esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Survival Analysis
#### 2.2.2 COX Proportional Hazard Regression Model  
To analyze the influence of both the driving style and distraction state on the departure duration, the multivariate parametric or semi-parametric survival analysis model would be needed. The parametric model requires the survival time variable to satisfy one non-negative parametric distribution (e.g., Gamma distribution, Weibull distribution, or Log-Normal distribution). However, LDD values did not meet the requirement (details are provided in the Section 3.1.2). Hence, the COX model, the most widely used semi-parametric model which does not have any distribution requirement, was used in this study.  
Since each participant performed 12 groups of tests, 12 LDD values may be correlated at individual levels. Therefore, the heterogeneity among the participants needs to be considered. For this purpose, a COX survival analysis model with gamma frailty was adopted for the study. The COX regression model with gamma frailty can be written as:  
\[h(t,X)=\alpha_{j}h_{0}(t)e^{(\beta^{T}X)}\sqrt{a^{2}+b^{2}} \tag{6}\]  
where \(X=[X_{1j}(t),X_{2j}(t)](j=1,2,\cdots,J)\) is the vector of explanatory covariates, \(\beta=[\beta_{1},\beta_{2},\cdots,\beta_{J}]\) is the regression coefficient of each covariate, \(h_{0}(t)\) is the baseline hazard, and \(\alpha_{j}\) is the gamma-distributed frailty with mean one and variance \(\theta\).  
The hazard ratio between variable \(X\) and control variable \(\overline{X}\) is given by Equation (7):  
\[h(t,X)/h(t,\overline{X})=(\alpha_{j}h_{0}(t)e^{\frac{\int\limits_{t}^{\infty }\beta_{1}^{T}x_{j}}{\beta_{2}}})/(\alpha_{j}h_{0}(t)e^{\frac{\int\limits_{t} ^{\infty}\beta_{1}^{T}\overline{x}_{j}}{\beta_{2}}})=e^{\frac{\int\limits_{t} ^{\infty}\beta_{1}^{T}(x_{j}-\overline{x}_{j})}{\beta_{2}}} \tag{7}\]
# An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
## 1 Introduction
### Contributions  
In this paper, we propose a novel algorithm to find the shortest path within a tubular space that connects two points at the tube ends. Our contributions include: (1) the description of the ESP geometrical structure inside tubular spaces with mathematical proof; (2) the proposition of a novel algorithm for finding the shortest path in tubular spaces based on the observed data; and (3) the numerical validation and comparison results with [PERSON]'s algorithm by considering various types of tubular spaces. As a result, the solution obtainedby using the proposed algorithm is shorter, smoother and faster than that provided by [PERSON]'s method.  
The remainder of this paper is organized as follows. Section 2 formulates the problem. The basics of [PERSON]'s algorithm are described in Section 3. Then, we present the proposed algorithm in Section 4. Our computational results are provided in Section 5. After that, Section 6 includes some brief discussions. Section 7 concludes the paper.
## 2 Places, Instruments, and Data
### Data
#### 2.3.1 Buoy Measurements  
The TAO buoy array was obtained from the NDBC, supported primarily by the US National Oceanic and Atmospheric Administration (NOAA). The TAO array comprises 55 moored buoys near the central equatorial Pacific Ocean. It is a sensor network telemetering oceanographic and metrological data in real-time by taking the advantage of the Argos satellite system. Therefore, the measured sea surface winds are appropriate for the instant performance assessment at low and moderate wind speeds.  
The NDBC TAO measurements mainly include surface winds, sea surface temperature, ocean currents, precipitation, humidity, salinity, and pressure. Figure 1 shows the distribution of the mooring buoys we used: they are arranged in eight columns from 165\({}^{\circ}\)E to 95\({}^{\circ}\)W longitudes and 8\({}^{\circ}\)S to 9\({}^{\circ}\)N latitudes. The temporal resolution is 10 min. The real-time data have a 1-h delay of obtainment and the historic data has a lag of one day. For the wind speed measurements, the NOAA NDBC buoy data have an accuracy of 1.0 m/s or 10%, which is the highest accuracy among the three datasets in this paper.  
The wind from the TAO program is at a 4-m reference height, which can be calibrated into the standard \(U_{10}\) by the following equation [40]:  
\[U_{10}=U_{2}8.87403/\text{ln}(z/0.0016), \tag{1}\]  
where \(z\) (m) is the observation height, \(U_{z}\) is the wind speed at the observation height, and U\({}_{10}\) is the 10-m reference height wind speed.
