esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 6 Performance Evaluation Results  
This section presents the performance evaluation results. Section 6.1 discusses the evaluation results of the congestion use case using two traffic strategies: variable speed limit and re-routing. Section 6.2 shows the evaluation results of the pollution use case with a variable speed limit as the traffic strategy. Finally, Section 6.3 presents the algorithm for decision-making for both use cases, and Section 6.4 presents the evaluation of the simulation time of the digital twin.  
Before presenting the evaluation results for the proposed traffic strategies, it is important to clarify the behavioral assumptions used in the simulations. Specifically, the simulation framework assumes full compliance from drivers; all vehicles are expected to follow the recommended speed limits or re-routing instructions issued by the digital twin. Thus, two corner cases have been considered in the work, as follows:  
* In the best-case scenario, full compliance allows us to assess the maximum potential effectiveness of the proposed strategies.
* In the worst-case scenario, complete non-compliance is equivalent to applying no strategy at all, serving as a baseline for comparison.  
Due to the lack of empirical data from the infrastructure operator regarding partial compliance behaviors, intermediate cases were not modeled.
## 3 Results and Discussion
### Photostability Test on DHP Formulations  
The stressing test was performed on the NF and NM specialties currently on the market, containing the drugs at a concentrations of 20 and 40 mg/mL, respectively. The samples were exposed to light in the original bottles and analyzed immediately before the experiment (zero time) and after 120 min of irradiation. Before recording the spectra, an aliquot of the solution was diluted with ethanol to obtain a concentration of 20 ug/mL. At the end of the experiments, the residual concentrations of NF and NS formulations were 74.33 and 91.08%, respectively. The photoprotective capacity of the red glass was then tested on all the studied DHPs, formulated in solution with excipients commonly used in pharmaceutical technology. These formulations were prepared in 95% PEG:ethanol (8:2) at a drug concentration of 20 mg/mL, very similar to those used in commercial specialties, and exposed to light under the same instrumental conditions, using the red glass as a light shield. The use of these solvents in the formulation of DHPs can be advantageous, as it  
Figure 5: Absorbance spectra of quartz, transparent, amber, and red glass.  
has been reported that the formation of hydrogen bonds with alcohols could significantly increase photostability [27].  
The samples were analyzed at time zero and after 120 min of irradiation. The samples were diluted with ethanol to 20 \(\upmu\)g/mL before analysis. The residual concentration of the drugs after 120 min of stressing irradiation are listed in Table 2. The parameter t\({}_{10}\) could not be calculated, as for most drugs the residual concentrations remained above 90% at the end of the experiments. The residual concentrations of NF and NS formulations were 84.33 and 93.15%, respectively, while the values for all the other DHPs were all above 95%.  
The results showed a further increase in the stability of the NF and NS samples, even with respect to the values recorded on the pharmaceutical specialties. It was found that the photostability of the two products was also improved compared to the relative standard solutions, but this could be due to the higher concentration of the drugs. This dependence had already been reported in previous works [28]. However, the degradation test was repeated on hydroalcoholic solutions of the drugs at a concentration of 20 mg/mL, identical to the PEG/EtOH solutions, obtaining comparable results. This allowed us to exclude the influence of PEG on the photodegradation rate of the products.  
The results obtained with the use of red glass can be considered very satisfactory in consideration of the stressful irradiation conditions and the exposure time in the tests adopted. At the same time, they allow us to estimate a potential high stabilization of DHP drugs under normal conditions of storage and use.
# Assessment of Spatial Variability in Ground Models Using Mini-Cone Penetration Testing
## 4 Discussion of the Results
### CL Analysis  
#### 4.3.1 Horizontal CL  
Figure 5 presents the horizontal \(CL\)s for Case 1 and Case 2. The horizontal \(CL\) was calculated by taking the average of the measured cone penetration resistances at each of the 18 locations. Figure 5a presents the \(CL\) for the silica sand layer, and Figure 5b,c separate the calculations of \(CL\)s for silica sand and weathered soil grounds, respectively.  
Figure 4: Horizontal coefficient of variation: (**a**) Case 1; (**b**) Case 2.  
For the application of the _CL_ model, Markovian and Gaussian models were utilized in the calculations.  
As can be seen from Figure 5a, the horizontal _CL_s using the Markovian model and the Gaussian model were found to be 332.56 mm and 397.69 mm, respectively. Figure 5b shows the calculation results for the horizontal _CL_s in the silica sand layer, with the Markovian model and the Gaussian model yielding 180.83 mm and 267.23 mm, respectively. Figure 5c displays the calculation results for the horizontal _CL_s in the weathered soil layer, with the Markovian model and the Gaussian model resulting in 467.68 mm and 557.59 mm, respectively.  
