esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Convolutional Features Extractor  
CNN has shown a good ability to produce a rich representation of the input image by embedding it into a fixed feature vector. In many tasks, it is natural to use CNN as the encoder to extract the features and networks which are pre-trained on ImageNet dataset might obtain great performance. In general, our architecture can adapt any deep convolution networks for feature extraction, thus, we conduct our encoder network with GoogLeNet, VGG and ResNet-101,152.  
First, we employ the opencv toolkit to split each video into 16 video frames with fixed interval, and then, we resize all frames into 224 \(\times\) 224 and extract feature cubes from the different layers in different networks. For example, in GoogLeNet, we pick up the last convolutional layer, and we extract feature cubes from the last pooling layer and in both ResNet-101 and ResNet-152, also known as, block4 unit3 conv2. Simply, we represent these feature cubes as \(D\times K\times K\) (e.g., 512 \(\times\) 14 \(\times\) 14 used in VGG Net). Especially, \(K\times K\) is the size of feature map of the input image and \(D\) is the number of the feature dimensions. Hence, at each time-step \(t\), we extract \(K^{2}\)\(D\)-dimensional vectors. We refer to these vectors as feature slices into a feature cube, and denoted as:  
\[\begin{split} X_{t}=[X_{t,1},\cdots,X_{t,K^{2}}]\\ X_{t,i}\in R^{D}\end{split} \tag{1}\]  
where each vertical feature vector is the same region of feature map in different dimensions. At time step \(t\), these \(K^{2}\) vertical feature slices to different overlapping regions in the input space and our model chooses to focus its attention on these \(K^{2}\) regions.
## 3 Results
### THz Results  
The above-described measurements demonstrate that the used laser diode is mode-locked. We used the optimum parameters (injection current, fiber length) to drive the THz setup (Figure 1, blue part) with the laser diode. The transimpedance amplifier (TIA) before the lock-in amplifier is set to a gain of \(10^{7}\) V/A. The lock-in frequency is set to 17.3 kHz and the amplitude of the external sine wave reference is set to 7 V. The HV amplifier is used to increase the reference voltage by a factor of 15 to 105 V for the emitter antenna, which results in a maximum photocurrent of 205 \(\upmu\)A. The integration time, \(t_{\text{c}}\), is set to 1 ms. At each time step, a waiting time of 10 ms is introduced as the settling time for the lock-in amplifier. The scan window is 120 mm at a step size of 0.025 mm. The optical power is 22 mW at the THz emitter and 27 mW at the THz receiver.  
Figure 3a,c shows the THz time-domain traces recorded with the FPL1009P. The time between the pulses is \(\Delta t=23\) ps for both time-traces and corresponds to the inverse repetition rate of the laser, at 43.47 GHz. The laser's high repetition rate causes the discrete spectrum shown in Figure 3b,d  
obtained by Fourier transformation. The measurements shown in Figure 3c,d are done with a metallic mesh-filter inside the THz path. This filter acts as a high pass filter attenuating the low frequency components (particularly \(<\)400 GHz) in the spectrum.  
The peak-to-peak amplitude is 14.1 mV without the filter (Figure 3a) and 8.3 mV with the mesh filter (Figure 3c), corresponding to a decrease to 60% in peak-to-peak amplitude. Due to an unwanted resonance in the antenna leads, the emitter-receiver pair produces a particularly strong line around 50 GHz, which causes ringing after the main pulse. This ringing is visible in the red shaded areas in Figure 3a, which is heavily attenuated by the filter, as shown in Figure 3c. The red shaded part of the pulse in Figure 3a has a peak at 3.5 mV, whereas this peak is reduced to 0.98 mV with the use of the filter. This is a reduction factor of 3.6 in amplitude of the unwanted resonance. The visibility of the filter effect is more pronounced in the frequency domain. The first peak at 43.47 GHz in Figure 3d is suppressed by 7.5 dB, as compared to Figure 3b, but it is still dominant considering the peak dynamic range. Therefore, we neglected it for the determination of the peak dynamic range. The peak dynamic range is 51 dB without and 48 dB with the filter. Using the filter decreased the bandwidth from 1.4 THz (without filter) to 1.3 THz (with filter).
