esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Gate Allocation Problem Based on Multi-Commodity Network Flow Model
## 3 Problem Description
The transferring arc \(\widehat{BA}\) points to the direction of the next arrival flight to be allocated from the departing flight that has been serviced, indicating the next flight served by the designated gate. This type of arc exists if the landing time of the arrival flight to be allocated is greater than the departure time of the last flight currently served by the gate. A buffer time greater than a certain buffer time is required between the two flights operated by the same gate. The leaving arc \(\widehat{BD}\) is the gate recovery arc, which flows from the corresponding flight departure node \(d_{j}\) to the destination node \(D\), indicating that the gate is idle and is about to be recycled. The directing arc \(\widehat{OD}\) flows directly from the origin node \(O\) to the destination node \(D\), indicating that the gate is never occupied. The arc set can be concluded as follows: \[E=\big{\{}\widehat{OA},\widehat{AB},\widehat{BA},\widehat{BD},\widehat{OD} \big{\}},a_{i}\in A,d_{j}\in B,i,j\in[1,N]\] (2)  
The gate flows through the direction of the arc in the network as a commodity. It starts from the origin node and flows through the intermediate nodes to the destination node to form a complete commodity flow. The multi-commodity network flow model is the interleaved network formed by multiple commodity flows. Figure 1b shows a feasible allocation scheme for three gates \(G=\big{\{}G_{1},G_{2},G_{3}\big{\}}\) and five flight pairs \(F=\big{\{}a_{i},d_{i}\big{\}}\), \(i=1,2,\ldots,5\). The blue line indicates the overall flow of \(G_{1}\), which is first allocated to the arrival flight \(a_{2}\), and then serves the corresponding departure flight \(d_{2}\). Due to the landing time of flight \(a_{4}\) being greater than the departure time of flight \(d_{2}\) and the service time of the \(G_{1}\) satisfying the corresponding buffer time, \(G_{1}\) is allocated to the arrival flight \(a_{4}\) and the departure flight \(d_{4}\) and then is in an idle state. The red line indicates the work process of the \(G_{2}\), which is first assigned to the arrival flight \(a_{1}\) and the departure flight \(d_{1}\), and then to the arrival flight \(a_{3}\) and the departure flight \(d_{3}\) under certain conditions. After the second process, it is found that \(G_{2}\) can still be allocated to arrival flight \(a_{5}\) and departure flight \(d_{5}\). \(G_{2}\) is not assigned again after serving three pairs of flights. The black line represents the state of \(G_{3}\), which means that the gate has not been allocated and is always in an idle state. According to the modeling process described above, some potential limitations can be found, such as the paired existence of flights (i.e., arrival and departure flights) and the first-come-first-served gate allocation strategy. In practice, most scenarios follow these requirements, so these potential limitations are somewhat acceptable.
## 3 Proposed System
### Service Model Training Module (SMTM)  
In the Service Model Training Module (SMTM), a diverse range of time-series forecasting models are trained using preprocessed data. In this paper, we trained models ranging from traditional deep learning models, such as a CNN (convolution neural network), an RNN (recurrent neural network), and LSTM (Long short-term memory), to attention-based transformer models and GluonTS models, including TFT (Temporal Fusion Transformer), etc. [22]. A list of the trained models is given in Table 2. Each model is trained with the same training dataset to ensure fair selection among several candidates.  
The proposed system does not include statistical time-series models, such as ARIMA (Auto-Regressive Integrated Moving Average) or Prophet. Even though the models are commonly used because they require relatively little training time or less training data than deep learning models, it is difficult for them to consider explicit variables, events, or categorical information, so these models need to be trained every time they are used. This can also cause low forecasting performance.  
For the deep learning models, we applied a parameter tuning mechanism to ensure the forecasting performance of each model. The system offers various lengths of forecasting horizons, which refers to the future target days to predict, from 7 days to 30 days, with a 7-day interval. The deep learning models are trained and stored on the database, and their attributes, including the number of parameters, complexity, and execution time, are used to generate the meta-features.