The comparative analysis of the horizontal _CL_s for Case 1 and Case 2 revealed that the silica sand ground exhibited smaller _CL_s in Case 2 than those in Case 1, despite the _CV_. Moreover, although larger _CL_s were expected in the silica sand ground due to greater variability in the weathered soil ground, smaller _CL_s were calculated for the silica sand ground compared to the weathered soil ground. As shown in Figure 5a,b, this smaller _CL_ observed in the silica sand ground for Case 2 is considered to be influenced by the penetration depth. Additionally, the impact of having fewer data points, as opposed to calculating vertical CLs, is also thought to be a factor.
## 4 Models and Problem Definition
### Budgeted Competitive Influence Maximization Problem  
In this paper, we assume that we have known knowledge that seed set of competitor \(B\) is \(S_{B}\subset V\) and each node \(u\) is associated with an arbitrary cost \(c(u)\geq 0\) to add in \(S_{A}\). We define _Budgeted Competitive Influence Maximization_ (BCIM) as follows:  
Definition 2: BCIM problem. Given a directed graph \(G=(V,E)\) representing a social network under \(\mathsf{TCLT}\) model, \(B\)-seed set \(S_{B}\subset V\), a budget \(L>0\), and time constraint \(\tau\), find \(A\)-seed set \(S_{A}\subseteq V\setminus S_{B}\) with total cost \(\sum_{u\in A}c(u)\leq L\) to maximize \(\mathbb{I}(S_{A})\).  
Theorem 1: BCIM problem is NP-hard and calculating the objective function \(\mathbb{I}(\cdot)\) is #P-Hard.  
Proof: We see that, when \(S_{B}=\emptyset\) and \(\tau=n\), the \(\mathsf{TCLT}\) model becomes well-known LT model [1] and BCIM becomes \(\mathsf{IM}\) problem [1]. In other words, \(\mathsf{IM}\) is a special case of BCIM so BCIM is NP-hard problem and calculating the influence \(\mathbb{I}(S_{A})\) is #P-hard.  
Figure 1: Illustrating for TB-WPP and TB-PP rules.  
Although the objective function in IM problem is _monotone_ and _submodular_ function, unfortunately, the objective function in BCIM is neither _submodular_ nor _supermodular_. Therefore, we cannot use the nature greedy for optimizing submodular and supermodular function to get an approximation guarantee.  
The function \(\mathbb{I}(\cdot)\) is neither submodular nor supermodular under \(\mathsf{TCLT}\) model  
Proof.: We prove that by counter example (see Figure 2). Consider an instance of BCIM problem \(G=(V,E)\) with \(V=\{a,b,c,d,e,f\}\), \(E=\{(a,b),(b,c),(c,e),(c,d),(c,f)\}\), and \(\tau=2\). The \(A\)-weight and \(B\)-weight on each edge is equal to 1, and we set \(S_{B}=\{f\}\). In this example, we have \(\mathbb{I}(\emptyset)=0\), \(\mathbb{I}(\{a\})=2\), \(\mathbb{I}(\{a,c\})=5\), and \(\mathbb{I}(\{a,b,c\})=5\). Therefore, \(\mathbb{I}(\{a,c\})-\mathbb{I}(\{a\})=3>\mathbb{I}(\{a\})-\mathbb{I}(\emptyset)=2\). That is, \(\mathbb{I}(\cdot)\) is not submodular. On other hand, we have \(\mathbb{I}(\{a,c\})-\mathbb{I}(\{a\})=3>\mathbb{I}(\{a,b,c\})-\mathbb{I}(\{a, c\})=0\). Therefore, \(\mathbb{I}(\cdot)\) is also not supermodular.