# Intelligent Interference Management in UAV-Based HetNets +
## 7 Conclusions  
We studied how 3 GPP LTE FeIcIC parameters can be tuned in a UAV-HetNet to maximize the mean and median spectral efficiency of a cellular network. Computationally efficient AI and greedy approaches were compared with an optimal exhaustive search. The AI approach was implemented using DDQN RL with a single agent to model all UAVs and BSs in the network. We observed that, for the scenarios that we considered, the greedy algorithm achieved the optimal mean and median SE, while the AI approach achieved 93.46% of the optimal median SE and 95.83% of the optimal mean SE.  
**Author Contributions:** Formal analysis, S.S., A.K., I.G., M.L.S.; Investigation, S.S., A.K., I.G., M.L.S.; Methodology, S.S., A.K., I.G., M.L.S.; Writing--original draft, S.S.; Writing--review & editing, I.G. and M.L.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded in part by NSF under the grant numbers CNS-1453678 and CNS-1738093.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Evaluation of the Image Data
#### 2.4.2 Image Registration  
elastix [35; 37] is an open-source image toolbox for intensity-based image registration. The main task of image registration is to map one image to another and gain information about the spatial relationship between them. The main applications are aligning different image modalities, comparing images of different recording times, or preparing for segmentation and classification [35].  
In our case, the challenge was to find the temporal relationship between frame \(I_{N}(\mathbf{x})\) and the following frame \(I_{N+1}(\mathbf{x})\); whereas, \(\mathbf{x}=[x,y]^{T}\) is the position occupied by a material point [38]. The relation between \(I_{N}(\mathbf{x})\) and \(I_{N+1}(\mathbf{x})\) is expressed by the transformation \(T(\mathbf{x})\). The optimal transformation perfectly aligns the two frames. To find this transformation, a cost function:  
\[\mathcal{S}(T,I_{N},I_{N+1}) \tag{4}\]  
was defined and minimized. To find the minimum, an iterative process was used [39]. In many cases, \(T\) is not well defined, because it is not necessarily possible to map every pixel in the first frame to a corresponding pixel in the following frame. For more details on the registration process itself, we refer to [PERSON] et al. [35].  
We used a non-rigid b-spline registration with a multiresolution approach [40]. We applied a normalized correlation coefficient (NCC) metric. The NCC metric had no spatial boundaries; therefore, regularization was necessary to avoid non-physical displacements (e.g., folding). We regularized the registration in two ways: First, we adjusted the maximum step size of the optimizer to the expected displacements. In that way, we avoided too large displacements. Secondly, we added a bending energy penalty \(\mathcal{P}(T)\) to the cost function \(\mathcal{S}(T,I_{N},I_{N+1})\) in Equation (4), resulting in a regularized cost function:  
\[\mathcal{C}(T,I_{N},I_{N+1})=\gamma_{1}\mathcal{S}(T,I_{N},I_{N+1})+\gamma_{2} \mathcal{P}(T) \tag{5}\]  
The bending energy penalty smoothed the displacement field according to the bending of a thin metal plate [41; 42]. The values of \(\gamma_{1}\) and \(\gamma_{2}\) weighted the influence of the two metrics on the registration process. The estimation of the values was an empiric and iterative process. In our registration, we used relative weighting, in which both metrics were weighted the same.
# STGAT: Spatial-Temporal Graph Attention Neural Network for Stock Prediction
## 3 System Framework
### Seasonal-Trend Decomposition Based on Loess
#### 3.1.1 Loess  
Loess, as a nonparametric smoothing method, employs iterative reweighting to robustly fit local regression surfaces, effectively capturing trends and seasonal variations in data. \(\hat{g}(x)\), as the Loess regression curve used for smoothing, is computed in the following way.  