## 3 Materials and Methods
### Multi-Task Attention Fusion (MTAF) Module  
To fuse multi-modal features from Sentinel-1 and Sentinel-2 data, we introduce the Multi-Task Attention Fusion (MTAF) module proposed by [PERSON] et al. [31] into the next stage of the VIT framework's encoder for multi-modal feature fusion. This module uses a multi-task learning framework combined with the attention mechanism to dynamically assign weights to each modality (as shown in Figure 6). At each time step t, the MTAF module processes the feature maps \(F_{s}1^{t}\) from Sentinel-1 and \(F_{s}2^{t}\) from Sentinel-2. The modality-specific attention weights \(\alpha_{s}1^{t}\) and \(\alpha_{s}2^{t}\) are calculated through a scoring mechanism and then normalized using the Softmax function (see Formulas (5) and (6)):  
\[\alpha_{s}1^{t},\alpha_{s}2^{t}=\text{Softmax}\big{(}W_{a}\text{-concat}\big{(} F_{s}1^{t},F_{s}2^{t}\big{)}\big{)} \tag{5}\]  
\[F_{\text{fused}}^{t}=\alpha_{s}1^{t}\cdot F_{s}1^{t}+\alpha_{s}2^{t}\cdot F_{ s}2^{t} \tag{6}\]  
where \(W_{a}\) is the learnable parameter matrix, and \"\(concat\)\" represents feature concatenation. The fused feature map is obtained through a weighted combination.
# Investigation of the Environmentally-Friendly Refrigerant R152a for Air Conditioning Purposes
## 2 Material and Methods  
### Examined System  
In this work, a simple heat pump system for the cooling of a building is investigated, as Figure 1a illustrates. R152a is the examined working fluid which is a relatively environmentally-friendly fluid as it is discussed by the literature review of Section 1. The present system is studied under different ambient temperatures (\(T_{amb}\)), indoor space temperatures (\(T_{ind}\)), and different rotational speeds. In the nominal conditions, it has cooling capacity 5 kW at 2000 rpm for \(T_{amb}\) = 35 \({}^{\circ}\)C and \(T_{ind}\) = 25 \({}^{\circ}\)C. It is useful to state that air is used in the condenser and evaporator as the external heat transfer fluid. Figure 1b shows the thermodynamic cycle in the pressure-specific enthalpy (p-h) diagram.  
Figure 1: (**a**) The examined cooling system. (**b**) The p-h diagram of the thermodynamic cycle.  
It is important to state that in the present system, some assumptions have been done in order to perform a parametric study. The superheating after the evaporator and the subcooling after the condenser have been selected to be negligible. This assumption has been done in order to investigate a simple vapor compression cycle and to make suitable comparisons among the different examined operating scenarios. The expansion in the throttling valve is assumed to be ideal so there are no thermal losses. Moreover, the overall heat transfer coefficients (_ULA_) in the heat exchangers of the condenser and evaporator are constant for all the examined cases. Furthermore, the mass flow rate in the examined system has been selected to be controlled by changing the rotational speed of the compressor. About the symbols of the Figure 1a, it has to be said that the mass flow rates and the heat rates are symbolized with (\(m\)) and (\(Q\)) respectively, while the dots are missing from these symbols for simplicity reasons. All the parameters defined in the nomenclature in a clear way.
## 2 Materials and Methods
### Quality and Bias Assessment  
The included studies were evaluated using the CASP tool, the Cochrane risk of bias Tool, and the Newcastle-Ottawa Scale. The Critical Appraisal Skills Programme tool offers a checklist to assess the qualities of the studies reviewed. The risk of bias in the included studies was assessed systematically using the Cochrane Risk of Bias Tool, which examines bias across domains such as randomization, blinding, and selective reporting. The quality of the non-randomized studies reviewed was systematically assessed using the Newcastle-Ottawa Scale, which evaluates studies based on selection, comparability, and outcome domains. Most studies had clear aims, utilized appropriate methodologies addressing research questions relevant to ML algorithms, and presented results with well-supported conclusions. Ethical considerations were generally observed; although, some studies lacked detail. The CASP assessments rated most of the studies as high quality because of appropriate research designs that were relevant and rigorous, including the support of well-defined datasets. However, References [37; 38] were rated as moderate quality studies because of unclear representativeness and generalizability concerns. Furthermore, since all the studies reviewed were not randomized trials, randomization, and allocation concealment were not  
Figure 1: PRISMA diagram (source: [27]).  
