esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
1. A process was proposed to assess the suitability of multilayer UUS based on the impacts of geological environments. The case study of Xiong'an New Area was used to establish an assessment indicator system, including four types of criteria indicators: topography, geotechnical characteristics, hydrogeological conditions, and adverse geological phenomena. The suitability of four vertical layers within a depth of 100 m was identified, providing guidance for urban planners and constructors. To ensure an objective and rational assessment, the entropy and CRITIC combined method was employed to determine the weights of sub-criteria indicators, considering the discreteness, correlation, and comparative intensity of data. Compared with previously used combination by other researcher, this paper demonstrated a better method. This combined weighting method can be applied to similar regions for the suitability assessment of UUS.
2. The evaluation results indicated that the suitability of UUS in Xiong'an New Area varied depending on the depth of strata. Deeper layers showed better suitability, while shallower layers showed relatively worse suitability. The proportions of acrage with good and moderate suitability were as follows: 45.31% for shallow layer, 57.94% for sub-shallow layer, 58.31% for sub-deep layer, and 57.97% for deep layer. When considering the plane distribution state, the good and moderate suitable zones were predominantly located in the north, south, and southwest of the study area. Conversely, the inferior and worse suitable zones were mainly found around Baiyangdian Lake. Although the factors influencing the suitability of the four layers differed, the main indicators were the bearing capacity of soils, compression modulus of soils, groundwater buried depth, and ground elevation, which carried higher weights. Groundwater buried depth was significantly affected by artificial exploitation of groundwater, while the other three indicators are primarily influenced by the sedimentary process of strata.
3. The paper focused on the geological indicators affecting the utilization of UUS in Xiong'an New Area. However, it did not include socioeconomic factors and excavation types of underground facilities. Although the UUS utilization in Xiong'an New Area is still in the initial stage, future studies should consider socioeconomic progress, existing underground infrastructures, and the difficulty of excavation based on the evaluation results.  
Conceptualization, H.L. and Q.H.; methodology, H.L.; software, H.L. and Z.L.; validation, H.L. and Z.L.; investigation, H.L. and Z.L.; data curation, H.L. and Z.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and Q.H.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by China Geological Survey project (Grant No. DD20221727).  
Not applicable.  
**Acknowledgments:** The authors would like to thank [PERSON] and [PERSON] for their valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 4 Preliminaries
### Function Secret Sharing
#### 4.4.1 Secure Computation with FSS in the Preprocessing Model.  
[PERSON] et al. [44; 45] observe that FSS can be used to perform two-party secure computation with optimal online communication. However, the generation of FSS keys either requires a trusted third party or relies on expensive distributed generation. [PERSON] [46] extends this approach to a three-party setting, where one party generates and distributes FSS keys to the other two parties in the offline phase.  
A computation function is represented as a circuit, where \(f_{i}\) denotes the gates to be evaluated, and \(w_{i}\) represents input and output wires. Each gate maps an input from a group \(\mathbb{G}^{in}\) to an output in a group \(\mathbb{G}^{out}\), where gates with multiple input or output wires can be represented using product groups. To ensure secure gate computation, an offset function is used to conceal the inputs and outputs. We first introduce the following definition.  
**Definition 2** (Offset Function and FSS Gate).: _Let \(\mathcal{G}=\{g:\mathbb{G}^{in}\rightarrow\mathbb{G}^{out}\}\) be a computation gate; the offset function family \(\hat{\mathcal{G}}\) of \(\mathcal{G}\) is given by_  
\[\hat{\mathcal{G}}:=\left\{g[^{in},r^{out}]:\mathbb{G}^{in}\rightarrow\mathbb{G }^{out}\mid\begin{array}{l}g:\mathbb{G}^{in}\rightarrow\mathbb{G}^{out} \in\mathcal{G},\\ r^{in}\in\mathbb{G}^{in},r^{out}\in\mathbb{G}^{out}\end{array}\right\}\]  
_where \(g[^{in},r^{out}](x):=g(x-r^{in})+r^{out}\)._  
The term FSS gate for \(\mathcal{G}\) denotes an FSS scheme for the corresponding offset function family.  
We describe the high-level idea using [PERSON]'s three-party model.  
**Offline Stage.** For a gate \(g\) where the input and output wires are \(w_{i}\) and \(w_{j}\), \(P_{2}\) generates the FSS keys \((k_{0}^{g},k_{1}^{g})\) corresponding to the offset function \(g[^{r_{i},r_{i}}](x):=g(x-r_{i})+r_{i}\), where \(r_{i}\) and \(r_{i}\) are the random masks for \(w_{i}\) and \(w_{j}\) sampled by \(P_{2}\). The resulting triplet \((k_{b}^{g},\langle r_{i}\rangle_{b},\langle r_{j}\rangle_{b})\) is then distributed to \(P_{b}\), \(b\in\{0,1\}\).  
