esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators
## 3 Simulation Analysis and Experimental Validation
### Experimental Validation
Under the above experimental conditions, the motion range of the end was X = 0.19-0.23 m and Y = -0.1-0.1 m; the manipulator's workspace is shown in Figure 9. The deviation of the tip position caused by the cable length error seen from the X and Y directions is shown in Figures 10 and 11. Figure 8: Experimental prototype and overall layout. Figure 9: The workspace of experimental prototype. It can be seen from the Table 3 that the experimental results were basically as expected: At the points where the deviation of the tip position was large (such as the second and third experimental points), the tip position produced a deviation of about a few millimeters when the length of the first cable shortened 1 mm; at the points where the deviation of the tip position was small (such as the seventh, eighth, and ninth experimental points), the tip position produced a deviation of about a fraction of a millimeter; at the jump points where the deviation of the tip position suddenly changed (such as the fourth, fifth, and sixth experimental points), the deviation of the tip position at the jump point was nearly doubled compared to the previous point; when the manipulator was in a singular state (all joint angles were zero), the deviation of the tip position in the X direction was smaller than that in the Y direction. As shown in Figure 12a, all joint angles were zero, and the tip pose of the manipulator was 0.228 m, 0 m and 0\({}^{\circ}\). At this point, the manipulator was in a singular state. We added \(-\)1 mm of cable length error to this singular configuration, and the changes of the pose are shown in Figure 12b. From the results of the simulation and experiment, the theoretical deviations in the simulation of the tip position in the X and Y directions were \(-\)0.0415 and 2.4299 mm; the actual deviations in the experiment were \(-\)0.0495 and 2.2675 mm. The absolute differences of the simulation and experiment were \(-\)0.0080 and \(-\)0.1624 mm; that is, the deviation of the tip position in the X direction was significantly smaller than that in the Y direction, because the manipulator lost the freedom of the X direction in this singular state. In this section, by comparing the simulation and the experimental measurement results, we verified the correctness regarding the influence trend of the cable length error on the tip position. However, the precise change of the tip position caused by the cable length error could not be obtained. This is because only the main influence factor (cable length error) was analyzed in the simulation, and there were other secondary factors which had the characteristics of diversity and randomness during the actual experiment process, such as the experimental system error, mathematical calculation error, measurement error, and so on.
## 3 System Mode and Terminologies
### Terminologies  
Throughout this paper, we use the following terminologies:  
* \(Source_{j}\) the \(jth\) sensor node. \(Sink_{i}\) is the \(ith\) mobile sink.
* \(Agent_{i}\) is the agent node belonging to \(Sink_{i}\). One mobile sink has just only on agent node
* \(N\) is the number of source nodes and \(M\) is the number of mobile sinks
* \(R\) is the communication range of sensor node.
* \(RN_{j}^{k}\) is the \(kth\) relay node on the path \(Path_{j}\). \(RN_{j}^{k+1}\) is the next relay node of \(RN_{j}^{k}\) and \(RN_{j}^{k-1}\) is the previous relay node of \(RN_{j}^{k}\).
* \(N_{Path_{j}}\) is the number of relay nodes in \(Path_{j}\). \(N_{Path}^{Sink_{i}}\) is the number of routing paths of \(Sink_{i}\).
* \(Path_{j}\) is the path from \(jth\) source node in the network to its nearest mobile sink \(Sink_{i}\), \(Path_{j}=\{Source_{j}\), \(RN_{j}^{1},RN_{j}^{2},\ldots,RN_{j}^{k}\cdot\ldots,RN_{j}^{N_{Path_{j}}},Agent_ {i},Sink_{i}\}\).
* \(PTree_{i}\) is the routing tree of \(Sink_{i}\). \(Path_{j}\in PTree_{i}\) and \(RN_{j}^{k}\in PTree_{i}\).
* \(lf(RN_{j}^{k})\) means the lifetime of the \(RN_{j}^{k}\). \(lf(PTree_{i})\) means the lifetime of the \(lf(PTree_{i})\).
* \(Delay(RN_{j}^{k})\) is the delay of the \(RN_{j}^{k}\). \(Delay\)\((PTree_{i})\) means the overall delay of the \(PTree_{i}\).
* \(Len(Path_{j})\) means the length of the \(Path_{j}\). \(Len(PTree_{i})\) means the overall length of the \(PTree_{i}\).
* \(E_{cons}(RN_{j}^{k})\) is the energy consumed by \(RN_{j}^{k}\) due to data forwarding.
* \(E_{cons}^{Path}\)\((Path_{j})\) is the total energy consumption of routing path \(Path_{j}\).
