esa-sceva/satcom-chunk-collection huggingface dataset:
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Communication Overhead
#### 5.4.2 Cost for Client-Server Communication  
Each client needs to send fixed numbers of parameters in the network, and hence, their format is fixed. Therefore, this overhead depends on the size and number of required parameters. For our algorithm, the packet size is a combination of the message, GS, and the client's portion of the secret key, which is \((Upd,\sigma_{GS},S_{i})\). Let us assume that the size of the updated message is 1024 bits. This is also common for all other algorithms. However, most algorithms use a digital signature instead of a GS; therefore, it will vary from other algorithms' results.  
\(\sigma_{GS}\) has nine variables: \((c_{1},c_{2},c_{3},c,s_{\alpha},s_{\beta},s_{\alpha},s_{\beta_{1}},s_{\beta_{2}})\). Each one contains 171 bits. Hence, \(\sigma_{GS}=9\times 171=1539\) bits. The message format is given in Table 7. The total size of the message is given in Equation (29):  
\[L_{M} =L_{GID}+L_{MID}+L_{RAND}+L_{Payload}+L_{GS}+L_{TTL}+L_{TS}+L_{S_{i}}\] \[=16+16+128+1024+1539+8+32+1024 \tag{29}\] \[=3787\;\text{bits}=473\;\text{bytes}\]  
In algorithms that use a personal signature (not a GS), the client sends two encrypted messages to the server: one for encryption and the other for the signature. Thus, there is no separate section for the signature in the final packet, which reduces the packet's size. However, clients may need to send separate messages to preserve their privacy. Hence, the message sizes of the existing algorithms can be obtained as follows:
## 4 Results
### Fairness  
Resource fairness was the main constraint considered in this study. The resource fairness constraint was satisfied in all cases (the P0, the proposed strategy, and PP). To determine the effect of resource fairness, [PERSON]'s fairness index [36], which is used to determine the fairness of a set of values in sharing systems, was calculated based on the electricity bill savings per allocated capacity of each member as follows:  
\[\text{[PERSON]'s fairness index}=\frac{1}{M}\frac{\left(\sum_{m\in\mathcal{M}}\chi_{m} \right)^{2}}{\sum_{m\in\mathcal{M}}\chi_{m}^{2}}, \tag{22}\]  
where \(\chi_{m}=\frac{B_{m}(\cdot)-B_{m}(c_{m,t})}{\gamma_{m}\sum_{t\in\mathcal{T}} \beta t}\) is the electricity bill saving per allocated capacity of member \(m\).  
Figure 9 shows the variation in the Jain's fairness index with the community solar capacity, where the optimal case for the Jain's fairness index is when the value is 1. The results show that the PP case has the optimal fairness index, and the proposed strategy case has a higher fairness index than that of P0.  
Figure 10 shows the distribution of the electricity bill savings per allocated capacity of each member when the community solar capacity is 100 kWp. As shown in Figure 10c, the PP case has the distribution with the highest sharpness, and the distribution of the P0 case  
Figure 8: Electricity bill savings per community solar capacity.  
Figure 9: Jain’s fairness index based on the electricity bill savings per allocated capacity of each member.  
in Figure 10a has a heavier tail compared to that of the proposed strategy case in Figure 10b, which reduces the fairness index.
# Prism-Based Spatial Heterodyne Spectrometer with a Fixed Fringe Localization Plane
## 2 Principles and Designs
### Simplification of the Motion Control System  
Tuning the spectrometer with the structure shown in Figure 2 requires tilting four optics: two bimirrors and two mirrors. The motion control system is more complex than that of a grating-based TSHS, which requires tilting only two gratings. We drew on the orthogonal interferometer based on a Michelson interferometer designed by [PERSON] et al. [20] and modified it into the form of a Mach-Zehnder interferometer. After simplifying the motion control system, it allows tuning by tilting only two optics at the expense of a larger spectrometer size. As shown in Figure 5a, the collimated light is divided by the beam splitter into arm A and arm B. Arm A contains mirror 1 and arm B contains a bimirror. The angle between the two mirrors of the bimirror is \(90^{\circ}\). The mirror 1 and bimirror, each placed at \(22.5^{\circ}\), reflect the beams onto a right-angle prism coated with mirrors. Then the two beams incident parallelly to prism 1 and prism 2, respectively. The bimirror acts like a roof prism, deflecting the beam top-to-bottom.  