applicable using the Cochrane risk of bias tool. Most studies exhibited a moderate risk of bias because of the absence of blinding the outcome assessments, incomplete data handling, and selective reporting. Conflicts of interest were often addressed, though not universally stated. Generalizability was frequently limited, with biases introduced by the reliance on specific datasets or settings. The Newcastle-Ottawa Scale highlighted concerns about representativeness and comparability in some studies, though exposure ascertainment was consistently clear. Supplementary Material: SM S8 presents the full assessment, and the domains considered.
## 5 Results and Discussion
### Trip Volume Distribution  
The analysis of HDT activity patterns reveals clear disparities in trip volumes across vehicle types, reflecting the multifaceted nature of Shanghai's logistics ecosystem. Figure 5 presents the trip volume distribution by vehicle type. Tractor trucks account for the largest share of total trips, comprising 38.66% of the dataset. This dominance underscores their pivotal role in facilitating intercity and multi-modal freight flows. As one of the world's leading maritime trade hubs, Shanghai annually handles over 47 million TEUs at its ports, necessitating high-frequency tractor operations for container transfers among terminals, logistics parks, and inland distribution centers. This finding aligns with Shanghai's positioning as a \"gateway economy\", in which long-haul connectivity and multi-modal logistics efficiency are of strategic importance.  
Figure 4: Demonstration of identifying multi-level threshold for trip volume. Subplots **(a–h)** represent the Lorenz curves constructed by grids screened at each threshold level. The obtained eight-level thresholds are 1984, 624, 230, 93, 38, 16, 6, and 2, respectively.  
General cargo trucks constitute the second-largest group, comprising 32.96% of total trips. Their significant presence reflects their operational versatility in serving Shanghai's dense network of retail, commercial, and manufacturing zones. These vehicles are integral to the daily distribution of consumer products, electronics, and industrial supplies across diverse urban subregions.  
Dump trucks (10.73%) and specialized trucks, such as concrete mixers and tankers (8.06%), exhibit moderate activity levels, closely tied to the dynamics of construction and chemical material transport. Although their absolute trip volumes remain substantial, their relative shares are limited by sector-specific operational demands, such as construction schedules, project locations, and the cyclical nature of materials logistics. Fence-sided trucks (7.5%) also represent a critical portion of urban freight trips. These vehicles, known for their open-body designs, are commonly used to transport building materials, agricultural goods, irregular cargoes, and other cargoes requiring ventilation. Owing to their structural design, fence-sided trucks may also require specific safety, emission, and cargo exposure considerations, which merit dedicated regulatory attention.  
In contrast, refrigerated trucks (1.4%) and car carriers (0.69%) represent the smallest segments of the freight system, not due to inactivity but due to their highly specialized functions. Refrigerated trucks serve the city's growing cold-chain logistics sector, operating mainly within food distribution centers and pharmaceutical hubs, in which trip patterns are shaped by the perishable nature of goods. Car carriers, on the other hand, are primarily deployed in the automotive industry, transporting vehicles between assembly plants, storage depots, and ports.
## 3 Results
### Heat Pump Investigation  
The energy behavior of the high-temperature heat pump is presented in Figure 2 for different combinations of the temperature levels in the evaporator and in the condenser devices. It is obvious that the \(\mathrm{COP_{hp}}\) increases with the evaporator temperature increase and it also increases with the condenser temperature reduction. The aforementioned behavior is reasonable because the \(\mathrm{COP_{hp}}\) of the heat pump increases when there is a lower difference between the temperatures of the condenser and the evaporator devices. For the condenser temperature of 70 \({}^{\circ}\)C, the maximum \(\mathrm{COP_{hp}}\) is found at 5.087 for \(\mathrm{T_{evap}}\) = 35 \({}^{\circ}\)C, while the minimum is 1.867 for \(\mathrm{T_{evap}}\) = \(-\)15 \({}^{\circ}\)C.  
Figure 2: Performance of the heat pump for different operating conditions.  