* \(G(V,E)\) or \(G\) means the connected graph containing all candidate relay nodes.
* \(Set(V)\) is the set of all sensor nodes of \(G(V,E)\), i.e., the set of all candidate relay nodes.
* \(N_{V}\) is the number of sensor nodes in \(Set(V)\). \(N_{V}^{\prime}\) is the number of sensor nodes in \(Set(V)\) except the agent node, \(N_{V}^{\prime}=N-1\), i.e., the number of \(Set(V)\)-\(\{Agent_{i}\}\).
* \(Neig(node_{i})\) is the set of all those sensor nodes within the communication range of \(node_{i}\), therefore, \(Neig(node_{i})=\{node_{j}\}\)\(|\)\(0<dis(node_{i}\), \(node_{j})\)\(\in\) R\(\}\).
* \(NextRelay(node_{d})\) is the sensor node which is selected as the next relay node for \(node_{d}\). Therefore, \(NextRelay(node_{d})=\{node_{j}\}\)\(|\)\(\forall node_{j}\)\(dis(node_{j}\), \(Agent)<dis(node_{d}\), \(Agent)\)}.
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 1 Introduction  
The recently standardized 5G New Radio technology is facing principal challenges in terms of its millimeter wave (mmWave) deployment option. Specifically, the inherently large propagation losses and sensitivity to static [1] and dynamic [2] blockage phenomena require very dense deployments to provide continuous service to users [3; 4; 5] resulting in extreme capital expenditures (CAPEX) for network operators. At the same time, mmWave deployments are critical for achieving IMT-2020 requirements set forth by ITU-T in M.2410 specification [6]. On top of this, similar problems will be inherent in the next evolution of communications systems operating in the sub-terahertz (sub-THz) band [7; 8].  
The problem of costly deployments is expected to be alleviated with the recently proposed by 3 GPP Integrated Access and Backhaul (IAB) technology. Specifically, by utilizing multi-hop communications, in 3 GPP Releases 15-17, a fixed infrastructure based on IAB technology has been proposed [9] by utilizing cost-efficient relays, called IAB nodes. In the forthcoming Release 18, this concept is expected to be extended to mobile IAB nodes, forming 3 GPP compliant multi-hop relaying on-the-fly [10; 11].  
Highway scenarios are expected to be one of the natural early adopters of this technology. In these deployments, providing full coverage is extremely costly, especially, on not-so-frequently utilized roads. Here, vehicles may serve as mobile relays creating the so-called \"bridges\" that will potentially allow for decreasing the required density of mmWave/sub-THz base stations (BSs) providing ubiquitous coverage. However, in such deployments, there are inherent trade-offs between bridge length in terms of relaying vehicles and the provided bridge rate as the latter is determined by the link in worst signal-to-noise ratio (SNR) conditions. Furthermore, in most cases, the worst link happens to the last one offloading the traffic to the mmWave/THz BS as the bridge is often interrupted once the vehicle with direct infrastructure connectivity is achieved.
# A Boundary Distance-Based Symbolic Aggregate Approximation Method for Time Series Data
## 4 Experimental Validation
### Result Analysis
We firstly compare the classification error rates with different \(w\) while \(\alpha\) is fixed at 3, and then with different \(\alpha\) while \(w\) is fixed at 4 (to illustrate the classification error rates using small parameters). Secondly, we use \(w\), which varies, while \(\alpha\) is fixed at 10, and then \(\alpha\) varies while \(w\) is fixed at 128 (to illustrate the classification error rates using large parameters). SAX-TD and SAX-BD has lower error rates than the other two methods when the parameters are small and large, SAX-BD has lower error rates than the SAX-TD. The results are shown in Figure 7.  
The dimensionality reduction ratios are calculated using the w when the four methods achieve their smallest classification error rates on each data set, shown in Figure 8. The SAX-TD and SAX-BD representation use more values than SAX, SAX-TD use fewer values than ESAX. In fact, our method has a low dimensionality reduction ratio in majority datasets, and even uses fewer values than SAX-TD. Figure 7: The classification error rates of SAX, ESAX, SAX-TD and SAX-BD with different parameters \(w\) and \(\alpha\). For (**a**), on Gun-Point, w varies while \(\alpha\) is fixed at 3, for (**b**), on Gun-Point, varies while w is fixed at 4. For (**c**), on Yoga, w varies while \(\alpha\) is fixed at 10, for (**d**), on Yoga, varies while w is fixed at 128. Figure 8: _Cont._  
We also recorded the running time of SAX-TD and SAX-BD with different \(\alpha\) from 3 to 10 shown in Figure 9. The experimental results indicated that we have made a greater improvement at the cost of only a little time, and that's well worth it.
