esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 2 The KMALL Data Format
### MRZ Datagrams  
Multibeam Raw Range and Depth (MRZ) is the most common type of multibeam datagram from Kongsberg Maritime. It replaces several old datagrams from the.all format: raw range (N 78), depth (XYZ 88), seabed image (Y 89) datagram, quality factor (O 79) and runtime (R 52).  
MRZ datagrams are stored in.kmal files. Usually, a file with MRZ datagrams does not contain MWC datagrams, although some exceptions may occur. MRZ datagrams have a maximum size of 366.456 kB, yet they can be smaller. Its structure is shown in Figure 4.  
Figure 4: MRZ datagram as described by Kongsberg (410224 Revision H). Sizes of data elements not to scale.  
Figure 3: Number of samples per ping in a test file from a Kongsberg EM2040 echosounder.  
Figure 2: Raw representation as a 1270 \(\times\) 256-pixel picture of water column amplitude samples from a ping acquired by a Kongsberg EM2040 echosounder. Note that the black area on the right does not contain any sample.  
The first three data structures of an MRZ datagram are the same as in an MWC datagram. _PingInfo_ contains common information for all the beams in the current ping. It also contains the number of SectorInfo elements and its size in bytes. The number of elements will be smaller than nine and the size will be smaller than 48 bytes. Each _SectorInfo_ element contains information about the transmit sectors, such as the frequency or the angle. _RxInfo_, as in the MWC case, contains information from the receiver. However, the structure type is not the same. It also contains the number of bottom soundings, the number of extra detections (soundings in the water column), the number of extra detections classes and the bytes in each class. In _ExtraDetClassInfo_, we find the number of detections of each class. Note that the sum of all these fields must equal the number of extra detections given in RxInfo. The _Sounding_ structure contains data for each sounding such as reflectivity and two-way travel time. It also contains the number of seabed imaging samples, _SlnumSamples_, in the last struct.
# STPA-RL: Integrating Reinforcement Learning into STPA for Loss Scenario Exploration
## 3 Proposed Approach: Stpa-Rl
### System Environment Modeling  
#### 3.1.1 Definition of Process Model for State Representation  
In STPA, the Process Model represents the system state information necessary for the controller to provide control actions. It defines the environment's states based on the available information. The Process Model specifies the system's operational characteristics, input and output variables, operational constraints, and other relevant factors. If the Process Model for constructing the system's learning environment is not fully defined, additional Process Model definitions can be derived from specific situations or contexts obtained during the derivation of UCAs to complement the existing STPA analysis results.  
We model our problem as an MDP. Following the MDPs formulation, the state space is denoted by \(S\). The Process Model defines the \(S\), which represents the system's different states. It can be denoted as \(S=\{s_{1},\ldots,s_{m}\}\), where each \(s\) represents the individual state of the system.
## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.1 Results of the Basic Experiments
On the above, despite the failure of CABIR to achieve the top performance in all experimental indexes over the three datasets, its optimality rate has reached 79.1%. Further analysis reveals that the indexes in which no optimal results have been achieved are all associated with the datasets UCM-Captions and Sydney Captions. It appears to us that this may result from the higher repeatability and coarser semantic granularity in the textual descriptions of the two datasets--a more rigorous and more distinctive text is more advantageous to unleashing the effect of the CAL.
# Railway Cloud: Management and Orchestration Functionality Designed as Microservices
## 5 Microservices for Railway Cloud Orchestration
### Validation of Microservices  
Key performance indicators (KPIs) are essential metrics measuring the smooth operation of microservices and applications. Measuring KPIs and statistical performance analysis could reveal management operation improvement opportunities. In order to demonstrate the benefits of the proposed approach for fulfilling the strong mission critical requirements of railway operations and to validate the synthesised microservices, latency, injected by the microservices' API, is evaluated.  
Latency is referred to as one of the functional metrics related to the performance of microservices. It is, in general, time-variable and could be measured in a specified time interval and described by a profile over time. The concept of latency is wide, but in this paper, latency is defined as the time taken for an HTTP request, generated by a railway cloud orchestration application to get to the server that hosts microservices, and the time taken by the respective HTTP response to travel back.  
The RESTful approach is based on the well-known client-server pattern and considering that the requirement about the cross-platform property of the implementation must be kept, the programming tool Java is used for the numerical simulation. Both sides of the interface are deployed in a single group in order to avoid any influence of outer traffic. This makes the results as reference for a lower bound estimation of the latency with as modest resources allocated as four cores of the CPU and memory of four GBs. The client- and server-side are multi-threaded, but, purposefully, no scale-up is allowed. The results in terms of latency, shown in Figure 9, represent a frame of two thousand operations over the interface in a consecutive manner without any delays between.  
