^{\circ}\)) to WNW (292.5\({}^{\circ}\)), is also depicted in Figure 2 for reference. It is observed from Figure 2b that the prevailing wind blows from NNE or SSW, i.e., along the axis of the gorge and normal to the bridge alignment. It is also shown in Figure 2a that the prevailing wind has a relatively larger wind speed compared with the wind in other directions. The relative angle between the wind direction and the bridge alignment has a significant influence on the resultant bridge dynamic response. Many researchers have reported that the wind with the direction normal to the bridge alignment (i.e., cross-wind) could induce the largest bridge dynamic responses, while the wind with direction parallel to the bridge axis could barely affect the bridge dynamic responses [28]. Therefore, it is assumed that the contribution of cross-wind to fatigue damage accumulation is much more significant than that caused by the wind parallel to the bridge alignment [2]. To this end, in the present study, all the winds were decomposed into two components, one normal to the bridge axis and one parallel to the bridge axis, and only the cross-wind was adopted for the subsequent analysis. As a result, the cross-winds at the bridge site were obtained and eight cross-winds were chosen to represent the cross-wind ranges in the bridge location with the probability occurrence, as tabulated in Table 2.
## 3 Offset Correction
### Full-Consensus Algorithm
Let \(\pi\) be the left eigenvector of \(\Gamma\), corresponding to the zero eigenvalue, and satisfying \(\pi\Gamma=0\) and \(\pi\mathbf{1}=1\). **Lemma 2** ([55; 56; 57]). : _Let \(T=[\mathbf{1}\vdots T^{*}]\), where \(n\times(n-1)\) matrix \(T^{*}\) satisfies \(\text{span}T^{*}=\text{span}\{\Gamma\}\). Then, \(T^{-1}=\left[ \begin{array}{c}\mathcal{T}\\ \mathcal{S}^{*}\end{array} \right]\), where \(S^{*}\) is an \((n-1)\times n\) matrix satisfying \(S^{*}T^{*}=I_{n-1}\) and_  
\[T^{-1}\Gamma T=\left[ \begin{array}{c}0&\vdots 0_{1\times(n-1)}\\ 0_{(n-1)\times 1}&\vdots\Gamma^{*}\end{array} \right],\]_where \(\Gamma^{*}\) is Hurwitz._  
Theorem 3.1: _Let A1-A3 be satisfied. Then, there exists \(\delta^{\prime}>0\), such that, for all \(0<\delta<\delta^{\prime}\), we have for all \(t\) the following solution of (6):_  
\[\hat{f}_{t}=\pi\hat{f}_{0}\mathbf{1}+S^{*}\hat{f}_{t}^{[2]}, \tag{7}\]  
_where \(\hat{f}_{t}^{[2]}\) is generated by the following recursion:_  
\[\hat{f}_{t+1}^{[2]}=(I+\delta\Gamma^{*})\hat{f}_{t}^{[2]}+\delta\Gamma^{*} \hat{v}_{t}^{[2]} \tag{8}\]  
_with \(\hat{f}_{0}^{[2]}=S^{*}[\beta^{1}\cdots\beta^{n}]^{T}\), \(\hat{v}_{t}=T^{-1}v_{t}=[\hat{v}_{t}^{[1]T};\hat{v}_{t}^{[2]T}]^{T}\), \(\dim\{\hat{v}_{t}^{[1]}\}=1\), \(\dim\{\hat{v}_{t}^{[2]}\}=n-1\). Furthermore, \(\|\hat{f}_{t}^{[2]}\|<\infty\) and \(\lim\|v_{t}\|\to 0\) implies \(\|\hat{f}_{t}^{[2]}\|\to 0\)._  
Proof: Introduce \(\tilde{f}_{t}=T^{-1}\hat{f}_{t}\)[33; 34; 44; 55]; then, if \(\tilde{f}_{t}=[\hat{f}_{t}^{[1]T}\vdots\hat{f}_{t}^{[2]T}]^{T}\), \(\dim\{\hat{f}_{t}^{[1]}\}=1\), \(\dim\{\hat{f}_{t}^{[2]}\}=n-1\), then (6) provides  
\[\hat{f}_{t+1}^{[1]}=\tilde{f}_{t}^{[1]}, \tag{9}\]  
together with the recursion (8) for \(\hat{f}_{t}^{[2]}\), with \(\hat{f}_{0}=T^{-1}[\beta^{1}\cdots\beta^{n}]^{T}\). According to Lemma 2, \(\Gamma^{*}\) is time-invariant and Hurwitz [52; 55]; it follows that, for a sufficiently small \(\delta^{\prime}\), the condition \(\max_{i}|\lambda_{i}\{I+\delta\Gamma^{*}\}|<1\) holds. Therefore, (8) is exponentially asymptotically stable and also B.I.B.O.-stable [52], directly implying the assertion of the theorem.