Given an independent time series \(T=(x_{1},y_{1}),(x_{2},y_{2}),\ldots(x_{n},y_{n})\), each \(x_{i}\) means time and \(y_{i}\) stands for the corresponding value. First, choose a positive integer \(q\) as the number of values that are closest to \(x\), and then calculate the neighborhood weight \(W\) for \(x_{i}\) according to the distance between \(x_{i}\) and \(x\) when \(q\leq n\):  
\[W(u)=\left\{\begin{array}{ll}(1-u^{3})^{3}&\text{for }0\leq u<1\\ 0&\text{for }u\geq 1.\end{array}\right. \tag{1}\]In Equation (1), \(u\) represents the distance measure between point \(x_{i}\) and \(x\), and \(v_{i}(x)\) is the neighborhood weight of \(x_{i}\), which is calculated as follows:  
\[v_{i}(x)=W(\frac{|x_{i}-x|}{\lambda_{q}(x)}). \tag{2}\]  
In Equation (2), \(\lambda_{q}(x)\) stands for the distance between the center and edge point of the sliding window. Therefore, the longer \(\lambda_{q}(x)\) is, the smaller the weight value \(v_{i}(x)\) is.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 5 Simulations and Parameter Effects
### Simulation Environment  
A simulator was developed to simulate the performance of the proposed protocol. The program was developed using Java RMI. Parameters similar to those of FELGossiping [19] were used in the simulations. The nodes were randomly deployed in an 80 m \(\times\) 60 m area with one sink node located at the centre of the area at (40, 30). The locations of the rest of the nodes were randomised. The transmission limit for sensing was set to 20 m. All sensor nodes shared the same initial energy of 0.5 joules. The simulation parameters are summarised in Table 2.  
The performance of the proposed protocol was assessed based on a series of evaluation metrics that are relevant within the context of WSNs and the IoT. The evaluation metrics were as follows:  
1. _End-to-end delay_: This metric refers to the average time difference between the time at which the message was sent, i.e., the sending time, and the time at which the message was successfully received by the sink node, i.e., the receiving time.
2. _Rebroadcast nodes_: This measure represents the percentage of nodes that have received and rebroadcast the message.
3. _Saved rebroadcasts (SRB)_: This metric is related to the above metric and is used to compute the percentage of bandwidth utilisation. The measure is defined in Equation (9): \[SRB=(v-n)/v\] (9) where \(v\) is the total number of nodes that have received the message and \(n\) is the total number of nodes that have retransmitted the message [33].
4. _Network lifetime_: This measure refers to the time from the start of the network simulation up until the death of the last node in the network. This measure represents the number of rounds until the death of the last node in the network [34].  
The evaluation framework considers the scalability of the proposed protocol as the number of nodes is increased from 50 to 250 nodes in a fixed-area network. Each simulation was executed ten times with a confidence interval of 95%, and the results were averaged and presented as line graphs, with the number of nodes plotted on the x-axis and the performance measure plotted on a linear scale on the \(y\)-axis.
# Review of Acoustic Sources Alternatives to a Dodecahedron Speaker
## 1 Introduction
### Acoustic Measurements with Omnidirectional Sources  
A practical implementation of a pulsating sphere producing an omnidirectional field is useful in many fields of acoustics. An omnidirectional sound source is required in many acoustic measurements set by the International Organization for Standardization (ISO) as well as many standards set by national organizations, such as the American National Standards Institute (ANSI), the German Institute for Standardization (DIN-Deutsches Institut fur Normung) and the British Standards Institution (BSI).  
Due to the large number of standards, we will only mention standards for acoustic measurements by the ISO where a source with omnidirectional characteristics is required, such as ISO 3382-1 [8], ISO 3382-2 [9], ISO 3382-3 [10], ISO 3382-3 [10], ISO 354 [11], ISO 17497-1 [12], ISO 17497-2 [13], ISO 16283-1 [14], ISO 16283-3 [15] and ISO 10140-2 [16].  
ISO 3382-1 describes the appropriate measurement of the impulse response and acoustic parameters of performance spaces, ISO 3382-2 of ordinary rooms and ISO 3382-3 of open-plan offices. An impulse response is the temporal evolution of the sound pressure observed at a point in a room as a result of the emission of a Dirac impulse at another point in the room. Acoustic parameters can be obtained directly from the impulse response and used to assess the acoustic quality of a space and provide guidance for possible improvements. ISO 354 describes the measurement of the sound absorption coefficient of materials performed in a reverberation chamber. ISO 17497-1 describes the measurement of the random-incidence scattering coefficient in a reverberation room (part two in a free field). ISO 16283-1 describes the measurement of the airborne sound insulation in buildings (part three for facade sound insulation) and ISO 10140-2 describes the laboratory measurement of sound insulation of building elements.  
