esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response of Railway Subgrade Under Train Load and Freeze-Thaw Action
## 2 Three-Dimensional Finite Element Model of Railway Subgrade
### Freeze-Thaw Action  
Owing to the effectively infinite extent of the railway roadbed, a two-dimensional model has been employed to replicate the thermal conduction process. During the phase transitions of soil thawing and freezing, the simulation incorporates heat transfer through the soil matrix, as well as the heat exchange associated with the ice-water phase change, while neglecting other thermal phenomena. In view of the low water content of the soil in the test section, the convection, water evaporation, etc., during the melting process are ignored. For the heat transfer problem, it contains a phase change, the variable enthalpy \(H\) (J/m\({}^{3}\)) [35] is introduced in the calculation to account for the latent heat of the material during the ice-water phase change, the change of temperature with time at the phase change interface is intermittent. The change in enthalpy with time is continuous, there is no need to keep track of the two-phase interface when solving the enthalpy distribution by numerical methods, it makes possible to treat the liquid-phase region and solid-phase region in unison. After the enthalpy field is solved, the temperature field is solved.  
\[H(T)=\int\rho c(T)dT \tag{5}\]  
\[\frac{\partial H}{\partial t}=\frac{\partial H}{\partial T}\frac{\partial T}{ \partial t}=\rho c(T)\frac{\partial T}{\partial t} \tag{6}\]  
Therefore, the soil temperature field control differential equation is:  
\[\frac{\partial H}{\partial t}=\frac{\partial}{\partial x}(\lambda\frac{ \partial T}{\partial x})+\frac{\partial}{\partial y}(\lambda\frac{\partial T }{\partial y}) \tag{7}\]  
where \(\rho\) is the density of the material (kg/m\({}^{3}\)), \(c(T)\) is the specific heat of the soil as a function of temperature (J/(kg\({}^{\circ}\)C)), \(\lambda\) is the coefficient of thermal conductivity of the material (W/(m\({}^{\circ}\)C)), \(T\) is the temperature (\({}^{\circ}\)C), \(t\) is the time (s).  
According to the survey data, the region experiences an average annual temperature of approximately 7 \({}^{\circ}\)C. Furthermore, the thermometric extremes recorded within the area are characterized by a maximum temperature of around 30 \({}^{\circ}\)C and a minimum temperature of approximately \(-\)15 \({}^{\circ}\)C. The seasonal freezing and thawing process of subgrade soil is essentially a process of energy change. The model of subgrade temperature field is established by numerical simulation method, and the mechanical characteristics of railway subgrade at different temperatures are analyzed according to the boundary conditions of subgrade temperature field and related thermodynamic parameters.
# FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
###### Abstract  
Federated learning (FL) is an emerging decentralized machine learning framework enabling private global model training by collaboratively leveraging local client data without transferring it centrally. Unlike traditional distributed optimization, FL trains the model at the local client and then aggregates it at the server. While this approach reduces communication costs, the local datasets of different clients are non-Independent and Identically Distributed (non-IID), which may make the local model inconsistent. The present study suggests a FL algorithm that leverages regularization and data sharing (FedRDS). The local loss function is adapted by introducing a regularization term in each round of training so that the local model will gradually move closer to the global model. However, when the client data distribution gap becomes large, adding regularization items will increase the degree of client drift. Based on this, we used a data-sharing method in which a portion of server data is taken out as a shared dataset during the initialization. We then evenly distributed these data to each client to mitigate the problem of client drift by reducing the difference in client data distribution. Analysis of experimental outcomes indicates that FedRDS surpasses some known FL methods in various image classification tasks, enhancing both communication efficacy and accuracy.  
federated learning; non-IID data; regularization; data sharing; machine learning +
Footnote †: journal:  
differences in data collection and resulting in data heterogeneity. The presence of heterogeneity among clients can cause variations in their objective functions and optimization directions, leading to drift in local updates and slower convergence speeds. This difference in data distribution is a crucial factor responsible for the inherent equilibrium, minimizing the loss of locally trained experience and reducing globally trained experience loss.