When the beams of the two arms are incident into the prisms placed in the same direction, the cross-section of the beam of arm B is upside down. Thus, while the two beams are refracted by the prisms in a same outgoing direction, they are equivalent to being tilted at the same angle relative to the optical axis in opposite directions. After this,  
Figure 5: Improvements on the preliminary design. (**a**) Layout of the spectrometer after simplifying the motion control system. (**b**) Schematic diagram of the anti-aliasing prisms of the two arms. The tilting angle of the half-surfaces is exaggerated in this diagram. As a comparison, the dashed lines show the shape before modification.  
the beams of the two arms pass through the bimirror assembly and mirror 2 and return to the right-angle prism in turn. Then, the beam of arm A passes through mirror 1 again and the beam of arm B passes through the bimirror again, being deflected bottom-to-top. Finally, the two beams return to the beam splitter for recombination.  
This modified structure assembles the two bimirrors of the two arms of the prism-bimirror-mirror TSHS into a bimirror assembly by fixing them together and tilting them synchronously. Similarly, the two mirrors of the two arms are combined into mirror 2. Therefore, tuning can be performed by simply tilting the bimirror assembly and mirror 2 around the rotating spindle, simplifying the motion control system.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 3 Alternative Categorization
### Space  
Finally, considering the power levels at which the RFPA operates efficiently is essential in practical design. In ideal cases when \(P_{diss}+\sum_{h=2}^{\infty}P_{out,hf}=0\) in Equation (4), efficiency can be expressed as Equation (10).  
\[\eta=\frac{P_{out,fo}}{P_{out,df}} \tag{14}\]  
Here, \(P_{out,fo}\) is the total output power delivered to the load and \(P_{out,df}\) is the maximum output power that can be delivered efficiently to the load for the RFPA designed at that level. In this context, the term space refers to the deviation in input/output power levels from the optimal power levels required for efficiently operating the RFPA.  
The input/output characteristics of RFPAs are inherently non-linear, displaying linear behavior only at low input drive levels before reaching saturation beyond a certain threshold, as illustrated in Figure 1b. To achieve the best trade-off between efficiency and linearity, the RFPA is operated at its saturation point, denoted as \(P_{in,1 dB}\), where the corresponding output power is \(P_{out,1 dB}\).  
Figure 6: Trajectory of fundamental and second harmonic impedance of class J/B continuum in Smith chart (\(\delta=[-1\) to \(1]\)).  
In Equation (14), \(P_{out,1 dB}\) can be referred to as \(P_{out,df}\) without loss of generality. In a similar manner, the efficiency vs. the output power, \(P_{out,fo}\), follows a similar trend.  
The term efficiency at back-off power is used to describe the efficiency levels of the RFPA when it operates at reduced power levels. In cases involving communication, especially where modulation plays a crucial role, one important metric to consider is the Peak-to-Average Power Ratio (PAPR). As shown in Figure 7, the efficiency of the amplifier decreases significantly below the nominal output power level.  
Effective solutions to address these limitations have been developed, with approaches such as supply modulation and load modulation becoming popular, as discussed in [PERSON] [44] and [PERSON] et al. [45]. Thus, in an abstract manner, the domain of design space can be sub-categorized into two key subsets: supply modulation and load modulation.  
Envelope Tracking is one example of supply modulation technique, while Doherty-Chireix design provides an example of a load modulation technique [45]. Moreover, tunable load networks have also been used by [PERSON] et al. [46] to deal with the efficiency at the back-off level.