The time frame is chosen well after the initial lazy instantiation of the objects. It might be observed that the mean of the latency is about 2 milliseconds, and the major part of the value is up to about 4 milliseconds. However, a minority of values exist that hit almost 50 milliseconds, and this is a clear signal that, if the load increases, one should expect further degradation of the indicator.  
Figure 9: Latency values in a frame of two thousand operations over the interface between the RMAO and RCP.
## 3 Methodology
### The Advanced Whale-Based Optimization Algorithm for Signal Detection in Underwater MI-MIMO Systems
Figure 4a illustrates the whale's initial state, where it is randomly distributed in the solution space without any target. When the control parameters \(p<0.5\) and \(\left|\overset{\rightarrow}{A}\right|<1\), the whale moves towards the best signal vector using an encircling prey strategy, as shown in Figure 4b. On the other hand, when \(p>0.5\), the whale adopts an ascending spiral strategy to approach the best vector, as illustrated in Figure 4c. Figure 4d demonstrates that when \(p<0.5\) and \(\left|\overset{\rightarrow}{A}\right|\geq 1\), the whale randomly searches the signal vector in the solution space. The AWOA for underwater MI-MIMO signal detection is shown as Algorithm 1:  
```
1:Input: \(\text{N}_{\text{T}},\text{N}_{\text{R}}\), \(Max\_\), and \(G\_number\)
2:Output: \(X\_aim\)
3:\(\text{W}_{\text{Halton}}=\text{Halton}^{*}\ W_{i}\ (i=1,2\ \ldots\ \text{n})\) //Use Halton to initialize the whale population \(\underset{\vec{X}}{\mathbf{X}}=argmin\left\{|\mathbf{Y}-\mathbf{H}\mathbf{X} |^{2}\right\}\)
4:\(=argmax\underset{\vec{X}\in\vec{S}}{\mathbf{Z}}\mathbf{X}^{\text{T}}\mathbf{ H}^{\text{T}}\mathbf{Y}-\mathbf{X}^{\text{T}}\mathbf{H}\mathbf{H}\mathbf{X}\) //Compute the fitness of each search unit to determine the optimal value
5:\(//\)Gaussian swims out of the local saddle point
6:While (t < \(Max\_z\))
7:Fori = 1 to n do \(\overset{\rightarrow}{a}= \begin{cases}2-\tan(2\times\pi\times\frac{t}{\text{ Max}\_z})&t\leq\frac{1}{2}Max\_z\\ \sin(((t-\frac{1}{2}Max\_z)/Max\_z)\times\pi)&t>\frac{1}{2}Max\_z\\ \end{cases} \)
8:\(\beta=2-\cos(0.5\times(t/Max\_z)\times\pi+1)\) \(\overset{\rightarrow}{A}=2\cdot\overset{\rightarrow}{a}\cdot\overset{ \rightarrow}{r}-\overset{\rightarrow}{a}\) //Calculates and updates all parameters and adaptive weight
9:If(1) \(p<0.5\)
10:If(2) \(\left|\overset{\rightarrow}{A}\right|<1\) \(\overset{\rightarrow}{D}=\left|\overset{\rightarrow}{C}\times\overset{ \rightarrow}{X}_{aim}-\overset{\rightarrow}{X}(t)\right|\) \(\overset{\rightarrow}{X}(t+1)=\beta\times\overset{\rightarrow}{X}_{aim}- \overset{\rightarrow}{A}\overset{\rightarrow}{\times D}\)
12:ElseIf(2) \(\left|\overset{\rightarrow}{A}\right|\geq 1\) \(\overset{\rightarrow}{D}=\left|\overset{\rightarrow}{C}\times\overset{ \rightarrow}{X}_{m}-\overset{\rightarrow}{X}(t)\right|\) \(\overset{\rightarrow}{X}(t+1)=\beta\times\overset{\rightarrow}{X}_{m}- \overset{\rightarrow}{A}\overset{\rightarrow}{\times D}\)
14:endif(2)
15:ElseIf(1) \(p\geq 0.5\) \(\overset{\rightarrow}{D}=\left|\overset{\rightarrow}{X}_{aim}-\overset{ \rightarrow}{X}(t)\right|\) \(\overset{\rightarrow}{X}(t+1)=\beta\times\overset{\rightarrow}{D}\times \varepsilon^{\text{l}}\times\cos(2\pi l)+\overset{\rightarrow}{X}_{aim}\)
17:\(//\)Calculate the fitness of the whale after updating the position
18:\(t=t+1\)
19:End while
```  
**Algorithm 1**The AWOA for underwater MI-MIMO signal detection
## 2 Related Work and Technologies  
Several authors have addressed the issue of monitoring people and their health status remotely. In answer to this question, several low-cost technologies were selected that allow us to ensure optimal reference values in various metrics, namely reliability, quality of service (QoS) and total cost of ownership (TCO). To this end, this chapter provides to further analysis and a literature review on the subject, to provide a more comprehensive view of long-range communication technologies and their applications. The most promising areas in the use of remote monitoring technologies are, naturally, mobile communications--3G, 4G and, in the near future, 5G--but also the use of wireless communication technologies such as LoRa, SigFox, Weightless-N/Nwave, Long Term Evolution for Machines (LTE-M) and Narrowband IoT (NB-IoT), among others, these being the most relevant, as mentioned and analyzed in [21], and the TV whitespace (TVWS) analyzed in [22].  