# Secure ECDSA SRAM-PUF Based on Universal Single/Double Scalar Multiplication Architecture
## 5 Implementation Results
### ASIC Results
The quality result of SRAM-PUF in our design is compared with existing research in Table 2.  
To measure the temperature resistance and voltage resistance of the SRAM-PUF, we conducted BER measurements on the SRAM-PUF in our design across a temperature range from 0 \({}^{\circ}\)C to 85 \({}^{\circ}\)C and a voltage range from 0.8 V to 1.2 V (\(\pm\)20% of the standard voltage). The most error-prone case arises when the temperature and voltage are 85 \({}^{\circ}\)C and 1.2 V, respectively. In this case, the BER reaches 11.21%, approaching the error correction capability upper limit of the \(RM(2,8)\) in our design. In this extreme scenario, we conducted 100 complete experiments for each chip, and no response errors occurred. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research Work** & **ASIC Process (nm)** & **Area Per Bit** & **GE Per Bit** & **BER** \\  & & **(\(\upmu\)m\({}^{2}\)/bit)** & **(GE/bit)** & \\ \hline
[22] & 65 & 49.12 & 25.58 & \(2.7\times 10^{-10}\) \\
[23] & 14 & 1.84 & 11.83 & \(1.45\times 10^{-2}\) \\
[24] & 65 & 50.7 & 26.40 & \(1.56\times 10^{-2}\) \\
[25] & 65 & 17.9 & 9.32 & \(2.5\times 10^{-2}\) \\ This work & 40 & 15.40 & 21.45 & \(3\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PUF implementation results comparison.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 4 Working of Algorithm  
We consider an ad hoc wireless tactical network comprised of \(N\) nodes, connected through wireless links of same-channel properties, and has a unique identifier denoted as ID. Each network node is connected to a set of neighbors, moves with consistent speed, and has low mobility during protocol functioning. In our network model, the NBR_table corresponds to discover 1- to 4-hop neighbors and routing paths. The algorithm can work for any number of SDRs, usually ranging from 10 to 80 radios. We assume that there are only \(Q\) nodes (half of the radios, \(Q<N\)) which act as traffic sources, and the TRANS_table is maintained to list all intended transmissions involved. Now, assume a topology of 10 SDRs with IDs 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10. In the first TDMA cycle, each SDR sends the NBR_DSCV packet in its time slot with the information they have in their NBR_table as depicted in Figure 6. Suppose that \(N_{1}\) sends NBR_DSCV packet with no information in its NBR_table and that it made its entry in the NBR_table of SDRs \(N_{3}\), \(N_{4}\), \(N_{6}\), and \(N_{9}\) as shown in Table 3 (the colors of each transmission show entries, added by matching colored arrows in Figure 6). Then, \(N_{2}\) sends the packet to its neighbors \(N_{3}\), \(N_{4}\), \(N_{7}\), \(N_{8}\), and \(N_{10}\). Similarly, \(N_{3}\) transmits the discovery packet to not only announce itself to its 1-hop neighbors but also shares hop information of \(N_{1}\) to \(N_{2}\) and \(N_{2}\) to \(N_{1}\), as they are not the neighbors of each other but of \(N_{3}\). The NBR_DSCV packet of \(N_{3}\) is shown in Figure 7, where the two-bit code is the hop information of each node which is at 1- or 3-hop away from \(N_{3}\) i.e., \(N_{1}\) and \(N_{2}\) are represented at 1-hop with bit code 01. All other remaining SDRs similarly send NBR_DSCV packets. After one-time neighbor discovery, each SDR knows about its 1- to 4-hop neighbors. Figure 6: Flow diagram of one-time neighbor discovery process for \(N\) nodes, where \(N=\{1,2,3,...,10\}\).