## 3 Experiment  
### Experimental Data Sources  
Experiments were conducted using the bearing degradation dataset PHM2012, which was published in 2012 and contains the full-life vibration signal data of bearings from normal stage to degradation, provided by the PRONOSTIA experimental platform of FEMTO-ST Institute in France [35]. This experimental platform can accelerate the rolling bearing degradation process under specific conditions and record the experimental data such as rotational speed, load, vibration signals, etc., at the same time, which can provide data support for the RUL prediction of rolling bearings. The RONOSTIA experimental platform is shown in Figure 7.  
The PHM2012 dataset contains the full-life vibration signal data of 17 bearings under three operating conditions, and different bearing numbers represent the experimental bearings under different operating conditions, e.g., bearing 1-1 represents the first experimental bearing under operating condition 1, and the PHM2012 data are shown in Table 1.  
Figure 6: The training process of CNN-GRU-MHA.  
The PHM2012 data include vibration signals in both horizontal and vertical directions, and the signals in the horizontal direction can reflect the degradation of the bearings's more quickly and accurately, so only the vibration signals in the horizontal direction are used in this paper for the prediction of bearing life.  
Three groups of 12 different migration experiments were set up to verify the prediction effect of the CNN-GRU-MHA model with a small amount of training data, and the experimental data are shown in Table 2.  
In Table 2, the source domain data are used as training data, and the target domain data are divided into test data and validation data in a ratio of 1:1. The training and test data contain labels and the validation data do not contain labels.
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 3 Approach
### Faster Neural Networks  
Efforts to design faster neural networks have been centered on reducing floating-point operations (FLOPs). However, it is important to note that a decrease in FLOPs does not necessarily translate to a proportional reduction in latency. [PERSON] et al. [11] emphasize that the low FLOPS observed is primarily attributed to frequent memory access, particularly in operators like DWConv [10].  
In response to these challenges, the authors introduce partial convolution (PConv) as a solution. PConv aims to improve spatial feature extraction while simultaneously minimizing redundant computation and memory access. This innovation is incorporated into the FasterNet architecture, a new neural network family featuring four hierarchical stages. Each stage integrates an embedding or merging layer for spatial downsampling and channel expansion. The FasterNet block structure, which is present within each stage, consists of a PConv layer, followed by two pointwise convolution [9] layers. PConv utilizes specific consecutive channels as representatives for computation, with an increased number of channels in the middle layer, and incorporates a shortcut connection to reuse input features. The overall architecture of FasterNet and how PConv works is shown in Figure 2.  
Figure 1: The overall architecture of the improved YOLOv8 network.  
FasterNet has many variants. In our experiments, we replaced the YOLOv8s' Darknet-53 backbone with FasterNet-T0. This replacement led to a 22.52% reduction in model parameters (8.6 M vs. 11.1 M) and a 23.59% reduction in flops (21.7 B vs. 28.4 B), while causing only a slight drop in AP and AP50, of 0.81% and 0.87%, respectively.
## 5 Potential Challenges
### Multicell NOMA Networks  
While many studies have been conducted on NOMA, the vast majority of attention has been paid to single-cell NOMA connections. Accordingly, further research is required in this area for real-world applications. While there are many advantages to multicell settings, interference concerns can reduce performance for users on the cell's periphery. Microcell collaboration in multicell NOMA-provided heterogeneous networks will likely resolve this ancillary issue in addition to persistent intracluster and intercluster interference constraints.
# Progress on the Application of Nanomaterial Expansion in Oil Displacement
## 2 Principle of Enhanced Oil Recovery by Nanomaterials  
In recent years, nanomaterials have been widely used in the field of oil exploitation to improve its efficiency. The main mechanisms whereby nanomaterials improve oil recovery include reducing interfacial tension [31], changing wettability, and reducing crude oil viscosity [32]. Considering the relationship between different mechanisms is the key factor to improving oil recovery. Adding surfactants to an oil displacement agent can effectively improve the wettability of the rock surface, thereby enhancing the emulsification. Reduction in interfacial tension is an important factor in improving oil recovery [33]. A reduction in interfacial tension can improve the flow of crude oil in the pore throat so that oil droplets can pass through small pores more smoothly, thereby improving the oil recovery efficiency [34]. Wettability is one of the important parameters in improving oil recovery. Changing the wettability can help the oil displacement fluid peel the crude oil from the rock gap to improve the oil recovery rate [35].
