esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The building segment is responsible for a considerable percentage of world energy consumption, with heating, ventilation, and air conditioning (HVAC) systems being among the main energy users. Residential buildings account for ~60% energy consumption in the world [1]. Increasing energy sustainability needs, indoor comfort requirements, and operational efficiency concerns have made intelligent, data-centered HVAC management solutions more necessary. Innovative developments in artificial intelligence (AI), Internet of Things (IoT), communication protocols, and multi-objective optimization [2] have accelerated the creation of predictive and adaptive control systems to harmonize energy saving with occupant convenience.  
Schedule-based and rule-based conventional HVAC control approaches tend to lag in handling dynamic and nonlinear indoor spaces with varying occupancy rates, external weather fluctuations, and thermal inertia in buildings. Unlike such conventional approaches, AI-based models, especially those that incorporate deep learning and reinforcement learning (RL), can learn intricate patterns from past data and adjust control strategies in real time. Methods in reinforcement learning have shown promising outcomes in maximizing building energy performance by allowing agents to dynamically modify control actions in response to environmental feedback. Effective practical deployment rests not only on learning performance but also on seamless, reliable communication among sensors, actuators, and controllers.  
Here, industrial-grade communication protocols like Modbus TCP have gained more significance. Modbus TCP offers an open, high-data-rate, and reliable device-level communication protocol over IP-based networks that facilitates real-time data acquisition and actuation needed for smart HVAC control [3].  
To provide a comprehensive understanding of the proposed AI-based indoor temperature control system, this paper is organized into six main sections. Section 1 provides the introduction of the paper, Section 2 consists of the related work, Section 3 provides an overview of the methodology, Section 4 presents the experimental setup to conduct this study and discuss the results, while Sections 5 and 6 discuss the conclusion and future work of the study.
## 3 Teleoperation Control Design with Wave Variable and Virtual Force Feedback  
### Modified Wave Variable Architecture  
In a wave variable-based teleoperation control scheme, the leader robot sends desired motion information to the follower manipulator and receives force feedback conversely through communication modules, where the wave variable method is adopted to deal with inherent system time delay and guarantee the passivity, namely the stability of the teleoperation system at the cost of insufficient transparency including position-tracking and force feedback. Therefore, a design of modified wave variable architecture is proposed in this section inspired by the bilateral teleoperation schemes in [36], as shown in Figure 6, where \(\dot{X}_{m}\) and \(\dot{X}_{s}\) are the velocities of leader and follower; \(F_{m}\) and \(F_{s}\) denote the forces of leader and follower; \(\dot{X}_{sd}\) represents the desired velocity of the follower. To improve the system transparency, two transform parameters are introduced, among which the impedance parameter \(b\) is used for position-tracking performance and the gain coefficient \(K\) reduces force feedback error. Furthermore, a first-order low-pass filter expressed as \(\lambda/(s+\lambda)\) is adopted in the forward communication channel to eliminate high-frequency noise of velocity caused by manual operation and ensure the system passivity by dissipating inherit energy [33].  
Figure 5: Simplified kinematics model between joint angles and end-effector position.  
Figure 6: Modified wave variable architecture.  
In the proposed architecture, the transformed wave variables transmitted in the communication channel can be derived as:  
\[u_{m}(t)=\frac{b\cdot\dot{X}_{m}(t)+F_{s}(t-T)}{K}\otimes L^{-1}(\frac{\lambda}{ s+\lambda}) \tag{16}\]  
\[v_{s}(t)=\frac{F_{s}(t)}{K} \tag{17}\]  
where \(T\) is the constant time delay in communication channel between the leader and follower, \(\otimes\) and \(L^{-1}\) represent convolution and inverse Laplace transform, respectively.  
