esa-sceva/satcom-chunk-collection huggingface dataset:
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 2 Methodology
### F-ALS Method and Its Limitations  
The SGDs to be considered are those in the line of sight (LOS) of the SAR image, which can be mapped onto the ground plane of the DEM. For simplicity, we assume a locally flat earth. This is a rough approximation for calculating the local terrain slope and incidence angle, of which the results are not distorted by this assumption. Figure 1 illustrates the phenomena of foreshortening, layover, and shadow in the object space (DEM) and are detailed below.  
Figure 1a shows scatterers A-E on the surface of a hill illuminated by the radar. The points A'-E', located on the radar image plane, represent the positions where the scatterers A-E are imaged. The lengths of A'B' and D'E' equal those of AB and DE, respectively, while the length of BC' is shorter than that of BC. This means that the image is geometrically distorted in the sense that the hill appears to tilt towards the radar. This is the phenomenon of foreshortening.  
Figure 1b shows a steep hill. It is seen that the slope BC is imaged in reversed order and is superimposed on the images of the sections AB and CD. This is the called layover, where the slope BC is also referred to as the \"active layover\", whereas sections AB and CD, located at the both ends of the slope BC, are \"near passive\" and \"far passive\", respectively. Figure 1c shows an even steeper hill. In this case, the section of terrain EFG cannot receive the signal transmitted by the radar, leading to points E-G not being imaged. This is the phenomenon of shadow, where the slope EF is defined as the \"active shadow\", whereas the section FG, located at the end of the slope EF and which cannot be reached by the radar beam since it is blanked by the hillside EF, is referred to as the \"passive shadow\".  
Except for passive distortion, these phenomena are related to the surface slope angle \(\chi\) and incidence angle \(\theta\), which can be calculated by the F-ALS method based on the relationship between the slope angle acquired by DEM and the incidence angle acquired by SAR imaging geometry:  
\[facing-sensor\quad\left\{\begin{array}{l l}\chi_{ij}<\theta_{ij}& foreshortening\\ \chi_{ij}=\theta_{ij}&worst-resolution\\ \chi_{ij}>\theta_{ij}&active-layover\end{array}\right. \tag{1}\]  
\[facing-away-sensor\quad\left\{\begin{array}{l l}\chi_{ij}<90-\theta_{ij}& resolution-enhancing\\ \chi_{ij}>90-\theta_{ij}&active-shadow\end{array}\right.\]  
where the DEM grid is defined by \((i,j)\) with \(i=1,\dots,m\); \(j=1,\dots,m\), \(\chi_{ij}\) is the slope angle of each point of the DEM, and \(\theta_{ij}\) is the incidence angle of each point of the SAR.  
This indicates that the areas affected by the foreshortening and active layover/shadow can be identified by the F-ALS method but cannot be when identifying the areas affected by the passive distortion. Therefore, it is important to develop a method that can simulate passive distortion.
# Building Equi-Width Histograms on Homomorphically Encrypted Data
## 2 Equi-Width Histograms on Homomorphically Encrypted Data
### Threat Model Definition and Assumptions  
Our privacy-preserving histogram construction technique targets edge-cloud deployments in IoT environments, where sensitive data collected from distributed sensors is encrypted using homomorphic encryption and aggregated before being transmitted to the cloud. The data sources (i.e., sensors) are considered to be trusted entities. We assume that sensor data is encrypted at the source immediately upon collection using the private encryption keys managed by a trusted HE controller. Data is then transmitted to more capable but untrusted edge/cloud processors, where the histogram building and outlier detection modules are deployed, as shown in Figure 4. These edge/cloud processors are not granted access to private encryption/decryption keys, but only to the evaluation public key to perform the histogram construction and anomaly detection. The HE controller is the only trusted entity responsible for distributing keys, validating computation circuits, and enforcing policies. Only a single trusted controller governs each deployment.  