The COP is approximated with a polynomial expression which is given in Equation (25) by using the (T\({}_{\text{cond}}\)) and the (T\({}_{\text{evap}}\)) as parameters. The mean deviation of the Equation (25) results and the real calculated values by the code in Engineering Equation Solver were found at 1.21% which is a relatively low value. Thus, Equation (25) can be accepted as a reliable approximation for estimating the heat pump's efficiency.  
\[\text{COP}=12.61-0.3556\cdot\text{T}_{\text{cond}}+4.538\cdot\text{10}^{-3} \cdot\text{T}_{\text{cond}}^{2}-2.165\cdot\text{10}^{-5}\cdot\text{T}_{\text{ cond}}^{3}+0.3427\cdot\text{T}_{\text{evap}}+9.962\cdot\text{10}^{-3}.\]  
\[\text{T}_{\text{evap}}^{2}+1.69\cdot\text{10}^{-5}\cdot\text{T}_{\text{evap}}^{ 3}-7.495\cdot\text{10}^{-3}\cdot\text{T}_{\text{cond}}\cdot\text{T}_{\text{ evap}}-2.314\cdot\text{10}^{-4}\cdot\text{T}_{\text{cond}}\cdot\text{T}_{ \text{evap}}^{2}+4.592\cdot\text{10}^{-5}\cdot\text{T}_{\text{cond}}^{2}. \tag{25}\]  
Moreover, it has to be added that the approximation equation is valid for the following conditions:  
\[40^{\circ}\text{C}\leq\text{T}_{\text{cond}}\leq 80^{\circ}\text{C}\text{ and }- 15^{\circ}\text{C}\leq\text{T}_{\text{cond}}\leq 35^{\circ}\text{C}\]  
However, in the case of T\({}_{\text{cond}}\) = 40 \({}^{\circ}\)C, the maximum evaporator temperature is T\({}_{\text{evap,max}}\) = 20 \({}^{\circ}\)C, while in the case of T\({}_{\text{cond}}\) = 50 \({}^{\circ}\)C, the maximum evaporator temperature is T\({}_{\text{evap,max}}\) = 30 \({}^{\circ}\)C.  
Figure 3 illustrates the COP calculated with the code and with the aforementioned equation, and it is clear that the results are very close to each other. This fact validates the high accuracy of the approximation expression.
# Analysis of the Impact of Multipath on Galileo System Measurements
## 3 Methodology
### Phase Multipath  
The multipath effect for carrier-phase observations was determined using observations recorded on a very short baseline. In this case, the DD observations allow us to completely eliminate the impact of the troposphere, ionosphere, and orbit error as well as the receiver and satellite clock errors. The DD equation of phase observations can be represented as follows:  
\[\
abla\Delta L^{ij}_{ab,f}=\
abla\Delta\rho^{ij}_{ab}+\
abla\Delta\lambda_{f} N^{ij}_{ab,f}+\
abla\Delta m^{ij}_{ab,f}+\
abla\Delta e^{ij}_{ab,f} \tag{21}\]  
where \(\
abla\Delta\) denotes the DD operator.  
Based on the known satellite-receiver distances and the resolved DD ambiguities, the residues of the DD observations (\(\widehat{\
abla\Delta L}\)) can be expressed as follows:  
\[\widehat{\
abla\Delta L}^{ij}_{ab,f}=\
abla\Delta m^{ij}_{ab,f}+\
abla\Delta e^{ ij}_{ab,f} \tag{22}\]  
Subsequently, the magnitude of the STD of the multipath error can be formulated similarly to \(\widehat{\
abla\Delta C}\):  
\[\sigma^{2}_{\widehat{\
abla\Delta L}^{ij}_{ab,f}}=\sigma^{2}_{\
abla\Delta m^{ ij}_{ab,f}}+\sigma^{2}_{\
abla\Delta c^{ij}_{ab,f}} \tag{23}\]  
Assuming \(\
abla\Delta m^{ij}_{ab,f}(t_{1})\simeq\
abla\Delta m^{ij}_{ab,f}(t_{2})\) for adjacent epochs, the TD and its STD can be formulated as follows:  
\[\widehat{\
abla\Delta L}^{ij}_{ab,f}(t_{12})=\
abla\Delta e^{ij}_{ab,f}(t_{12}) \tag{24}\]  
\[\sigma^{2}_{\
abla\Delta L^{ij}_{ab,f}(t_{12})}=\sigma^{2}_{\
abla\Delta e^{ ij}_{ab,f}(t_{12})}(1-q_{t}) \tag{25}\]  
The STD of the multipath error and measurement noise for undifferenced observations is therefore determined as follows:  
\[\sigma_{\epsilon}=\frac{\sigma_{\widehat{\
abla\Delta L}^{ij}_{ab,f}(t_{12})} }{\sqrt{8(1-q_{t})}} \tag{26}\]  
\[\sigma_{L}=\frac{\sigma_{\widehat{\
abla\Delta L}^{ij}_{ab,f}}}{2} \tag{27}\]  
\[\sigma_{m}=\sqrt{\sigma_{L}^{2}-\sigma_{\epsilon}^{2}} \tag{28}\]  
The time correlation of the carrier-phase observations was determined in the same way as for the code observations. For GPS observations, the estimated correlation coefficient values were 0.20 for L1C/A and L2C(L) observations, 0.54 for L2W, and 0.01 for LSQ. For Galileo observations, the time correlation coefficient is similar for all phase observations at the level of 0.01-0.06.