## 5 Results
### Lidars' Performance in Presence of the Motion
#### 5.2.4 Vertical Motion along Z Axis (Heave)  
The statistical analysis of lidar data during heave motion (tests 3, 4, 5, 36, 38, and 56) is presented in the fifth row of Figure 6. The moving lidars were subjected to vertical displacements with \(f_{0}\) ranging from 0.1 Hz to 0.4 Hz. Test cases 03, 36, and 38 have \(A_{0}=40\) cm. The mean wind speeds measured by the moving lidars were similar to or slightly lower than those measured by the reference lidars at both heights, except for test 56, which showed a large discrepancy between the reference and moving ZephIR 300 (Figure 6 Heave: a, b).  
In terms of TI estimation, WindCube V1 lidars displayed a noticeable discrepancy between moving and reference measurements, whereas ZephIR 300 showed similar TI estimations (Figure 6 Heave: c, d). The APE between moving and reference lidars reached 5% at 85 m for WindCube V1 and was less than 3% for ZephIR 300. At 145 m, the APE remained below 2% for ZephIR 300 but reached 24.3% for WindCube V1, which is a considerable amount (Figure 7 Heave: a, b). The correlation between the lidars varied from 0.9 in test 3 to 0.2 in test 36. Correlation was consistently lower at 145 m for all test cases. As motion frequency increased, the correlation of WindCube V1 lidars decreased, while the correlation of ZephIR 300 increased (Figure 7 Heave: c, d).
# Campus City Project: Challenge Living Lab for Smart Cities
## 3 Campus City Challenge--Smart Energy
### Challenge Implementation Results  
The challenge was implemented during August 2019-June 2020. A team consisting of three 9 th-semester Mechanical Engineering students was assigned to solve the Smart Energy challenge. Some results of the challenge and the student experience from the teacher's perspective are discussed below.  
Figure 6 shows that the students were able to obtain data through low-cost sensors. However, they did not demonstrate the same ability in the use of Matlab(r) to interpolate the conditions at points where there was no sampling, nor the use of scales to demonstrate a real behavior of CO\({}_{2}\) concentration. During the challenge, it was noted that the students struggled to use the mathematical equation to calculate the number of room air changes necessary to maintain the CO\({}_{2}\) concentration at acceptable levels, according to regulations. However, they demonstrated competency in database consulting.  
An excerpt of the students' conclusions is presented below:  
_\"Inexpensive sensors were used to perform classroom measurements that measure CO\({}_{2}\) concentration, temperature, and percentage of humidity within the classroom. According to the data obtained, color maps were constructed in Matlab(r) to visualize the CO\({}_{2}\) concentration during a class session. The concentration of CO\({}_{2}\) remains over 2000 ppm, reaching a peak value of up to 2883 ppm in the afternoon, which is well above the limit recommended by the regulations that tell us the standard of 1000 ppm.\"_  
_\"Likewise, calculations of the liters of air per second that each person should have in the room were made. Taking into account an average concentration from outside such as 400 ppm, the highest amount of CO\({}_{2}\) concentration, and the variable found with the average weight and height of a Mexican, we obtain 2.17 L per second for each person, being well below what gives us the regulation of 5 L per second per person.\"_  
_\"The air quality inside the classrooms of the Tecologico de Monterrey is not adequate to ensure the well-being of its occupants. For this reason, it is proposed that installed air conditioners achieve adequate ventilation so that students and teachers have the security that they will stay healthy and that academic performance increases. Likewise, a filter system in the air conditioners capable of eliminating viruses is necessary to avoid contingencies such as COVID. In this way, students and teachers will feel protected by the Institution since the necessary hygiene measures will be taken to eliminate any virus.\"_  
The COVID-19 Pandemic affected the normal development of the Smart Energy Challenge since by the middle of spring 2020 students could not get access to the campus and therefore, they could not obtain new data. This fact affected their motivation, and their achievements were much lower compared to the students that had participated in the previous semester (fall 2019).  