**Online Stage.** To obtain the correct result, \(P_{b}\) also needs to perform an offset shift on the input to the FSS evaluation function. After obtaining the secret share of \(x_{i}\), \(P_{b}\) computes \(\langle x_{i}\rangle_{b}+\langle r_{i}\rangle_{b}\) and sends it to \(P_{1-b}\). Both parties now can reveal the same offset input \(x_{i}+r_{i}\), evaluate the corresponding FSS key, and obtain the share of \(g(x_{i})\) via \(\mathbf{Eval}(k_{b}^{g},b,x_{i}+r_{i})-\langle r_{j}\rangle_{b}\).
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Security
#### 3.2.1 Authentication and Authorization  
The authors in [14] have used the CoAP for the purpose of integration between use of Authentication, Authorization and Accounting (AAA) infrastructures and Extensible Authentication Protocol (EAP). Being a lightweight transport protocol, the use of CoAP covers the restrictions of Low-Power Wide Area Networks (LP-WAN) such as constrained bandwidth, low memory, and restricted resources in terms of CPU and energy consumption. The proposed scheme named as Low-Overhead CoAP-EAP (LO-CoAP-EAP) provides a solution for network access authentication forconstrained devices in LP-WAN. CoAP in this scheme enables the design of a network authentication service which can be used independently and does not depend on any LP-WAN technology. The CoAP is used for communication between the constrained devices. The architecture contains (i) a smart object acting as a CoAP server, (ii) a controller as CoAP client and (iii) a AAA server. The exchange of CoAP POST messages is employed to obtain the identity of the smart object for providing it with the required services.
## 3 Results and Discussion
### RF Treatment of Nutrient Broths without Bacteria
#### 3.2.3 Power Consumption  
The maximum power consumption of the samples was calculated using the recorded voltage and current (P = UI). However, it should be taken into account that not all emitted power was absorbed by the sample, as the environment and the electrical system also consume a certain amount of the electromagnetic energy.  
The evolution of the power consumption during the RF treatments of TSB and BHI is illustrated in Figure 5. For both media, the power consumption decreased during the first minutes of the treatments, remaining at an approximately constant value afterwards, i.e., 12.14-12.25 W and 12.67-12.85 W, for TSB and BHI, respectively. Over the course of the entire treatment, the power consumption in TSB was in general also significantly lower than in BHI. The maximum power consumption in samples exposed to RF energy can be described by Equation (1) [27].  
\[P=2\pi\text{f}\epsilon_{0}\epsilon^{\prime\prime}\bigg{|}\overset{\rightarrow}{E_{m} }\bigg{|}^{2} \tag{1}\]  
where \(P\) (W/m\({}^{3}\)) is the power consumption caused by the conversion from electromagnetic to thermal energy; f (Hz) is the frequency; \(\epsilon_{0}\) is the permittivity of vacuum (8.854 \(\times\) 10\({}^{-12}\) F/m); \(\epsilon^{\prime\prime}\) is the dielectric loss factor; and \(\bigg{|}\overset{\rightarrow}{E_{m}}\bigg{|}\) (V/m) is the electric field intensity in the sample. The change in power consumption over the course of the treatments occurs according to Equation (1). The most probable cause for the decrease in power absorption observed in Figure 5 is a change in the electrical capacitance of the media with increasing temperature, which is possibly due to a decrease in the dielectric loss factor. In addition, the decrease in power consumption during the first treatment minutes for both media is a possible cause for the relatively fast decrease in frequency observed during that time period, as seen in Figure 4. In future studies, the quantification of the temperature-dependent dielectric properties of TSB and BHI could elucidate the underlying physical mechanisms causing the observed frequency and power consumption profiles.  
Figure 4: Frequency profiles in TSB (**A**) and BHI (**B**) during RF treatments. Individual replicates and the average frequency profile are shown.
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Deep Learning Techniques
#### 5.4.1 Hyperparameters' Optimization  
In this step, hyperparameter tuning was performed to achieve optimal performance of the ANN. To do this, a Grid Search method was applied to the MLP model provided by the _Scikit-Learn_ library. The study was focused on configuring hidden layers and the number of neurons per layer (_hidden_layer_sizes_), as well as other hyperparameters such as the activation function (_activation_) and the optimization algorithm (_solver_). To prevent overfitting during Grid Search execution, a maximum number of 100 iterations was set along with early stopping, which was activated if no significant improvements occurred in 10 consecutive iterations.  
The entire search process took around 2.34 h on a server with 32 processors. In Table 9, it can be seen that the Stochastic Gradient Descent (SGD) optimization algorithm generally outperformed the _Adam_ algorithm. Regarding training times, Table 10 represents the impact of both the layer structure and the number of neurons configured in the network. Similarly, the dimensions also affected the achieved accuracies, with some cases of overfitting leading to lower classification accuracies.  