## 1 Introduction
Consequently, reducing the ripple of the RSOA is of great significance for enhancing the linewidth of external cavity lasers, and thereby improving the performance of coherent optical communication systems and fiber-optic sensing applications. In this paper, a four-layer Nb\({}_{2}\)O\({}_{5}\) film system is designed as the coating material for the output facet of the RSOA. Under test conditions of 25 \({}^{\circ}\)C and 180 mA, the 3 dB spectral width of the RSOA's spontaneous emission spectrum reaches 79.4 nm, spanning the wavelength range from 1497.2 nm to 1576.6 nm. Within this range, the ripple is maintained at less than 1 dB, with the ripple near 1550 nm being approximately 0.5 dB. To the best of our knowledge, this represents the best performance reported to date. The narrow-linewidth external cavity laser fabricated with this RSOA achieves a linewidth of 299.8 Hz at 180 mA, while maintaining an output power of 12.6 mW at room temperature (25 \({}^{\circ}\)C). Furthermore, by adjusting parameters such as the FP standard cavity and filtering within the external cavity, the lasing spectrum of an external cavity laser constructed with this RSOA can cover wavelengths from 1535.83 nm to 1561.42 nm. This capability holds significant practical value for the realization of narrow-linewidth lasers with a broad tuning range.
# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 2 Background
### Quantum and Classical Machine Learning  
The increasing importance of Machine Learning in recent years has led to many relevant studies that investigate the promise of quantum computers for Machine Learning [25; 26; 27]. Quantum Machine Learning (QML) is the field that concerns the integration of quantum algorithms into classical Machine Learning models. Classical Machine Learning algorithms are used to analyze large amounts of data, and quantum computing helps by using qubits and quantum operators to increase the computation and data storage speed of these algorithms. The QML field is still at the forefront of computer science research, and some improvements (the so-called \"Quantum Advantage\") have not been proven theoretically, yet. With the concept of the \"Quantum Advantage\", the researchers refer to the advantage of Quantum Models over classical approaches, by leveraging quantum effects. While some attainable advantage of the Quantum Models over generic classical computations has been proven, there is no certainty that the \"advantage\" may bring complete \"supremacy\" in the future; the question is still an open debate. The strong quantum speedup is still debatable if a lower bound for the classical algorithms has not been found yet. Indeed, even if the Quantum Advantage was demonstrated for some problems (such as [PERSON]'s factoring algorithm [28]), the demonstration represents a quantum speedup over the best-known classical counterpart, but it may be, theoretically, disrupted by improvements in classical calculation techniques and lower bound verifications.  
The main difference between a quantum and a classical calculator is the basic unit of calculation. In the classic case, a process is based on the bit, which can assume only two states, generally represented as 0 and 1, corresponding to the state of charge of a transistor. On the other hand, in the quantum scenario, the equivalent of the bit is the quantum bit (or qubit), which follows properties deriving from the postulates of quantum mechanics. Superposition and entanglement are two of the key concepts of quantum theory and contribute to the great computational capacity of quantum computers. The combination of these two principles allows one to create computer systems characterized by a great calculation capacity and speed of execution. While in classical computer science, a system consisting of two bits can store only one of the four possible binary combinations (00, 01, 10, 11), a two-qubit quantum system can store all four combinations.  