Figure 6: CO\({}_{2}\) concentration profile in two classrooms at Tecologico de Monterrey.
## 2 Materials and Methods
### Wireless Sensor Network
#### 2.1.2 Data Transmission  
The WSN utilizes LoRa technology for message transmission. Each node in the network is equipped with a TTGO board featuring an embedded LoRa antenna, eliminating the need for additional electronics and wiring to establish communication between nodes.  
Figure 2: Block diagram of the sensor node’s components.  
Due to the substantial number of variables contained in a single measurement, high transmission speeds are not required, especially considering that each message consists of approximately 120 bytes.  
The WSN implements a custom media access control (MAC) protocol on a star topology, which utilizes a message structure based on JavaScript object notation (JSON) strings. These JSON messages contain vital information such as transmitter address and receiver address, as well as message types, enabling effective communication and status monitoring within the network.  
In terms of the WSN operation, a master-controlled communication approach is followed. When the master node is powered up, it fetches the configuration from the web platform, which includes the sensor node address list and the time intervals between measurements. Upon reaching the scheduled time, the master node initiates a broadcast message to trigger the sensor nodes to take soil measurements using the JXBS-3001 sensor.  
After a 5 s interval, the master node iterates through the node address list, sending data request commands to collect the recently obtained data. To ensure reliable data retrieval, if a sensor node fails to respond, the data request command is sent up to four additional times with a 1 s timeout for each attempt. Each received measurement from the sensor nodes is appended to a list.  
The master node, which is connected to the internet, sends all the accumulated data to an endpoint on the web platform using an HTTP-POST request. The payload of this request is in JSON format, containing all the measurements as the request body, and includes an application//JSON header to specify the data type.  
The developed MAC protocol offers scalability, allowing the WSN to accommodate up to 252 sensor nodes. Additionally, it provides the flexibility to incorporate various types of sensor nodes, expanding beyond soil parameter measurements. This opens up opportunities to integrate sensors for air temperature, humidity, water pH, electrical conductivity, and other environmental factors.
## 3 Numerical Results and Discussions
### Single- and Double-Edge-Based Detection
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{2}{c}{**Fixed PQD**} & \multicolumn{2}{c}{**Random PQD**} \\ \cline{3-6}  & & **AWGN** & **IMPN** & **AWGN** & **IMPN** \\ \hline \multirow{2}{*}{Prop.} & MV & 100/0 & 100/0.1 & 89.8/8.6 & 89.2/11.6 \\  & RMS & 100/0 & 100/0.1 & 89.6/6.4 & 88.8/11.6 \\ \hline Conv. & WC-Det. & 100/0 & 100/8.3 & 80.1/11 & 74.6/58.2 \\ \hline \end{tabular}
\end{table} 
Table 4: Single-edge-based PQD detection performance (DR/FER (%)) of the proposed and conventional wavelet-correlation-based-detection (WC-Det) algorithms, where a swell signal with 120% \(pu\) is used over Gaussian noise (AWGN) with signal-to-noise ratio (SNR) = 35 dB or IMPN with SNR = 35 dB, \(\mu\) = 400, \(\psi\) = 0.02%. A comparison of Tables 4 and 5 indicates that the proposed scheme is superior to the existing schemes in terms of both single- and double-edge-based detection performance. Particularly, for the double-edge-based detection of random PQD (Table 5), the proposed MV or RMS scheme has much more improved performance indices (i.e., in terms of DR and FER; even FER results are omitted in Table 5 for simplicity) than the existing schemes. A comparison between the proposed metrics indicates that MV is slightly better than RMS, especially in terms of double-edge-based DR.