However, only a limited subset of patients actually benefit from these treatments, with an overall 3-year survival rate of approximately 50% [1; 2]. Based on the recent literature, several modifiable patient-level factors, such as diet, exercise, body composition, and gut microbiota (GM), may influence melanoma progression and therapeutic response [3]. In particular, GM has shown great promise as a biomarker of clinical benefit and a therapeutic target. Indeed, both experimental and human studies have demonstrated the importance of GM in modulating the efficacy of anticancer therapy and patients' susceptibility to side effects [4]. With particular reference to melanoma, GM is known to play a leading role in the therapeutic response, especially to immune checkpoint inhibitors, by promoting local and systemic inflammation or inducing immunosuppressive phenotypes, thereby enhancing or counteracting the anti-tumor immune response [5; 6; 7; 8; 9]. This awareness has paved the way for several clinical trials aimed at manipulating GM (e.g., through diet, bacterial consortia, or even fecal microbiota transplantation) toward a more favorable profile associated with better prognosis and overall survival [10; 11; 12; 13]. However, reliable and consistent GM signatures of response to therapy have yet to be identified [14]. No less important is the fact that insights into the functional contribution of GM (such as those provided via metagenomics, metatranscriptomics, or metabolomics) are still very scarce, although they may provide new and interesting opportunities for adjuvant treatment [13]. Regarding body composition, obesity is an established risk factor for several malignancies, though it was unexpectedly associated with better outcomes in patients with metastatic melanoma who received targeted therapy or immunotherapy [3], thus suggesting a stage- and treatment-dependent relationship. Similarly, sarcopenia has been identified as a poor prognostic factor [15], though the strength of its association with clinical outcomes in advanced melanoma remains controversial [16]. In an attempt to further investigate the impact of the above-mentioned modifiable factors on therapeutic responses in melanoma patients, we profiled the GM and fecal metabolome, and we thoroughly characterized the nutritional status (including anthropometry, body composition, and biochemical parameters) of patients with advanced melanoma prior to receiving targeted therapy or immunotherapy. To our knowledge, although exploratory, this paper represents the first study to consider such factors together in order to identify potential early integrated signatures of response to therapy in these patients.
# Fragility Assessment of Traffic Embarkments Exposed to Earthquake-Induced Liquefaction
## 3 Model Description
### Ground Motions  
For the purpose of the fragility analysis, a set of 30 GMs was selected from the strong-motion database that combines recordings from the Next Generation Attenuation (NGA) [31] and from the Reference database for seismic ground-motion prediction in Europe (RESORCE) database [32]. The selection of GMs was performed according to the procedure proposed by [PERSON] et al. [33], considering the following constraints regarding the magnitude (\(M\)), source-to-site distance (\(R\)), and shear-wave velocity in the upper 30 m of soil (\(v_{s,30}\)): 5.5 \(<\)\(M\) \(<\) 7.5; 5 km \(<\)\(R\) \(<\) 50 km; and \(v_{s,30}\) \(>\) 500 m/s. The maximum scale factor in the ground-motion selection was limited to 2.0. The GMs were selected in such a way that the mean of the horizontal acceleration spectra matched the elastic spectrum according to Eurocode 8 (EC8: [34]) for soil type A by being conditioned to the peak ground acceleration of 0.25g. Note that the EC8's criterion [34] for the \(v_{s,30}\) of soil type A (\(v_{s,30}\) \(>\) 800 m/s) had to be relaxed due to the limited number of GMs recorded from rock outcrop or rock-equivalent stiff soil. Figure 3 shows a comparison of the EC8 spectrum for soil class A and the mean spectrum obtained for the selected set of 30 GMs. The duration of the selected GMs varied between 7.4 s and 61.0 s (average duration of 17.4 s).  
The decision on the number of GMs was based on a literature review. The typical number of GMs for the estimation of the distribution of structural response ranged between 14 and 200, but most of the studies considered 20 to 40 GMs [35]. Recently, [PERSON] et al. [36] recommended using 20 GMs to obtain sufficient accuracy of the results in the case of multiple stripe analysis.  