In such a deployment, we consider several potential risks, including the execution of tampered circuits by malicious edge nodes, replay attacks aiming to skew the histogram, and statistical inference from aggregate outputs. To mitigate these threats, all data is encrypted at the source using homomorphic encryption, and we assume that only validated, signed computation circuits approved by the trusted controller are allowed to run. Additionally, we assume the existence of authenticated channels over which the encrypted data is transmitted, and that policy enforcement is centralized under a single trusted HE controller that governs key management and computation integrity. However, this setup introduces a single point of failure, as the HE controller is solely responsible for key management, policy enforcement, and circuit validation.  
Figure 4: Deployment sketch and threat model.  
Figure 3: Vectorial approach of detecting outliers using equi-width histograms.
# The Thermo-Pressure Concept: A New Model in Diabetic Foot Risk Stratification
## 4 Acquisition of Plantar Pressure Data  
A Tekscan High-Resolution (HR) Mat (Tekscan, Boston, MA, USA) was used for collecting plantar pressure data.  
The HR pressure mat was calibrated before collecting data from each participant as recommended by the manufacturer. The weight of each participant was measured separately using a calibrated hospital scale and inputted into the Research FootMat(tm) Software Version 7.5. The two-step gait protocol, a previously validated method, was utilized for all participants for acquiring data. This method was demonstrated to all participants before initiating data collection. All data were collected at 55 Hz. Three trials were recorded for each participant whilst walking at their own preferred speed and looking straight ahead since the literature suggests that this is sufficient to ensure reliability in the acquisition of plantar pressure and force data [21]. Trials were excluded and repeated if the participant misplaced their foot on the pressure mat, or did not continue to walk on the mat for more than two steps or paused on the mat during walking. Standardised instructions were given to each participant by the same researcher. Each trial produced eight stance phases, four stance phases for the left foot and four stance phases for the right foot. Before analysing plantar pressure data, the first and last step for each participant were discarded as these could influence the mean result.  
All the pressure recordings for both left and right feet were divided into five regions of interest (ROIs) by applying masks in the regions of the hallux, 1 st metatarasophalangeal joint (MP)), 2 nd to 4 th MPJs, 5 th MPJ and heel (Figure 2). From each division of the foot, data relating to peak plantar pressure (PPP) were extracted and documented as this variable plays an important role in the development of ulcers. Peak plantar pressure was defined as the highest observed pressure to a particular area.
## 2 Topics and Results  
### Enhanced PLT Reactivity in Diabetic Patients Has Been Considered a \"Pro-Thrombolic State\"  
PLT studies are important for regular treatment, successful management, and improved outcomes of DM2. In retrospective and prospective studies, higher values of hematometric indices--namely PLT count, mean platelet volume (MPV), and platelet distribution width (PDW)--are risk indicators for developing diabetes-related vascular complications. Easily identifiable increases in thrombocyte volume indices--MPV, PDW, and PCR (plateletcrit)--during routine automated hematological analysis may be useful early diagnostic and prognostic markers, especially for thrombogenesis in DM2. Researchers have characterized PLTs from patients with DM2 as \"highly reactive, playing a key role in the development of diabetic vascular complications\" [16; 17].  
The processes of micro- and macro-clot formation in DM2 are biochemically induced by increased production of different PLT factors--transcription, fibrinogen/fibrin, thromboglobulin, serotonin, P-selectin, d2-dimer, thromboxane A2, etc.--in cases of PLT hyperactivation [16; 17; 18; 19].  
Platelet Signaling, Hyperaggregation, and Abnormalities in Patients with DM2 Play a Crucial Role in Thrombic (Clot Formation) Complications and Thromboembolism during DM2 Micro- and Macrangopathies  
Targeted studies [16] of these pathological states would be useful for the development of new DM2 treatment strategies and effective medicines (drugs, agents) to reduce PLT hyperactivation during DM2.  