In view of the panoply of similar technologies with applications in remote monitoring, it is difficult from the outset to select one that best meets the requirements defined in order to answer our question. Thus, based on several studies published in scientific journals, the authors selected those that, in general, could answer our question. However, there are restrictions that may lead us to choose one technology over another due to a set of technical requirements at the outset, namely low acquisition cost, low energy consumption, ease of implementation, robustness and availability in the marketplace.
## 4 Numerical Results
In our example, the routing policy of a sensor is to first relay the packets with a lower time stamp and then send its own packets, which explains the higher allocation of sensors 1 and 2 in Figure 11.  
Finally, signalling is a very important aspect to take into account when considering distributed optimization techniques. The advantage is clear: the network operates at the desired optimal point (proportional fairness in our examples). Notwithstanding, as opposite to other policies such as FCFS, sensors need to exchange information thus draining the sensor batteries. Therefore, distributed optimization approaches make sense if the amount of signalling information is kept small. In Figure 13  
Figure 12: Fairness comparison. we compare the proposed CDM against the classical dual decomposition approach in terms of iterations required to converge to the optimal solution. In this example, requests, maximum transmission rates, minimum guaranteed rates and sensor priorities are picked at random using uniform distributions. In particular, \(d_{i},c_{i}\backsim\mathcal{U}[0,50]\) kbps, \(m_{i}\backsim\mathcal{U}[0,0.5]\) kbps and \(p_{i}\backsim\mathcal{U}[0,2]\). In our simulations, the step-size in dual decomposition is adjusted to provide good performance in general, fixing it to \(\alpha^{k}=\frac{0.5}{\sqrt{k}}\) (note that the CDM has no parameter to configure). See in Figure 13 one numerical example. Note that although one iteration of the CDM implies twice the amount of signalling required in one iteration of dual decomposition, the number of iterations in the latter is excessively large and makes the technique impractical in the WSN context. On the contrary, the CDM finds the optimal solution using 10-30 iterations in general. In other words, the CDM requires about \(40\,N\) to \(120\,N\) messages to achieve a fair distribution of the available rate whereas the so-called dual decomposition solution needs to exchange up to \(5,000\,N\) in our example. If we consider 32-bit messages, our solution requires \(56.25\) kb in total whereas the dual decomposition approach needs \(2343.75\) kb of signalling.
## 2 Integration of Artificial Intelligence (AI) in Robotic Surgery
### Machine Learning in Robotic Surgery  
Machine learning, a subset of AI, empowers robotic systems to enhance the precision of surgical procedures. By analyzing complex datasets, including preoperative imaging, intraoperative dynamics, and patient-specific variables, AI algorithms can offer predictive analytics for a wide range of surgical outcomes. For example, machine learning has been employed to track anatomical changes in real-time, which is crucial during procedures like prostatectomy and nephrectomy. [PERSON] et al. presented an innovative approach for aligning preoperative imaging with real-time endoscopic views during partial nephrectomy procedures [4]. Their method leverages subsurface anatomical features, most notably vascular pulsation patterns, alongside color and texture information to automatically match the surgical field with preoperative scans. Machine learning algorithms are used to analyze the visual data related to color and texture. This research represents significant advancement by integrating vascular pulsation as a novel cue for enhancing intraoperative image registration. Moreover, the proposed system includes a tissue-specific deformation model, allowing for accurate adaptation to the dynamic, non-rigid changes that occur during surgery [4].