## 3 Results and Discussion
### Application of the Proposed Model to the G-Band Images Dataset
#### 3.7.3 Desktop Application for Semi-Automatic Chromosome Classification  
In Figure 17, a screenshot of the developed semi-automated chromosome segmentation desktop app is shown. The cytogeneticist can read chromosome images from the microscope connected to the computer that hosts the desktop application. Once the chromosome image has been acquired, the cytogeneticist can choose between two segmentation modes: the automatic or the semi-automatic mode. In the semi-automatic mode, the cytogeneticist interacts with the app by manually selecting the approximated medial axis to aid the segmentation process of the displayed chromosomes.  
Figure 16: Phase 1 and 2 confusion matrices for the 3 wide classes classification. (**a**) Phase 1 confusion matrix for the Group 1 versus all classifier. (**b**) Phase 1 confusion matrix for the Group 2 versus all classifier. (**c**) Phase 1 confusion matrix for the Group 3 versus all classifier. (**d**) Phase 2 confusion matrix for group 1. (**e**) Phase 2 confusion matrix for group 2. (**f**) Phase 2 confusion matrix for group 3.  
The segmented chromosome are then automatically classified by the previously trained two-stage classifier to generate a preliminary karyotype with the classification label for each chromosome.  
The cytogeneticist would use the generated results to build the final karyogram, including the possibility to manually correct misclassified chromosomes. In Figure 18, an example of a preliminary karyogram is shown. This karyogram is the output generated by the proposed system, and may need additional intervention of the cytogeneticist. In this specific situation, the misclassified chromosomes must be manually arranged by the cytogeneticist to have the final karyogram complete.  
Figure 17: Screenshot of the proposed chromosome segmentation application used in manual chromosome segmentation mode.  
Figure 18: Screenshot of the proposed chromosome segmentation application, where a preliminary karyogram is shown.
## 2 Methods
### LCA Methodology
#### 2.3.4 Inventory Analysis of the Manufacturing Stage
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Element** & **Unit** & **Quantity/Unit** & **Unit of Measurement** & **Replacement** & **Total Amount** \\ \hline Heat pump & & & & & \\ Compressor & 1 & 5 & kg & 2 & 10 \\ Controller & 1 & 1 & unit & 2 & 2 \\ Inverter & 1 & 1 & unit & 3.3 & 3.3 \\ Expansion valve & 1 & 1 & unit & 2 & 2 \\ Heat exchanger-evaporator & 1 & 22 & kg & 1.2 & 26.4 \\ Fan-evaporator & 1 & 1 & unit & 2 & 2 \\
4-way valve & 1 & 1 & unit & 2 & 2 \\ Refrigerant R32 & 1 & 3.5 & kg & 2 & 7 \\ Expansion vessel & 1 & 8 & kg & 1.2 & 9.6 \\ Heat exchanger–condenser & 1 & 9.5 & kg & 1.2 & 11.4 \\ Electric heater & 1 & 1.2 & kg & 2 & 2.4 \\ Manual 2-way valve & 1 & 0.4 & kg & 2 & 0.8 \\ Circulating pump & 1 & 4.7 & kg & 2 & 9.4 \\ Diverter 3-way valve & 1 & 1 & kg & 2 & 2 \\ Hydraulic separator & 1 & 12 & kg & 1.2 & 14.4 \\ Security valve & 1 & 0.1 & kg & 1.2 & 0.12 \\ Flow rate sensor & 1 & 1 & unit & 2 & 2 \\ Electric storage & & & & & \\ Electric battery & 1 & 225 & kg & 0.75 & 168.8 \\ DC–DC converter & 1 & 40 & kg & 1.5 & 60 \\ AC–DC converter & 1 & 50 & kg & 1.5 & 75 \\ Electric controller (PLC) & 1 & 0.5 & kg & 1.5 & 0.75 \\ Current transducer & 3 & 0.24 & kg & 1.5 & 1.08 \\ Line filter & 1 & 1.8 & kg & 1.5 & 2.7 \\ Fuses & 8 & 0.02 & kg & 1.5 & 0.24 \\ Aux power supply & 1 & 0.75 & kg & 1.5 & 1125 \\ Grid monitoring & 1 & 0.36 & kg & 1.5 & 0.54 \\ LED indicators & 5 & 0.05 & kg & 1.5 & 0.375 \\ Insolation monitoring & 1 & 0.39 & kg & 1.5 & 0.585 \\ Connectors & 4 & 0.03 & kg & 1.5 & 0.18 \\ Time delay relay & 1 & 0.07 & kg & 1.5 & 0.105 \\ Power relay & 1 & 0.86 & kg & 1.5 & 1.29 \\ Relays & 5 & 0.06 & kg & 1.5 & 0.45 \\ Grid contactor & 1 & 0.84 & kg & 1.5 & 1.26 \\ DC contacts & 3 & 1 & kg & 1.5 & 4.5 \\ Battery contactor & 1 & 5.2 & kg & 1.5 & 7.8 \\ Circuit breakers + aux & 7 & 0.8 & kg & 1.5 & 8.4 \\ Precharge resistor & 1 & 0.01 & kg & 1.5 & 0.015 \\ Switches & 4 & 0.2 & kg & 1.5 & 1.2 \\ Smart meters & 3 & 0.1 & kg & 1.5 & 0.45 \\ Ethernet switch & 1 & 0.28 & kg & 1.5 & 0.42 \\ Terminal blocks & 84 & 0.01 & kg & 1.5 & 1.26 \\ Electric cabinet + acc. & 1 & 30 & kg & 1.5 & 45 \\ DIN rail & 6 & 0.8 & kg & 1.5 & 7.2 \\ Wires & 1 & 30 & kg & 1.5 & 45 \\ Screws & 150 & 0.04 & kg & 1.5 & 9 \\ Nuts & 100 & 0.03 & kg & 1.5 & 4.5 \\ Washers & 100 & 0.01 & kg & 1.5 & 1.5 \\ Slotted wiring duct & 3 & 0.5 & kg & 1.5 & 2.25 \\ Strain relief bar & 2 & 0.01 & kg & 1.5 & 0.03 \\ Latent storage & & & & & \\ \hline RPW-HEX & 1 & 220 & kg & 1.5 & 330 \\ Paraffin & 1 & 80 & kg & 1.5 & 120 \\ Sensible storage & & & & & \\ DHW tank & 1 & 10 & unit & 0.7 & 6.7 \\ PV panels & & & & & \\ PV panels & 1 & 80 & m\({}^{2}\) & 1.2 & 96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inventory of the innovative system.
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 5 Monitoring of Agricultural Parameters
### Haystack Monitoring
#### 5.1.1 Moisture  
Moisture is a key determinant for the potential self-combustion of hay bales. Thus, moisture content should be measured as it precedes the combustion itself and if below a certain threshold precludes the event of a haystack fire. The inconsistency due to irregular moisture sources such as dew or steam can be reduced by taking an intrusive sample past the surface layer of the bale [64].  
\begin{table}
\begin{tabular}{c c} \hline
**Sensor Type** & **Sensor/Devices Operations** \\ \hline Passive Infrared (PIR) & PIR sensor traces the range of a person’s movement in the field. The sensor also has a light detection property: while tracking an object, rising temperatures are converted into voltage for analysing crop growth [56]. \\ Soil moisture & Measures the quantity of water and level of moisture across the field [57]. Utilised to monitor the greenhouse irrigation system [58]. Changes in the soil temperature affect the absorption of soil nutrients and moisture. Utilised to map the exact amount of nutrients in soil and the water surface [59]. \\ Humidity & Measuring the temperature and moisture content of the air to determine humidity in air and its potential impact on the growth of plant leaves, photosynthesis and pollination [60]. \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{l c c} \hline
**Crowbar Status** & **Temperature (°C)** & **Management** \\ \hline Can handle the bar with no & & \\ discomfort & \(<\)50 & Check daily \\ Can handle for short time only & 50–60 & Check twice daily, remove \\ Can only touch bar briefly & 60–70 & Check every 2–4 h, move hay to \\  & & improve airflow \\ Bar is too hot to hold & \(>\)70 & Call Fire Brigade, do not walk \\  & & on stack \\ \hline \end{tabular}
\end{table}
Table 5: Temperature interpretation using a crowbar [61].  