According to the architecture shown in Figure 6, the received desired velocity in follower \(\dot{X}_{sd}\) and force feedback \(F_{m}\) in leader can be written as:  
\[\dot{X}_{sd}(t)=\frac{K\cdot u_{m}(t-T)}{b}-\frac{F_{s}(t)}{b} \tag{18}\]  
\[F_{m}(t)=K\cdot v_{s}(t-T)+K\cdot b\cdot\dot{X}_{m}(t) \tag{19}\]  
Hence, by substituting Equations (15) and (16) into Equations (17) and (18), the transmission relationships between the leader and follower could be derived as:  
\[\dot{X}_{sd}(t)=\dot{X}_{m}(t-T)\otimes L^{-1}(\frac{\lambda}{s+\lambda})+ \frac{1}{b}\cdot(F_{s}(t-2\cdot T)\otimes L^{-1}(\frac{\lambda}{s+\lambda})- F_{s}(t)) \tag{20}\]  
\[F_{m}(t)=F_{s}(t-T)+K\cdot b\cdot\dot{X}_{m}(t) \tag{21}\]
## 3 Experimental Setup
### Case II: Forced Vibration  
To study the robustness of the ECTMD in the forced vibration case, a motor with an unbalanced mass is used to generate the harmonic excitation on the free end of the cantilever beam. The frequency of the excitation load can be varied from 0 Hz to 5 Hz.  
Figure 7 plots the steady-state response of the cantilever beam in a wide frequency domain under different detuned situation. The results show that, when the frequency of the excitation load is larger or smaller than that of the cantilever beam, some new resonance peaks are excited in both tuned and detuned situations (\(\gamma\) = 0%, \(\gamma\) = 14.7% and \(\gamma\) = \(-\)13.3%), and the displacement responses of the cantilever beam with the TMD near the resonance peaks are 1.63-2.99 times that with the ECTMD in both tuned and detuned (\(\gamma\) = 0, \(\gamma\) = 14.7% and \(\gamma\) = \(-\)13.3%) situations. Therefore, the vibration performances plotted in Figure 7 demonstrate that the robustness of the ECTMD is much better than the TMD in the forced vibration case.  
Please note that in Figure 7c the curve (blue dashed line) for the vibration of the primary structure with TMD control is not continuous. The missing segment corresponds to the worst-case vibration of the primary structure in the case of the TMD control, and was not recorded since the vibration of the primary structure was so severe that the primary structure starts to impact the mass of the TMD.
## 7 Conclusions  
In this paper, with the analysis of the network structure and the protocol characteristics of Modbus and 5G protocols, we proposed a protocol adaptation scheme that resolves the Modbus protocol and converts the data between Modbus and 5G. On this basis, we designed a wireless PLC prototype and performed extensive experiments to evaluate the end-to-end delay and reliability. The results showed that the latency of the protocol adaptation is smaller than that of the protocol transparent transmission, while there is a small reliability loss. Meanwhile, both the speed and reliability of Modbus TCP adaptation are better than those of Modbus RTU adaptation. Generally, the performance of the wireless PLC prototype can meet the basic requirements as a Modbus-based wired PLC, such as remote control and automated production.  
However, there are still some challenges that should well addressed in future works. First, the configuration of the wireless PLC prototype should be further optimized since automation engineers generally do not have extensive knowledge on Linux programming. Second, the current wireless PLC prototype completely depends on commercial 5G modules and must be operated on a licensed band. Thus, we can develop a software-defined prototype to operate on unlicensed band for self-organized networking. Third, the prototype should be tested in a practical factory with hash environment. Only in this way can the wireless PLC prototype really be utilized to enhance intelligent manufacturing while accelerating the applications of 5G. Last but not least, with 5G, the energy supplements and energy efficiency should be considered, which will enable wireless PLC to achieve flexible deployments. Furthermore, the wireless PLC prototype with respect to latency, reliability, spectrum, and energy efficiency should be continuously optimized to satisfy critical industrial control applications in the 6G era.  
Conceptualization, C.X. ([PERSON]); methodology, X.D., Y.T.; software, X.D., Y.T.; validation, X.D., Y.T.; formal analysis, X.D., X.J.; investigation, X.L., L.L.; Writing--Original draft preparation, X.D., C.X. ([PERSON]); Writing--Review and editing, C.X. ([PERSON]); supervision, C.X. ([PERSON]), X.L. All authors have read and agreed to the published version of the manuscript.  
Figure 21: The reliability comparison between Modbus RTU and Modbus TCP for protocol adaptation.  
**Funding:** This research was funded by National Key Research and Development Program of China under Grant 2020 YFB1701900, National Natural Science Foundation of China under Grants 62173322, 92267108, 62133014, 61972389, 61903356, and Youth Innovation Promotion Association CAS under Grants 2019202, 2020207, Y2021062.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Hydrogen vs. Batteries: Comparative Safety Assessments for a High-Speed Passenger Ferry
## 6 Results and Discussion
### Hydrogen Version
#### 6.1.1 Scenario 1: Leakage Diameter 3 mm at the Inlet of Bottom Right Stack  
The hydrogen release rate (Q\({}_{\text{gas}}\)) for a leakage of 3 mm was calculated on the basis of Equation (2): Q\({}_{\text{gas}}\) = 5.25 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/s. Leakages of small piping diameters (3 mm or less) were considered to be the most likely failure scenarios for the hydrogen pipes.  