## 2 Theory of TRM-SBDC UWA Communication  
### The Process Chain of the Communication System  
The paper applies the time reversal technique and SBDC technique to the PDS schemes and calls it the TRM-SBDC UWA communication system. Figure 1 shows the flow chart of the communication system. As shown in Figure 1, after the signal passes through the UWA channel, it is processed by TRM, so that the signal is temporally compressed and the ISI resulting from multipath spreads can be suppressed. Then, we use copy-correlation technique [15] to process the signal and get a series of correlation peaks, thereby calculating the time delay of each symbol. In addition, SBDC technique is performed to compensate the Doppler and help us obtain the correct decoding.
## 3 Results and Discussion  
### Daily Water Use Efficiency  
Figure 2a shows an example of daily WUE\({}_{\mathrm{dt}}\) varying with meteorological conditions at Qianyanzhou during July 2023. There are large day-to-day variations in the WUE\({}_{\mathrm{dt}}\) values, ranging between 4 and 9 mg CO\({}_{2}\) per g H\({}_{2}\)O. These large variations in WUE can also be observed at the Dinghushan broadleaved forest (not shown). The WUE\({}_{\mathrm{dt}}\) values were the lowest around 15 July 2003 when VPD, T\({}_{\mathrm{a}}\), and radiation were high. WUE\({}_{\mathrm{dt}}\) was the highest around 29 July 2003, when VPD, T\({}_{\mathrm{a}}\), and radiation were comparatively low. Daily WUE\({}_{\mathrm{dt}}\) showed low sensitivity to soil moisture content, suggesting that soil moisture was not a controlling factor for WUE\({}_{\mathrm{dt}}\) at this site.  
The mean WUE\({}_{\rm dt}\) over 2003-2005 was 7.387 mg CO\({}_{2}\) per g H\({}_{2}\)O for the broadleaved forest at Dinghushan and 9.014 mg CO\({}_{2}\) per g H\({}_{2}\)O for the coniferous forest at Qianyanzhou (Table 2). The coniferous forest also showed larger variations in WUE\({}_{\rm dt}\) (Figure 3), likely due to the large variations in VPD and air temperature (Figure 1). In addition, the soil of the coniferous forest is much drier than that of the broadleaved forest. The means are similar to those of forests in other subtropical regions (i.e., a pine ecosystem [28]), but the variations are smaller, as only summertime was focused on.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Broadleaved Forest** & **Coniferous Forest** \\ \hline WUE (mg CO\({}_{2}\) g\({}^{-1}\) H\({}_{2}\)O) & 7.387 \(\pm\) 1.385 & 9.014 \(\pm\) 3.466 \\ C–W coupling (\(r\)) & 0.551 \(\pm\) 0.236 & 0.585 \(\pm\) 0.254 \\ \hline \end{tabular}
\end{table}
Table 2: The mean and standard deviation of WUE\({}_{\rm dt}\) and diurnal C–W coupling (correlation coefficient \(r\) between hourly GPP and ET) in the broadleaved forest at Dinghushan and in the coniferous forest at Qianyanzhou on summer no-rain days over 2003–2005. The form of values in the table is mean \(\pm\) standard deviation.  
Figure 2: An example of daily variations in daytime water use efficiency (WUE\({}_{\rm dt}\), **(a)**, blue hollow squares) and diurnal carbon–water coupling (C–W coupling, **(b)**, cyan hollow squares) in July 2003 at Qianyanzhou, along with meteorological conditions. The solid lines in yellow, red, cyan, and blue indicate the daily means of shortwave radiation (SR), air temperature (T\({}_{\rm a}\)), vapor pressure deficit (VPD), and soil water content (SWC), respectively, during the daytime (6 a.m.–6 p.m.).
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 6 TWT in Wi-Fi 6
### Additional Improvements  
Access points of overlapping BSSs may cooperate to avoid collisions. Typically, they may allocate different RUs within their BSSs or they may schedule SPs sequentially.  
A station participating in a TWT session should stay awake during SPs. With Intra-PPDU Power Save, a station can temporarily sleep within an SP if it receives a frame from the same BSS and if this frame is not relevant to it. This means that the station sets its NAV timer, turns to sleep mode, and wakes up only at the end of the current PPDU [5; 111].  
Opportunistic Power Save (OPS) removes the obligation to negotiate the SPs, combining the classic PSM and the novel TWT broadcast agreements [5]. It divides the beacon interval into sub-intervals. The access point schedules a broadcast SP in every sub-interval and tells certain stations to stay awake and transmit or receive data. The system does not need explicit negotiations; the schedule is propagated by a traffic indication map frame at the beginning of every broadcast SP. The stations receive this frame and if they have no activity in the current SP, they turn to sleep mode until the next SP. Thus, OPS reduces the overheads of the SPs' negotiations, which may be costly in dense networks. In addition, the access point assigns different stations to independent sub-intervals, making the system more efficient through load balancing [5].