Moisture content of a given material can be measured by four different methods, such as, absolute methods, wet chemical methods, electrical methods and spectrometric methods [65]. Absolute methods directly determine the actual moisture content of a material and therefore can be used as reference methods. Wet chemical methods use various chemical reagents to directly measure the moisture content. Finally, electrical methods use electrical energy and spectrometric methods use light energy to indirectly correlate with actual moisture content of a given material [66].
## 2 Test Samples and Methods  
### Preparations of Experimental Samples  
In this study, the process of producing a pre-stressed beam model includes the following steps. Firstly, a steel reinforcement framework is constructed, then a pre-stressed duct is embedded (corrugated pipe), and grouting defect channels and internal defects are set up in the beam. Subsequently, the concrete is pre-molded before pouring, and after it is poured, it is allowed to solidify; then, the beam formwork is removed. Next, the grouting defect pipes are cut off, steel strands and anchorages are installed, pre-stress is applied, the pre-stressed ducts are grouted, and, finally, the cement slurry is cleaned at the grouting defect locations. Upon completion of the entire process, the production of the pre-stressed bridge beam model is successfully achieved. An overall diagram of the pre-stressed bridge beam model is shown in Figure 1.  
[PERSON] et al. [15]. utilized inkjet 3D printing technology for the fabrication of cement mortar specimens. The performance, structure, and properties of the inkjet 3D-printed cement mortar specimens were investigated through the combination of an experimental analysis and a numerical simulation. These properties were subsequently employed for finite element analysis (FEA) modeling. In this paper, the material properties of the experimental samples are defined as shown in Table 1.  
The pre-stressed bridge beam slab model was pre-set with four pre-stressed pipes numbered N1, N2, N3, and N4 from top to bottom. There were three defects with a length of 0.5 m in the four pipes. Two of these were located at a distance of 4.25 m to 4.75 m and 6.25 m to 6.75 m in the N1 pipe from the A end. Another was located at a distance of 3.25 m to 3.75 m in the N2 pipe from the A end. Except for the pre-set grouting defects, the N1 and N2 pipelines were densely grouted at other positions. The N3 pipeline was fully grouted, while the N4 pipeline was completely empty and not grouted. A model is shown in Figures 2 and 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density \(\rho\)** & \multirow{2}{*}{**Elastic Modulus E Pa**} & \multirow{2}{*}{**Poisson’s Ratio \(\mu\)**} \\  & **kg/m\({}^{3}\)** & & \\ \hline C30 concrete & 2360 & \(3\times 10^{10}\) & 0.2 \\ Mortar & 2360 & \(3\times 10^{10}\) & 0.2 \\ Metal corrugated pipe & 7800 & \(21\times 10^{10}\) & 0.3 \\ Steel strand & 7800 & \(21\times 10^{10}\) & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of experimental samples.  
Figure 1: Overall view of pre-stressed bridge beam model.
# Co-Production of Hydrogen and Methanol Using Fuel Mix Systems: Technical and Economic Assessment
## 3 Results and Discussion
### Process Performance Analysis
#### 3.2.2 Specific Energy Consumption and Process Efficiency  
Specific energy consumption and process efficiency indicators were used in this study to evaluate the reliability and sustainability of the process. It can be seen from the results that case 1 and case 2 produce 8.9 kg and 11.5 kg of fuel (including both H\({}_{2}\) and methanol) per unit (GJ) energy consumed. By incorporating the heating values of the feedstocks and the fuel produced, the process efficiencies are calculated as 47.1% and 50.6% for case 1 and case 2, respectively. The comparison between the process efficiencies of two cases reveals that case 2 offers 3.5% higher process efficiency and 29% higher fuel production capacity than the base case (case 1) design (Figure 5).
