esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presented an evaluation of the deep learning approach to determine surface roughness using vibration signal data. The three predictive models of 1-D CNN, FFT-DNN, and FFT-LSTM were evaluated in terms of their training and prediction performances. The results are summarized as follows. All three models have good training performance in sufficient data conditions, with 1-D CNN exhibiting superior training performance. After training the models, the predictive performance of all the three models achieve satisfactory accuracy with less than 20% MAPE at medium Ra ranges between 0.4 to 0.7. Comparing the three predictive models, FFT-DNN has the worst prediction at the lowest Ra range with 48.75% MAPE and 1-D CNN is the worst at the highest Ra range with 36.67% MAPE. 1-D CNN can extract elaborate features to present better prediction ability and achieves a highly accurate level of prediction at lower Ra values with 26.47% MAPE. FFT-LSTM utilizes temporal modeling forecasting to perform well at higher Ra ranges with 23.5% MAPE. Furthermore, the experiments also revealed that 1-D CNN can extract the features automatically and extract more information compared to the FFT extractor. Based on these results, vibration signal data combined with the 1-D CNN and FFT-LSTM models are recommended for prediction of surface roughness during milling processes.  
[PERSON]-[PERSON] wrote the paper, implemented the algorithms and performed the experiments; [PERSON]-[PERSON] implemented the algorithms and performed the experiments; [PERSON]-[PERSON] revised the paper; [PERSON]-[PERSON] conceived, designed the algorithms and experiments, and revised the paper.  
This research is supported by the Ministry of Science and Technology under the grants MOST 107-2218-E-002-071, MOST 107-2218-E-492-009 and MOST 107-2420-H-005-001.  
The authors declare no conflict of interest.  
Figure 17: Cross-validation for testing. (**a**) 1-D CNN; (**b**) FFT-DNN; (**c**) FFT-LSTM.
## 3 NDF Analysis and Space Sampling Cues
If \((u_{n},v_{m})\) are the corresponding sampling points, with \(u_{n}\in(u_{1},u_{2},\cdots,u_{N})\), \(v_{m}\in(v_{1},v_{2},\cdots,v_{M})\) and \(NM=N_{T}\), the sensors' positions in Cartesian coordinates can be obtained by the following transformations  
\[ \begin{array}{l}x_{nm}=hu_{n}/\sqrt{1-(u_{n}^{2}+v_{m}^{2})}\\ y_{nm}=hv_{m}/\sqrt{1-(u_{n}^{2}+v_{m}^{2})}\end{array}  \tag{9}\]  
These are non-linear transformations, therefore the uniform sampling in the \(u-v\) plane translates into a non-uniform sampling in the \(x-y\) plane. This is shown in Figure 3 (top panels). It is clear that this would lead to a quite unusual sensor arrangement. On the contrary, a uniform sampling in \(x-y\) would result non-uniform in \(u-v\). In Figure 3 (bottom panels) the relative behavior is shown. It can be concluded that in order to properly sample the field in the \(u-v\) domain, either a non-uniform sampling can be adopted in the \(x-y\) plane or a uniform sampling can be used, having care that Equation (8) is satisfied in the central part of the observation domain. In the latter case, \(u-v\) data are over-sampled in the peripheral zone as is shown in Figure 3 (bottom panels). Figure 2: Illustrating the behavior of the lower bound of \(N_{T}\) as a function of the investigation domain normalized side, for different values of the ratio \(r_{O}/h\). The importance of choosing appropriate sampling points is illustrated in Figure 4, where the singular values corresponding to the even sampling in the \(u-v\) domain are compared to those corresponding to equally spaced samples in the \(x-y\) domain. The number of samples and the spatial observation region are the same in both cases. As can be seen, uniform sampling in the \(u-v\) leads to a \"flat\" singular values behavior before the index provided by Equation (6) (\(n=200\) for the present case), by contrast, uniform sampling in \(x-y\) leads to singular values that start decaying even before the critical index. This of course implies a different effect of noise on the inversion [35]. Figure 3: Relationship between the sampling points in the spectral (panels (**a**) and (**c**)) and spatial (panels (**b**) and (**d**)) domains for \(\chi_{in}=5\) and different measurement configurations. (**a**) shows the uniform sampling inside a circular \(u-v\) domain for \(r_{O}/h=1\) (blue stars) and \(r_{O}/h=0.5\) (red circles), whereas the corresponding non-uniform sampling in the \(x-y\) plane is reported in (**b**). As opposed, in (**d**) a uniform sampling is employed for a square \(x-y\) domain of side \(2X_{max}=2h\) (blue stars) and \(2X_{max}=h\) (red circles), whereas the corresponding points in the spectral domain are reported in (**c**).