# Generalized Beam Theory for Thin-Walled Beams with Curvilinear Open Cross-Sections
###### Abstract  
The use of the Generalized Beam Theory (GBT) is extended to thin-walled beams with curvilinear cross-sections. After defining the kinematic features of the walls, where their curvature is consistently accounted for, the displacement of the points is assumed as linear combination of unknown amplitudes and pre-established trial functions. The latter, and specifically their in-plane components, are chosen as dynamic modes of a curved beam in the shape of the member cross-section. Moreover, the out-of-plane components come from the imposition of the Vlasov internal constraint of shear indeformable middle surface. For a case study of semi-annular cross-section, i.e., constant curvature, the modes are analytically evaluated and the procedure is implemented for two different load conditions. Outcomes are compared to those of a FEM model.  
generalized beam theory (GBT); curved cross-section; thin-walled beam; cross-section deformation Article
## 4 Results and Discussion
### Phase 1: Assess the Potential and Maturity for IoT Solution Implementation
#### 4.1.6 Is There a Need for Real-Time Data Collection?  
This issue, closely linked to the capabilities of companies to know in real time how different activities are unfolding along the product chain, from production, storage for sale, infers the need for real-time data collection. This will take a set of sensors, communication systems and data acquisition and processing. In Table 5, item 6 shows this need, and about 70% responded affirmatively. These same companies indicated that the parameter which requires real-time data collection was temperature.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 8 Evaluation
### Communication Overhead  
We evaluate network-wide communication overhead by comparing the communication overhead with a reference model through simulations. Because this is the first proposal of a log-integrated DPN to the best of our knowledge, we developed a model, referred to as the Output Accumulation Model (OAM), for baseline comparison, which achieves the goal of maintaining a complete record of nodes' outputs at the end of a round.  
In OAM, we assume that nodes are organized as a Report Tree and deliver their output along this tree. However, no logger exists in the DPN. Instead, the submitter collects and retains all node outputs at the end of a round. Therefore, each node should relay its output and inputs, and data are accumulated from one node to the next. At the end of the round, when all the outputs have reached the submitter, it broadcasts the complete output record back to the nodes, allowing each node to verify the accuracy of its entry in the total records. This broadcast is performed as a context-aware delivery in the reverse direction of the Report Tree. Each node extracts relevant parts from the broadcast message for its descendants and forwards the extracted parts to its children, similar to the secure aggregation model in Wireless Sensor Networks (WSNs) [21]. If a node confirms that the received part matches its output for that round, it returns a positive acknowledgment to the submitter. Once all acknowledgments are received, the submitter can confirm its comprehensive record for the round and then submit the final result. OAM achieves post-process tamper-evident logging by storing a cryptographic digest such as a hash chain  
Figure 9: Storage overhead.  
on the confirmed records. However, it lacks support for in-process tamper-evidence or efficient data-flow reconstruction.  
Figure 10 illustrates the communication overhead of DPTracer and OAM. In the simulation, we explore various DPN structures by adjusting the aggregation factor \(d\) and measured the total message sizes for both models with varying numbers of collector nodes. In DPTracer, the communication overhead increases more when \(d\) is higher. When the number of collector nodes is 300, the total message size of DPTracer increases by 6.8% when \(d\) doubles from \(d=6\) to \(d=12\). In contrast, the communication overhead of OAM decreases as \(d\) increases. This reduction is caused by fewer levels being in the Report Tree, which lowers the burden of delivering accumulated reports and the final broadcast OAM. For example, when \(d=6\), OAM incurs 68.7% more communication overhead than DPTracer. However, when \(d=9\), the performance gap decreases to 26%, and it further reduces to 17.7% when \(d=12\). In all scenarios, DPTracer exhibits lower communication overheads than OAM because it combines the delivery and verification processes with the logger, suppressing the onerous confirmation process at the end of the round and the burden of nodes relaying cumulative inputs as required in OAM.
# Rapid Estimation of Moisture Content in Unpeeled Potato Tubers Using Hyperspectral Imaging
## 2 Materials and Methods
### Model Evaluation  
The evaluation metrics are used to validate a model based on accuracy or error rate, whereas some evaluation metrics are used as an analyzer for model selection or used as a discriminator to differentiate and choose the best model [53]. The evaluation metrics R\({}^{2}\) and RMSE of the training and testing datasets are used to assess a generated model's prediction abilities [46; 54; 55]. Meanwhile, the relative performance to deviation (RPD) is used to examine the predictability of the results [46; 50; 56]. According to [PERSON] et al. [56] and [PERSON] et al. [57], a model's predictive performance is regarded as 'excellent' when the RPD  
Figure 2: Moisture content of unpeeled potatoes.  
value exceeds 1.4 and 'poor' when the RPD value is less than 1.4. The following equations are used to determine the R\({}^{2}\), RMSE, and RPD:  
\[R^{2}=\frac{\sum\left(y_{i,p}-y_{i,m}\right)^{2}}{\sum\left(y_{i,p}- \overline{y}\right)^{2}}\] \[RMSE=\sqrt{\frac{1}{m}\sum\limits_{i=m}^{m}\left(y_{i,p}-y_{i,m} \right)^{2}}\] \[RPD=\frac{SD}{RMSE}\]  
where \(y_{p}\) represents the predicted value, \(y_{m}\) represents the main value for the \(i\)th sample, \(m\) represents the number of samples, \(\overline{y}\) is the average of the true loads of m samples, and SD represents the standard deviation of the predicted values in the training (\(T\)) and testing (\(V\)) sets. Figure 3 depicts a schematic representation of the complete data processing, analysis, and model generation process.  