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 3 Battery SOC Model-Driven State-of-the-Art Method  
The Thevenin model serves as a commonly employed equivalent circuit model (ECM) for batteries. This model is constructed using one or more resistance-capacitance (RC) pairs, along with an additional resistor and a voltage source [3], as illustrated in Figure 1.  
The Ah (Ampere-hour) Coulomb-counting method is a widely adopted approach for estimating the State of Charge (\(SOC\)), as used in [16], and shown in (1).  
\[SOC_{k}=SOC_{k-1}+\frac{\eta}{Q_{n}}\int_{k-1}^{k}\mathrm{I}_{k}\,\mathrm{d}k \tag{1}\]  
Here, \(Q_{n}\) represents the rated capacity of the battery, while \(\eta\) denotes the charge-discharge efficiency, which depends on factors such as the charge and discharge rate, operating temperature, and other conditions.  
Compared to the first-order \(1 RC\) model (which has only one \(RC\) branch in the electrical circuit), the \(2 RC\) model has a smaller estimation error. This indicates that the \(2 RC\) model provides enhanced accuracy, stability, and robustness. It outperforms both the \(1 RC\) and \(3 RC\) models, and also minimizes computational complexity [17]. Therefore, for the purpose of modeling the battery, the second-order \(RC\) model is chosen.  
Considering [PERSON]'s voltage law across the full loop, and defining \(\tau_{n}=R_{n}C_{n}\), where \(n\subset[1,2]\), it becomes the following:  
\[Vt_{k}=OCV_{k}-R_{0}I_{k}-V1_{k}-V2_{k} \tag{2}\] \[V1_{k}=V1_{k-1}e^{-t/\tau_{1}}+R_{1}(1-e^{-t/\tau_{1}})I_{k}\] (3) \[V2_{k}=V2_{k-1}e^{-t/\tau_{2}}+R_{2}(1-e^{-t/\tau_{2}})I_{k} \tag{4}\]  
Combining (1) to (4), it provides the state matrix in (5).  
\[\begin{bmatrix}SOC_{k}\\ V1_{k}\\ V2_{k}\end{bmatrix}=\begin{bmatrix}1&0&0\\ 0&e^{-t/\tau_{1}}&0\\ 0&0&e^{-t/\tau_{2}}\end{bmatrix}*\begin{bmatrix}SOC_{k-1}\\ V1_{k-1}\\ V2_{k-1}\end{bmatrix}+\begin{bmatrix}\frac{\eta\times k}{Q_{n}}\\ R_{1}(1-e^{-t/\tau_{1}})\\ R_{2}(1-e^{-t/\tau_{2}})\end{bmatrix}*\mathrm{I}_{k} \tag{5}\]
## 2 Background Literature
### Related Studies  
Previous studies have comprehensively approached the relationship involving strategy and complexity in SCs. Table 1 shows related studies and their specific contributions, positioning the current study at the forefront of the relevant literature. Figure 1: Relationship between meta-strategies in SCs and uncertainty. Source: Authors’ own creation based on [38]. The following analysis reflects the individual and comparative contributions of selected previous studies [9; 10; 11; 12; 13; 14; 15; 16]. Study [9] addresses complexity in SCs by mitigating the ripple effects of disruptions. It aims for a balance among robustness, complexity, and efficiency rather than simply minimizing complexity. By strategically managing the number of suppliers, product differentiation, and interrelationships, this study posits that a balanced approach to complexity can lead to broader objectives, such as reduced vulnerability to disruptions. The complexity is examined in a systemic manner, considering its interactions with other performance objectives. Study [10] introduces a theoretical development of an entropy-based metric to assess the structural complexity of SCs. It lays foundational theoretical and conceptual frameworks for quantifying complexity through information theory, particularly [PERSON]'s entropy. Prior to this work, complexity was primarily addressed qualitatively or with ad hoc metrics, and this study pioneers a quantitative approach that facilitates comparisons of complexity across different SCs. Study [11] identifies empirical drivers of complexity within SCs through a methodological framework combining SAP-LAP and AHP, specifically within the context of mining equipment manufacturing in India. This study not only validates existing theoretical perspectives on complexity drivers but also offers empirical evidence and prioritization. By applying structured methodologies to a practical case study, it transitions the discourse on complexity drivers from abstraction to actionable insights, enabling managers in analogous contexts