## 3 Results and Discussion
### Measurements
#### 3.2.3 Influence of Soiling on the Infrared Thermographic Pattern  
Figure 17 shows the thermogram of the module removed from the dust-covered roof. Upon washing, the apparent temperature values were significantly reduced. A temperature increase in the upper left corner of the triangle was also observed as a result of the reflected radiation from the production hall on the left side of the test site, which can be seen in Figure 13 of the measurement setup. The reflected temperature was read from the chrome-plated stand with the emissivity set to 1. During the thermographic measurements, it was found that the used cameras were not suitable for measurements at high solar irradiance levels due to the poor visibility of the displays. It is recommended to use cameras with optical viewfinder.  
Due to the observed significant effect of shading, because of soiling, on the thermal pattern, all modules were subjected to the washing process. The procedure was carried out in three steps. The first stage was the removal of large particles and the rest of the glass with a whisk, followed by washing, which dissolves the stubborn deposits of particles from the module. This was followed by rinsing the surface with clean water and wiping with a clean cloth. Figure 18 shows the appearance of the module before washing and the necessary equipment on the ground. On average, one litre of water per module was needed to clean the modules.  
Figure 17: Thermographic analysis of the PV module 1 before and after washing.  
Figure 18: Dusted PV module appearance and washing equipment used for cleaning.  
Figure 19 shows the need for washing, i.e., the effects of impurities on the thermal pattern of the module, which is common due to shading [28]. Figure 19 illustrates the temperature deviation of the readings before and after cleaning. The analysis was performed on the first 5 rows whose measurement data were not affected by the reflected radiation of the production hall, which is difficult to see on the dirty module. The significance of the influence of shading is clearly seen in Figure 17, where the temperature values of individual cells after cleaning cannot determine the pattern of stochastic change.
## 5 Fingerprinting
### Offline Step  
One of the main challenges in the fingerprinting approach is collecting and maintaining a proper fingerprint database. The most straightforward approach to generating the radio map or dataset is to measure and record the fingerprints over all areas. However, due to the ample space, it is not practical to obtain detailed measurements and conduct costly site surveys. Moreover, the fingerprint map becomes outdated due to the radio signal features and dynamic environment. This problem is discussed in [56], where the effect of an incomplete dataset is explored by applying several interpolations and extrapolations to recover the missing data. The work of [57] explores deterministic adaptive path loss model interpolation for radio map generation, focusing on improving the speed of the process and decreasing the time of radio map construction. Each AP's RSS path loss model is extracted using available reference points, LS, and interpolation. Gaussian regression is another probabilistic method used for this purpose. The authors of [58] exploit Gaussian Process Regression (GPR) to predict the RSS spatial distribution based on the available dataset. To achieve this, instead of using a basic zero mean function and a single squared exponential as kernel function for GPR, compound GPR kernel functions are used. In [59], more information regarding radio map generation techniques can be found. Experiments in the real world also make this comparison.  
It is worth mentioning that most fingerprinting localisation exploits the collected raw data or their scaled value. However, this is not efficient in the presence of heterogeneity. The heterogeneity of device pairs, i.e., transmitter and receiver, can affect the RF-based localisation, as each device uses different hardware parameters, such as antenna gain for transmitters. The different signatures might be recorded in the same situation for each transmitter-receiver combination. To remedy this issue, other variants of perceived  
Figure 6: Fingerprinting block diagram.  
measurements are used. For example, differences in signal strengths are used in [60] to address both device heterogeneity and temporal variation of RSS. Another differential RSS-based localisation is handled in [61]. Finally, the authors of [62] present hyperbolic location fingerprinting in which fingerprints are recorded as RSS log ratios between pairs of base stations instead of absolute RSS.