From the pathological and pathophysiological points of view, DM2 and related states (obesity, metabolic syndrome, impaired glucose tolerance, insulin resistance, etc.) are closely associated with subclinical (chronic) inflammation. Thus, several inflammatory hematometric biomarkers for PLTs and erythrocytes (RBCs), such as MPV, PDW, MCV, and RDW, are significantly elevated in DM2 compared to data for healthy individuals (controls) [19; 20; 21; 22; 23; 24]. MPV is significantly elevated in diabetic study groups compared with healthy individuals, suggesting that this hematological index is closely associated with DM2 development [19; 21].  
Further prospective studies with a larger cohort are necessary to characterize the probable relationship between MPV and metabolic (glycemic) control (concomitant HbA1c values as markers of glycemic control in patients with DM2). Further results could be significant for DM2 treatment, based on the principle of patient-specific healthcare in DM2 management.
# Impact of Temperature and Humidity on Key Mechanical Properties of Corrugated Board
###### Abstract
In connection with transport conditions, some researchers analysed the influence of various factors, such as stacking load [24], storage time and conditions [25], and hanging of the package on the pallet [26], on the load-bearing capacity of the boxes. The problem of determining the material parameters of corrugated board is an important and, in some conditions, complicated issue. A reliable laboratory testing turns out to be crucial to obtain values that reflect the actual material behaviour. In 2021, [PERSON] et al. determined the effect of creasing on the values of strength parameters of cardboard in bending, shearing, twisting and edge crushing tests [27]. [PERSON] et al. analysed the effect of span length on the bending parameters of cardboard in the three-point bending test [28]. However, due to the action of the shear force in the three-point bending test, for a more effective assessment, it may be necessary to adopt more advanced models [29; 30]. Another testing issue studied in the literature was the loss of stability in the edge crush test, which was analysed on the case studies of corrugated board [31; 32] and sandwich panels with corrugated core [33]. Additionally, in the bending test, local deformations on supports can be encountered, the influence of which was analysed for different types of corrugated board [34] and sandwich panels [35; 36]. From a general standpoint, the main task of corrugated cardboard packaging is to protect transported goods, so it is particularly important to know the actual material parameters of the cardboard from which the boxes are made. For this reason, the main aim of this research is to check the influence of relative humidity and temperature, which can significantly affect the strength of the cardboard. Laboratory tests of bending, shearing, twisting and edge crushing were performed in standard conditions (23 \({}^{\circ}\)C and 50% RH) and in several other combinations of temperature and relative humidity. The data obtained can also be used in the design process of packaging, for possible employment in scenarios different from the standard conditions, in view of perspective engineering research and industrial applications.
# Recommender Systems in the Real Estate Market--A Survey
## 4 Methodological Approaches
### Collaborative Filtering
#### 4.1.1 Model-Based Collaborative Filtering  
Five papers among the selected ones used model-based CF to provide recommendation lists in a real estate context. [PERSON] et al. [22] proposed two geographical proximity boosted real estate CF models, assuming that users' preferences are highly related to properties' geographical proximity. In this regard, they added two geographical-based regularization terms to the weighted regularized matrix factorization (WRMF) [43] and showed that the proposed geographical proximity boosted approaches perform better compared to the regular WRMF, PMF [44], SVD++ [45], UKNN, and IKNN.  
[PERSON] et al. [23] proposed the _SeoulHouse2 Vec_ RS which is an embedding-based housing RS using a neural network collaborative model. In this model, user ids and property ids are fed into a fully connected neural network to predict the user-item ratings obtained through a survey. In the trained model, the users and items are mapped to low-dimensional vectors (embeddings) which can be used to provide recommendations.  
[PERSON] et al. [19] used a simple deep neural network architecture that obtains the user-item interactions as input to learn the embeddings. They compared Adam and stochastic gradient descent (SGD) optimizers and L1, L2, and ElasticNet regularizers to avoid overfitting. They reported that the model with SGD and the L1 regularizer has the best performance.  
[PERSON] et al. [20] cast the real estate recommendation problem as a session-based recommendation task where the RS should predict the next item of a session given the previous items in the session. They specifically proposed a two-step recommendation task. In the first step, they used the gated orthogonal recurrent unit (GORU) [46] with the Top1 loss function as a session-based recommender to generate an initial recommendation list that contains the most probable next items given the current items in the session. Then, the final ranking is formed based on the weighted cosine similarity of the last item in the session and the candidate items in the initial list. They showed that the proposed method performs better compared to GRU4 REC [47; 48], BPR, and KNN.  