In order to cover the entire range of fragility curves, the GMs were scaled to eight intensity levels in terms of maximum peak ground acceleration at bedrock (0.25 g, 0.375 g, 0.5 g, 0.625 g, 0.75 g, 1.0 g, 1.25 g and 1.5 g). Additional intensity levels (0.1 g, 0.15 g, 0.2 g and 0.3 g) were used in the analyses with loose sand, where the threshold values of crest settlements were exceeded in the majority of performed cases, even at low intensities.
## 2 Measured Parameters  
The set of parameters that have been measured along these years can be categorized into three groups: geomagnetic parameters, ionospheric parameters and HF channel parameters. All of them are explained in detail below.
# Groundwater Prediction Using Machine-Learning Tools
## 4 Groundwater Prediction Methodology
### Feature Selection
#### 4.3.2 Other Local Spatiotemporal Features, and Rescaling  
Because of the geographical and seasonal nature of groundwater levels, the following spatiotemporal features were also deemed to be significant and were used:  
* Pixel's \(x,y\) coordinate;
* Time stamp (0,..., 11) (0 = January,...11 = December)  
Since most of the pixel values are low and high values were relatively infrequent, the pixel values were replaced by their square roots to regularize the scale. The square root transformation was similarly applied to inputs in [64], and to outputs in [65; 66].  
Figure 4: Feature importance of same pixel of previous months, where f(0) stands for same month previous year, and f(11) stands for previous month.
# Repair Composite Adhesion Strength: A Comparison of Testing Methods
## 1 Introduction
The repair system is effective if it provides load transfer and ensures uniform distribution of stresses in the element [17]. Therefore, great attention needs to be paid to the determination of the adhesion strength of the repair composite to the concrete base when developing repair systems. As noted [15; 18], the connection between the repair material and the concrete substrate can weaken and eventually fail due to various stresses (Figure 1). The variability of bond test results, depending on the adhesive bond failure mode [19; 20; 21], combined with the existence of multiple measurement approaches [22], poses a significant challenge for data comparison. The testing method significantly influences the measured bond strength, as demonstrated in [22]. Manufacturers' reported values may significantly overestimate actual strength depending on the method used. Consequently, a comparative analysis of various bond strength measurement methods is essential to establish the range of values obtained by each method [23]. The Ukrainian standard [24] determines adhesive strength by measuring the pull-off force using a Pull-Off Adhesion Tester on metal pullers attached to ceramic tiles, bonded to concrete mock-ups with a repair mortar. The European  
Figure 1: Types of adhesive bond loss: \(\sigma_{t}\)internal shrinkage stress; \(S_{\mathrm{f}}\) —tensile strength of the repair material; \(f_{t}\)—tensile strength of the concrete substrate; \(f_{t}\)—adhesive strength. regulatory standards [25; 26] define adhesion as the separation force measured similarly, but using metal pullers on circular dollies cut from the repair composite. The circular dollies used in the assessment are fabricated by cutting sections from a continuous layer of repair composite applied to a concrete substrate. It is important to note that the existing Ukrainian [24] and European [26] standards do not account for all relevant structural parameters beyond the scope of general recommendations. Consequently, this study aims to conduct a comprehensive evaluation of the adhesion strength of repair composites to concrete substrates and to assess the reproducibility of results when employing the measurement methods specified in the Ukrainian [24] and European [26] standards.
## 3 Results and Discussion
### Comparison of LOD with Other Techniques  
A comparison of the LOD obtained by APNDP-AES with other techniques is shown in Table 2. The results show that the LOD of Cu in this study is lower than that of other studies and is in the same order of magnitude as that of ICP-OES.  
The LOD of APNDP-AES is much lower than the standard value of Cu for drinking water provided by WHO [36]. Because APNDP has a small experimental device, lower LOD and a relatively high detection sensitivity, it has great potential for portable and in situ application. In the future, it can be optimized by changing different types of pneumatic nebulizers and distance between the nozzle and the discharge region and so on to reduce the LOD. Finally, it has a better application prospect of in situ detection and lower consumption.