An acoustic source is also necessary for the measurement of head-related transfer functions (HRTFs) which are important for auralization purposes in many fields, such as virtual and augmented reality. HRTFs are defined as the free-field transfer functions, from a point sound source to each of the two ears on a fixed head. In practice HRTFs, describe the overall filtering effect imposed by anatomical structures. As stated by [PERSON] [17], \"in far-field HRTF measurements, the point sound source needed can be approximated by a common, small, loudspeaker system, where measurement errors caused by the size and directivity of the loudspeaker, as well as the multiple scattering between subject and loudspeaker, are negligible\". However, in near-field measurements, the size of the source and scattering between subject and source is a key element for accurate measurements [18]. Under such conditions, a commonly used loudspeaker can no longer be regarded as a point source. Therefore the appropriate sound source is necessary for the measurement of the HRTFs. In relevant research [19], a dodecahedral sound source with a small radius (0.035 m) is proposed.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 2 Preliminaries  
In this study, multiple authoritative scientific databases were chosen during the literature review process to ensure the comprehensiveness and quality of the selected literature. The databases used include Web of Science, IEEE Xplore, MDPI, Engineering Index (Ei), and Science Direct. Regarding the literature selection, studies published from 2019 to 2023 on the applications of blockchain and the Internet of Things (IoT) in agriculture and animal husbandry were included. Keywords such as \"blockchain\", \"Internet of Things\", \"agriculture\", and \"animal husbandry\" were employed to optimize the search results. The inclusion criteria focused on peer-reviewed journal articles and conference papers to ensure the scientific rigor and authority of the research, while the exclusion criteria removed literature that was not directly relevant to the research topic or of insufficient quality, such as non-peer-reviewed preprints. A preliminary search was conducted in the selected databases, followed by screening based on the defined selection criteria.  
Figure 1: Organization of this paper.  
This section succinctly presents the core technologies and application areas of the agricultural and pastoral IoT, as well as the key technologies of blockchain. In addition, we also explore how these two technologies can be combined and applied in the agricultural and pastoral IoT.
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 3 Collaboration between Seaports and Dry Ports
### Indicator Selection  
1. Customer satisfaction degree From the perspective of the supply chain, the performance indicators of customer satisfaction degree for the cooperation between dry ports and seaports include customer satisfaction degree, market share, customs clearance time, and generalized transportation cost. 2. Financial indicators Enterprises are sensitive to financial data, and the relevant financial data cannot be obtained directly. In view of this, the indicators indirectly related to enterprise finance are selected. Since the financial status of dry port enterprises is directly proportional to their annual throughput and trade volume, the growth amount of import and export is taken as an indicator to indirectly reflect the financial performance of both parties. 3. Cooperative relationship of both partiesFor the cooperation between seaports and dry ports, the cooperation performance indicators include customs declaration and inspection business, information sharing degree, connected railways, and the cooperation degree of both parties. Since it is difficult to characterize the degree of cooperation between the two parties, it is necessary to quantify the signed cooperation agreement with a score in the range of 0-10. (4) Non-market tools  
In addition to external cooperation evaluation indicators, the internal factors of dry ports should also be considered. (1) Regional transport scale is the most fundamental reason for the emergence of dry ports, and the cargo capacity of dry ports determines that they can absorb more goods. (2) The number of relevant promotion policies for developing dry ports is one of the indicators for measuring the competitiveness of dry ports. (3) The port facility level of dry ports depends on the amount of fixed asset investment (which is a monetary activity), and it is an important indicator reflecting the scale of fixed asset investment. According to the cooperation performance evaluation indicators for dry ports and seaports, the specific cooperation performance evaluation system is shown in Table 1.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.7. Resistance to Ephemeral Secret Leakage Attacks  
If adversary \(\mathcal{A}\) acquires the session-specific random numbers \(n_{i1}\), \(n_{i2}\), and \(n_{i3}\) generated by \(U_{r_{i}}\), \(IoT_{SL_{j}}\), and the \(CS\) during the authentication phase, \(\mathcal{A}\) seeks to obtain the session key \(SK=h(UID_{i}||SI_{j})\oplus h(n_{i1}||n_{i2}||n_{i3})\). The session key is derived from \(UID_{i}\), \(SI_{j}\), and the random numbers. However, \(\mathcal{A}\) cannot derive \(SI_{j}\) from the publicly transmitted messages because \(SI_{j}\) is masked using the server's master key \(S_{k}\). Thus, our scheme can withstand ESL attacks.