[PERSON] et al. [21] incorporated the item side-information in NeuMF and factorization machines (FM) [49] and evaluated them in two different scenarios: normal recommendation tasks and item cold-start recommendation tasks. They showed that NeuMF with side information performs better compared to FM in both scenarios.
# Steering Pull Model and Its Sensitivity Analysis
## 2 Tire Pulling Force
### Wheel Alignment  
#### 2.3.1 Kingpin and Caster  
When a tire is turned by external forces, such as driver steering force or tire pulling force, the tire turns with respect to a kingpin axis. This kingpin axis, as viewed from the front of the vehicle, is set at an angle relative to the vertical line. This angle is called'steering angle inclination (SAI)' or 'king pin inclination (KPI)' [10; 14]. In the same manner, the kingpin axis is set at an angle relative to the vertical line as viewed from the side of the vehicle. This angle is called 'caster angle'. These KPI and caster angle are described in Figure 4. Because of the KPI, the tire basically turns towards the ground as it turns. As the reaction from the ground, the tire lifts up the vehicle. Assuming the camber angle is neglected as it is small, the lifting moment by the KPI of left and right tires can be represented by Equation (5) [15].  
\[M_{lift}=(F_{zl}-F_{zr})d_{kp}\sin\
u\cos\delta-(F_{zl}+F_{zr})d\sin\lambda\sin\delta \tag{5}\]  
where \(M_{lift}\) = lifting moment, \(F_{zl}\) = normal force at left tire, \(F_{zr}\) = normal force at right tire, \(d_{kp}\) = kingpin offset, \(\
u\) = caster angle, \(\lambda\) = kingpin angle, \(\delta\) = tire turning angle. By dividing the lifting moment by pneumatic trail of the tire, as described in Equation (6), the lifting force is obtained.  
\[F_{lift}=\frac{(F_{zl}-F_{zr})d_{kp}\sin\
u\cos\delta-(F_{zl}+F_{zr})d\sin \lambda\sin\delta}{2t_{p}} \tag{6}\]  
where \(F_{lift}\) = lifting force due to caster and kingpin, \(t_{p}\) = pneumatic trail.  
In order to analyze the relation between tire contact area and lateral force, the tire model proposed by [PERSON] is employed [16]. Due to the friction force generated between the tires and ground, the treads of the tire are deformed to the opposite direction of the tire turning direction. Assuming a parabolic force distribution at the contact patch, the lateral force of the tire can be obtained by Equation (7) [16].  
\[F_{y}=\frac{8 kba^{2}}{6\theta}\bigg{[}1-\bigg{(}\frac{x_{S}}{2a}\bigg{)}^{3} \bigg{]} \tag{7}\]  
where \(k\) = lateral stiffness of tire per unit area, \(a\) = half length of contact patch, \(b\) = half width of contact patch, \(\theta\) = constant (a function of tire parameters and normal force), \(x_{S}\) = initiation point in contact patch for sliding.  
Figure 4: Kingpin and caster (**a**) Kingpin (**b**) Caster.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 5 Application Examples, Challenges and Perspectives
[PERSON] et al. reported high-speed PIV measurements with 6 kHz using pulsed illumination [197]. Time-resolved DGV spray measurements were performed with continuous-wave illumination by [PERSON] et al. [83]. Similar DGV measurements of a high-pressure injection with rates up to 250 kHz were demonstrated by [PERSON] et al. [84] and [PERSON] et al. [86]. Due to the continuous illumination and the high speed of the injection, the scattered light intensity, as well as the flow velocity varies during the exposure time of 0.45 \(\upmu\)s. In order to improve the temporal resolution by shortening the effective exposure time, high-speed DGV measurements with pulsed lasers are desired. However, short pulses widen the spectrum of the light, which finally deteriorates the sensitivity of DGV if the spectrum is wider than the spectral width of the molecular filter. According to the Fourier limit of laser pulses [198], the typically maximal acceptable bandwidth (full width at half maximum intensity) of \(\Delta f=100\) MHz corresponds to a Gaussian pulse with the duration (full width at half maximum intensity) of \(\Delta t=\frac{0.441}{\Delta f}=4.4\) ns. Hence, the realization, characterization and application of high-speed DGV measurements with pulsed lasers seem feasible and remain to be investigated in future research.