Figure 14: Analysis of RF model accuracy based on the number of features used with RFECV method.  
Figure 15: Analysis of variance based on the number of components used in PCA.  
Therefore, the analysis of the results determined that the optimal MLP configuration was given by a structure of two hidden layers with five neurons each (5, 5), the Rectifier Lineal Unit (RELU) activation function, and the SGD optimization algorithm. Figures 16 and 17 represent the evolution of the accuracy values and the loss function, respectively. The optimal model converged at iteration 41, achieving an accuracy of 97.66% and a loss function value of 0.0712.
## 4 Design and Development
### Data Structures and File Formats
#### 4.2.2. CSV Log Format for Evaluation Results  
The access evaluation results are stored in a structured CSV format with the following fields: user_id, resource_id, status, latency_ms, and timestamp. An example is shown in Listing 3.  
**Listing 3.** Excerpt from simulation_results.csv.  
user01, MIN4701, Granted, 42, 2025-04-25T07 : 22 : 11Z  
user02, MIN5288, Denied, 38, 2025-04-25T07 : 22 : 12Z  
The use of structured JSON for inputs and CSV for logging enabled reproducible experimentation, streamlined access auditing, and seamless integration with data analytics tools such as Python (Pandas), Excel, and machine learning pipelines.
## 4 Results
### Analysis of Energy Management in the Three Irrigation Types  
Figure 7 shows the energy management with the proposal of a predictive control, based on MPC models with a control horizon of one day for the proposed optimal irrigation, where the energy or energy demand of the pumps (Demand_b) sent by the expert system is the 4 h turn-on, represented by the purple line, the optimizer decides the pump start-up time automatically and thus optimizes the sources that provide energy to the system, identifying the best time for the batteries to operate in charge or in discharge mode. The available energy of the BESS system (Energy_B) is represented by the red curve. It can be observed that there is no use of the diesel generator (P_diesel), i.e., it does not generate cost in the system, keeping the brown curve at zero. The solar power (P_GS), represented by the lead curve, indicates that it is used to recharge the batteries when conditions are more opportune. The yellow line represents when the batteries are in charge mode (P_Battery_Charge), and the light blue curve indicates when the BESS system is in discharge mode (P_Battery_Discharge), while the blue curve represents the energy not supplied (P_Nosupplied), which is kept at zero, i.e., the system is maintained with the necessary energy all the time. It can also be observed that the green curve is the solar shedding (P_solar_ shedding) the excess solar energy when more energy is produced than is demanded.  
After reviewing the energy consumption with the irrigation techniques of the crop in Scenario 2, the curves of energy consumption without management can be analyzed, i.e., the pumps are turned on according to a fixed schedule and the system must supply energy whatever the source (traditional irrigation, technified irrigation), whereas in the proposed irrigation with optimal energy management, the optimizer has the flexibility to see the most appropriate time to turn on the pump, taking advantage of solar energy and batteries, to avoid the ignition of the diesel generator as much as possible.  
There are no proposals describing optimal irrigation simultaneously with the energy management used to supply the electrical irrigation demand. However, a comparative analysis was performed concerning a smart irrigation system proposed in [31], in which the control system is based on fuzzy logic. The ignition pump was analyzed in order to establish the differences found in our proposal regarding energy consumption. In [31], the water pump powered up for 7 h of growing period (three days). Compared to our proposed smart irrigation, the pump powers up for 3 h during the growing period, achieving a good performance in the crop. The powering up of the pump is related to the cost of energy consumption. Therefore, [31] presents a higher cost. Moreover, unlike [31], our proposal includes the management of a microgrid based on renewable sources.
# Influence of Deep Foundation Pit Excavation on Adjacent Pipelines: A Case Study in Nanjing, China
## 1 Introduction
However, this model overlooks the continuity of foundation deformation, leading to improved dual-parameter models in many studies. In the field of deep foundation pit engineering, most historical research has concentrated on singular soil or rock strata, leaving a gap in the understanding of the effects of excavations in complex strata, which include layers of both soil and rock. This study, focusing on a deep foundation pit project in Nanjing's soft soil floodplain, aimed to fill this gap. By combining field monitoring data with advanced numerical simulations using Plaxis 3D, this research delved into the intricate dynamics of composite soil-rock strata and their influence on pipeline deformation. This study elucidates not only he deformation patterns of pipelines in proximity to deep foundation pits but also how variables such as the excavation depth and the burial depth of pipelines interplay in this context. The insights gained are pivotal for the design and execution of large-scale foundation pits in similar soft soil regions, offering a significant contribution to both theoretical understanding and practical application in geotechnical engineering.