# High-Power, High-Efficiency GaSb-Based Laser with Compositionally Linearly Graded AlGaAsSb Layer
## 2 Composition-Graded AlGaAsSb Layer Epitaxy
Therefore, the Al shutter should be closed as soon as the composition-graded layer growth is over to avoid the influence of an Al temperature overshoot, and an interruption is necessary for temperature stabilization. Figure 4: Al BEP response to Al temperature ramp-up. Al growth rate is linearly modified by ramping up Al cell temperature. In-time shutter closure is necessary to prevent composition fluctuation caused by temperature overshoot. Figure 3: As BEP response to As valve position ramp-up. As valve position ramps up from Al\({}_{0.25}\)Ga\({}_{0.75}\)As\({}_{0.02}\)Sb\({}_{0.98}\) growth condition to Al\({}_{0.5}\)Ga\({}_{0.5}\)As\({}_{0.04}\)Sb\({}_{0.96}\) growth condition, and As BEP exhibits quasi-linear increase. As BEP settles in Al\({}_{0.5}\)Ga\({}_{0.5}\)As\({}_{0.04}\)Sb\({}_{0.96}\) growth condition when As valve position reaches its destination. Based on the abovementioned linear Al and As flux modulation experiment, we can estimate the thickness of the composition-graded layer by using Equation (3). The Al growth rate is assumed to vary linearly with time, and GaSb-lattice-matched growth is guaranteed by As BEP modulation. \[d=A\int_{0}^{T}(v_{Ga}+v_{Al})dt=A\int_{0}^{T}(v_{Ga}+at+v_{Al0})dt=AT(v_{Ga}+v_{ Al0})+\frac{aAT^{2}}{2} \tag{3}\]  
where d is the thickness of the composition-graded layer, \(T\) is the duration of the growth process, \(A\) is a constant depending on the lattice of the GaSb substrate, and \(a\) is the ramp rate of the Al growth rate. In our linear Al modulation experiment, \(a\) was set to 4.4 (A/h)/s to obtain quasi-equilibrium growth. A thickness of 210 nm and a growth duration of 827 s can be obtained, according to Equation (3), when the Ga growth rate is set to 0.5 ML/s and the Al growth rate ramps from 0.16 ML/s to 0.5 ML/s. A composition-graded layer sample was grown by adopting linear As and Al BEP modification parameters. The sample consisted of a 210 nm composition-graded AlGaAsSb layer and a 10 nm GaSb cap layer. The Al content varied linearly from 0.25 to 0.5 in the composition-graded layer. The sample was characterized by AFM and HRXRD, the results are illustrated in Figure 5. In the 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m AFM scanning result, clear atomic steps and 1.7 A RMS surface roughness indicate high epitaxial quality. A single sharp HRXRD peak implies lattice-matching growth. Similar procedures were adopted to verify the Al\({}_{0.5}\)Ga\({}_{0.5}\)As\({}_{0.04}\)Sb\({}_{0.96}\) to Al\({}_{0.25}\)Ga\({}_{0.75}\)As\({}_{0.02}\)Sb\({}_{0.98}\) composition-graded layer. The AFM and HRXRD results also suggest high-quality and GaSb-lattice-matching growth.
## 2 Materials and Methods
### Data Analysis and Statistical Methods  
We first computed the spatial distribution of mean SOS/EOS/LOS for the period 2010 to 2019. The variations in SOS/EOS dates at the pixel level were analysed to identify the underlying causes. Then, we presented the inter-annual SOS, EOS, and LOS dates extracted from TIMESAT software for nine growing seasons using the boxplot. Each boxplot displays the first quartile, median, and third quartile of the yearly phenological metrics. In addition, the overall mean of 10-year SOS, EOS, and LOS was also calculated and shown in the representative boxplots. The advance and delay in SOS/EOS dates were assessed and correlated to the events occurring during those particular times using climate data. Any extreme weather and climate events during the 10-year period were also noted. We computed the mean, median, standard deviations, and dispersion index for each phenological metric to evaluate the degree of fluctuation or dispersion in the dataset. The accuracy of the MODIS-based SOS/EOS/LOS were evaluated and validated against the Sentinel-based SOS/EOS/LOS for two growing seasons (2017/18 and 2018/19). Comparisons were made using one-way analysis of variance (ANOVA).  
The Mann-Kendall test was applied to evaluate the significance of climatic trend data. It is the most widely used test in the field of meteorology because of its suitability for abnormal distribution data [70]. This test also offers better results for time-series data compared with straight linear functions. Ten years of monthly climatic data for total rainfall and average monthly temperatures were used for this purpose. This test reveals the presence or absence of upward or downward monotonic trends in the total rainfall/average temperature time series data. The results were evaluated based on the \(p\)-value and Tau values. The magnitude of the increase or decrease in the climate trend was evaluated using [PERSON]'s slope test. [PERSON]'s test for single change point detection was conducted when there was a monotonic trend in the data.  