In [29], the authors conducted research in which they distinguished four ways to combine quantum computing and Machine Learning. There are several approaches to applying quantum principles. Those methodologies are reported in Figure
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 3 Proposed Approaches  
In this work, in order to take full advantage of different attention mechanisms, we propose a dual-path attention module, which combines the CBAM and self-attention. Besides, to improve the convergence efficiency of CRL, a weighted cluster-range loss and a criticality-enhancement loss are proposed from different standpoints.
## 2 Materials and Methods
### Flux Data Preprocessing, Quality Control, and Gap Filling  
All raw data were processed and corrected using EddyPro 7.0.8 software (Li-COR, Inc.). We applied the coordination correction twice, the Webb-Pearman-Leuning density-fluctuation correction [27], and the time-lag correction to the final validated data.  
To ensure the reliability of the results, we carefully controlled the quality of the processed data. Initially, we excluded the data detected based on low signals and abnormal machine states. In addition, we discarded the flux data with \"Flag 2\" [28]. We applied the moving point method to remove invalid gas flux data collected under low-turbulence conditions when the friction velocity (u*) was below the seasonal u* thresholds [29]. Finally, all abnormal values beyond the conventional threshold were deleted. Overall, the data collected over 4 years indicated that we maintained approximately 61% and 53% of the CO2 fluxes in LWS and HWS, respectively.  
The original NEE was gap-filled using a marginal distribution sampling algorithm and then separated into GPP and RE based on the \"night-time\" partition theory [30]. The friction velocity threshold and partitioning of the NEE were conducted using the \"REddyProc 1.3.2\" R package [31].
## 3 Methodology
### Model Evaluation
#### 3.4.2 Receiver Operating Characteristic Curve  
The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) was used to measure the models' performance [54]. The AUC is widely recognized as an effective measure for evaluating classification performance, as it quantifies how well the model distinguishes between positive and negative classes [55]. AUC values range from 0 to 1, with values greater than 0.8 generally indicating strong model performance [56].
## 3 Experiment and Analyses  
Five pseudolites have been set up in the laboratory. The ranging signals with C/A codes are transmitted on the GPS L1 frequency. The RF front end of the receiver is a USRP and a full series of GNSS signals can be captured with a suitable daughter board. Taking a certain mark as the origin point, the two horizontal directions of the floor are \(X\) and \(Y\) axis, respectively. The \(Z\)-axis is perpendicular to the floor and a right hand indoor independent coordinate system is established. All the experiments are carried out on the floor. In addition, the scope of the laboratory is about 10m\(\times\)7m\(\times\)4m. The pseudolites constellation is as shown in Figure 4a,b. The HDOP (Horizontal Dilution of Precision) on the floor under this condition of pseudolite layout is shown in Figure 4c. HDOP is less than 6 in 92.8% of the laboratory area, and the mean value of HDOP is 3.1055.  
Two sets of experiments were performed. It should be noted that this pseudolite system is in good condition in most cases. In most of the other experiments, high precision positioning results can be obtained in our laboratory. The faults in the following two groups of experiments are accidental. However, these kinds of accidental faults should also be considered in high-precision positioning.The first group of experiments with a zero baseline is named Experiment 1. The purpose is to verify the availability of an indoor positioning system by eliminating all the spatial correlation errors (for example, multipath).Through conducting a zero-baseline experiment, the receiver bias is estimated.A total of 20 epochs are sampled with a sampling interval of 1s. The second group is a short baseline dynamic experiment, including the static stage before moving, named Experiment 2.The sampling interval is set to 1 s, and a total of 123 epochs are sampled. Firstly, the static measurement of the rover station at an initial position is maintained and then the rover station is controlled to move back and forth on the fixed rail for two periods. One period is divided into two datasets and a total of four datasets are obtained.  
Figure 4: (**a**) Pseudolite Constellation in the Laboratory, (**b**) The Horizontal Locations of Pseudolite and Initial Position of Rover Stations, (**c**) HDOP of Pseudolite System(On the Floor).