The total volume flow rate of air is 0.16 m\({}^{3}\)/s to achieve 30 ACH, considering that the volume of the room was 19.19 cubic meters. Hence, for each vent, the exhaust flow rate was 0.04 m\({}^{3}\)/s.  
Hydrogen leakage was detected first by the middle sensor, 5.6 s after the release, as can be observed in Figure 9a, and 1 s later, hydrogen flow was stopped in the room. With 30 ACH, flammable mass concentrations can be avoided since the maximum percentage is 1% = 0.01 mol/mol, and the flammability range for hydrogen is 4-75%. Hence, there was no ignition risk (Figure 9b,c), even in the presence of ignition sources, as long as the leakage was stopped 1 s after detection. Leakage and potential malfunction of multiple systems (two or three sensors) can delay the detection of gas and potentially create a hazardous atmosphere with 30 ACH ventilation rate. However, it is considered a highly unlikely scenario.  
Figure 9: Leakage diameter 3 mm at the inlet of the bottom right stack: (**a**) detection at 5.6 s; (**b**) concentrations at 10 s; (**c**) concentrations at 40 s.
## 3 FedCKA
### Measuring Layer-Wise Similarity  
FedCKA is designed to regularize dissimilar updates in layers that should naturally be similar. However, there is currently no standard for measuring the similarity of layers between neural networks. While there are classical methods of applying univariate or multivariate analysis for comparing matrices, these methods are not suitable for comparing the similarity of layers and representations of different neural networks ([PERSON] et al., 2010). As for norms, [PERSON] et al. (2011) concluded that a layer's robustness to re-initialization is not correlated with the l2-norm or lo-norm. This suggests that using these norms to regularize dissimilar updates, as in previous works, may be inaccurate.  
[PERSON] et al. (2010) concluded that similarity metrics for comparing the representation of different neural networks should be invariant to orthogonal transformations and isotropic scaling, while they are invertible to linear transformation. The study introduced centered kernel alignments (CKAa) and showed that the metric is most consistent in measuring the similarity between representation of neural networks. Thus, FedCKA regularizes local updates using the CKA metric as a similarity measure.  
Figure 2: CKA similarity between clients at the end of training (refer to _Experimental Setup_ for more information on training).
## 3 Molecular Characterization of Coat Color Affecting Genes in Rabbits
### A Mutation in the Lippah Gene Causes the Rex Locus \(R^{1}\)  
Rex rabbits show an interesting breed-specific phenotype mainly determined by the absence (or almost complete absence) of guard and awn hairs that gives a fur constituted by short and soft hair coat. Early classical genetic studies analyzed the segregation of three independent natural Rex mutants that occurred in three independent rabbit populations and established a similar genetic determinism, each due to an autosomal recessive allele [30]. Due to uncertainty in establishing homology across populations, the presence of three independent _Rex_ loci (\(R^{1}\), \(R^{2}\), and \(R^{3}\), having the recessive alleles indicated as \(r^{1}\), \(r^{2}\), and \(r^{3}\), respectively) was suggested [94; 95; 96; 97]. The mutation that occurred in the French rabbit strain (\(r^{1}\)) was first assigned to rabbit chromosome 14 in an experimental design based on microsatellite analysis of rabbit families established to follow the segregation of the derived _Rex_ phenotypic variant [98]. A refined analysis, using the same approach and applying a comparative mapping, identified that a single nucleotide deletion in exon 9 of the lipase member H (_LIPH_) gene was the causative mutation of this _Rex_ locus [98]. This frameshift mutation (indicated with c.1362 delA) introduces a downstream premature stop codon that alters the C-terminal region of the protein. _LIPH_ encodes a membrane-bound member of the mammalian triglyceride lipase family that catalyzes the production of 2-acyl lysophosphatidic acid (LPA). This molecule is a lipid mediator with several biological properties on smooth muscle contraction, platelet aggregation, and stimulation of cell proliferation and motility. Some mutations affecting the structure and function of this gene in mice and humans have been shown to affect hair structure and growth among other defects (e.g., [99; 100; 101; 102; 103]). Transcriptional analysis of this gene in Rex and normal rabbit skins in both fetal and adult stages indicated that the mutated phenotype is associated to a three-time reduced expression level of _LIPH_ in rabbits homozygous for the c.1362 delA mutation [104].