## 4 Data and Estimation Results
### Illustration of Volatility Jumps  
Figure 1 graphs the daily return rate series of EUA and WTI. To illustrate volatility regimes, we use a 21-trading-day rolling window to calculate their volatilities and graph  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **EUA** & **WTI** \\ \hline \multicolumn{3}{c}{_Panel A: Natural log level_} \\ \hline Mean & 2.4113 & 4.1823 \\ Q1 & 1.8213 & 3.9300 \\ Median (Q2) & 2.9143 & 4.4894 \\ Q3 & 2.3116 & 4.2015 \\ S.D. & 0.7081 & 0.3417 \\ Skewness & 0.4987 & \(-\)0.6036 \\ Kurtosis & 2.4039 & 3.4786 \\ Correlation & \(-\)0.1626 & \\ \hline \multicolumn{3}{c}{_Panel B: Return rate (Logarithmic change)_} \\ \hline Mean & 0.0574 & 0.0376 \\ Q1 & \(-\)1.3462 & \(-\)1.0658 \\ Median (Q2) & 1.5629 & 1.1675 \\ Q3 & 0.0000 & 0.0000 \\ S.D. & 3.0350 & 2.6399 \\ Skewness & \(-\)0.9978 & 0.6581 \\ Kurtosis & 21.3959 & 29.6411 \\ Correlation & 0.1791 & \\ \hline \end{tabular} Notes: The sample consists of 3311 daily observations from 16 March 2009 to 24 November 2021. The WTI oil price on 20 April 2021 is negative, which causes incalculable return rates (logarithmic change) on 20 and 21 April 2021. We exclude the two days in our analysis. The data source is DATASTREAM.  
\end{table}
Table 1: Basic statistics of EUA and WTI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **EUA** & **WTI** \\ \hline \multicolumn{3}{c}{_Panel A: Price level (Natural logarithm)_} \\ \hline ADF & 0.1902 & \(-\)2.4291 \\ Phillips–Perron & 0.2785 & \(-\)2.3620 \\ ADF-GLS & 0.0606 & \(-\)1.6053 \\ NGP & 0.1487 & \(-\)5.7049 \\ \hline \multicolumn{3}{c}{_Panel B: Return rate (Logarithmic change)_} \\ \hline ADF & \(-\)42.9309 *** & \(-\)19.1259 *** \\ Phillips–Perron & \(-\)56.9186 *** & \(-\)57.7969 *** \\ ADF-GLS & \(-\)2.5954 *** & \(-\)2.7100 *** \\ NGP & \(-\)4.4264 & \(-\)7.3027 * \\ \hline \end{tabular} Notes: This study employs four unit root tests for the log levels and return rates (first difference) of EUA and WTI, including the ADF test [42], Phillips–Perron test [43], ADF-GLS test [44], and the NGP test [45]. We use the maximum lag length for 15-order by Schwarz Info Criterion when conducting the unit root test. The ***, **, and * denote the significance in the 1%, 5%, and 10%. The data source is consistent with Table 1. Overall, the test results indicate that EUA and WTI price levels are non-stationary while their return rates are stationary.  
\end{table}
Table 2: Unit root tests.  
them in Figure 2. The figure shows that return volatilities on EUA and WTI are non-constant and show some specific peaks (i.e., prominent movements). These peaks offer evidence of volatility regimes (i.e., HV versus LV) in carbon-energy markets. For instance, the peak identified in May 2020 corresponds to the economic and financial distresses due to the COVID-19 pandemic.