## 3 Multiplier-Free Modified Godard Timing Recovery  
The Godard algorithm is a technique that estimates the timing error of a signal with at least two samples per symbol based on frequency domain coefficients [6]. Equation (2) shows an adaption of the discretized representation as used in [14].  
\[\text{CT}=\sum_{k=0}^{\frac{N}{2}-1}\widetilde{r}_{k}\widetilde{r}_{k+N/2}^{*} \tag{2}\]  
Here, \(\widetilde{r}_{k}\) is the discrete Fourier transform of the received time domain signal \(r_{n}\), \(N\) is the size of the DFT, and \((.)^{*}\) is the complex conjugate.  
By calculating the imaginary part of this CT (as shown in Equation (3)), the Godard technique is realized.  
\[\hat{\tau}_{\epsilon,\text{Godard}}=\sum_{k=0}^{\frac{N}{2}-1}\text{Im}\{ \widetilde{r}_{k}\widetilde{r}_{k+N/2}^{*}\} \tag{3}\]  
The estimate \(\hat{\tau}_{\epsilon,\text{Godard}}\) follows a sinusoidal shape, which is commonly referred to as an s-curve (see Section 4 for further details). For positive estimates, the samples were taken too late with respect to the symbols, while for negative estimates, the samples were taken too early. Due to the sinusoidal shape, there is no unique correspondence between the timing error and the value of the estimate. As a result, this scheme cannot be used for feedforward structures. For feedback structures, the sign of the estimate can be used to control a feedback loop. The loop operates towards the zero crossing of the estimate between too late and too early and therefore the ideal sampling phase. When calculating the angle instead of the imaginary part, a linear dependence between the timing error and the estimate can be observed. A normalization by \(1/2\pi\) provides the estimate for a feedforward timing correction.
# Experimental Study on Cooling Performance of a Hybrid Microchannel and Jet Impingement Heat Sink
## 5 Conclusions  
In this work, the cooling performance of a hybrid heat sink with micro-jets and microchannels was experimentally studied with a working fluid of R134a. The thermal conductivity of the AlN substrate was measured by using a laser flash analyzer (LFA 447), and the thermal resistances of silicone grease were determined by the steady-state method. The parameters including temperature, thermal resistance and pressure drop were investigated at different flow rates and heat fluxes. The largest heat flux of 400 W/cm\({}^{2}\) was achieved on a heating area of 210 mm\({}^{2}\) in the experiments. The lowest thermal resistance of the heat sink was 0.11 K/W, and the pressure drop was between 1.54 and 13.46 kPa. Results show that the combination of jet impingement and microchannel structures has excellent cooling performance and provides a promising solution for the thermal management of high-power devices with both a large area and a high heat flux.  
Data curation, R.X.; formal analysis, R.X., P.Z., L.C. and Y.Z.; resources, Y.H.; supervision, L.C. and Y.H.; writing--original draft, R.X.; writing--review and editing, R.X. and P.Z. All authors have read and agreed to the published version of the manuscript.  
This project is supported by the National Natural Science Foundation of China (52176020), the Youth Innovation Team of Shaanxi Universities and Xi'an Jiaotong University Special Research Project for Basic Research Business Expenses (xzy022022030).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
This research proposes an analytical method for the prediction of molten pool size in laser-based powder bed fusion (LPBF) additive manufacturing with the consideration of process conditions-dependent absorptivity. Under different process conditions, the melting modes in LPBF are different, which induces the difference in laser absorptivity. An empirical model of absorptivity was used to calculate the laser absorptivity under various process conditions. An analytical point-moving heat source model was employed to calculate the temperature distribution of the build-in LPBF, with absorptivity, material properties, and process conditions as inputs. The molten pool width, length, and depth were determined by comparing the predicted temperature profile with the melting temperature of the material. To validate the proposed method, the predicted molten pool width, and depth of Ti6 Al4V were compared with the reported experimental measurements under various process conditions. The predicted molten pool widths were very close to the measured results, and the predictions of molten pool depth were also acceptable. The computational time of the presented model is less than 200s, which shows better computational efficiency than most methods based on numerical iterations, such as the finite element method (FEM). The sensitivity of molten pool width and depth to normalized enthalpy w also discussed. The presented analytical method can be a potential tool for the research of molten pool size and related defects in LPBF.  