###### Abstract  
This paper presents the design of a system to support the identification of victims of disasters and terrorist attacks. The system, called ID Victim (IDV), is a web application using a mobile app and data server. The DVI (Disaster Victim Identification) procedure, an international standard developed by Interpol, is used. The purpose of the IDV system is to facilitate and expedite the process of determining victims' identities. A neural identification module was developed and trained on approximately 13,000 images from the LFW dataset and fine-tuned using 400 simulated PostMortem (PM) and AnteMortem (AM) images. Postmortem data include photographs of victims while antemortem data consist of pre-disaster photos of potential victims. The module generates a hypothesis, linking PM to AM, which is then verified. The module achieved test identification accuracy of up to 60% for 25 sample PM and AM sets. The system partially automates photo comparisons by DVI teams, improving efficiency, reducing identification time, and limiting the exposure of operators to graphic images. Implementing the system as a mobile application accelerates the process by enabling direct data entry during victim examinations on-site.  
computer vision; CNN module compilation; deep metric learning; disaster victim verification +
Footnote †: journal: Applied sciences
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 2 Related Works  
### Passenger Behavior Prediction  
Passenger behavior prediction has garnered significant attention in urban data analysis. Early research [7] proposed a multidimensional analysis of travel behavior to understand people's travel demands. In recent years, travel behavior analysis has been commonly used in urban function perception and smart transportation development. In machine learning methods, neural network models have been effective in solving time series prediction problems [1]. [PERSON] et al. [2] considered temporal features in neural network models to predict station-based passenger flow. [PERSON] and [PERSON] [8] used passenger flow data from the six time steps before the prediction period as input, combining empirical mode decomposition and NN models to predict passenger flow. [PERSON] and [PERSON] [9] combined a non-homogeneous Poisson process with a single-layer neural network model to predict short-term public transit passenger flow. [PERSON] et al. [10] proposed a new hierarchical hybrid model for short-term passenger flow prediction, based on time series models, deep belief networks, and an improved incremental extreme learning machine. [PERSON] et al. [3] applied LSTM networks to develop an hourly subway passenger flow prediction model. [PERSON] et al. [4] used convolutional neural networks to predict short-term passenger flow during special events. The work in [5] proposed a short-term passenger flow prediction method for subways based on ARIMA, incorporating weather conditions. [PERSON] et al. [6] proposed a trajectory sequence encoding method and applied it to an interpretable StTP model with adaptive location awareness. [PERSON] et al. [11] proposed an end-to-end framework for multi-step prediction, using an attention mechanism to predict the number of passengers alighting at all stations. Please see Table 1 for a review summary.  
Figure 1: Passenger behavior prediction with a dynamic graph to trace passengers’ travel patterns. This task has three dynamics: (1) travel records are infinitely growing; (2) periodic patterns over long intervals and abrupt changes over short intervals. (3) evolving relationships between passengers and stations.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 4 Hybrid Positioning Systems
### RSS-IMU Hybrid Systems  
The availability of wireless networks deployed inside millions of buildings around the world make RSS based positioning systems an attractive option for mass market hybrid systems because there is no need of investing in a wireless infrastructure. Note that, as stated in Section 2.1.3 the RSS can be computed just listening to the network, i.e., without any additional hardware, as far as most of the wireless standards of communication already include the RSS field in the radio packets.  
The most common kind of RSS hybrid system is one that combines it with inertial sensors. The motivation is clear: both systems have complementary errors. The inertial based systems obtain highly accurate positions estimations in the short term while the RSS based systems are less accurate but the estimations of position are time invariant. An example of this kind of hybrid systems is found in [47] where the authors developed a system that combines the position estimation of a WiFi  
Figure 10: Simultaneous localization and mapping.  
probabilistic fingerprinting with the information of a foot mounted SHS using an Extended Kalman Filter (EKF) for the fusion of the systems. Similarly, in [48; 49; 64] the step information of a hip mounted IMU is combined with the position estimations of a range based RSS system. [PERSON] et al. [50] combine a strapdown foot mounted inertial system with the RSS of RFID tags using an EKF. Table 3 summarizes the RSS-IMU hybrid positioning systems showing the main characteristics of the underlying RSS and IMU systems as well as the parameters and results of the experimental evaluation.