to pinpoint specific sources of complexity. Study [12] conducts a comprehensive literature review outlining the various drivers of complexity present in SCs. It synthesizes existing knowledge related to operational, structural, and informational complexities, serving as an essential foundation for understanding what contributes to the intricate nature of SCs. Study [13] illustrates the detrimental impact of complexity on specific SC objectives, such as reducing deforestation in the palm oil SC. It identifies key drivers and demonstrates how multiple actors, geographical factors, and flow dynamics can obstruct crucial sustainability commitments. Study [14] explores the relationship between complexity and performance, establishing a methodological and empirical linkage between SC complexity and manufacturing operational performance. It quantifies how complexity, influenced by plant design and
# FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
## 3 Technical Background
### Binarized Neural Networks (BNNs)  
Recent studies, as summarized in the previous section, have revealed that accurate classification can be acquired by using one or two bit numbers for weights and activation operations in convolution layers, instead of using floating-point operations in CNN applications. BNNs stand out with their small memory requirements, low arithmetic precision, high speed, and low energy consumption [35]. In addition, BNNs are an especially attractive option for implementation on FPGAs because all their operations can be performed in binary. For this reason, a tera-operations-per-second (TOPS) level can be reached in BNN applications on FPGAs. BNNs quantize both the image and the core matrix at \((-1,1)\) when the convolution process is performed on the CPU through multiplication operations. This quantization is conducted in the range of (0, 1) on the FPGA platform by XNor logic operation. Thus, instead of multiplication during convolution, the XNor gate can be implemented on FPGA in a much faster way. The difference between a convolution operation performed with 32-bit weights and XNOR can be seen in Figure 6.
# Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges
## 3 Device Requirements for Differential Imaging
### Bandwidth, Measurement Rate, and Antenna Array  
The bandwidth of the sounding signal determines the range resolution performance of the radar. Hence, it should be as large as possible. The bandwidth is fixed by the width of the sounding pulse or the chip width of the M-sequence, respectively. The upper-band limits are, however, often restricted by the frequency-dependent propagation losses of the test scenario. Because these losses grow with the increasing target distance, the range resolution will worsen the deeper a target is located in a lossy material.  
The measurement rate \(\phi_{R}\) has to respect the Nyquist sampling criteria of the temporal target variation. For heart rate monitoring, for example, at least several tens of impulse response functions per second have to be collected if one is also interested in the higher harmonics of the heart motion.  
In the UWB case, \(\lambda/2\)-antenna spacing is not an issue, so often a low number of antenna positions is already sufficient for localization purposes in a scenario with a low number of targets. The antennas must be kept in stable positions, because even minor movements cause signal modulation, which represses the target modulation. In order to permit measurements in highly time-varying scenarios, parallel operation of all receiver channels is required. To illuminate the scenario from different aspect angles, several transmitting antennas need to be involved. Because orthogonal UWB signals are usually not available or difficult to generate, the antennas are not allowed to transmit in parallel. Consequently, the different transmitters have to be activated sequentially. This lowers the measurement rate wherefore the number of transmitters should be limited to the minimum, because the Nyquist sampling criteria for the temporal target variation applies to the whole cycle of data collection.