analytical thermal model; molten pool dimensions; laser absorptivity; powder bed fusion; high computational efficiency Article
## 5 Conclusions  
The energy consumption issues of battery-powered Tx anchor and Rx nodes are successfully addressed by the suggested energy-efficient MAC protocol incorporating Q-learning. The technique improves network performance by emphasizing low collision rates and energy economy while dynamically optimizing transmission energy without previous interference information. The simulation findings indicate a considerable decrease in end-to-end latency, packet collisions, PDR, and channel utilization, as well as a possible boost in network throughput of up to 23%. However, with these advantages, the protocol also has some disadvantages, including the need for a large amount of training data and processing capacity for the Q-learning model. Subsequently, we investigated and concentrated on augmenting the flexibility of the procedure, carrying out empirical investigations for validation, which in turn strengthened the scalability of our approach.  
Conceptualization, Q.G. and W.U.R.; methodology, Q.G. and W.U.R.; software, W.U.R. and F.Z.; validation, Q.G., W.U.R. and F.Z.; formal analysis, F.Z., M.B., and W.A.; investigation, W.U.R., Q.G., and F.Z.; resources, Q.G. and F.Z.; data curation, S.U.K., M.B., M.I.K., and W.A.; writing--original W.U.R.; writing--review and editing, W.U.R., S.U.K., M.B., M.I.K., and W.A.; visualization, W.U.R., M.I.K., F.Z., and S.U.K.; supervision, Q.G. and F.Z.; project administration, Q.G. and F.Z.; funding acquisition, Q.G. and F.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 25: Energy consumption vs. number of nodes.  
**Funding:** This work was supported by: 1. Shenzhen Science and Technology Program under Grant No. JSGG20220831103800001. 2. Key Research and Development Program of ShanDong Province under Grant No. 2022 CXGC020409.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Chemicals and Analytical Methods
#### 2.2.3 HPLC Reagents, Equipment and Procedure  
Acetonitrile, methanol, and water were of HPLC grade and were purchased from Carlo Erba (Milano, Italy). The reference standard of AFM\({}_{1}\) with purity grade of 99.6% was supplied by Sigma-Aldrich (Milano, Italy). Working standard solutions (0.2, 0.6, 0.9, 2.0, 5.0) \(\upmu\)g/L were prepared by appropriate dilution in mobile phase just before analysis.  
Chromotagraphic separations were performed on an HPLC system Agilent Technologies 1100 Series (Waldbronn, Germany), consisting of a quaternary pump provided with a micro vacuum degasser, a thermostated autosampler, a 20 \(\upmu\)L loop, a thermostated column compartment, and a fluorescence detector. All chromatographic separations were performed using Agilent columns ZORBAX Eclipse XDB-C18 (250 mm \(\times\) 4.6 mm i.d., particle size 5 \(\upmu\)m). The mobile phase consisted of water, acetonitrile, and methanol flowed at 0.8 mL/min. The optimized elution gradient is detailed in Table 1. The injection volume was 30 \(\upmu\)L and the column compartment temperature was set at 30 \({}^{\circ}\)C. Fluorescence detection was performed at the excitation and emission wavelengths of 360 and 440 nm, respectively.  
Details about sample preparation step are reported elsewhere [38]. Briefly, 10 g of skimmed milk were purified by an immunoaffinity column (RIDA Aflatoxin column, R-Biopharm, Darmstadt, Germany). The eluate was evaporated to dryness at 40 \({}^{\circ}\)C under a nitrogen stream and the residue was solubilized in 500 \(\upmu\)L of mobile phase and then injected. The procedure resulted in a 20-fold concentration of AFM\({}_{1}\) evaluated against a known concentration added to a blank real sample. HPLC/FLD method allowed to detect AFM\({}_{1}\) content at concentration between 0.010 and 0.250 \(\upmu\)g/kg. Figure 3 shows an example of AFM\({}_{1}\) analysis performed using HPLC/FLD method.  