## 3 TDoA Method Experimental Validation
### Experimental Setup  
To prove the effectiveness of the method, a massive campaign of measurement was carried out in a semi-anechoic chamber scenario with the SBA anchor installed on a rotating platform controlled by a computer, thus spanning the entire azimuthal domain. Measurements were acquired through the utilization of a calibrated vector network analyzer (VNA), connected to a computer to get measurements synchronized with the SBA antenna switching, as depicted in Figure 10. The VNA equipment is able to provide a measurement of the radio-link transfer function as shown in Section 2.3. Synchronized  
Figure 8: Ideal and wrapped phase differences respect to reference antenna #1.  
Figure 9: Ideal phase differences respect to reference antenna #1 for frequency fit \(f_{\text{FIT}}=\frac{\text{B}}{\text{M}}\).  
measurement routines are used to fulfill the conditions on sampling periodicity as given in Section 2.3, Figure 4. Due to high communication times between computer and VNA, the synchronization condition is satisfied acquiring more than one signal period for each antenna measurement.  
The adoption of the laboratory instrumentation is due to the need to remove all the possible cause of measurement uncertainty, and thus permits us to focus on the methodological aspects of this work. In particular, the validation of calibrated VNA as equivalent to time domain signal sampling was demonstrated by the authors of [15]. Figure 11 shows a picture of the laboratory set-up schematically represented in the Figure 10, which includes absorbing panels and the VNA hidden by the same panels; the SBA is installed in a rotating platform, while the target in positioned on a teflon pole and moved in different position and getting different heights. The target is emulated by only the transmitting antenna, which is identical to the SBA individual element; the reader can refer to the Table 1 for a summary of the transmitting antenna features.  
Nevertheless, to prove method robustness towards a lack of synchronization between the signal transmission and signal reception in the experiment, a random time shift has been introduced in each set of time observations throughout the SBA antennas. Each time observation is given as the Inverse Fourier Transform of an \(S_{21}\) measurement trace considering a bandwidth \(\mathrm{B}=3\,\mathrm{GHz}\) centered in \(f_{0}=5.5\,\mathrm{GHz}\) and a number of points \(M=801\).
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 4 Experimental Evaluations  
### Evaluation Metrics  
Since we have a regression problem, the metrics we used for performance evaluation include mean absolute error, mean squared error, root mean square error, and mean absolute percentage error. Mean absolute error (\(MAE\)) measures the average of the absolute values between prediction (\(\widehat{y}\)) and actual observation (\(y\)) over all the instances where all the differences have equal weight. Mean absolute error is less sensitive to outliers.  
\[MAE=\frac{1}{N}\sum_{i=1}^{n}\lvert y_{i}-\widehat{y}_{i}\rvert \tag{8}\]  
where \(n\) is the total instances. The mean squared error (\(MSE\)) tells how good a regression line is to a set of data points. Mean squared error takes the distance of each point to the regression line. After finding the distance, any negative signs are removed by taking the square of these values. It also gives more weight to larger differences from the regression line.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\widehat{y}_{i})^{2} \tag{9}\]  
Root mean squared error measures (\(RMSE\)) the average magnitude of the error. It is the square root of the average squared differences between prediction and actual values. Root mean squared is desirable when large errors are undesirable since it gives higher weightage to large errors. MSE uses the square operation to remove each error value's sign and punish large errors. The square root reverses this operation, although it ensures that the result remains positive.  
\[RMSE=\sqrt{MSE} \tag{10}\]  
The mean absolute percentage error (\(MAPE\)) is the mean of the absolute percentage errors of predictions. The mean absolute percentage error is scale-independent and is not affected by the global scaling of the target variable. The lower the value for \(MAPE\), the better the machine learning model is at predicting values. The error measurement is more intuitive to understand as a percentage than other measures such as the mean square error because many other error measurements are relative to the range of values.  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\left\lvert\frac{y_{i}-\widehat{y}_{i}}{y _{i}}\right\rvert \tag{11}\]
## 4 Method
### Fasteso
#### 4.1.3 Adaptive Switching Gains with Fuzzy Logic Rules  
The fuzzy logic controller was proposed many years ago [31]. In this part, the fuzzy rules are designed according to the fuzzy control theory for effectively optimizing the switching gain based on the states of the sliding-mode surface.  