## 2 Materials and Methods
### Real-Time qPCR (RT-qPCR) Analysis  
Total RNA was isolated from cells with RNeasy Mini Kit (Qiagen, Hilden, Germany). Total RNA (1 ug) was reversely transcribed with high capacity cDNA reverse transcription kit (Applied Biosystems, Foster City, CA, USA). RT-qPCR was performed using TaqMan probes with the universal PCR Master Mix (Applied Biosystems, Foster City, CA, USA) using StepOnePlus real time PCR system (Applied Biosystems, Foster City, CA, USA). The TaqMan probes used were Hemeoxygenase 1 (_Hmox1_, Mm00516005_m1), NAD(P)H dehydrogenase quinone 1 (_Nq01_, Mm01253561_m1); Thioredoxin 1 (_Txn1_, Mm00726847_s1); \(\beta\)-actin (_Acbt_, Mm00607939_s1). Based on the reference _Acbt_ gene, the relative gene expression analysis was performed using the delta-delta Ct formula.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### Integration of the Vision-Based Technologies Together and with Other RTLS
#### 4.3.3 Photo/Videogrammetry Integration with UWB
In [32], a case study was done for reconstruction of a building in which a camera and a Pozyx UWB device were mounted on an unmanned aerial vehicle (UAV) in a very close range from each other (less than the UWB accuracy range). In this way, first, the positioning of the receivers was done by both geo-referencing approaches included DG such as using RTS or GNSS devices, and self-positioning approach using the UWB system. The first approach was more accurate since the exact positioning of the receivers has an important role in the exactness of a proper transformation (i.e., scaling factor, translation, and rotation). After that, they achieved the position of other receivers in the same coordinate system by using trilateration and extended Kalman filter (EKF) techniques. The computed roving tag positions in the UWB coordinate system were used to estimate the transformation parameters (i.e., scaling and transition factors) for mapping the photogrammetry point clouds from the photo to the UWB frame. They concluded that introducing different weighting factors for various reading ranges, might improve the scale factor estimation. Besides, from these positioning data which will be used as reference positions, the camera external parameters can also be extracted [32; 51]. The integration of these two sensing technologies provides accurate and timely positioning and visual data for various applications, especially in areas with limitation to use a positioning technology such as GNSS (e.g., in the tunnels or area covered with tall buildings) or using laser scanners due to limitation associated with scanning positions. Furthermore, there is no need for any extra devices for geo-referencing of images. In fact, in the case of direct geo-referencing using the UWB self-positioning technique, there is no need to use control points (CPs) anymore. However, the data acquired by UWB tag (s) need to get converted to the camera referenced frame and the photo coordinate system which may cause an error propagation phenomenon due to the intrinsic inaccuracy in each technology [32; 51].
## 1 Introduction
### Information Entropy Baselines for Network Services  
Each packet traversing a network typically contains identifiers that associate that packet with a network service--for example, the File Transfer Protocol (FTP), where the payload of each packet usually represents a fragment of the content being transferred. Packet payload represents a rich source of high dimensional data, and techniques that examine this low-level information are termed Deep Packet Inspection (DPI). In the past, we enjoyed almost complete visibility of this content since older network services (such as HTTP) encoded content in plaintext (i.e., unencrypted). Today, networks are dominated by encrypted services, such as HTTPS, where payload is effectively treated as a 'black-box' (without resorting to technologies that can unpack the data in transit, such as SSL intercept),although, there remain some important legacy services that do not encrypt data, as shown in Figure 2.  
We know that network services exhibit markedly different entropy profiles, since some are known to be plaintext, and some are partially or fully encrypted--as illustrated in Figure 2. This gives us some intuition on what level of information entropy to expect when analysing the content of network traffic. However, since there are no published baselines for service level information entropy, even if we dynamically compute payload entropy (e.g., within an active flow), there are no 'ground truth' values with which to compare. Baseline data can prove very useful in determining whether the characteristics of flow content are deviating significantly from expected bounds, and this may be a strong indicator of anomalous activity, such as a covert channel, compromised protocol, or even data theft--as discussed in Section 2.