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
## 2 Materials and Methods
### Density Model
#### 2.4.3 Density Calculation  
The density is calculated as follows. If the line developed between a pair of consecutive positions of a ship crosses two or more cells, the length of the line segment that crosses a cell can be calculated. To calculate the occupancy time of a ship in each cell, the length of the line segment is divided by the total line length and then multiplied by the total line time. The density in each individual cell is then determined by calculating the time value of each line segment and adding all time values associated with the cell:  
\[D_{i}=\sum_{j=1}^{n}\frac{S_{j}}{L_{j}}\times T_{j} \tag{3}\]  
where \(D_{i}\) is the ship density (h) in cell \(i\), \(L_{j}\) is the total length (km) of the line, \(S_{j}\) is the partial length (km) of the line \(j\) intersecting the cell \(i\), \(T_{j}\) is the total time (h) along the line \(j\) and \(n\) is the number of lines associated with the cell. Therefore, the density is the time spent by each ship (in hours) in a given cell during the full timespan (e.g., one month).
## 2 Materials and Methods
### Data Analysis
#### 2.6.2 Time-Series Analysis  
The primary motor cortex (M1), supplementary motor area (SMA), and prefrontal cortex (PFC) were selected as the regions of interest (ROIs). During the task, the concentration changes of HbO were measured from each channel of the ROIs. For each ROIs, the grand average of the hemodynamic response was computed. To compare the activities among the different brain regions, the peak values of the concentration changes of HbO were obtained from the averages of the ROI channels (M1: channels 2, 4, and 10; SMA: channels 11, 12, 13, 17, 19, 21, and 22; PFC: channels 38, 39, 41, 42, 45, 46, and 47). A total of 33 and 39 data samples were used for CG and IG, respectively (CG: 33 samples = 3 trials \(\times\) 11 subjects, IG: 39 samples = 3 trials \(\times\) 13 subjects).
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation
#### 2.1.3 Rate Matcher  
The main functionality of the rate matching block is to match the number of bits in the transport block to the number of bits that can be transmitted in a given allocation [11]. In addition, it controls the turbo encoder's rate, which gives a \(1/3\) rate, as the rate can be adjusted according to the downlink control information (DCI) and the channel quality information. Using rate matching, any arbitrary code rate can be achieved from a fixed-rate mother code [14]. As shown in Figure 3, the design of the rate matcher block consists of the following [10]:  
* The 32-bit serial-to-parallel converters, one for each stream.
* One port RAMs of size \(128\times 32\) bit as the maximum number of rows corresponding to TBS of 2536 bit is 81 rows.
* A control unit.  
The block's operation starts with the write state. As the input streams are received serially and then turned into parallel to be stored in the 2D array, a 32-bit serial-to-parallel converter is used to write data row by row [10; 11]. Afterward, the read state starts from the ninth column in the first sub-block interleaver or from the nineteenth column in the second sub-block interleaver if the redundancy version value (\(rv_{idx}\)) equals 0 or 2, respectively. The staring column, \(k_{0}\), is determined according to the following equation [14; 15]:  
\[k_{0}=R_{subblock}^{Tc}.(24.rv_{idx}+2) \tag{10}\]  
where \(R_{subblock}^{Tc}\) is the number of rows.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 5 Conclusions  
In this paper, we propose two routing algorithms for SR in SDN. Our goal is to reduce the consumption of network resources and optimize the load balance of the network as well as select the optimal path in combination with the business preference and actual situation of the network. First, we use the MOPSO algorithm to optimize the link weight and set two objective functions for load balance and network cost. Then, we choose the first \(k\) shortest paths among all the paths. Finally, the path with the maximum comprehensive evaluation value is selected from the \(k\) paths. Furthermore, we propose an improved MOPSO algorithm to optimize the link weight. Simulation results show that our proposed algorithms have better performance than other algorithms in terms of the consumption of network resources, load balance, maximum link utilization. Further, our proposed algorithm can choose the best path according to the user's preference and obtain larger the value of the performance comprehensive evaluation. Our algorithms tend toward theoretical simulation, and further work will perform experiments on real networks and take into account the network throughput and rejection rate for a more comprehensive algorithm.  
[PERSON]. (PhD student) is the main author of the current paper. She has put forward the main idea, proposed the plan, designed the experiment, and wrote the manuscript. [PERSON] is the PhD advisor. He has suggested improvements in the main ideas of manuscript research and writing. [PERSON] has analyzed the proposed method, proposed improvement suggestions, and modified the grammar of the paper. The final draft that all authors read and approve.  
This work is supported by the 111 project (NO.B17007), Director Funds of Beijing Key Laboratory of Network System Architecture and Convergence (NO.2017 BKL-NSAC-ZJ-01) and National Natural Science Foundation of China (NSFC) (NO.61872401).  
The authors declare no conflict of interest.