Finally, using selected pre-SOS/EOS climate data and the SOS/EOS of each growing season, we fitted linear mixed models with the SOS/EOS as the outcome, rainfall and/or temperature as predictor variables, and pixel as random effect. The random effects included in the models account for the correlation among multiple observations from each pixel as well as the heterogeneity in the SOS/EOS values across pixels. These models were developed to evaluate the SOS/EOS sensitivity to rainfall and/or temperature. We conducted Akaike information criterion (AIC) comparisons to discriminate between mixed model random effect structures, i.e., random intercept vs. random intercept and slope models. The models with the lowest AIC values and that met the assumptions of homogeneity of variance, normality of error, and linearity [71] were chosen for SOS and EOS. All statistical computations were performed in the statistical package R version 3.6.2. [72]. The R packages used for the analysis were raster [73], rgdal [74], sp [75], tidyverse [76], trend [77] and lme4 [78].
## 5 Architecture, Optimization, and Technical Challenges in LLM4 Rec
### Paradigms of LLM4 Rec  
#### 5.1.1 Discriminative Paradigm  
Discriminative LLM4 Rec systems focus on identifying or ranking items that best match a user's preferences based on given data. These models excel in tasks where classification, ranking, or prediction is the primary objective. For example, BERT4 Rec fine-tunes a BERT-based architecture to predict the next item in a user's sequence, leveraging the model's bidirectional encoding to understand user-item interactions effectively. Examples of discriminative LLM4 Rec systems include (1) BERT4 Rec, which utilizes masked language modeling to predict the next item in sequential recommendations and (2) PALR, which focuses on learning personalization-aware ranking mechanisms for user-item interactions. Key characteristics of the discriminative paradigm include  
* Task Orientation: Designed for tasks like next-item prediction, click-through rate (CTR) estimation, and personalized ranking.
* Fine-tuning: Most discriminative systems require supervised fine-tuning on domain-specific datasets to optimize performance.
* Precision: These models are highly accurate for structured and feature-rich recommendation tasks.
# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
## 8 Conclusions  
In this work, time-domain numerical simulations were performed to analyze the global dynamic performance of a bottom-set gillnet under various current and wave conditions. Simulation tools were developed to design a sensor-based monitoring system and develop the corresponding machine-based problem-detection or decision-making processes. Computer simulations were carried out for a typical range of measure environmental data on the southwest coast of Korea. The target net-system was a bottom-set gillnet constructed by employing an actual design used in Korea. A reliable equivalent net model with Morison forces on instantaneous net/mooring positions was developed. The convergence of the equivalent net model was confirmed by increasing the number of elements used for a finer model. From a series of computer simulations, the following conclusions can be drawn:  
* The stretching performance of the gillnet is good under different current loads.
* The effects of wave excitations are small in operating conditions at the given submergence depth. Dynamic responses of the net are, however, substantial in the fishery-prohibition condition, which significantly increases the mooring tension through nonlinear snap loading.
* In different line-failure scenarios, the proposed monitoring system, in combination with three different kinds of sensors, can effectively detect specific problems from the combined patterns of sensor signals.
* The proposed algorithm, based on the analysis of sensor signals and the corresponding statistical data, can be used for failure detection.
* An example of a machine-based problem-detection algorithm using sensor signals has been developed and successfully demonstrated using a number of different failure scenarios.
* In order to develop a more sophisticated algorithm for many different failure cases, big-data analysis combined with machine learning might be a favorable solution.  
All authors contributed equally contributed to the publication of this article with regard to the design of the target model, validation of the numerical model, parametric study, analysis, and writing.  
This research is a part of a project titled 'Development of Automatic Identification Monitoring System for Fishing Gear', which is funded by the Ministry of Oceans and Fisheries, Korea.  
The authors declare no conflict of interest.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 6 3 GPP Release 19: 5G-Advanced Growth  
5G-Advanced evolution continues in the ongoing Release 19, enhancing a set of commercially driven features and introducing a suite of innovative features that enhance the capabilities of 5G. Release 19 acts as a bridge toward 6G by conducting forward-looking studies on channel modeling.
## 4 Data Analysis and Findings
### Hypotheses Testing Measurements  
The main objective of this section is to enable hypotheses testing. However, two antecedent tests, the Coefficient of Determination (R\({}^{2}\)) and Predictive Relevance (Q\({}^{2}\)), are required prior to hypotheses testing in order to better understand, discuss, and argue the results of statistical significance (\(p\)-values and \(t\)-values). On the one hand, R\({}^{2}\) will predict the variance inflicted by the entire framework of OUM on each dependent variable, while on the other, Q\({}^{2}\) will elaborate the predictive capability of each dependent variable within OUM. Following the above antecedent tests, the statistical significance (\(p\)-values and \(t\)-values) will be evaluated according to the OUM's 25 hypotheses.