## 2 Models
### Parcel-Receiving Station and Public Air-Route Planning
#### 2.4.2 Lower-Layer Model  
1. Objective function  
The lower-layer model of the logistics UAV public air-route planning problem can be expressed as a minimization problem as:  
\[\text{min}C_{4}=\sum_{k\in E}\sum_{v=1}^{n_{s}^{k}}\sqrt{\left(x_{v}-x_{v-1} \right)^{2}+\left(y_{v}-y_{v-1}\right)^{2}}(D_{v}+S_{v}+L_{v}+\sum_{w\in S}r_{ kw}) \tag{17}\]  
Function (17) refers to the cost of public air-route operations, which reflects the safety and efficiency of the planning results, including the air range of the route between the warehouse and each station and the flight risk of the public air route.  
2. Constraints  
\[|R_{s}|\geq 1 \tag{18}\]  
\[\sum_{k\in R_{s}}\sum_{v=1}^{n_{s}^{k}}\sqrt{\left(x_{v}-x_{v-1}\right)^{2}+ \left(y_{v}-y_{v-1}\right)^{2}}\leq(1+\gamma)\sum_{v=1}^{n_{s}}\sqrt{\left(x_ {v}-x_{v-1}\right)^{2}+\left(y_{v}-y_{v-1}\right)^{2}} \tag{19}\]  
\[\sum_{k\in R_{s}}\sum_{v=1}^{n_{s}^{k}}\sqrt{\left(x_{v}-x_{v-1}\right)^{2}+ \left(y_{v}-y_{v-1}\right)^{2}}\leq AR_{\text{max}} \tag{20}\]  
Equation (18) is the route accessibility constraint. Each station can be reached by a set of edges in the public air route; that is, the number of edges in each route set \(R_{s}\) should not be less than 1. Equation (19) is the air-range negative optimization constraint; the route air range of each station in the public air route should be within an acceptable ratio compared to the air range of the single-air-route planning result. Taking Figure 1b as an example, assuming that the air range of OB-BA between O and A exceeds the acceptable ratio compared to the air range of OA, the transit route of OB-BA cannot be selected for the route of A when planning the public air route; instead, the direct route of OA should be selected. Equation (20) is the maximum air-range constraint, indicating that the route air range should be less than the maximum acceptable travel distance of the UAVs.
## 2 System Model
### Transmission Protocol  
This paper assumes that each user will generate one downlink flow but will generate uplink flow on a probabilistic basis. Services generated by different users are independent of each other. \(\Gamma^{d}\) and \(\Gamma^{u}\) represent the downlink user (data flow) set and single uplink user (data flow) set in integrated networks, respectively. The data flow is temporarily stored in the buffer and scheduled by the system according to the resource status.  
The eMBB and mMTC services studied in this paper have different QoS requirements. The eMBB service requires the guarantee of soft QoS, which means the eMBB service can tolerate a specific QoS reduction. However, the data flow cannot be available once the data rate decreases beyond a necessary threshold. mMTC service isn't sensitive to transmission delay and requires limited system resources for its transmission. In order to make the relationship between the QoS and data rate, we adopt the method of constructing utility function [1-4]. This paper adopts the definition of the utility function in [5].  
\[U^{S}(R)=\left\{\begin{array}{l}(1-p_{1})e^{q_{1}(R-R_{th})},R<R_{th}\\ 1-p_{1}e^{-q_{1}(R-R_{th})},R\geq R_{th}\end{array}\right. \tag{10}\]  
\[U^{B}(R)=P_{2}(1-e^{-q_{2}R}),R\geq 0 \tag{11}\]  
Parameters \(p_{1}\), \(q_{1}\), \(p_{2}\), \(q_{2}\) are used to characterize the slope of the utility function. Symbol \(R\) indicates the data rate for soft QoS service. The symbol \(R_{th}\) is a threshold of data rate. QoS requirements can be met effectively for the best-effort service, as long as some RRs are allocated for the flow.