## 5 The Proposed Solution
### Preparation  
1. The key pair of preparation steps for the function owner: - Run \(HE.Setup(\lambda)\to pp_{he}\) to generate \(pp_{he}=(N,g)\). - Run \(HE.KeyGen(pp_{he})\rightarrow(pk_{f},sk_{f})\) to generate \((pk_{f},sk_{f})\)=\((g^{a},a)\).
2. The function owner encrypts the matrix and uploads it to \(CSP_{1}\): - For each element \(f_{i}\) in the matrix \(\mathbf{F}\in\mathbb{Z}_{q}^{m\times d}\), \(0<i\leq m\times d\), run \(HE.Enc(pk_{f},f_{i})\to ct_{i}\) to generate \(ct_{i}=(g^{a\tau_{i}}g^{\tau_{i}}(1+f_{i}N))\), where \(r_{i}\xleftarrow{\$}\mathbb{Z}_{N^{2}}\). - Upload \(\mathbf{ct}=(ct_{1},\dots,ct_{m\cdot d})\) to \(CSP_{1}\).
3. The function owner generates the verification data \(\mathbf{ver}\): - Run \(\mathbf{VerM}.Prepare(\mathbf{F})\rightarrow\mathbf{ver}\) to generate \(\mathbf{ver}=(\mathbf{T},\mathbf{S})\). It holds that \(\mathbf{T}=g^{t}=(g^{t_{1}},\dots,g^{t_{m}})\), \(\mathbf{S}=g^{s}=(g^{s_{1}},\dots,g^{s_{d}})\), where \(\mathbf{t}\xleftarrow{\$}\mathbb{Z}_{q}^{m}\) and \(\mathbf{s}=(s_{1},\dots,s_{d})=\mathbf{t}\cdot\mathbf{F}\).
4. The request user encrypts the input vector and sends it to \(CSP_{1}\): - Run \(LHE.Setup(\lambda)\to pp_{he}\) to generate the public parameters \(pp_{he}=(n,f,q,\chi)\) for the request user. - Run \(LHE.KeyGen(pp_{he})\to sk\) to generate the secret key \(sk=s\) for the request user. - For each \(x_{i}\) in the input vector \(\mathbf{x}=(x_{1},\dots,x_{d})^{\top},i\in[d]\), \(x_{i}\in\mathbb{Z}_{p}[x]/\langle f(x)\rangle\), run \(LHE.Enc(sk,x_{i})\to c_{i}\) to generate the ciphertext \(c_{i}=(u_{i},v_{i})=(a_{i}s+pe_{i}+x_{i},-a_{i})\), where \(a_{i}\xleftarrow{\$}R_{q},e_{i}\xleftarrow{\$}\chi\) and \(c_{i}\in R_{q}^{2}\). - The user sends the ciphertext \(\mathbf{c}=(c_{1},\dots,c_{d})^{\top}\) to \(CSP_{1}\) and requests computation.
5. \(CSP_{1}\) asks the function owner for permission.
6. The function owner will generate a temporary key pair and a re-encryption key if permission is granted: - The function owner runs \(HE.KeyGen(pp_{he})\rightarrow(pk_{f}^{\prime},sk_{f}^{\prime})\) again to generate a temporary public/secret key pair \((pk_{f}^{\prime}=g^{a^{\prime}},sk_{f}^{\prime}=a^{\prime})\). - Run \(PRE.ReKeyGen(sk_{f},sk_{f}^{\prime})\to rk_{f\to f^{\prime}}\) to generate a re-encryption key \(rk_{f\to f^{\prime}}=\frac{sk_{f}^{\prime}}{sk_{f}^{\prime}}=\frac{a^{ \prime}}{a}\). - The function owner sends \(pk_{f}^{\prime}\) and \(rk_{f\to f^{\prime}}\) to \(CSP_{1}\) and sends \(sk_{f}^{\prime}\) to \(CSP_{2}\).