Figure 3: Schematic workflow of data processing.  
The variable importance in the projection (VIP) and the PLSR regression coefficients, often known as \(\beta\)-coefficients, are frequently employed methods for choosing variables. In this paper, the PLSR regression \(\beta\)-coefficients of each variable were used to choose significant wavelengths. The vector of regression coefficients (\(\beta\)) was considered as it is a single measure of the extent of correlation between each variable and the response [58]. Based on [42; 59], the PLSR model makes use of wavelengths with significant positive or negative \(\beta\)-coefficients, as they are crucial and contain valuable information. Similarly, variables with low absolute values of this filter measure and wavelengths with \(\beta\)-coefficient less than the standard deviation of all \(\beta\)-coefficients were removed from the investigation [58].  
VIP is another technique that is frequently utilized for determining effective spectral regions. The VIP technique identifies variables by first computing the VIP score, and then eliminates variables with VIP scores below a specific threshold [42; 58]. On the basis of the suggestions from [42], the threshold value in this study was determined at 1.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Encryption and Digital Signature Algorithm
#### 4.2.1 Security Parameters of SPHINCS-256  
In our proposed scheme, we use SPHINCS-256 to generate digital signature for integrity and authentication. It uses the following security parameters to provide a quality trade-off between speed and signature size [33].  
* Security parameter \(n\) referring to the bit-length of hashes in HORST and WOTS, \(n=256\).
* Bit length of message, \(m=512\).
* Height of the hypertree, \(h=60\).
* Layers of the hypertree, \(d=12\).
* WOTS parameter, \(w=16\), used for generating One-time signature.
* Number of HORST secret key elements, \(t=2\)16.
* Number of published HORST secret key elements, \(k=32\).  
Footnote 16: [[https://www.hORST.com/](https://www.hORST.com/)]([https://www.hORST.com/](https://www.hORST.com/))
## 2 Project Overview  
The Jinshui River flood control project in Zhengzhou comprises dual-line underground jacking engineering spanning 5.44 km, featuring seven receiving wells and eight working wells along its route, as illustrated in Figure 1. The caissions are constructed with circular reinforced concrete structures with an outer diameter of 12.0 m, an inner diameter of 10.0 m, and a height of 28.85 m, divided into three sections with respective heights of 10.0 m, 9.15 m, and 9.70 m from crown to invert. The invert of the caisson is equipped with a 2.0 m high blade, with a tread width of 0.40 m, utilizing self-weight for undrained excavation.  
According to the engineering geological survey report, the strata where the caissons are situated consist of miscellaneous fill soil, loess-like light silty soil, and loess-like medium silty soil from top to bottom. These soils exhibit moderate collapsibility, with the groundwater level being 5.66 m above the bottom of the well. The material parameters for the soil and relevant caisson structures are listed in Tables 1 and 2, respectively.  
of the ground and caisson structure, promptly detect and address any safety hazards, rectify design and construction deficiencies, prepare for subsequent safe constructions, ensure a safe excavation of the caisson, achieve construction information, and compare the results with FE simulations to further its validation. Four monitoring points were symmetrically arranged around the caisson, with the overall monitoring layout detailed in Figure 2. To mitigate the influence of temperature fluctuations on strain gauges, corrective measures were taken to compensate for temperature effects during preliminary tests, and sunshade measures were implemented during monitoring.
# Survey of Smart Parking Systems
## 4 Vehicle Detection Techniques (VDT) Implemented in SPS
### In-Roadway Sensors  
IRS detection technology is based on monitoring car parks using sensors located in these spaces, which allow vehicle arrival and departure to be detected from the corresponding place [292]. The IRS is usually located on the pavement or road surface. Most have an ultrasonic [263] or magnetic [109] detection type that reacts when the vehicle is parked in the parking slot.  