## 4 Spatial and Temporal Variation Characteristics of TEC in the Polar Regions  
The previous section has shown that the SCH model has higher mapping accuracy with respect to the three models for the polar regions during about one solar cycle. Therefore, starting from this section, we analyze the spatial and temporal variations of the polar ionospheric TEC based on the SCH model solely.  
Figure 4: Annually ionospheric TEC mapping bias (**top**) and RMS (**bottom**) errors of the four models in the Antarctic from 2010 to 2018.  
Figure 3: Annually ionospheric total electron content (TEC) mapping bias (**top**) and RMS (**bottom**) errors of the four models in the Arctic from 2006 to 2018.
## 3 Preliminaries
### Challenges  
Existing work has shown that each of the aforementioned design goals is feasible, but designing a system that finds a balance and achieves the goals in tandem is challenging. Some key challenges to consider are as follows:  
* Classical computer vision algorithms are fast and suitable for real-time streaming but have relatively low accuracy compared to DNN-based methods. While there are DNN models built for devices with low computing power, there is much to be explored in leveraging edge computing for tasks that require intensive computation.
* Edge computing has alleviated the bottleneck in data processing for deep learning, but it has also introduced a new set of privacy challenges. The exposure of sensitive data to the cloudlet needs to be minimized, though this often comes at the cost of increased latency due to preprocessing or reduced utility.
* Popular object-tracking methods in videos rely on known kernels or approximate the location of objects of interest to optimize computation and execution time. However, this may result in the leakage of PII, which may nullify any desired benefits.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 5 Evaluation Protocols and Metrics
### Key Metrics
#### 5.1.5 SODA  
SODA (Story Oriented Dense Video Captioning Evaluation Framework) is a metric specifically designed to evaluate video story descriptions [12]. It takes into account the temporal ordering of events and penalizes redundant captions to ensure accuracy. SODA assesses the quality and relevance of generated captions by maximizing the sum of Intersection over Union (_IoU_) scores and computing an \(F\)-measure based on METEOR scores. The framework comprises two main components: optimal matching using dynamic programming, and an \(F\)-measure specifically tailored for evaluating video story descriptions.  
1. Optimal Matching Using Dynamic Programming SODA uses dynamic programming to find the best match between generated and reference captions, maximizing their _IoU_ score sum while considering temporal order. The _IoU_ between a generated caption \(p\) and a reference caption \(g\) is defined as: \[IoU(g,p)=\frac{\max(0,\min(e(g),e(p))-\max(s(g),s(p)))}{\max(e(g),e(p))-\min(s (g),s(p))},\] (14)where \(s(.)\) and \(e(.)\) represent the start and end times of the event proposals corresponding to the captions.  
Given a set of reference captions \(G\) and generated captions \(P\), SODA finds the optimal matching by solving:  
\[\mathbb{S}[i][j]=\max_{\begin{subarray}{c}k=0,\ldots,j-1\\ l=0,\ldots,j-1\end{subarray}}(\mathbb{S}[k][l]+\mathbb{C}[k+1][l+1]), \tag{15}\]  
where \(\mathbb{S}[i][j]\) holds the maximum score of optimal matching between the first \(i\) generated captions and the first \(j\) reference captions, and \(\mathbb{C}[i][j]\) is the cost matrix based on IoU scores.
2. \(F\)-measure for Evaluating Video Story Description To penalize redundant captions and ensure the generated captions cover all relevant events, SODA computes _Precision_ and _Recall_ based on the optimal matching and derives \(F\)-measure scores: \[Precision(G,P)=\frac{\sum_{p\in P}\max_{g\in G}f(g,p)}{|P|},\] (16) \[Recall(G,P)=\frac{\sum_{g\in G}\max_{p\in P}f(g,p)}{|G|},\] (17) \[F\text{-measure}(G,P)=\frac{2\times Precision(G,P)\times Recall(G,P)}{ Precision(G,P)+Recall(G,P)},\] (18) where \(f(g,p)\) represents an evaluation metric like METEOR.