Normally, for purpose of enhancing the ability of observer on anti-disturbance and undertaking the generation of sliding mode, the sliding mode switching gain is often chosen to be too large, but this would further enhance the chattering noise in the disturbance estimation. Therefore, it is feasible to introduce the intelligent method to effectively estimate the switching gain according to the sliding mode arrival condition to alleviate the system chattering problem. For example, as the system state is far from the sliding surface, it means that the absolute value of difference \(|e|\) is relatively large, the proportional factor \(k_{f}\) should be enlarged to drive the system state back. Similarly, as the system state is close to the sliding surface, proportional factor \(k_{f}\) should be smaller.  
The proposed fuzzy logic system in this work consists of one input variable and one output variable which would be quantized first. The input and output variables are divided into four fuzzy subsets. Let \(|e|=\left\{\begin{array}{cccc}ZR&PS&PM&PB\end{array}\right\}\) denote the input variable and \(k_{f}=\left\{\begin{array}{cccc}ZR&PS&PM&PB\end{array}\right\}\) represent the output variable, respectively. Each rank is depicted by a membership function of trigonometric function, as shown in Figures 4 and 5, where the fuzzy language is defined as \(ZR\)(zero), \(PS\)(positive small), \(PM\)(positive middle), and \(PB\)(positive big).  
Figure 3: Sketch of the saturation function.  
Then define the fuzzy rules as  
_Rule_ 1: If \(|e|\) is PB, then \(k_{f}\) is PB.  
_Rule_ 2: If \(|e|\) is PM, then \(k_{f}\) is PM.  
_Rule_ 3: If \(|e|\) is PS, then \(k_{f}\) is PS.  
_Rule_ 4: If \(|e|\) is ZR, then \(k_{f}\) is ZR.  
Finally, as for the fuzzy outputs, according to the work in [32], the center of gravity algorithm is adopted as the defuzzification method to convert the fuzzy subset duty cycle changes to real numbers, which is given as  
\[k_{f}=\frac{\int_{0}^{3}k_{f}\cdot\mu_{out}\cdot dk_{f}}{\int_{0}^{3}\mu_{out} \cdot dk_{f}} \tag{36}\]  
where \(\mu_{out}\) represents the resulting output membership function. It takes every rule into account performing the union of the resulting output membership function \(\mu_{out,i}\) of each \(Rule\) i \((i=1,...,4)\), which means the maximum operation between them.
## 3 Results  
### Change and Distribution of the Annual GPP, ER, and NEP  
Simulation results indicate that AGQIP had an average annual GPP, ER, and NEP of 330.9 Tg C yr\({}^{-1}\), 315.9 Tg C yr\({}^{-1}\), and 15.25 Tg C yr\({}^{-1}\), respectively. From 2000 to 2020, GPP, ER, and NEP all significantly increased (\(p<0.05\)) at rates of 0.002 Pg C yr\({}^{-2}\), 0.013 Pg C yr\({}^{-2}\), and 0.0007 Pg C yr\({}^{-2}\), respectively, but displayed noticeable inter-annual fluctuations (Figure 3a-c). It was noted that from 2012 to 2018, both the GPP and NEP dropped sharply and then rebounded rapidly (Figure 3a,c). As shown, the mean annual GPP, ER, and NEP gradually increased from northwest to southeast generally, which was roughly consistent with the hydrothermal gradient of the QTP (Figure 3d-f). The one-way analysis of variance (ANOVA) based on the NEP pixel values of meadows and steppes showed significant differences (\(p<0.05\)) in NEP between these two categories of grasslands. The majority of meadows had higher NEP values, with a mean of 82.03 gC m\({}^{-2}\)yr\({}^{-1}\) and a range of -153.42 to 429.89 gC m\({}^{-2}\)yr\({}^{-1}\), indicating that they primarily functioned as carbon sinks (Figure 3f). In contrast, most steppes functioned as carbon sources, as evidenced by their low NEP values (mean \(-56.62\) gC m\({}^{-2}\)yr\({}^{-1}\)) (Figure 3f).