There are also wireless optical communication systems that use the modulation of visible light waves they receive to sense information; this technology is called visible light communication (VLC) [156]. In the event that a vehicle is parked in a parking slot, a VLC module will no longer receive light and will indicate a change of state. Therefore, through the light modulations, one can send data and, thus, get real-time parking information.  
IRS is not widely implemented and does not have a greater tendency to be used by any SPS in particular. Each of these IRS sensors has its own limitations that must be taken into account when establishing an SPS. Because they all require being slotted into the bitumen, this makes them expensive in terms of installation and maintenance. They are also sensitive to temperature and weather conditions [4]. Ultrasonic sensors suffer from surrounding noise, and magnetic sensors are affected by air temperature and pressure. These inconveniences lead to the need for frequent maintenance of the systems using these sensors. In addition, they need power from either cables or batteries, which implies an additional cost in their use.
# Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems
## 3 Deep Learning-Based Receiver  
The traditional maximum likelihood decoding scheme becomes practically infeasible and complex as the number of parameters to be detected is increased. Therefore, deep learning-based detection can be investigated as a good alternative detection scheme [38; 39]. A Convolutional Neural Network (CNN) with multiple hidden layers is used in this work. The ReLu activation function is used for hidden layers, and a softmax activation function is used for the output layer. Since symbol level detection is considered, we generate training data of constellation symbols of different M-order PSK along with channel information \(H\). The proposed CNN includes three CNN layers followed by the pooling layers and two dense layers, and it ends with the output layer with the softmax activation function. For training, we use the following loss function with \(l_{2}\) norm [40]:  
\[\text{F}(\theta)=\sum_{k=1}^{K}\frac{1}{|S_{k}|}\sum_{s_{k}\in S_{k}}||s_{k}- \hat{s}_{k}||^{2} \tag{22}\]  
where \(k\) is the constellation symbol, and \(\theta\) refers to weight and bias. Figure 8 shows the general framework of the multiuser communication system using CNN and NOMA.
## 3 Building Elements
### Pre-Processing  
1. The Sender (A) generates a random integer \(a_{A}\) such that \(1<a_{A}<p^{*}-1\); considering that \(p^{*}\approxeq 2^{1024}\). Consequently, the calculation \(\beta_{A}=\alpha^{a_{A}}\bmod p^{*}\) is performed. Finally, \(\beta_{A}\) is sent to the receiver (B). Similarly, the receiver (B) randomly obtains an integer \(a_{B}\) with \(1<a_{B}<p^{*}-1\), then computes the value \(\beta_{B}=\alpha^{a_{B}}\bmod p^{*}\) and transmits it to the sender. From here, the sender has \(\beta_{B}\) and performs the following calculation: \(\beta=\beta_{B}^{a_{B}}\bmod p^{*}\). Note that the receiver can also generate the integer \(\beta\). Consequently,both the sender and receiver have the same information. As can be seen, \(\beta_{A},\beta_{B}\) are public; although, \(\beta\) is private.
2. At this moment 128 strings \(\beta_{i}=\alpha^{i}\times\beta\) mod \(p^{*}\) are built, with \(i=1\cdots 128\). Note that each \(\beta_{i}\) is taken as a 1024-bits message. While if a \(\beta_{i}\) has a length less than 1024 bits, then zeros are added to the left until the string reaches that magnitude.
3. The sender randomly generates two positive integers that satisfy \(1<C^{1},C^{2}<2^{512}\) for sending them to the receiver. In addition, using the Block-Chain technique, two 512-bit binary strings are associated with each integer \(C^{1},C^{2}\). Afterward, they are divided into blocks of 8 bits, resulting in 64 blocks of 8 bits for each integer. In fact, the result of the joint of both strings is 128 strings of one byte. The number associated with each block is \(b_{i}\) for \(i=1,2,\cdots,64\), and the range of \(b_{i}\) is from 0 to 255.
4. This research proposes to apply the Hash SHA-512 function to the chain \(\beta_{i}\). If \(b_{i}=0\), the Hash-SHA algorithm is applied once to the chain \(\beta_{i}\). In contrast, if \(0<b_{i}\leq 255\), the Hash-SHA algorithm is executed \(b_{i}+1\) times in a chained manner [51], starting from the block \(\beta_{i}\). Therefore, as a result of this process, there are 128 strings of 512 bits, and each of them is associated with an integer. It is important to say that the sender publishes these strings. On the other hand, the receiver can replicate the procedure to know the associated values with each block of the 512-bit strings. Consequently, the receiver (B) knows the integers \(C^{1},C^{2}\).