ELISA and HPLC/FLD methods are currently used in laboratory for official control activity. ELISA method is very advantageous for speed and simplicity of operation and HPLC/FLD method, because of its specificity and high sensitivity, allows accurate confirmation analyses of milk samples resulted as suspicious by the screening method.
## 3 Methods and Materials
### Methods  
For RTC hot spot identification, this study used the spatial statistics toolbox of ArcGIS 10.5. Supporting tools such as Google Earth and Google Maps were also utilized. For RTC hot spot identification, the procedure used in this study can be divided into the following steps: (i) projecting the map, (ii) computing the severity value of each crash, (iii) analyzing the spatial pattern of RTCs to measure the fluctuating threshold distance of RTC clustering by using _Moran's I_ index of incremental spatial autocorrelation tool and (iv) identifying and prioritizing highly clustered RTCs or hot spot zones. The detailed steps and the theoretical basis are discussed as follows.  
Figure 1: Map of the study area in Ethiopia.  
Instead of using a geographical coordinate system, the map used to identify crash hot spots was a projected coordinate system. The map dataset obtained from Ethiopian Geospatial Information Agency (EGIA) was in a geographic coordinate system. Since distance is among parameters to be considered in crash hot spot analysis [50], the analyzed maps were projected using the UTM (Universal Transverse Mercator) coordinate system in World Geodetic System WGS84.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 4 Globally-Referenced Electro-Optical SLAM (GEOSLAM)  
This section describes a simultaneous localization and mapping (SLAM) pipeline capable of globally-referenced collaborative multi-session digital mapping. The pipeline combines visual measurements from a stereo visible-light camera system with position measurements from GNSS signals. The objective of this pipeline is to demonstrate the development of an accurate digital map based on multiple mapping sessions with standard GNSS position estimates. The following sections detail the visual SLAM pipeline, the integration of GNSS measurements, and the techniques for multi-session mapping developed in GEOSLAM.
## 4 Discussion  
This study aimed to enhance the movement quality of the Mechatronic Rehabilitation Device (MWR) by addressing the influences of friction forces and gravity, particularly on the \(\varphi_{2}\) axis responsible for palmar and dorsal flexion of the hand. The experimental results revealed that the mass of the mechanism's components, and external factors, such as gravity, significantly affected the control system's ability to maintain the desired angular position and velocity in the \(\varphi_{2}\) axis.  
Initially, the control system exhibited challenges in achieving smooth motion, with noticeable position errors and oscillations during both linear and sinusoidal input signals. The angular velocity \(\omega_{2}\) showed sudden drops at the start of movements, indicating the system's struggle to overcome inertia and friction without compensation.  
By implementing compensation functions \(f_{kg}(\varphi_{2})\) and \(f_{kd}(\varphi_{2})\), derived from the experimentally determined drive torques needed to maintain specific angular positions, the control system was modified, taking into account the gravitational and frictional influences on the \(\varphi_{2}\) axis. Introducing these compensation functions led to a significant improvement in movement quality. Specifically, the integral of the absolute value of the position error decreased by more than half for both linear and sinusoidal inputs after compensation, indicating enhanced accuracy and smoother motion.  
The improved performance underscores the importance of addressing mechanical factors in the design and control of rehabilitation devices. By compensating for the weight of the mechanism and frictional forces, the MWR device achieves more precise movements, which are critical for effective rehabilitation therapy. This enhancement improves patient  
Figure 18: Summary of angular velocity errors for sinusoidal input signal \(\varphi_{2}\): \(\hat{e_{2}}(t)\)-angular velocity error, \(e_{2k}^{*}(t)\)-angular velocity error with compensation function.  
safety and comfort and allows for the more accurate tracking of rehabilitation progress, potentially leading to better therapeutic outcomes.  
However, the study has limitations that warrant further investigation. The compensation functions were developed based on experiments conducted without patient interaction. Future studies should involve clinical trials with patients to evaluate the effectiveness of the compensation strategy in real-world rehabilitation scenarios. Additionally, while the \(\varphi_{2}\) axis was the focus due to its significant impact, exploring compensation strategies for the other axes (\(\varphi_{1}\) and \(\varphi_{3}\)) could provide a more comprehensive improvement in the device's overall performance.