# A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method
## 5 Results and Discussion
### Ranging Resolution  
In conventional ultrasound ranging techniques, the ranging resolution is equal to the half-wavelength of the wave. Unlike amplitude-based ranging methods, phase-based ranging methods, such as the cross-correlation method used in this paper, can improve the ranging resolution to subwavelength. The time resolution is 0.4 ns in theory, and the theoretical ranging resolution can achieve 1 \(\upmu\)m.  
In this situation, the target was fixed on the displacement platform. The ranging results by conventional ultrasonic ranging and the proposed system with a step distance of 0.05 mm are shown in Figure 10. At each measuring point, we measured 10 times repeatedly. The deviation of ultrasonic ranging was larger than the proposed method.  
Figure 10: Ranging results with a step distance of 0.05 mm: (**a**) Ultrasonic ranging; (**b**) the proposed ranging.  
Figure 9: Sound velocity measured at different places in the water tank.  
The ranging results from the proposed system with a step distance of 0.01 mm are shown in Figure 11. The deviation was less than 0.004 mm, and the linearity was good. The ranging resolution could reach at least 0.01 mm in practice.
## 2 Feasible Design Space for ET Operation
### PA Implementation and Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Freq. (GHz)** & **Back Off (dB)** & \begin{tabular}{c} **Pout (dBm)** \\ **@ Low Supply** \\ \end{tabular} & \
\begin{tabular}{c} **Power Added** \\ **Efficiency (PAE) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Gain (dB)** \\ **@ Low Supply** \\ \end{tabular} \\ \hline
[12] & 2.68 & 7.0 & 34.5 & 50.1 & 10.2 \\
[13] & 1.85 & 7.5 & 22.5 & 46.0 & 9.5 \\
[14] & 3.6 & 6.5 & 32.5 & 68.5 & 12.3 \\
[15] & 1.8–2.7 & 8.0 & 35.0–36.3 & 59.0–65.0 & 8.0–11.0 \\ This work & 1.9–2.2 & 6.7 & 35.1–36.0 & 67.8–72.4 & 10.5–12.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary and comparison of the RF PA performances (under low supply voltage condition) used in envelope tracking (ET) architecture. Figure 6: (a) Input and output matching network schematic of the proposed PA circuit; (b) Matching network layout of the proposed PA circuit. Figure 7: Measured results of the fabricated PA at various drain biases (from 5 V to 27.5 V with 25 V step).
## 1 Graphene and Graphene Nanoplatelets
Considering this, graphene nanoplatelets are already employed in several technological fields. In fact, GnPs-based materials show increased tribology [26; 27], mechanical [17; 28; 29; 30; 31], biomedical [32; 33; 34], gas barrier [35; 36], flame retardant [37; 38], and heat conduction [39; 40; 41; 42] properties. Furthermore, GnPs can transform plastic in an electrical conductor, converting it into a conformable material for electronics [43; 44; 45]. Finally, GnPs showed good potential for enhancing the thermal conductivity of polymer matrixes [46], making them suitable as thermal interface materials [39; 47]. In this review, we will focus on GnP-based applications related in areas such as flexible and wearable electronics, motion and structural sensors, and reinforced bio-nanocomposites. In particular, we will show that GnPs unveils large-scale and unique uses (from antennas to energy harvesting) in the field of flexible electronics. We will discuss the potential uses of GnPs in smart fabrics, and the steps needed to reach a wide distribution of wearable technology. We will display many different approaches and materials employed to fabricate strain and pressure sensors, structure health monitoring systems, and stretchable devices. Finally, we will present recent advances in the field of GnP-reinforced bioplastics, and the potential of these nanoflakes to fill the performance gap between long-lasting traditional plastics and green and sustainable biopolymers.
## 4 Experiments and Analysis
### Comparative Experiments with Existing Methods
Superiority over baseline: We compare our approach with a baseline model, which is often a standard practice in the field of computer vision. By demonstrating an improvement from the baseline model's performance, we highlight the effectiveness of our proposed methodologies. Our experiments are carried out in the same training/testing data setting, ensuring a fair evaluation. 3. Impact of data variability: Data variability can indeed affect model performance. To address this concern, our experimentation is designed to include a diverse range of object classes, sizes, and complexities, which often reflect real-world scenarios. We understand that by changing the dataset, the performance might vary, and it could be an interesting direction for future research.