# Client-Aware Negotiation for Secure and Efficient Data Transmission
###### Abstract  
In Wireless Sensor Networks (WSNs), server clusters, and other systems requiring secure transmission, the overhead of data encryption and transmission is often not negligible. Unfortunately, a conflict exists between security and efficiency in processing data. Therefore, this paper proposes a strategy to overcome this conflict, called Client-Aware Negotiation for Secure and Efficient Data Transmission (CAN-SEAT). This strategy allows a client with different security transmission requirements to use the appropriate data security transmission without modifying the client. Two methods are designed for different clients. The first method is based on two-way authentication and renegotiation. After handshakes, the appropriate data security transmission scheme is selected according to the client requirements. Another method is based on redirection, which can be applied when the client does not support two-way authentication or renegotiation. For the characteristics of different architecture, this paper classifies and discusses symmetric key algorithms, asymmetric key algorithms, and hardware encryption instructions. In four application scenarios, the CAN-SEAT strategy is tested. Compared with the general transmission strategy, when only software encryption is used, the data processing and transmission cost can be reduced by 89.41% in the best case and by 15.40% in the worst case. When supporting hardware encryption, the cost can be reduced by 85.30% and 24.63%, respectively. A good effect was produced on the experimental platforms XiLinx, FT-2000+, and Intel processors. To the best of our knowledge, for Client-Aware Negotiation (CAN), this is the first method to be successfully deployed on a general system. CAN-SEAT can be easily combined with other energy-efficient strategies.  
SSL/TLS protocol; data security transmission; two-way authentication; client-aware negotiation; energy saving +
Footnote †: journal:  
_Article_  
Client-Aware Negotiation for Secure and Efficient Data Transmission
# Systematic Mapping Study on Remote Sensing in Agriculture
## 4 Discussion
### Limitations of the Mapping Study  
The last step in the execution of a systematic mapping study is the evaluation of the limitations and weak points of the study itself. This analysis has to consider all the steps of the process. Several possible limitations have been recognized for the present study:  
* Using only Scopus as a source of publications. Other relevant publications that are not indexed in Scopus could have impacted the final results. However, our focus was to provide an overview on the most highly cited papers. For this reason, we chose to focus on Scopus as it is one of the largest databases available. Moreover, it has one of the most complete searching methods allowing Boolean combinations and wildcards.
* Some missing terms in the search string might have impacted the results. In order to reduce this limitation, we formed the search string to include a broad range of terms of interest to this study. Besides, the search string was refine in several trials, by observing some papers that were not initially included. Therefore, we consider this threat is low.  
* Other classification criteria not present in this study might have provided interesting views on the selected papers. The eight mapping questions included provide interesting findings to researchers and practitioners, although other questions could be also useful.
* The exclusion criterion of six cites per year could have rejected some interesting papers. This criterion was added to select only the most relevant works in the literature. This has shown to be very restrictive both for recent papers (only two papers from 2019 received more than six citations) and for older publications (e.g., the papers before year 2000 require more than 120 citations). However, as the purpose of the mapping study is to analyze the trends of the most relevant works, this is not a threat to the validity of the study.